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rwasp_multipleregression.wasp

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Multiple Regression

Date of computation

Sat, 13 Dec 2014 10:43:32 +0000

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Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2014/Dec/13/t1418467466kclwsuxexndig4o.htm/, Retrieved Thu, 16 May 2024 22:09:47 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=266958, Retrieved Thu, 16 May 2024 22:09:47 +0000

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-       [Multiple Regression] [] [2014-12-13 10:43:32] [58179e1d3a5a39b9daf58e365d8a3352] [Current]

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12.9 26 50 4 13 12 13 13 21 2 149 96 86
12.8 37 54 5 14 11 11 11 22 0 148 88 71
7.4 67 71 4 16 13 14 10 18 0 158 114 108
6.7 43 54 4 14 11 15 9 23 4 128 69 64
12.6 52 65 9 13 10 14 8 12 0 224 176 119
14.8 52 73 8 15 7 11 26 20 -1 159 114 97
13.3 43 52 11 13 10 13 10 22 0 105 121 129
11.1 84 84 4 20 15 16 10 21 1 159 110 153
8.2 67 42 4 17 12 14 8 19 0 167 158 78
11.4 49 66 6 15 12 14 13 22 3 165 116 80
6.4 70 65 4 16 10 15 11 15 -1 159 181 99
12 58 73 4 17 14 13 12 19 4 176 141 147
6.3 68 75 4 11 6 14 24 18 1 54 35 40
11.3 62 72 11 16 12 11 21 15 0 91 80 57
11.9 43 66 4 16 14 12 5 20 -2 163 152 120
9.3 56 70 4 15 11 14 14 21 -4 124 97 71
10 74 81 6 14 12 12 9 15 2 121 84 68
13.8 63 69 8 16 13 15 17 23 2 148 101 137
10.8 58 71 5 17 11 14 18 21 -4 221 107 79
11.7 63 68 9 15 7 12 23 25 2 149 112 101
10.9 53 70 4 14 11 12 9 9 2 244 171 111
16.1 57 68 7 14 7 12 14 30 0 148 137 189
9.9 64 67 4 15 12 14 10 23 -3 150 66 81
11.5 53 76 4 17 13 16 8 16 2 153 93 63
8.3 29 70 7 14 9 12 10 16 0 94 105 69
11.7 54 60 12 16 11 12 19 19 4 156 131 71
9 58 72 7 15 12 14 11 25 2 132 102 64
10.8 51 71 8 16 12 15 12 23 2 105 120 85
10.4 54 70 4 8 5 14 11 10 -4 151 77 55
12.7 56 64 9 17 13 13 10 14 3 131 108 69
11.8 47 76 4 10 6 16 14 26 2 157 168 96
13 50 68 4 16 6 15 11 24 -1 162 75 100
10.8 35 76 4 16 12 13 13 24 -3 163 107 68
12.3 30 65 7 16 11 16 15 18 0 59 62 57
11.3 68 67 4 8 6 16 15 23 1 187 121 105
11.6 56 75 4 14 11 15 14 23 -3 116 97 69
10.9 43 60 4 16 12 13 12 19 3 148 126 49
12.1 67 73 4 19 13 12 13 21 0 155 104 50
13.3 62 63 4 19 14 14 7 18 0 125 148 93
10.1 57 70 4 14 12 14 8 27 0 116 146 58
14.3 54 66 12 13 14 10 20 13 3 138 97 74
9.3 61 64 4 15 11 16 16 28 0 164 118 107
12.5 56 70 5 11 10 14 11 23 2 162 58 65
7.6 41 75 15 9 7 14 26 21 -1 99 63 58
9.2 53 60 10 12 7 15 15 19 3 186 50 70
14.5 46 66 5 13 10 16 20 17 2 188 94 95
12.3 51 59 9 17 12 15 15 25 2 177 127 136
12.6 37 78 4 7 5 13 17 14 -2 139 128 82
13 42 67 7 15 10 12 19 16 0 162 146 102
12.6 38 59 5 12 12 12 13 24 -2 108 69 65
13.2 66 66 4 15 11 14 8 20 0 159 186 90
7.7 53 71 4 16 12 15 9 24 6 110 85 83
10.5 49 66 4 14 11 11 12 22 0 96 54 70
10.9 49 72 4 16 12 14 9 22 -2 87 106 77
4.3 59 71 6 13 10 16 14 20 1 97 34 37
10.3 40 59 10 16 9 13 14 10 0 127 60 81
11.4 63 78 4 10 7 11 13 22 2 74 62 71
5.6 34 65 11 12 9 12 16 20 2 114 64 40
8.8 32 65 14 14 10 12 14 22 -3 95 98 43
9 67 71 4 16 12 14 11 20 1 121 35 32
9.6 61 72 4 18 14 12 11 17 -4 130 55 76
6.4 60 66 5 12 9 13 14 18 1 52 54 30
11.6 63 69 4 15 12 14 15 19 0 118 51 51

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	5 seconds
R Server	'Sir Ronald Aylmer Fisher' @ fisher.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 & 5 seconds \tabularnewline
R Server & 'Sir Ronald Aylmer Fisher' @ fisher.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=266958&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]5 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Sir Ronald Aylmer Fisher' @ fisher.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=266958&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=266958&T=0

As an alternative you can also use a QR Code:

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

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	5 seconds
R Server	'Sir Ronald Aylmer Fisher' @ fisher.wessa.net

