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R Software Module

rwasp_multipleregression.wasp

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Multiple Regression

Date of computation

Thu, 03 Sep 2015 02:12:24 +0100

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Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2015/Sep/03/t1441242801s2ayidjinfyw9wk.htm/, Retrieved Thu, 16 May 2024 22:42:20 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=280432, Retrieved Thu, 16 May 2024 22:42:20 +0000

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Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)

-       [Multiple Regression] [Multiple Lin Regr...] [2015-09-03 01:12:24] [71b0783e9a96780cfc39e1fa76ab729e] [Current]

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0 26 13 13 13 96 21 12.9
1 57 8 13 16 70 22 12.2
0 37 14 11 11 88 22 12.8
1 67 16 14 10 114 18 7.4
1 43 14 15 9 69 23 6.7
1 52 13 14 8 176 12 12.6
0 52 15 11 26 114 20 14.8
1 43 13 13 10 121 22 13.3
1 84 20 16 10 110 21 11.1
1 67 17 14 8 158 19 8.2
1 49 15 14 13 116 22 11.4
1 70 16 15 11 181 15 6.4
1 52 12 15 8 77 20 10.6
0 58 17 13 12 141 19 12.0
0 68 11 14 24 35 18 6.3
0 62 16 11 21 80 15 11.3
1 43 16 12 5 152 20 11.9
0 56 15 14 14 97 21 9.3
1 56 13 13 11 99 21 9.6
0 74 14 12 9 84 15 10.0
1 63 16 15 17 101 23 13.8
0 58 17 14 18 107 21 10.8
1 63 15 12 23 112 25 11.7
1 53 14 12 9 171 9 10.9
1 57 14 12 14 137 30 16.1
0 51 16 15 13 77 20 13.4
1 64 15 14 10 66 23 9.9
0 53 17 16 8 93 16 11.5
0 29 14 12 10 105 16 8.3
0 54 16 12 19 131 19 11.7
1 58 15 14 11 102 25 9.0
1 43 16 16 16 161 18 9.7
1 51 16 15 12 120 23 10.8
1 53 10 12 11 127 21 10.3
0 54 8 14 11 77 10 10.4
1 56 17 13 10 108 14 12.7
1 61 14 14 13 85 22 9.3
0 47 10 16 14 168 26 11.8
1 39 14 12 8 48 23 5.9
1 48 12 14 11 152 23 11.4
1 50 16 15 11 75 24 13.0
1 35 16 13 13 107 24 10.8
1 30 16 16 15 62 18 12.3
0 68 8 16 15 121 23 11.3
1 49 16 12 16 124 15 11.8
1 61 15 12 12 72 19 7.9
0 67 8 16 12 40 16 12.7
1 47 13 12 17 58 25 12.3
1 56 14 15 14 97 23 11.6
1 50 13 12 15 88 17 6.7
1 43 16 13 12 126 19 10.9
1 67 19 12 13 104 21 12.1
1 62 19 14 7 148 18 13.3
1 57 14 14 8 146 27 10.1
0 41 15 11 16 80 21 5.7
1 54 13 10 20 97 13 14.3
0 45 10 12 14 25 8 8.0
1 48 16 11 10 99 29 13.3
1 61 15 16 16 118 28 9.3
0 56 11 14 11 58 23 12.5
0 41 9 14 26 63 21 7.6
1 43 16 15 9 139 19 15.9
0 53 12 15 15 50 19 9.2
1 44 12 14 12 60 20 9.1
0 66 14 13 21 152 18 11.1
1 58 14 11 20 142 19 13.0
1 46 13 16 20 94 17 14.5
0 37 15 12 10 66 19 12.2
0 51 17 15 15 127 25 12.3
0 51 14 14 10 67 19 11.4
1 66 9 14 9 75 23 14.6
0 37 7 13 17 128 14 12.6
1 59 13 6 10 41 28 NA
0 42 15 12 19 146 16 13.0
1 38 12 12 13 69 24 12.6
0 66 15 14 8 186 20 13.2
0 34 14 14 11 81 12 9.9
1 53 16 15 9 85 24 7.7
0 49 14 11 12 54 22 10.5
0 55 13 13 10 46 12 13.4
0 49 16 14 9 106 22 10.9
1 59 13 16 14 34 20 4.3
0 40 16 13 14 60 10 10.3
1 58 16 14 10 95 23 11.8
1 60 16 16 8 57 17 11.2
0 63 10 11 13 62 22 11.4
0 56 12 13 9 36 24 8.6
0 54 12 13 14 56 18 13.2
1 52 12 15 8 54 21 12.6
1 34 12 12 16 64 20 5.6
1 69 19 13 14 76 20 9.9
0 32 14 12 14 98 22 8.8
1 48 13 14 8 88 19 7.7
0 67 16 14 11 35 20 9.0
1 58 15 16 11 102 26 7.3
1 57 12 15 13 61 23 11.4
1 42 8 14 12 80 24 13.6
1 64 10 13 13 49 21 7.9
1 58 16 14 9 78 21 10.7
0 66 16 15 10 90 19 10.3
1 61 18 12 11 55 17 9.6
1 52 12 7 13 96 20 14.2
0 51 16 12 17 43 11 8.5
0 55 10 15 15 52 8 13.5
0 60 12 13 14 54 18 6.4
0 56 11 11 10 51 18 9.6
0 63 15 14 15 51 19 11.6
1 61 7 13 14 38 19 11.1

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	6 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=280432&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=280432&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=280432&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	6 seconds
R Server	'Herman Ole Andreas Wold' @ wold.wessa.net

