FreeStatistics.org

Free Statistics

of Irreproducible Research!

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 computationSun, 14 Dec 2014 13:07:24 +0000
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2014/Dec/14/t1418562472oyw43716mgja3te.htm/, Retrieved Sun, 19 May 2024 15:54:16 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=267539, Retrieved Sun, 19 May 2024 15:54:16 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact106

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Chi-Squared Test, McNemar Test, and Fisher Exact Test] [3.1 Chi kwadraat ...] [2014-12-14 11:44:27] [765bd0d5d4a0c852014c120c6930661d]
- RMPD    [Multiple Regression] [3 Multiple regres...] [2014-12-14 13:07:24] [706bcb1d0c5210dc074174906fafd7a3] [Current]
Feedback Forum

Post a new message

Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
48	41	12	0.5
50	146	45	7.5
150	182	37	9
154	192	37	9.5
109	263	108	8.5
68	35	10	7
194	439	68	8
158	214	72	10
159	341	143	7
67	58	9	8.5
147	292	55	9
39	85	17	9.5
100	200	37	4
111	158	27	6
138	199	37	8
101	297	58	5.5
131	227	66	9.5
101	108	21	7.5
114	86	19	7
165	302	78	7.5
114	148	35	8
111	178	48	7
75	120	27	7
82	207	43	6
121	157	30	10
32	128	25	2.5
150	296	69	9
117	323	72	8
71	79	23	6
165	70	13	8.5
154	146	61	6
126	246	43	9
138	145	22	8
149	196	51	8
145	199	67	9
120	127	36	5.5
138	91	21	5
109	153	44	7
132	299	45	5.5
172	228	34	9
169	190	36	2
114	180	72	8.5
156	212	39	9
172	269	43	8.5
68	130	25	9
89	179	56	7.5
167	243	80	10
113	190	40	9
115	299	73	7.5
78	121	34	6
118	137	72	10.5
87	305	42	8.5
173	157	61	8
2	96	23	10
162	183	74	10.5
49	52	16	6.5
122	238	66	9.5
96	40	9	8.5
100	226	41	7.5
82	190	57	5
100	214	48	8
115	145	51	10
141	119	53	7
165	222	29	7.5
165	222	29	7.5
110	159	55	9.5
118	165	54	6
158	249	43	10
146	125	51	7
49	122	20	3
90	186	79	6
121	148	39	7
155	274	61	10
104	172	55	7
147	84	30	3.5
110	168	55	8
108	102	22	10
113	106	37	5.5
115	2	2	6
61	139	38	6.5
60	95	27	6.5
109	130	56	8.5
68	72	25	4
111	141	39	9.5
77	113	33	8
73	206	43	8.5
151	268	57	5.5
89	175	43	7
78	77	23	9
110	125	44	8
220	255	54	10
65	111	28	8
141	132	36	6
117	211	39	8
122	92	16	5
63	76	23	9
44	171	40	4.5
52	83	24	8.5
62	119	29	7
131	266	78	9.5
101	186	57	8.5
42	50	37	7.5
152	117	27	7.5
107	219	61	5
77	246	27	7
154	279	69	8
103	148	34	5.5
96	137	44	8.5
154	130	21	7.5
175	181	34	9.5
57	98	39	7
112	226	51	8
143	234	34	8.5
49	138	31	3.5
110	85	13	6.5
131	66	12	6.5
167	236	51	10.5
56	106	24	8.5
137	135	19	8
86	122	30	10
121	218	81	10
149	199	42	9.5
168	112	22	9
140	278	85	10
88	94	27	7.5
168	113	25	4.5
94	84	22	4.5
51	86	19	0.5
48	62	14	6.5
145	222	45	4.5
66	167	45	5.5
85	82	28	5
109	207	51	6
63	184	41	4
102	83	31	8
162	183	74	10.5
128	85	24	8.5
86	89	19	6.5
114	225	51	8
164	237	73	8.5
119	102	24	5.5
126	221	61	7
132	128	23	5
142	91	14	3.5
83	198	54	5
94	204	51	9
81	158	62	8.5
166	138	36	5
110	226	59	9.5
64	44	24	3
93	196	26	1.5
104	83	54	6
105	79	39	0.5
49	52	16	6.5
88	105	36	7.5
95	116	31	4.5
102	83	31	8
99	196	42	9
63	153	39	7.5
76	157	25	8.5
109	75	31	7
117	106	38	9.5
57	58	31	6.5
120	75	17	9.5
73	74	22	6
91	185	55	8
108	265	62	9.5
105	131	51	8
117	139	30	8
119	196	49	9
31	78	16	5

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time6 seconds
R Server'Herman Ole Andreas Wold' @ wold.wessa.net
R Framework error message
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.

\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 & 6 seconds \tabularnewline
R Server & 'Herman Ole Andreas Wold' @ wold.wessa.net \tabularnewline
R Framework error message & 
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=267539&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]6 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Herman Ole Andreas Wold' @ wold.wessa.net[/C][/ROW]
[ROW][C]R Framework error message[/C][C]
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=267539&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=267539&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 time6 seconds
R Server'Herman Ole Andreas Wold' @ wold.wessa.net
R Framework error message
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.






Multiple Linear Regression - Estimated Regression Equation
Ex [t] = + 4.99006 + 0.0108363LFM[t] + 0.00128928B[t] + 0.0217172CH[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Ex
[t] =  +  4.99006 +  0.0108363LFM[t] +  0.00128928B[t] +  0.0217172CH[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=267539&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Ex
[t] =  +  4.99006 +  0.0108363LFM[t] +  0.00128928B[t] +  0.0217172CH[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=267539&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=267539&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
Ex [t] = + 4.99006 + 0.0108363LFM[t] + 0.00128928B[t] + 0.0217172CH[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)4.990060.48307510.331.18667e-195.93334e-20
LFM0.01083630.004501782.4070.01716910.00858453
B0.001289280.003189740.40420.6865850.343292
CH0.02171720.01077692.0150.04549080.0227454

\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) & 4.99006 & 0.483075 & 10.33 & 1.18667e-19 & 5.93334e-20 \tabularnewline
LFM & 0.0108363 & 0.00450178 & 2.407 & 0.0171691 & 0.00858453 \tabularnewline
B & 0.00128928 & 0.00318974 & 0.4042 & 0.686585 & 0.343292 \tabularnewline
CH & 0.0217172 & 0.0107769 & 2.015 & 0.0454908 & 0.0227454 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=267539&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]4.99006[/C][C]0.483075[/C][C]10.33[/C][C]1.18667e-19[/C][C]5.93334e-20[/C][/ROW]
[ROW][C]LFM[/C][C]0.0108363[/C][C]0.00450178[/C][C]2.407[/C][C]0.0171691[/C][C]0.00858453[/C][/ROW]
[ROW][C]B[/C][C]0.00128928[/C][C]0.00318974[/C][C]0.4042[/C][C]0.686585[/C][C]0.343292[/C][/ROW]
[ROW][C]CH[/C][C]0.0217172[/C][C]0.0107769[/C][C]2.015[/C][C]0.0454908[/C][C]0.0227454[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=267539&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=267539&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)4.990060.48307510.331.18667e-195.93334e-20
LFM0.01083630.004501782.4070.01716910.00858453
B0.001289280.003189740.40420.6865850.343292
CH0.02171720.01077692.0150.04549080.0227454






Multiple Linear Regression - Regression Statistics
Multiple R0.373796
R-squared0.139723
Adjusted R-squared0.124269
F-TEST (value)9.04121
F-TEST (DF numerator)3
F-TEST (DF denominator)167
p-value1.397e-05
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1.96369
Sum Squared Residuals643.965

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.373796 \tabularnewline
R-squared & 0.139723 \tabularnewline
Adjusted R-squared & 0.124269 \tabularnewline
F-TEST (value) & 9.04121 \tabularnewline
F-TEST (DF numerator) & 3 \tabularnewline
F-TEST (DF denominator) & 167 \tabularnewline
p-value & 1.397e-05 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 1.96369 \tabularnewline
Sum Squared Residuals & 643.965 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=267539&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.373796[/C][/ROW]
[ROW][C]R-squared[/C][C]0.139723[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.124269[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]9.04121[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]3[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]167[/C][/ROW]
[ROW][C]p-value[/C][C]1.397e-05[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]1.96369[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]643.965[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=267539&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=267539&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 R0.373796
R-squared0.139723
Adjusted R-squared0.124269
F-TEST (value)9.04121
F-TEST (DF numerator)3
F-TEST (DF denominator)167
p-value1.397e-05
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1.96369
Sum Squared Residuals643.965






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
10.55.82367-5.32367
27.56.697390.80261
397.65371.3463
49.57.709941.79006
58.58.85576-0.355763
675.989231.01077
789.13508-1.13508
8108.541751.45825
9710.2582-3.25825
108.55.986332.51367
1198.153920.846078
129.55.891463.60854
1347.13509-3.13509
1466.98297-0.982968
1587.545580.454418
165.57.72705-2.22705
179.58.135631.36437
187.56.679840.820162
1976.748910.251089
207.58.86136-1.36136
2187.176320.823678
2277.46482-0.464815
2376.543870.456133
2467.07936-1.07936
25107.155192.84481
262.56.04478-3.54478
2798.495630.504371
2888.23799-0.237992
2966.36079-0.360793
308.57.150631.34937
3168.17184-2.17184
3297.606451.39355
3387.15020.849797
3487.964950.0350451
3598.272950.727048
365.57.23598-1.73598
3757.05886-2.05886
3877.32404-0.324042
395.57.78323-2.28323
4097.886261.11374
4127.84819-5.84819
428.58.021120.478885
4397.800831.19917
448.58.134570.365429
4596.437472.56253
467.57.401440.0985575
47108.85041.1496
4897.328221.67178
497.58.20709-0.707094
5066.72969-0.729686
5110.58.009022.49098
528.57.238181.26182
5388.39192-0.391917
54105.6354.365
5510.58.588561.91144
566.55.935560.564438
579.58.052281.44772
588.56.277382.22262
597.57.255480.244519
6057.36149-2.36149
6187.392030.60797
62107.530772.46923
6377.82242-0.822424
647.57.69408-0.194079
657.57.69408-0.194079
669.57.58151.9185
6767.65421-1.65421
68107.957082.04292
6977.84091-0.840907
7036.11268-3.11268
7167.9208-1.9208
7277.33905-0.339045
73108.347711.65229
7477.53325-0.533245
753.57.34282-3.84282
7687.593110.406894
77106.769673.23033
785.57.15477-1.65477
7966.28226-0.282256
806.56.65554-0.155544
816.56.349090.15091
828.57.554990.945006
8346.36269-2.36269
849.57.221662.27834
8586.686821.31318
868.56.980551.51945
875.58.20976-2.70976
8877.11396-0.113962
8996.434072.56593
9087.298780.701222
91108.875551.12445
9286.445621.55438
9367.46999-1.46999
9487.376920.623075
9556.77819-1.77819
9696.270232.72977
974.56.55602-2.05602
988.56.181782.31822
9976.445140.55486
1009.58.446511.05349
1018.57.562220.937779
1027.56.313191.18681
1037.57.37440.125602
10457.75665-2.75665
10576.727990.27201
10688.51706-0.517057
1075.57.03541-1.53541
1088.57.162541.33746
1097.57.282530.217472
1109.57.858171.64183
11176.581060.418945
11287.602690.397311
1138.57.579740.920263
1143.56.3722-2.8722
1156.56.57397-0.0739736
1166.56.75532-0.255323
11710.58.211582.28842
1188.56.254782.24522
11987.061320.938678
120106.73083.2692
121108.341421.65858
1229.57.773371.72663
12397.432751.56725
124108.711531.28847
1257.56.651220.848782
1264.57.49919-2.99919
1274.56.59476-2.09476
1280.56.06622-5.56622
1296.55.894180.605816
1304.57.82483-3.32483
1315.56.89785-1.39785
13256.62496-1.62496
13367.54568-1.54568
13446.80039-2.80039
13586.875611.12439
13610.58.588561.91144
1378.57.007921.49208
1386.56.449360.0506383
13987.623070.376928
1408.58.65814-0.158139
1415.56.93231-1.43231
14277.96512-0.965123
14357.08498-2.08498
1443.56.95019-3.45019
14557.31749-2.31749
14697.379271.62073
1478.57.417981.08202
14857.74864-2.74864
1499.57.754751.74525
15036.26153-3.26153
1511.56.81519-5.31519
15267.39678-1.39678
1530.57.0767-6.5767
1546.55.935560.564438
1557.56.860860.639145
1564.56.84231-2.34231
15786.875611.12439
15897.227681.77232
1597.56.716980.783016
1608.56.558971.94103
16176.941150.058846
1629.57.219832.28017
1636.56.355750.144254
1649.56.756312.74369
16566.3543-0.354302
16687.409130.590866
1679.57.848511.65149
16887.404350.595648
16987.088640.911358
17097.596431.40357
17155.77403-0.774029