Multiple Linear Regression - Estimated Regression Equation
TOT[t] = + 10.5609 -0.0427501AMS.I[t] + 0.012359AMS.E[t] -0.103387AMS.A[t] -0.0236817CONFSTATTOT[t] + 0.132482CONFSOFTTOT[t] -0.373983STRESSTOT[t] + 0.107024CESDTOT[t] + 0.0131311NUMERACYTOT[t] -0.0145957DECTESTTOT[t] + 0.00902141LFM[t] + 0.00311912BLOG[t] + 0.0374795HOURS[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
TOT[t] =  +  10.5609 -0.0427501AMS.I[t] +  0.012359AMS.E[t] -0.103387AMS.A[t] -0.0236817CONFSTATTOT[t] +  0.132482CONFSOFTTOT[t] -0.373983STRESSTOT[t] +  0.107024CESDTOT[t] +  0.0131311NUMERACYTOT[t] -0.0145957DECTESTTOT[t] +  0.00902141LFM[t] +  0.00311912BLOG[t] +  0.0374795HOURS[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=266958&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]TOT[t] =  +  10.5609 -0.0427501AMS.I[t] +  0.012359AMS.E[t] -0.103387AMS.A[t] -0.0236817CONFSTATTOT[t] +  0.132482CONFSOFTTOT[t] -0.373983STRESSTOT[t] +  0.107024CESDTOT[t] +  0.0131311NUMERACYTOT[t] -0.0145957DECTESTTOT[t] +  0.00902141LFM[t] +  0.00311912BLOG[t] +  0.0374795HOURS[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=266958&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=266958&T=1

As an alternative you can also use a QR Code:

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

Multiple Linear Regression - Estimated Regression Equation
TOT[t] = + 10.5609 -0.0427501AMS.I[t] + 0.012359AMS.E[t] -0.103387AMS.A[t] -0.0236817CONFSTATTOT[t] + 0.132482CONFSOFTTOT[t] -0.373983STRESSTOT[t] + 0.107024CESDTOT[t] + 0.0131311NUMERACYTOT[t] -0.0145957DECTESTTOT[t] + 0.00902141LFM[t] + 0.00311912BLOG[t] + 0.0374795HOURS[t] + e[t]

Multiple Linear Regression - Ordinary Least Squares
Variable	Parameter	S.D.	T-STATH0: parameter = 0	2-tail p-value	1-tail p-value
(Intercept)	10.5609	4.44312	2.377	0.021326	0.010663
AMS.I	-0.0427501	0.0260467	-1.641	0.107014	0.0535068
AMS.E	0.012359	0.0405897	0.3045	0.762022	0.381011
AMS.A	-0.103387	0.117735	-0.8781	0.384071	0.192036
CONFSTATTOT	-0.0236817	0.157682	-0.1502	0.881222	0.440611
CONFSOFTTOT	0.132482	0.175431	0.7552	0.453685	0.226842
STRESSTOT	-0.373983	0.187416	-1.995	0.0514548	0.0257274
CESDTOT	0.107024	0.0781679	1.369	0.17707	0.0885349
NUMERACYTOT	0.0131311	0.0672935	0.1951	0.84608	0.42304
DECTESTTOT	-0.0145957	0.126116	-0.1157	0.908328	0.454164
LFM	0.00902141	0.00910839	0.9905	0.326723	0.163362
BLOG	0.00311912	0.00941872	0.3312	0.741906	0.370953
HOURS	0.0374795	0.011701	3.203	0.00236682	0.00118341

