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Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationTue, 30 Oct 2012 14:29:15 -0400
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2012/Oct/30/t1351621952jo9udcfkn72h7z4.htm/, Retrieved Sat, 04 May 2024 02:56:30 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=185286, Retrieved Sat, 04 May 2024 02:56:30 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact70

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Multiple Regression] [workshop 7] [2012-10-30 18:29:15] [18a55f974a2e8651a7d8da0218fcbdb6] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
43	44	124	15	26	0	252
50	54	97	22	29	3	255
88	71	165	39	39	2	404
58	61	139	28	20	0	306
59	44	118	24	30	1	276
70	59	155	25	35	0	344
75	54	137	41	33	0	340
51	46	143	31	25	1	297
65	57	159	25	38	1	345
49	47	130	29	15	3	273
62	56	147	38	30	0	333
74	68	134	18	28	0	322
46	72	132	25	24	1	300
52	60	138	35	21	1	307
71	77	169	36	26	1	380
61	45	116	33	19	1	275
56	58	107	27	22	0	270
91	89	124	26	24	2	356
50	37	124	22	21	1	255
58	56	153	26	39	1	333
50	66	136	29	25	0	306
49	56	117	25	19	0	266
56	54	170	24	34	1	339
39	38	127	32	20	0	256
45	53	127	29	29	0	283
26	64	130	34	14	1	269
51	68	125	45	19	1	309
50	58	124	20	11	0	263
43	46	121	41	15	2	268
47	41	118	36	25	2	269
36	43	128	28	22	0	257
46	45	125	35	17	1	269
43	43	122	31	15	4	258
41	57	135	37	18	1	289
38	48	145	27	21	0	279
26	34	111	20	21	2	214
49	56	157	34	10	0	306
37	55	129	25	15	1	262
56	52	118	29	11	0	266
36	57	113	25	18	1	250
42	56	122	27	13	0	260
35	48	130	32	13	1	259
36	44	127	26	14	1	248
36	61	101	26	9	0	233
37	55	116	29	13	1	251
50	46	140	29	25	2	292
38	51	98	24	14	2	227
40	43	95	32	20	1	231
54	40	131	36	14	2	277
35	55	117	35	17	0	259
47	47	81	33	21	0	229
46	44	123	18	16	0	247
37	57	104	28	19	1	246
45	38	99	28	17	0	227
37	48	133	22	29	1	270
38	33	88	26	16	0	201
43	66	112	30	14	0	265
46	47	137	24	17	0	271
34	60	103	28	23	2	250
34	45	116	24	15	3	237
32	52	103	22	12	0	221
31	36	113	23	15	2	220
50	50	134	30	14	1	279
34	62	110	26	14	1	247
26	36	84	28	17	1	192
42	54	110	30	12	0	248
40	45	100	26	14	1	226
36	46	76	17	13	0	188
48	71	123	33	24	0	299
33	40	88	29	21	0	211
37	42	77	21	10	1	188
37	37	76	11	17	1	179
24	37	65	14	9	0	149
37	42	76	15	15	0	185
34	60	103	23	16	0	236
22	50	91	24	13	4	204
33	31	83	10	9	0	166
40	42	98	11	26	1	218
32	33	65	7	10	1	148
32	40	68	13	6	3	162
30	35	74	18	14	2	173
20	42	64	16	9	2	153
26	35	90	14	14	2	181
35	34	77	14	10	0	170
32	42	59	15	11	1	160
36	40	83	20	11	0	190
35	60	79	19	10	0	203
22	40	60	14	13	1	150
40	47	84	20	19	1	211
30	31	78	12	10	1	162
34	31	77	13	16	3	174
37	47	100	17	21	0	222
25	42	79	15	17	0	178
41	52	103	14	18	1	229
40	62	85	19	40	1	247
25	46	74	17	18	0	180
34	59	90	11	24	1	219
25	49	97	20	16	2	209
32	54	68	16	31	1	202
19	70	85	12	15	1	202
46	60	103	22	18	1	250
27	43	80	15	24	0	189
48	52	92	21	21	0	234
49	61	100	12	19	0	241
43	53	84	12	18	1	211
55	63	94	17	27	0	256
52	55	80	17	15	1	220
40	35	89	17	12	0	193
54	68	124	18	26	0	290
47	58	99	10	24	0	238
52	43	100	10	35	0	240
38	45	79	9	21	1	193
53	56	96	11	30	1	247
43	51	100	18	32	1	245
56	55	115	15	55	1	297
42	48	74	15	39	0	218
50	49	77	18	48	1	243
52	58	108	22	50	3	293
38	43	64	13	52	1	211
48	45	83	14	51	1	242
57	68	103	20	47	2	297
50	63	96	24	45	0	278
47	76	106	16	41	1	287
59	109	122	30	49	1	370
72	95	105	21	59	0	352
79	86	112	23	51	2	353
74	68	105	21	62	1	331
64	57	96	20	35	0	272
77	79	144	27	74	0	401
67	70	113	16	67	2	335
61	62	112	28	48	3	314
49	65	113	25	56	1	309
57	62	126	43	54	1	343
43	77	116	30	70	1	337
96	94	156	42	74	0	462
60	76	91	23	60	0	310
65	65	108	19	60	0	317
67	71	151	19	77	2	387
44	52	132	36	55	2	321
71	78	120	20	64	1	354
43	67	160	27	55	2	354
57	61	118	24	49	1	310
59	94	155	23	62	2	395
45	77	122	26	54	3	327
47	86	123	31	59	3	349
46	72	146	51	73	1	389
58	102	156	39	72	0	427
52	57	127	32	58	0	326
46	94	128	30	59	1	358
51	97	147	46	62	2	405
50	67	128	31	50	4	330
51	70	139	31	51	4	346
76	74	130	40	74	1	395
47	59	118	29	49	4	306
47	65	147	43	68	1	371
53	71	98	17	62	2	303
45	63	141	53	41	2	345
39	58	138	47	60	1	343
44	72	130	49	53	0	348
39	53	145	44	44	1	326
48	73	123	48	50	4	346
42	62	116	51	48	2	321
30	77	90	47	47	1	292
37	98	110	44	50	4	343
30	70	102	33	41	3	279
36	56	109	47	46	3	297
37	67	111	41	40	2	298
30	33	93	36	47	2	241
50	48	120	46	41	5	310
29	52	81	24	33	1	220
25	69	84	17	29	5	229
34	55	87	22	33	1	232
42	52	110	30	35	3	272
26	43	90	24	39	1	223
28	49	108	18	40	5	248
25	68	101	24	45	3	266
39	59	87	24	34	2	245
51	48	118	28	50	5	300
43	50	82	19	38	1	233
27	44	86	22	27	1	207
31	57	103	26	31	3	251
22	50	93	14	35	1	215
19	52	83	16	33	5	208
38	74	91	21	25	3	252
24	35	69	15	22	1	166
35	37	95	23	48	0	238
29	53	96	29	33	4	244
30	45	105	17	37	0	234
34	62	121	24	30	2	273
28	57	101	18	51	5	260
55	68	111	22	52	4	312
46	69	130	8	49	3	305
67	83	134	26	43	9	362
71	81	161	22	51	3	389
113	95	186	34	63	1	492
62	89	244	25	65	2	487
43	84	145	20	63	2	357
105	87	170	35	93	15	505
63	100	164	38	78	5	448
67	63	124	24	62	5	345
73	94	154	14	99	0	434
60	95	126	25	67	6	379
58	95	173	31	74	8	439
71	98	140	17	59	6	391
65	83	142	32	62	3	387
65	121	129	27	54	5	401
77	148	171	30	58	5	489
65	93	107	19	58	0	342
41	79	98	36	34	1	289
105	129	185	27	57	13	516
54	85	142	28	47	0	356
63	86	135	38	36	2	360
52	88	126	26	45	3	340
106	92	126	25	39	11	399
76	86	134	30	57	9	392
54	69	119	27	33	0	302
61	103	134	30	45	4	377
51	112	133	50	49	0	395
65	87	129	48	62	6	397
65	82	96	34	42	1	320
71	82	150	41	57	6	407
31	71	113	26	42	3	286
52	64	99	39	49	3	306
53	97	164	33	36	12	395
80	130	127	38	34	7	416
71	114	148	28	49	2	412
82	117	166	36	61	2	464
53	89	115	20	78	3	358
73	121	199	39	60	7	499
66	142	141	22	97	12	480
53	117	149	32	87	7	445
82	143	131	32	77	6	471
100	131	171	31	66	12	511
74	125	178	28	52	5	462
73	147	181	44	91	8	544
64	107	129	40	55	3	398

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time11 seconds
R Server'Gwilym Jenkins' @ jenkins.wessa.net

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 11 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ jenkins.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185286&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]11 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ jenkins.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185286&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185286&T=0

As an alternative you can also use a QR Code:  
QR Code


The GUIDs for individual cells are displayed in the table below:

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time11 seconds
R Server'Gwilym Jenkins' @ jenkins.wessa.net






Multiple Linear Regression - Estimated Regression Equation
totaal_aantal_faillissementen[t] = + 5.52246339358112e-14 + 1Industrie[t] + 1Bouw[t] + 1Handel[t] + 1Horeca[t] + 1`Financi\303\253le_dienstverlening`[t] + 1gezondheidszorg[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
totaal_aantal_faillissementen[t] =  +  5.52246339358112e-14 +  1Industrie[t] +  1Bouw[t] +  1Handel[t] +  1Horeca[t] +  1`Financi\303\253le_dienstverlening`[t] +  1gezondheidszorg[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185286&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]totaal_aantal_faillissementen[t] =  +  5.52246339358112e-14 +  1Industrie[t] +  1Bouw[t] +  1Handel[t] +  1Horeca[t] +  1`Financi\303\253le_dienstverlening`[t] +  1gezondheidszorg[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185286&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185286&T=1

As an alternative you can also use a QR Code:  
QR Code


The GUIDs for individual cells are displayed in the table below:

Multiple Linear Regression - Estimated Regression Equation
totaal_aantal_faillissementen[t] = + 5.52246339358112e-14 + 1Industrie[t] + 1Bouw[t] + 1Handel[t] + 1Horeca[t] + 1`Financi\303\253le_dienstverlening`[t] + 1gezondheidszorg[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)5.52246339358112e-1406.878500
Industrie10630984635441257400
Bouw10771504056079549200
Handel101033228696050211400
Horeca10447121341698367200
`Financi\303\253le_dienstverlening`10782743205230276300
gezondheidszorg10114076606932544300

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Ordinary Least Squares \tabularnewline
Variable & Parameter & S.D. & T-STATH0: parameter = 0 & 2-tail p-value & 1-tail p-value \tabularnewline
(Intercept) & 5.52246339358112e-14 & 0 & 6.8785 & 0 & 0 \tabularnewline
Industrie & 1 & 0 & 6309846354412574 & 0 & 0 \tabularnewline
Bouw & 1 & 0 & 7715040560795492 & 0 & 0 \tabularnewline
Handel & 1 & 0 & 10332286960502114 & 0 & 0 \tabularnewline
Horeca & 1 & 0 & 4471213416983672 & 0 & 0 \tabularnewline
`Financi\303\253le_dienstverlening` & 1 & 0 & 7827432052302763 & 0 & 0 \tabularnewline
gezondheidszorg & 1 & 0 & 1140766069325443 & 0 & 0 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185286&T=2

[TABLE]
[ROW][C]Multiple Linear Regression - Ordinary Least Squares[/C][/ROW]
[ROW][C]Variable[/C][C]Parameter[/C][C]S.D.[/C][C]T-STATH0: parameter = 0[/C][C]2-tail p-value[/C][C]1-tail p-value[/C][/ROW]
[ROW][C](Intercept)[/C][C]5.52246339358112e-14[/C][C]0[/C][C]6.8785[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Industrie[/C][C]1[/C][C]0[/C][C]6309846354412574[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Bouw[/C][C]1[/C][C]0[/C][C]7715040560795492[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Handel[/C][C]1[/C][C]0[/C][C]10332286960502114[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Horeca[/C][C]1[/C][C]0[/C][C]4471213416983672[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]`Financi\303\253le_dienstverlening`[/C][C]1[/C][C]0[/C][C]7827432052302763[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]gezondheidszorg[/C][C]1[/C][C]0[/C][C]1140766069325443[/C][C]0[/C][C]0[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185286&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185286&T=2

As an alternative you can also use a QR Code:  
QR Code


The GUIDs for individual cells are displayed in the table below:

Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)5.52246339358112e-1406.878500
Industrie10630984635441257400
Bouw10771504056079549200
Handel101033228696050211400
Horeca10447121341698367200
`Financi\303\253le_dienstverlening`10782743205230276300
gezondheidszorg10114076606932544300






Multiple Linear Regression - Regression Statistics
Multiple R1
R-squared1
Adjusted R-squared1
F-TEST (value)3.3931656029304e+32
F-TEST (DF numerator)6
F-TEST (DF denominator)229
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.81302979682973e-14
Sum Squared Residuals1.81210829006809e-25

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 1 \tabularnewline
R-squared & 1 \tabularnewline
Adjusted R-squared & 1 \tabularnewline
F-TEST (value) & 3.3931656029304e+32 \tabularnewline
F-TEST (DF numerator) & 6 \tabularnewline
F-TEST (DF denominator) & 229 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 2.81302979682973e-14 \tabularnewline
Sum Squared Residuals & 1.81210829006809e-25 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185286&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]1[/C][/ROW]
[ROW][C]R-squared[/C][C]1[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]1[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]3.3931656029304e+32[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]6[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]229[/C][/ROW]
[ROW][C]p-value[/C][C]0[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]2.81302979682973e-14[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]1.81210829006809e-25[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185286&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185286&T=3

As an alternative you can also use a QR Code:  
QR Code


The GUIDs for individual cells are displayed in the table below:

Multiple Linear Regression - Regression Statistics
Multiple R1
R-squared1
Adjusted R-squared1
F-TEST (value)3.3931656029304e+32
F-TEST (DF numerator)6
F-TEST (DF denominator)229
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.81302979682973e-14
Sum Squared Residuals1.81210829006809e-25






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
12522524.03785829928857e-13
2255255-5.84192650743475e-14
3404404-6.19825900963619e-15
4306306-3.58754103822894e-14
5276276-1.56903680101126e-14
63443444.53213675306535e-14
73403401.95613333935572e-16
8297297-9.09483912344494e-15
9345345-4.51960581632876e-15
10273273-5.60293429503933e-15
11333333-5.32302149932726e-15
12322322-6.09046223307207e-15
13300300-2.55920953143812e-15
14307307-5.14237840290863e-15
153803804.96505244810587e-15
16275275-1.48895607874764e-16
172702702.85554333918835e-15
183563561.58509968548298e-14
19255255-1.3432524688378e-14
20333333-6.70531984916407e-15
21306306-3.97086374521214e-15
22266266-5.43266132367192e-16
23339339-2.02883613002546e-15
24256256-9.18508495286011e-15
25283283-1.66752646139642e-15
262692695.77866820487169e-15
273093095.85628560737786e-16
28263263-4.08821749901804e-15
29268268-1.27652031416849e-15
30269269-2.65751351228944e-15
31257257-7.64651803636983e-15
32269269-6.12620186767517e-15
33258258-5.87667018293084e-15
34289289-1.55153255911191e-15
35279279-1.17761177844507e-14
362142142.0931889669574e-15
37306306-1.76755065107884e-15
38262262-1.26279105166358e-15
39266266-2.6874267308277e-15
40250250-6.45254854800818e-15
41260260-1.78728290117255e-15
42259259-3.00075089828705e-15
43248248-1.02264004073573e-14
44233233-3.62238747159633e-15
45251251-7.82335046918773e-15
46292292-7.5148238105658e-15
472272274.20979892808007e-17
48231231-8.96162041779693e-15
492772774.01357181234945e-16
50259259-4.10268906450505e-15
512292293.4076415218173e-15
52247247-1.17876431823324e-14
53246246-5.48117884051284e-15
542272273.02663799336714e-15
55270270-4.77477584027962e-15
56201201-2.06605425068185e-15
57265265-9.65280518756684e-16
58271271-7.21710330993843e-15
59250250-4.94360561356464e-15
60237237-1.02461118530128e-14
612212217.92261177098496e-16
622202201.88230239924117e-15
63279279-6.58702969322543e-15
64247247-8.46814406779883e-15
651921923.77181737675228e-15
66248248-5.91545134109724e-15
67226226-2.73397386944172e-15
681881889.12211375095781e-15
69299299-1.27539251622862e-15
70211211-2.52578619761422e-15
711881883.14659955005803e-15
721791795.19493108562732e-15
73149149-1.53866869776593e-14
741851854.88569089523233e-15
75236236-3.37491029943921e-15
762042043.75395192442314e-15
771661666.60432810386625e-15
78218218-5.40600509344828e-15
79148148-1.24553712566783e-14
80162162-1.55061775822295e-14
81173173-1.01571766255238e-15
82153153-1.42439834706394e-14
83181181-4.49093555267958e-15
841701703.15423265280186e-15
85160160-6.4308186689974e-15
86190190-7.66062233038408e-16
872032031.81038394174582e-15
88150150-4.76528113776096e-15
892112117.61938648139692e-17
90162162-1.50127845211783e-14
91174174-7.65904786950017e-16
92222222-4.92413308707159e-15
931781781.64238170215295e-15
94229229-8.53248292627627e-15
95247247-4.77519072508711e-15
961801802.64136020256263e-15
972192191.16343110604293e-15
982092095.89766393687948e-17
992022027.5963825211622e-15
100202202-3.43681878467963e-15
101250250-4.98997336715108e-15
1021891891.53393302958922e-15
103234234-5.36333918789656e-15
104241241-5.79145753956258e-15
1052112111.07345127643039e-15
106256256-7.35050070003397e-15
107220220-4.82150762776776e-15
108193193-1.38131160334644e-14
109290290-5.00753383336561e-15
110238238-1.20220151453967e-14
111240240-1.27158984519284e-14
1121931931.33402465513625e-15
113247247-1.16282460312783e-14
114245245-9.17310793491945e-15
1152972972.82707078887966e-15
1162182181.14657301088953e-14
117243243-1.06823201828367e-14
1182932935.84514974261385e-16
1192112116.47963036757429e-15
120242242-7.19533904672582e-15
121297297-4.34712325095105e-16
122278278-2.23030319563382e-15
123287287-1.78874383441621e-15
1243703709.07505679928313e-15
125352352-5.89674958431437e-15
126353353-4.73019848120257e-15
127331331-6.940529749379e-15
1282722729.3451737416527e-15
129401401-4.60207498798898e-15
130335335-2.83980020997866e-15
1313143142.21206405316133e-15
1323093091.83301738675507e-15
133343343-2.27824177459443e-15
134337337-2.56229317764999e-15
135462462-4.85077734363226e-15
136310310-8.52338981746998e-16
1373173171.46374057515752e-15
1383873879.03067669842329e-16
139321321-2.44174856545483e-16
140354354-4.49227529460853e-15
141354354-6.08405356527478e-15
142310310-3.59857190773066e-15
143395395-1.64814816091733e-15
1443273271.91266925814986e-15
1453493493.39377254961322e-15
146389389-2.96260424953827e-15
147427427-1.62776173674122e-15
148326326-6.73754243522582e-15
1493583583.55420079557036e-15
1504054056.6780108326712e-15
151330330-5.33122419704152e-15
152346346-2.18850140569691e-15
1533953958.02198512142492e-15
1543063065.6680025641304e-16
155371371-8.00105118747082e-15
1563033031.88657463991067e-15
157345345-1.64973000410195e-15
1583433433.94720239379526e-15
159348348-2.24530474377703e-15
1603263265.90733940555227e-15
1613463468.06918689881815e-15
1623213219.38219970971832e-15
1632922926.89162177975199e-15
1643433437.86107381509455e-15
1652792798.18632745035535e-15
1662972975.720664596246e-15
1672982986.89735536508333e-15
1682412412.16978594086801e-15
1693103108.64445207516161e-15
1702202205.38393253537911e-15
1712292293.17678199280198e-17
172232232-2.9912290504133e-15
1732722721.38604367311386e-15
1742232238.11670048354231e-15
175248248-3.9878745333072e-15
1762662666.34814029257498e-16
177245245-9.50014583487218e-15
1783003001.06868295454307e-15
179233233-2.48627373977135e-16
180207207-9.27583159986701e-16
1812512511.33779649201863e-15
1822152153.79986876766545e-16
1832082087.1856069858165e-15
184252252-8.41812785988633e-15
1851661669.66653433039894e-16
186238238-8.31578890222451e-15
187244244-5.40742972998485e-15
188234234-9.4701770405001e-15
189273273-5.25407330816427e-15
1902602601.48136655149494e-18
1913123125.91209482223254e-16
192305305-3.82792048734735e-15
1933623624.94735120333166e-15
1943893893.21293024980292e-15
195492492-1.51767929281926e-14
196487487-3.50533982301806e-14
197357357-5.33020139335334e-15
1985055057.48662715579884e-15
199448448-1.10365442186694e-14
2003453456.80802405919443e-16
201434434-2.26891423242688e-14
2023793796.88602778821162e-15
203439439-1.2408206584292e-14
2043913914.19484022223571e-15
2053873872.10915401720704e-15
2064014013.65608698117103e-15
2074894894.65576553917683e-15
208342342-1.02035470430174e-15
2092892895.24368638057159e-15
210516516-8.73172981253657e-15
2113563561.68126566997077e-16
2123603603.32771057559326e-15
213340340-2.70557865667373e-15
2143993993.65069538644716e-15
2153923925.46668055933456e-15
2163023021.85704626673114e-15
2173773773.04252776456733e-15
2183953951.14911055375505e-14
2193973971.35138982747523e-14
2203203207.82332374195907e-15
2214074079.42404599931171e-15
2222862861.62265580091258e-15
2233063065.34222040932643e-15
2243953951.21034955388982e-14
2254164161.94435880435367e-14
2264124121.64761616244076e-15
2274644642.87759751886903e-15
2283583584.22656045687789e-15
2294994998.20834551917277e-17
230480480-6.48784483881044e-16
231445445-2.20570365980277e-14
2324714711.38640246481875e-15
2335115113.60106363807204e-15
234462462-3.50507915831599e-15
2355445448.85353742243196e-16
2363983986.19726279152269e-15