Multiple Linear Regression - Estimated Regression Equation
TOT[t] = + 11.2853 -0.238128Gender[t] -0.00394087AMS.I[t] -0.0733071CONFSTATTOT[t] -0.121082STRESSTOT[t] + 0.0142712CESDTOT[t] + 0.0193415BEREK[t] + 0.0264642NUM[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
TOT[t] =  +  11.2853 -0.238128Gender[t] -0.00394087AMS.I[t] -0.0733071CONFSTATTOT[t] -0.121082STRESSTOT[t] +  0.0142712CESDTOT[t] +  0.0193415BEREK[t] +  0.0264642NUM[t]  + e[t] \tabularnewline
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=280432&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]TOT[t] =  +  11.2853 -0.238128Gender[t] -0.00394087AMS.I[t] -0.0733071CONFSTATTOT[t] -0.121082STRESSTOT[t] +  0.0142712CESDTOT[t] +  0.0193415BEREK[t] +  0.0264642NUM[t]  + e[t][/C][/ROW]
[ROW][C][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=280432&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=280432&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] = + 11.2853 -0.238128Gender[t] -0.00394087AMS.I[t] -0.0733071CONFSTATTOT[t] -0.121082STRESSTOT[t] + 0.0142712CESDTOT[t] + 0.0193415BEREK[t] + 0.0264642NUM[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)	+11.29	2.735	+4.1270e+00	7.671e-05	3.835e-05
Gender	-0.2381	0.5077	-4.6900e-01	0.6401	0.32
AMS.I	-0.003941	0.02223	-1.7730e-01	0.8597	0.4298
CONFSTATTOT	-0.07331	0.08923	-8.2150e-01	0.4133	0.2067
STRESSTOT	-0.1211	0.1462	-8.2840e-01	0.4094	0.2047
CESDTOT	+0.01427	0.05921	+2.4100e-01	0.81	0.405
BEREK	+0.01934	0.006482	+2.9840e+00	0.003584	0.001792
NUM	+0.02646	0.05617	+4.7110e-01	0.6386	0.3193

\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) & +11.29 &  2.735 & +4.1270e+00 &  7.671e-05 &  3.835e-05 \tabularnewline
Gender & -0.2381 &  0.5077 & -4.6900e-01 &  0.6401 &  0.32 \tabularnewline
AMS.I & -0.003941 &  0.02223 & -1.7730e-01 &  0.8597 &  0.4298 \tabularnewline
CONFSTATTOT & -0.07331 &  0.08923 & -8.2150e-01 &  0.4133 &  0.2067 \tabularnewline
STRESSTOT & -0.1211 &  0.1462 & -8.2840e-01 &  0.4094 &  0.2047 \tabularnewline
CESDTOT & +0.01427 &  0.05921 & +2.4100e-01 &  0.81 &  0.405 \tabularnewline
BEREK & +0.01934 &  0.006482 & +2.9840e+00 &  0.003584 &  0.001792 \tabularnewline
NUM & +0.02646 &  0.05617 & +4.7110e-01 &  0.6386 &  0.3193 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=280432&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]+11.29[/C][C] 2.735[/C][C]+4.1270e+00[/C][C] 7.671e-05[/C][C] 3.835e-05[/C][/ROW]
[ROW][C]Gender[/C][C]-0.2381[/C][C] 0.5077[/C][C]-4.6900e-01[/C][C] 0.6401[/C][C] 0.32[/C][/ROW]
[ROW][C]AMS.I[/C][C]-0.003941[/C][C] 0.02223[/C][C]-1.7730e-01[/C][C] 0.8597[/C][C] 0.4298[/C][/ROW]
[ROW][C]CONFSTATTOT[/C][C]-0.07331[/C][C] 0.08923[/C][C]-8.2150e-01[/C][C] 0.4133[/C][C] 0.2067[/C][/ROW]
[ROW][C]STRESSTOT[/C][C]-0.1211[/C][C] 0.1462[/C][C]-8.2840e-01[/C][C] 0.4094[/C][C] 0.2047[/C][/ROW]
[ROW][C]CESDTOT[/C][C]+0.01427[/C][C] 0.05921[/C][C]+2.4100e-01[/C][C] 0.81[/C][C] 0.405[/C][/ROW]
[ROW][C]BEREK[/C][C]+0.01934[/C][C] 0.006482[/C][C]+2.9840e+00[/C][C] 0.003584[/C][C] 0.001792[/C][/ROW]
[ROW][C]NUM[/C][C]+0.02646[/C][C] 0.05617[/C][C]+4.7110e-01[/C][C] 0.6386[/C][C] 0.3193[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=280432&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=280432&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)	+11.29	2.735	+4.1270e+00	7.671e-05	3.835e-05
Gender	-0.2381	0.5077	-4.6900e-01	0.6401	0.32
AMS.I	-0.003941	0.02223	-1.7730e-01	0.8597	0.4298
CONFSTATTOT	-0.07331	0.08923	-8.2150e-01	0.4133	0.2067
STRESSTOT	-0.1211	0.1462	-8.2840e-01	0.4094	0.2047
CESDTOT	+0.01427	0.05921	+2.4100e-01	0.81	0.405
BEREK	+0.01934	0.006482	+2.9840e+00	0.003584	0.001792
NUM	+0.02646	0.05617	+4.7110e-01	0.6386	0.3193

Multiple Linear Regression - Regression Statistics
Multiple R	0.3045
R-squared	0.09273
Adjusted R-squared	0.02858
F-TEST (value)	1.446
F-TEST (DF numerator)	7
F-TEST (DF denominator)	99
p-value	0.1958
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	2.356
Sum Squared Residuals	549.7

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R &  0.3045 \tabularnewline
R-squared &  0.09273 \tabularnewline
Adjusted R-squared &  0.02858 \tabularnewline
F-TEST (value) &  1.446 \tabularnewline
F-TEST (DF numerator) & 7 \tabularnewline
F-TEST (DF denominator) & 99 \tabularnewline
p-value &  0.1958 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation &  2.356 \tabularnewline
Sum Squared Residuals &  549.7 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=280432&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C] 0.3045[/C][/ROW]
[ROW][C]R-squared[/C][C] 0.09273[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C] 0.02858[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C] 1.446[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]7[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]99[/C][/ROW]
[ROW][C]p-value[/C][C] 0.1958[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C] 2.356[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C] 549.7[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=280432&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=280432&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	0.3045
R-squared	0.09273
Adjusted R-squared	0.02858
F-TEST (value)	1.446
F-TEST (DF numerator)	7
F-TEST (DF denominator)	99
p-value	0.1958
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	2.356
Sum Squared Residuals	549.7

