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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 computationThu, 18 Dec 2014 22:27:32 +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/18/t1418941699sx0rughtlwijb72.htm/, Retrieved Sun, 19 May 2024 17:00:33 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=271308, Retrieved Sun, 19 May 2024 17:00:33 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact66

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Multiple Regression] [] [2014-12-18 22:27:32] [a3de03a8fa2b95b1b988206b9ba33408] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
48	41	23	12	34	0,75
50	146	16	45	61	1,5
150	182	33	37	70	3
154	192	32	37	69	2,25
109	263	37	108	145	3
68	35	14	10	23	1,5
194	439	52	68	120	3
158	214	75	72	147	3
159	341	72	143	215	3
67	58	15	9	24	0,75
147	292	29	55	84	3
39	85	13	17	30	2,25
100	200	40	37	77	1,5
111	158	19	27	46	1,5
138	199	24	37	61	2,25
101	297	121	58	178	3
131	227	93	66	160	3
101	108	36	21	57	1,5
114	86	23	19	42	2,25
165	302	85	78	163	2,25
114	148	41	35	75	1,5
111	178	46	48	94	2,25
75	120	18	27	45	1,5
82	207	35	43	78	2,25
121	157	17	30	47	2,25
32	128	4	25	29	3
150	296	28	69	97	3
117	323	44	72	116	3
71	79	10	23	32	1,5
165	70	38	13	50	3
154	146	57	61	118	3
126	246	23	43	66	2,25
149	196	36	51	86	2,25
145	199	22	67	89	2,25
120	127	40	36	76	3
109	153	31	44	75	2,25
132	299	11	45	57	3
172	228	38	34	72	3
169	190	24	36	60	1,5
114	180	37	72	109	2,25
156	212	37	39	76	3
172	269	22	43	65	2,25
68	130	15	25	40	1,5
89	179	2	56	58	2,25
167	243	43	80	123	2,25
113	190	31	40	71	1,5
115	299	29	73	102	2,25
78	121	45	34	80	1,5
118	137	25	72	97	2,25
87	305	4	42	46	3
173	157	31	61	93	3
2	96	-4	23	19	3
162	183	66	74	140	3
49	52	61	16	78	1,5
122	238	32	66	98	2,25
96	40	31	9	40	1,5
100	226	39	41	80	2,25
82	190	19	57	76	2,25
100	214	31	48	79	2,25
115	145	36	51	87	3
141	119	42	53	95	1,5
165	222	21	29	49	2,25
165	222	21	29	49	2,25
110	159	25	55	80	3
118	165	32	54	86	2,25
158	249	26	43	69	3
146	125	28	51	79	2,25
49	122	32	20	52	1,5
90	186	41	79	120	3
121	148	29	39	69	1,5
155	274	33	61	94	3
104	172	17	55	72	3
147	84	13	30	43	3
110	168	32	55	87	3
108	102	30	22	52	2,25
113	106	34	37	71	2,25
115	2	59	2	61	0,75
61	139	13	38	51	3
60	95	23	27	50	0,75
109	130	10	56	67	1,5
68	72	5	25	30	1,5
111	141	31	39	70	3
77	113	19	33	52	1,5
73	206	32	43	75	2,25
151	268	30	57	87	3
89	175	25	43	69	3
78	77	48	23	72	1,5
110	125	35	44	79	3
220	255	67	54	121	3
65	111	15	28	43	1,5
141	132	22	36	58	1,5
117	211	18	39	57	2,25
122	92	33	16	50	1,5
63	76	46	23	69	1,5
44	171	24	40	64	2,25
52	83	14	24	38	1,5
131	266	12	78	90	3
101	186	38	57	96	3
42	50	12	37	49	0,75
152	117	28	27	56	1,5
107	219	41	61	102	1,5
77	246	12	27	40	2,25
154	279	31	69	100	2,25
103	148	33	34	67	1,5
96	137	34	44	78	2,25
175	181	21	34	55	2,25
57	98	20	39	59	0,75
112	226	44	51	96	2,25
143	234	52	34	86	3
49	138	7	31	38	0,75
110	85	29	13	43	0,75
131	66	11	12	23	3
167	236	26	51	77	3
56	106	24	24	48	3
137	135	7	19	26	3
86	122	60	30	91	1,5
121	218	13	81	94	3
149	199	20	42	62	3
168	112	52	22	74	3
140	278	28	85	114	3
88	94	25	27	52	1,5
168	113	39	25	64	2,25
94	84	9	22	31	0,75
51	86	19	19	38	0,75
48	62	13	14	27	2,25
145	222	60	45	105	3
66	167	19	45	64	2,25
85	82	34	28	62	3
109	207	14	51	65	2,25
63	184	17	41	58	3
102	83	45	31	76	1,5
162	183	66	74	140	3
86	89	48	19	68	0,75
114	225	29	51	80	1,5
164	237	-2	73	71	3
119	102	51	24	76	3
126	221	2	61	63	3
132	128	24	23	46	2,25
142	91	40	14	53	2,25
83	198	20	54	74	3
94	204	19	51	70	1,5
81	158	16	62	78	2,25
166	138	20	36	56	2,25
110	226	40	59	100	2,25
64	44	27	24	51	0,75
93	196	25	26	52	2,25
104	83	49	54	102	1,5
105	79	39	39	78	2,25
49	52	61	16	78	1,5
88	105	19	36	55	0,75
95	116	67	31	98	1,5
102	83	45	31	76	1,5
99	196	30	42	73	2,25
63	153	8	39	47	1,5
76	157	19	25	45	1,5
109	75	52	31	83	3
117	106	22	38	60	2,25
57	58	17	31	48	1,5
120	75	33	17	50	0,75
73	74	34	22	56	2,25
91	185	22	55	77	3
108	265	30	62	91	3
105	131	25	51	76	1,5
117	139	38	30	68	1,5
119	196	26	49	74	2,25
31	78	13	16	29	0,75

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

\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
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=271308&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]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=271308&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=271308&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






Multiple Linear Regression - Estimated Regression Equation
PE[t] = + 0.96948 + 0.00452956LFM[t] + 0.00297178B[t] + 0.0359152PRH[t] + 0.0433646CH[t] -0.036941H[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
PE[t] =  +  0.96948 +  0.00452956LFM[t] +  0.00297178B[t] +  0.0359152PRH[t] +  0.0433646CH[t] -0.036941H[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=271308&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]PE[t] =  +  0.96948 +  0.00452956LFM[t] +  0.00297178B[t] +  0.0359152PRH[t] +  0.0433646CH[t] -0.036941H[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=271308&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=271308&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
PE[t] = + 0.96948 + 0.00452956LFM[t] + 0.00297178B[t] + 0.0359152PRH[t] + 0.0433646CH[t] -0.036941H[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)0.969480.1528866.3412.22989e-091.11495e-09
LFM0.004529560.001478533.0640.002567140.00128357
B0.002971780.0009869263.0110.003025480.00151274
PRH0.03591520.09948950.3610.7185790.35929
CH0.04336460.09920840.43710.6626240.331312
H-0.0369410.0991686-0.37250.7100080.355004

\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) & 0.96948 & 0.152886 & 6.341 & 2.22989e-09 & 1.11495e-09 \tabularnewline
LFM & 0.00452956 & 0.00147853 & 3.064 & 0.00256714 & 0.00128357 \tabularnewline
B & 0.00297178 & 0.000986926 & 3.011 & 0.00302548 & 0.00151274 \tabularnewline
PRH & 0.0359152 & 0.0994895 & 0.361 & 0.718579 & 0.35929 \tabularnewline
CH & 0.0433646 & 0.0992084 & 0.4371 & 0.662624 & 0.331312 \tabularnewline
H & -0.036941 & 0.0991686 & -0.3725 & 0.710008 & 0.355004 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=271308&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]0.96948[/C][C]0.152886[/C][C]6.341[/C][C]2.22989e-09[/C][C]1.11495e-09[/C][/ROW]
[ROW][C]LFM[/C][C]0.00452956[/C][C]0.00147853[/C][C]3.064[/C][C]0.00256714[/C][C]0.00128357[/C][/ROW]
[ROW][C]B[/C][C]0.00297178[/C][C]0.000986926[/C][C]3.011[/C][C]0.00302548[/C][C]0.00151274[/C][/ROW]
[ROW][C]PRH[/C][C]0.0359152[/C][C]0.0994895[/C][C]0.361[/C][C]0.718579[/C][C]0.35929[/C][/ROW]
[ROW][C]CH[/C][C]0.0433646[/C][C]0.0992084[/C][C]0.4371[/C][C]0.662624[/C][C]0.331312[/C][/ROW]
[ROW][C]H[/C][C]-0.036941[/C][C]0.0991686[/C][C]-0.3725[/C][C]0.710008[/C][C]0.355004[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=271308&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=271308&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)0.969480.1528866.3412.22989e-091.11495e-09
LFM0.004529560.001478533.0640.002567140.00128357
B0.002971780.0009869263.0110.003025480.00151274
PRH0.03591520.09948950.3610.7185790.35929
CH0.04336460.09920840.43710.6626240.331312
H-0.0369410.0991686-0.37250.7100080.355004






Multiple Linear Regression - Regression Statistics
Multiple R0.594881
R-squared0.353884
Adjusted R-squared0.333693
F-TEST (value)17.5267
F-TEST (DF numerator)5
F-TEST (DF denominator)160
p-value8.01581e-14
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.599948
Sum Squared Residuals57.59

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.594881 \tabularnewline
R-squared & 0.353884 \tabularnewline
Adjusted R-squared & 0.333693 \tabularnewline
F-TEST (value) & 17.5267 \tabularnewline
F-TEST (DF numerator) & 5 \tabularnewline
F-TEST (DF denominator) & 160 \tabularnewline
p-value & 8.01581e-14 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 0.599948 \tabularnewline
Sum Squared Residuals & 57.59 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=271308&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.594881[/C][/ROW]
[ROW][C]R-squared[/C][C]0.353884[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.333693[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]17.5267[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]5[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]160[/C][/ROW]
[ROW][C]p-value[/C][C]8.01581e-14[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]0.599948[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]57.59[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=271308&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=271308&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 R0.594881
R-squared0.353884
Adjusted R-squared0.333693
F-TEST (value)17.5267
F-TEST (DF numerator)5
F-TEST (DF denominator)160
p-value8.01581e-14
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.599948
Sum Squared Residuals57.59