\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) & 10.5609 & 4.44312 & 2.377 & 0.021326 & 0.010663 \tabularnewline
AMS.I & -0.0427501 & 0.0260467 & -1.641 & 0.107014 & 0.0535068 \tabularnewline
AMS.E & 0.012359 & 0.0405897 & 0.3045 & 0.762022 & 0.381011 \tabularnewline
AMS.A & -0.103387 & 0.117735 & -0.8781 & 0.384071 & 0.192036 \tabularnewline
CONFSTATTOT & -0.0236817 & 0.157682 & -0.1502 & 0.881222 & 0.440611 \tabularnewline
CONFSOFTTOT & 0.132482 & 0.175431 & 0.7552 & 0.453685 & 0.226842 \tabularnewline
STRESSTOT & -0.373983 & 0.187416 & -1.995 & 0.0514548 & 0.0257274 \tabularnewline
CESDTOT & 0.107024 & 0.0781679 & 1.369 & 0.17707 & 0.0885349 \tabularnewline
NUMERACYTOT & 0.0131311 & 0.0672935 & 0.1951 & 0.84608 & 0.42304 \tabularnewline
DECTESTTOT & -0.0145957 & 0.126116 & -0.1157 & 0.908328 & 0.454164 \tabularnewline
LFM & 0.00902141 & 0.00910839 & 0.9905 & 0.326723 & 0.163362 \tabularnewline
BLOG & 0.00311912 & 0.00941872 & 0.3312 & 0.741906 & 0.370953 \tabularnewline
HOURS & 0.0374795 & 0.011701 & 3.203 & 0.00236682 & 0.00118341 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=266958&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]10.5609[/C][C]4.44312[/C][C]2.377[/C][C]0.021326[/C][C]0.010663[/C][/ROW]
[ROW][C]AMS.I[/C][C]-0.0427501[/C][C]0.0260467[/C][C]-1.641[/C][C]0.107014[/C][C]0.0535068[/C][/ROW]
[ROW][C]AMS.E[/C][C]0.012359[/C][C]0.0405897[/C][C]0.3045[/C][C]0.762022[/C][C]0.381011[/C][/ROW]
[ROW][C]AMS.A[/C][C]-0.103387[/C][C]0.117735[/C][C]-0.8781[/C][C]0.384071[/C][C]0.192036[/C][/ROW]
[ROW][C]CONFSTATTOT[/C][C]-0.0236817[/C][C]0.157682[/C][C]-0.1502[/C][C]0.881222[/C][C]0.440611[/C][/ROW]
[ROW][C]CONFSOFTTOT[/C][C]0.132482[/C][C]0.175431[/C][C]0.7552[/C][C]0.453685[/C][C]0.226842[/C][/ROW]
[ROW][C]STRESSTOT[/C][C]-0.373983[/C][C]0.187416[/C][C]-1.995[/C][C]0.0514548[/C][C]0.0257274[/C][/ROW]
[ROW][C]CESDTOT[/C][C]0.107024[/C][C]0.0781679[/C][C]1.369[/C][C]0.17707[/C][C]0.0885349[/C][/ROW]
[ROW][C]NUMERACYTOT[/C][C]0.0131311[/C][C]0.0672935[/C][C]0.1951[/C][C]0.84608[/C][C]0.42304[/C][/ROW]
[ROW][C]DECTESTTOT[/C][C]-0.0145957[/C][C]0.126116[/C][C]-0.1157[/C][C]0.908328[/C][C]0.454164[/C][/ROW]
[ROW][C]LFM[/C][C]0.00902141[/C][C]0.00910839[/C][C]0.9905[/C][C]0.326723[/C][C]0.163362[/C][/ROW]
[ROW][C]BLOG[/C][C]0.00311912[/C][C]0.00941872[/C][C]0.3312[/C][C]0.741906[/C][C]0.370953[/C][/ROW]
[ROW][C]HOURS[/C][C]0.0374795[/C][C]0.011701[/C][C]3.203[/C][C]0.00236682[/C][C]0.00118341[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=266958&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=266958&T=2

As an alternative you can also use a QR Code:

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

Multiple Linear Regression - Ordinary Least Squares
Variable	Parameter	S.D.	T-STATH0: parameter = 0	2-tail p-value	1-tail p-value
(Intercept)	10.5609	4.44312	2.377	0.021326	0.010663
AMS.I	-0.0427501	0.0260467	-1.641	0.107014	0.0535068
AMS.E	0.012359	0.0405897	0.3045	0.762022	0.381011
AMS.A	-0.103387	0.117735	-0.8781	0.384071	0.192036
CONFSTATTOT	-0.0236817	0.157682	-0.1502	0.881222	0.440611
CONFSOFTTOT	0.132482	0.175431	0.7552	0.453685	0.226842
STRESSTOT	-0.373983	0.187416	-1.995	0.0514548	0.0257274
CESDTOT	0.107024	0.0781679	1.369	0.17707	0.0885349
NUMERACYTOT	0.0131311	0.0672935	0.1951	0.84608	0.42304
DECTESTTOT	-0.0145957	0.126116	-0.1157	0.908328	0.454164
LFM	0.00902141	0.00910839	0.9905	0.326723	0.163362
BLOG	0.00311912	0.00941872	0.3312	0.741906	0.370953
HOURS	0.0374795	0.011701	3.203	0.00236682	0.00118341

Multiple Linear Regression - Regression Statistics
Multiple R	0.635935
R-squared	0.404413
Adjusted R-squared	0.261472
F-TEST (value)	2.82923
F-TEST (DF numerator)	12
F-TEST (DF denominator)	50
p-value	0.00494061
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	2.06239
Sum Squared Residuals	212.672

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.635935 \tabularnewline
R-squared & 0.404413 \tabularnewline
Adjusted R-squared & 0.261472 \tabularnewline
F-TEST (value) & 2.82923 \tabularnewline
F-TEST (DF numerator) & 12 \tabularnewline
F-TEST (DF denominator) & 50 \tabularnewline
p-value & 0.00494061 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 2.06239 \tabularnewline
Sum Squared Residuals & 212.672 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=266958&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.635935[/C][/ROW]
[ROW][C]R-squared[/C][C]0.404413[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.261472[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]2.82923[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]12[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]50[/C][/ROW]
[ROW][C]p-value[/C][C]0.00494061[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]2.06239[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]212.672[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=266958&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=266958&T=3

As an alternative you can also use a QR Code:

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

Multiple Linear Regression - Regression Statistics
Multiple R	0.635935
R-squared	0.404413
Adjusted R-squared	0.261472
F-TEST (value)	2.82923
F-TEST (DF numerator)	12
F-TEST (DF denominator)	50
p-value	0.00494061
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	2.06239
Sum Squared Residuals	212.672