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 252 & 252 & 4.03785829928857e-13 \tabularnewline
2 & 255 & 255 & -5.84192650743475e-14 \tabularnewline
3 & 404 & 404 & -6.19825900963619e-15 \tabularnewline
4 & 306 & 306 & -3.58754103822894e-14 \tabularnewline
5 & 276 & 276 & -1.56903680101126e-14 \tabularnewline
6 & 344 & 344 & 4.53213675306535e-14 \tabularnewline
7 & 340 & 340 & 1.95613333935572e-16 \tabularnewline
8 & 297 & 297 & -9.09483912344494e-15 \tabularnewline
9 & 345 & 345 & -4.51960581632876e-15 \tabularnewline
10 & 273 & 273 & -5.60293429503933e-15 \tabularnewline
11 & 333 & 333 & -5.32302149932726e-15 \tabularnewline
12 & 322 & 322 & -6.09046223307207e-15 \tabularnewline
13 & 300 & 300 & -2.55920953143812e-15 \tabularnewline
14 & 307 & 307 & -5.14237840290863e-15 \tabularnewline
15 & 380 & 380 & 4.96505244810587e-15 \tabularnewline
16 & 275 & 275 & -1.48895607874764e-16 \tabularnewline
17 & 270 & 270 & 2.85554333918835e-15 \tabularnewline
18 & 356 & 356 & 1.58509968548298e-14 \tabularnewline
19 & 255 & 255 & -1.3432524688378e-14 \tabularnewline
20 & 333 & 333 & -6.70531984916407e-15 \tabularnewline
21 & 306 & 306 & -3.97086374521214e-15 \tabularnewline
22 & 266 & 266 & -5.43266132367192e-16 \tabularnewline
23 & 339 & 339 & -2.02883613002546e-15 \tabularnewline
24 & 256 & 256 & -9.18508495286011e-15 \tabularnewline
25 & 283 & 283 & -1.66752646139642e-15 \tabularnewline
26 & 269 & 269 & 5.77866820487169e-15 \tabularnewline
27 & 309 & 309 & 5.85628560737786e-16 \tabularnewline
28 & 263 & 263 & -4.08821749901804e-15 \tabularnewline
29 & 268 & 268 & -1.27652031416849e-15 \tabularnewline
30 & 269 & 269 & -2.65751351228944e-15 \tabularnewline
31 & 257 & 257 & -7.64651803636983e-15 \tabularnewline
32 & 269 & 269 & -6.12620186767517e-15 \tabularnewline
33 & 258 & 258 & -5.87667018293084e-15 \tabularnewline
34 & 289 & 289 & -1.55153255911191e-15 \tabularnewline
35 & 279 & 279 & -1.17761177844507e-14 \tabularnewline
36 & 214 & 214 & 2.0931889669574e-15 \tabularnewline
37 & 306 & 306 & -1.76755065107884e-15 \tabularnewline
38 & 262 & 262 & -1.26279105166358e-15 \tabularnewline
39 & 266 & 266 & -2.6874267308277e-15 \tabularnewline
40 & 250 & 250 & -6.45254854800818e-15 \tabularnewline
41 & 260 & 260 & -1.78728290117255e-15 \tabularnewline
42 & 259 & 259 & -3.00075089828705e-15 \tabularnewline
43 & 248 & 248 & -1.02264004073573e-14 \tabularnewline
44 & 233 & 233 & -3.62238747159633e-15 \tabularnewline
45 & 251 & 251 & -7.82335046918773e-15 \tabularnewline
46 & 292 & 292 & -7.5148238105658e-15 \tabularnewline
47 & 227 & 227 & 4.20979892808007e-17 \tabularnewline
48 & 231 & 231 & -8.96162041779693e-15 \tabularnewline
49 & 277 & 277 & 4.01357181234945e-16 \tabularnewline
50 & 259 & 259 & -4.10268906450505e-15 \tabularnewline
51 & 229 & 229 & 3.4076415218173e-15 \tabularnewline
52 & 247 & 247 & -1.17876431823324e-14 \tabularnewline
53 & 246 & 246 & -5.48117884051284e-15 \tabularnewline
54 & 227 & 227 & 3.02663799336714e-15 \tabularnewline
55 & 270 & 270 & -4.77477584027962e-15 \tabularnewline
56 & 201 & 201 & -2.06605425068185e-15 \tabularnewline
57 & 265 & 265 & -9.65280518756684e-16 \tabularnewline
58 & 271 & 271 & -7.21710330993843e-15 \tabularnewline
59 & 250 & 250 & -4.94360561356464e-15 \tabularnewline
60 & 237 & 237 & -1.02461118530128e-14 \tabularnewline
61 & 221 & 221 & 7.92261177098496e-16 \tabularnewline
62 & 220 & 220 & 1.88230239924117e-15 \tabularnewline
63 & 279 & 279 & -6.58702969322543e-15 \tabularnewline
64 & 247 & 247 & -8.46814406779883e-15 \tabularnewline
65 & 192 & 192 & 3.77181737675228e-15 \tabularnewline
66 & 248 & 248 & -5.91545134109724e-15 \tabularnewline
67 & 226 & 226 & -2.73397386944172e-15 \tabularnewline
68 & 188 & 188 & 9.12211375095781e-15 \tabularnewline
69 & 299 & 299 & -1.27539251622862e-15 \tabularnewline
70 & 211 & 211 & -2.52578619761422e-15 \tabularnewline
71 & 188 & 188 & 3.14659955005803e-15 \tabularnewline
72 & 179 & 179 & 5.19493108562732e-15 \tabularnewline
73 & 149 & 149 & -1.53866869776593e-14 \tabularnewline
74 & 185 & 185 & 4.88569089523233e-15 \tabularnewline
75 & 236 & 236 & -3.37491029943921e-15 \tabularnewline
76 & 204 & 204 & 3.75395192442314e-15 \tabularnewline
77 & 166 & 166 & 6.60432810386625e-15 \tabularnewline
78 & 218 & 218 & -5.40600509344828e-15 \tabularnewline
79 & 148 & 148 & -1.24553712566783e-14 \tabularnewline
80 & 162 & 162 & -1.55061775822295e-14 \tabularnewline
81 & 173 & 173 & -1.01571766255238e-15 \tabularnewline
82 & 153 & 153 & -1.42439834706394e-14 \tabularnewline
83 & 181 & 181 & -4.49093555267958e-15 \tabularnewline
84 & 170 & 170 & 3.15423265280186e-15 \tabularnewline
85 & 160 & 160 & -6.4308186689974e-15 \tabularnewline
86 & 190 & 190 & -7.66062233038408e-16 \tabularnewline
87 & 203 & 203 & 1.81038394174582e-15 \tabularnewline
88 & 150 & 150 & -4.76528113776096e-15 \tabularnewline
89 & 211 & 211 & 7.61938648139692e-17 \tabularnewline
90 & 162 & 162 & -1.50127845211783e-14 \tabularnewline
91 & 174 & 174 & -7.65904786950017e-16 \tabularnewline
92 & 222 & 222 & -4.92413308707159e-15 \tabularnewline
93 & 178 & 178 & 1.64238170215295e-15 \tabularnewline
94 & 229 & 229 & -8.53248292627627e-15 \tabularnewline
95 & 247 & 247 & -4.77519072508711e-15 \tabularnewline
96 & 180 & 180 & 2.64136020256263e-15 \tabularnewline
97 & 219 & 219 & 1.16343110604293e-15 \tabularnewline
98 & 209 & 209 & 5.89766393687948e-17 \tabularnewline
99 & 202 & 202 & 7.5963825211622e-15 \tabularnewline
100 & 202 & 202 & -3.43681878467963e-15 \tabularnewline
101 & 250 & 250 & -4.98997336715108e-15 \tabularnewline
102 & 189 & 189 & 1.53393302958922e-15 \tabularnewline
103 & 234 & 234 & -5.36333918789656e-15 \tabularnewline
104 & 241 & 241 & -5.79145753956258e-15 \tabularnewline
105 & 211 & 211 & 1.07345127643039e-15 \tabularnewline
106 & 256 & 256 & -7.35050070003397e-15 \tabularnewline
107 & 220 & 220 & -4.82150762776776e-15 \tabularnewline
108 & 193 & 193 & -1.38131160334644e-14 \tabularnewline
109 & 290 & 290 & -5.00753383336561e-15 \tabularnewline
110 & 238 & 238 & -1.20220151453967e-14 \tabularnewline
111 & 240 & 240 & -1.27158984519284e-14 \tabularnewline
112 & 193 & 193 & 1.33402465513625e-15 \tabularnewline
113 & 247 & 247 & -1.16282460312783e-14 \tabularnewline
114 & 245 & 245 & -9.17310793491945e-15 \tabularnewline
115 & 297 & 297 & 2.82707078887966e-15 \tabularnewline
116 & 218 & 218 & 1.14657301088953e-14 \tabularnewline
117 & 243 & 243 & -1.06823201828367e-14 \tabularnewline
118 & 293 & 293 & 5.84514974261385e-16 \tabularnewline
119 & 211 & 211 & 6.47963036757429e-15 \tabularnewline
120 & 242 & 242 & -7.19533904672582e-15 \tabularnewline
121 & 297 & 297 & -4.34712325095105e-16 \tabularnewline
122 & 278 & 278 & -2.23030319563382e-15 \tabularnewline
123 & 287 & 287 & -1.78874383441621e-15 \tabularnewline
124 & 370 & 370 & 9.07505679928313e-15 \tabularnewline
125 & 352 & 352 & -5.89674958431437e-15 \tabularnewline
126 & 353 & 353 & -4.73019848120257e-15 \tabularnewline
127 & 331 & 331 & -6.940529749379e-15 \tabularnewline
128 & 272 & 272 & 9.3451737416527e-15 \tabularnewline
129 & 401 & 401 & -4.60207498798898e-15 \tabularnewline
130 & 335 & 335 & -2.83980020997866e-15 \tabularnewline
131 & 314 & 314 & 2.21206405316133e-15 \tabularnewline
132 & 309 & 309 & 1.83301738675507e-15 \tabularnewline
133 & 343 & 343 & -2.27824177459443e-15 \tabularnewline
134 & 337 & 337 & -2.56229317764999e-15 \tabularnewline
135 & 462 & 462 & -4.85077734363226e-15 \tabularnewline
136 & 310 & 310 & -8.52338981746998e-16 \tabularnewline
137 & 317 & 317 & 1.46374057515752e-15 \tabularnewline
138 & 387 & 387 & 9.03067669842329e-16 \tabularnewline
139 & 321 & 321 & -2.44174856545483e-16 \tabularnewline
140 & 354 & 354 & -4.49227529460853e-15 \tabularnewline
141 & 354 & 354 & -6.08405356527478e-15 \tabularnewline
142 & 310 & 310 & -3.59857190773066e-15 \tabularnewline
143 & 395 & 395 & -1.64814816091733e-15 \tabularnewline
144 & 327 & 327 & 1.91266925814986e-15 \tabularnewline
145 & 349 & 349 & 3.39377254961322e-15 \tabularnewline
146 & 389 & 389 & -2.96260424953827e-15 \tabularnewline
147 & 427 & 427 & -1.62776173674122e-15 \tabularnewline
148 & 326 & 326 & -6.73754243522582e-15 \tabularnewline
149 & 358 & 358 & 3.55420079557036e-15 \tabularnewline
150 & 405 & 405 & 6.6780108326712e-15 \tabularnewline
151 & 330 & 330 & -5.33122419704152e-15 \tabularnewline
152 & 346 & 346 & -2.18850140569691e-15 \tabularnewline
153 & 395 & 395 & 8.02198512142492e-15 \tabularnewline
154 & 306 & 306 & 5.6680025641304e-16 \tabularnewline
155 & 371 & 371 & -8.00105118747082e-15 \tabularnewline
156 & 303 & 303 & 1.88657463991067e-15 \tabularnewline
157 & 345 & 345 & -1.64973000410195e-15 \tabularnewline
158 & 343 & 343 & 3.94720239379526e-15 \tabularnewline
159 & 348 & 348 & -2.24530474377703e-15 \tabularnewline
160 & 326 & 326 & 5.90733940555227e-15 \tabularnewline
161 & 346 & 346 & 8.06918689881815e-15 \tabularnewline
162 & 321 & 321 & 9.38219970971832e-15 \tabularnewline
163 & 292 & 292 & 6.89162177975199e-15 \tabularnewline
164 & 343 & 343 & 7.86107381509455e-15 \tabularnewline
165 & 279 & 279 & 8.18632745035535e-15 \tabularnewline
166 & 297 & 297 & 5.720664596246e-15 \tabularnewline
167 & 298 & 298 & 6.89735536508333e-15 \tabularnewline
168 & 241 & 241 & 2.16978594086801e-15 \tabularnewline
169 & 310 & 310 & 8.64445207516161e-15 \tabularnewline
170 & 220 & 220 & 5.38393253537911e-15 \tabularnewline
171 & 229 & 229 & 3.17678199280198e-17 \tabularnewline
172 & 232 & 232 & -2.9912290504133e-15 \tabularnewline
173 & 272 & 272 & 1.38604367311386e-15 \tabularnewline
174 & 223 & 223 & 8.11670048354231e-15 \tabularnewline
175 & 248 & 248 & -3.9878745333072e-15 \tabularnewline
176 & 266 & 266 & 6.34814029257498e-16 \tabularnewline
177 & 245 & 245 & -9.50014583487218e-15 \tabularnewline
178 & 300 & 300 & 1.06868295454307e-15 \tabularnewline
179 & 233 & 233 & -2.48627373977135e-16 \tabularnewline
180 & 207 & 207 & -9.27583159986701e-16 \tabularnewline
181 & 251 & 251 & 1.33779649201863e-15 \tabularnewline
182 & 215 & 215 & 3.79986876766545e-16 \tabularnewline
183 & 208 & 208 & 7.1856069858165e-15 \tabularnewline
184 & 252 & 252 & -8.41812785988633e-15 \tabularnewline
185 & 166 & 166 & 9.66653433039894e-16 \tabularnewline
186 & 238 & 238 & -8.31578890222451e-15 \tabularnewline
187 & 244 & 244 & -5.40742972998485e-15 \tabularnewline
188 & 234 & 234 & -9.4701770405001e-15 \tabularnewline
189 & 273 & 273 & -5.25407330816427e-15 \tabularnewline
190 & 260 & 260 & 1.48136655149494e-18 \tabularnewline
191 & 312 & 312 & 5.91209482223254e-16 \tabularnewline
192 & 305 & 305 & -3.82792048734735e-15 \tabularnewline
193 & 362 & 362 & 4.94735120333166e-15 \tabularnewline
194 & 389 & 389 & 3.21293024980292e-15 \tabularnewline
195 & 492 & 492 & -1.51767929281926e-14 \tabularnewline
196 & 487 & 487 & -3.50533982301806e-14 \tabularnewline
197 & 357 & 357 & -5.33020139335334e-15 \tabularnewline
198 & 505 & 505 & 7.48662715579884e-15 \tabularnewline
199 & 448 & 448 & -1.10365442186694e-14 \tabularnewline
200 & 345 & 345 & 6.80802405919443e-16 \tabularnewline
201 & 434 & 434 & -2.26891423242688e-14 \tabularnewline
202 & 379 & 379 & 6.88602778821162e-15 \tabularnewline
203 & 439 & 439 & -1.2408206584292e-14 \tabularnewline
204 & 391 & 391 & 4.19484022223571e-15 \tabularnewline
205 & 387 & 387 & 2.10915401720704e-15 \tabularnewline
206 & 401 & 401 & 3.65608698117103e-15 \tabularnewline
207 & 489 & 489 & 4.65576553917683e-15 \tabularnewline
208 & 342 & 342 & -1.02035470430174e-15 \tabularnewline
209 & 289 & 289 & 5.24368638057159e-15 \tabularnewline
210 & 516 & 516 & -8.73172981253657e-15 \tabularnewline
211 & 356 & 356 & 1.68126566997077e-16 \tabularnewline
212 & 360 & 360 & 3.32771057559326e-15 \tabularnewline
213 & 340 & 340 & -2.70557865667373e-15 \tabularnewline
214 & 399 & 399 & 3.65069538644716e-15 \tabularnewline
215 & 392 & 392 & 5.46668055933456e-15 \tabularnewline
216 & 302 & 302 & 1.85704626673114e-15 \tabularnewline
217 & 377 & 377 & 3.04252776456733e-15 \tabularnewline
218 & 395 & 395 & 1.14911055375505e-14 \tabularnewline
219 & 397 & 397 & 1.35138982747523e-14 \tabularnewline
220 & 320 & 320 & 7.82332374195907e-15 \tabularnewline
221 & 407 & 407 & 9.42404599931171e-15 \tabularnewline
222 & 286 & 286 & 1.62265580091258e-15 \tabularnewline
223 & 306 & 306 & 5.34222040932643e-15 \tabularnewline
224 & 395 & 395 & 1.21034955388982e-14 \tabularnewline
225 & 416 & 416 & 1.94435880435367e-14 \tabularnewline
226 & 412 & 412 & 1.64761616244076e-15 \tabularnewline
227 & 464 & 464 & 2.87759751886903e-15 \tabularnewline
228 & 358 & 358 & 4.22656045687789e-15 \tabularnewline
229 & 499 & 499 & 8.20834551917277e-17 \tabularnewline
230 & 480 & 480 & -6.48784483881044e-16 \tabularnewline
231 & 445 & 445 & -2.20570365980277e-14 \tabularnewline
232 & 471 & 471 & 1.38640246481875e-15 \tabularnewline
233 & 511 & 511 & 3.60106363807204e-15 \tabularnewline
234 & 462 & 462 & -3.50507915831599e-15 \tabularnewline
235 & 544 & 544 & 8.85353742243196e-16 \tabularnewline
236 & 398 & 398 & 6.19726279152269e-15 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185286&T=4