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	12.9	11.25	1.646
2	12.2	10.83	1.373
3	12.8	11.22	1.577
4	7.4	10.74	-3.339
5	6.7	10.11	-3.407
6	12.6	12.03	0.57
7	14.8	11.75	3.046
8	13.3	11.42	1.884
9	11.1	10.14	0.9613
10	8.2	11.51	-3.315
11	11.4	11.07	0.3293
12	6.4	11.84	-5.437
13	10.6	10.28	0.3209
14	12	11.64	0.3623
15	6.3	10.01	-3.712
16	11.3	10.78	0.5198
17	11.9	11.79	0.1076
18	9.3	10.9	-1.602
19	9.6	10.93	-1.327
20	10	10.66	-0.6645
21	13.8	10.61	3.185
22	10.8	11	-0.1976
23	11.7	11.4	0.2976
24	10.9	12.03	-1.133
25	16.1	11.99	4.113
26	13.4	10.3	3.101
27	9.9	10.03	-0.1282
28	11.5	10.23	1.271
29	8.3	11.29	-2.989
30	11.7	11.75	-0.05433
31	9	10.82	-1.815
32	9.7	11.59	-1.886
33	10.8	10.96	-0.158
34	10.3	11.82	-1.521
35	10.4	10.7	-0.3018
36	12.7	10.61	2.092
37	9.3	10.5	-1.197
38	11.8	12.57	-0.767
39	5.9	10.07	-4.165
40	11.4	11.99	-0.5888
41	13	10.1	2.896
42	10.8	11.05	-0.2525
43	12.3	9.708	2.592
44	11.3	11.66	-0.3566
45	11.8	11.25	0.5482
46	7.9	10.32	-2.421
47	12.7	9.866	2.834
48	12.3	10.48	1.818
49	11.6	10.67	0.9314
50	6.7	10.81	-4.11
51	10.9	11.24	-0.3419
52	12.1	10.69	1.41
53	13.3	11.15	2.146
54	10.1	11.75	-1.654
55	5.7	11.02	-5.324
56	14.3	11.18	3.124
57	8	9.817	-1.817
58	13.3	11.18	2.122
59	9.3	11.02	-1.722
60	12.5	10.45	2.049
61	7.6	10.91	-3.314
62	15.9	11.21	4.692
63	9.2	10.06	-0.8645
64	9.1	10.16	-1.06
65	11.1	12.14	-1.041
66	13	12	1.005
67	14.5	10.53	3.971
68	12.2	10.51	1.691
69	12.3	11.35	0.9461
70	11.4	10.3	1.096
71	14.6	10.62	3.98
72	12.6	12.14	0.4589
73	NA	NA	0.9143
74	13	11.12	1.88
75	12.6	11.87	0.7285
76	13.2	13.77	-0.5711
77	9.9	12.46	-2.557
78	7.7	7.732	-0.03193
79	10.5	6.992	3.508
80	13.4	13.48	-0.08502
81	10.9	15.91	-5.011
82	4.3	4.006	0.2944
83	10.3	9.039	1.261
84	11.8	9.967	1.833
85	11.2	10.74	0.461
86	11.4	12.87	-1.471
87	8.6	5.778	2.822
88	13.2	10.46	2.739
89	12.6	17.58	-4.976
90	5.6	5.707	-0.1074
91	9.9	12.46	-2.557
92	8.8	11.63	-2.829
93	7.7	8.216	-0.5164
94	9	12.3	-3.3
95	7.3	6.001	1.299
96	11.4	8.754	2.646
97	13.6	15.88	-2.277
98	7.9	7.343	0.5566
99	10.7	10.82	-0.1224
100	10.3	10.4	-0.105
101	9.6	7.087	2.513
102	14.2	15.52	-1.324
103	8.5	4.951	3.549
104	13.5	17.42	-3.916
105	6.4	7.332	-0.9318
106	9.6	7.946	1.654
107	11.6	10.66	0.9428
108	11.1	NA	NA