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	12.9	12.5787	0.321286
2	12.8	11.8784	0.921579
3	7.4	11.4036	-4.00356
4	6.7	9.46803	-2.76803
5	12.6	12.0356	0.564439
6	14.8	13.3567	1.44329
7	13.3	11.9019	1.39809
8	11.1	11.9677	-0.867668
9	8.2	9.78212	-1.58212
10	11.4	11.1455	0.254532
11	6.4	10.3926	-3.99264
12	12	14.1729	-2.17291
13	6.3	8.35178	-2.05178
14	11.3	10.4114	0.888639
15	11.9	13.3819	-1.48194
16	9.3	10.3996	-1.09955
17	10	9.58182	0.418179
18	13.8	12.5041	1.29591
19	10.8	11.8099	-1.00986
20	11.7	12.1016	-0.401557
21	10.9	13.3317	-2.43174
22	16.1	15.0873	1.01271
23	9.9	10.2492	-0.349185
24	11.5	9.22553	2.27447
25	8.3	10.8775	-2.57754
26	11.7	11.0455	0.654524
27	9	9.63038	-0.630379
28	10.8	10.0966	0.703384
29	10.4	8.97194	1.42806
30	12.7	9.80036	2.89964
31	11.8	11.0007	0.799279
32	13	10.6069	2.39311
33	10.8	12.0426	-1.2426
34	12.3	9.15643	3.14357
35	11.3	10.5827	0.717313
36	11.6	9.97558	1.62442
37	10.9	10.4544	0.445564
38	12.1	10.2336	1.8664
39	13.3	10.505	2.79504
40	10.1	9.48466	0.615342
41	14.3	12.2229	2.07708
42	9.3	11.3869	-2.08688
43	12.5	9.87232	2.62768
44	7.6	9.99918	-2.39918
45	9.2	9.30486	-0.104865
46	14.5	11.8107	2.68929
47	12.3	12.7514	-0.451402
48	12.6	11.9235	0.67653
49	13	13.3349	-0.334893
50	12.6	11.3278	1.27217
51	13.2	10.5144	2.68558
52	7.7	9.91932	-2.21932
53	10.5	11.1115	-0.611496
54	10.9	10.2003	0.699703
55	4.3	7.44331	-3.14331
56	10.3	10.4962	-0.196245
57	11.4	10.1679	1.23211
58	5.6	9.86693	-4.26693
59	8.8	9.65966	-0.859662
60	9	7.96113	1.03887
61	9.6	11.0218	-1.42181
62	6.4	7.8231	-1.4231
63	11.6	9.29561	2.30439