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
10.751.39917-0.649173
21.51.90249-0.402487
332.39360.6064
42.252.44246-0.192462
532.900580.0994248
61.51.468320.031682
733.53629-0.536291
832.706680.293323
933.54778-0.547776
100.751.48775-0.737749
1132.826640.173364
122.251.49460.7554
131.52.21343-0.713434
141.52.09575-0.59575
152.252.399-0.148998
1632.594970.405026
1732.529060.470935
181.51.84588-0.345885
192.251.839880.410122
202.253.02818-0.778184
211.52.14538-0.645383
222.252.26238-0.0123846
231.51.82078-0.320784
242.252.196370.0536253
252.252.15940.0906042
2631.65131.3487
2732.943070.0569324
2832.876690.123312
291.51.70028-0.200275
3032.006350.99365
3132.434280.565718
322.252.52388-0.273884
332.252.55447-0.30447
342.252.62546-0.375464
3532.080660.919339
362.252.168720.0812767
3732.696780.303218
3832.605550.394447
391.52.50624-1.00624
402.252.44532-0.195315
4132.518680.481325
422.252.80162-0.551621
431.51.80902-0.309025
442.252.26223-0.0122296
452.252.91784-0.667838
461.52.2711-0.771103
472.252.81812-0.568117
481.51.81767-0.317672
492.252.34796-0.0979564
5032.535630.464367
5132.542760.457237
5231.415681.58432
5332.654750.345251
541.51.349220.150776
552.252.6205-0.370503
561.51.5492-0.0492015
572.252.31742-0.0674203
582.252.2522-0.0021975
592.252.33493-0.0849305
6032.211960.788037
611.52.25916-0.759157
622.252.57828-0.328277
632.252.57828-0.328277
6432.26790.732102
652.252.30836-0.058361
6632.674670.325332
672.252.30115-0.0511497
681.51.64963-0.149632
6932.39530.604701
701.52.14121-0.641212
7132.843820.156182
7232.287560.71244
7332.064330.935673
7432.287460.712537
752.251.872340.377661
762.251.999130.250875
770.751.44865-0.698648
7831.889621.11038
790.751.67342-0.923417
801.52.16206-0.662056
811.51.64692-0.146919
8232.110.889997
831.51.84656-0.346556
842.252.155710.0942857
8532.785250.214748
8632.10630.893699
871.51.61318-0.113176
8832.085940.91406
8933.00193-0.00193388
901.51.75824-0.258243
911.52.20911-0.709105
922.252.35854-0.10854
931.51.82748-0.327475
941.51.58125-0.0812538
952.251.909280.34072
961.51.59148-0.09148
9732.842080.157922
9832.269940.730059
990.751.53367-0.783674
1001.52.11345-0.613445
1011.52.45475-0.954745
1022.252.17350.076499
1032.252.90759-0.657588
1041.52.0604-0.560399
1052.252.059210.190787
1062.252.49691-0.246906
1070.751.7489-0.998904
1082.252.39394-0.143941
10932.477670.522335
1100.751.79349-1.04349
1110.751.73715-0.987151
11231.824791.17521
11332.728190.27181
11431.667691.33231
11532.106090.893912
1161.51.8158-0.315799
11732.672380.327619
11832.485040.514956
11932.151260.848737
12032.910120.0898834
1211.51.79522-0.295221
1222.252.186840.0631587
1230.751.77698-1.02698
1240.751.55862-0.808619
1252.251.447740.802256
12632.513520.486484
1272.252.034290.21571
12831.743161.25684
1292.252.3916-0.141603
13032.047580.952421
1311.51.83112-0.331124
13232.654750.345251
1330.751.65938-0.90938
1341.52.45236-0.952356
13532.887610.112386
13631.876531.12347
13732.586760.413244
1382.252.107830.142165
1392.251.968950.281051
14032.26020.739795
1411.52.30962-0.809616
1422.252.187770.0622337
1432.252.34223-0.0922264
1442.252.44037-0.190374
1450.751.5166-0.7666
1462.252.077630.172374
1471.52.02076-0.520764
1482.251.890370.359631
1491.51.349220.150776
1500.751.89188-1.14188
1511.51.87492-0.374918
1521.51.83112-0.331124
1532.252.202450.047548
1541.51.95184-0.451839
1551.51.88446-0.384455
15631.831881.16812
1572.252.035980.214023
1581.51.58172-0.0817211
1590.751.81126-1.06126
1602.251.626490.623508
16132.262180.73782
16232.650630.349375
1631.52.13635-0.636346
1641.52.06624-0.566244
1652.252.41599-0.165994
1660.751.43114-0.681137