[TABLE]
[ROW][C]Multiple Linear Regression - Actuals, Interpolation, and Residuals[/C][/ROW]
[ROW][C]Time or Index[/C][C]Actuals[/C][C]InterpolationForecast[/C][C]ResidualsPrediction Error[/C][/ROW]
[ROW][C]1[/C][C]252[/C][C]252[/C][C]4.03785829928857e-13[/C][/ROW]
[ROW][C]2[/C][C]255[/C][C]255[/C][C]-5.84192650743475e-14[/C][/ROW]
[ROW][C]3[/C][C]404[/C][C]404[/C][C]-6.19825900963619e-15[/C][/ROW]
[ROW][C]4[/C][C]306[/C][C]306[/C][C]-3.58754103822894e-14[/C][/ROW]
[ROW][C]5[/C][C]276[/C][C]276[/C][C]-1.56903680101126e-14[/C][/ROW]
[ROW][C]6[/C][C]344[/C][C]344[/C][C]4.53213675306535e-14[/C][/ROW]
[ROW][C]7[/C][C]340[/C][C]340[/C][C]1.95613333935572e-16[/C][/ROW]
[ROW][C]8[/C][C]297[/C][C]297[/C][C]-9.09483912344494e-15[/C][/ROW]
[ROW][C]9[/C][C]345[/C][C]345[/C][C]-4.51960581632876e-15[/C][/ROW]
[ROW][C]10[/C][C]273[/C][C]273[/C][C]-5.60293429503933e-15[/C][/ROW]
[ROW][C]11[/C][C]333[/C][C]333[/C][C]-5.32302149932726e-15[/C][/ROW]
[ROW][C]12[/C][C]322[/C][C]322[/C][C]-6.09046223307207e-15[/C][/ROW]
[ROW][C]13[/C][C]300[/C][C]300[/C][C]-2.55920953143812e-15[/C][/ROW]
[ROW][C]14[/C][C]307[/C][C]307[/C][C]-5.14237840290863e-15[/C][/ROW]
[ROW][C]15[/C][C]380[/C][C]380[/C][C]4.96505244810587e-15[/C][/ROW]
[ROW][C]16[/C][C]275[/C][C]275[/C][C]-1.48895607874764e-16[/C][/ROW]
[ROW][C]17[/C][C]270[/C][C]270[/C][C]2.85554333918835e-15[/C][/ROW]
[ROW][C]18[/C][C]356[/C][C]356[/C][C]1.58509968548298e-14[/C][/ROW]
[ROW][C]19[/C][C]255[/C][C]255[/C][C]-1.3432524688378e-14[/C][/ROW]
[ROW][C]20[/C][C]333[/C][C]333[/C][C]-6.70531984916407e-15[/C][/ROW]
[ROW][C]21[/C][C]306[/C][C]306[/C][C]-3.97086374521214e-15[/C][/ROW]
[ROW][C]22[/C][C]266[/C][C]266[/C][C]-5.43266132367192e-16[/C][/ROW]
[ROW][C]23[/C][C]339[/C][C]339[/C][C]-2.02883613002546e-15[/C][/ROW]
[ROW][C]24[/C][C]256[/C][C]256[/C][C]-9.18508495286011e-15[/C][/ROW]
[ROW][C]25[/C][C]283[/C][C]283[/C][C]-1.66752646139642e-15[/C][/ROW]
[ROW][C]26[/C][C]269[/C][C]269[/C][C]5.77866820487169e-15[/C][/ROW]
[ROW][C]27[/C][C]309[/C][C]309[/C][C]5.85628560737786e-16[/C][/ROW]
[ROW][C]28[/C][C]263[/C][C]263[/C][C]-4.08821749901804e-15[/C][/ROW]
[ROW][C]29[/C][C]268[/C][C]268[/C][C]-1.27652031416849e-15[/C][/ROW]
[ROW][C]30[/C][C]269[/C][C]269[/C][C]-2.65751351228944e-15[/C][/ROW]
[ROW][C]31[/C][C]257[/C][C]257[/C][C]-7.64651803636983e-15[/C][/ROW]
[ROW][C]32[/C][C]269[/C][C]269[/C][C]-6.12620186767517e-15[/C][/ROW]
[ROW][C]33[/C][C]258[/C][C]258[/C][C]-5.87667018293084e-15[/C][/ROW]
[ROW][C]34[/C][C]289[/C][C]289[/C][C]-1.55153255911191e-15[/C][/ROW]
[ROW][C]35[/C][C]279[/C][C]279[/C][C]-1.17761177844507e-14[/C][/ROW]
[ROW][C]36[/C][C]214[/C][C]214[/C][C]2.0931889669574e-15[/C][/ROW]
[ROW][C]37[/C][C]306[/C][C]306[/C][C]-1.76755065107884e-15[/C][/ROW]
[ROW][C]38[/C][C]262[/C][C]262[/C][C]-1.26279105166358e-15[/C][/ROW]
[ROW][C]39[/C][C]266[/C][C]266[/C][C]-2.6874267308277e-15[/C][/ROW]
[ROW][C]40[/C][C]250[/C][C]250[/C][C]-6.45254854800818e-15[/C][/ROW]
[ROW][C]41[/C][C]260[/C][C]260[/C][C]-1.78728290117255e-15[/C][/ROW]
[ROW][C]42[/C][C]259[/C][C]259[/C][C]-3.00075089828705e-15[/C][/ROW]
[ROW][C]43[/C][C]248[/C][C]248[/C][C]-1.02264004073573e-14[/C][/ROW]
[ROW][C]44[/C][C]233[/C][C]233[/C][C]-3.62238747159633e-15[/C][/ROW]
[ROW][C]45[/C][C]251[/C][C]251[/C][C]-7.82335046918773e-15[/C][/ROW]
[ROW][C]46[/C][C]292[/C][C]292[/C][C]-7.5148238105658e-15[/C][/ROW]
[ROW][C]47[/C][C]227[/C][C]227[/C][C]4.20979892808007e-17[/C][/ROW]
[ROW][C]48[/C][C]231[/C][C]231[/C][C]-8.96162041779693e-15[/C][/ROW]
[ROW][C]49[/C][C]277[/C][C]277[/C][C]4.01357181234945e-16[/C][/ROW]
[ROW][C]50[/C][C]259[/C][C]259[/C][C]-4.10268906450505e-15[/C][/ROW]
[ROW][C]51[/C][C]229[/C][C]229[/C][C]3.4076415218173e-15[/C][/ROW]
[ROW][C]52[/C][C]247[/C][C]247[/C][C]-1.17876431823324e-14[/C][/ROW]
[ROW][C]53[/C][C]246[/C][C]246[/C][C]-5.48117884051284e-15[/C][/ROW]
[ROW][C]54[/C][C]227[/C][C]227[/C][C]3.02663799336714e-15[/C][/ROW]
[ROW][C]55[/C][C]270[/C][C]270[/C][C]-4.77477584027962e-15[/C][/ROW]
[ROW][C]56[/C][C]201[/C][C]201[/C][C]-2.06605425068185e-15[/C][/ROW]
[ROW][C]57[/C][C]265[/C][C]265[/C][C]-9.65280518756684e-16[/C][/ROW]
[ROW][C]58[/C][C]271[/C][C]271[/C][C]-7.21710330993843e-15[/C][/ROW]
[ROW][C]59[/C][C]250[/C][C]250[/C][C]-4.94360561356464e-15[/C][/ROW]
[ROW][C]60[/C][C]237[/C][C]237[/C][C]-1.02461118530128e-14[/C][/ROW]
[ROW][C]61[/C][C]221[/C][C]221[/C][C]7.92261177098496e-16[/C][/ROW]
[ROW][C]62[/C][C]220[/C][C]220[/C][C]1.88230239924117e-15[/C][/ROW]
[ROW][C]63[/C][C]279[/C][C]279[/C][C]-6.58702969322543e-15[/C][/ROW]
[ROW][C]64[/C][C]247[/C][C]247[/C][C]-8.46814406779883e-15[/C][/ROW]
[ROW][C]65[/C][C]192[/C][C]192[/C][C]3.77181737675228e-15[/C][/ROW]
[ROW][C]66[/C][C]248[/C][C]248[/C][C]-5.91545134109724e-15[/C][/ROW]
[ROW][C]67[/C][C]226[/C][C]226[/C][C]-2.73397386944172e-15[/C][/ROW]
[ROW][C]68[/C][C]188[/C][C]188[/C][C]9.12211375095781e-15[/C][/ROW]
[ROW][C]69[/C][C]299[/C][C]299[/C][C]-1.27539251622862e-15[/C][/ROW]
[ROW][C]70[/C][C]211[/C][C]211[/C][C]-2.52578619761422e-15[/C][/ROW]
[ROW][C]71[/C][C]188[/C][C]188[/C][C]3.14659955005803e-15[/C][/ROW]
[ROW][C]72[/C][C]179[/C][C]179[/C][C]5.19493108562732e-15[/C][/ROW]
[ROW][C]73[/C][C]149[/C][C]149[/C][C]-1.53866869776593e-14[/C][/ROW]
[ROW][C]74[/C][C]185[/C][C]185[/C][C]4.88569089523233e-15[/C][/ROW]
[ROW][C]75[/C][C]236[/C][C]236[/C][C]-3.37491029943921e-15[/C][/ROW]
[ROW][C]76[/C][C]204[/C][C]204[/C][C]3.75395192442314e-15[/C][/ROW]
[ROW][C]77[/C][C]166[/C][C]166[/C][C]6.60432810386625e-15[/C][/ROW]
[ROW][C]78[/C][C]218[/C][C]218[/C][C]-5.40600509344828e-15[/C][/ROW]
[ROW][C]79[/C][C]148[/C][C]148[/C][C]-1.24553712566783e-14[/C][/ROW]
[ROW][C]80[/C][C]162[/C][C]162[/C][C]-1.55061775822295e-14[/C][/ROW]
[ROW][C]81[/C][C]173[/C][C]173[/C][C]-1.01571766255238e-15[/C][/ROW]
[ROW][C]82[/C][C]153[/C][C]153[/C][C]-1.42439834706394e-14[/C][/ROW]
[ROW][C]83[/C][C]181[/C][C]181[/C][C]-4.49093555267958e-15[/C][/ROW]
[ROW][C]84[/C][C]170[/C][C]170[/C][C]3.15423265280186e-15[/C][/ROW]
[ROW][C]85[/C][C]160[/C][C]160[/C][C]-6.4308186689974e-15[/C][/ROW]
[ROW][C]86[/C][C]190[/C][C]190[/C][C]-7.66062233038408e-16[/C][/ROW]
[ROW][C]87[/C][C]203[/C][C]203[/C][C]1.81038394174582e-15[/C][/ROW]
[ROW][C]88[/C][C]150[/C][C]150[/C][C]-4.76528113776096e-15[/C][/ROW]
[ROW][C]89[/C][C]211[/C][C]211[/C][C]7.61938648139692e-17[/C][/ROW]
[ROW][C]90[/C][C]162[/C][C]162[/C][C]-1.50127845211783e-14[/C][/ROW]
[ROW][C]91[/C][C]174[/C][C]174[/C][C]-7.65904786950017e-16[/C][/ROW]
[ROW][C]92[/C][C]222[/C][C]222[/C][C]-4.92413308707159e-15[/C][/ROW]
[ROW][C]93[/C][C]178[/C][C]178[/C][C]1.64238170215295e-15[/C][/ROW]
[ROW][C]94[/C][C]229[/C][C]229[/C][C]-8.53248292627627e-15[/C][/ROW]
[ROW][C]95[/C][C]247[/C][C]247[/C][C]-4.77519072508711e-15[/C][/ROW]
[ROW][C]96[/C][C]180[/C][C]180[/C][C]2.64136020256263e-15[/C][/ROW]
[ROW][C]97[/C][C]219[/C][C]219[/C][C]1.16343110604293e-15[/C][/ROW]
[ROW][C]98[/C][C]209[/C][C]209[/C][C]5.89766393687948e-17[/C][/ROW]
[ROW][C]99[/C][C]202[/C][C]202[/C][C]7.5963825211622e-15[/C][/ROW]
[ROW][C]100[/C][C]202[/C][C]202[/C][C]-3.43681878467963e-15[/C][/ROW]
[ROW][C]101[/C][C]250[/C][C]250[/C][C]-4.98997336715108e-15[/C][/ROW]
[ROW][C]102[/C][C]189[/C][C]189[/C][C]1.53393302958922e-15[/C][/ROW]
[ROW][C]103[/C][C]234[/C][C]234[/C][C]-5.36333918789656e-15[/C][/ROW]
[ROW][C]104[/C][C]241[/C][C]241[/C][C]-5.79145753956258e-15[/C][/ROW]
[ROW][C]105[/C][C]211[/C][C]211[/C][C]1.07345127643039e-15[/C][/ROW]
[ROW][C]106[/C][C]256[/C][C]256[/C][C]-7.35050070003397e-15[/C][/ROW]
[ROW][C]107[/C][C]220[/C][C]220[/C][C]-4.82150762776776e-15[/C][/ROW]
[ROW][C]108[/C][C]193[/C][C]193[/C][C]-1.38131160334644e-14[/C][/ROW]
[ROW][C]109[/C][C]290[/C][C]290[/C][C]-5.00753383336561e-15[/C][/ROW]
[ROW][C]110[/C][C]238[/C][C]238[/C][C]-1.20220151453967e-14[/C][/ROW]
[ROW][C]111[/C][C]240[/C][C]240[/C][C]-1.27158984519284e-14[/C][/ROW]
[ROW][C]112[/C][C]193[/C][C]193[/C][C]1.33402465513625e-15[/C][/ROW]
[ROW][C]113[/C][C]247[/C][C]247[/C][C]-1.16282460312783e-14[/C][/ROW]
[ROW][C]114[/C][C]245[/C][C]245[/C][C]-9.17310793491945e-15[/C][/ROW]
[ROW][C]115[/C][C]297[/C][C]297[/C][C]2.82707078887966e-15[/C][/ROW]
[ROW][C]116[/C][C]218[/C][C]218[/C][C]1.14657301088953e-14[/C][/ROW]
[ROW][C]117[/C][C]243[/C][C]243[/C][C]-1.06823201828367e-14[/C][/ROW]
[ROW][C]118[/C][C]293[/C][C]293[/C][C]5.84514974261385e-16[/C][/ROW]
[ROW][C]119[/C][C]211[/C][C]211[/C][C]6.47963036757429e-15[/C][/ROW]
[ROW][C]120[/C][C]242[/C][C]242[/C][C]-7.19533904672582e-15[/C][/ROW]
[ROW][C]121[/C][C]297[/C][C]297[/C][C]-4.34712325095105e-16[/C][/ROW]
[ROW][C]122[/C][C]278[/C][C]278[/C][C]-2.23030319563382e-15[/C][/ROW]
[ROW][C]123[/C][C]287[/C][C]287[/C][C]-1.78874383441621e-15[/C][/ROW]
[ROW][C]124[/C][C]370[/C][C]370[/C][C]9.07505679928313e-15[/C][/ROW]
[ROW][C]125[/C][C]352[/C][C]352[/C][C]-5.89674958431437e-15[/C][/ROW]
[ROW][C]126[/C][C]353[/C][C]353[/C][C]-4.73019848120257e-15[/C][/ROW]
[ROW][C]127[/C][C]331[/C][C]331[/C][C]-6.940529749379e-15[/C][/ROW]
[ROW][C]128[/C][C]272[/C][C]272[/C][C]9.3451737416527e-15[/C][/ROW]
[ROW][C]129[/C][C]401[/C][C]401[/C][C]-4.60207498798898e-15[/C][/ROW]
[ROW][C]130[/C][C]335[/C][C]335[/C][C]-2.83980020997866e-15[/C][/ROW]
[ROW][C]131[/C][C]314[/C][C]314[/C][C]2.21206405316133e-15[/C][/ROW]
[ROW][C]132[/C][C]309[/C][C]309[/C][C]1.83301738675507e-15[/C][/ROW]
[ROW][C]133[/C][C]343[/C][C]343[/C][C]-2.27824177459443e-15[/C][/ROW]
[ROW][C]134[/C][C]337[/C][C]337[/C][C]-2.56229317764999e-15[/C][/ROW]
[ROW][C]135[/C][C]462[/C][C]462[/C][C]-4.85077734363226e-15[/C][/ROW]
[ROW][C]136[/C][C]310[/C][C]310[/C][C]-8.52338981746998e-16[/C][/ROW]
[ROW][C]137[/C][C]317[/C][C]317[/C][C]1.46374057515752e-15[/C][/ROW]
[ROW][C]138[/C][C]387[/C][C]387[/C][C]9.03067669842329e-16[/C][/ROW]
[ROW][C]139[/C][C]321[/C][C]321[/C][C]-2.44174856545483e-16[/C][/ROW]
[ROW][C]140[/C][C]354[/C][C]354[/C][C]-4.49227529460853e-15[/C][/ROW]
[ROW][C]141[/C][C]354[/C][C]354[/C][C]-6.08405356527478e-15[/C][/ROW]
[ROW][C]142[/C][C]310[/C][C]310[/C][C]-3.59857190773066e-15[/C][/ROW]
[ROW][C]143[/C][C]395[/C][C]395[/C][C]-1.64814816091733e-15[/C][/ROW]
[ROW][C]144[/C][C]327[/C][C]327[/C][C]1.91266925814986e-15[/C][/ROW]
[ROW][C]145[/C][C]349[/C][C]349[/C][C]3.39377254961322e-15[/C][/ROW]
[ROW][C]146[/C][C]389[/C][C]389[/C][C]-2.96260424953827e-15[/C][/ROW]
[ROW][C]147[/C][C]427[/C][C]427[/C][C]-1.62776173674122e-15[/C][/ROW]
[ROW][C]148[/C][C]326[/C][C]326[/C][C]-6.73754243522582e-15[/C][/ROW]
[ROW][C]149[/C][C]358[/C][C]358[/C][C]3.55420079557036e-15[/C][/ROW]
[ROW][C]150[/C][C]405[/C][C]405[/C][C]6.6780108326712e-15[/C][/ROW]
[ROW][C]151[/C][C]330[/C][C]330[/C][C]-5.33122419704152e-15[/C][/ROW]
[ROW][C]152[/C][C]346[/C][C]346[/C][C]-2.18850140569691e-15[/C][/ROW]
[ROW][C]153[/C][C]395[/C][C]395[/C][C]8.02198512142492e-15[/C][/ROW]
[ROW][C]154[/C][C]306[/C][C]306[/C][C]5.6680025641304e-16[/C][/ROW]
[ROW][C]155[/C][C]371[/C][C]371[/C][C]-8.00105118747082e-15[/C][/ROW]
[ROW][C]156[/C][C]303[/C][C]303[/C][C]1.88657463991067e-15[/C][/ROW]
[ROW][C]157[/C][C]345[/C][C]345[/C][C]-1.64973000410195e-15[/C][/ROW]
[ROW][C]158[/C][C]343[/C][C]343[/C][C]3.94720239379526e-15[/C][/ROW]
[ROW][C]159[/C][C]348[/C][C]348[/C][C]-2.24530474377703e-15[/C][/ROW]
[ROW][C]160[/C][C]326[/C][C]326[/C][C]5.90733940555227e-15[/C][/ROW]
[ROW][C]161[/C][C]346[/C][C]346[/C][C]8.06918689881815e-15[/C][/ROW]
[ROW][C]162[/C][C]321[/C][C]321[/C][C]9.38219970971832e-15[/C][/ROW]
[ROW][C]163[/C][C]292[/C][C]292[/C][C]6.89162177975199e-15[/C][/ROW]
[ROW][C]164[/C][C]343[/C][C]343[/C][C]7.86107381509455e-15[/C][/ROW]
[ROW][C]165[/C][C]279[/C][C]279[/C][C]8.18632745035535e-15[/C][/ROW]
[ROW][C]166[/C][C]297[/C][C]297[/C][C]5.720664596246e-15[/C][/ROW]
[ROW][C]167[/C][C]298[/C][C]298[/C][C]6.89735536508333e-15[/C][/ROW]
[ROW][C]168[/C][C]241[/C][C]241[/C][C]2.16978594086801e-15[/C][/ROW]
[ROW][C]169[/C][C]310[/C][C]310[/C][C]8.64445207516161e-15[/C][/ROW]
[ROW][C]170[/C][C]220[/C][C]220[/C][C]5.38393253537911e-15[/C][/ROW]
[ROW][C]171[/C][C]229[/C][C]229[/C][C]3.17678199280198e-17[/C][/ROW]
[ROW][C]172[/C][C]232[/C][C]232[/C][C]-2.9912290504133e-15[/C][/ROW]
[ROW][C]173[/C][C]272[/C][C]272[/C][C]1.38604367311386e-15[/C][/ROW]
[ROW][C]174[/C][C]223[/C][C]223[/C][C]8.11670048354231e-15[/C][/ROW]
[ROW][C]175[/C][C]248[/C][C]248[/C][C]-3.9878745333072e-15[/C][/ROW]
[ROW][C]176[/C][C]266[/C][C]266[/C][C]6.34814029257498e-16[/C][/ROW]
[ROW][C]177[/C][C]245[/C][C]245[/C][C]-9.50014583487218e-15[/C][/ROW]
[ROW][C]178[/C][C]300[/C][C]300[/C][C]1.06868295454307e-15[/C][/ROW]
[ROW][C]179[/C][C]233[/C][C]233[/C][C]-2.48627373977135e-16[/C][/ROW]
[ROW][C]180[/C][C]207[/C][C]207[/C][C]-9.27583159986701e-16[/C][/ROW]
[ROW][C]181[/C][C]251[/C][C]251[/C][C]1.33779649201863e-15[/C][/ROW]
[ROW][C]182[/C][C]215[/C][C]215[/C][C]3.79986876766545e-16[/C][/ROW]
[ROW][C]183[/C][C]208[/C][C]208[/C][C]7.1856069858165e-15[/C][/ROW]
[ROW][C]184[/C][C]252[/C][C]252[/C][C]-8.41812785988633e-15[/C][/ROW]
[ROW][C]185[/C][C]166[/C][C]166[/C][C]9.66653433039894e-16[/C][/ROW]
[ROW][C]186[/C][C]238[/C][C]238[/C][C]-8.31578890222451e-15[/C][/ROW]
[ROW][C]187[/C][C]244[/C][C]244[/C][C]-5.40742972998485e-15[/C][/ROW]
[ROW][C]188[/C][C]234[/C][C]234[/C][C]-9.4701770405001e-15[/C][/ROW]
[ROW][C]189[/C][C]273[/C][C]273[/C][C]-5.25407330816427e-15[/C][/ROW]
[ROW][C]190[/C][C]260[/C][C]260[/C][C]1.48136655149494e-18[/C][/ROW]
[ROW][C]191[/C][C]312[/C][C]312[/C][C]5.91209482223254e-16[/C][/ROW]
[ROW][C]192[/C][C]305[/C][C]305[/C][C]-3.82792048734735e-15[/C][/ROW]
[ROW][C]193[/C][C]362[/C][C]362[/C][C]4.94735120333166e-15[/C][/ROW]
[ROW][C]194[/C][C]389[/C][C]389[/C][C]3.21293024980292e-15[/C][/ROW]
[ROW][C]195[/C][C]492[/C][C]492[/C][C]-1.51767929281926e-14[/C][/ROW]
[ROW][C]196[/C][C]487[/C][C]487[/C][C]-3.50533982301806e-14[/C][/ROW]
[ROW][C]197[/C][C]357[/C][C]357[/C][C]-5.33020139335334e-15[/C][/ROW]
[ROW][C]198[/C][C]505[/C][C]505[/C][C]7.48662715579884e-15[/C][/ROW]
[ROW][C]199[/C][C]448[/C][C]448[/C][C]-1.10365442186694e-14[/C][/ROW]
[ROW][C]200[/C][C]345[/C][C]345[/C][C]6.80802405919443e-16[/C][/ROW]
[ROW][C]201[/C][C]434[/C][C]434[/C][C]-2.26891423242688e-14[/C][/ROW]
[ROW][C]202[/C][C]379[/C][C]379[/C][C]6.88602778821162e-15[/C][/ROW]
[ROW][C]203[/C][C]439[/C][C]439[/C][C]-1.2408206584292e-14[/C][/ROW]
[ROW][C]204[/C][C]391[/C][C]391[/C][C]4.19484022223571e-15[/C][/ROW]
[ROW][C]205[/C][C]387[/C][C]387[/C][C]2.10915401720704e-15[/C][/ROW]
[ROW][C]206[/C][C]401[/C][C]401[/C][C]3.65608698117103e-15[/C][/ROW]
[ROW][C]207[/C][C]489[/C][C]489[/C][C]4.65576553917683e-15[/C][/ROW]
[ROW][C]208[/C][C]342[/C][C]342[/C][C]-1.02035470430174e-15[/C][/ROW]
[ROW][C]209[/C][C]289[/C][C]289[/C][C]5.24368638057159e-15[/C][/ROW]
[ROW][C]210[/C][C]516[/C][C]516[/C][C]-8.73172981253657e-15[/C][/ROW]
[ROW][C]211[/C][C]356[/C][C]356[/C][C]1.68126566997077e-16[/C][/ROW]
[ROW][C]212[/C][C]360[/C][C]360[/C][C]3.32771057559326e-15[/C][/ROW]
[ROW][C]213[/C][C]340[/C][C]340[/C][C]-2.70557865667373e-15[/C][/ROW]
[ROW][C]214[/C][C]399[/C][C]399[/C][C]3.65069538644716e-15[/C][/ROW]
[ROW][C]215[/C][C]392[/C][C]392[/C][C]5.46668055933456e-15[/C][/ROW]
[ROW][C]216[/C][C]302[/C][C]302[/C][C]1.85704626673114e-15[/C][/ROW]
[ROW][C]217[/C][C]377[/C][C]377[/C][C]3.04252776456733e-15[/C][/ROW]
[ROW][C]218[/C][C]395[/C][C]395[/C][C]1.14911055375505e-14[/C][/ROW]
[ROW][C]219[/C][C]397[/C][C]397[/C][C]1.35138982747523e-14[/C][/ROW]
[ROW][C]220[/C][C]320[/C][C]320[/C][C]7.82332374195907e-15[/C][/ROW]
[ROW][C]221[/C][C]407[/C][C]407[/C][C]9.42404599931171e-15[/C][/ROW]
[ROW][C]222[/C][C]286[/C][C]286[/C][C]1.62265580091258e-15[/C][/ROW]
[ROW][C]223[/C][C]306[/C][C]306[/C][C]5.34222040932643e-15[/C][/ROW]
[ROW][C]224[/C][C]395[/C][C]395[/C][C]1.21034955388982e-14[/C][/ROW]
[ROW][C]225[/C][C]416[/C][C]416[/C][C]1.94435880435367e-14[/C][/ROW]
[ROW][C]226[/C][C]412[/C][C]412[/C][C]1.64761616244076e-15[/C][/ROW]
[ROW][C]227[/C][C]464[/C][C]464[/C][C]2.87759751886903e-15[/C][/ROW]
[ROW][C]228[/C][C]358[/C][C]358[/C][C]4.22656045687789e-15[/C][/ROW]
[ROW][C]229[/C][C]499[/C][C]499[/C][C]8.20834551917277e-17[/C][/ROW]
[ROW][C]230[/C][C]480[/C][C]480[/C][C]-6.48784483881044e-16[/C][/ROW]
[ROW][C]231[/C][C]445[/C][C]445[/C][C]-2.20570365980277e-14[/C][/ROW]
[ROW][C]232[/C][C]471[/C][C]471[/C][C]1.38640246481875e-15[/C][/ROW]
[ROW][C]233[/C][C]511[/C][C]511[/C][C]3.60106363807204e-15[/C][/ROW]
[ROW][C]234[/C][C]462[/C][C]462[/C][C]-3.50507915831599e-15[/C][/ROW]
[ROW][C]235[/C][C]544[/C][C]544[/C][C]8.85353742243196e-16[/C][/ROW]
[ROW][C]236[/C][C]398[/C][C]398[/C][C]6.19726279152269e-15[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185286&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185286&T=4