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 12.9 & 12.5787 & 0.321286 \tabularnewline
2 & 12.8 & 11.8784 & 0.921579 \tabularnewline
3 & 7.4 & 11.4036 & -4.00356 \tabularnewline
4 & 6.7 & 9.46803 & -2.76803 \tabularnewline
5 & 12.6 & 12.0356 & 0.564439 \tabularnewline
6 & 14.8 & 13.3567 & 1.44329 \tabularnewline
7 & 13.3 & 11.9019 & 1.39809 \tabularnewline
8 & 11.1 & 11.9677 & -0.867668 \tabularnewline
9 & 8.2 & 9.78212 & -1.58212 \tabularnewline
10 & 11.4 & 11.1455 & 0.254532 \tabularnewline
11 & 6.4 & 10.3926 & -3.99264 \tabularnewline
12 & 12 & 14.1729 & -2.17291 \tabularnewline
13 & 6.3 & 8.35178 & -2.05178 \tabularnewline
14 & 11.3 & 10.4114 & 0.888639 \tabularnewline
15 & 11.9 & 13.3819 & -1.48194 \tabularnewline
16 & 9.3 & 10.3996 & -1.09955 \tabularnewline
17 & 10 & 9.58182 & 0.418179 \tabularnewline
18 & 13.8 & 12.5041 & 1.29591 \tabularnewline
19 & 10.8 & 11.8099 & -1.00986 \tabularnewline
20 & 11.7 & 12.1016 & -0.401557 \tabularnewline
21 & 10.9 & 13.3317 & -2.43174 \tabularnewline
22 & 16.1 & 15.0873 & 1.01271 \tabularnewline
23 & 9.9 & 10.2492 & -0.349185 \tabularnewline
24 & 11.5 & 9.22553 & 2.27447 \tabularnewline
25 & 8.3 & 10.8775 & -2.57754 \tabularnewline
26 & 11.7 & 11.0455 & 0.654524 \tabularnewline
27 & 9 & 9.63038 & -0.630379 \tabularnewline
28 & 10.8 & 10.0966 & 0.703384 \tabularnewline
29 & 10.4 & 8.97194 & 1.42806 \tabularnewline
30 & 12.7 & 9.80036 & 2.89964 \tabularnewline
31 & 11.8 & 11.0007 & 0.799279 \tabularnewline
32 & 13 & 10.6069 & 2.39311 \tabularnewline
33 & 10.8 & 12.0426 & -1.2426 \tabularnewline
34 & 12.3 & 9.15643 & 3.14357 \tabularnewline
35 & 11.3 & 10.5827 & 0.717313 \tabularnewline
36 & 11.6 & 9.97558 & 1.62442 \tabularnewline
37 & 10.9 & 10.4544 & 0.445564 \tabularnewline
38 & 12.1 & 10.2336 & 1.8664 \tabularnewline
39 & 13.3 & 10.505 & 2.79504 \tabularnewline
40 & 10.1 & 9.48466 & 0.615342 \tabularnewline
41 & 14.3 & 12.2229 & 2.07708 \tabularnewline
42 & 9.3 & 11.3869 & -2.08688 \tabularnewline
43 & 12.5 & 9.87232 & 2.62768 \tabularnewline
44 & 7.6 & 9.99918 & -2.39918 \tabularnewline
45 & 9.2 & 9.30486 & -0.104865 \tabularnewline
46 & 14.5 & 11.8107 & 2.68929 \tabularnewline
47 & 12.3 & 12.7514 & -0.451402 \tabularnewline
48 & 12.6 & 11.9235 & 0.67653 \tabularnewline
49 & 13 & 13.3349 & -0.334893 \tabularnewline
50 & 12.6 & 11.3278 & 1.27217 \tabularnewline
51 & 13.2 & 10.5144 & 2.68558 \tabularnewline
52 & 7.7 & 9.91932 & -2.21932 \tabularnewline
53 & 10.5 & 11.1115 & -0.611496 \tabularnewline
54 & 10.9 & 10.2003 & 0.699703 \tabularnewline
55 & 4.3 & 7.44331 & -3.14331 \tabularnewline
56 & 10.3 & 10.4962 & -0.196245 \tabularnewline
57 & 11.4 & 10.1679 & 1.23211 \tabularnewline
58 & 5.6 & 9.86693 & -4.26693 \tabularnewline
59 & 8.8 & 9.65966 & -0.859662 \tabularnewline
60 & 9 & 7.96113 & 1.03887 \tabularnewline
61 & 9.6 & 11.0218 & -1.42181 \tabularnewline
62 & 6.4 & 7.8231 & -1.4231 \tabularnewline
63 & 11.6 & 9.29561 & 2.30439 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=266958&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]12.9[/C][C]12.5787[/C][C]0.321286[/C][/ROW]
[ROW][C]2[/C][C]12.8[/C][C]11.8784[/C][C]0.921579[/C][/ROW]
[ROW][C]3[/C][C]7.4[/C][C]11.4036[/C][C]-4.00356[/C][/ROW]
[ROW][C]4[/C][C]6.7[/C][C]9.46803[/C][C]-2.76803[/C][/ROW]
[ROW][C]5[/C][C]12.6[/C][C]12.0356[/C][C]0.564439[/C][/ROW]
[ROW][C]6[/C][C]14.8[/C][C]13.3567[/C][C]1.44329[/C][/ROW]
[ROW][C]7[/C][C]13.3[/C][C]11.9019[/C][C]1.39809[/C][/ROW]
[ROW][C]8[/C][C]11.1[/C][C]11.9677[/C][C]-0.867668[/C][/ROW]
[ROW][C]9[/C][C]8.2[/C][C]9.78212[/C][C]-1.58212[/C][/ROW]
[ROW][C]10[/C][C]11.4[/C][C]11.1455[/C][C]0.254532[/C][/ROW]
[ROW][C]11[/C][C]6.4[/C][C]10.3926[/C][C]-3.99264[/C][/ROW]
[ROW][C]12[/C][C]12[/C][C]14.1729[/C][C]-2.17291[/C][/ROW]
[ROW][C]13[/C][C]6.3[/C][C]8.35178[/C][C]-2.05178[/C][/ROW]
[ROW][C]14[/C][C]11.3[/C][C]10.4114[/C][C]0.888639[/C][/ROW]
[ROW][C]15[/C][C]11.9[/C][C]13.3819[/C][C]-1.48194[/C][/ROW]
[ROW][C]16[/C][C]9.3[/C][C]10.3996[/C][C]-1.09955[/C][/ROW]
[ROW][C]17[/C][C]10[/C][C]9.58182[/C][C]0.418179[/C][/ROW]
[ROW][C]18[/C][C]13.8[/C][C]12.5041[/C][C]1.29591[/C][/ROW]
[ROW][C]19[/C][C]10.8[/C][C]11.8099[/C][C]-1.00986[/C][/ROW]
[ROW][C]20[/C][C]11.7[/C][C]12.1016[/C][C]-0.401557[/C][/ROW]
[ROW][C]21[/C][C]10.9[/C][C]13.3317[/C][C]-2.43174[/C][/ROW]
[ROW][C]22[/C][C]16.1[/C][C]15.0873[/C][C]1.01271[/C][/ROW]
[ROW][C]23[/C][C]9.9[/C][C]10.2492[/C][C]-0.349185[/C][/ROW]
[ROW][C]24[/C][C]11.5[/C][C]9.22553[/C][C]2.27447[/C][/ROW]
[ROW][C]25[/C][C]8.3[/C][C]10.8775[/C][C]-2.57754[/C][/ROW]
[ROW][C]26[/C][C]11.7[/C][C]11.0455[/C][C]0.654524[/C][/ROW]
[ROW][C]27[/C][C]9[/C][C]9.63038[/C][C]-0.630379[/C][/ROW]
[ROW][C]28[/C][C]10.8[/C][C]10.0966[/C][C]0.703384[/C][/ROW]
[ROW][C]29[/C][C]10.4[/C][C]8.97194[/C][C]1.42806[/C][/ROW]
[ROW][C]30[/C][C]12.7[/C][C]9.80036[/C][C]2.89964[/C][/ROW]
[ROW][C]31[/C][C]11.8[/C][C]11.0007[/C][C]0.799279[/C][/ROW]
[ROW][C]32[/C][C]13[/C][C]10.6069[/C][C]2.39311[/C][/ROW]
[ROW][C]33[/C][C]10.8[/C][C]12.0426[/C][C]-1.2426[/C][/ROW]
[ROW][C]34[/C][C]12.3[/C][C]9.15643[/C][C]3.14357[/C][/ROW]
[ROW][C]35[/C][C]11.3[/C][C]10.5827[/C][C]0.717313[/C][/ROW]
[ROW][C]36[/C][C]11.6[/C][C]9.97558[/C][C]1.62442[/C][/ROW]
[ROW][C]37[/C][C]10.9[/C][C]10.4544[/C][C]0.445564[/C][/ROW]
[ROW][C]38[/C][C]12.1[/C][C]10.2336[/C][C]1.8664[/C][/ROW]
[ROW][C]39[/C][C]13.3[/C][C]10.505[/C][C]2.79504[/C][/ROW]
[ROW][C]40[/C][C]10.1[/C][C]9.48466[/C][C]0.615342[/C][/ROW]
[ROW][C]41[/C][C]14.3[/C][C]12.2229[/C][C]2.07708[/C][/ROW]
[ROW][C]42[/C][C]9.3[/C][C]11.3869[/C][C]-2.08688[/C][/ROW]
[ROW][C]43[/C][C]12.5[/C][C]9.87232[/C][C]2.62768[/C][/ROW]
[ROW][C]44[/C][C]7.6[/C][C]9.99918[/C][C]-2.39918[/C][/ROW]
[ROW][C]45[/C][C]9.2[/C][C]9.30486[/C][C]-0.104865[/C][/ROW]
[ROW][C]46[/C][C]14.5[/C][C]11.8107[/C][C]2.68929[/C][/ROW]
[ROW][C]47[/C][C]12.3[/C][C]12.7514[/C][C]-0.451402[/C][/ROW]
[ROW][C]48[/C][C]12.6[/C][C]11.9235[/C][C]0.67653[/C][/ROW]
[ROW][C]49[/C][C]13[/C][C]13.3349[/C][C]-0.334893[/C][/ROW]
[ROW][C]50[/C][C]12.6[/C][C]11.3278[/C][C]1.27217[/C][/ROW]
[ROW][C]51[/C][C]13.2[/C][C]10.5144[/C][C]2.68558[/C][/ROW]
[ROW][C]52[/C][C]7.7[/C][C]9.91932[/C][C]-2.21932[/C][/ROW]
[ROW][C]53[/C][C]10.5[/C][C]11.1115[/C][C]-0.611496[/C][/ROW]
[ROW][C]54[/C][C]10.9[/C][C]10.2003[/C][C]0.699703[/C][/ROW]
[ROW][C]55[/C][C]4.3[/C][C]7.44331[/C][C]-3.14331[/C][/ROW]
[ROW][C]56[/C][C]10.3[/C][C]10.4962[/C][C]-0.196245[/C][/ROW]
[ROW][C]57[/C][C]11.4[/C][C]10.1679[/C][C]1.23211[/C][/ROW]
[ROW][C]58[/C][C]5.6[/C][C]9.86693[/C][C]-4.26693[/C][/ROW]
[ROW][C]59[/C][C]8.8[/C][C]9.65966[/C][C]-0.859662[/C][/ROW]
[ROW][C]60[/C][C]9[/C][C]7.96113[/C][C]1.03887[/C][/ROW]
[ROW][C]61[/C][C]9.6[/C][C]11.0218[/C][C]-1.42181[/C][/ROW]
[ROW][C]62[/C][C]6.4[/C][C]7.8231[/C][C]-1.4231[/C][/ROW]
[ROW][C]63[/C][C]11.6[/C][C]9.29561[/C][C]2.30439[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=266958&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=266958&T=4