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 0.75 & 1.39917 & -0.649173 \tabularnewline
2 & 1.5 & 1.90249 & -0.402487 \tabularnewline
3 & 3 & 2.3936 & 0.6064 \tabularnewline
4 & 2.25 & 2.44246 & -0.192462 \tabularnewline
5 & 3 & 2.90058 & 0.0994248 \tabularnewline
6 & 1.5 & 1.46832 & 0.031682 \tabularnewline
7 & 3 & 3.53629 & -0.536291 \tabularnewline
8 & 3 & 2.70668 & 0.293323 \tabularnewline
9 & 3 & 3.54778 & -0.547776 \tabularnewline
10 & 0.75 & 1.48775 & -0.737749 \tabularnewline
11 & 3 & 2.82664 & 0.173364 \tabularnewline
12 & 2.25 & 1.4946 & 0.7554 \tabularnewline
13 & 1.5 & 2.21343 & -0.713434 \tabularnewline
14 & 1.5 & 2.09575 & -0.59575 \tabularnewline
15 & 2.25 & 2.399 & -0.148998 \tabularnewline
16 & 3 & 2.59497 & 0.405026 \tabularnewline
17 & 3 & 2.52906 & 0.470935 \tabularnewline
18 & 1.5 & 1.84588 & -0.345885 \tabularnewline
19 & 2.25 & 1.83988 & 0.410122 \tabularnewline
20 & 2.25 & 3.02818 & -0.778184 \tabularnewline
21 & 1.5 & 2.14538 & -0.645383 \tabularnewline
22 & 2.25 & 2.26238 & -0.0123846 \tabularnewline
23 & 1.5 & 1.82078 & -0.320784 \tabularnewline
24 & 2.25 & 2.19637 & 0.0536253 \tabularnewline
25 & 2.25 & 2.1594 & 0.0906042 \tabularnewline
26 & 3 & 1.6513 & 1.3487 \tabularnewline
27 & 3 & 2.94307 & 0.0569324 \tabularnewline
28 & 3 & 2.87669 & 0.123312 \tabularnewline
29 & 1.5 & 1.70028 & -0.200275 \tabularnewline
30 & 3 & 2.00635 & 0.99365 \tabularnewline
31 & 3 & 2.43428 & 0.565718 \tabularnewline
32 & 2.25 & 2.52388 & -0.273884 \tabularnewline
33 & 2.25 & 2.55447 & -0.30447 \tabularnewline
34 & 2.25 & 2.62546 & -0.375464 \tabularnewline
35 & 3 & 2.08066 & 0.919339 \tabularnewline
36 & 2.25 & 2.16872 & 0.0812767 \tabularnewline
37 & 3 & 2.69678 & 0.303218 \tabularnewline
38 & 3 & 2.60555 & 0.394447 \tabularnewline
39 & 1.5 & 2.50624 & -1.00624 \tabularnewline
40 & 2.25 & 2.44532 & -0.195315 \tabularnewline
41 & 3 & 2.51868 & 0.481325 \tabularnewline
42 & 2.25 & 2.80162 & -0.551621 \tabularnewline
43 & 1.5 & 1.80902 & -0.309025 \tabularnewline
44 & 2.25 & 2.26223 & -0.0122296 \tabularnewline
45 & 2.25 & 2.91784 & -0.667838 \tabularnewline
46 & 1.5 & 2.2711 & -0.771103 \tabularnewline
47 & 2.25 & 2.81812 & -0.568117 \tabularnewline
48 & 1.5 & 1.81767 & -0.317672 \tabularnewline
49 & 2.25 & 2.34796 & -0.0979564 \tabularnewline
50 & 3 & 2.53563 & 0.464367 \tabularnewline
51 & 3 & 2.54276 & 0.457237 \tabularnewline
52 & 3 & 1.41568 & 1.58432 \tabularnewline
53 & 3 & 2.65475 & 0.345251 \tabularnewline
54 & 1.5 & 1.34922 & 0.150776 \tabularnewline
55 & 2.25 & 2.6205 & -0.370503 \tabularnewline
56 & 1.5 & 1.5492 & -0.0492015 \tabularnewline
57 & 2.25 & 2.31742 & -0.0674203 \tabularnewline
58 & 2.25 & 2.2522 & -0.0021975 \tabularnewline
59 & 2.25 & 2.33493 & -0.0849305 \tabularnewline
60 & 3 & 2.21196 & 0.788037 \tabularnewline
61 & 1.5 & 2.25916 & -0.759157 \tabularnewline
62 & 2.25 & 2.57828 & -0.328277 \tabularnewline
63 & 2.25 & 2.57828 & -0.328277 \tabularnewline
64 & 3 & 2.2679 & 0.732102 \tabularnewline
65 & 2.25 & 2.30836 & -0.058361 \tabularnewline
66 & 3 & 2.67467 & 0.325332 \tabularnewline
67 & 2.25 & 2.30115 & -0.0511497 \tabularnewline
68 & 1.5 & 1.64963 & -0.149632 \tabularnewline
69 & 3 & 2.3953 & 0.604701 \tabularnewline
70 & 1.5 & 2.14121 & -0.641212 \tabularnewline
71 & 3 & 2.84382 & 0.156182 \tabularnewline
72 & 3 & 2.28756 & 0.71244 \tabularnewline
73 & 3 & 2.06433 & 0.935673 \tabularnewline
74 & 3 & 2.28746 & 0.712537 \tabularnewline
75 & 2.25 & 1.87234 & 0.377661 \tabularnewline
76 & 2.25 & 1.99913 & 0.250875 \tabularnewline
77 & 0.75 & 1.44865 & -0.698648 \tabularnewline
78 & 3 & 1.88962 & 1.11038 \tabularnewline
79 & 0.75 & 1.67342 & -0.923417 \tabularnewline
80 & 1.5 & 2.16206 & -0.662056 \tabularnewline
81 & 1.5 & 1.64692 & -0.146919 \tabularnewline
82 & 3 & 2.11 & 0.889997 \tabularnewline
83 & 1.5 & 1.84656 & -0.346556 \tabularnewline
84 & 2.25 & 2.15571 & 0.0942857 \tabularnewline
85 & 3 & 2.78525 & 0.214748 \tabularnewline
86 & 3 & 2.1063 & 0.893699 \tabularnewline
87 & 1.5 & 1.61318 & -0.113176 \tabularnewline
88 & 3 & 2.08594 & 0.91406 \tabularnewline
89 & 3 & 3.00193 & -0.00193388 \tabularnewline
90 & 1.5 & 1.75824 & -0.258243 \tabularnewline
91 & 1.5 & 2.20911 & -0.709105 \tabularnewline
92 & 2.25 & 2.35854 & -0.10854 \tabularnewline
93 & 1.5 & 1.82748 & -0.327475 \tabularnewline
94 & 1.5 & 1.58125 & -0.0812538 \tabularnewline
95 & 2.25 & 1.90928 & 0.34072 \tabularnewline
96 & 1.5 & 1.59148 & -0.09148 \tabularnewline
97 & 3 & 2.84208 & 0.157922 \tabularnewline
98 & 3 & 2.26994 & 0.730059 \tabularnewline
99 & 0.75 & 1.53367 & -0.783674 \tabularnewline
100 & 1.5 & 2.11345 & -0.613445 \tabularnewline
101 & 1.5 & 2.45475 & -0.954745 \tabularnewline
102 & 2.25 & 2.1735 & 0.076499 \tabularnewline
103 & 2.25 & 2.90759 & -0.657588 \tabularnewline
104 & 1.5 & 2.0604 & -0.560399 \tabularnewline
105 & 2.25 & 2.05921 & 0.190787 \tabularnewline
106 & 2.25 & 2.49691 & -0.246906 \tabularnewline
107 & 0.75 & 1.7489 & -0.998904 \tabularnewline
108 & 2.25 & 2.39394 & -0.143941 \tabularnewline
109 & 3 & 2.47767 & 0.522335 \tabularnewline
110 & 0.75 & 1.79349 & -1.04349 \tabularnewline
111 & 0.75 & 1.73715 & -0.987151 \tabularnewline
112 & 3 & 1.82479 & 1.17521 \tabularnewline
113 & 3 & 2.72819 & 0.27181 \tabularnewline
114 & 3 & 1.66769 & 1.33231 \tabularnewline
115 & 3 & 2.10609 & 0.893912 \tabularnewline
116 & 1.5 & 1.8158 & -0.315799 \tabularnewline
117 & 3 & 2.67238 & 0.327619 \tabularnewline
118 & 3 & 2.48504 & 0.514956 \tabularnewline
119 & 3 & 2.15126 & 0.848737 \tabularnewline
120 & 3 & 2.91012 & 0.0898834 \tabularnewline
121 & 1.5 & 1.79522 & -0.295221 \tabularnewline
122 & 2.25 & 2.18684 & 0.0631587 \tabularnewline
123 & 0.75 & 1.77698 & -1.02698 \tabularnewline
124 & 0.75 & 1.55862 & -0.808619 \tabularnewline
125 & 2.25 & 1.44774 & 0.802256 \tabularnewline
126 & 3 & 2.51352 & 0.486484 \tabularnewline
127 & 2.25 & 2.03429 & 0.21571 \tabularnewline
128 & 3 & 1.74316 & 1.25684 \tabularnewline
129 & 2.25 & 2.3916 & -0.141603 \tabularnewline
130 & 3 & 2.04758 & 0.952421 \tabularnewline
131 & 1.5 & 1.83112 & -0.331124 \tabularnewline
132 & 3 & 2.65475 & 0.345251 \tabularnewline
133 & 0.75 & 1.65938 & -0.90938 \tabularnewline
134 & 1.5 & 2.45236 & -0.952356 \tabularnewline
135 & 3 & 2.88761 & 0.112386 \tabularnewline
136 & 3 & 1.87653 & 1.12347 \tabularnewline
137 & 3 & 2.58676 & 0.413244 \tabularnewline
138 & 2.25 & 2.10783 & 0.142165 \tabularnewline
139 & 2.25 & 1.96895 & 0.281051 \tabularnewline
140 & 3 & 2.2602 & 0.739795 \tabularnewline
141 & 1.5 & 2.30962 & -0.809616 \tabularnewline
142 & 2.25 & 2.18777 & 0.0622337 \tabularnewline
143 & 2.25 & 2.34223 & -0.0922264 \tabularnewline
144 & 2.25 & 2.44037 & -0.190374 \tabularnewline
145 & 0.75 & 1.5166 & -0.7666 \tabularnewline
146 & 2.25 & 2.07763 & 0.172374 \tabularnewline
147 & 1.5 & 2.02076 & -0.520764 \tabularnewline
148 & 2.25 & 1.89037 & 0.359631 \tabularnewline
149 & 1.5 & 1.34922 & 0.150776 \tabularnewline
150 & 0.75 & 1.89188 & -1.14188 \tabularnewline
151 & 1.5 & 1.87492 & -0.374918 \tabularnewline
152 & 1.5 & 1.83112 & -0.331124 \tabularnewline
153 & 2.25 & 2.20245 & 0.047548 \tabularnewline
154 & 1.5 & 1.95184 & -0.451839 \tabularnewline