As an alternative you can also use a QR Code:  
QR Code


The GUIDs for individual cells are displayed in the table below:

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
12522524.03785829928857e-13
2255255-5.84192650743475e-14
3404404-6.19825900963619e-15
4306306-3.58754103822894e-14
5276276-1.56903680101126e-14
63443444.53213675306535e-14
73403401.95613333935572e-16
8297297-9.09483912344494e-15
9345345-4.51960581632876e-15
10273273-5.60293429503933e-15
11333333-5.32302149932726e-15
12322322-6.09046223307207e-15
13300300-2.55920953143812e-15
14307307-5.14237840290863e-15
153803804.96505244810587e-15
16275275-1.48895607874764e-16
172702702.85554333918835e-15
183563561.58509968548298e-14
19255255-1.3432524688378e-14
20333333-6.70531984916407e-15
21306306-3.97086374521214e-15
22266266-5.43266132367192e-16
23339339-2.02883613002546e-15
24256256-9.18508495286011e-15
25283283-1.66752646139642e-15
262692695.77866820487169e-15
273093095.85628560737786e-16
28263263-4.08821749901804e-15
29268268-1.27652031416849e-15
30269269-2.65751351228944e-15
31257257-7.64651803636983e-15
32269269-6.12620186767517e-15
33258258-5.87667018293084e-15
34289289-1.55153255911191e-15
35279279-1.17761177844507e-14
362142142.0931889669574e-15
37306306-1.76755065107884e-15
38262262-1.26279105166358e-15
39266266-2.6874267308277e-15
40250250-6.45254854800818e-15
41260260-1.78728290117255e-15
42259259-3.00075089828705e-15
43248248-1.02264004073573e-14
44233233-3.62238747159633e-15
45251251-7.82335046918773e-15
46292292-7.5148238105658e-15
472272274.20979892808007e-17
48231231-8.96162041779693e-15
492772774.01357181234945e-16
50259259-4.10268906450505e-15
512292293.4076415218173e-15
52247247-1.17876431823324e-14
53246246-5.48117884051284e-15
542272273.02663799336714e-15
55270270-4.77477584027962e-15
56201201-2.06605425068185e-15
57265265-9.65280518756684e-16
58271271-7.21710330993843e-15
59250250-4.94360561356464e-15
60237237-1.02461118530128e-14
612212217.92261177098496e-16
622202201.88230239924117e-15
63279279-6.58702969322543e-15
64247247-8.46814406779883e-15
651921923.77181737675228e-15
66248248-5.91545134109724e-15
67226226-2.73397386944172e-15
681881889.12211375095781e-15
69299299-1.27539251622862e-15
70211211-2.52578619761422e-15
711881883.14659955005803e-15
721791795.19493108562732e-15
73149149-1.53866869776593e-14
741851854.88569089523233e-15
75236236-3.37491029943921e-15
762042043.75395192442314e-15
771661666.60432810386625e-15
78218218-5.40600509344828e-15
79148148-1.24553712566783e-14
80162162-1.55061775822295e-14
81173173-1.01571766255238e-15
82153153-1.42439834706394e-14
83181181-4.49093555267958e-15
841701703.15423265280186e-15
85160160-6.4308186689974e-15
86190190-7.66062233038408e-16
872032031.81038394174582e-15
88150150-4.76528113776096e-15
892112117.61938648139692e-17
90162162-1.50127845211783e-14
91174174-7.65904786950017e-16
92222222-4.92413308707159e-15
931781781.64238170215295e-15
94229229-8.53248292627627e-15
95247247-4.77519072508711e-15
961801802.64136020256263e-15
972192191.16343110604293e-15
982092095.89766393687948e-17
992022027.5963825211622e-15
100202202-3.43681878467963e-15
101250250-4.98997336715108e-15
1021891891.53393302958922e-15
103234234-5.36333918789656e-15
104241241-5.79145753956258e-15
1052112111.07345127643039e-15
106256256-7.35050070003397e-15
107220220-4.82150762776776e-15
108193193-1.38131160334644e-14
109290290-5.00753383336561e-15
110238238-1.20220151453967e-14
111240240-1.27158984519284e-14
1121931931.33402465513625e-15
113247247-1.16282460312783e-14
114245245-9.17310793491945e-15
1152972972.82707078887966e-15
1162182181.14657301088953e-14
117243243-1.06823201828367e-14
1182932935.84514974261385e-16
1192112116.47963036757429e-15
120242242-7.19533904672582e-15
121297297-4.34712325095105e-16
122278278-2.23030319563382e-15
123287287-1.78874383441621e-15
1243703709.07505679928313e-15
125352352-5.89674958431437e-15
126353353-4.73019848120257e-15
127331331-6.940529749379e-15
1282722729.3451737416527e-15
129401401-4.60207498798898e-15
130335335-2.83980020997866e-15
1313143142.21206405316133e-15
1323093091.83301738675507e-15
133343343-2.27824177459443e-15
134337337-2.56229317764999e-15
135462462-4.85077734363226e-15
136310310-8.52338981746998e-16
1373173171.46374057515752e-15
1383873879.03067669842329e-16
139321321-2.44174856545483e-16
140354354-4.49227529460853e-15
141354354-6.08405356527478e-15
142310310-3.59857190773066e-15
143395395-1.64814816091733e-15
1443273271.91266925814986e-15
1453493493.39377254961322e-15
146389389-2.96260424953827e-15
147427427-1.62776173674122e-15
148326326-6.73754243522582e-15
1493583583.55420079557036e-15
1504054056.6780108326712e-15
151330330-5.33122419704152e-15
152346346-2.18850140569691e-15
1533953958.02198512142492e-15
1543063065.6680025641304e-16
155371371-8.00105118747082e-15
1563033031.88657463991067e-15
157345345-1.64973000410195e-15
1583433433.94720239379526e-15
159348348-2.24530474377703e-15
1603263265.90733940555227e-15
1613463468.06918689881815e-15
1623213219.38219970971832e-15
1632922926.89162177975199e-15
1643433437.86107381509455e-15
1652792798.18632745035535e-15
1662972975.720664596246e-15
1672982986.89735536508333e-15
1682412412.16978594086801e-15
1693103108.64445207516161e-15
1702202205.38393253537911e-15
1712292293.17678199280198e-17
172232232-2.9912290504133e-15
1732722721.38604367311386e-15
1742232238.11670048354231e-15
175248248-3.9878745333072e-15
1762662666.34814029257498e-16
177245245-9.50014583487218e-15
1783003001.06868295454307e-15
179233233-2.48627373977135e-16
180207207-9.27583159986701e-16
1812512511.33779649201863e-15
1822152153.79986876766545e-16
1832082087.1856069858165e-15
184252252-8.41812785988633e-15
1851661669.66653433039894e-16
186238238-8.31578890222451e-15
187244244-5.40742972998485e-15
188234234-9.4701770405001e-15
189273273-5.25407330816427e-15
1902602601.48136655149494e-18
1913123125.91209482223254e-16
192305305-3.82792048734735e-15
1933623624.94735120333166e-15
1943893893.21293024980292e-15
195492492-1.51767929281926e-14
196487487-3.50533982301806e-14
197357357-5.33020139335334e-15
1985055057.48662715579884e-15
199448448-1.10365442186694e-14
2003453456.80802405919443e-16
201434434-2.26891423242688e-14
2023793796.88602778821162e-15
203439439-1.2408206584292e-14
2043913914.19484022223571e-15
2053873872.10915401720704e-15
2064014013.65608698117103e-15
2074894894.65576553917683e-15
208342342-1.02035470430174e-15
2092892895.24368638057159e-15
210516516-8.73172981253657e-15
2113563561.68126566997077e-16
2123603603.32771057559326e-15
213340340-2.70557865667373e-15
2143993993.65069538644716e-15
2153923925.46668055933456e-15
2163023021.85704626673114e-15
2173773773.04252776456733e-15
2183953951.14911055375505e-14
2193973971.35138982747523e-14
2203203207.82332374195907e-15
2214074079.42404599931171e-15
2222862861.62265580091258e-15
2233063065.34222040932643e-15
2243953951.21034955388982e-14
2254164161.94435880435367e-14
2264124121.64761616244076e-15
2274644642.87759751886903e-15
2283583584.22656045687789e-15
2294994998.20834551917277e-17
230480480-6.48784483881044e-16
231445445-2.20570365980277e-14
2324714711.38640246481875e-15
2335115113.60106363807204e-15
234462462-3.50507915831599e-15
2355445448.85353742243196e-16
2363983986.19726279152269e-15