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 0.5 & 5.82367 & -5.32367 \tabularnewline
2 & 7.5 & 6.69739 & 0.80261 \tabularnewline
3 & 9 & 7.6537 & 1.3463 \tabularnewline
4 & 9.5 & 7.70994 & 1.79006 \tabularnewline
5 & 8.5 & 8.85576 & -0.355763 \tabularnewline
6 & 7 & 5.98923 & 1.01077 \tabularnewline
7 & 8 & 9.13508 & -1.13508 \tabularnewline
8 & 10 & 8.54175 & 1.45825 \tabularnewline
9 & 7 & 10.2582 & -3.25825 \tabularnewline
10 & 8.5 & 5.98633 & 2.51367 \tabularnewline
11 & 9 & 8.15392 & 0.846078 \tabularnewline
12 & 9.5 & 5.89146 & 3.60854 \tabularnewline
13 & 4 & 7.13509 & -3.13509 \tabularnewline
14 & 6 & 6.98297 & -0.982968 \tabularnewline
15 & 8 & 7.54558 & 0.454418 \tabularnewline
16 & 5.5 & 7.72705 & -2.22705 \tabularnewline
17 & 9.5 & 8.13563 & 1.36437 \tabularnewline
18 & 7.5 & 6.67984 & 0.820162 \tabularnewline
19 & 7 & 6.74891 & 0.251089 \tabularnewline
20 & 7.5 & 8.86136 & -1.36136 \tabularnewline
21 & 8 & 7.17632 & 0.823678 \tabularnewline
22 & 7 & 7.46482 & -0.464815 \tabularnewline
23 & 7 & 6.54387 & 0.456133 \tabularnewline
24 & 6 & 7.07936 & -1.07936 \tabularnewline
25 & 10 & 7.15519 & 2.84481 \tabularnewline
26 & 2.5 & 6.04478 & -3.54478 \tabularnewline
27 & 9 & 8.49563 & 0.504371 \tabularnewline
28 & 8 & 8.23799 & -0.237992 \tabularnewline
29 & 6 & 6.36079 & -0.360793 \tabularnewline
30 & 8.5 & 7.15063 & 1.34937 \tabularnewline
31 & 6 & 8.17184 & -2.17184 \tabularnewline
32 & 9 & 7.60645 & 1.39355 \tabularnewline
33 & 8 & 7.1502 & 0.849797 \tabularnewline
34 & 8 & 7.96495 & 0.0350451 \tabularnewline
35 & 9 & 8.27295 & 0.727048 \tabularnewline
36 & 5.5 & 7.23598 & -1.73598 \tabularnewline
37 & 5 & 7.05886 & -2.05886 \tabularnewline
38 & 7 & 7.32404 & -0.324042 \tabularnewline
39 & 5.5 & 7.78323 & -2.28323 \tabularnewline
40 & 9 & 7.88626 & 1.11374 \tabularnewline
41 & 2 & 7.84819 & -5.84819 \tabularnewline
42 & 8.5 & 8.02112 & 0.478885 \tabularnewline
43 & 9 & 7.80083 & 1.19917 \tabularnewline
44 & 8.5 & 8.13457 & 0.365429 \tabularnewline
45 & 9 & 6.43747 & 2.56253 \tabularnewline
46 & 7.5 & 7.40144 & 0.0985575 \tabularnewline
47 & 10 & 8.8504 & 1.1496 \tabularnewline
48 & 9 & 7.32822 & 1.67178 \tabularnewline
49 & 7.5 & 8.20709 & -0.707094 \tabularnewline
50 & 6 & 6.72969 & -0.729686 \tabularnewline
51 & 10.5 & 8.00902 & 2.49098 \tabularnewline
52 & 8.5 & 7.23818 & 1.26182 \tabularnewline
53 & 8 & 8.39192 & -0.391917 \tabularnewline
54 & 10 & 5.635 & 4.365 \tabularnewline
55 & 10.5 & 8.58856 & 1.91144 \tabularnewline
56 & 6.5 & 5.93556 & 0.564438 \tabularnewline
57 & 9.5 & 8.05228 & 1.44772 \tabularnewline
58 & 8.5 & 6.27738 & 2.22262 \tabularnewline
59 & 7.5 & 7.25548 & 0.244519 \tabularnewline
60 & 5 & 7.36149 & -2.36149 \tabularnewline
61 & 8 & 7.39203 & 0.60797 \tabularnewline
62 & 10 & 7.53077 & 2.46923 \tabularnewline
63 & 7 & 7.82242 & -0.822424 \tabularnewline
64 & 7.5 & 7.69408 & -0.194079 \tabularnewline
65 & 7.5 & 7.69408 & -0.194079 \tabularnewline
66 & 9.5 & 7.5815 & 1.9185 \tabularnewline
67 & 6 & 7.65421 & -1.65421 \tabularnewline
68 & 10 & 7.95708 & 2.04292 \tabularnewline
69 & 7 & 7.84091 & -0.840907 \tabularnewline
70 & 3 & 6.11268 & -3.11268 \tabularnewline
71 & 6 & 7.9208 & -1.9208 \tabularnewline
72 & 7 & 7.33905 & -0.339045 \tabularnewline
73 & 10 & 8.34771 & 1.65229 \tabularnewline
74 & 7 & 7.53325 & -0.533245 \tabularnewline
75 & 3.5 & 7.34282 & -3.84282 \tabularnewline
76 & 8 & 7.59311 & 0.406894 \tabularnewline
77 & 10 & 6.76967 & 3.23033 \tabularnewline
78 & 5.5 & 7.15477 & -1.65477 \tabularnewline
79 & 6 & 6.28226 & -0.282256 \tabularnewline
80 & 6.5 & 6.65554 & -0.155544 \tabularnewline
81 & 6.5 & 6.34909 & 0.15091 \tabularnewline
82 & 8.5 & 7.55499 & 0.945006 \tabularnewline
83 & 4 & 6.36269 & -2.36269 \tabularnewline
84 & 9.5 & 7.22166 & 2.27834 \tabularnewline
85 & 8 & 6.68682 & 1.31318 \tabularnewline
86 & 8.5 & 6.98055 & 1.51945 \tabularnewline
87 & 5.5 & 8.20976 & -2.70976 \tabularnewline
88 & 7 & 7.11396 & -0.113962 \tabularnewline
89 & 9 & 6.43407 & 2.56593 \tabularnewline
90 & 8 & 7.29878 & 0.701222 \tabularnewline
91 & 10 & 8.87555 & 1.12445 \tabularnewline
92 & 8 & 6.44562 & 1.55438 \tabularnewline
93 & 6 & 7.46999 & -1.46999 \tabularnewline
94 & 8 & 7.37692 & 0.623075 \tabularnewline
95 & 5 & 6.77819 & -1.77819 \tabularnewline
96 & 9 & 6.27023 & 2.72977 \tabularnewline
97 & 4.5 & 6.55602 & -2.05602 \tabularnewline
98 & 8.5 & 6.18178 & 2.31822 \tabularnewline
99 & 7 & 6.44514 & 0.55486 \tabularnewline
100 & 9.5 & 8.44651 & 1.05349 \tabularnewline
101 & 8.5 & 7.56222 & 0.937779 \tabularnewline
102 & 7.5 & 6.31319 & 1.18681 \tabularnewline
103 & 7.5 & 7.3744 & 0.125602 \tabularnewline
104 & 5 & 7.75665 & -2.75665 \tabularnewline
105 & 7 & 6.72799 & 0.27201 \tabularnewline
106 & 8 & 8.51706 & -0.517057 \tabularnewline
107 & 5.5 & 7.03541 & -1.53541 \tabularnewline
108 & 8.5 & 7.16254 & 1.33746 \tabularnewline
109 & 7.5 & 7.28253 & 0.217472 \tabularnewline
110 & 9.5 & 7.85817 & 1.64183 \tabularnewline
111 & 7 & 6.58106 & 0.418945 \tabularnewline
112 & 8 & 7.60269 & 0.397311 \tabularnewline
113 & 8.5 & 7.57974 & 0.920263 \tabularnewline
114 & 3.5 & 6.3722 & -2.8722 \tabularnewline
115 & 6.5 & 6.57397 & -0.0739736 \tabularnewline
116 & 6.5 & 6.75532 & -0.255323 \tabularnewline
117 & 10.5 & 8.21158 & 2.28842 \tabularnewline
118 & 8.5 & 6.25478 & 2.24522 \tabularnewline
119 & 8 & 7.06132 & 0.938678 \tabularnewline
120 & 10 & 6.7308 & 3.2692 \tabularnewline
121 & 10 & 8.34142 & 1.65858 \tabularnewline
122 & 9.5 & 7.77337 & 1.72663 \tabularnewline
123 & 9 & 7.43275 & 1.56725 \tabularnewline
124 & 10 & 8.71153 & 1.28847 \tabularnewline
125 & 7.5 & 6.65122 & 0.848782 \tabularnewline
126 & 4.5 & 7.49919 & -2.99919 \tabularnewline
127 & 4.5 & 6.59476 & -2.09476 \tabularnewline
128 & 0.5 & 6.06622 & -5.56622 \tabularnewline
129 & 6.5 & 5.89418 & 0.605816 \tabularnewline
130 & 4.5 & 7.82483 & -3.32483 \tabularnewline
131 & 5.5 & 6.89785 & -1.39785 \tabularnewline
132 & 5 & 6.62496 & -1.62496 \tabularnewline
133 & 6 & 7.54568 & -1.54568 \tabularnewline
134 & 4 & 6.80039 & -2.80039 \tabularnewline
135 & 8 & 6.87561 & 1.12439 \tabularnewline
136 & 10.5 & 8.58856 & 1.91144 \tabularnewline
137 & 8.5 & 7.00792 & 1.49208 \tabularnewline
138 & 6.5 & 6.44936 & 0.0506383 \tabularnewline
139 & 8 & 7.62307 & 0.376928 \tabularnewline
140 & 8.5 & 8.65814 & -0.158139 \tabularnewline
141 & 5.5 & 6.93231 & -1.43231 \tabularnewline
142 & 7 & 7.96512 & -0.965123 \tabularnewline
143 & 5 & 7.08498 & -2.08498 \tabularnewline
144 & 3.5 & 6.95019 & -3.45019 \tabularnewline
145 & 5 & 7.31749 & -2.31749 \tabularnewline
146 & 9 & 7.37927 & 1.62073 \tabularnewline
147 & 8.5 & 7.41798 & 1.08202 \tabularnewline
148 & 5 & 7.74864 & -2.74864 \tabularnewline
149 & 9.5 & 7.75475 & 1.74525 \tabularnewline
150 & 3 & 6.26153 & -3.26153 \tabularnewline
151 & 1.5 & 6.81519 & -5.31519 \tabularnewline
152 & 6 & 7.39678 & -1.39678 \tabularnewline
153 & 0.5 & 7.0767 & -6.5767 \tabularnewline
154 & 6.5 & 5.93556 & 0.564438 \tabularnewline
155 & 7.5 & 6.86086 & 0.639145 \tabularnewline
156 & 4.5 & 6.84231 & -2.34231 \tabularnewline
157 & 8 & 6.87561 & 1.12439 \tabularnewline
158 & 9 & 7.22768 & 1.77232 \tabularnewline
159 & 7.5 & 6.71698 & 0.783016 \tabularnewline
160 & 8.5 & 6.55897 & 1.94103 \tabularnewline
161 & 7 & 6.94115 & 0.058846 \tabularnewline
162 & 9.5 & 7.21983 & 2.28017 \tabularnewline
163 & 6.5 & 6.35575 & 0.144254 \tabularnewline
164 & 9.5 & 6.75631 & 2.74369 \tabularnewline
165 & 6 & 6.3543 & -0.354302 \tabularnewline
166 & 8 & 7.40913 & 0.590866 \tabularnewline
167 & 9.5 & 7.84851 & 1.65149 \tabularnewline
168 & 8 & 7.40435 & 0.595648 \tabularnewline
169 & 8 & 7.08864 & 0.911358 \tabularnewline
170 & 9 & 7.59643 & 1.40357 \tabularnewline
171 & 5 & 5.77403 & -0.774029 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=267539&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]0.5[/C][C]5.82367[/C][C]-5.32367[/C][/ROW]
[ROW][C]2[/C][C]7.5[/C][C]6.69739[/C][C]0.80261[/C][/ROW]
[ROW][C]3[/C][C]9[/C][C]7.6537[/C][C]1.3463[/C][/ROW]
[ROW][C]4[/C][C]9.5[/C][C]7.70994[/C][C]1.79006[/C][/ROW]
[ROW][C]5[/C][C]8.5[/C][C]8.85576[/C][C]-0.355763[/C][/ROW]
[ROW][C]6[/C][C]7[/C][C]5.98923[/C][C]1.01077[/C][/ROW]
[ROW][C]7[/C][C]8[/C][C]9.13508[/C][C]-1.13508[/C][/ROW]
[ROW][C]8[/C][C]10[/C][C]8.54175[/C][C]1.45825[/C][/ROW]
[ROW][C]9[/C][C]7[/C][C]10.2582[/C][C]-3.25825[/C][/ROW]
[ROW][C]10[/C][C]8.5[/C][C]5.98633[/C][C]2.51367[/C][/ROW]
[ROW][C]11[/C][C]9[/C][C]8.15392[/C][C]0.846078[/C][/ROW]
[ROW][C]12[/C][C]9.5[/C][C]5.89146[/C][C]3.60854[/C][/ROW]
[ROW][C]13[/C][C]4[/C][C]7.13509[/C][C]-3.13509[/C][/ROW]
[ROW][C]14[/C][C]6[/C][C]6.98297[/C][C]-0.982968[/C][/ROW]
[ROW][C]15[/C][C]8[/C][C]7.54558[/C][C]0.454418[/C][/ROW]
[ROW][C]16[/C][C]5.5[/C][C]7.72705[/C][C]-2.22705[/C][/ROW]
[ROW][C]17[/C][C]9.5[/C][C]8.13563[/C][C]1.36437[/C][/ROW]
[ROW][C]18[/C][C]7.5[/C][C]6.67984[/C][C]0.820162[/C][/ROW]
[ROW][C]19[/C][C]7[/C][C]6.74891[/C][C]0.251089[/C][/ROW]
[ROW][C]20[/C][C]7.5[/C][C]8.86136[/C][C]-1.36136[/C][/ROW]
[ROW][C]21[/C][C]8[/C][C]7.17632[/C][C]0.823678[/C][/ROW]
[ROW][C]22[/C][C]7[/C][C]7.46482[/C][C]-0.464815[/C][/ROW]
[ROW][C]23[/C][C]7[/C][C]6.54387[/C][C]0.456133[/C][/ROW]
[ROW][C]24[/C][C]6[/C][C]7.07936[/C][C]-1.07936[/C][/ROW]
[ROW][C]25[/C][C]10[/C][C]7.15519[/C][C]2.84481[/C][/ROW]
[ROW][C]26[/C][C]2.5[/C][C]6.04478[/C][C]-3.54478[/C][/ROW]
[ROW][C]27[/C][C]9[/C][C]8.49563[/C][C]0.504371[/C][/ROW]
[ROW][C]28[/C][C]8[/C][C]8.23799[/C][C]-0.237992[/C][/ROW]
[ROW][C]29[/C][C]6[/C][C]6.36079[/C][C]-0.360793[/C][/ROW]
[ROW][C]30[/C][C]8.5[/C][C]7.15063[/C][C]1.34937[/C][/ROW]
[ROW][C]31[/C][C]6[/C][C]8.17184[/C][C]-2.17184[/C][/ROW]
[ROW][C]32[/C][C]9[/C][C]7.60645[/C][C]1.39355[/C][/ROW]
[ROW][C]33[/C][C]8[/C][C]7.1502[/C][C]0.849797[/C][/ROW]
[ROW][C]34[/C][C]8[/C][C]7.96495[/C][C]0.0350451[/C][/ROW]
[ROW][C]35[/C][C]9[/C][C]8.27295[/C][C]0.727048[/C][/ROW]
[ROW][C]36[/C][C]5.5[/C][C]7.23598[/C][C]-1.73598[/C][/ROW]
[ROW][C]37[/C][C]5[/C][C]7.05886[/C][C]-2.05886[/C][/ROW]
[ROW][C]38[/C][C]7[/C][C]7.32404[/C][C]-0.324042[/C][/ROW]
[ROW][C]39[/C][C]5.5[/C][C]7.78323[/C][C]-2.28323[/C][/ROW]
[ROW][C]40[/C][C]9[/C][C]7.88626[/C][C]1.11374[/C][/ROW]
[ROW][C]41[/C][C]2[/C][C]7.84819[/C][C]-5.84819[/C][/ROW]
[ROW][C]42[/C][C]8.5[/C][C]8.02112[/C][C]0.478885[/C][/ROW]
[ROW][C]43[/C][C]9[/C][C]7.80083[/C][C]1.19917[/C][/ROW]
[ROW][C]44[/C][C]8.5[/C][C]8.13457[/C][C]0.365429[/C][/ROW]
[ROW][C]45[/C][C]9[/C][C]6.43747[/C][C]2.56253[/C][/ROW]
[ROW][C]46[/C][C]7.5[/C][C]7.40144[/C][C]0.0985575[/C][/ROW]
[ROW][C]47[/C][C]10[/C][C]8.8504[/C][C]1.1496[/C][/ROW]
[ROW][C]48[/C][C]9[/C][C]7.32822[/C][C]1.67178[/C][/ROW]
[ROW][C]49[/C][C]7.5[/C][C]8.20709[/C][C]-0.707094[/C][/ROW]
[ROW][C]50[/C][C]6[/C][C]6.72969[/C][C]-0.729686[/C][/ROW]