As an alternative you can also use a QR Code:

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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	12.9	12.5787	0.321286
2	12.8	11.8784	0.921579
3	7.4	11.4036	-4.00356
4	6.7	9.46803	-2.76803
5	12.6	12.0356	0.564439
6	14.8	13.3567	1.44329
7	13.3	11.9019	1.39809
8	11.1	11.9677	-0.867668
9	8.2	9.78212	-1.58212
10	11.4	11.1455	0.254532
11	6.4	10.3926	-3.99264
12	12	14.1729	-2.17291
13	6.3	8.35178	-2.05178
14	11.3	10.4114	0.888639
15	11.9	13.3819	-1.48194
16	9.3	10.3996	-1.09955
17	10	9.58182	0.418179
18	13.8	12.5041	1.29591
19	10.8	11.8099	-1.00986
20	11.7	12.1016	-0.401557
21	10.9	13.3317	-2.43174
22	16.1	15.0873	1.01271
23	9.9	10.2492	-0.349185
24	11.5	9.22553	2.27447
25	8.3	10.8775	-2.57754
26	11.7	11.0455	0.654524
27	9	9.63038	-0.630379
28	10.8	10.0966	0.703384
29	10.4	8.97194	1.42806
30	12.7	9.80036	2.89964
31	11.8	11.0007	0.799279
32	13	10.6069	2.39311
33	10.8	12.0426	-1.2426
34	12.3	9.15643	3.14357
35	11.3	10.5827	0.717313
36	11.6	9.97558	1.62442
37	10.9	10.4544	0.445564
38	12.1	10.2336	1.8664
39	13.3	10.505	2.79504
40	10.1	9.48466	0.615342
41	14.3	12.2229	2.07708
42	9.3	11.3869	-2.08688
43	12.5	9.87232	2.62768
44	7.6	9.99918	-2.39918
45	9.2	9.30486	-0.104865
46	14.5	11.8107	2.68929
47	12.3	12.7514	-0.451402
48	12.6	11.9235	0.67653
49	13	13.3349	-0.334893
50	12.6	11.3278	1.27217
51	13.2	10.5144	2.68558
52	7.7	9.91932	-2.21932
53	10.5	11.1115	-0.611496
54	10.9	10.2003	0.699703
55	4.3	7.44331	-3.14331
56	10.3	10.4962	-0.196245
57	11.4	10.1679	1.23211
58	5.6	9.86693	-4.26693
59	8.8	9.65966	-0.859662
60	9	7.96113	1.03887
61	9.6	11.0218	-1.42181
62	6.4	7.8231	-1.4231
63	11.6	9.29561	2.30439