155 & 1.5 & 1.88446 & -0.384455 \tabularnewline
156 & 3 & 1.83188 & 1.16812 \tabularnewline
157 & 2.25 & 2.03598 & 0.214023 \tabularnewline
158 & 1.5 & 1.58172 & -0.0817211 \tabularnewline
159 & 0.75 & 1.81126 & -1.06126 \tabularnewline
160 & 2.25 & 1.62649 & 0.623508 \tabularnewline
161 & 3 & 2.26218 & 0.73782 \tabularnewline
162 & 3 & 2.65063 & 0.349375 \tabularnewline
163 & 1.5 & 2.13635 & -0.636346 \tabularnewline
164 & 1.5 & 2.06624 & -0.566244 \tabularnewline
165 & 2.25 & 2.41599 & -0.165994 \tabularnewline
166 & 0.75 & 1.43114 & -0.681137 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=271308&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]0.75[/C][C]1.39917[/C][C]-0.649173[/C][/ROW]
[ROW][C]2[/C][C]1.5[/C][C]1.90249[/C][C]-0.402487[/C][/ROW]
[ROW][C]3[/C][C]3[/C][C]2.3936[/C][C]0.6064[/C][/ROW]
[ROW][C]4[/C][C]2.25[/C][C]2.44246[/C][C]-0.192462[/C][/ROW]
[ROW][C]5[/C][C]3[/C][C]2.90058[/C][C]0.0994248[/C][/ROW]
[ROW][C]6[/C][C]1.5[/C][C]1.46832[/C][C]0.031682[/C][/ROW]
[ROW][C]7[/C][C]3[/C][C]3.53629[/C][C]-0.536291[/C][/ROW]
[ROW][C]8[/C][C]3[/C][C]2.70668[/C][C]0.293323[/C][/ROW]
[ROW][C]9[/C][C]3[/C][C]3.54778[/C][C]-0.547776[/C][/ROW]
[ROW][C]10[/C][C]0.75[/C][C]1.48775[/C][C]-0.737749[/C][/ROW]
[ROW][C]11[/C][C]3[/C][C]2.82664[/C][C]0.173364[/C][/ROW]
[ROW][C]12[/C][C]2.25[/C][C]1.4946[/C][C]0.7554[/C][/ROW]
[ROW][C]13[/C][C]1.5[/C][C]2.21343[/C][C]-0.713434[/C][/ROW]
[ROW][C]14[/C][C]1.5[/C][C]2.09575[/C][C]-0.59575[/C][/ROW]
[ROW][C]15[/C][C]2.25[/C][C]2.399[/C][C]-0.148998[/C][/ROW]
[ROW][C]16[/C][C]3[/C][C]2.59497[/C][C]0.405026[/C][/ROW]
[ROW][C]17[/C][C]3[/C][C]2.52906[/C][C]0.470935[/C][/ROW]
[ROW][C]18[/C][C]1.5[/C][C]1.84588[/C][C]-0.345885[/C][/ROW]
[ROW][C]19[/C][C]2.25[/C][C]1.83988[/C][C]0.410122[/C][/ROW]
[ROW][C]20[/C][C]2.25[/C][C]3.02818[/C][C]-0.778184[/C][/ROW]
[ROW][C]21[/C][C]1.5[/C][C]2.14538[/C][C]-0.645383[/C][/ROW]
[ROW][C]22[/C][C]2.25[/C][C]2.26238[/C][C]-0.0123846[/C][/ROW]
[ROW][C]23[/C][C]1.5[/C][C]1.82078[/C][C]-0.320784[/C][/ROW]
[ROW][C]24[/C][C]2.25[/C][C]2.19637[/C][C]0.0536253[/C][/ROW]
[ROW][C]25[/C][C]2.25[/C][C]2.1594[/C][C]0.0906042[/C][/ROW]
[ROW][C]26[/C][C]3[/C][C]1.6513[/C][C]1.3487[/C][/ROW]
[ROW][C]27[/C][C]3[/C][C]2.94307[/C][C]0.0569324[/C][/ROW]
[ROW][C]28[/C][C]3[/C][C]2.87669[/C][C]0.123312[/C][/ROW]
[ROW][C]29[/C][C]1.5[/C][C]1.70028[/C][C]-0.200275[/C][/ROW]
[ROW][C]30[/C][C]3[/C][C]2.00635[/C][C]0.99365[/C][/ROW]
[ROW][C]31[/C][C]3[/C][C]2.43428[/C][C]0.565718[/C][/ROW]
[ROW][C]32[/C][C]2.25[/C][C]2.52388[/C][C]-0.273884[/C][/ROW]
[ROW][C]33[/C][C]2.25[/C][C]2.55447[/C][C]-0.30447[/C][/ROW]
[ROW][C]34[/C][C]2.25[/C][C]2.62546[/C][C]-0.375464[/C][/ROW]
[ROW][C]35[/C][C]3[/C][C]2.08066[/C][C]0.919339[/C][/ROW]
[ROW][C]36[/C][C]2.25[/C][C]2.16872[/C][C]0.0812767[/C][/ROW]
[ROW][C]37[/C][C]3[/C][C]2.69678[/C][C]0.303218[/C][/ROW]
[ROW][C]38[/C][C]3[/C][C]2.60555[/C][C]0.394447[/C][/ROW]
[ROW][C]39[/C][C]1.5[/C][C]2.50624[/C][C]-1.00624[/C][/ROW]
[ROW][C]40[/C][C]2.25[/C][C]2.44532[/C][C]-0.195315[/C][/ROW]
[ROW][C]41[/C][C]3[/C][C]2.51868[/C][C]0.481325[/C][/ROW]
[ROW][C]42[/C][C]2.25[/C][C]2.80162[/C][C]-0.551621[/C][/ROW]
[ROW][C]43[/C][C]1.5[/C][C]1.80902[/C][C]-0.309025[/C][/ROW]
[ROW][C]44[/C][C]2.25[/C][C]2.26223[/C][C]-0.0122296[/C][/ROW]
[ROW][C]45[/C][C]2.25[/C][C]2.91784[/C][C]-0.667838[/C][/ROW]
[ROW][C]46[/C][C]1.5[/C][C]2.2711[/C][C]-0.771103[/C][/ROW]
[ROW][C]47[/C][C]2.25[/C][C]2.81812[/C][C]-0.568117[/C][/ROW]
[ROW][C]48[/C][C]1.5[/C][C]1.81767[/C][C]-0.317672[/C][/ROW]
[ROW][C]49[/C][C]2.25[/C][C]2.34796[/C][C]-0.0979564[/C][/ROW]
[ROW][C]50[/C][C]3[/C][C]2.53563[/C][C]0.464367[/C][/ROW]
[ROW][C]51[/C][C]3[/C][C]2.54276[/C][C]0.457237[/C][/ROW]
[ROW][C]52[/C][C]3[/C][C]1.41568[/C][C]1.58432[/C][/ROW]
[ROW][C]53[/C][C]3[/C][C]2.65475[/C][C]0.345251[/C][/ROW]
[ROW][C]54[/C][C]1.5[/C][C]1.34922[/C][C]0.150776[/C][/ROW]
[ROW][C]55[/C][C]2.25[/C][C]2.6205[/C][C]-0.370503[/C][/ROW]
[ROW][C]56[/C][C]1.5[/C][C]1.5492[/C][C]-0.0492015[/C][/ROW]
[ROW][C]57[/C][C]2.25[/C][C]2.31742[/C][C]-0.0674203[/C][/ROW]
[ROW][C]58[/C][C]2.25[/C][C]2.2522[/C][C]-0.0021975[/C][/ROW]
[ROW][C]59[/C][C]2.25[/C][C]2.33493[/C][C]-0.0849305[/C][/ROW]
[ROW][C]60[/C][C]3[/C][C]2.21196[/C][C]0.788037[/C][/ROW]
[ROW][C]61[/C][C]1.5[/C][C]2.25916[/C][C]-0.759157[/C][/ROW]
[ROW][C]62[/C][C]2.25[/C][C]2.57828[/C][C]-0.328277[/C][/ROW]
[ROW][C]63[/C][C]2.25[/C][C]2.57828[/C][C]-0.328277[/C][/ROW]
[ROW][C]64[/C][C]3[/C][C]2.2679[/C][C]0.732102[/C][/ROW]
[ROW][C]65[/C][C]2.25[/C][C]2.30836[/C][C]-0.058361[/C][/ROW]
[ROW][C]66[/C][C]3[/C][C]2.67467[/C][C]0.325332[/C][/ROW]
[ROW][C]67[/C][C]2.25[/C][C]2.30115[/C][C]-0.0511497[/C][/ROW]
[ROW][C]68[/C][C]1.5[/C][C]1.64963[/C][C]-0.149632[/C][/ROW]
[ROW][C]69[/C][C]3[/C][C]2.3953[/C][C]0.604701[/C][/ROW]
[ROW][C]70[/C][C]1.5[/C][C]2.14121[/C][C]-0.641212[/C][/ROW]
[ROW][C]71[/C][C]3[/C][C]2.84382[/C][C]0.156182[/C][/ROW]
[ROW][C]72[/C][C]3[/C][C]2.28756[/C][C]0.71244[/C][/ROW]
[ROW][C]73[/C][C]3[/C][C]2.06433[/C][C]0.935673[/C][/ROW]
[ROW][C]74[/C][C]3[/C][C]2.28746[/C][C]0.712537[/C][/ROW]
[ROW][C]75[/C][C]2.25[/C][C]1.87234[/C][C]0.377661[/C][/ROW]
[ROW][C]76[/C][C]2.25[/C][C]1.99913[/C][C]0.250875[/C][/ROW]
[ROW][C]77[/C][C]0.75[/C][C]1.44865[/C][C]-0.698648[/C][/ROW]
[ROW][C]78[/C][C]3[/C][C]1.88962[/C][C]1.11038[/C][/ROW]
[ROW][C]79[/C][C]0.75[/C][C]1.67342[/C][C]-0.923417[/C][/ROW]
[ROW][C]80[/C][C]1.5[/C][C]2.16206[/C][C]-0.662056[/C][/ROW]
[ROW][C]81[/C][C]1.5[/C][C]1.64692[/C][C]-0.146919[/C][/ROW]
[ROW][C]82[/C][C]3[/C][C]2.11[/C][C]0.889997[/C][/ROW]
[ROW][C]83[/C][C]1.5[/C][C]1.84656[/C][C]-0.346556[/C][/ROW]
[ROW][C]84[/C][C]2.25[/C][C]2.15571[/C][C]0.0942857[/C][/ROW]
[ROW][C]85[/C][C]3[/C][C]2.78525[/C][C]0.214748[/C][/ROW]
[ROW][C]86[/C][C]3[/C][C]2.1063[/C][C]0.893699[/C][/ROW]
[ROW][C]87[/C][C]1.5[/C][C]1.61318[/C][C]-0.113176[/C][/ROW]
[ROW][C]88[/C][C]3[/C][C]2.08594[/C][C]0.91406[/C][/ROW]
[ROW][C]89[/C][C]3[/C][C]3.00193[/C][C]-0.00193388[/C][/ROW]
[ROW][C]90[/C][C]1.5[/C][C]1.75824[/C][C]-0.258243[/C][/ROW]
[ROW][C]91[/C][C]1.5[/C][C]2.20911[/C][C]-0.709105[/C][/ROW]
[ROW][C]92[/C][C]2.25[/C][C]2.35854[/C][C]-0.10854[/C][/ROW]
[ROW][C]93[/C][C]1.5[/C][C]1.82748[/C][C]-0.327475[/C][/ROW]
[ROW][C]94[/C][C]1.5[/C][C]1.58125[/C][C]-0.0812538[/C][/ROW]
[ROW][C]95[/C][C]2.25[/C][C]1.90928[/C][C]0.34072[/C][/ROW]
[ROW][C]96[/C][C]1.5[/C][C]1.59148[/C][C]-0.09148[/C][/ROW]
[ROW][C]97[/C][C]3[/C][C]2.84208[/C][C]0.157922[/C][/ROW]
[ROW][C]98[/C][C]3[/C][C]2.26994[/C][C]0.730059[/C][/ROW]
[ROW][C]99[/C][C]0.75[/C][C]1.53367[/C][C]-0.783674[/C][/ROW]
[ROW][C]100[/C][C]1.5[/C][C]2.11345[/C][C]-0.613445[/C][/ROW]
[ROW][C]101[/C][C]1.5[/C][C]2.45475[/C][C]-0.954745[/C][/ROW]
[ROW][C]102[/C][C]2.25[/C][C]2.1735[/C][C]0.076499[/C][/ROW]
[ROW][C]103[/C][C]2.25[/C][C]2.90759[/C][C]-0.657588[/C][/ROW]
[ROW][C]104[/C][C]1.5[/C][C]2.0604[/C][C]-0.560399[/C][/ROW]
[ROW][C]105[/C][C]2.25[/C][C]2.05921[/C][C]0.190787[/C][/ROW]
[ROW][C]106[/C][C]2.25[/C][C]2.49691[/C][C]-0.246906[/C][/ROW]
[ROW][C]107[/C][C]0.75[/C][C]1.7489[/C][C]-0.998904[/C][/ROW]
[ROW][C]108[/C][C]2.25[/C][C]2.39394[/C][C]-0.143941[/C][/ROW]
[ROW][C]109[/C][C]3[/C][C]2.47767[/C][C]0.522335[/C][/ROW]