[ROW][C]51[/C][C]10.5[/C][C]8.00902[/C][C]2.49098[/C][/ROW]
[ROW][C]52[/C][C]8.5[/C][C]7.23818[/C][C]1.26182[/C][/ROW]
[ROW][C]53[/C][C]8[/C][C]8.39192[/C][C]-0.391917[/C][/ROW]
[ROW][C]54[/C][C]10[/C][C]5.635[/C][C]4.365[/C][/ROW]
[ROW][C]55[/C][C]10.5[/C][C]8.58856[/C][C]1.91144[/C][/ROW]
[ROW][C]56[/C][C]6.5[/C][C]5.93556[/C][C]0.564438[/C][/ROW]
[ROW][C]57[/C][C]9.5[/C][C]8.05228[/C][C]1.44772[/C][/ROW]
[ROW][C]58[/C][C]8.5[/C][C]6.27738[/C][C]2.22262[/C][/ROW]
[ROW][C]59[/C][C]7.5[/C][C]7.25548[/C][C]0.244519[/C][/ROW]
[ROW][C]60[/C][C]5[/C][C]7.36149[/C][C]-2.36149[/C][/ROW]
[ROW][C]61[/C][C]8[/C][C]7.39203[/C][C]0.60797[/C][/ROW]
[ROW][C]62[/C][C]10[/C][C]7.53077[/C][C]2.46923[/C][/ROW]
[ROW][C]63[/C][C]7[/C][C]7.82242[/C][C]-0.822424[/C][/ROW]
[ROW][C]64[/C][C]7.5[/C][C]7.69408[/C][C]-0.194079[/C][/ROW]
[ROW][C]65[/C][C]7.5[/C][C]7.69408[/C][C]-0.194079[/C][/ROW]
[ROW][C]66[/C][C]9.5[/C][C]7.5815[/C][C]1.9185[/C][/ROW]
[ROW][C]67[/C][C]6[/C][C]7.65421[/C][C]-1.65421[/C][/ROW]
[ROW][C]68[/C][C]10[/C][C]7.95708[/C][C]2.04292[/C][/ROW]
[ROW][C]69[/C][C]7[/C][C]7.84091[/C][C]-0.840907[/C][/ROW]
[ROW][C]70[/C][C]3[/C][C]6.11268[/C][C]-3.11268[/C][/ROW]
[ROW][C]71[/C][C]6[/C][C]7.9208[/C][C]-1.9208[/C][/ROW]
[ROW][C]72[/C][C]7[/C][C]7.33905[/C][C]-0.339045[/C][/ROW]
[ROW][C]73[/C][C]10[/C][C]8.34771[/C][C]1.65229[/C][/ROW]
[ROW][C]74[/C][C]7[/C][C]7.53325[/C][C]-0.533245[/C][/ROW]
[ROW][C]75[/C][C]3.5[/C][C]7.34282[/C][C]-3.84282[/C][/ROW]
[ROW][C]76[/C][C]8[/C][C]7.59311[/C][C]0.406894[/C][/ROW]
[ROW][C]77[/C][C]10[/C][C]6.76967[/C][C]3.23033[/C][/ROW]
[ROW][C]78[/C][C]5.5[/C][C]7.15477[/C][C]-1.65477[/C][/ROW]
[ROW][C]79[/C][C]6[/C][C]6.28226[/C][C]-0.282256[/C][/ROW]
[ROW][C]80[/C][C]6.5[/C][C]6.65554[/C][C]-0.155544[/C][/ROW]
[ROW][C]81[/C][C]6.5[/C][C]6.34909[/C][C]0.15091[/C][/ROW]
[ROW][C]82[/C][C]8.5[/C][C]7.55499[/C][C]0.945006[/C][/ROW]
[ROW][C]83[/C][C]4[/C][C]6.36269[/C][C]-2.36269[/C][/ROW]
[ROW][C]84[/C][C]9.5[/C][C]7.22166[/C][C]2.27834[/C][/ROW]
[ROW][C]85[/C][C]8[/C][C]6.68682[/C][C]1.31318[/C][/ROW]
[ROW][C]86[/C][C]8.5[/C][C]6.98055[/C][C]1.51945[/C][/ROW]
[ROW][C]87[/C][C]5.5[/C][C]8.20976[/C][C]-2.70976[/C][/ROW]
[ROW][C]88[/C][C]7[/C][C]7.11396[/C][C]-0.113962[/C][/ROW]
[ROW][C]89[/C][C]9[/C][C]6.43407[/C][C]2.56593[/C][/ROW]
[ROW][C]90[/C][C]8[/C][C]7.29878[/C][C]0.701222[/C][/ROW]
[ROW][C]91[/C][C]10[/C][C]8.87555[/C][C]1.12445[/C][/ROW]
[ROW][C]92[/C][C]8[/C][C]6.44562[/C][C]1.55438[/C][/ROW]
[ROW][C]93[/C][C]6[/C][C]7.46999[/C][C]-1.46999[/C][/ROW]
[ROW][C]94[/C][C]8[/C][C]7.37692[/C][C]0.623075[/C][/ROW]
[ROW][C]95[/C][C]5[/C][C]6.77819[/C][C]-1.77819[/C][/ROW]
[ROW][C]96[/C][C]9[/C][C]6.27023[/C][C]2.72977[/C][/ROW]
[ROW][C]97[/C][C]4.5[/C][C]6.55602[/C][C]-2.05602[/C][/ROW]
[ROW][C]98[/C][C]8.5[/C][C]6.18178[/C][C]2.31822[/C][/ROW]
[ROW][C]99[/C][C]7[/C][C]6.44514[/C][C]0.55486[/C][/ROW]
[ROW][C]100[/C][C]9.5[/C][C]8.44651[/C][C]1.05349[/C][/ROW]
[ROW][C]101[/C][C]8.5[/C][C]7.56222[/C][C]0.937779[/C][/ROW]
[ROW][C]102[/C][C]7.5[/C][C]6.31319[/C][C]1.18681[/C][/ROW]
[ROW][C]103[/C][C]7.5[/C][C]7.3744[/C][C]0.125602[/C][/ROW]
[ROW][C]104[/C][C]5[/C][C]7.75665[/C][C]-2.75665[/C][/ROW]
[ROW][C]105[/C][C]7[/C][C]6.72799[/C][C]0.27201[/C][/ROW]
[ROW][C]106[/C][C]8[/C][C]8.51706[/C][C]-0.517057[/C][/ROW]
[ROW][C]107[/C][C]5.5[/C][C]7.03541[/C][C]-1.53541[/C][/ROW]
[ROW][C]108[/C][C]8.5[/C][C]7.16254[/C][C]1.33746[/C][/ROW]
[ROW][C]109[/C][C]7.5[/C][C]7.28253[/C][C]0.217472[/C][/ROW]
[ROW][C]110[/C][C]9.5[/C][C]7.85817[/C][C]1.64183[/C][/ROW]
[ROW][C]111[/C][C]7[/C][C]6.58106[/C][C]0.418945[/C][/ROW]
[ROW][C]112[/C][C]8[/C][C]7.60269[/C][C]0.397311[/C][/ROW]
[ROW][C]113[/C][C]8.5[/C][C]7.57974[/C][C]0.920263[/C][/ROW]
[ROW][C]114[/C][C]3.5[/C][C]6.3722[/C][C]-2.8722[/C][/ROW]
[ROW][C]115[/C][C]6.5[/C][C]6.57397[/C][C]-0.0739736[/C][/ROW]
[ROW][C]116[/C][C]6.5[/C][C]6.75532[/C][C]-0.255323[/C][/ROW]
[ROW][C]117[/C][C]10.5[/C][C]8.21158[/C][C]2.28842[/C][/ROW]
[ROW][C]118[/C][C]8.5[/C][C]6.25478[/C][C]2.24522[/C][/ROW]
[ROW][C]119[/C][C]8[/C][C]7.06132[/C][C]0.938678[/C][/ROW]
[ROW][C]120[/C][C]10[/C][C]6.7308[/C][C]3.2692[/C][/ROW]
[ROW][C]121[/C][C]10[/C][C]8.34142[/C][C]1.65858[/C][/ROW]
[ROW][C]122[/C][C]9.5[/C][C]7.77337[/C][C]1.72663[/C][/ROW]
[ROW][C]123[/C][C]9[/C][C]7.43275[/C][C]1.56725[/C][/ROW]
[ROW][C]124[/C][C]10[/C][C]8.71153[/C][C]1.28847[/C][/ROW]
[ROW][C]125[/C][C]7.5[/C][C]6.65122[/C][C]0.848782[/C][/ROW]
[ROW][C]126[/C][C]4.5[/C][C]7.49919[/C][C]-2.99919[/C][/ROW]
[ROW][C]127[/C][C]4.5[/C][C]6.59476[/C][C]-2.09476[/C][/ROW]
[ROW][C]128[/C][C]0.5[/C][C]6.06622[/C][C]-5.56622[/C][/ROW]
[ROW][C]129[/C][C]6.5[/C][C]5.89418[/C][C]0.605816[/C][/ROW]
[ROW][C]130[/C][C]4.5[/C][C]7.82483[/C][C]-3.32483[/C][/ROW]
[ROW][C]131[/C][C]5.5[/C][C]6.89785[/C][C]-1.39785[/C][/ROW]
[ROW][C]132[/C][C]5[/C][C]6.62496[/C][C]-1.62496[/C][/ROW]
[ROW][C]133[/C][C]6[/C][C]7.54568[/C][C]-1.54568[/C][/ROW]
[ROW][C]134[/C][C]4[/C][C]6.80039[/C][C]-2.80039[/C][/ROW]
[ROW][C]135[/C][C]8[/C][C]6.87561[/C][C]1.12439[/C][/ROW]
[ROW][C]136[/C][C]10.5[/C][C]8.58856[/C][C]1.91144[/C][/ROW]
[ROW][C]137[/C][C]8.5[/C][C]7.00792[/C][C]1.49208[/C][/ROW]
[ROW][C]138[/C][C]6.5[/C][C]6.44936[/C][C]0.0506383[/C][/ROW]
[ROW][C]139[/C][C]8[/C][C]7.62307[/C][C]0.376928[/C][/ROW]
[ROW][C]140[/C][C]8.5[/C][C]8.65814[/C][C]-0.158139[/C][/ROW]
[ROW][C]141[/C][C]5.5[/C][C]6.93231[/C][C]-1.43231[/C][/ROW]
[ROW][C]142[/C][C]7[/C][C]7.96512[/C][C]-0.965123[/C][/ROW]
[ROW][C]143[/C][C]5[/C][C]7.08498[/C][C]-2.08498[/C][/ROW]
[ROW][C]144[/C][C]3.5[/C][C]6.95019[/C][C]-3.45019[/C][/ROW]
[ROW][C]145[/C][C]5[/C][C]7.31749[/C][C]-2.31749[/C][/ROW]
[ROW][C]146[/C][C]9[/C][C]7.37927[/C][C]1.62073[/C][/ROW]
[ROW][C]147[/C][C]8.5[/C][C]7.41798[/C][C]1.08202[/C][/ROW]
[ROW][C]148[/C][C]5[/C][C]7.74864[/C][C]-2.74864[/C][/ROW]
[ROW][C]149[/C][C]9.5[/C][C]7.75475[/C][C]1.74525[/C][/ROW]
[ROW][C]150[/C][C]3[/C][C]6.26153[/C][C]-3.26153[/C][/ROW]
[ROW][C]151[/C][C]1.5[/C][C]6.81519[/C][C]-5.31519[/C][/ROW]
[ROW][C]152[/C][C]6[/C][C]7.39678[/C][C]-1.39678[/C][/ROW]
[ROW][C]153[/C][C]0.5[/C][C]7.0767[/C][C]-6.5767[/C][/ROW]
[ROW][C]154[/C][C]6.5[/C][C]5.93556[/C][C]0.564438[/C][/ROW]
[ROW][C]155[/C][C]7.5[/C][C]6.86086[/C][C]0.639145[/C][/ROW]
[ROW][C]156[/C][C]4.5[/C][C]6.84231[/C][C]-2.34231[/C][/ROW]
[ROW][C]157[/C][C]8[/C][C]6.87561[/C][C]1.12439[/C][/ROW]
[ROW][C]158[/C][C]9[/C][C]7.22768[/C][C]1.77232[/C][/ROW]
[ROW][C]159[/C][C]7.5[/C][C]6.71698[/C][C]0.783016[/C][/ROW]
[ROW][C]160[/C][C]8.5[/C][C]6.55897[/C][C]1.94103[/C][/ROW]
[ROW][C]161[/C][C]7[/C][C]6.94115[/C][C]0.058846[/C][/ROW]
[ROW][C]162[/C][C]9.5[/C][C]7.21983[/C][C]2.28017[/C][/ROW]
[ROW][C]163[/C][C]6.5[/C][C]6.35575[/C][C]0.144254[/C][/ROW]
[ROW][C]164[/C][C]9.5[/C][C]6.75631[/C][C]2.74369[/C][/ROW]
[ROW][C]165[/C][C]6[/C][C]6.3543[/C][C]-0.354302[/C][/ROW]
[ROW][C]166[/C][C]8[/C][C]7.40913[/C][C]0.590866[/C][/ROW]
[ROW][C]167[/C][C]9.5[/C][C]7.84851[/C][C]1.65149[/C][/ROW]
[ROW][C]168[/C][C]8[/C][C]7.40435[/C][C]0.595648[/C][/ROW]
[ROW][C]169[/C][C]8[/C][C]7.08864[/C][C]0.911358[/C][/ROW]
[ROW][C]170[/C][C]9[/C][C]7.59643[/C][C]1.40357[/C][/ROW]
[ROW][C]171[/C][C]5[/C][C]5.77403[/C][C]-0.774029[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=267539&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=267539&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
10.55.82367-5.32367
27.56.697390.80261
397.65371.3463
49.57.709941.79006
58.58.85576-0.355763
675.989231.01077
789.13508-1.13508
8108.541751.45825
9710.2582-3.25825
108.55.986332.51367
1198.153920.846078
129.55.891463.60854
1347.13509-3.13509
1466.98297-0.982968
1587.545580.454418
165.57.72705-2.22705
179.58.135631.36437
187.56.679840.820162
1976.748910.251089
207.58.86136-1.36136
2187.176320.823678
2277.46482-0.464815
2376.543870.456133
2467.07936-1.07936
25107.155192.84481
262.56.04478-3.54478
2798.495630.504371
2888.23799-0.237992
2966.36079-0.360793
308.57.150631.34937
3168.17184-2.17184
3297.606451.39355
3387.15020.849797
3487.964950.0350451
3598.272950.727048
365.57.23598-1.73598
3757.05886-2.05886
3877.32404-0.324042
395.57.78323-2.28323
4097.886261.11374
4127.84819-5.84819
428.58.021120.478885
4397.800831.19917
448.58.134570.365429
4596.437472.56253
467.57.401440.0985575
47108.85041.1496
4897.328221.67178
497.58.20709-0.707094
5066.72969-0.729686
5110.58.009022.49098
528.57.238181.26182
5388.39192-0.391917
54105.6354.365
5510.58.588561.91144
566.55.935560.564438
579.58.052281.44772
588.56.277382.22262
597.57.255480.244519
6057.36149-2.36149
6187.392030.60797
62107.530772.46923
6377.82242-0.822424
647.57.69408-0.194079
657.57.69408-0.194079
669.57.58151.9185
6767.65421-1.65421
68107.957082.04292
6977.84091-0.840907
7036.11268-3.11268
7167.9208-1.9208
7277.33905-0.339045
73108.347711.65229
7477.53325-0.533245
753.57.34282-3.84282
7687.593110.406894
77106.769673.23033
785.57.15477-1.65477
7966.28226-0.282256
806.56.65554-0.155544
816.56.349090.15091
828.57.554990.945006
8346.36269-2.36269
849.57.221662.27834
8586.686821.31318
868.56.980551.51945
875.58.20976-2.70976
8877.11396-0.113962
8996.434072.56593
9087.298780.701222
91108.875551.12445
9286.445621.55438
9367.46999-1.46999
9487.376920.623075
9556.77819-1.77819
9696.270232.72977
974.56.55602-2.05602
988.56.181782.31822
9976.445140.55486
1009.58.446511.05349
1018.57.562220.937779
1027.56.313191.18681
1037.57.37440.125602
10457.75665-2.75665
10576.727990.27201
10688.51706-0.517057
1075.57.03541-1.53541
1088.57.162541.33746
1097.57.282530.217472
1109.57.858171.64183
11176.581060.418945
11287.602690.397311
1138.57.579740.920263
1143.56.3722-2.8722
1156.56.57397-0.0739736
1166.56.75532-0.255323
11710.58.211582.28842
1188.56.254782.24522
11987.061320.938678
120106.73083.2692
121108.341421.65858
1229.57.773371.72663
12397.432751.56725
124108.711531.28847
1257.56.651220.848782
1264.57.49919-2.99919
1274.56.59476-2.09476
1280.56.06622-5.56622
1296.55.894180.605816
1304.57.82483-3.32483
1315.56.89785-1.39785
13256.62496-1.62496
13367.54568-1.54568
13446.80039-2.80039
13586.875611.12439
13610.58.588561.91144
1378.57.007921.49208
1386.56.449360.0506383
13987.623070.376928
1408.58.65814-0.158139
1415.56.93231-1.43231
14277.96512-0.965123
14357.08498-2.08498
1443.56.95019-3.45019
14557.31749-2.31749
14697.379271.62073
1478.57.417981.08202
14857.74864-2.74864
1499.57.754751.74525
15036.26153-3.26153
1511.56.81519-5.31519
15267.39678-1.39678
1530.57.0767-6.5767
1546.55.935560.564438
1557.56.860860.639145
1564.56.84231-2.34231
15786.875611.12439
15897.227681.77232
1597.56.716980.783016
1608.56.558971.94103
16176.941150.058846
1629.57.219832.28017
1636.56.355750.144254
1649.56.756312.74369
16566.3543-0.354302
16687.409130.590866
1679.57.848511.65149
16887.404350.595648
16987.088640.911358
17097.596431.40357
17155.77403-0.774029