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 &  12.9 &  11.25 &  1.646 \tabularnewline
2 &  12.2 &  10.83 &  1.373 \tabularnewline
3 &  12.8 &  11.22 &  1.577 \tabularnewline
4 &  7.4 &  10.74 & -3.339 \tabularnewline
5 &  6.7 &  10.11 & -3.407 \tabularnewline
6 &  12.6 &  12.03 &  0.57 \tabularnewline
7 &  14.8 &  11.75 &  3.046 \tabularnewline
8 &  13.3 &  11.42 &  1.884 \tabularnewline
9 &  11.1 &  10.14 &  0.9613 \tabularnewline
10 &  8.2 &  11.51 & -3.315 \tabularnewline
11 &  11.4 &  11.07 &  0.3293 \tabularnewline
12 &  6.4 &  11.84 & -5.437 \tabularnewline
13 &  10.6 &  10.28 &  0.3209 \tabularnewline
14 &  12 &  11.64 &  0.3623 \tabularnewline
15 &  6.3 &  10.01 & -3.712 \tabularnewline
16 &  11.3 &  10.78 &  0.5198 \tabularnewline
17 &  11.9 &  11.79 &  0.1076 \tabularnewline
18 &  9.3 &  10.9 & -1.602 \tabularnewline
19 &  9.6 &  10.93 & -1.327 \tabularnewline
20 &  10 &  10.66 & -0.6645 \tabularnewline
21 &  13.8 &  10.61 &  3.185 \tabularnewline
22 &  10.8 &  11 & -0.1976 \tabularnewline
23 &  11.7 &  11.4 &  0.2976 \tabularnewline
24 &  10.9 &  12.03 & -1.133 \tabularnewline
25 &  16.1 &  11.99 &  4.113 \tabularnewline
26 &  13.4 &  10.3 &  3.101 \tabularnewline
27 &  9.9 &  10.03 & -0.1282 \tabularnewline
28 &  11.5 &  10.23 &  1.271 \tabularnewline
29 &  8.3 &  11.29 & -2.989 \tabularnewline
30 &  11.7 &  11.75 & -0.05433 \tabularnewline
31 &  9 &  10.82 & -1.815 \tabularnewline
32 &  9.7 &  11.59 & -1.886 \tabularnewline
33 &  10.8 &  10.96 & -0.158 \tabularnewline
34 &  10.3 &  11.82 & -1.521 \tabularnewline
35 &  10.4 &  10.7 & -0.3018 \tabularnewline
36 &  12.7 &  10.61 &  2.092 \tabularnewline
37 &  9.3 &  10.5 & -1.197 \tabularnewline
38 &  11.8 &  12.57 & -0.767 \tabularnewline
39 &  5.9 &  10.07 & -4.165 \tabularnewline
40 &  11.4 &  11.99 & -0.5888 \tabularnewline
41 &  13 &  10.1 &  2.896 \tabularnewline
42 &  10.8 &  11.05 & -0.2525 \tabularnewline
43 &  12.3 &  9.708 &  2.592 \tabularnewline
44 &  11.3 &  11.66 & -0.3566 \tabularnewline
45 &  11.8 &  11.25 &  0.5482 \tabularnewline
46 &  7.9 &  10.32 & -2.421 \tabularnewline
47 &  12.7 &  9.866 &  2.834 \tabularnewline
48 &  12.3 &  10.48 &  1.818 \tabularnewline
49 &  11.6 &  10.67 &  0.9314 \tabularnewline
50 &  6.7 &  10.81 & -4.11 \tabularnewline
51 &  10.9 &  11.24 & -0.3419 \tabularnewline
52 &  12.1 &  10.69 &  1.41 \tabularnewline
53 &  13.3 &  11.15 &  2.146 \tabularnewline
54 &  10.1 &  11.75 & -1.654 \tabularnewline
55 &  5.7 &  11.02 & -5.324 \tabularnewline
56 &  14.3 &  11.18 &  3.124 \tabularnewline
57 &  8 &  9.817 & -1.817 \tabularnewline
58 &  13.3 &  11.18 &  2.122 \tabularnewline
59 &  9.3 &  11.02 & -1.722 \tabularnewline
60 &  12.5 &  10.45 &  2.049 \tabularnewline
61 &  7.6 &  10.91 & -3.314 \tabularnewline
62 &  15.9 &  11.21 &  4.692 \tabularnewline
63 &  9.2 &  10.06 & -0.8645 \tabularnewline
64 &  9.1 &  10.16 & -1.06 \tabularnewline
65 &  11.1 &  12.14 & -1.041 \tabularnewline
66 &  13 &  12 &  1.005 \tabularnewline
67 &  14.5 &  10.53 &  3.971 \tabularnewline
68 &  12.2 &  10.51 &  1.691 \tabularnewline
69 &  12.3 &  11.35 &  0.9461 \tabularnewline
70 &  11.4 &  10.3 &  1.096 \tabularnewline
71 &  14.6 &  10.62 &  3.98 \tabularnewline
72 &  12.6 &  12.14 &  0.4589 \tabularnewline
73 & NA & NA &  0.9143 \tabularnewline
74 &  13 &  11.12 &  1.88 \tabularnewline
75 &  12.6 &  11.87 &  0.7285 \tabularnewline
76 &  13.2 &  13.77 & -0.5711 \tabularnewline
77 &  9.9 &  12.46 & -2.557 \tabularnewline
78 &  7.7 &  7.732 & -0.03193 \tabularnewline
79 &  10.5 &  6.992 &  3.508 \tabularnewline
80 &  13.4 &  13.48 & -0.08502 \tabularnewline
81 &  10.9 &  15.91 & -5.011 \tabularnewline
82 &  4.3 &  4.006 &  0.2944 \tabularnewline
83 &  10.3 &  9.039 &  1.261 \tabularnewline
84 &  11.8 &  9.967 &  1.833 \tabularnewline
85 &  11.2 &  10.74 &  0.461 \tabularnewline
86 &  11.4 &  12.87 & -1.471 \tabularnewline
87 &  8.6 &  5.778 &  2.822 \tabularnewline
88 &  13.2 &  10.46 &  2.739 \tabularnewline
89 &  12.6 &  17.58 & -4.976 \tabularnewline
90 &  5.6 &  5.707 & -0.1074 \tabularnewline
91 &  9.9 &  12.46 & -2.557 \tabularnewline
92 &  8.8 &  11.63 & -2.829 \tabularnewline
93 &  7.7 &  8.216 & -0.5164 \tabularnewline
94 &  9 &  12.3 & -3.3 \tabularnewline
95 &  7.3 &  6.001 &  1.299 \tabularnewline
96 &  11.4 &  8.754 &  2.646 \tabularnewline
97 &  13.6 &  15.88 & -2.277 \tabularnewline
98 &  7.9 &  7.343 &  0.5566 \tabularnewline
99 &  10.7 &  10.82 & -0.1224 \tabularnewline
100 &  10.3 &  10.4 & -0.105 \tabularnewline
101 &  9.6 &  7.087 &  2.513 \tabularnewline