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
16	0.528715	0.942571	0.471285
17	0.484706	0.969412	0.515294
18	0.333949	0.667898	0.666051
19	0.28485	0.569701	0.71515
20	0.271228	0.542455	0.728772
21	0.267109	0.534217	0.732891
22	0.187963	0.375927	0.812037
23	0.125158	0.250315	0.874842
24	0.268428	0.536856	0.731572
25	0.409602	0.819204	0.590398
26	0.338245	0.67649	0.661755
27	0.263103	0.526207	0.736897
28	0.202035	0.404071	0.797965
29	0.188007	0.376014	0.811993
30	0.201562	0.403125	0.798438
31	0.202361	0.404722	0.797639
32	0.244168	0.488336	0.755832
33	0.19259	0.385181	0.80741
34	0.393803	0.787605	0.606197
35	0.346079	0.692158	0.653921
36	0.353252	0.706503	0.646748
37	0.317353	0.634705	0.682647
38	0.373796	0.747592	0.626204
39	0.451613	0.903226	0.548387
40	0.356096	0.712192	0.643904
41	0.292198	0.584395	0.707802
42	0.333336	0.666673	0.666664
43	0.324911	0.649822	0.675089
44	0.340424	0.680848	0.659576
45	0.26692	0.533839	0.73308
46	0.303271	0.606542	0.696729
47	0.215914	0.431828	0.784086