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
70.9307850.1384310.0692153
80.8710250.257950.128975
90.922380.1552390.0776196
100.9265420.1469150.0734577
110.8890760.2218490.110924
120.9450970.1098060.0549028
130.9680770.06384620.0319231
140.9575480.08490420.0424521
150.9348170.1303660.065183
160.9150110.1699780.084989
170.8982660.2034690.101734
180.8605040.2789920.139496
190.8237850.3524290.176215
200.7866420.4267160.213358
210.7333830.5332340.266617
220.6751180.6497640.324882
230.6125790.7748420.387421
240.5502390.8995230.449761
250.5755210.8489570.424479
260.6217960.7564080.378204
270.572160.855680.42784
280.5355890.9288210.464411
290.4780430.9560870.521957
300.4478150.8956290.552185
310.5099940.9800120.490006
320.480870.9617390.51913
330.4260920.8521840.573908
340.3708840.7417690.629116
350.3251010.6502030.674899
360.3327050.665410.667295
370.3869940.7739880.613006
380.3353220.6706430.664678
390.3492590.6985170.650741
400.3065990.6131980.693401
410.7436690.5126630.256331
420.7094690.5810630.290531
430.6802220.6395560.319778
440.6344320.7311360.365568
450.6719950.6560110.328005
460.627390.745220.37261
470.6024770.7950460.397523
480.5902780.8194440.409722
490.5463420.9073160.453658
500.5029220.9941570.497078
510.5328530.9342930.467147
520.5163610.9672780.483639
530.4708950.941790.529105
540.637920.724160.36208
550.6353790.7292430.364621
560.5932150.8135710.406785
570.5716090.8567820.428391
580.5716240.8567530.428376
590.5253690.9492620.474631
600.5509060.8981880.449094
610.5081310.9837380.491869
620.5254950.9490110.474505
630.4918670.9837350.508133
640.4465430.8930860.553457
650.4019670.8039350.598033
660.3940020.7880030.605998
670.3864040.7728090.613596
680.3894710.7789410.610529
690.3576730.7153460.642327
700.4339390.8678780.566061
710.4354970.8709940.564503
720.3937590.7875180.606241
730.380860.7617190.61914
740.3427810.6855620.657219
750.4757380.9514760.524262
760.4327510.8655020.567249
770.5039880.9920240.496012
780.4944990.9889970.505501
790.4537190.9074370.546281
800.4103780.8207570.589622
810.3682270.7364550.631773
820.3343950.6687890.665605
830.3539690.7079380.646031
840.3655390.7310780.634461
850.3416490.6832990.658351
860.3253270.6506550.674673
870.3634190.7268380.636581
880.3227740.6455490.677226
890.3518710.7037420.648129
900.3153620.6307250.684638
910.2875880.5751750.712412
920.2747380.5494770.725262
930.2599520.5199040.740048
940.2286810.4573620.771319
950.2217770.4435530.778223
960.2576740.5153480.742326
970.2596120.5192240.740388
980.2808730.5617460.719127
990.2500150.500030.749985
1000.2228530.4457070.777147
1010.1962770.3925540.803723
1020.1806710.3613430.819329
1030.1527430.3054860.847257
1040.1856050.3712110.814395
1050.1588710.3177410.841129
1060.1393320.2786630.860668
1070.1289620.2579240.871038
1080.1160980.2321950.883902
1090.09576980.191540.90423
1100.08841120.1768220.911589
1110.07294590.1458920.927054
1120.05854180.1170840.941458
1130.04879130.09758260.951209
1140.05925370.1185070.940746
1150.04785230.09570470.952148
1160.03821940.07643870.961781
1170.0403270.0806540.959673
1180.04733210.09466420.952668
1190.04233580.08467160.957664
1200.07239960.1447990.9276
1210.0625030.1250060.937497
1220.06228030.1245610.93772
1230.06897990.137960.93102
1240.05707230.1141450.942928
1250.05005090.1001020.949949
1260.05573720.1114740.944263
1270.05155220.1031040.948448
1280.172080.3441590.82792
1290.1508790.3017580.849121
1300.1906350.381270.809365
1310.1717670.3435330.828233
1320.1529060.3058120.847094
1330.1408940.2817880.859106
1340.173970.347940.82603
1350.1589960.3179920.841004
1360.1512420.3024840.848758
1370.1655090.3310190.834491
1380.1371330.2742670.862867
1390.108580.2171610.89142
1400.08440010.16880.9156
1410.06700930.1340190.932991
1420.05598920.1119780.944011
1430.04710630.09421270.952894
1440.05491830.1098370.945082
1450.07096440.1419290.929036
1460.05615730.1123150.943843
1470.04172040.08344080.95828
1480.05304820.1060960.946952
1490.04124440.08248880.958756
1500.04945070.09890140.950549
1510.5232930.9534140.476707
1520.4601090.9202190.539891
1530.9924270.01514630.00757315
1540.9879980.02400320.0120016
1550.9781340.04373140.0218657
1560.9996170.0007650970.000382548
1570.9989560.002087170.00104358
1580.9973160.005368480.00268424
1590.9940780.0118450.00592248
1600.9920.01600080.00800039
1610.9930730.01385390.00692693
1620.9864980.02700490.0135024
1630.9720010.05599710.0279985
1640.9996470.0007060380.000353019