102 &  14.2 &  15.52 & -1.324 \tabularnewline
103 &  8.5 &  4.951 &  3.549 \tabularnewline
104 &  13.5 &  17.42 & -3.916 \tabularnewline
105 &  6.4 &  7.332 & -0.9318 \tabularnewline
106 &  9.6 &  7.946 &  1.654 \tabularnewline
107 &  11.6 &  10.66 &  0.9428 \tabularnewline
108 &  11.1 & NA & NA \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=280432&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] 11.25[/C][C] 1.646[/C][/ROW]
[ROW][C]2[/C][C] 12.2[/C][C] 10.83[/C][C] 1.373[/C][/ROW]
[ROW][C]3[/C][C] 12.8[/C][C] 11.22[/C][C] 1.577[/C][/ROW]
[ROW][C]4[/C][C] 7.4[/C][C] 10.74[/C][C]-3.339[/C][/ROW]
[ROW][C]5[/C][C] 6.7[/C][C] 10.11[/C][C]-3.407[/C][/ROW]
[ROW][C]6[/C][C] 12.6[/C][C] 12.03[/C][C] 0.57[/C][/ROW]
[ROW][C]7[/C][C] 14.8[/C][C] 11.75[/C][C] 3.046[/C][/ROW]
[ROW][C]8[/C][C] 13.3[/C][C] 11.42[/C][C] 1.884[/C][/ROW]
[ROW][C]9[/C][C] 11.1[/C][C] 10.14[/C][C] 0.9613[/C][/ROW]
[ROW][C]10[/C][C] 8.2[/C][C] 11.51[/C][C]-3.315[/C][/ROW]
[ROW][C]11[/C][C] 11.4[/C][C] 11.07[/C][C] 0.3293[/C][/ROW]
[ROW][C]12[/C][C] 6.4[/C][C] 11.84[/C][C]-5.437[/C][/ROW]
[ROW][C]13[/C][C] 10.6[/C][C] 10.28[/C][C] 0.3209[/C][/ROW]
[ROW][C]14[/C][C] 12[/C][C] 11.64[/C][C] 0.3623[/C][/ROW]
[ROW][C]15[/C][C] 6.3[/C][C] 10.01[/C][C]-3.712[/C][/ROW]
[ROW][C]16[/C][C] 11.3[/C][C] 10.78[/C][C] 0.5198[/C][/ROW]
[ROW][C]17[/C][C] 11.9[/C][C] 11.79[/C][C] 0.1076[/C][/ROW]
[ROW][C]18[/C][C] 9.3[/C][C] 10.9[/C][C]-1.602[/C][/ROW]
[ROW][C]19[/C][C] 9.6[/C][C] 10.93[/C][C]-1.327[/C][/ROW]
[ROW][C]20[/C][C] 10[/C][C] 10.66[/C][C]-0.6645[/C][/ROW]
[ROW][C]21[/C][C] 13.8[/C][C] 10.61[/C][C] 3.185[/C][/ROW]
[ROW][C]22[/C][C] 10.8[/C][C] 11[/C][C]-0.1976[/C][/ROW]
[ROW][C]23[/C][C] 11.7[/C][C] 11.4[/C][C] 0.2976[/C][/ROW]
[ROW][C]24[/C][C] 10.9[/C][C] 12.03[/C][C]-1.133[/C][/ROW]
[ROW][C]25[/C][C] 16.1[/C][C] 11.99[/C][C] 4.113[/C][/ROW]
[ROW][C]26[/C][C] 13.4[/C][C] 10.3[/C][C] 3.101[/C][/ROW]
[ROW][C]27[/C][C] 9.9[/C][C] 10.03[/C][C]-0.1282[/C][/ROW]
[ROW][C]28[/C][C] 11.5[/C][C] 10.23[/C][C] 1.271[/C][/ROW]
[ROW][C]29[/C][C] 8.3[/C][C] 11.29[/C][C]-2.989[/C][/ROW]
[ROW][C]30[/C][C] 11.7[/C][C] 11.75[/C][C]-0.05433[/C][/ROW]
[ROW][C]31[/C][C] 9[/C][C] 10.82[/C][C]-1.815[/C][/ROW]
[ROW][C]32[/C][C] 9.7[/C][C] 11.59[/C][C]-1.886[/C][/ROW]
[ROW][C]33[/C][C] 10.8[/C][C] 10.96[/C][C]-0.158[/C][/ROW]
[ROW][C]34[/C][C] 10.3[/C][C] 11.82[/C][C]-1.521[/C][/ROW]
[ROW][C]35[/C][C] 10.4[/C][C] 10.7[/C][C]-0.3018[/C][/ROW]
[ROW][C]36[/C][C] 12.7[/C][C] 10.61[/C][C] 2.092[/C][/ROW]
[ROW][C]37[/C][C] 9.3[/C][C] 10.5[/C][C]-1.197[/C][/ROW]
[ROW][C]38[/C][C] 11.8[/C][C] 12.57[/C][C]-0.767[/C][/ROW]
[ROW][C]39[/C][C] 5.9[/C][C] 10.07[/C][C]-4.165[/C][/ROW]
[ROW][C]40[/C][C] 11.4[/C][C] 11.99[/C][C]-0.5888[/C][/ROW]
[ROW][C]41[/C][C] 13[/C][C] 10.1[/C][C] 2.896[/C][/ROW]
[ROW][C]42[/C][C] 10.8[/C][C] 11.05[/C][C]-0.2525[/C][/ROW]
[ROW][C]43[/C][C] 12.3[/C][C] 9.708[/C][C] 2.592[/C][/ROW]
[ROW][C]44[/C][C] 11.3[/C][C] 11.66[/C][C]-0.3566[/C][/ROW]
[ROW][C]45[/C][C] 11.8[/C][C] 11.25[/C][C] 0.5482[/C][/ROW]
[ROW][C]46[/C][C] 7.9[/C][C] 10.32[/C][C]-2.421[/C][/ROW]
[ROW][C]47[/C][C] 12.7[/C][C] 9.866[/C][C] 2.834[/C][/ROW]
[ROW][C]48[/C][C] 12.3[/C][C] 10.48[/C][C] 1.818[/C][/ROW]
[ROW][C]49[/C][C] 11.6[/C][C] 10.67[/C][C] 0.9314[/C][/ROW]
[ROW][C]50[/C][C] 6.7[/C][C] 10.81[/C][C]-4.11[/C][/ROW]
[ROW][C]51[/C][C] 10.9[/C][C] 11.24[/C][C]-0.3419[/C][/ROW]
[ROW][C]52[/C][C] 12.1[/C][C] 10.69[/C][C] 1.41[/C][/ROW]
[ROW][C]53[/C][C] 13.3[/C][C] 11.15[/C][C] 2.146[/C][/ROW]
[ROW][C]54[/C][C] 10.1[/C][C] 11.75[/C][C]-1.654[/C][/ROW]
[ROW][C]55[/C][C] 5.7[/C][C] 11.02[/C][C]-5.324[/C][/ROW]
[ROW][C]56[/C][C] 14.3[/C][C] 11.18[/C][C] 3.124[/C][/ROW]
[ROW][C]57[/C][C] 8[/C][C] 9.817[/C][C]-1.817[/C][/ROW]
[ROW][C]58[/C][C] 13.3[/C][C] 11.18[/C][C] 2.122[/C][/ROW]
[ROW][C]59[/C][C] 9.3[/C][C] 11.02[/C][C]-1.722[/C][/ROW]
[ROW][C]60[/C][C] 12.5[/C][C] 10.45[/C][C] 2.049[/C][/ROW]
[ROW][C]61[/C][C] 7.6[/C][C] 10.91[/C][C]-3.314[/C][/ROW]
[ROW][C]62[/C][C] 15.9[/C][C] 11.21[/C][C] 4.692[/C][/ROW]
[ROW][C]63[/C][C] 9.2[/C][C] 10.06[/C][C]-0.8645[/C][/ROW]
[ROW][C]64[/C][C] 9.1[/C][C] 10.16[/C][C]-1.06[/C][/ROW]
[ROW][C]65[/C][C] 11.1[/C][C] 12.14[/C][C]-1.041[/C][/ROW]
[ROW][C]66[/C][C] 13[/C][C] 12[/C][C] 1.005[/C][/ROW]
[ROW][C]67[/C][C] 14.5[/C][C] 10.53[/C][C] 3.971[/C][/ROW]
[ROW][C]68[/C][C] 12.2[/C][C] 10.51[/C][C] 1.691[/C][/ROW]
[ROW][C]69[/C][C] 12.3[/C][C] 11.35[/C][C] 0.9461[/C][/ROW]
[ROW][C]70[/C][C] 11.4[/C][C] 10.3[/C][C] 1.096[/C][/ROW]