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
100.3802386249971990.7604772499943990.619761375002801
110.9169639885366760.1660720229266470.0830360114633236
120.00461276747523290.00922553495046580.995387232524767
130.002347460874705810.004694921749411620.997652539125294
140.6681835278736620.6636329442526760.331816472126338
150.9077638753630380.1844722492739250.0922361246369623
160.9999998873927172.25214566408953e-071.12607283204477e-07
170.5757825578763440.8484348842473120.424217442123656
180.9999999999646557.06891863326092e-113.53445931663046e-11
190.9512160692688690.09756786146226190.0487839307311309
204.91647362216502e-059.83294724433003e-050.999950835263778
210.00048087996413940.00096175992827880.999519120035861
220.9999955913155768.81736884736351e-064.40868442368175e-06
234.55434840719873e-059.10869681439745e-050.999954456515928
240.7981708242597280.4036583514805440.201829175740272
250.04002617433124630.08005234866249270.959973825668754
260.2487611106496030.4975222212992070.751238889350397
270.9999992378161911.5243676176209e-067.6218380881045e-07
280.0001389503412143120.0002779006824286240.999861049658786
290.3678596220216960.7357192440433920.632140377978304
300.006602055882906840.01320411176581370.993397944117093
310.9993005423840360.001398915231928270.000699457615964134
320.9952685354712320.009462929057535870.00473146452876794
331.29907344895159e-062.59814689790318e-060.999998700926551
340.5901508970125350.819698205974930.409849102987465
350.5492871883463140.9014256233073720.450712811653686
360.8845360865756680.2309278268486650.115463913424332
370.4169017396980070.8338034793960140.583098260301993
380.002737239129748650.00547447825949730.997262760870251
390.3443381384122620.6886762768245250.655661861587738
400.004268202835638750.008536405671277510.995731797164361
411.83913010533517e-183.67826021067034e-181
420.0004589186958789770.0009178373917579550.999541081304121
437.42826928026694e-050.0001485653856053390.999925717307197
444.5740562565629e-059.1481125131258e-050.999954259437434
450.7542371856379590.4915256287240820.245762814362041
460.9995648212795540.0008703574408916650.000435178720445833
470.9995121391920870.0009757216158256530.000487860807912826
483.31820175432259e-076.63640350864518e-070.999999668179825
490.03720791742866650.0744158348573330.962792082571334
500.9999999919080251.61839503750225e-088.09197518751125e-09
511.02184684074268e-152.04369368148537e-150.999999999999999
524.90613414976683e-069.81226829953366e-060.99999509386585
530.0003418369996569270.0006836739993138540.999658163000343
540.01938007846980280.03876015693960560.980619921530197
550.000448862444314480.000897724888628960.999551137555685
561.33651049389454e-052.67302098778909e-050.999986634895061
570.99997718724684.56255063995102e-052.28127531997551e-05
582.31571322971797e-394.63142645943593e-391
590.9999999999996487.03383983886761e-133.5169199194338e-13
600.001987356465659540.003974712931319080.99801264353434
613.82453614006699e-087.64907228013398e-080.999999961754639
626.39809016582556e-091.27961803316511e-080.99999999360191
637.38965438501423e-191.47793087700285e-181
640.9999999999995568.87119539362348e-134.43559769681174e-13
6512.36839411680581e-301.18419705840291e-30
660.946316762678350.10736647464330.0536832373216502
671.10773950093894e-102.21547900187789e-100.999999999889226
680.9999999999978744.25131860541913e-122.12565930270957e-12
693.37261202580125e-116.7452240516025e-110.999999999966274
701.0096061919153e-152.01921238383061e-150.999999999999999
7112.60634543159316e-191.30317271579658e-19
720.9999999999543419.13173326028668e-114.56586663014334e-11
730.9999988062032962.38759340906443e-061.19379670453222e-06
740.001387281018091030.002774562036182070.998612718981909
750.9999519922663859.6015467230959e-054.80077336154795e-05
761.41279587772536e-192.82559175545072e-191
775.65051985076578e-121.13010397015316e-110.99999999999435
784.42052481733013e-118.84104963466025e-110.999999999955795
790.9999999990587381.88252324279311e-099.41261621396556e-10
802.83815172405478e-095.67630344810956e-090.999999997161848
810.9999989630379172.07392416506017e-061.03696208253008e-06
820.9990926842171790.001814631565641650.000907315782820826
832.6916561747878e-105.38331234957559e-100.999999999730834
843.03189031071778e-206.06378062143555e-201
8511.10691086119089e-465.53455430595444e-47
860.9999984739926693.05201466198159e-061.5260073309908e-06
8718.43370230888514e-244.21685115444257e-24
888.88226390966032e-131.77645278193206e-120.999999999999112
890.9999999999841473.17066968249006e-111.58533484124503e-11
906.9220209559343e-261.38440419118686e-251
9113.77952984937245e-211.88976492468623e-21
920.9999999969130396.17392167307732e-093.08696083653866e-09
931.15764664705211e-202.31529329410422e-201
942.55950519599679e-145.11901039199358e-140.999999999999974
950.9999966669114986.66617700472317e-063.33308850236159e-06
960.9999982618710233.47625795411059e-061.7381289770553e-06
971.0182431345757e-072.03648626915141e-070.999999898175687
980.9999999995584628.83075072721798e-104.41537536360899e-10
990.007634123871242550.01526824774248510.992365876128757
1003.75831997958844e-167.51663995917689e-161
1012.33458991221556e-194.66917982443113e-191
10211.17306984114158e-165.8653492057079e-17
1030.5151211503049170.9697576993901660.484878849695083
1041.16664561830886e-302.33329123661771e-301
1051.6456381778401e-073.2912763556802e-070.999999835436182
1064.29127337216136e-118.58254674432272e-110.999999999957087
1070.01379373172842890.02758746345685790.986206268271571
1080.9999999999971145.77258929294967e-122.88629464647484e-12
1090.9463857704930440.1072284590139120.0536142295069558
1100.9999999999999991.93865020262824e-159.6932510131412e-16
1117.11573927537232e-071.42314785507446e-060.999999288426072
1120.9999961683680237.66326395496359e-063.8316319774818e-06
1135.30775336235885e-351.06155067247177e-341
1140.9999965302598186.93948036457986e-063.46974018228993e-06
1150.004505325582900420.009010651165800850.9954946744171
1163.43715707996029e-206.87431415992058e-201
1172.70613277410753e-105.41226554821506e-100.999999999729387
1181.29148481588542e-162.58296963177084e-161
1190.0226089649113760.0452179298227520.977391035088624
1201.9198159652763e-153.8396319305526e-150.999999999999998
1211.44561725827483e-452.89123451654966e-451
1225.62200695492756e-541.12440139098551e-531
1230.002431571545039090.004863143090078170.997568428454961
1240.0002803607279555260.0005607214559110510.999719639272044
1258.74663958612848e-081.7493279172257e-070.999999912533604
1263.15711422752394e-086.31422845504788e-080.999999968428858
1278.33900113364865e-061.66780022672973e-050.999991660998866
1280.003461324862345540.006922649724691080.996538675137654
1291.38318263978079e-392.76636527956158e-391
1300.9628798046755640.07424039064887260.0371201953244363
1310.9999715090460925.69819078151013e-052.84909539075507e-05
1320.9981373221863470.003725355627306990.00186267781365349
1332.15525815975901e-154.31051631951801e-150.999999999999998
1340.6764526023145930.6470947953708130.323547397685407
1350.9981414807594770.003717038481046070.00185851924052304
1360.2983910407901580.5967820815803160.701608959209842
1372.97797474117528e-335.95594948235056e-331
1380.999912621166660.0001747576666789948.73788333394968e-05
1390.9999999997324675.35065210633234e-102.67532605316617e-10
1400.999977519716574.49605668590291e-052.24802834295145e-05
1413.69732179110955e-417.39464358221909e-411
1420.9537371663573170.09252566728536590.0462628336426829
1430.01397391808891280.02794783617782570.986026081911087
1440.9999999995749588.50084867955417e-104.25042433977708e-10
1450.9998440125147790.0003119749704424710.000155987485221235
1462.08065897659298e-434.16131795318596e-431
1470.9997334931125270.0005330137749459110.000266506887472955
1480.001503104263451180.003006208526902360.998496895736549
1495.50772232041777e-050.0001101544464083550.999944922776796
1500.1940133724444770.3880267448889530.805986627555523
1510.9966423450791260.006715309841748040.00335765492087402
1525.96033521302185e-461.19206704260437e-451
1538.59809239295582e-241.71961847859116e-231
15415.43759515098953e-182.71879757549476e-18
1552.7288702336741e-065.45774046734821e-060.999997271129766
1560.0681624344765640.1363248689531280.931837565523436
1570.627140581723670.7457188365526590.37285941827633
1580.9999954769639549.04607209220718e-064.52303604610359e-06
1591.30342613734204e-082.60685227468409e-080.999999986965739
1600.9999999960073967.98520729156692e-093.99260364578346e-09
1610.9866217735821980.02675645283560420.0133782264178021
1629.20284406744043e-181.84056881348809e-171
1630.00334919294587460.006698385891749190.996650807054125
1640.0006231762104394140.001246352420878830.999376823789561
1650.9675604536455940.06487909270881190.032439546354406
1661.0066279142524e-132.01325582850479e-130.999999999999899
1671.48530462090826e-082.97060924181653e-080.999999985146954
1682.08043272942245e-074.16086545884491e-070.999999791956727
1693.30872957753186e-116.61745915506372e-110.999999999966913
1703.20305579045675e-126.4061115809135e-120.999999999996797
1710.876816636687540.2463667266249210.12318336331246
1720.9999020405152560.0001959189694880389.79594847440188e-05
1731.41920173991025e-202.8384034798205e-201
1740.6025766725224080.7948466549551840.397423327477592
1750.01402882582059580.02805765164119170.985971174179404
1766.62444423004397e-071.32488884600879e-060.999999337555577
1775.82161966319664e-101.16432393263933e-090.999999999417838
1780.001730414659266750.003460829318533490.998269585340733
1790.0002731888893622390.0005463777787244780.999726811110638
1800.0005919272713313210.001183854542662640.999408072728669
1813.23174723507074e-106.46349447014149e-100.999999999676825
1821.12770939445224e-082.25541878890448e-080.999999988722906
1830.9999867349105252.65301789503967e-051.32650894751984e-05
1849.29725069937053e-221.85945013987411e-211
1851.31052133398331e-102.62104266796661e-100.999999999868948
1860.06434055210025210.1286811042005040.935659447899748
1870.5652807533190030.8694384933619940.434719246680997
1882.27043882924003e-184.54087765848006e-181
1890.1016102770909480.2032205541818960.898389722909052
1901.98536828848039e-113.97073657696079e-110.999999999980146
1917.62155874187804e-151.52431174837561e-140.999999999999992
1920.09041769613009270.1808353922601850.909582303869907
1930.0003900318177645580.0007800636355291160.999609968182235
1941.98979869616754e-333.97959739233507e-331
1950.02922090575278070.05844181150556130.970779094247219
1961.72234979208092e-243.44469958416184e-241
1970.001898408782311320.003796817564622640.998101591217689
1982.3774146171336e-064.7548292342672e-060.999997622585383
1991.14182047436808e-252.28364094873615e-251
2000.02364305754830740.04728611509661490.976356942451693
2018.98562081827058e-131.79712416365412e-120.999999999999101
2022.15642983253103e-304.31285966506205e-301
2033.24944491544771e-336.49888983089541e-331
2041.29376370560115e-392.5875274112023e-391
2053.04439303348925e-256.0887860669785e-251
2063.90694193921227e-257.81388387842454e-251
2073.92989251811971e-117.85978503623943e-110.999999999960701
2080.5859813913731540.8280372172536920.414018608626846
2093.96308675859818e-197.92617351719636e-191
2100.9901447609140860.01971047817182780.00985523908591392
2110.7816430716718660.4367138566562690.218356928328134
2120.914549869335710.1709002613285810.0854501306642903
2130.6168693262541350.766261347491730.383130673745865
2140.999891137192610.0002177256147797630.000108862807389882
2150.9997959836992090.0004080326015828420.000204016300791421
2160.0766344452524960.1532688905049920.923365554747504
2170.9996290661328430.0007418677343140670.000370933867157033
2182.14769825520974e-114.29539651041949e-110.999999999978523
2190.3088498660080450.6176997320160890.691150133991955
2200.5850603256786250.829879348642750.414939674321375
2210.8493415767794350.3013168464411290.150658423220565
2220.9976676232074070.004664753585186280.00233237679259314
2230.7402788167139830.5194423665720350.259721183286017
2240.771707132373550.45658573525290.22829286762645
2250.01978189569333560.03956379138667120.980218104306664
2260.5490796181063510.9018407637872980.450920381893649