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
7 & 0.930785 & 0.138431 & 0.0692153 \tabularnewline
8 & 0.871025 & 0.25795 & 0.128975 \tabularnewline
9 & 0.92238 & 0.155239 & 0.0776196 \tabularnewline
10 & 0.926542 & 0.146915 & 0.0734577 \tabularnewline
11 & 0.889076 & 0.221849 & 0.110924 \tabularnewline
12 & 0.945097 & 0.109806 & 0.0549028 \tabularnewline
13 & 0.968077 & 0.0638462 & 0.0319231 \tabularnewline
14 & 0.957548 & 0.0849042 & 0.0424521 \tabularnewline
15 & 0.934817 & 0.130366 & 0.065183 \tabularnewline
16 & 0.915011 & 0.169978 & 0.084989 \tabularnewline
17 & 0.898266 & 0.203469 & 0.101734 \tabularnewline
18 & 0.860504 & 0.278992 & 0.139496 \tabularnewline
19 & 0.823785 & 0.352429 & 0.176215 \tabularnewline
20 & 0.786642 & 0.426716 & 0.213358 \tabularnewline
21 & 0.733383 & 0.533234 & 0.266617 \tabularnewline
22 & 0.675118 & 0.649764 & 0.324882 \tabularnewline
23 & 0.612579 & 0.774842 & 0.387421 \tabularnewline
24 & 0.550239 & 0.899523 & 0.449761 \tabularnewline
25 & 0.575521 & 0.848957 & 0.424479 \tabularnewline
26 & 0.621796 & 0.756408 & 0.378204 \tabularnewline
27 & 0.57216 & 0.85568 & 0.42784 \tabularnewline
28 & 0.535589 & 0.928821 & 0.464411 \tabularnewline
29 & 0.478043 & 0.956087 & 0.521957 \tabularnewline
30 & 0.447815 & 0.895629 & 0.552185 \tabularnewline
31 & 0.509994 & 0.980012 & 0.490006 \tabularnewline
32 & 0.48087 & 0.961739 & 0.51913 \tabularnewline
33 & 0.426092 & 0.852184 & 0.573908 \tabularnewline
34 & 0.370884 & 0.741769 & 0.629116 \tabularnewline
35 & 0.325101 & 0.650203 & 0.674899 \tabularnewline
36 & 0.332705 & 0.66541 & 0.667295 \tabularnewline
37 & 0.386994 & 0.773988 & 0.613006 \tabularnewline
38 & 0.335322 & 0.670643 & 0.664678 \tabularnewline
39 & 0.349259 & 0.698517 & 0.650741 \tabularnewline
40 & 0.306599 & 0.613198 & 0.693401 \tabularnewline
41 & 0.743669 & 0.512663 & 0.256331 \tabularnewline
42 & 0.709469 & 0.581063 & 0.290531 \tabularnewline
43 & 0.680222 & 0.639556 & 0.319778 \tabularnewline
44 & 0.634432 & 0.731136 & 0.365568 \tabularnewline
45 & 0.671995 & 0.656011 & 0.328005 \tabularnewline
46 & 0.62739 & 0.74522 & 0.37261 \tabularnewline
47 & 0.602477 & 0.795046 & 0.397523 \tabularnewline
48 & 0.590278 & 0.819444 & 0.409722 \tabularnewline
49 & 0.546342 & 0.907316 & 0.453658 \tabularnewline
50 & 0.502922 & 0.994157 & 0.497078 \tabularnewline
51 & 0.532853 & 0.934293 & 0.467147 \tabularnewline
52 & 0.516361 & 0.967278 & 0.483639 \tabularnewline
53 & 0.470895 & 0.94179 & 0.529105 \tabularnewline
54 & 0.63792 & 0.72416 & 0.36208 \tabularnewline
55 & 0.635379 & 0.729243 & 0.364621 \tabularnewline
56 & 0.593215 & 0.813571 & 0.406785 \tabularnewline
57 & 0.571609 & 0.856782 & 0.428391 \tabularnewline
58 & 0.571624 & 0.856753 & 0.428376 \tabularnewline
59 & 0.525369 & 0.949262 & 0.474631 \tabularnewline
60 & 0.550906 & 0.898188 & 0.449094 \tabularnewline
61 & 0.508131 & 0.983738 & 0.491869 \tabularnewline
62 & 0.525495 & 0.949011 & 0.474505 \tabularnewline
63 & 0.491867 & 0.983735 & 0.508133 \tabularnewline
64 & 0.446543 & 0.893086 & 0.553457 \tabularnewline
65 & 0.401967 & 0.803935 & 0.598033 \tabularnewline
66 & 0.394002 & 0.788003 & 0.605998 \tabularnewline
67 & 0.386404 & 0.772809 & 0.613596 \tabularnewline
68 & 0.389471 & 0.778941 & 0.610529 \tabularnewline
69 & 0.357673 & 0.715346 & 0.642327 \tabularnewline
70 & 0.433939 & 0.867878 & 0.566061 \tabularnewline
71 & 0.435497 & 0.870994 & 0.564503 \tabularnewline
72 & 0.393759 & 0.787518 & 0.606241 \tabularnewline
73 & 0.38086 & 0.761719 & 0.61914 \tabularnewline
74 & 0.342781 & 0.685562 & 0.657219 \tabularnewline
75 & 0.475738 & 0.951476 & 0.524262 \tabularnewline
76 & 0.432751 & 0.865502 & 0.567249 \tabularnewline
77 & 0.503988 & 0.992024 & 0.496012 \tabularnewline
78 & 0.494499 & 0.988997 & 0.505501 \tabularnewline
79 & 0.453719 & 0.907437 & 0.546281 \tabularnewline
80 & 0.410378 & 0.820757 & 0.589622 \tabularnewline
81 & 0.368227 & 0.736455 & 0.631773 \tabularnewline
82 & 0.334395 & 0.668789 & 0.665605 \tabularnewline
83 & 0.353969 & 0.707938 & 0.646031 \tabularnewline
84 & 0.365539 & 0.731078 & 0.634461 \tabularnewline
85 & 0.341649 & 0.683299 & 0.658351 \tabularnewline
86 & 0.325327 & 0.650655 & 0.674673 \tabularnewline
87 & 0.363419 & 0.726838 & 0.636581 \tabularnewline
88 & 0.322774 & 0.645549 & 0.677226 \tabularnewline
89 & 0.351871 & 0.703742 & 0.648129 \tabularnewline
90 & 0.315362 & 0.630725 & 0.684638 \tabularnewline
91 & 0.287588 & 0.575175 & 0.712412 \tabularnewline
92 & 0.274738 & 0.549477 & 0.725262 \tabularnewline
93 & 0.259952 & 0.519904 & 0.740048 \tabularnewline
94 & 0.228681 & 0.457362 & 0.771319 \tabularnewline
95 & 0.221777 & 0.443553 & 0.778223 \tabularnewline
96 & 0.257674 & 0.515348 & 0.742326 \tabularnewline
97 & 0.259612 & 0.519224 & 0.740388 \tabularnewline
98 & 0.280873 & 0.561746 & 0.719127 \tabularnewline
99 & 0.250015 & 0.50003 & 0.749985 \tabularnewline
100 & 0.222853 & 0.445707 & 0.777147 \tabularnewline
101 & 0.196277 & 0.392554 & 0.803723 \tabularnewline
102 & 0.180671 & 0.361343 & 0.819329 \tabularnewline
103 & 0.152743 & 0.305486 & 0.847257 \tabularnewline
104 & 0.185605 & 0.371211 & 0.814395 \tabularnewline
105 & 0.158871 & 0.317741 & 0.841129 \tabularnewline
106 & 0.139332 & 0.278663 & 0.860668 \tabularnewline
107 & 0.128962 & 0.257924 & 0.871038 \tabularnewline
108 & 0.116098 & 0.232195 & 0.883902 \tabularnewline
109 & 0.0957698 & 0.19154 & 0.90423 \tabularnewline
110 & 0.0884112 & 0.176822 & 0.911589 \tabularnewline
111 & 0.0729459 & 0.145892 & 0.927054 \tabularnewline
112 & 0.0585418 & 0.117084 & 0.941458 \tabularnewline
113 & 0.0487913 & 0.0975826 & 0.951209 \tabularnewline
114 & 0.0592537 & 0.118507 & 0.940746 \tabularnewline
115 & 0.0478523 & 0.0957047 & 0.952148 \tabularnewline
116 & 0.0382194 & 0.0764387 & 0.961781 \tabularnewline
117 & 0.040327 & 0.080654 & 0.959673 \tabularnewline
118 & 0.0473321 & 0.0946642 & 0.952668 \tabularnewline
119 & 0.0423358 & 0.0846716 & 0.957664 \tabularnewline
120 & 0.0723996 & 0.144799 & 0.9276 \tabularnewline
121 & 0.062503 & 0.125006 & 0.937497 \tabularnewline
122 & 0.0622803 & 0.124561 & 0.93772 \tabularnewline
123 & 0.0689799 & 0.13796 & 0.93102 \tabularnewline
124 & 0.0570723 & 0.114145 & 0.942928 \tabularnewline
125 & 0.0500509 & 0.100102 & 0.949949 \tabularnewline
126 & 0.0557372 & 0.111474 & 0.944263 \tabularnewline
127 & 0.0515522 & 0.103104 & 0.948448 \tabularnewline
128 & 0.17208 & 0.344159 & 0.82792 \tabularnewline
129 & 0.150879 & 0.301758 & 0.849121 \tabularnewline
130 & 0.190635 & 0.38127 & 0.809365 \tabularnewline
131 & 0.171767 & 0.343533 & 0.828233 \tabularnewline
132 & 0.152906 & 0.305812 & 0.847094 \tabularnewline
133 & 0.140894 & 0.281788 & 0.859106 \tabularnewline
134 & 0.17397 & 0.34794 & 0.82603 \tabularnewline
135 & 0.158996 & 0.317992 & 0.841004 \tabularnewline
136 & 0.151242 & 0.302484 & 0.848758 \tabularnewline
137 & 0.165509 & 0.331019 & 0.834491 \tabularnewline
138 & 0.137133 & 0.274267 & 0.862867 \tabularnewline
139 & 0.10858 & 0.217161 & 0.89142 \tabularnewline
140 & 0.0844001 & 0.1688 & 0.9156 \tabularnewline
141 & 0.0670093 & 0.134019 & 0.932991 \tabularnewline
142 & 0.0559892 & 0.111978 & 0.944011 \tabularnewline
143 & 0.0471063 & 0.0942127 & 0.952894 \tabularnewline
144 & 0.0549183 & 0.109837 & 0.945082 \tabularnewline
145 & 0.0709644 & 0.141929 & 0.929036 \tabularnewline
146 & 0.0561573 & 0.112315 & 0.943843 \tabularnewline
147 & 0.0417204 & 0.0834408 & 0.95828 \tabularnewline
148 & 0.0530482 & 0.106096 & 0.946952 \tabularnewline
149 & 0.0412444 & 0.0824888 & 0.958756 \tabularnewline
150 & 0.0494507 & 0.0989014 & 0.950549 \tabularnewline
151 & 0.523293 & 0.953414 & 0.476707 \tabularnewline
152 & 0.460109 & 0.920219 & 0.539891 \tabularnewline
153 & 0.992427 & 0.0151463 & 0.00757315 \tabularnewline
154 & 0.987998 & 0.0240032 & 0.0120016 \tabularnewline
155 & 0.978134 & 0.0437314 & 0.0218657 \tabularnewline