[ROW][C]110[/C][C]0.75[/C][C]1.79349[/C][C]-1.04349[/C][/ROW]
[ROW][C]111[/C][C]0.75[/C][C]1.73715[/C][C]-0.987151[/C][/ROW]
[ROW][C]112[/C][C]3[/C][C]1.82479[/C][C]1.17521[/C][/ROW]
[ROW][C]113[/C][C]3[/C][C]2.72819[/C][C]0.27181[/C][/ROW]
[ROW][C]114[/C][C]3[/C][C]1.66769[/C][C]1.33231[/C][/ROW]
[ROW][C]115[/C][C]3[/C][C]2.10609[/C][C]0.893912[/C][/ROW]
[ROW][C]116[/C][C]1.5[/C][C]1.8158[/C][C]-0.315799[/C][/ROW]
[ROW][C]117[/C][C]3[/C][C]2.67238[/C][C]0.327619[/C][/ROW]
[ROW][C]118[/C][C]3[/C][C]2.48504[/C][C]0.514956[/C][/ROW]
[ROW][C]119[/C][C]3[/C][C]2.15126[/C][C]0.848737[/C][/ROW]
[ROW][C]120[/C][C]3[/C][C]2.91012[/C][C]0.0898834[/C][/ROW]
[ROW][C]121[/C][C]1.5[/C][C]1.79522[/C][C]-0.295221[/C][/ROW]
[ROW][C]122[/C][C]2.25[/C][C]2.18684[/C][C]0.0631587[/C][/ROW]
[ROW][C]123[/C][C]0.75[/C][C]1.77698[/C][C]-1.02698[/C][/ROW]
[ROW][C]124[/C][C]0.75[/C][C]1.55862[/C][C]-0.808619[/C][/ROW]
[ROW][C]125[/C][C]2.25[/C][C]1.44774[/C][C]0.802256[/C][/ROW]
[ROW][C]126[/C][C]3[/C][C]2.51352[/C][C]0.486484[/C][/ROW]
[ROW][C]127[/C][C]2.25[/C][C]2.03429[/C][C]0.21571[/C][/ROW]
[ROW][C]128[/C][C]3[/C][C]1.74316[/C][C]1.25684[/C][/ROW]
[ROW][C]129[/C][C]2.25[/C][C]2.3916[/C][C]-0.141603[/C][/ROW]
[ROW][C]130[/C][C]3[/C][C]2.04758[/C][C]0.952421[/C][/ROW]
[ROW][C]131[/C][C]1.5[/C][C]1.83112[/C][C]-0.331124[/C][/ROW]
[ROW][C]132[/C][C]3[/C][C]2.65475[/C][C]0.345251[/C][/ROW]
[ROW][C]133[/C][C]0.75[/C][C]1.65938[/C][C]-0.90938[/C][/ROW]
[ROW][C]134[/C][C]1.5[/C][C]2.45236[/C][C]-0.952356[/C][/ROW]
[ROW][C]135[/C][C]3[/C][C]2.88761[/C][C]0.112386[/C][/ROW]
[ROW][C]136[/C][C]3[/C][C]1.87653[/C][C]1.12347[/C][/ROW]
[ROW][C]137[/C][C]3[/C][C]2.58676[/C][C]0.413244[/C][/ROW]
[ROW][C]138[/C][C]2.25[/C][C]2.10783[/C][C]0.142165[/C][/ROW]
[ROW][C]139[/C][C]2.25[/C][C]1.96895[/C][C]0.281051[/C][/ROW]
[ROW][C]140[/C][C]3[/C][C]2.2602[/C][C]0.739795[/C][/ROW]
[ROW][C]141[/C][C]1.5[/C][C]2.30962[/C][C]-0.809616[/C][/ROW]
[ROW][C]142[/C][C]2.25[/C][C]2.18777[/C][C]0.0622337[/C][/ROW]
[ROW][C]143[/C][C]2.25[/C][C]2.34223[/C][C]-0.0922264[/C][/ROW]
[ROW][C]144[/C][C]2.25[/C][C]2.44037[/C][C]-0.190374[/C][/ROW]
[ROW][C]145[/C][C]0.75[/C][C]1.5166[/C][C]-0.7666[/C][/ROW]
[ROW][C]146[/C][C]2.25[/C][C]2.07763[/C][C]0.172374[/C][/ROW]
[ROW][C]147[/C][C]1.5[/C][C]2.02076[/C][C]-0.520764[/C][/ROW]
[ROW][C]148[/C][C]2.25[/C][C]1.89037[/C][C]0.359631[/C][/ROW]
[ROW][C]149[/C][C]1.5[/C][C]1.34922[/C][C]0.150776[/C][/ROW]
[ROW][C]150[/C][C]0.75[/C][C]1.89188[/C][C]-1.14188[/C][/ROW]
[ROW][C]151[/C][C]1.5[/C][C]1.87492[/C][C]-0.374918[/C][/ROW]
[ROW][C]152[/C][C]1.5[/C][C]1.83112[/C][C]-0.331124[/C][/ROW]
[ROW][C]153[/C][C]2.25[/C][C]2.20245[/C][C]0.047548[/C][/ROW]
[ROW][C]154[/C][C]1.5[/C][C]1.95184[/C][C]-0.451839[/C][/ROW]
[ROW][C]155[/C][C]1.5[/C][C]1.88446[/C][C]-0.384455[/C][/ROW]
[ROW][C]156[/C][C]3[/C][C]1.83188[/C][C]1.16812[/C][/ROW]
[ROW][C]157[/C][C]2.25[/C][C]2.03598[/C][C]0.214023[/C][/ROW]
[ROW][C]158[/C][C]1.5[/C][C]1.58172[/C][C]-0.0817211[/C][/ROW]
[ROW][C]159[/C][C]0.75[/C][C]1.81126[/C][C]-1.06126[/C][/ROW]
[ROW][C]160[/C][C]2.25[/C][C]1.62649[/C][C]0.623508[/C][/ROW]
[ROW][C]161[/C][C]3[/C][C]2.26218[/C][C]0.73782[/C][/ROW]
[ROW][C]162[/C][C]3[/C][C]2.65063[/C][C]0.349375[/C][/ROW]
[ROW][C]163[/C][C]1.5[/C][C]2.13635[/C][C]-0.636346[/C][/ROW]
[ROW][C]164[/C][C]1.5[/C][C]2.06624[/C][C]-0.566244[/C][/ROW]
[ROW][C]165[/C][C]2.25[/C][C]2.41599[/C][C]-0.165994[/C][/ROW]
[ROW][C]166[/C][C]0.75[/C][C]1.43114[/C][C]-0.681137[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=271308&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=271308&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
10.751.39917-0.649173
21.51.90249-0.402487
332.39360.6064
42.252.44246-0.192462
532.900580.0994248
61.51.468320.031682
733.53629-0.536291
832.706680.293323
933.54778-0.547776
100.751.48775-0.737749
1132.826640.173364
122.251.49460.7554
131.52.21343-0.713434
141.52.09575-0.59575
152.252.399-0.148998
1632.594970.405026
1732.529060.470935
181.51.84588-0.345885
192.251.839880.410122
202.253.02818-0.778184
211.52.14538-0.645383
222.252.26238-0.0123846
231.51.82078-0.320784
242.252.196370.0536253
252.252.15940.0906042
2631.65131.3487
2732.943070.0569324
2832.876690.123312
291.51.70028-0.200275
3032.006350.99365
3132.434280.565718
322.252.52388-0.273884
332.252.55447-0.30447
342.252.62546-0.375464
3532.080660.919339
362.252.168720.0812767
3732.696780.303218
3832.605550.394447
391.52.50624-1.00624
402.252.44532-0.195315
4132.518680.481325
422.252.80162-0.551621
431.51.80902-0.309025
442.252.26223-0.0122296
452.252.91784-0.667838
461.52.2711-0.771103
472.252.81812-0.568117
481.51.81767-0.317672
492.252.34796-0.0979564
5032.535630.464367
5132.542760.457237
5231.415681.58432
5332.654750.345251
541.51.349220.150776
552.252.6205-0.370503
561.51.5492-0.0492015
572.252.31742-0.0674203
582.252.2522-0.0021975
592.252.33493-0.0849305
6032.211960.788037
611.52.25916-0.759157
622.252.57828-0.328277
632.252.57828-0.328277
6432.26790.732102
652.252.30836-0.058361
6632.674670.325332
672.252.30115-0.0511497
681.51.64963-0.149632
6932.39530.604701
701.52.14121-0.641212
7132.843820.156182
7232.287560.71244
7332.064330.935673
7432.287460.712537
752.251.872340.377661
762.251.999130.250875
770.751.44865-0.698648
7831.889621.11038
790.751.67342-0.923417
801.52.16206-0.662056
811.51.64692-0.146919
8232.110.889997
831.51.84656-0.346556
842.252.155710.0942857
8532.785250.214748
8632.10630.893699
871.51.61318-0.113176
8832.085940.91406
8933.00193-0.00193388
901.51.75824-0.258243
911.52.20911-0.709105
922.252.35854-0.10854
931.51.82748-0.327475
941.51.58125-0.0812538
952.251.909280.34072
961.51.59148-0.09148
9732.842080.157922
9832.269940.730059
990.751.53367-0.783674
1001.52.11345-0.613445
1011.52.45475-0.954745
1022.252.17350.076499
1032.252.90759-0.657588
1041.52.0604-0.560399
1052.252.059210.190787
1062.252.49691-0.246906
1070.751.7489-0.998904
1082.252.39394-0.143941
10932.477670.522335
1100.751.79349-1.04349
1110.751.73715-0.987151
11231.824791.17521
11332.728190.27181
11431.667691.33231
11532.106090.893912
1161.51.8158-0.315799
11732.672380.327619
11832.485040.514956
11932.151260.848737
12032.910120.0898834
1211.51.79522-0.295221
1222.252.186840.0631587
1230.751.77698-1.02698
1240.751.55862-0.808619
1252.251.447740.802256
12632.513520.486484
1272.252.034290.21571
12831.743161.25684
1292.252.3916-0.141603
13032.047580.952421
1311.51.83112-0.331124
13232.654750.345251
1330.751.65938-0.90938
1341.52.45236-0.952356
13532.887610.112386
13631.876531.12347
13732.586760.413244
1382.252.107830.142165
1392.251.968950.281051
14032.26020.739795
1411.52.30962-0.809616
1422.252.187770.0622337
1432.252.34223-0.0922264
1442.252.44037-0.190374
1450.751.5166-0.7666
1462.252.077630.172374
1471.52.02076-0.520764
1482.251.890370.359631
1491.51.349220.150776
1500.751.89188-1.14188
1511.51.87492-0.374918
1521.51.83112-0.331124
1532.252.202450.047548
1541.51.95184-0.451839
1551.51.88446-0.384455
15631.831881.16812
1572.252.035980.214023
1581.51.58172-0.0817211
1590.751.81126-1.06126
1602.251.626490.623508
16132.262180.73782
16232.650630.349375
1631.52.13635-0.636346
1641.52.06624-0.566244
1652.252.41599-0.165994
1660.751.43114-0.681137