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
16 & 0.528715 & 0.942571 & 0.471285 \tabularnewline
17 & 0.484706 & 0.969412 & 0.515294 \tabularnewline
18 & 0.333949 & 0.667898 & 0.666051 \tabularnewline
19 & 0.28485 & 0.569701 & 0.71515 \tabularnewline
20 & 0.271228 & 0.542455 & 0.728772 \tabularnewline
21 & 0.267109 & 0.534217 & 0.732891 \tabularnewline
22 & 0.187963 & 0.375927 & 0.812037 \tabularnewline
23 & 0.125158 & 0.250315 & 0.874842 \tabularnewline
24 & 0.268428 & 0.536856 & 0.731572 \tabularnewline
25 & 0.409602 & 0.819204 & 0.590398 \tabularnewline
26 & 0.338245 & 0.67649 & 0.661755 \tabularnewline
27 & 0.263103 & 0.526207 & 0.736897 \tabularnewline
28 & 0.202035 & 0.404071 & 0.797965 \tabularnewline
29 & 0.188007 & 0.376014 & 0.811993 \tabularnewline
30 & 0.201562 & 0.403125 & 0.798438 \tabularnewline
31 & 0.202361 & 0.404722 & 0.797639 \tabularnewline
32 & 0.244168 & 0.488336 & 0.755832 \tabularnewline
33 & 0.19259 & 0.385181 & 0.80741 \tabularnewline
34 & 0.393803 & 0.787605 & 0.606197 \tabularnewline
35 & 0.346079 & 0.692158 & 0.653921 \tabularnewline
36 & 0.353252 & 0.706503 & 0.646748 \tabularnewline
37 & 0.317353 & 0.634705 & 0.682647 \tabularnewline
38 & 0.373796 & 0.747592 & 0.626204 \tabularnewline
39 & 0.451613 & 0.903226 & 0.548387 \tabularnewline
40 & 0.356096 & 0.712192 & 0.643904 \tabularnewline
41 & 0.292198 & 0.584395 & 0.707802 \tabularnewline
42 & 0.333336 & 0.666673 & 0.666664 \tabularnewline
43 & 0.324911 & 0.649822 & 0.675089 \tabularnewline
44 & 0.340424 & 0.680848 & 0.659576 \tabularnewline
45 & 0.26692 & 0.533839 & 0.73308 \tabularnewline
46 & 0.303271 & 0.606542 & 0.696729 \tabularnewline
47 & 0.215914 & 0.431828 & 0.784086 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=266958&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]16[/C][C]0.528715[/C][C]0.942571[/C][C]0.471285[/C][/ROW]
[ROW][C]17[/C][C]0.484706[/C][C]0.969412[/C][C]0.515294[/C][/ROW]
[ROW][C]18[/C][C]0.333949[/C][C]0.667898[/C][C]0.666051[/C][/ROW]
[ROW][C]19[/C][C]0.28485[/C][C]0.569701[/C][C]0.71515[/C][/ROW]
[ROW][C]20[/C][C]0.271228[/C][C]0.542455[/C][C]0.728772[/C][/ROW]
[ROW][C]21[/C][C]0.267109[/C][C]0.534217[/C][C]0.732891[/C][/ROW]
[ROW][C]22[/C][C]0.187963[/C][C]0.375927[/C][C]0.812037[/C][/ROW]
[ROW][C]23[/C][C]0.125158[/C][C]0.250315[/C][C]0.874842[/C][/ROW]
[ROW][C]24[/C][C]0.268428[/C][C]0.536856[/C][C]0.731572[/C][/ROW]
[ROW][C]25[/C][C]0.409602[/C][C]0.819204[/C][C]0.590398[/C][/ROW]
[ROW][C]26[/C][C]0.338245[/C][C]0.67649[/C][C]0.661755[/C][/ROW]
[ROW][C]27[/C][C]0.263103[/C][C]0.526207[/C][C]0.736897[/C][/ROW]
[ROW][C]28[/C][C]0.202035[/C][C]0.404071[/C][C]0.797965[/C][/ROW]
[ROW][C]29[/C][C]0.188007[/C][C]0.376014[/C][C]0.811993[/C][/ROW]
[ROW][C]30[/C][C]0.201562[/C][C]0.403125[/C][C]0.798438[/C][/ROW]
[ROW][C]31[/C][C]0.202361[/C][C]0.404722[/C][C]0.797639[/C][/ROW]
[ROW][C]32[/C][C]0.244168[/C][C]0.488336[/C][C]0.755832[/C][/ROW]
[ROW][C]33[/C][C]0.19259[/C][C]0.385181[/C][C]0.80741[/C][/ROW]
[ROW][C]34[/C][C]0.393803[/C][C]0.787605[/C][C]0.606197[/C][/ROW]
[ROW][C]35[/C][C]0.346079[/C][C]0.692158[/C][C]0.653921[/C][/ROW]
[ROW][C]36[/C][C]0.353252[/C][C]0.706503[/C][C]0.646748[/C][/ROW]
[ROW][C]37[/C][C]0.317353[/C][C]0.634705[/C][C]0.682647[/C][/ROW]
[ROW][C]38[/C][C]0.373796[/C][C]0.747592[/C][C]0.626204[/C][/ROW]
[ROW][C]39[/C][C]0.451613[/C][C]0.903226[/C][C]0.548387[/C][/ROW]
[ROW][C]40[/C][C]0.356096[/C][C]0.712192[/C][C]0.643904[/C][/ROW]
[ROW][C]41[/C][C]0.292198[/C][C]0.584395[/C][C]0.707802[/C][/ROW]
[ROW][C]42[/C][C]0.333336[/C][C]0.666673[/C][C]0.666664[/C][/ROW]
[ROW][C]43[/C][C]0.324911[/C][C]0.649822[/C][C]0.675089[/C][/ROW]
[ROW][C]44[/C][C]0.340424[/C][C]0.680848[/C][C]0.659576[/C][/ROW]
[ROW][C]45[/C][C]0.26692[/C][C]0.533839[/C][C]0.73308[/C][/ROW]
[ROW][C]46[/C][C]0.303271[/C][C]0.606542[/C][C]0.696729[/C][/ROW]
[ROW][C]47[/C][C]0.215914[/C][C]0.431828[/C][C]0.784086[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=266958&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=266958&T=5

As an alternative you can also use a QR Code:

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

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
16	0.528715	0.942571	0.471285
17	0.484706	0.969412	0.515294
18	0.333949	0.667898	0.666051
19	0.28485	0.569701	0.71515
20	0.271228	0.542455	0.728772
21	0.267109	0.534217	0.732891
22	0.187963	0.375927	0.812037
23	0.125158	0.250315	0.874842
24	0.268428	0.536856	0.731572
25	0.409602	0.819204	0.590398
26	0.338245	0.67649	0.661755
27	0.263103	0.526207	0.736897
28	0.202035	0.404071	0.797965
29	0.188007	0.376014	0.811993
30	0.201562	0.403125	0.798438
31	0.202361	0.404722	0.797639
32	0.244168	0.488336	0.755832
33	0.19259	0.385181	0.80741
34	0.393803	0.787605	0.606197
35	0.346079	0.692158	0.653921
36	0.353252	0.706503	0.646748
37	0.317353	0.634705	0.682647
38	0.373796	0.747592	0.626204
39	0.451613	0.903226	0.548387
40	0.356096	0.712192	0.643904
41	0.292198	0.584395	0.707802
42	0.333336	0.666673	0.666664
43	0.324911	0.649822	0.675089
44	0.340424	0.680848	0.659576
45	0.26692	0.533839	0.73308
46	0.303271	0.606542	0.696729
47	0.215914	0.431828	0.784086

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	0	0	OK
5% type I error level	0	0	OK
10% type I error level	0	0	OK

\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 & 0 & 0 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=266958&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]0[/C][C]0[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=266958&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=266958&T=6

As an alternative you can also use a 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 tests	OK/NOK
1% type I error level	0	0	OK
5% type I error level	0	0	OK
10% type I error level	0	0	OK

Figure 1

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Figure 3

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Figure 4

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Figure 5

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Figure 6

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Figure 7

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Figure 8

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Figure 9

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Figure 10

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Parameters (Session):

par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 1 ; 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 <- '1'
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')
}

Free Statistics

Description of Statistical Computation

Tree of Dependent Computations

Dataset

Tables (Output of Computation)

Figures (Output of Computation)

Input Parameters & R Code