156 & 0.999617 & 0.000765097 & 0.000382548 \tabularnewline
157 & 0.998956 & 0.00208717 & 0.00104358 \tabularnewline
158 & 0.997316 & 0.00536848 & 0.00268424 \tabularnewline
159 & 0.994078 & 0.011845 & 0.00592248 \tabularnewline
160 & 0.992 & 0.0160008 & 0.00800039 \tabularnewline
161 & 0.993073 & 0.0138539 & 0.00692693 \tabularnewline
162 & 0.986498 & 0.0270049 & 0.0135024 \tabularnewline
163 & 0.972001 & 0.0559971 & 0.0279985 \tabularnewline
164 & 0.999647 & 0.000706038 & 0.000353019 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=267539&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]7[/C][C]0.930785[/C][C]0.138431[/C][C]0.0692153[/C][/ROW]
[ROW][C]8[/C][C]0.871025[/C][C]0.25795[/C][C]0.128975[/C][/ROW]
[ROW][C]9[/C][C]0.92238[/C][C]0.155239[/C][C]0.0776196[/C][/ROW]
[ROW][C]10[/C][C]0.926542[/C][C]0.146915[/C][C]0.0734577[/C][/ROW]
[ROW][C]11[/C][C]0.889076[/C][C]0.221849[/C][C]0.110924[/C][/ROW]
[ROW][C]12[/C][C]0.945097[/C][C]0.109806[/C][C]0.0549028[/C][/ROW]
[ROW][C]13[/C][C]0.968077[/C][C]0.0638462[/C][C]0.0319231[/C][/ROW]
[ROW][C]14[/C][C]0.957548[/C][C]0.0849042[/C][C]0.0424521[/C][/ROW]
[ROW][C]15[/C][C]0.934817[/C][C]0.130366[/C][C]0.065183[/C][/ROW]
[ROW][C]16[/C][C]0.915011[/C][C]0.169978[/C][C]0.084989[/C][/ROW]
[ROW][C]17[/C][C]0.898266[/C][C]0.203469[/C][C]0.101734[/C][/ROW]
[ROW][C]18[/C][C]0.860504[/C][C]0.278992[/C][C]0.139496[/C][/ROW]
[ROW][C]19[/C][C]0.823785[/C][C]0.352429[/C][C]0.176215[/C][/ROW]
[ROW][C]20[/C][C]0.786642[/C][C]0.426716[/C][C]0.213358[/C][/ROW]
[ROW][C]21[/C][C]0.733383[/C][C]0.533234[/C][C]0.266617[/C][/ROW]
[ROW][C]22[/C][C]0.675118[/C][C]0.649764[/C][C]0.324882[/C][/ROW]
[ROW][C]23[/C][C]0.612579[/C][C]0.774842[/C][C]0.387421[/C][/ROW]
[ROW][C]24[/C][C]0.550239[/C][C]0.899523[/C][C]0.449761[/C][/ROW]
[ROW][C]25[/C][C]0.575521[/C][C]0.848957[/C][C]0.424479[/C][/ROW]
[ROW][C]26[/C][C]0.621796[/C][C]0.756408[/C][C]0.378204[/C][/ROW]
[ROW][C]27[/C][C]0.57216[/C][C]0.85568[/C][C]0.42784[/C][/ROW]
[ROW][C]28[/C][C]0.535589[/C][C]0.928821[/C][C]0.464411[/C][/ROW]
[ROW][C]29[/C][C]0.478043[/C][C]0.956087[/C][C]0.521957[/C][/ROW]
[ROW][C]30[/C][C]0.447815[/C][C]0.895629[/C][C]0.552185[/C][/ROW]
[ROW][C]31[/C][C]0.509994[/C][C]0.980012[/C][C]0.490006[/C][/ROW]
[ROW][C]32[/C][C]0.48087[/C][C]0.961739[/C][C]0.51913[/C][/ROW]
[ROW][C]33[/C][C]0.426092[/C][C]0.852184[/C][C]0.573908[/C][/ROW]
[ROW][C]34[/C][C]0.370884[/C][C]0.741769[/C][C]0.629116[/C][/ROW]
[ROW][C]35[/C][C]0.325101[/C][C]0.650203[/C][C]0.674899[/C][/ROW]
[ROW][C]36[/C][C]0.332705[/C][C]0.66541[/C][C]0.667295[/C][/ROW]
[ROW][C]37[/C][C]0.386994[/C][C]0.773988[/C][C]0.613006[/C][/ROW]
[ROW][C]38[/C][C]0.335322[/C][C]0.670643[/C][C]0.664678[/C][/ROW]
[ROW][C]39[/C][C]0.349259[/C][C]0.698517[/C][C]0.650741[/C][/ROW]
[ROW][C]40[/C][C]0.306599[/C][C]0.613198[/C][C]0.693401[/C][/ROW]
[ROW][C]41[/C][C]0.743669[/C][C]0.512663[/C][C]0.256331[/C][/ROW]
[ROW][C]42[/C][C]0.709469[/C][C]0.581063[/C][C]0.290531[/C][/ROW]
[ROW][C]43[/C][C]0.680222[/C][C]0.639556[/C][C]0.319778[/C][/ROW]
[ROW][C]44[/C][C]0.634432[/C][C]0.731136[/C][C]0.365568[/C][/ROW]
[ROW][C]45[/C][C]0.671995[/C][C]0.656011[/C][C]0.328005[/C][/ROW]
[ROW][C]46[/C][C]0.62739[/C][C]0.74522[/C][C]0.37261[/C][/ROW]
[ROW][C]47[/C][C]0.602477[/C][C]0.795046[/C][C]0.397523[/C][/ROW]
[ROW][C]48[/C][C]0.590278[/C][C]0.819444[/C][C]0.409722[/C][/ROW]
[ROW][C]49[/C][C]0.546342[/C][C]0.907316[/C][C]0.453658[/C][/ROW]
[ROW][C]50[/C][C]0.502922[/C][C]0.994157[/C][C]0.497078[/C][/ROW]
[ROW][C]51[/C][C]0.532853[/C][C]0.934293[/C][C]0.467147[/C][/ROW]
[ROW][C]52[/C][C]0.516361[/C][C]0.967278[/C][C]0.483639[/C][/ROW]
[ROW][C]53[/C][C]0.470895[/C][C]0.94179[/C][C]0.529105[/C][/ROW]
[ROW][C]54[/C][C]0.63792[/C][C]0.72416[/C][C]0.36208[/C][/ROW]
[ROW][C]55[/C][C]0.635379[/C][C]0.729243[/C][C]0.364621[/C][/ROW]
[ROW][C]56[/C][C]0.593215[/C][C]0.813571[/C][C]0.406785[/C][/ROW]
[ROW][C]57[/C][C]0.571609[/C][C]0.856782[/C][C]0.428391[/C][/ROW]
[ROW][C]58[/C][C]0.571624[/C][C]0.856753[/C][C]0.428376[/C][/ROW]
[ROW][C]59[/C][C]0.525369[/C][C]0.949262[/C][C]0.474631[/C][/ROW]
[ROW][C]60[/C][C]0.550906[/C][C]0.898188[/C][C]0.449094[/C][/ROW]
[ROW][C]61[/C][C]0.508131[/C][C]0.983738[/C][C]0.491869[/C][/ROW]
[ROW][C]62[/C][C]0.525495[/C][C]0.949011[/C][C]0.474505[/C][/ROW]
[ROW][C]63[/C][C]0.491867[/C][C]0.983735[/C][C]0.508133[/C][/ROW]
[ROW][C]64[/C][C]0.446543[/C][C]0.893086[/C][C]0.553457[/C][/ROW]
[ROW][C]65[/C][C]0.401967[/C][C]0.803935[/C][C]0.598033[/C][/ROW]
[ROW][C]66[/C][C]0.394002[/C][C]0.788003[/C][C]0.605998[/C][/ROW]
[ROW][C]67[/C][C]0.386404[/C][C]0.772809[/C][C]0.613596[/C][/ROW]
[ROW][C]68[/C][C]0.389471[/C][C]0.778941[/C][C]0.610529[/C][/ROW]
[ROW][C]69[/C][C]0.357673[/C][C]0.715346[/C][C]0.642327[/C][/ROW]
[ROW][C]70[/C][C]0.433939[/C][C]0.867878[/C][C]0.566061[/C][/ROW]
[ROW][C]71[/C][C]0.435497[/C][C]0.870994[/C][C]0.564503[/C][/ROW]
[ROW][C]72[/C][C]0.393759[/C][C]0.787518[/C][C]0.606241[/C][/ROW]
[ROW][C]73[/C][C]0.38086[/C][C]0.761719[/C][C]0.61914[/C][/ROW]
[ROW][C]74[/C][C]0.342781[/C][C]0.685562[/C][C]0.657219[/C][/ROW]
[ROW][C]75[/C][C]0.475738[/C][C]0.951476[/C][C]0.524262[/C][/ROW]
[ROW][C]76[/C][C]0.432751[/C][C]0.865502[/C][C]0.567249[/C][/ROW]
[ROW][C]77[/C][C]0.503988[/C][C]0.992024[/C][C]0.496012[/C][/ROW]
[ROW][C]78[/C][C]0.494499[/C][C]0.988997[/C][C]0.505501[/C][/ROW]
[ROW][C]79[/C][C]0.453719[/C][C]0.907437[/C][C]0.546281[/C][/ROW]
[ROW][C]80[/C][C]0.410378[/C][C]0.820757[/C][C]0.589622[/C][/ROW]
[ROW][C]81[/C][C]0.368227[/C][C]0.736455[/C][C]0.631773[/C][/ROW]
[ROW][C]82[/C][C]0.334395[/C][C]0.668789[/C][C]0.665605[/C][/ROW]
[ROW][C]83[/C][C]0.353969[/C][C]0.707938[/C][C]0.646031[/C][/ROW]
[ROW][C]84[/C][C]0.365539[/C][C]0.731078[/C][C]0.634461[/C][/ROW]
[ROW][C]85[/C][C]0.341649[/C][C]0.683299[/C][C]0.658351[/C][/ROW]
[ROW][C]86[/C][C]0.325327[/C][C]0.650655[/C][C]0.674673[/C][/ROW]
[ROW][C]87[/C][C]0.363419[/C][C]0.726838[/C][C]0.636581[/C][/ROW]
[ROW][C]88[/C][C]0.322774[/C][C]0.645549[/C][C]0.677226[/C][/ROW]
[ROW][C]89[/C][C]0.351871[/C][C]0.703742[/C][C]0.648129[/C][/ROW]
[ROW][C]90[/C][C]0.315362[/C][C]0.630725[/C][C]0.684638[/C][/ROW]
[ROW][C]91[/C][C]0.287588[/C][C]0.575175[/C][C]0.712412[/C][/ROW]
[ROW][C]92[/C][C]0.274738[/C][C]0.549477[/C][C]0.725262[/C][/ROW]
[ROW][C]93[/C][C]0.259952[/C][C]0.519904[/C][C]0.740048[/C][/ROW]
[ROW][C]94[/C][C]0.228681[/C][C]0.457362[/C][C]0.771319[/C][/ROW]
[ROW][C]95[/C][C]0.221777[/C][C]0.443553[/C][C]0.778223[/C][/ROW]
[ROW][C]96[/C][C]0.257674[/C][C]0.515348[/C][C]0.742326[/C][/ROW]
[ROW][C]97[/C][C]0.259612[/C][C]0.519224[/C][C]0.740388[/C][/ROW]
[ROW][C]98[/C][C]0.280873[/C][C]0.561746[/C][C]0.719127[/C][/ROW]
[ROW][C]99[/C][C]0.250015[/C][C]0.50003[/C][C]0.749985[/C][/ROW]
[ROW][C]100[/C][C]0.222853[/C][C]0.445707[/C][C]0.777147[/C][/ROW]
[ROW][C]101[/C][C]0.196277[/C][C]0.392554[/C][C]0.803723[/C][/ROW]
[ROW][C]102[/C][C]0.180671[/C][C]0.361343[/C][C]0.819329[/C][/ROW]
[ROW][C]103[/C][C]0.152743[/C][C]0.305486[/C][C]0.847257[/C][/ROW]
[ROW][C]104[/C][C]0.185605[/C][C]0.371211[/C][C]0.814395[/C][/ROW]
[ROW][C]105[/C][C]0.158871[/C][C]0.317741[/C][C]0.841129[/C][/ROW]
[ROW][C]106[/C][C]0.139332[/C][C]0.278663[/C][C]0.860668[/C][/ROW]
[ROW][C]107[/C][C]0.128962[/C][C]0.257924[/C][C]0.871038[/C][/ROW]
[ROW][C]108[/C][C]0.116098[/C][C]0.232195[/C][C]0.883902[/C][/ROW]
[ROW][C]109[/C][C]0.0957698[/C][C]0.19154[/C][C]0.90423[/C][/ROW]
[ROW][C]110[/C][C]0.0884112[/C][C]0.176822[/C][C]0.911589[/C][/ROW]
[ROW][C]111[/C][C]0.0729459[/C][C]0.145892[/C][C]0.927054[/C][/ROW]
[ROW][C]112[/C][C]0.0585418[/C][C]0.117084[/C][C]0.941458[/C][/ROW]
[ROW][C]113[/C][C]0.0487913[/C][C]0.0975826[/C][C]0.951209[/C][/ROW]
[ROW][C]114[/C][C]0.0592537[/C][C]0.118507[/C][C]0.940746[/C][/ROW]
[ROW][C]115[/C][C]0.0478523[/C][C]0.0957047[/C][C]0.952148[/C][/ROW]