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
90.3877650.775530.612235
100.4375720.8751440.562428
110.3414210.6828410.658579
120.6013770.7972460.398623
130.5564680.8870640.443532
140.5574430.8851140.442557
150.4588910.9177830.541109
160.4484770.8969530.551523
170.3608350.7216710.639165
180.3068740.6137480.693126
190.2768410.5536820.723159
200.3480260.6960530.651974
210.3092850.6185690.690715
220.2432110.4864220.756789
230.1935220.3870450.806478
240.1504340.3008680.849566
250.1185310.2370620.881469
260.3297230.6594450.670277
270.2810450.5620910.718955
280.227650.4552990.77235
290.1817760.3635520.818224
300.3770.7540.623
310.3566770.7133530.643323
320.305310.6106210.69469
330.2571640.5143280.742836
340.2220820.4441650.777918
350.2637710.5275410.736229
360.2174070.4348150.782593
370.1863120.3726240.813688
380.1616430.3232870.838357
390.2438840.4877680.756116
400.2050660.4101330.794934
410.1914250.3828490.808575
420.1750320.3500640.824968
430.1530190.3060390.846981
440.1248480.2496960.875152
450.1243230.2486460.875677
460.1461020.2922030.853898
470.1315290.2630580.868471
480.1338370.2676730.866163
490.1079390.2158780.892061
500.1133930.2267860.886607
510.1016390.2032790.898361
520.2866720.5733430.713328
530.2615860.5231720.738414
540.2363630.4727260.763637
550.2124590.4249190.787541
560.1820070.3640140.817993
570.1519940.3039870.848006
580.1248160.2496320.875184
590.1020070.2040140.897993
600.1179470.2358940.882053
610.1368170.2736330.863183
620.1168050.2336110.883195
630.1004660.2009320.899534
640.1125960.2251910.887404
650.09171920.1834380.908281
660.08110320.1622060.918897
670.06482460.1296490.935175
680.05422590.1084520.945774
690.05362950.1072590.94637
700.06080870.1216170.939191
710.05005620.1001120.949944
720.05504290.1100860.944957
730.0782130.1564260.921787
740.08346610.1669320.916534
750.07184060.1436810.928159
760.05908120.1181620.940919
770.06837030.1367410.93163
780.1076530.2153070.892347
790.1511140.3022270.848886
800.1619670.3239330.838033
810.1391410.2782810.860859
820.1692230.3384470.830777
830.1523840.3047670.847616
840.1270860.2541730.872914
850.1074760.2149530.892524
860.1317720.2635450.868228
870.1114770.2229550.888523
880.1416120.2832230.858388
890.1231970.2463930.876803
900.1063730.2127460.893627
910.1148180.2296360.885182
920.09526560.1905310.904734
930.08254230.1650850.917458
940.06711340.1342270.932887
950.05802360.1160470.941976
960.04698270.09396540.953017
970.03724550.07449110.962754
980.04251410.08502810.957486
990.0479210.09584210.952079
1000.0487610.0975220.951239
1010.0695490.1390980.930451
1020.05571830.1114370.944282
1030.0629050.125810.937095
1040.06174880.1234980.938251
1050.04987080.09974160.950129
1060.04372070.08744140.956279
1070.05957230.1191450.940428
1080.04799220.09598450.952008
1090.04165130.08330260.958349
1100.06121820.1224360.938782
1110.0886180.1772360.911382
1120.1465160.2930320.853484
1130.1228760.2457520.877124
1140.2553770.5107540.744623
1150.3027940.6055880.697206
1160.2763390.5526770.723661
1170.2456640.4913270.754336
1180.2294050.458810.770595
1190.2532970.5065940.746703
1200.215220.4304390.78478
1210.1837860.3675710.816214
1220.1520370.3040750.847963
1230.1842390.3684790.815761
1240.1966730.3933470.803327
1250.2497640.4995280.750236
1260.2194440.4388880.780556
1270.187580.3751590.81242
1280.354380.7087590.64562
1290.3054810.6109610.694519
1300.4202590.8405180.579741
1310.3765450.753090.623455
1320.3278470.6556940.672153
1330.3905430.7810860.609457
1340.4981250.9962510.501875
1350.436820.873640.56318
1360.5531820.8936360.446818
1370.5314240.9371530.468576
1380.4838820.9677630.516118
1390.4748780.9497560.525122
1400.5238780.9522440.476122
1410.552830.894340.44717
1420.4840090.9680170.515991
1430.4250940.8501890.574906
1440.4257470.8514940.574253
1450.389610.779220.61039
1460.332170.6643410.66783
1470.4089120.8178240.591088
1480.3405960.6811920.659404
1490.2675840.5351680.732416
1500.3752150.750430.624785
1510.5406010.9187970.459399
1520.6443530.7112930.355647
1530.545340.9093190.45466
1540.4293530.8587060.570647
1550.3293780.6587570.670622
1560.2491610.4983230.750839
1570.4074310.8148630.592569