[ROW][C]71[/C][C] 14.6[/C][C] 10.62[/C][C] 3.98[/C][/ROW]
[ROW][C]72[/C][C] 12.6[/C][C] 12.14[/C][C] 0.4589[/C][/ROW]
[ROW][C]73[/C][C]NA[/C][C]NA[/C][C] 0.9143[/C][/ROW]
[ROW][C]74[/C][C] 13[/C][C] 11.12[/C][C] 1.88[/C][/ROW]
[ROW][C]75[/C][C] 12.6[/C][C] 11.87[/C][C] 0.7285[/C][/ROW]
[ROW][C]76[/C][C] 13.2[/C][C] 13.77[/C][C]-0.5711[/C][/ROW]
[ROW][C]77[/C][C] 9.9[/C][C] 12.46[/C][C]-2.557[/C][/ROW]
[ROW][C]78[/C][C] 7.7[/C][C] 7.732[/C][C]-0.03193[/C][/ROW]
[ROW][C]79[/C][C] 10.5[/C][C] 6.992[/C][C] 3.508[/C][/ROW]
[ROW][C]80[/C][C] 13.4[/C][C] 13.48[/C][C]-0.08502[/C][/ROW]
[ROW][C]81[/C][C] 10.9[/C][C] 15.91[/C][C]-5.011[/C][/ROW]
[ROW][C]82[/C][C] 4.3[/C][C] 4.006[/C][C] 0.2944[/C][/ROW]
[ROW][C]83[/C][C] 10.3[/C][C] 9.039[/C][C] 1.261[/C][/ROW]
[ROW][C]84[/C][C] 11.8[/C][C] 9.967[/C][C] 1.833[/C][/ROW]
[ROW][C]85[/C][C] 11.2[/C][C] 10.74[/C][C] 0.461[/C][/ROW]
[ROW][C]86[/C][C] 11.4[/C][C] 12.87[/C][C]-1.471[/C][/ROW]
[ROW][C]87[/C][C] 8.6[/C][C] 5.778[/C][C] 2.822[/C][/ROW]
[ROW][C]88[/C][C] 13.2[/C][C] 10.46[/C][C] 2.739[/C][/ROW]
[ROW][C]89[/C][C] 12.6[/C][C] 17.58[/C][C]-4.976[/C][/ROW]
[ROW][C]90[/C][C] 5.6[/C][C] 5.707[/C][C]-0.1074[/C][/ROW]
[ROW][C]91[/C][C] 9.9[/C][C] 12.46[/C][C]-2.557[/C][/ROW]
[ROW][C]92[/C][C] 8.8[/C][C] 11.63[/C][C]-2.829[/C][/ROW]
[ROW][C]93[/C][C] 7.7[/C][C] 8.216[/C][C]-0.5164[/C][/ROW]
[ROW][C]94[/C][C] 9[/C][C] 12.3[/C][C]-3.3[/C][/ROW]
[ROW][C]95[/C][C] 7.3[/C][C] 6.001[/C][C] 1.299[/C][/ROW]
[ROW][C]96[/C][C] 11.4[/C][C] 8.754[/C][C] 2.646[/C][/ROW]
[ROW][C]97[/C][C] 13.6[/C][C] 15.88[/C][C]-2.277[/C][/ROW]
[ROW][C]98[/C][C] 7.9[/C][C] 7.343[/C][C] 0.5566[/C][/ROW]
[ROW][C]99[/C][C] 10.7[/C][C] 10.82[/C][C]-0.1224[/C][/ROW]
[ROW][C]100[/C][C] 10.3[/C][C] 10.4[/C][C]-0.105[/C][/ROW]
[ROW][C]101[/C][C] 9.6[/C][C] 7.087[/C][C] 2.513[/C][/ROW]
[ROW][C]102[/C][C] 14.2[/C][C] 15.52[/C][C]-1.324[/C][/ROW]
[ROW][C]103[/C][C] 8.5[/C][C] 4.951[/C][C] 3.549[/C][/ROW]
[ROW][C]104[/C][C] 13.5[/C][C] 17.42[/C][C]-3.916[/C][/ROW]
[ROW][C]105[/C][C] 6.4[/C][C] 7.332[/C][C]-0.9318[/C][/ROW]
[ROW][C]106[/C][C] 9.6[/C][C] 7.946[/C][C] 1.654[/C][/ROW]
[ROW][C]107[/C][C] 11.6[/C][C] 10.66[/C][C] 0.9428[/C][/ROW]
[ROW][C]108[/C][C] 11.1[/C][C]NA[/C][C]NA[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=280432&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=280432&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	11.25	1.646
2	12.2	10.83	1.373
3	12.8	11.22	1.577
4	7.4	10.74	-3.339
5	6.7	10.11	-3.407
6	12.6	12.03	0.57
7	14.8	11.75	3.046
8	13.3	11.42	1.884
9	11.1	10.14	0.9613
10	8.2	11.51	-3.315
11	11.4	11.07	0.3293
12	6.4	11.84	-5.437
13	10.6	10.28	0.3209
14	12	11.64	0.3623
15	6.3	10.01	-3.712
16	11.3	10.78	0.5198
17	11.9	11.79	0.1076
18	9.3	10.9	-1.602
19	9.6	10.93	-1.327
20	10	10.66	-0.6645
21	13.8	10.61	3.185
22	10.8	11	-0.1976
23	11.7	11.4	0.2976
24	10.9	12.03	-1.133
25	16.1	11.99	4.113
26	13.4	10.3	3.101
27	9.9	10.03	-0.1282
28	11.5	10.23	1.271
29	8.3	11.29	-2.989
30	11.7	11.75	-0.05433
31	9	10.82	-1.815
32	9.7	11.59	-1.886
33	10.8	10.96	-0.158
34	10.3	11.82	-1.521
35	10.4	10.7	-0.3018
36	12.7	10.61	2.092
37	9.3	10.5	-1.197
38	11.8	12.57	-0.767
39	5.9	10.07	-4.165
40	11.4	11.99	-0.5888
41	13	10.1	2.896
42	10.8	11.05	-0.2525
43	12.3	9.708	2.592
44	11.3	11.66	-0.3566
45	11.8	11.25	0.5482
46	7.9	10.32	-2.421
47	12.7	9.866	2.834
48	12.3	10.48	1.818
49	11.6	10.67	0.9314
50	6.7	10.81	-4.11
51	10.9	11.24	-0.3419
52	12.1	10.69	1.41
53	13.3	11.15	2.146
54	10.1	11.75	-1.654
55	5.7	11.02	-5.324
56	14.3	11.18	3.124
57	8	9.817	-1.817
58	13.3	11.18	2.122
59	9.3	11.02	-1.722
60	12.5	10.45	2.049
61	7.6	10.91	-3.314
62	15.9	11.21	4.692
63	9.2	10.06	-0.8645
64	9.1	10.16	-1.06
65	11.1	12.14	-1.041
66	13	12	1.005
67	14.5	10.53	3.971
68	12.2	10.51	1.691
69	12.3	11.35	0.9461
70	11.4	10.3	1.096
71	14.6	10.62	3.98
72	12.6	12.14	0.4589
73	NA	NA	0.9143
74	13	11.12	1.88
75	12.6	11.87	0.7285
76	13.2	13.77	-0.5711
77	9.9	12.46	-2.557
78	7.7	7.732	-0.03193
79	10.5	6.992	3.508
80	13.4	13.48	-0.08502
81	10.9	15.91	-5.011
82	4.3	4.006	0.2944
83	10.3	9.039	1.261
84	11.8	9.967	1.833
85	11.2	10.74	0.461
86	11.4	12.87	-1.471
87	8.6	5.778	2.822
88	13.2	10.46	2.739
89	12.6	17.58	-4.976
90	5.6	5.707	-0.1074
91	9.9	12.46	-2.557
92	8.8	11.63	-2.829
93	7.7	8.216	-0.5164
94	9	12.3	-3.3
95	7.3	6.001	1.299
96	11.4	8.754	2.646
97	13.6	15.88	-2.277
98	7.9	7.343	0.5566
99	10.7	10.82	-0.1224
100	10.3	10.4	-0.105
101	9.6	7.087	2.513
102	14.2	15.52	-1.324
103	8.5	4.951	3.549
104	13.5	17.42	-3.916
105	6.4	7.332	-0.9318
106	9.6	7.946	1.654
107	11.6	10.66	0.9428
108	11.1	NA	NA