[ROW][C]116[/C][C]0.0382194[/C][C]0.0764387[/C][C]0.961781[/C][/ROW]
[ROW][C]117[/C][C]0.040327[/C][C]0.080654[/C][C]0.959673[/C][/ROW]
[ROW][C]118[/C][C]0.0473321[/C][C]0.0946642[/C][C]0.952668[/C][/ROW]
[ROW][C]119[/C][C]0.0423358[/C][C]0.0846716[/C][C]0.957664[/C][/ROW]
[ROW][C]120[/C][C]0.0723996[/C][C]0.144799[/C][C]0.9276[/C][/ROW]
[ROW][C]121[/C][C]0.062503[/C][C]0.125006[/C][C]0.937497[/C][/ROW]
[ROW][C]122[/C][C]0.0622803[/C][C]0.124561[/C][C]0.93772[/C][/ROW]
[ROW][C]123[/C][C]0.0689799[/C][C]0.13796[/C][C]0.93102[/C][/ROW]
[ROW][C]124[/C][C]0.0570723[/C][C]0.114145[/C][C]0.942928[/C][/ROW]
[ROW][C]125[/C][C]0.0500509[/C][C]0.100102[/C][C]0.949949[/C][/ROW]
[ROW][C]126[/C][C]0.0557372[/C][C]0.111474[/C][C]0.944263[/C][/ROW]
[ROW][C]127[/C][C]0.0515522[/C][C]0.103104[/C][C]0.948448[/C][/ROW]
[ROW][C]128[/C][C]0.17208[/C][C]0.344159[/C][C]0.82792[/C][/ROW]
[ROW][C]129[/C][C]0.150879[/C][C]0.301758[/C][C]0.849121[/C][/ROW]
[ROW][C]130[/C][C]0.190635[/C][C]0.38127[/C][C]0.809365[/C][/ROW]
[ROW][C]131[/C][C]0.171767[/C][C]0.343533[/C][C]0.828233[/C][/ROW]
[ROW][C]132[/C][C]0.152906[/C][C]0.305812[/C][C]0.847094[/C][/ROW]
[ROW][C]133[/C][C]0.140894[/C][C]0.281788[/C][C]0.859106[/C][/ROW]
[ROW][C]134[/C][C]0.17397[/C][C]0.34794[/C][C]0.82603[/C][/ROW]
[ROW][C]135[/C][C]0.158996[/C][C]0.317992[/C][C]0.841004[/C][/ROW]
[ROW][C]136[/C][C]0.151242[/C][C]0.302484[/C][C]0.848758[/C][/ROW]
[ROW][C]137[/C][C]0.165509[/C][C]0.331019[/C][C]0.834491[/C][/ROW]
[ROW][C]138[/C][C]0.137133[/C][C]0.274267[/C][C]0.862867[/C][/ROW]
[ROW][C]139[/C][C]0.10858[/C][C]0.217161[/C][C]0.89142[/C][/ROW]
[ROW][C]140[/C][C]0.0844001[/C][C]0.1688[/C][C]0.9156[/C][/ROW]
[ROW][C]141[/C][C]0.0670093[/C][C]0.134019[/C][C]0.932991[/C][/ROW]
[ROW][C]142[/C][C]0.0559892[/C][C]0.111978[/C][C]0.944011[/C][/ROW]
[ROW][C]143[/C][C]0.0471063[/C][C]0.0942127[/C][C]0.952894[/C][/ROW]
[ROW][C]144[/C][C]0.0549183[/C][C]0.109837[/C][C]0.945082[/C][/ROW]
[ROW][C]145[/C][C]0.0709644[/C][C]0.141929[/C][C]0.929036[/C][/ROW]
[ROW][C]146[/C][C]0.0561573[/C][C]0.112315[/C][C]0.943843[/C][/ROW]
[ROW][C]147[/C][C]0.0417204[/C][C]0.0834408[/C][C]0.95828[/C][/ROW]
[ROW][C]148[/C][C]0.0530482[/C][C]0.106096[/C][C]0.946952[/C][/ROW]
[ROW][C]149[/C][C]0.0412444[/C][C]0.0824888[/C][C]0.958756[/C][/ROW]
[ROW][C]150[/C][C]0.0494507[/C][C]0.0989014[/C][C]0.950549[/C][/ROW]
[ROW][C]151[/C][C]0.523293[/C][C]0.953414[/C][C]0.476707[/C][/ROW]
[ROW][C]152[/C][C]0.460109[/C][C]0.920219[/C][C]0.539891[/C][/ROW]
[ROW][C]153[/C][C]0.992427[/C][C]0.0151463[/C][C]0.00757315[/C][/ROW]
[ROW][C]154[/C][C]0.987998[/C][C]0.0240032[/C][C]0.0120016[/C][/ROW]
[ROW][C]155[/C][C]0.978134[/C][C]0.0437314[/C][C]0.0218657[/C][/ROW]
[ROW][C]156[/C][C]0.999617[/C][C]0.000765097[/C][C]0.000382548[/C][/ROW]
[ROW][C]157[/C][C]0.998956[/C][C]0.00208717[/C][C]0.00104358[/C][/ROW]
[ROW][C]158[/C][C]0.997316[/C][C]0.00536848[/C][C]0.00268424[/C][/ROW]
[ROW][C]159[/C][C]0.994078[/C][C]0.011845[/C][C]0.00592248[/C][/ROW]
[ROW][C]160[/C][C]0.992[/C][C]0.0160008[/C][C]0.00800039[/C][/ROW]
[ROW][C]161[/C][C]0.993073[/C][C]0.0138539[/C][C]0.00692693[/C][/ROW]
[ROW][C]162[/C][C]0.986498[/C][C]0.0270049[/C][C]0.0135024[/C][/ROW]
[ROW][C]163[/C][C]0.972001[/C][C]0.0559971[/C][C]0.0279985[/C][/ROW]
[ROW][C]164[/C][C]0.999647[/C][C]0.000706038[/C][C]0.000353019[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=267539&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=267539&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
70.9307850.1384310.0692153
80.8710250.257950.128975
90.922380.1552390.0776196
100.9265420.1469150.0734577
110.8890760.2218490.110924
120.9450970.1098060.0549028
130.9680770.06384620.0319231
140.9575480.08490420.0424521
150.9348170.1303660.065183
160.9150110.1699780.084989
170.8982660.2034690.101734
180.8605040.2789920.139496
190.8237850.3524290.176215
200.7866420.4267160.213358
210.7333830.5332340.266617
220.6751180.6497640.324882
230.6125790.7748420.387421
240.5502390.8995230.449761
250.5755210.8489570.424479
260.6217960.7564080.378204
270.572160.855680.42784
280.5355890.9288210.464411
290.4780430.9560870.521957
300.4478150.8956290.552185
310.5099940.9800120.490006
320.480870.9617390.51913
330.4260920.8521840.573908
340.3708840.7417690.629116
350.3251010.6502030.674899
360.3327050.665410.667295
370.3869940.7739880.613006
380.3353220.6706430.664678
390.3492590.6985170.650741
400.3065990.6131980.693401
410.7436690.5126630.256331
420.7094690.5810630.290531
430.6802220.6395560.319778
440.6344320.7311360.365568
450.6719950.6560110.328005
460.627390.745220.37261
470.6024770.7950460.397523
480.5902780.8194440.409722
490.5463420.9073160.453658
500.5029220.9941570.497078
510.5328530.9342930.467147
520.5163610.9672780.483639
530.4708950.941790.529105
540.637920.724160.36208
550.6353790.7292430.364621
560.5932150.8135710.406785
570.5716090.8567820.428391
580.5716240.8567530.428376
590.5253690.9492620.474631
600.5509060.8981880.449094
610.5081310.9837380.491869
620.5254950.9490110.474505
630.4918670.9837350.508133
640.4465430.8930860.553457
650.4019670.8039350.598033
660.3940020.7880030.605998
670.3864040.7728090.613596
680.3894710.7789410.610529
690.3576730.7153460.642327
700.4339390.8678780.566061
710.4354970.8709940.564503
720.3937590.7875180.606241
730.380860.7617190.61914
740.3427810.6855620.657219
750.4757380.9514760.524262
760.4327510.8655020.567249
770.5039880.9920240.496012
780.4944990.9889970.505501
790.4537190.9074370.546281
800.4103780.8207570.589622
810.3682270.7364550.631773
820.3343950.6687890.665605
830.3539690.7079380.646031
840.3655390.7310780.634461
850.3416490.6832990.658351
860.3253270.6506550.674673
870.3634190.7268380.636581
880.3227740.6455490.677226
890.3518710.7037420.648129
900.3153620.6307250.684638
910.2875880.5751750.712412
920.2747380.5494770.725262
930.2599520.5199040.740048
940.2286810.4573620.771319
950.2217770.4435530.778223
960.2576740.5153480.742326
970.2596120.5192240.740388
980.2808730.5617460.719127
990.2500150.500030.749985
1000.2228530.4457070.777147
1010.1962770.3925540.803723
1020.1806710.3613430.819329
1030.1527430.3054860.847257
1040.1856050.3712110.814395
1050.1588710.3177410.841129
1060.1393320.2786630.860668
1070.1289620.2579240.871038
1080.1160980.2321950.883902
1090.09576980.191540.90423
1100.08841120.1768220.911589
1110.07294590.1458920.927054
1120.05854180.1170840.941458
1130.04879130.09758260.951209
1140.05925370.1185070.940746
1150.04785230.09570470.952148
1160.03821940.07643870.961781
1170.0403270.0806540.959673
1180.04733210.09466420.952668
1190.04233580.08467160.957664
1200.07239960.1447990.9276
1210.0625030.1250060.937497
1220.06228030.1245610.93772
1230.06897990.137960.93102
1240.05707230.1141450.942928
1250.05005090.1001020.949949
1260.05573720.1114740.944263
1270.05155220.1031040.948448
1280.172080.3441590.82792
1290.1508790.3017580.849121
1300.1906350.381270.809365
1310.1717670.3435330.828233
1320.1529060.3058120.847094
1330.1408940.2817880.859106
1340.173970.347940.82603
1350.1589960.3179920.841004
1360.1512420.3024840.848758
1370.1655090.3310190.834491
1380.1371330.2742670.862867
1390.108580.2171610.89142
1400.08440010.16880.9156
1410.06700930.1340190.932991
1420.05598920.1119780.944011
1430.04710630.09421270.952894
1440.05491830.1098370.945082
1450.07096440.1419290.929036
1460.05615730.1123150.943843
1470.04172040.08344080.95828
1480.05304820.1060960.946952
1490.04124440.08248880.958756
1500.04945070.09890140.950549
1510.5232930.9534140.476707
1520.4601090.9202190.539891
1530.9924270.01514630.00757315
1540.9879980.02400320.0120016
1550.9781340.04373140.0218657
1560.9996170.0007650970.000382548
1570.9989560.002087170.00104358
1580.9973160.005368480.00268424
1590.9940780.0118450.00592248
1600.9920.01600080.00800039
1610.9930730.01385390.00692693
1620.9864980.02700490.0135024
1630.9720010.05599710.0279985
1640.9996470.0007060380.000353019