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
10 & 0.380238624997199 & 0.760477249994399 & 0.619761375002801 \tabularnewline
11 & 0.916963988536676 & 0.166072022926647 & 0.0830360114633236 \tabularnewline
12 & 0.0046127674752329 & 0.0092255349504658 & 0.995387232524767 \tabularnewline
13 & 0.00234746087470581 & 0.00469492174941162 & 0.997652539125294 \tabularnewline
14 & 0.668183527873662 & 0.663632944252676 & 0.331816472126338 \tabularnewline
15 & 0.907763875363038 & 0.184472249273925 & 0.0922361246369623 \tabularnewline
16 & 0.999999887392717 & 2.25214566408953e-07 & 1.12607283204477e-07 \tabularnewline
17 & 0.575782557876344 & 0.848434884247312 & 0.424217442123656 \tabularnewline
18 & 0.999999999964655 & 7.06891863326092e-11 & 3.53445931663046e-11 \tabularnewline
19 & 0.951216069268869 & 0.0975678614622619 & 0.0487839307311309 \tabularnewline
20 & 4.91647362216502e-05 & 9.83294724433003e-05 & 0.999950835263778 \tabularnewline
21 & 0.0004808799641394 & 0.0009617599282788 & 0.999519120035861 \tabularnewline
22 & 0.999995591315576 & 8.81736884736351e-06 & 4.40868442368175e-06 \tabularnewline
23 & 4.55434840719873e-05 & 9.10869681439745e-05 & 0.999954456515928 \tabularnewline
24 & 0.798170824259728 & 0.403658351480544 & 0.201829175740272 \tabularnewline
25 & 0.0400261743312463 & 0.0800523486624927 & 0.959973825668754 \tabularnewline
26 & 0.248761110649603 & 0.497522221299207 & 0.751238889350397 \tabularnewline
27 & 0.999999237816191 & 1.5243676176209e-06 & 7.6218380881045e-07 \tabularnewline
28 & 0.000138950341214312 & 0.000277900682428624 & 0.999861049658786 \tabularnewline
29 & 0.367859622021696 & 0.735719244043392 & 0.632140377978304 \tabularnewline
30 & 0.00660205588290684 & 0.0132041117658137 & 0.993397944117093 \tabularnewline
31 & 0.999300542384036 & 0.00139891523192827 & 0.000699457615964134 \tabularnewline
32 & 0.995268535471232 & 0.00946292905753587 & 0.00473146452876794 \tabularnewline
33 & 1.29907344895159e-06 & 2.59814689790318e-06 & 0.999998700926551 \tabularnewline
34 & 0.590150897012535 & 0.81969820597493 & 0.409849102987465 \tabularnewline
35 & 0.549287188346314 & 0.901425623307372 & 0.450712811653686 \tabularnewline
36 & 0.884536086575668 & 0.230927826848665 & 0.115463913424332 \tabularnewline
37 & 0.416901739698007 & 0.833803479396014 & 0.583098260301993 \tabularnewline
38 & 0.00273723912974865 & 0.0054744782594973 & 0.997262760870251 \tabularnewline
39 & 0.344338138412262 & 0.688676276824525 & 0.655661861587738 \tabularnewline
40 & 0.00426820283563875 & 0.00853640567127751 & 0.995731797164361 \tabularnewline
41 & 1.83913010533517e-18 & 3.67826021067034e-18 & 1 \tabularnewline
42 & 0.000458918695878977 & 0.000917837391757955 & 0.999541081304121 \tabularnewline
43 & 7.42826928026694e-05 & 0.000148565385605339 & 0.999925717307197 \tabularnewline
44 & 4.5740562565629e-05 & 9.1481125131258e-05 & 0.999954259437434 \tabularnewline
45 & 0.754237185637959 & 0.491525628724082 & 0.245762814362041 \tabularnewline
46 & 0.999564821279554 & 0.000870357440891665 & 0.000435178720445833 \tabularnewline
47 & 0.999512139192087 & 0.000975721615825653 & 0.000487860807912826 \tabularnewline
48 & 3.31820175432259e-07 & 6.63640350864518e-07 & 0.999999668179825 \tabularnewline
49 & 0.0372079174286665 & 0.074415834857333 & 0.962792082571334 \tabularnewline
50 & 0.999999991908025 & 1.61839503750225e-08 & 8.09197518751125e-09 \tabularnewline
51 & 1.02184684074268e-15 & 2.04369368148537e-15 & 0.999999999999999 \tabularnewline
52 & 4.90613414976683e-06 & 9.81226829953366e-06 & 0.99999509386585 \tabularnewline
53 & 0.000341836999656927 & 0.000683673999313854 & 0.999658163000343 \tabularnewline
54 & 0.0193800784698028 & 0.0387601569396056 & 0.980619921530197 \tabularnewline
55 & 0.00044886244431448 & 0.00089772488862896 & 0.999551137555685 \tabularnewline
56 & 1.33651049389454e-05 & 2.67302098778909e-05 & 0.999986634895061 \tabularnewline
57 & 0.9999771872468 & 4.56255063995102e-05 & 2.28127531997551e-05 \tabularnewline
58 & 2.31571322971797e-39 & 4.63142645943593e-39 & 1 \tabularnewline
59 & 0.999999999999648 & 7.03383983886761e-13 & 3.5169199194338e-13 \tabularnewline
60 & 0.00198735646565954 & 0.00397471293131908 & 0.99801264353434 \tabularnewline
61 & 3.82453614006699e-08 & 7.64907228013398e-08 & 0.999999961754639 \tabularnewline
62 & 6.39809016582556e-09 & 1.27961803316511e-08 & 0.99999999360191 \tabularnewline
63 & 7.38965438501423e-19 & 1.47793087700285e-18 & 1 \tabularnewline
64 & 0.999999999999556 & 8.87119539362348e-13 & 4.43559769681174e-13 \tabularnewline
65 & 1 & 2.36839411680581e-30 & 1.18419705840291e-30 \tabularnewline
66 & 0.94631676267835 & 0.1073664746433 & 0.0536832373216502 \tabularnewline
67 & 1.10773950093894e-10 & 2.21547900187789e-10 & 0.999999999889226 \tabularnewline
68 & 0.999999999997874 & 4.25131860541913e-12 & 2.12565930270957e-12 \tabularnewline
69 & 3.37261202580125e-11 & 6.7452240516025e-11 & 0.999999999966274 \tabularnewline
70 & 1.0096061919153e-15 & 2.01921238383061e-15 & 0.999999999999999 \tabularnewline
71 & 1 & 2.60634543159316e-19 & 1.30317271579658e-19 \tabularnewline
72 & 0.999999999954341 & 9.13173326028668e-11 & 4.56586663014334e-11 \tabularnewline
73 & 0.999998806203296 & 2.38759340906443e-06 & 1.19379670453222e-06 \tabularnewline
74 & 0.00138728101809103 & 0.00277456203618207 & 0.998612718981909 \tabularnewline
75 & 0.999951992266385 & 9.6015467230959e-05 & 4.80077336154795e-05 \tabularnewline
76 & 1.41279587772536e-19 & 2.82559175545072e-19 & 1 \tabularnewline
77 & 5.65051985076578e-12 & 1.13010397015316e-11 & 0.99999999999435 \tabularnewline
78 & 4.42052481733013e-11 & 8.84104963466025e-11 & 0.999999999955795 \tabularnewline
79 & 0.999999999058738 & 1.88252324279311e-09 & 9.41261621396556e-10 \tabularnewline
80 & 2.83815172405478e-09 & 5.67630344810956e-09 & 0.999999997161848 \tabularnewline
81 & 0.999998963037917 & 2.07392416506017e-06 & 1.03696208253008e-06 \tabularnewline
82 & 0.999092684217179 & 0.00181463156564165 & 0.000907315782820826 \tabularnewline
83 & 2.6916561747878e-10 & 5.38331234957559e-10 & 0.999999999730834 \tabularnewline
84 & 3.03189031071778e-20 & 6.06378062143555e-20 & 1 \tabularnewline
85 & 1 & 1.10691086119089e-46 & 5.53455430595444e-47 \tabularnewline
86 & 0.999998473992669 & 3.05201466198159e-06 & 1.5260073309908e-06 \tabularnewline
87 & 1 & 8.43370230888514e-24 & 4.21685115444257e-24 \tabularnewline
88 & 8.88226390966032e-13 & 1.77645278193206e-12 & 0.999999999999112 \tabularnewline
89 & 0.999999999984147 & 3.17066968249006e-11 & 1.58533484124503e-11 \tabularnewline
90 & 6.9220209559343e-26 & 1.38440419118686e-25 & 1 \tabularnewline
91 & 1 & 3.77952984937245e-21 & 1.88976492468623e-21 \tabularnewline
92 & 0.999999996913039 & 6.17392167307732e-09 & 3.08696083653866e-09 \tabularnewline
93 & 1.15764664705211e-20 & 2.31529329410422e-20 & 1 \tabularnewline
94 & 2.55950519599679e-14 & 5.11901039199358e-14 & 0.999999999999974 \tabularnewline
95 & 0.999996666911498 & 6.66617700472317e-06 & 3.33308850236159e-06 \tabularnewline
96 & 0.999998261871023 & 3.47625795411059e-06 & 1.7381289770553e-06 \tabularnewline
97 & 1.0182431345757e-07 & 2.03648626915141e-07 & 0.999999898175687 \tabularnewline
98 & 0.999999999558462 & 8.83075072721798e-10 & 4.41537536360899e-10 \tabularnewline
99 & 0.00763412387124255 & 0.0152682477424851 & 0.992365876128757 \tabularnewline
100 & 3.75831997958844e-16 & 7.51663995917689e-16 & 1 \tabularnewline
101 & 2.33458991221556e-19 & 4.66917982443113e-19 & 1 \tabularnewline
102 & 1 & 1.17306984114158e-16 & 5.8653492057079e-17 \tabularnewline
103 & 0.515121150304917 & 0.969757699390166 & 0.484878849695083 \tabularnewline
104 & 1.16664561830886e-30 & 2.33329123661771e-30 & 1 \tabularnewline
105 & 1.6456381778401e-07 & 3.2912763556802e-07 & 0.999999835436182 \tabularnewline
106 & 4.29127337216136e-11 & 8.58254674432272e-11 & 0.999999999957087 \tabularnewline
107 & 0.0137937317284289 & 0.0275874634568579 & 0.986206268271571 \tabularnewline
108 & 0.999999999997114 & 5.77258929294967e-12 & 2.88629464647484e-12 \tabularnewline
109 & 0.946385770493044 & 0.107228459013912 & 0.0536142295069558 \tabularnewline
110 & 0.999999999999999 & 1.93865020262824e-15 & 9.6932510131412e-16 \tabularnewline
111 & 7.11573927537232e-07 & 1.42314785507446e-06 & 0.999999288426072 \tabularnewline
112 & 0.999996168368023 & 7.66326395496359e-06 & 3.8316319774818e-06 \tabularnewline
113 & 5.30775336235885e-35 & 1.06155067247177e-34 & 1 \tabularnewline
114 & 0.999996530259818 & 6.93948036457986e-06 & 3.46974018228993e-06 \tabularnewline
115 & 0.00450532558290042 & 0.00901065116580085 & 0.9954946744171 \tabularnewline
116 & 3.43715707996029e-20 & 6.87431415992058e-20 & 1 \tabularnewline
117 & 2.70613277410753e-10 & 5.41226554821506e-10 & 0.999999999729387 \tabularnewline
118 & 1.29148481588542e-16 & 2.58296963177084e-16 & 1 \tabularnewline
119 & 0.022608964911376 & 0.045217929822752 & 0.977391035088624 \tabularnewline
120 & 1.9198159652763e-15 & 3.8396319305526e-15 & 0.999999999999998 \tabularnewline
121 & 1.44561725827483e-45 & 2.89123451654966e-45 & 1 \tabularnewline
122 & 5.62200695492756e-54 & 1.12440139098551e-53 & 1 \tabularnewline
123 & 0.00243157154503909 & 0.00486314309007817 & 0.997568428454961 \tabularnewline
124 & 0.000280360727955526 & 0.000560721455911051 & 0.999719639272044 \tabularnewline
125 & 8.74663958612848e-08 & 1.7493279172257e-07 & 0.999999912533604 \tabularnewline
126 & 3.15711422752394e-08 & 6.31422845504788e-08 & 0.999999968428858 \tabularnewline
127 & 8.33900113364865e-06 & 1.66780022672973e-05 & 0.999991660998866 \tabularnewline
128 & 0.00346132486234554 & 0.00692264972469108 & 0.996538675137654 \tabularnewline
129 & 1.38318263978079e-39 & 2.76636527956158e-39 & 1 \tabularnewline
130 & 0.962879804675564 & 0.0742403906488726 & 0.0371201953244363 \tabularnewline
131 & 0.999971509046092 & 5.69819078151013e-05 & 2.84909539075507e-05 \tabularnewline
132 & 0.998137322186347 & 0.00372535562730699 & 0.00186267781365349 \tabularnewline
133 & 2.15525815975901e-15 & 4.31051631951801e-15 & 0.999999999999998 \tabularnewline
134 & 0.676452602314593 & 0.647094795370813 & 0.323547397685407 \tabularnewline
135 & 0.998141480759477 & 0.00371703848104607 & 0.00185851924052304 \tabularnewline
136 & 0.298391040790158 & 0.596782081580316 & 0.701608959209842 \tabularnewline
137 & 2.97797474117528e-33 & 5.95594948235056e-33 & 1 \tabularnewline
138 & 0.99991262116666 & 0.000174757666678994 & 8.73788333394968e-05 \tabularnewline
139 & 0.999999999732467 & 5.35065210633234e-10 & 2.67532605316617e-10 \tabularnewline
140 & 0.99997751971657 & 4.49605668590291e-05 & 2.24802834295145e-05 \tabularnewline
141 & 3.69732179110955e-41 & 7.39464358221909e-41 & 1 \tabularnewline
142 & 0.953737166357317 & 0.0925256672853659 & 0.0462628336426829 \tabularnewline
143 & 0.0139739180889128 & 0.0279478361778257 & 0.986026081911087 \tabularnewline
144 & 0.999999999574958 & 8.50084867955417e-10 & 4.25042433977708e-10 \tabularnewline
145 & 0.999844012514779 & 0.000311974970442471 & 0.000155987485221235 \tabularnewline
146 & 2.08065897659298e-43 & 4.16131795318596e-43 & 1 \tabularnewline
147 & 0.999733493112527 & 0.000533013774945911 & 0.000266506887472955 \tabularnewline
148 & 0.00150310426345118 & 0.00300620852690236 & 0.998496895736549 \tabularnewline
149 & 5.50772232041777e-05 & 0.000110154446408355 & 0.999944922776796 \tabularnewline
150 & 0.194013372444477 & 0.388026744888953 & 0.805986627555523 \tabularnewline
151 & 0.996642345079126 & 0.00671530984174804 & 0.00335765492087402 \tabularnewline
152 & 5.96033521302185e-46 & 1.19206704260437e-45 & 1 \tabularnewline
153 & 8.59809239295582e-24 & 1.71961847859116e-23 & 1 \tabularnewline
154 & 1 & 5.43759515098953e-18 & 2.71879757549476e-18 \tabularnewline
155 & 2.7288702336741e-06 & 5.45774046734821e-06 & 0.999997271129766 \tabularnewline
156 & 0.068162434476564 & 0.136324868953128 & 0.931837565523436 \tabularnewline
157 & 0.62714058172367 & 0.745718836552659 & 0.37285941827633 \tabularnewline
158 & 0.999995476963954 & 9.04607209220718e-06 & 4.52303604610359e-06 \tabularnewline
159 & 1.30342613734204e-08 & 2.60685227468409e-08 & 0.999999986965739 \tabularnewline
160 & 0.999999996007396 & 7.98520729156692e-09 & 3.99260364578346e-09 \tabularnewline
161 & 0.986621773582198 & 0.0267564528356042 & 0.0133782264178021 \tabularnewline
162 & 9.20284406744043e-18 & 1.84056881348809e-17 & 1 \tabularnewline
163 & 0.0033491929458746 & 0.00669838589174919 & 0.996650807054125 \tabularnewline
164 & 0.000623176210439414 & 0.00124635242087883 & 0.999376823789561 \tabularnewline
165 & 0.967560453645594 & 0.0648790927088119 & 0.032439546354406 \tabularnewline
166 & 1.0066279142524e-13 & 2.01325582850479e-13 & 0.999999999999899 \tabularnewline
167 & 1.48530462090826e-08 & 2.97060924181653e-08 & 0.999999985146954 \tabularnewline
168 & 2.08043272942245e-07 & 4.16086545884491e-07 & 0.999999791956727 \tabularnewline
169 & 3.30872957753186e-11 & 6.61745915506372e-11 & 0.999999999966913 \tabularnewline
170 & 3.20305579045675e-12 & 6.4061115809135e-12 & 0.999999999996797 \tabularnewline
171 & 0.87681663668754 & 0.246366726624921 & 0.12318336331246 \tabularnewline
172 & 0.999902040515256 & 0.000195918969488038 & 9.79594847440188e-05 \tabularnewline
173 & 1.41920173991025e-20 & 2.8384034798205e-20 & 1 \tabularnewline
174 & 0.602576672522408 & 0.794846654955184 & 0.397423327477592 \tabularnewline
175 & 0.0140288258205958 & 0.0280576516411917 & 0.985971174179404 \tabularnewline
176 & 6.62444423004397e-07 & 1.32488884600879e-06 & 0.999999337555577 \tabularnewline
177 & 5.82161966319664e-10 & 1.16432393263933e-09 & 0.999999999417838 \tabularnewline
178 & 0.00173041465926675 & 0.00346082931853349 & 0.998269585340733 \tabularnewline
179 & 0.000273188889362239 & 0.000546377778724478 & 0.999726811110638 \tabularnewline
180 & 0.000591927271331321 & 0.00118385454266264 & 0.999408072728669 \tabularnewline
181 & 3.23174723507074e-10 & 6.46349447014149e-10 & 0.999999999676825 \tabularnewline
182 & 1.12770939445224e-08 & 2.25541878890448e-08 & 0.999999988722906 \tabularnewline
183 & 0.999986734910525 & 2.65301789503967e-05 & 1.32650894751984e-05 \tabularnewline
184 & 9.29725069937053e-22 & 1.85945013987411e-21 & 1 \tabularnewline
185 & 1.31052133398331e-10 & 2.62104266796661e-10 & 0.999999999868948 \tabularnewline
186 & 0.0643405521002521 & 0.128681104200504 & 0.935659447899748 \tabularnewline
187 & 0.565280753319003 & 0.869438493361994 & 0.434719246680997 \tabularnewline
188 & 2.27043882924003e-18 & 4.54087765848006e-18 & 1 \tabularnewline
189 & 0.101610277090948 & 0.203220554181896 & 0.898389722909052 \tabularnewline
190 & 1.98536828848039e-11 & 3.97073657696079e-11 & 0.999999999980146 \tabularnewline
191 & 7.62155874187804e-15 & 1.52431174837561e-14 & 0.999999999999992 \tabularnewline
192 & 0.0904176961300927 & 0.180835392260185 & 0.909582303869907 \tabularnewline
193 & 0.000390031817764558 & 0.000780063635529116 & 0.999609968182235 \tabularnewline
194 & 1.98979869616754e-33 & 3.97959739233507e-33 & 1 \tabularnewline
195 & 0.0292209057527807 & 0.0584418115055613 & 0.970779094247219 \tabularnewline
196 & 1.72234979208092e-24 & 3.44469958416184e-24 & 1 \tabularnewline
197 & 0.00189840878231132 & 0.00379681756462264 & 0.998101591217689 \tabularnewline
198 & 2.3774146171336e-06 & 4.7548292342672e-06 & 0.999997622585383 \tabularnewline
199 & 1.14182047436808e-25 & 2.28364094873615e-25 & 1 \tabularnewline
200 & 0.0236430575483074 & 0.0472861150966149 & 0.976356942451693 \tabularnewline
201 & 8.98562081827058e-13 & 1.79712416365412e-12 & 0.999999999999101 \tabularnewline
202 & 2.15642983253103e-30 & 4.31285966506205e-30 & 1 \tabularnewline
203 & 3.24944491544771e-33 & 6.49888983089541e-33 & 1 \tabularnewline
204 & 1.29376370560115e-39 & 2.5875274112023e-39 & 1 \tabularnewline
205 & 3.04439303348925e-25 & 6.0887860669785e-25 & 1 \tabularnewline
206 & 3.90694193921227e-25 & 7.81388387842454e-25 & 1 \tabularnewline
207 & 3.92989251811971e-11 & 7.85978503623943e-11 & 0.999999999960701 \tabularnewline
208 & 0.585981391373154 & 0.828037217253692 & 0.414018608626846 \tabularnewline
209 & 3.96308675859818e-19 & 7.92617351719636e-19 & 1 \tabularnewline
210 & 0.990144760914086 & 0.0197104781718278 & 0.00985523908591392 \tabularnewline
211 & 0.781643071671866 & 0.436713856656269 & 0.218356928328134 \tabularnewline
212 & 0.91454986933571 & 0.170900261328581 & 0.0854501306642903 \tabularnewline
213 & 0.616869326254135 & 0.76626134749173 & 0.383130673745865 \tabularnewline
214 & 0.99989113719261 & 0.000217725614779763 & 0.000108862807389882 \tabularnewline
215 & 0.999795983699209 & 0.000408032601582842 & 0.000204016300791421 \tabularnewline
216 & 0.076634445252496 & 0.153268890504992 & 0.923365554747504 \tabularnewline
217 & 0.999629066132843 & 0.000741867734314067 & 0.000370933867157033 \tabularnewline
218 & 2.14769825520974e-11 & 4.29539651041949e-11 & 0.999999999978523 \tabularnewline
219 & 0.308849866008045 & 0.617699732016089 & 0.691150133991955 \tabularnewline
220 & 0.585060325678625 & 0.82987934864275 & 0.414939674321375 \tabularnewline
221 & 0.849341576779435 & 0.301316846441129 & 0.150658423220565 \tabularnewline
222 & 0.997667623207407 & 0.00466475358518628 & 0.00233237679259314 \tabularnewline
223 & 0.740278816713983 & 0.519442366572035 & 0.259721183286017 \tabularnewline
224 & 0.77170713237355 & 0.4565857352529 & 0.22829286762645 \tabularnewline
225 & 0.0197818956933356 & 0.0395637913866712 & 0.980218104306664 \tabularnewline
226 & 0.549079618106351 & 0.901840763787298 & 0.450920381893649 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185286&T=5