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
9 & 0.387765 & 0.77553 & 0.612235 \tabularnewline
10 & 0.437572 & 0.875144 & 0.562428 \tabularnewline
11 & 0.341421 & 0.682841 & 0.658579 \tabularnewline
12 & 0.601377 & 0.797246 & 0.398623 \tabularnewline
13 & 0.556468 & 0.887064 & 0.443532 \tabularnewline
14 & 0.557443 & 0.885114 & 0.442557 \tabularnewline
15 & 0.458891 & 0.917783 & 0.541109 \tabularnewline
16 & 0.448477 & 0.896953 & 0.551523 \tabularnewline
17 & 0.360835 & 0.721671 & 0.639165 \tabularnewline
18 & 0.306874 & 0.613748 & 0.693126 \tabularnewline
19 & 0.276841 & 0.553682 & 0.723159 \tabularnewline
20 & 0.348026 & 0.696053 & 0.651974 \tabularnewline
21 & 0.309285 & 0.618569 & 0.690715 \tabularnewline
22 & 0.243211 & 0.486422 & 0.756789 \tabularnewline
23 & 0.193522 & 0.387045 & 0.806478 \tabularnewline
24 & 0.150434 & 0.300868 & 0.849566 \tabularnewline
25 & 0.118531 & 0.237062 & 0.881469 \tabularnewline
26 & 0.329723 & 0.659445 & 0.670277 \tabularnewline
27 & 0.281045 & 0.562091 & 0.718955 \tabularnewline
28 & 0.22765 & 0.455299 & 0.77235 \tabularnewline
29 & 0.181776 & 0.363552 & 0.818224 \tabularnewline
30 & 0.377 & 0.754 & 0.623 \tabularnewline
31 & 0.356677 & 0.713353 & 0.643323 \tabularnewline
32 & 0.30531 & 0.610621 & 0.69469 \tabularnewline
33 & 0.257164 & 0.514328 & 0.742836 \tabularnewline
34 & 0.222082 & 0.444165 & 0.777918 \tabularnewline
35 & 0.263771 & 0.527541 & 0.736229 \tabularnewline
36 & 0.217407 & 0.434815 & 0.782593 \tabularnewline
37 & 0.186312 & 0.372624 & 0.813688 \tabularnewline
38 & 0.161643 & 0.323287 & 0.838357 \tabularnewline
39 & 0.243884 & 0.487768 & 0.756116 \tabularnewline
40 & 0.205066 & 0.410133 & 0.794934 \tabularnewline
41 & 0.191425 & 0.382849 & 0.808575 \tabularnewline
42 & 0.175032 & 0.350064 & 0.824968 \tabularnewline
43 & 0.153019 & 0.306039 & 0.846981 \tabularnewline
44 & 0.124848 & 0.249696 & 0.875152 \tabularnewline
45 & 0.124323 & 0.248646 & 0.875677 \tabularnewline
46 & 0.146102 & 0.292203 & 0.853898 \tabularnewline
47 & 0.131529 & 0.263058 & 0.868471 \tabularnewline
48 & 0.133837 & 0.267673 & 0.866163 \tabularnewline
49 & 0.107939 & 0.215878 & 0.892061 \tabularnewline
50 & 0.113393 & 0.226786 & 0.886607 \tabularnewline
51 & 0.101639 & 0.203279 & 0.898361 \tabularnewline
52 & 0.286672 & 0.573343 & 0.713328 \tabularnewline
53 & 0.261586 & 0.523172 & 0.738414 \tabularnewline
54 & 0.236363 & 0.472726 & 0.763637 \tabularnewline
55 & 0.212459 & 0.424919 & 0.787541 \tabularnewline
56 & 0.182007 & 0.364014 & 0.817993 \tabularnewline
57 & 0.151994 & 0.303987 & 0.848006 \tabularnewline
58 & 0.124816 & 0.249632 & 0.875184 \tabularnewline
59 & 0.102007 & 0.204014 & 0.897993 \tabularnewline
60 & 0.117947 & 0.235894 & 0.882053 \tabularnewline
61 & 0.136817 & 0.273633 & 0.863183 \tabularnewline
62 & 0.116805 & 0.233611 & 0.883195 \tabularnewline
63 & 0.100466 & 0.200932 & 0.899534 \tabularnewline
64 & 0.112596 & 0.225191 & 0.887404 \tabularnewline
65 & 0.0917192 & 0.183438 & 0.908281 \tabularnewline
66 & 0.0811032 & 0.162206 & 0.918897 \tabularnewline
67 & 0.0648246 & 0.129649 & 0.935175 \tabularnewline
68 & 0.0542259 & 0.108452 & 0.945774 \tabularnewline
69 & 0.0536295 & 0.107259 & 0.94637 \tabularnewline
70 & 0.0608087 & 0.121617 & 0.939191 \tabularnewline
71 & 0.0500562 & 0.100112 & 0.949944 \tabularnewline
72 & 0.0550429 & 0.110086 & 0.944957 \tabularnewline
73 & 0.078213 & 0.156426 & 0.921787 \tabularnewline
74 & 0.0834661 & 0.166932 & 0.916534 \tabularnewline
75 & 0.0718406 & 0.143681 & 0.928159 \tabularnewline
76 & 0.0590812 & 0.118162 & 0.940919 \tabularnewline
77 & 0.0683703 & 0.136741 & 0.93163 \tabularnewline
78 & 0.107653 & 0.215307 & 0.892347 \tabularnewline
79 & 0.151114 & 0.302227 & 0.848886 \tabularnewline
80 & 0.161967 & 0.323933 & 0.838033 \tabularnewline
81 & 0.139141 & 0.278281 & 0.860859 \tabularnewline
82 & 0.169223 & 0.338447 & 0.830777 \tabularnewline
83 & 0.152384 & 0.304767 & 0.847616 \tabularnewline
84 & 0.127086 & 0.254173 & 0.872914 \tabularnewline
85 & 0.107476 & 0.214953 & 0.892524 \tabularnewline
86 & 0.131772 & 0.263545 & 0.868228 \tabularnewline
87 & 0.111477 & 0.222955 & 0.888523 \tabularnewline
88 & 0.141612 & 0.283223 & 0.858388 \tabularnewline
89 & 0.123197 & 0.246393 & 0.876803 \tabularnewline
90 & 0.106373 & 0.212746 & 0.893627 \tabularnewline
91 & 0.114818 & 0.229636 & 0.885182 \tabularnewline
92 & 0.0952656 & 0.190531 & 0.904734 \tabularnewline
93 & 0.0825423 & 0.165085 & 0.917458 \tabularnewline
94 & 0.0671134 & 0.134227 & 0.932887 \tabularnewline
95 & 0.0580236 & 0.116047 & 0.941976 \tabularnewline
96 & 0.0469827 & 0.0939654 & 0.953017 \tabularnewline
97 & 0.0372455 & 0.0744911 & 0.962754 \tabularnewline
98 & 0.0425141 & 0.0850281 & 0.957486 \tabularnewline
99 & 0.047921 & 0.0958421 & 0.952079 \tabularnewline
100 & 0.048761 & 0.097522 & 0.951239 \tabularnewline
101 & 0.069549 & 0.139098 & 0.930451 \tabularnewline
102 & 0.0557183 & 0.111437 & 0.944282 \tabularnewline
103 & 0.062905 & 0.12581 & 0.937095 \tabularnewline
104 & 0.0617488 & 0.123498 & 0.938251 \tabularnewline
105 & 0.0498708 & 0.0997416 & 0.950129 \tabularnewline
106 & 0.0437207 & 0.0874414 & 0.956279 \tabularnewline
107 & 0.0595723 & 0.119145 & 0.940428 \tabularnewline
108 & 0.0479922 & 0.0959845 & 0.952008 \tabularnewline
109 & 0.0416513 & 0.0833026 & 0.958349 \tabularnewline
110 & 0.0612182 & 0.122436 & 0.938782 \tabularnewline
111 & 0.088618 & 0.177236 & 0.911382 \tabularnewline
112 & 0.146516 & 0.293032 & 0.853484 \tabularnewline
113 & 0.122876 & 0.245752 & 0.877124 \tabularnewline
114 & 0.255377 & 0.510754 & 0.744623 \tabularnewline
115 & 0.302794 & 0.605588 & 0.697206 \tabularnewline
116 & 0.276339 & 0.552677 & 0.723661 \tabularnewline
117 & 0.245664 & 0.491327 & 0.754336 \tabularnewline
118 & 0.229405 & 0.45881 & 0.770595 \tabularnewline
119 & 0.253297 & 0.506594 & 0.746703 \tabularnewline
120 & 0.21522 & 0.430439 & 0.78478 \tabularnewline
121 & 0.183786 & 0.367571 & 0.816214 \tabularnewline
122 & 0.152037 & 0.304075 & 0.847963 \tabularnewline
123 & 0.184239 & 0.368479 & 0.815761 \tabularnewline
124 & 0.196673 & 0.393347 & 0.803327 \tabularnewline
125 & 0.249764 & 0.499528 & 0.750236 \tabularnewline
126 & 0.219444 & 0.438888 & 0.780556 \tabularnewline
127 & 0.18758 & 0.375159 & 0.81242 \tabularnewline
128 & 0.35438 & 0.708759 & 0.64562 \tabularnewline
129 & 0.305481 & 0.610961 & 0.694519 \tabularnewline
130 & 0.420259 & 0.840518 & 0.579741 \tabularnewline
131 & 0.376545 & 0.75309 & 0.623455 \tabularnewline
132 & 0.327847 & 0.655694 & 0.672153 \tabularnewline
133 & 0.390543 & 0.781086 & 0.609457 \tabularnewline
134 & 0.498125 & 0.996251 & 0.501875 \tabularnewline
135 & 0.43682 & 0.87364 & 0.56318 \tabularnewline
136 & 0.553182 & 0.893636 & 0.446818 \tabularnewline
137 & 0.531424 & 0.937153 & 0.468576 \tabularnewline
138 & 0.483882 & 0.967763 & 0.516118 \tabularnewline
139 & 0.474878 & 0.949756 & 0.525122 \tabularnewline
140 & 0.523878 & 0.952244 & 0.476122 \tabularnewline
141 & 0.55283 & 0.89434 & 0.44717 \tabularnewline
142 & 0.484009 & 0.968017 & 0.515991 \tabularnewline
143 & 0.425094 & 0.850189 & 0.574906 \tabularnewline
144 & 0.425747 & 0.851494 & 0.574253 \tabularnewline
145 & 0.38961 & 0.77922 & 0.61039 \tabularnewline
146 & 0.33217 & 0.664341 & 0.66783 \tabularnewline
147 & 0.408912 & 0.817824 & 0.591088 \tabularnewline
148 & 0.340596 & 0.681192 & 0.659404 \tabularnewline
149 & 0.267584 & 0.535168 & 0.732416 \tabularnewline
150 & 0.375215 & 0.75043 & 0.624785 \tabularnewline
151 & 0.540601 & 0.918797 & 0.459399 \tabularnewline
152 & 0.644353 & 0.711293 & 0.355647 \tabularnewline
153 & 0.54534 & 0.909319 & 0.45466 \tabularnewline
154 & 0.429353 & 0.858706 & 0.570647 \tabularnewline
155 & 0.329378 & 0.658757 & 0.670622 \tabularnewline
156 & 0.249161 & 0.498323 & 0.750839 \tabularnewline
157 & 0.407431 & 0.814863 & 0.592569 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=271308&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]9[/C][C]0.387765[/C][C]0.77553[/C][C]0.612235[/C][/ROW]
[ROW][C]10[/C][C]0.437572[/C][C]0.875144[/C][C]0.562428[/C][/ROW]
[ROW][C]11[/C][C]0.341421[/C][C]0.682841[/C][C]0.658579[/C][/ROW]
[ROW][C]12[/C][C]0.601377[/C][C]0.797246[/C][C]0.398623[/C][/ROW]
[ROW][C]13[/C][C]0.556468[/C][C]0.887064[/C][C]0.443532[/C][/ROW]
[ROW][C]14[/C][C]0.557443[/C][C]0.885114[/C][C]0.442557[/C][/ROW]
[ROW][C]15[/C][C]0.458891[/C][C]0.917783[/C][C]0.541109[/C][/ROW]
[ROW][C]16[/C][C]0.448477[/C][C]0.896953[/C][C]0.551523[/C][/ROW]
[ROW][C]17[/C][C]0.360835[/C][C]0.721671[/C][C]0.639165[/C][/ROW]
[ROW][C]18[/C][C]0.306874[/C][C]0.613748[/C][C]0.693126[/C][/ROW]
[ROW][C]19[/C][C]0.276841[/C][C]0.553682[/C][C]0.723159[/C][/ROW]
[ROW][C]20[/C][C]0.348026[/C][C]0.696053[/C][C]0.651974[/C][/ROW]
[ROW][C]21[/C][C]0.309285[/C][C]0.618569[/C][C]0.690715[/C][/ROW]
[ROW][C]22[/C][C]0.243211[/C][C]0.486422[/C][C]0.756789[/C][/ROW]
[ROW][C]23[/C][C]0.193522[/C][C]0.387045[/C][C]0.806478[/C][/ROW]
[ROW][C]24[/C][C]0.150434[/C][C]0.300868[/C][C]0.849566[/C][/ROW]
[ROW][C]25[/C][C]0.118531[/C][C]0.237062[/C][C]0.881469[/C][/ROW]
[ROW][C]26[/C][C]0.329723[/C][C]0.659445[/C][C]0.670277[/C][/ROW]
[ROW][C]27[/C][C]0.281045[/C][C]0.562091[/C][C]0.718955[/C][/ROW]
[ROW][C]28[/C][C]0.22765[/C][C]0.455299[/C][C]0.77235[/C][/ROW]
[ROW][C]29[/C][C]0.181776[/C][C]0.363552[/C][C]0.818224[/C][/ROW]
[ROW][C]30[/C][C]0.377[/C][C]0.754[/C][C]0.623[/C][/ROW]
[ROW][C]31[/C][C]0.356677[/C][C]0.713353[/C][C]0.643323[/C][/ROW]
[ROW][C]32[/C][C]0.30531[/C][C]0.610621[/C][C]0.69469[/C][/ROW]
[ROW][C]33[/C][C]0.257164[/C][C]0.514328[/C][C]0.742836[/C][/ROW]
[ROW][C]34[/C][C]0.222082[/C][C]0.444165[/C][C]0.777918[/C][/ROW]
[ROW][C]35[/C][C]0.263771[/C][C]0.527541[/C][C]0.736229[/C][/ROW]
[ROW][C]36[/C][C]0.217407[/C][C]0.434815[/C][C]0.782593[/C][/ROW]
[ROW][C]37[/C][C]0.186312[/C][C]0.372624[/C][C]0.813688[/C][/ROW]
[ROW][C]38[/C][C]0.161643[/C][C]0.323287[/C][C]0.838357[/C][/ROW]
[ROW][C]39[/C][C]0.243884[/C][C]0.487768[/C][C]0.756116[/C][/ROW]
[ROW][C]40[/C][C]0.205066[/C][C]0.410133[/C][C]0.794934[/C][/ROW]
[ROW][C]41[/C][C]0.191425[/C][C]0.382849[/C][C]0.808575[/C][/ROW]
[ROW][C]42[/C][C]0.175032[/C][C]0.350064[/C][C]0.824968[/C][/ROW]
[ROW][C]43[/C][C]0.153019[/C][C]0.306039[/C][C]0.846981[/C][/ROW]
[ROW][C]44[/C][C]0.124848[/C][C]0.249696[/C][C]0.875152[/C][/ROW]
[ROW][C]45[/C][C]0.124323[/C][C]0.248646[/C][C]0.875677[/C][/ROW]
[ROW][C]46[/C][C]0.146102[/C][C]0.292203[/C][C]0.853898[/C][/ROW]
[ROW][C]47[/C][C]0.131529[/C][C]0.263058[/C][C]0.868471[/C][/ROW]
[ROW][C]48[/C][C]0.133837[/C][C]0.267673[/C][C]0.866163[/C][/ROW]
[ROW][C]49[/C][C]0.107939[/C][C]0.215878[/C][C]0.892061[/C][/ROW]
[ROW][C]50[/C][C]0.113393[/C][C]0.226786[/C][C]0.886607[/C][/ROW]
[ROW][C]51[/C][C]0.101639[/C][C]0.203279[/C][C]0.898361[/C][/ROW]
[ROW][C]52[/C][C]0.286672[/C][C]0.573343[/C][C]0.713328[/C][/ROW]
[ROW][C]53[/C][C]0.261586[/C][C]0.523172[/C][C]0.738414[/C][/ROW]
[ROW][C]54[/C][C]0.236363[/C][C]0.472726[/C][C]0.763637[/C][/ROW]
[ROW][C]55[/C][C]0.212459[/C][C]0.424919[/C][C]0.787541[/C][/ROW]
[ROW][C]56[/C][C]0.182007[/C][C]0.364014[/C][C]0.817993[/C][/ROW]
[ROW][C]57[/C][C]0.151994[/C][C]0.303987[/C][C]0.848006[/C][/ROW]
[ROW][C]58[/C][C]0.124816[/C][C]0.249632[/C][C]0.875184[/C][/ROW]
[ROW][C]59[/C][C]0.102007[/C][C]0.204014[/C][C]0.897993[/C][/ROW]
[ROW][C]60[/C][C]0.117947[/C][C]0.235894[/C][C]0.882053[/C][/ROW]
[ROW][C]61[/C][C]0.136817[/C][C]0.273633[/C][C]0.863183[/C][/ROW]
[ROW][C]62[/C][C]0.116805[/C][C]0.233611[/C][C]0.883195[/C][/ROW]
[ROW][C]63[/C][C]0.100466[/C][C]0.200932[/C][C]0.899534[/C][/ROW]
[ROW][C]64[/C][C]0.112596[/C][C]0.225191[/C][C]0.887404[/C][/ROW]
[ROW][C]65[/C][C]0.0917192[/C][C]0.183438[/C][C]0.908281[/C][/ROW]