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
11	0.7887	0.4226	0.2113
12	0.9278	0.1444	0.07221
13	0.8841	0.2318	0.1159
14	0.814	0.3719	0.186
15	0.8486	0.3029	0.1514
16	0.7805	0.4391	0.2195
17	0.7178	0.5643	0.2822
18	0.6442	0.7116	0.3558
19	0.5847	0.8306	0.4153
20	0.5074	0.9852	0.4926
21	0.6131	0.7737	0.3869
22	0.5305	0.939	0.4695
23	0.4964	0.9928	0.5036
24	0.4331	0.8663	0.5669
25	0.4338	0.8677	0.5662
26	0.5536	0.8929	0.4464
27	0.4797	0.9594	0.5203
28	0.4704	0.9409	0.5296
29	0.5553	0.8895	0.4447
30	0.4924	0.9847	0.5076
31	0.4906	0.9813	0.5094
32	0.4566	0.9132	0.5434
33	0.391	0.782	0.609
34	0.3588	0.7176	0.6412
35	0.3434	0.6868	0.6566
36	0.3659	0.7318	0.6341
37	0.3212	0.6424	0.6788
38	0.2681	0.5362	0.7319
39	0.4276	0.8551	0.5724
40	0.3778	0.7557	0.6222
41	0.419	0.8381	0.581
42	0.3653	0.7305	0.6347
43	0.3848	0.7696	0.6152
44	0.3369	0.6738	0.6631
45	0.2853	0.5707	0.7147
46	0.2878	0.5755	0.7122
47	0.3342	0.6684	0.6658
48	0.3317	0.6634	0.6683
49	0.2869	0.5738	0.7131
50	0.4144	0.8288	0.5856
51	0.3643	0.7286	0.6357
52	0.3311	0.6622	0.6689
53	0.3211	0.6422	0.6789
54	0.3239	0.6478	0.6761
55	0.5337	0.9326	0.4663
56	0.5755	0.849	0.4245
57	0.5633	0.8734	0.4367
58	0.5714	0.8571	0.4286
59	0.5404	0.9193	0.4596
60	0.5389	0.9223	0.4611
61	0.5411	0.9178	0.4589
62	0.6554	0.6892	0.3446
63	0.6011	0.7978	0.3989
64	0.5519	0.8962	0.4481
65	0.5164	0.9672	0.4836
66	0.4617	0.9234	0.5383
67	0.6014	0.7973	0.3986
68	0.5817	0.8366	0.4183
69	0.588	0.824	0.412
70	0.5413	0.9174	0.4587
71	0.6028	0.7944	0.3972
72	0.5487	0.9027	0.4513
73	0.5102	0.9797	0.4898
74	0.562	0.8761	0.438
75	0.5392	0.9217	0.4608
76	0.486	0.972	0.514
77	0.4618	0.9235	0.5382
78	0.414	0.828	0.586
79	0.4071	0.8142	0.5929
80	0.3386	0.6773	0.6614
81	0.4759	0.9518	0.5241
82	0.406	0.8119	0.594
83	0.3623	0.7245	0.6377
84	0.3138	0.6277	0.6862
85	0.2465	0.4931	0.7535
86	0.1919	0.3838	0.8081
87	0.2248	0.4496	0.7752
88	0.2733	0.5467	0.7267
89	0.3748	0.7496	0.6252
90	0.2865	0.573	0.7135
91	0.2366	0.4732	0.7634
92	0.3098	0.6195	0.6902
93	0.2634	0.5268	0.7366
94	0.4209	0.8419	0.5791
95	0.2999	0.5998	0.7001
96	0.1865	0.3731	0.8135
97	0.1467	0.2934	0.8533