[TABLE]
[ROW][C]Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]p-values[/C][C]Alternative Hypothesis[/C][/ROW]
[ROW][C]breakpoint index[/C][C]greater[/C][C]2-sided[/C][C]less[/C][/ROW]
[ROW][C]10[/C][C]0.380238624997199[/C][C]0.760477249994399[/C][C]0.619761375002801[/C][/ROW]
[ROW][C]11[/C][C]0.916963988536676[/C][C]0.166072022926647[/C][C]0.0830360114633236[/C][/ROW]
[ROW][C]12[/C][C]0.0046127674752329[/C][C]0.0092255349504658[/C][C]0.995387232524767[/C][/ROW]
[ROW][C]13[/C][C]0.00234746087470581[/C][C]0.00469492174941162[/C][C]0.997652539125294[/C][/ROW]
[ROW][C]14[/C][C]0.668183527873662[/C][C]0.663632944252676[/C][C]0.331816472126338[/C][/ROW]
[ROW][C]15[/C][C]0.907763875363038[/C][C]0.184472249273925[/C][C]0.0922361246369623[/C][/ROW]
[ROW][C]16[/C][C]0.999999887392717[/C][C]2.25214566408953e-07[/C][C]1.12607283204477e-07[/C][/ROW]
[ROW][C]17[/C][C]0.575782557876344[/C][C]0.848434884247312[/C][C]0.424217442123656[/C][/ROW]
[ROW][C]18[/C][C]0.999999999964655[/C][C]7.06891863326092e-11[/C][C]3.53445931663046e-11[/C][/ROW]
[ROW][C]19[/C][C]0.951216069268869[/C][C]0.0975678614622619[/C][C]0.0487839307311309[/C][/ROW]
[ROW][C]20[/C][C]4.91647362216502e-05[/C][C]9.83294724433003e-05[/C][C]0.999950835263778[/C][/ROW]
[ROW][C]21[/C][C]0.0004808799641394[/C][C]0.0009617599282788[/C][C]0.999519120035861[/C][/ROW]
[ROW][C]22[/C][C]0.999995591315576[/C][C]8.81736884736351e-06[/C][C]4.40868442368175e-06[/C][/ROW]
[ROW][C]23[/C][C]4.55434840719873e-05[/C][C]9.10869681439745e-05[/C][C]0.999954456515928[/C][/ROW]
[ROW][C]24[/C][C]0.798170824259728[/C][C]0.403658351480544[/C][C]0.201829175740272[/C][/ROW]
[ROW][C]25[/C][C]0.0400261743312463[/C][C]0.0800523486624927[/C][C]0.959973825668754[/C][/ROW]
[ROW][C]26[/C][C]0.248761110649603[/C][C]0.497522221299207[/C][C]0.751238889350397[/C][/ROW]
[ROW][C]27[/C][C]0.999999237816191[/C][C]1.5243676176209e-06[/C][C]7.6218380881045e-07[/C][/ROW]
[ROW][C]28[/C][C]0.000138950341214312[/C][C]0.000277900682428624[/C][C]0.999861049658786[/C][/ROW]
[ROW][C]29[/C][C]0.367859622021696[/C][C]0.735719244043392[/C][C]0.632140377978304[/C][/ROW]
[ROW][C]30[/C][C]0.00660205588290684[/C][C]0.0132041117658137[/C][C]0.993397944117093[/C][/ROW]
[ROW][C]31[/C][C]0.999300542384036[/C][C]0.00139891523192827[/C][C]0.000699457615964134[/C][/ROW]
[ROW][C]32[/C][C]0.995268535471232[/C][C]0.00946292905753587[/C][C]0.00473146452876794[/C][/ROW]
[ROW][C]33[/C][C]1.29907344895159e-06[/C][C]2.59814689790318e-06[/C][C]0.999998700926551[/C][/ROW]
[ROW][C]34[/C][C]0.590150897012535[/C][C]0.81969820597493[/C][C]0.409849102987465[/C][/ROW]
[ROW][C]35[/C][C]0.549287188346314[/C][C]0.901425623307372[/C][C]0.450712811653686[/C][/ROW]
[ROW][C]36[/C][C]0.884536086575668[/C][C]0.230927826848665[/C][C]0.115463913424332[/C][/ROW]
[ROW][C]37[/C][C]0.416901739698007[/C][C]0.833803479396014[/C][C]0.583098260301993[/C][/ROW]
[ROW][C]38[/C][C]0.00273723912974865[/C][C]0.0054744782594973[/C][C]0.997262760870251[/C][/ROW]
[ROW][C]39[/C][C]0.344338138412262[/C][C]0.688676276824525[/C][C]0.655661861587738[/C][/ROW]
[ROW][C]40[/C][C]0.00426820283563875[/C][C]0.00853640567127751[/C][C]0.995731797164361[/C][/ROW]
[ROW][C]41[/C][C]1.83913010533517e-18[/C][C]3.67826021067034e-18[/C][C]1[/C][/ROW]
[ROW][C]42[/C][C]0.000458918695878977[/C][C]0.000917837391757955[/C][C]0.999541081304121[/C][/ROW]
[ROW][C]43[/C][C]7.42826928026694e-05[/C][C]0.000148565385605339[/C][C]0.999925717307197[/C][/ROW]
[ROW][C]44[/C][C]4.5740562565629e-05[/C][C]9.1481125131258e-05[/C][C]0.999954259437434[/C][/ROW]
[ROW][C]45[/C][C]0.754237185637959[/C][C]0.491525628724082[/C][C]0.245762814362041[/C][/ROW]
[ROW][C]46[/C][C]0.999564821279554[/C][C]0.000870357440891665[/C][C]0.000435178720445833[/C][/ROW]
[ROW][C]47[/C][C]0.999512139192087[/C][C]0.000975721615825653[/C][C]0.000487860807912826[/C][/ROW]
[ROW][C]48[/C][C]3.31820175432259e-07[/C][C]6.63640350864518e-07[/C][C]0.999999668179825[/C][/ROW]
[ROW][C]49[/C][C]0.0372079174286665[/C][C]0.074415834857333[/C][C]0.962792082571334[/C][/ROW]
[ROW][C]50[/C][C]0.999999991908025[/C][C]1.61839503750225e-08[/C][C]8.09197518751125e-09[/C][/ROW]
[ROW][C]51[/C][C]1.02184684074268e-15[/C][C]2.04369368148537e-15[/C][C]0.999999999999999[/C][/ROW]
[ROW][C]52[/C][C]4.90613414976683e-06[/C][C]9.81226829953366e-06[/C][C]0.99999509386585[/C][/ROW]
[ROW][C]53[/C][C]0.000341836999656927[/C][C]0.000683673999313854[/C][C]0.999658163000343[/C][/ROW]
[ROW][C]54[/C][C]0.0193800784698028[/C][C]0.0387601569396056[/C][C]0.980619921530197[/C][/ROW]
[ROW][C]55[/C][C]0.00044886244431448[/C][C]0.00089772488862896[/C][C]0.999551137555685[/C][/ROW]
[ROW][C]56[/C][C]1.33651049389454e-05[/C][C]2.67302098778909e-05[/C][C]0.999986634895061[/C][/ROW]
[ROW][C]57[/C][C]0.9999771872468[/C][C]4.56255063995102e-05[/C][C]2.28127531997551e-05[/C][/ROW]
[ROW][C]58[/C][C]2.31571322971797e-39[/C][C]4.63142645943593e-39[/C][C]1[/C][/ROW]
[ROW][C]59[/C][C]0.999999999999648[/C][C]7.03383983886761e-13[/C][C]3.5169199194338e-13[/C][/ROW]
[ROW][C]60[/C][C]0.00198735646565954[/C][C]0.00397471293131908[/C][C]0.99801264353434[/C][/ROW]
[ROW][C]61[/C][C]3.82453614006699e-08[/C][C]7.64907228013398e-08[/C][C]0.999999961754639[/C][/ROW]
[ROW][C]62[/C][C]6.39809016582556e-09[/C][C]1.27961803316511e-08[/C][C]0.99999999360191[/C][/ROW]
[ROW][C]63[/C][C]7.38965438501423e-19[/C][C]1.47793087700285e-18[/C][C]1[/C][/ROW]
[ROW][C]64[/C][C]0.999999999999556[/C][C]8.87119539362348e-13[/C][C]4.43559769681174e-13[/C][/ROW]
[ROW][C]65[/C][C]1[/C][C]2.36839411680581e-30[/C][C]1.18419705840291e-30[/C][/ROW]
[ROW][C]66[/C][C]0.94631676267835[/C][C]0.1073664746433[/C][C]0.0536832373216502[/C][/ROW]
[ROW][C]67[/C][C]1.10773950093894e-10[/C][C]2.21547900187789e-10[/C][C]0.999999999889226[/C][/ROW]
[ROW][C]68[/C][C]0.999999999997874[/C][C]4.25131860541913e-12[/C][C]2.12565930270957e-12[/C][/ROW]
[ROW][C]69[/C][C]3.37261202580125e-11[/C][C]6.7452240516025e-11[/C][C]0.999999999966274[/C][/ROW]
[ROW][C]70[/C][C]1.0096061919153e-15[/C][C]2.01921238383061e-15[/C][C]0.999999999999999[/C][/ROW]
[ROW][C]71[/C][C]1[/C][C]2.60634543159316e-19[/C][C]1.30317271579658e-19[/C][/ROW]
[ROW][C]72[/C][C]0.999999999954341[/C][C]9.13173326028668e-11[/C][C]4.56586663014334e-11[/C][/ROW]
[ROW][C]73[/C][C]0.999998806203296[/C][C]2.38759340906443e-06[/C][C]1.19379670453222e-06[/C][/ROW]
[ROW][C]74[/C][C]0.00138728101809103[/C][C]0.00277456203618207[/C][C]0.998612718981909[/C][/ROW]
[ROW][C]75[/C][C]0.999951992266385[/C][C]9.6015467230959e-05[/C][C]4.80077336154795e-05[/C][/ROW]
[ROW][C]76[/C][C]1.41279587772536e-19[/C][C]2.82559175545072e-19[/C][C]1[/C][/ROW]
[ROW][C]77[/C][C]5.65051985076578e-12[/C][C]1.13010397015316e-11[/C][C]0.99999999999435[/C][/ROW]
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[ROW][C]80[/C][C]2.83815172405478e-09[/C][C]5.67630344810956e-09[/C][C]0.999999997161848[/C][/ROW]
[ROW][C]81[/C][C]0.999998963037917[/C][C]2.07392416506017e-06[/C][C]1.03696208253008e-06[/C][/ROW]
[ROW][C]82[/C][C]0.999092684217179[/C][C]0.00181463156564165[/C][C]0.000907315782820826[/C][/ROW]
[ROW][C]83[/C][C]2.6916561747878e-10[/C][C]5.38331234957559e-10[/C][C]0.999999999730834[/C][/ROW]
[ROW][C]84[/C][C]3.03189031071778e-20[/C][C]6.06378062143555e-20[/C][C]1[/C][/ROW]
[ROW][C]85[/C][C]1[/C][C]1.10691086119089e-46[/C][C]5.53455430595444e-47[/C][/ROW]
[ROW][C]86[/C][C]0.999998473992669[/C][C]3.05201466198159e-06[/C][C]1.5260073309908e-06[/C][/ROW]
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[ROW][C]90[/C][C]6.9220209559343e-26[/C][C]1.38440419118686e-25[/C][C]1[/C][/ROW]
[ROW][C]91[/C][C]1[/C][C]3.77952984937245e-21[/C][C]1.88976492468623e-21[/C][/ROW]
[ROW][C]92[/C][C]0.999999996913039[/C][C]6.17392167307732e-09[/C][C]3.08696083653866e-09[/C][/ROW]
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[ROW][C]97[/C][C]1.0182431345757e-07[/C][C]2.03648626915141e-07[/C][C]0.999999898175687[/C][/ROW]
[ROW][C]98[/C][C]0.999999999558462[/C][C]8.83075072721798e-10[/C][C]4.41537536360899e-10[/C][/ROW]
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[ROW][C]102[/C][C]1[/C][C]1.17306984114158e-16[/C][C]5.8653492057079e-17[/C][/ROW]
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[ROW][C]106[/C][C]4.29127337216136e-11[/C][C]8.58254674432272e-11[/C][C]0.999999999957087[/C][/ROW]
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[ROW][C]111[/C][C]7.11573927537232e-07[/C][C]1.42314785507446e-06[/C][C]0.999999288426072[/C][/ROW]
[ROW][C]112[/C][C]0.999996168368023[/C][C]7.66326395496359e-06[/C][C]3.8316319774818e-06[/C][/ROW]
[ROW][C]113[/C][C]5.30775336235885e-35[/C][C]1.06155067247177e-34[/C][C]1[/C][/ROW]
[ROW][C]114[/C][C]0.999996530259818[/C][C]6.93948036457986e-06[/C][C]3.46974018228993e-06[/C][/ROW]
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[ROW][C]117[/C][C]2.70613277410753e-10[/C][C]5.41226554821506e-10[/C][C]0.999999999729387[/C][/ROW]
[ROW][C]118[/C][C]1.29148481588542e-16[/C][C]2.58296963177084e-16[/C][C]1[/C][/ROW]
[ROW][C]119[/C][C]0.022608964911376[/C][C]0.045217929822752[/C][C]0.977391035088624[/C][/ROW]
[ROW][C]120[/C][C]1.9198159652763e-15[/C][C]3.8396319305526e-15[/C][C]0.999999999999998[/C][/ROW]
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[ROW][C]122[/C][C]5.62200695492756e-54[/C][C]1.12440139098551e-53[/C][C]1[/C][/ROW]
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[ROW][C]125[/C][C]8.74663958612848e-08[/C][C]1.7493279172257e-07[/C][C]0.999999912533604[/C][/ROW]
[ROW][C]126[/C][C]3.15711422752394e-08[/C][C]6.31422845504788e-08[/C][C]0.999999968428858[/C][/ROW]
[ROW][C]127[/C][C]8.33900113364865e-06[/C][C]1.66780022672973e-05[/C][C]0.999991660998866[/C][/ROW]
[ROW][C]128[/C][C]0.00346132486234554[/C][C]0.00692264972469108[/C][C]0.996538675137654[/C][/ROW]
[ROW][C]129[/C][C]1.38318263978079e-39[/C][C]2.76636527956158e-39[/C][C]1[/C][/ROW]
[ROW][C]130[/C][C]0.962879804675564[/C][C]0.0742403906488726[/C][C]0.0371201953244363[/C][/ROW]
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[ROW][C]203[/C][C]3.24944491544771e-33[/C][C]6.49888983089541e-33[/C][C]1[/C][/ROW]
[ROW][C]204[/C][C]1.29376370560115e-39[/C][C]2.5875274112023e-39[/C][C]1[/C][/ROW]
[ROW][C]205[/C][C]3.04439303348925e-25[/C][C]6.0887860669785e-25[/C][C]1[/C][/ROW]
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[ROW][C]214[/C][C]0.99989113719261[/C][C]0.000217725614779763[/C][C]0.000108862807389882[/C][/ROW]
[ROW][C]215[/C][C]0.999795983699209[/C][C]0.000408032601582842[/C][C]0.000204016300791421[/C][/ROW]
[ROW][C]216[/C][C]0.076634445252496[/C][C]0.153268890504992[/C][C]0.923365554747504[/C][/ROW]
[ROW][C]217[/C][C]0.999629066132843[/C][C]0.000741867734314067[/C][C]0.000370933867157033[/C][/ROW]
[ROW][C]218[/C][C]2.14769825520974e-11[/C][C]4.29539651041949e-11[/C][C]0.999999999978523[/C][/ROW]
[ROW][C]219[/C][C]0.308849866008045[/C][C]0.617699732016089[/C][C]0.691150133991955[/C][/ROW]
[ROW][C]220[/C][C]0.585060325678625[/C][C]0.82987934864275[/C][C]0.414939674321375[/C][/ROW]
[ROW][C]221[/C][C]0.849341576779435[/C][C]0.301316846441129[/C][C]0.150658423220565[/C][/ROW]
[ROW][C]222[/C][C]0.997667623207407[/C][C]0.00466475358518628[/C][C]0.00233237679259314[/C][/ROW]
[ROW][C]223[/C][C]0.740278816713983[/C][C]0.519442366572035[/C][C]0.259721183286017[/C][/ROW]
[ROW][C]224[/C][C]0.77170713237355[/C][C]0.4565857352529[/C][C]0.22829286762645[/C][/ROW]
[ROW][C]225[/C][C]0.0197818956933356[/C][C]0.0395637913866712[/C][C]0.980218104306664[/C][/ROW]
[ROW][C]226[/C][C]0.549079618106351[/C][C]0.901840763787298[/C][C]0.450920381893649[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185286&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185286&T=5

As an alternative you can also use a QR Code:  
QR Code


The GUIDs for individual cells are displayed in the table below:

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
100.3802386249971990.7604772499943990.619761375002801
110.9169639885366760.1660720229266470.0830360114633236
120.00461276747523290.00922553495046580.995387232524767
130.002347460874705810.004694921749411620.997652539125294
140.6681835278736620.6636329442526760.331816472126338
150.9077638753630380.1844722492739250.0922361246369623
160.9999998873927172.25214566408953e-071.12607283204477e-07
170.5757825578763440.8484348842473120.424217442123656
180.9999999999646557.06891863326092e-113.53445931663046e-11
190.9512160692688690.09756786146226190.0487839307311309
204.91647362216502e-059.83294724433003e-050.999950835263778
210.00048087996413940.00096175992827880.999519120035861
220.9999955913155768.81736884736351e-064.40868442368175e-06
234.55434840719873e-059.10869681439745e-050.999954456515928
240.7981708242597280.4036583514805440.201829175740272
250.04002617433124630.08005234866249270.959973825668754
260.2487611106496030.4975222212992070.751238889350397
270.9999992378161911.5243676176209e-067.6218380881045e-07
280.0001389503412143120.0002779006824286240.999861049658786
290.3678596220216960.7357192440433920.632140377978304
300.006602055882906840.01320411176581370.993397944117093
310.9993005423840360.001398915231928270.000699457615964134
320.9952685354712320.009462929057535870.00473146452876794
331.29907344895159e-062.59814689790318e-060.999998700926551
340.5901508970125350.819698205974930.409849102987465
350.5492871883463140.9014256233073720.450712811653686
360.8845360865756680.2309278268486650.115463913424332
370.4169017396980070.8338034793960140.583098260301993
380.002737239129748650.00547447825949730.997262760870251
390.3443381384122620.6886762768245250.655661861587738
400.004268202835638750.008536405671277510.995731797164361
411.83913010533517e-183.67826021067034e-181
420.0004589186958789770.0009178373917579550.999541081304121
437.42826928026694e-050.0001485653856053390.999925717307197
444.5740562565629e-059.1481125131258e-050.999954259437434
450.7542371856379590.4915256287240820.245762814362041
460.9995648212795540.0008703574408916650.000435178720445833
470.9995121391920870.0009757216158256530.000487860807912826
483.31820175432259e-076.63640350864518e-070.999999668179825
490.03720791742866650.0744158348573330.962792082571334
500.9999999919080251.61839503750225e-088.09197518751125e-09
511.02184684074268e-152.04369368148537e-150.999999999999999
524.90613414976683e-069.81226829953366e-060.99999509386585
530.0003418369996569270.0006836739993138540.999658163000343
540.01938007846980280.03876015693960560.980619921530197
550.000448862444314480.000897724888628960.999551137555685
561.33651049389454e-052.67302098778909e-050.999986634895061
570.99997718724684.56255063995102e-052.28127531997551e-05
582.31571322971797e-394.63142645943593e-391
590.9999999999996487.03383983886761e-133.5169199194338e-13
600.001987356465659540.003974712931319080.99801264353434
613.82453614006699e-087.64907228013398e-080.999999961754639
626.39809016582556e-091.27961803316511e-080.99999999360191
637.38965438501423e-191.47793087700285e-181
640.9999999999995568.87119539362348e-134.43559769681174e-13
6512.36839411680581e-301.18419705840291e-30
660.946316762678350.10736647464330.0536832373216502
671.10773950093894e-102.21547900187789e-100.999999999889226
680.9999999999978744.25131860541913e-122.12565930270957e-12
693.37261202580125e-116.7452240516025e-110.999999999966274
701.0096061919153e-152.01921238383061e-150.999999999999999
7112.60634543159316e-191.30317271579658e-19
720.9999999999543419.13173326028668e-114.56586663014334e-11
730.9999988062032962.38759340906443e-061.19379670453222e-06
740.001387281018091030.002774562036182070.998612718981909
750.9999519922663859.6015467230959e-054.80077336154795e-05
761.41279587772536e-192.82559175545072e-191
775.65051985076578e-121.13010397015316e-110.99999999999435
784.42052481733013e-118.84104963466025e-110.999999999955795
790.9999999990587381.88252324279311e-099.41261621396556e-10
802.83815172405478e-095.67630344810956e-090.999999997161848
810.9999989630379172.07392416506017e-061.03696208253008e-06
820.9990926842171790.001814631565641650.000907315782820826
832.6916561747878e-105.38331234957559e-100.999999999730834
843.03189031071778e-206.06378062143555e-201
8511.10691086119089e-465.53455430595444e-47
860.9999984739926693.05201466198159e-061.5260073309908e-06
8718.43370230888514e-244.21685115444257e-24
888.88226390966032e-131.77645278193206e-120.999999999999112
890.9999999999841473.17066968249006e-111.58533484124503e-11
906.9220209559343e-261.38440419118686e-251
9113.77952984937245e-211.88976492468623e-21
920.9999999969130396.17392167307732e-093.08696083653866e-09
931.15764664705211e-202.31529329410422e-201
942.55950519599679e-145.11901039199358e-140.999999999999974
950.9999966669114986.66617700472317e-063.33308850236159e-06
960.9999982618710233.47625795411059e-061.7381289770553e-06
971.0182431345757e-072.03648626915141e-070.999999898175687
980.9999999995584628.83075072721798e-104.41537536360899e-10
990.007634123871242550.01526824774248510.992365876128757
1003.75831997958844e-167.51663995917689e-161
1012.33458991221556e-194.66917982443113e-191
10211.17306984114158e-165.8653492057079e-17
1030.5151211503049170.9697576993901660.484878849695083
1041.16664561830886e-302.33329123661771e-301
1051.6456381778401e-073.2912763556802e-070.999999835436182
1064.29127337216136e-118.58254674432272e-110.999999999957087
1070.01379373172842890.02758746345685790.986206268271571
1080.9999999999971145.77258929294967e-122.88629464647484e-12
1090.9463857704930440.1072284590139120.0536142295069558
1100.9999999999999991.93865020262824e-159.6932510131412e-16
1117.11573927537232e-071.42314785507446e-060.999999288426072
1120.9999961683680237.66326395496359e-063.8316319774818e-06
1135.30775336235885e-351.06155067247177e-341
1140.9999965302598186.93948036457986e-063.46974018228993e-06
1150.004505325582900420.009010651165800850.9954946744171
1163.43715707996029e-206.87431415992058e-201
1172.70613277410753e-105.41226554821506e-100.999999999729387
1181.29148481588542e-162.58296963177084e-161
1190.0226089649113760.0452179298227520.977391035088624
1201.9198159652763e-153.8396319305526e-150.999999999999998
1211.44561725827483e-452.89123451654966e-451
1225.62200695492756e-541.12440139098551e-531
1230.002431571545039090.004863143090078170.997568428454961
1240.0002803607279555260.0005607214559110510.999719639272044
1258.74663958612848e-081.7493279172257e-070.999999912533604
1263.15711422752394e-086.31422845504788e-080.999999968428858
1278.33900113364865e-061.66780022672973e-050.999991660998866
1280.003461324862345540.006922649724691080.996538675137654
1291.38318263978079e-392.76636527956158e-391
1300.9628798046755640.07424039064887260.0371201953244363
1310.9999715090460925.69819078151013e-052.84909539075507e-05
1320.9981373221863470.003725355627306990.00186267781365349
1332.15525815975901e-154.31051631951801e-150.999999999999998
1340.6764526023145930.6470947953708130.323547397685407
1350.9981414807594770.003717038481046070.00185851924052304
1360.2983910407901580.5967820815803160.701608959209842
1372.97797474117528e-335.95594948235056e-331
1380.999912621166660.0001747576666789948.73788333394968e-05
1390.9999999997324675.35065210633234e-102.67532605316617e-10
1400.999977519716574.49605668590291e-052.24802834295145e-05
1413.69732179110955e-417.39464358221909e-411
1420.9537371663573170.09252566728536590.0462628336426829
1430.01397391808891280.02794783617782570.986026081911087
1440.9999999995749588.50084867955417e-104.25042433977708e-10
1450.9998440125147790.0003119749704424710.000155987485221235
1462.08065897659298e-434.16131795318596e-431
1470.9997334931125270.0005330137749459110.000266506887472955
1480.001503104263451180.003006208526902360.998496895736549
1495.50772232041777e-050.0001101544464083550.999944922776796
1500.1940133724444770.3880267448889530.805986627555523
1510.9966423450791260.006715309841748040.00335765492087402
1525.96033521302185e-461.19206704260437e-451
1538.59809239295582e-241.71961847859116e-231
15415.43759515098953e-182.71879757549476e-18
1552.7288702336741e-065.45774046734821e-060.999997271129766
1560.0681624344765640.1363248689531280.931837565523436
1570.627140581723670.7457188365526590.37285941827633
1580.9999954769639549.04607209220718e-064.52303604610359e-06
1591.30342613734204e-082.60685227468409e-080.999999986965739
1600.9999999960073967.98520729156692e-093.99260364578346e-09
1610.9866217735821980.02675645283560420.0133782264178021
1629.20284406744043e-181.84056881348809e-171
1630.00334919294587460.006698385891749190.996650807054125
1640.0006231762104394140.001246352420878830.999376823789561
1650.9675604536455940.06487909270881190.032439546354406
1661.0066279142524e-132.01325582850479e-130.999999999999899
1671.48530462090826e-082.97060924181653e-080.999999985146954
1682.08043272942245e-074.16086545884491e-070.999999791956727
1693.30872957753186e-116.61745915506372e-110.999999999966913
1703.20305579045675e-126.4061115809135e-120.999999999996797
1710.876816636687540.2463667266249210.12318336331246
1720.9999020405152560.0001959189694880389.79594847440188e-05
1731.41920173991025e-202.8384034798205e-201
1740.6025766725224080.7948466549551840.397423327477592
1750.01402882582059580.02805765164119170.985971174179404
1766.62444423004397e-071.32488884600879e-060.999999337555577
1775.82161966319664e-101.16432393263933e-090.999999999417838
1780.001730414659266750.003460829318533490.998269585340733
1790.0002731888893622390.0005463777787244780.999726811110638
1800.0005919272713313210.001183854542662640.999408072728669
1813.23174723507074e-106.46349447014149e-100.999999999676825
1821.12770939445224e-082.25541878890448e-080.999999988722906
1830.9999867349105252.65301789503967e-051.32650894751984e-05
1849.29725069937053e-221.85945013987411e-211
1851.31052133398331e-102.62104266796661e-100.999999999868948
1860.06434055210025210.1286811042005040.935659447899748
1870.5652807533190030.8694384933619940.434719246680997
1882.27043882924003e-184.54087765848006e-181
1890.1016102770909480.2032205541818960.898389722909052
1901.98536828848039e-113.97073657696079e-110.999999999980146
1917.62155874187804e-151.52431174837561e-140.999999999999992
1920.09041769613009270.1808353922601850.909582303869907
1930.0003900318177645580.0007800636355291160.999609968182235
1941.98979869616754e-333.97959739233507e-331
1950.02922090575278070.05844181150556130.970779094247219
1961.72234979208092e-243.44469958416184e-241
1970.001898408782311320.003796817564622640.998101591217689
1982.3774146171336e-064.7548292342672e-060.999997622585383
1991.14182047436808e-252.28364094873615e-251
2000.02364305754830740.04728611509661490.976356942451693
2018.98562081827058e-131.79712416365412e-120.999999999999101
2022.15642983253103e-304.31285966506205e-301
2033.24944491544771e-336.49888983089541e-331
2041.29376370560115e-392.5875274112023e-391
2053.04439303348925e-256.0887860669785e-251
2063.90694193921227e-257.81388387842454e-251
2073.92989251811971e-117.85978503623943e-110.999999999960701
2080.5859813913731540.8280372172536920.414018608626846
2093.96308675859818e-197.92617351719636e-191
2100.9901447609140860.01971047817182780.00985523908591392
2110.7816430716718660.4367138566562690.218356928328134
2120.914549869335710.1709002613285810.0854501306642903
2130.6168693262541350.766261347491730.383130673745865
2140.999891137192610.0002177256147797630.000108862807389882
2150.9997959836992090.0004080326015828420.000204016300791421
2160.0766344452524960.1532688905049920.923365554747504
2170.9996290661328430.0007418677343140670.000370933867157033
2182.14769825520974e-114.29539651041949e-110.999999999978523
2190.3088498660080450.6176997320160890.691150133991955
2200.5850603256786250.829879348642750.414939674321375
2210.8493415767794350.3013168464411290.150658423220565
2220.9976676232074070.004664753585186280.00233237679259314
2230.7402788167139830.5194423665720350.259721183286017
2240.771707132373550.45658573525290.22829286762645
2250.01978189569333560.03956379138667120.980218104306664
2260.5490796181063510.9018407637872980.450920381893649






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level1600.737327188940092NOK
5% type I error level1710.788018433179724NOK
10% type I error level1780.820276497695853NOK

\begin{tabular}{lllllllll}
\hline
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
Description & # significant tests & % significant tests & OK/NOK \tabularnewline
1% type I error level & 160 & 0.737327188940092 & NOK \tabularnewline
5% type I error level & 171 & 0.788018433179724 & NOK \tabularnewline
10% type I error level & 178 & 0.820276497695853 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185286&T=6

[TABLE]
[ROW][C]Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]Description[/C][C]# significant tests[/C][C]% significant tests[/C][C]OK/NOK[/C][/ROW]
[ROW][C]1% type I error level[/C][C]160[/C][C]0.737327188940092[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]171[/C][C]0.788018433179724[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]178[/C][C]0.820276497695853[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185286&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185286&T=6

As an alternative you can also use a QR Code:  
QR Code


The GUIDs for individual cells are displayed in the table below:

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level1600.737327188940092NOK
5% type I error level1710.788018433179724NOK
10% type I error level1780.820276497695853NOK


Figures (Output of Computation)

Figure 1
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Figure 2
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Figure 5
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Figure 6
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Figure 7
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Figure 8
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Figure 9
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Figure 10
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Input Parameters & R Code

Parameters (Session):
par1 = 7 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
Parameters (R input):
par1 = 7 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
R code (references can be found in the software module):
library(lattice)
library(lmtest)
n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
par1 <- as.numeric(par1)
x <- t(y)
k <- length(x[1,])
n <- length(x[,1])
x1 <- cbind(x[,par1], x[,1:k!=par1])
mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
colnames(x1) <- mycolnames #colnames(x)[par1]
x <- x1
if (par3 == 'First Differences'){
x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
for (i in 1:n-1) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
}
if (par2 == 'Include Monthly Dummies'){
x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
for (i in 1:11){
x2[seq(i,n,12),i] <- 1
}
x <- cbind(x, x2)
}
if (par2 == 'Include Quarterly Dummies'){
x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
for (i in 1:3){
x2[seq(i,n,4),i] <- 1
}
x <- cbind(x, x2)
}
k <- length(x[1,])
if (par3 == 'Linear Trend'){
x <- cbind(x, c(1:n))
colnames(x)[k+1] <- 't'
}
x
k <- length(x[1,])
df <- as.data.frame(x)
(mylm <- lm(df))
(mysum <- summary(mylm))
if (n > n25) {
kp3 <- k + 3
nmkm3 <- n - k - 3
gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
numgqtests <- 0
numsignificant1 <- 0
numsignificant5 <- 0
numsignificant10 <- 0
for (mypoint in kp3:nmkm3) {
j <- 0
numgqtests <- numgqtests + 1
for (myalt in c('greater', 'two.sided', 'less')) {
j <- j + 1
gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
}
if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
}
gqarr
}
bitmap(file='test0.png')
plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
points(x[,1]-mysum$resid)
grid()
dev.off()
bitmap(file='test1.png')
plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
grid()
dev.off()
bitmap(file='test2.png')
hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
grid()
dev.off()
bitmap(file='test3.png')
densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
dev.off()
bitmap(file='test4.png')
qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
qqline(mysum$resid)
grid()
dev.off()
(myerror <- as.ts(mysum$resid))
bitmap(file='test5.png')
dum <- cbind(lag(myerror,k=1),myerror)
dum
dum1 <- dum[2:length(myerror),]
dum1
z <- as.data.frame(dum1)
z
plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
lines(lowess(z))
abline(lm(z))
grid()
dev.off()
bitmap(file='test6.png')
acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
grid()
dev.off()
bitmap(file='test7.png')
pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
grid()
dev.off()
bitmap(file='test8.png')
opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
plot(mylm, las = 1, sub='Residual Diagnostics')
par(opar)
dev.off()
if (n > n25) {
bitmap(file='test9.png')
plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
grid()
dev.off()
}
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
a<-table.row.end(a)
myeq <- colnames(x)[1]
myeq <- paste(myeq, '[t] = ', sep='')
for (i in 1:k){
if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ')
if (rownames(mysum$coefficients)[i] != '(Intercept)') {
myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
}
}
myeq <- paste(myeq, ' + e[t]')
a<-table.row.start(a)
a<-table.element(a, myeq)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable1.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,hyperlink('ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Variable',header=TRUE)
a<-table.element(a,'Parameter',header=TRUE)
a<-table.element(a,'S.D.',header=TRUE)
a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
a<-table.element(a,'2-tail p-value',header=TRUE)
a<-table.element(a,'1-tail p-value',header=TRUE)
a<-table.row.end(a)
for (i in 1:k){
a<-table.row.start(a)
a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
a<-table.element(a,mysum$coefficients[i,1])
a<-table.element(a, round(mysum$coefficients[i,2],6))
a<-table.element(a, round(mysum$coefficients[i,3],4))
a<-table.element(a, round(mysum$coefficients[i,4],6))
a<-table.element(a, round(mysum$coefficients[i,4]/2,6))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable2.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple R',1,TRUE)
a<-table.element(a, sqrt(mysum$r.squared))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'R-squared',1,TRUE)
a<-table.element(a, mysum$r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Adjusted R-squared',1,TRUE)
a<-table.element(a, mysum$adj.r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (value)',1,TRUE)
a<-table.element(a, mysum$fstatistic[1])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[2])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[3])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'p-value',1,TRUE)
a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
a<-table.element(a, mysum$sigma)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
a<-table.element(a, sum(myerror*myerror))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,x[i])
a<-table.element(a,x[i]-mysum$resid[i])
a<-table.element(a,mysum$resid[i])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable4.tab')
if (n > n25) {
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'p-values',header=TRUE)
a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'breakpoint index',header=TRUE)
a<-table.element(a,'greater',header=TRUE)
a<-table.element(a,'2-sided',header=TRUE)
a<-table.element(a,'less',header=TRUE)
a<-table.row.end(a)
for (mypoint in kp3:nmkm3) {
a<-table.row.start(a)
a<-table.element(a,mypoint,header=TRUE)
a<-table.element(a,gqarr[mypoint-kp3+1,1])
a<-table.element(a,gqarr[mypoint-kp3+1,2])
a<-table.element(a,gqarr[mypoint-kp3+1,3])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable5.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Description',header=TRUE)
a<-table.element(a,'# significant tests',header=TRUE)
a<-table.element(a,'% significant tests',header=TRUE)
a<-table.element(a,'OK/NOK',header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'1% type I error level',header=TRUE)
a<-table.element(a,numsignificant1)
a<-table.element(a,numsignificant1/numgqtests)
if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'5% type I error level',header=TRUE)
a<-table.element(a,numsignificant5)
a<-table.element(a,numsignificant5/numgqtests)
if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'10% type I error level',header=TRUE)
a<-table.element(a,numsignificant10)
a<-table.element(a,numsignificant10/numgqtests)
if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable6.tab')
}