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level40.0253165NOK
5% type I error level110.0696203NOK
10% type I error level240.151899NOK

\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 & 4 & 0.0253165 & NOK \tabularnewline
5% type I error level & 11 & 0.0696203 & NOK \tabularnewline
10% type I error level & 24 & 0.151899 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=267539&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]4[/C][C]0.0253165[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]11[/C][C]0.0696203[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]24[/C][C]0.151899[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=267539&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=267539&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 level40.0253165NOK
5% type I error level110.0696203NOK
10% type I error level240.151899NOK


Figures (Output of Computation)

Figure 1
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 2
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 3
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 4
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 5
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 6
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 7
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 8
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 9
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 10
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Input Parameters & R Code

Parameters (Session):
Parameters (R input):
par1 = 4 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
R code (references can be found in the software module):
par3 <- 'No Linear Trend'
par2 <- 'Do not include Seasonal Dummies'
par1 <- '4'
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$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
myeq <- paste(myeq, signif(mysum$coefficients[i,1],6), 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,signif(mysum$coefficients[i,1],6))
a<-table.element(a, signif(mysum$coefficients[i,2],6))
a<-table.element(a, signif(mysum$coefficients[i,3],4))
a<-table.element(a, signif(mysum$coefficients[i,4],6))
a<-table.element(a, signif(mysum$coefficients[i,4]/2,6))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable2.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple R',1,TRUE)
a<-table.element(a, signif(sqrt(mysum$r.squared),6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'R-squared',1,TRUE)
a<-table.element(a, signif(mysum$r.squared,6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Adjusted R-squared',1,TRUE)
a<-table.element(a, signif(mysum$adj.r.squared,6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (value)',1,TRUE)
a<-table.element(a, signif(mysum$fstatistic[1],6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
a<-table.element(a, signif(mysum$fstatistic[2],6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
a<-table.element(a, signif(mysum$fstatistic[3],6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'p-value',1,TRUE)
a<-table.element(a, signif(1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]),6))
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, signif(mysum$sigma,6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
a<-table.element(a, signif(sum(myerror*myerror),6))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,signif(x[i],6))
a<-table.element(a,signif(x[i]-mysum$resid[i],6))
a<-table.element(a,signif(mysum$resid[i],6))
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,signif(gqarr[mypoint-kp3+1,1],6))
a<-table.element(a,signif(gqarr[mypoint-kp3+1,2],6))
a<-table.element(a,signif(gqarr[mypoint-kp3+1,3],6))
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,signif(numsignificant1,6))
a<-table.element(a,signif(numsignificant1/numgqtests,6))
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,signif(numsignificant5,6))
a<-table.element(a,signif(numsignificant5/numgqtests,6))
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,signif(numsignificant10,6))
a<-table.element(a,signif(numsignificant10/numgqtests,6))
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')
}