[ROW][C]66[/C][C]0.0811032[/C][C]0.162206[/C][C]0.918897[/C][/ROW]
[ROW][C]67[/C][C]0.0648246[/C][C]0.129649[/C][C]0.935175[/C][/ROW]
[ROW][C]68[/C][C]0.0542259[/C][C]0.108452[/C][C]0.945774[/C][/ROW]
[ROW][C]69[/C][C]0.0536295[/C][C]0.107259[/C][C]0.94637[/C][/ROW]
[ROW][C]70[/C][C]0.0608087[/C][C]0.121617[/C][C]0.939191[/C][/ROW]
[ROW][C]71[/C][C]0.0500562[/C][C]0.100112[/C][C]0.949944[/C][/ROW]
[ROW][C]72[/C][C]0.0550429[/C][C]0.110086[/C][C]0.944957[/C][/ROW]
[ROW][C]73[/C][C]0.078213[/C][C]0.156426[/C][C]0.921787[/C][/ROW]
[ROW][C]74[/C][C]0.0834661[/C][C]0.166932[/C][C]0.916534[/C][/ROW]
[ROW][C]75[/C][C]0.0718406[/C][C]0.143681[/C][C]0.928159[/C][/ROW]
[ROW][C]76[/C][C]0.0590812[/C][C]0.118162[/C][C]0.940919[/C][/ROW]
[ROW][C]77[/C][C]0.0683703[/C][C]0.136741[/C][C]0.93163[/C][/ROW]
[ROW][C]78[/C][C]0.107653[/C][C]0.215307[/C][C]0.892347[/C][/ROW]
[ROW][C]79[/C][C]0.151114[/C][C]0.302227[/C][C]0.848886[/C][/ROW]
[ROW][C]80[/C][C]0.161967[/C][C]0.323933[/C][C]0.838033[/C][/ROW]
[ROW][C]81[/C][C]0.139141[/C][C]0.278281[/C][C]0.860859[/C][/ROW]
[ROW][C]82[/C][C]0.169223[/C][C]0.338447[/C][C]0.830777[/C][/ROW]
[ROW][C]83[/C][C]0.152384[/C][C]0.304767[/C][C]0.847616[/C][/ROW]
[ROW][C]84[/C][C]0.127086[/C][C]0.254173[/C][C]0.872914[/C][/ROW]
[ROW][C]85[/C][C]0.107476[/C][C]0.214953[/C][C]0.892524[/C][/ROW]
[ROW][C]86[/C][C]0.131772[/C][C]0.263545[/C][C]0.868228[/C][/ROW]
[ROW][C]87[/C][C]0.111477[/C][C]0.222955[/C][C]0.888523[/C][/ROW]
[ROW][C]88[/C][C]0.141612[/C][C]0.283223[/C][C]0.858388[/C][/ROW]
[ROW][C]89[/C][C]0.123197[/C][C]0.246393[/C][C]0.876803[/C][/ROW]
[ROW][C]90[/C][C]0.106373[/C][C]0.212746[/C][C]0.893627[/C][/ROW]
[ROW][C]91[/C][C]0.114818[/C][C]0.229636[/C][C]0.885182[/C][/ROW]
[ROW][C]92[/C][C]0.0952656[/C][C]0.190531[/C][C]0.904734[/C][/ROW]
[ROW][C]93[/C][C]0.0825423[/C][C]0.165085[/C][C]0.917458[/C][/ROW]
[ROW][C]94[/C][C]0.0671134[/C][C]0.134227[/C][C]0.932887[/C][/ROW]
[ROW][C]95[/C][C]0.0580236[/C][C]0.116047[/C][C]0.941976[/C][/ROW]
[ROW][C]96[/C][C]0.0469827[/C][C]0.0939654[/C][C]0.953017[/C][/ROW]
[ROW][C]97[/C][C]0.0372455[/C][C]0.0744911[/C][C]0.962754[/C][/ROW]
[ROW][C]98[/C][C]0.0425141[/C][C]0.0850281[/C][C]0.957486[/C][/ROW]
[ROW][C]99[/C][C]0.047921[/C][C]0.0958421[/C][C]0.952079[/C][/ROW]
[ROW][C]100[/C][C]0.048761[/C][C]0.097522[/C][C]0.951239[/C][/ROW]
[ROW][C]101[/C][C]0.069549[/C][C]0.139098[/C][C]0.930451[/C][/ROW]
[ROW][C]102[/C][C]0.0557183[/C][C]0.111437[/C][C]0.944282[/C][/ROW]
[ROW][C]103[/C][C]0.062905[/C][C]0.12581[/C][C]0.937095[/C][/ROW]
[ROW][C]104[/C][C]0.0617488[/C][C]0.123498[/C][C]0.938251[/C][/ROW]
[ROW][C]105[/C][C]0.0498708[/C][C]0.0997416[/C][C]0.950129[/C][/ROW]
[ROW][C]106[/C][C]0.0437207[/C][C]0.0874414[/C][C]0.956279[/C][/ROW]
[ROW][C]107[/C][C]0.0595723[/C][C]0.119145[/C][C]0.940428[/C][/ROW]
[ROW][C]108[/C][C]0.0479922[/C][C]0.0959845[/C][C]0.952008[/C][/ROW]
[ROW][C]109[/C][C]0.0416513[/C][C]0.0833026[/C][C]0.958349[/C][/ROW]
[ROW][C]110[/C][C]0.0612182[/C][C]0.122436[/C][C]0.938782[/C][/ROW]
[ROW][C]111[/C][C]0.088618[/C][C]0.177236[/C][C]0.911382[/C][/ROW]
[ROW][C]112[/C][C]0.146516[/C][C]0.293032[/C][C]0.853484[/C][/ROW]
[ROW][C]113[/C][C]0.122876[/C][C]0.245752[/C][C]0.877124[/C][/ROW]
[ROW][C]114[/C][C]0.255377[/C][C]0.510754[/C][C]0.744623[/C][/ROW]
[ROW][C]115[/C][C]0.302794[/C][C]0.605588[/C][C]0.697206[/C][/ROW]
[ROW][C]116[/C][C]0.276339[/C][C]0.552677[/C][C]0.723661[/C][/ROW]
[ROW][C]117[/C][C]0.245664[/C][C]0.491327[/C][C]0.754336[/C][/ROW]
[ROW][C]118[/C][C]0.229405[/C][C]0.45881[/C][C]0.770595[/C][/ROW]
[ROW][C]119[/C][C]0.253297[/C][C]0.506594[/C][C]0.746703[/C][/ROW]
[ROW][C]120[/C][C]0.21522[/C][C]0.430439[/C][C]0.78478[/C][/ROW]
[ROW][C]121[/C][C]0.183786[/C][C]0.367571[/C][C]0.816214[/C][/ROW]
[ROW][C]122[/C][C]0.152037[/C][C]0.304075[/C][C]0.847963[/C][/ROW]
[ROW][C]123[/C][C]0.184239[/C][C]0.368479[/C][C]0.815761[/C][/ROW]
[ROW][C]124[/C][C]0.196673[/C][C]0.393347[/C][C]0.803327[/C][/ROW]
[ROW][C]125[/C][C]0.249764[/C][C]0.499528[/C][C]0.750236[/C][/ROW]
[ROW][C]126[/C][C]0.219444[/C][C]0.438888[/C][C]0.780556[/C][/ROW]
[ROW][C]127[/C][C]0.18758[/C][C]0.375159[/C][C]0.81242[/C][/ROW]
[ROW][C]128[/C][C]0.35438[/C][C]0.708759[/C][C]0.64562[/C][/ROW]
[ROW][C]129[/C][C]0.305481[/C][C]0.610961[/C][C]0.694519[/C][/ROW]
[ROW][C]130[/C][C]0.420259[/C][C]0.840518[/C][C]0.579741[/C][/ROW]
[ROW][C]131[/C][C]0.376545[/C][C]0.75309[/C][C]0.623455[/C][/ROW]
[ROW][C]132[/C][C]0.327847[/C][C]0.655694[/C][C]0.672153[/C][/ROW]
[ROW][C]133[/C][C]0.390543[/C][C]0.781086[/C][C]0.609457[/C][/ROW]
[ROW][C]134[/C][C]0.498125[/C][C]0.996251[/C][C]0.501875[/C][/ROW]
[ROW][C]135[/C][C]0.43682[/C][C]0.87364[/C][C]0.56318[/C][/ROW]
[ROW][C]136[/C][C]0.553182[/C][C]0.893636[/C][C]0.446818[/C][/ROW]
[ROW][C]137[/C][C]0.531424[/C][C]0.937153[/C][C]0.468576[/C][/ROW]
[ROW][C]138[/C][C]0.483882[/C][C]0.967763[/C][C]0.516118[/C][/ROW]
[ROW][C]139[/C][C]0.474878[/C][C]0.949756[/C][C]0.525122[/C][/ROW]
[ROW][C]140[/C][C]0.523878[/C][C]0.952244[/C][C]0.476122[/C][/ROW]
[ROW][C]141[/C][C]0.55283[/C][C]0.89434[/C][C]0.44717[/C][/ROW]
[ROW][C]142[/C][C]0.484009[/C][C]0.968017[/C][C]0.515991[/C][/ROW]
[ROW][C]143[/C][C]0.425094[/C][C]0.850189[/C][C]0.574906[/C][/ROW]
[ROW][C]144[/C][C]0.425747[/C][C]0.851494[/C][C]0.574253[/C][/ROW]
[ROW][C]145[/C][C]0.38961[/C][C]0.77922[/C][C]0.61039[/C][/ROW]
[ROW][C]146[/C][C]0.33217[/C][C]0.664341[/C][C]0.66783[/C][/ROW]
[ROW][C]147[/C][C]0.408912[/C][C]0.817824[/C][C]0.591088[/C][/ROW]
[ROW][C]148[/C][C]0.340596[/C][C]0.681192[/C][C]0.659404[/C][/ROW]
[ROW][C]149[/C][C]0.267584[/C][C]0.535168[/C][C]0.732416[/C][/ROW]
[ROW][C]150[/C][C]0.375215[/C][C]0.75043[/C][C]0.624785[/C][/ROW]
[ROW][C]151[/C][C]0.540601[/C][C]0.918797[/C][C]0.459399[/C][/ROW]
[ROW][C]152[/C][C]0.644353[/C][C]0.711293[/C][C]0.355647[/C][/ROW]
[ROW][C]153[/C][C]0.54534[/C][C]0.909319[/C][C]0.45466[/C][/ROW]
[ROW][C]154[/C][C]0.429353[/C][C]0.858706[/C][C]0.570647[/C][/ROW]
[ROW][C]155[/C][C]0.329378[/C][C]0.658757[/C][C]0.670622[/C][/ROW]
[ROW][C]156[/C][C]0.249161[/C][C]0.498323[/C][C]0.750839[/C][/ROW]
[ROW][C]157[/C][C]0.407431[/C][C]0.814863[/C][C]0.592569[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=271308&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=271308&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
90.3877650.775530.612235
100.4375720.8751440.562428
110.3414210.6828410.658579
120.6013770.7972460.398623
130.5564680.8870640.443532
140.5574430.8851140.442557
150.4588910.9177830.541109
160.4484770.8969530.551523
170.3608350.7216710.639165
180.3068740.6137480.693126
190.2768410.5536820.723159
200.3480260.6960530.651974
210.3092850.6185690.690715
220.2432110.4864220.756789
230.1935220.3870450.806478
240.1504340.3008680.849566
250.1185310.2370620.881469
260.3297230.6594450.670277
270.2810450.5620910.718955
280.227650.4552990.77235
290.1817760.3635520.818224
300.3770.7540.623
310.3566770.7133530.643323
320.305310.6106210.69469
330.2571640.5143280.742836
340.2220820.4441650.777918
350.2637710.5275410.736229
360.2174070.4348150.782593
370.1863120.3726240.813688
380.1616430.3232870.838357
390.2438840.4877680.756116
400.2050660.4101330.794934
410.1914250.3828490.808575
420.1750320.3500640.824968
430.1530190.3060390.846981
440.1248480.2496960.875152
450.1243230.2486460.875677
460.1461020.2922030.853898
470.1315290.2630580.868471
480.1338370.2676730.866163
490.1079390.2158780.892061
500.1133930.2267860.886607
510.1016390.2032790.898361
520.2866720.5733430.713328
530.2615860.5231720.738414
540.2363630.4727260.763637
550.2124590.4249190.787541
560.1820070.3640140.817993
570.1519940.3039870.848006
580.1248160.2496320.875184
590.1020070.2040140.897993
600.1179470.2358940.882053
610.1368170.2736330.863183
620.1168050.2336110.883195
630.1004660.2009320.899534
640.1125960.2251910.887404
650.09171920.1834380.908281
660.08110320.1622060.918897
670.06482460.1296490.935175
680.05422590.1084520.945774
690.05362950.1072590.94637
700.06080870.1216170.939191
710.05005620.1001120.949944
720.05504290.1100860.944957
730.0782130.1564260.921787
740.08346610.1669320.916534
750.07184060.1436810.928159
760.05908120.1181620.940919
770.06837030.1367410.93163
780.1076530.2153070.892347
790.1511140.3022270.848886
800.1619670.3239330.838033
810.1391410.2782810.860859
820.1692230.3384470.830777
830.1523840.3047670.847616
840.1270860.2541730.872914
850.1074760.2149530.892524
860.1317720.2635450.868228
870.1114770.2229550.888523
880.1416120.2832230.858388
890.1231970.2463930.876803
900.1063730.2127460.893627
910.1148180.2296360.885182
920.09526560.1905310.904734
930.08254230.1650850.917458
940.06711340.1342270.932887
950.05802360.1160470.941976
960.04698270.09396540.953017
970.03724550.07449110.962754
980.04251410.08502810.957486
990.0479210.09584210.952079
1000.0487610.0975220.951239
1010.0695490.1390980.930451
1020.05571830.1114370.944282
1030.0629050.125810.937095
1040.06174880.1234980.938251
1050.04987080.09974160.950129
1060.04372070.08744140.956279
1070.05957230.1191450.940428
1080.04799220.09598450.952008
1090.04165130.08330260.958349
1100.06121820.1224360.938782
1110.0886180.1772360.911382
1120.1465160.2930320.853484
1130.1228760.2457520.877124
1140.2553770.5107540.744623
1150.3027940.6055880.697206
1160.2763390.5526770.723661
1170.2456640.4913270.754336
1180.2294050.458810.770595
1190.2532970.5065940.746703
1200.215220.4304390.78478
1210.1837860.3675710.816214
1220.1520370.3040750.847963
1230.1842390.3684790.815761
1240.1966730.3933470.803327
1250.2497640.4995280.750236
1260.2194440.4388880.780556
1270.187580.3751590.81242
1280.354380.7087590.64562
1290.3054810.6109610.694519
1300.4202590.8405180.579741
1310.3765450.753090.623455
1320.3278470.6556940.672153
1330.3905430.7810860.609457
1340.4981250.9962510.501875
1350.436820.873640.56318
1360.5531820.8936360.446818
1370.5314240.9371530.468576
1380.4838820.9677630.516118
1390.4748780.9497560.525122
1400.5238780.9522440.476122
1410.552830.894340.44717
1420.4840090.9680170.515991
1430.4250940.8501890.574906
1440.4257470.8514940.574253
1450.389610.779220.61039
1460.332170.6643410.66783
1470.4089120.8178240.591088
1480.3405960.6811920.659404
1490.2675840.5351680.732416
1500.3752150.750430.624785
1510.5406010.9187970.459399
1520.6443530.7112930.355647
1530.545340.9093190.45466
1540.4293530.8587060.570647
1550.3293780.6587570.670622
1560.2491610.4983230.750839
1570.4074310.8148630.592569






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level00OK
10% type I error level90.0604027OK

\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 & 0 & 0 & OK \tabularnewline
5% type I error level & 0 & 0 & OK \tabularnewline
10% type I error level & 9 & 0.0604027 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=271308&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]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]9[/C][C]0.0604027[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=271308&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=271308&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 level00OK
5% type I error level00OK
10% type I error level90.0604027OK


Figures (Output of Computation)

Figure 1
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Figure 9
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Figure 10
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Input Parameters & R Code

Parameters (Session):
par1 = 6 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
Parameters (R input):
par1 = 6 ; 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$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
myeq <- paste(myeq, signif(mysum$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')
}