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
11 &  0.7887 &  0.4226 &  0.2113 \tabularnewline
12 &  0.9278 &  0.1444 &  0.07221 \tabularnewline
13 &  0.8841 &  0.2318 &  0.1159 \tabularnewline
14 &  0.814 &  0.3719 &  0.186 \tabularnewline
15 &  0.8486 &  0.3029 &  0.1514 \tabularnewline
16 &  0.7805 &  0.4391 &  0.2195 \tabularnewline
17 &  0.7178 &  0.5643 &  0.2822 \tabularnewline
18 &  0.6442 &  0.7116 &  0.3558 \tabularnewline
19 &  0.5847 &  0.8306 &  0.4153 \tabularnewline
20 &  0.5074 &  0.9852 &  0.4926 \tabularnewline
21 &  0.6131 &  0.7737 &  0.3869 \tabularnewline
22 &  0.5305 &  0.939 &  0.4695 \tabularnewline
23 &  0.4964 &  0.9928 &  0.5036 \tabularnewline
24 &  0.4331 &  0.8663 &  0.5669 \tabularnewline
25 &  0.4338 &  0.8677 &  0.5662 \tabularnewline
26 &  0.5536 &  0.8929 &  0.4464 \tabularnewline
27 &  0.4797 &  0.9594 &  0.5203 \tabularnewline
28 &  0.4704 &  0.9409 &  0.5296 \tabularnewline
29 &  0.5553 &  0.8895 &  0.4447 \tabularnewline
30 &  0.4924 &  0.9847 &  0.5076 \tabularnewline
31 &  0.4906 &  0.9813 &  0.5094 \tabularnewline
32 &  0.4566 &  0.9132 &  0.5434 \tabularnewline
33 &  0.391 &  0.782 &  0.609 \tabularnewline
34 &  0.3588 &  0.7176 &  0.6412 \tabularnewline
35 &  0.3434 &  0.6868 &  0.6566 \tabularnewline
36 &  0.3659 &  0.7318 &  0.6341 \tabularnewline
37 &  0.3212 &  0.6424 &  0.6788 \tabularnewline
38 &  0.2681 &  0.5362 &  0.7319 \tabularnewline
39 &  0.4276 &  0.8551 &  0.5724 \tabularnewline
40 &  0.3778 &  0.7557 &  0.6222 \tabularnewline
41 &  0.419 &  0.8381 &  0.581 \tabularnewline
42 &  0.3653 &  0.7305 &  0.6347 \tabularnewline
43 &  0.3848 &  0.7696 &  0.6152 \tabularnewline
44 &  0.3369 &  0.6738 &  0.6631 \tabularnewline
45 &  0.2853 &  0.5707 &  0.7147 \tabularnewline
46 &  0.2878 &  0.5755 &  0.7122 \tabularnewline
47 &  0.3342 &  0.6684 &  0.6658 \tabularnewline
48 &  0.3317 &  0.6634 &  0.6683 \tabularnewline
49 &  0.2869 &  0.5738 &  0.7131 \tabularnewline
50 &  0.4144 &  0.8288 &  0.5856 \tabularnewline
51 &  0.3643 &  0.7286 &  0.6357 \tabularnewline
52 &  0.3311 &  0.6622 &  0.6689 \tabularnewline
53 &  0.3211 &  0.6422 &  0.6789 \tabularnewline
54 &  0.3239 &  0.6478 &  0.6761 \tabularnewline
55 &  0.5337 &  0.9326 &  0.4663 \tabularnewline
56 &  0.5755 &  0.849 &  0.4245 \tabularnewline
57 &  0.5633 &  0.8734 &  0.4367 \tabularnewline
58 &  0.5714 &  0.8571 &  0.4286 \tabularnewline
59 &  0.5404 &  0.9193 &  0.4596 \tabularnewline
60 &  0.5389 &  0.9223 &  0.4611 \tabularnewline
61 &  0.5411 &  0.9178 &  0.4589 \tabularnewline
62 &  0.6554 &  0.6892 &  0.3446 \tabularnewline
63 &  0.6011 &  0.7978 &  0.3989 \tabularnewline
64 &  0.5519 &  0.8962 &  0.4481 \tabularnewline
65 &  0.5164 &  0.9672 &  0.4836 \tabularnewline
66 &  0.4617 &  0.9234 &  0.5383 \tabularnewline
67 &  0.6014 &  0.7973 &  0.3986 \tabularnewline
68 &  0.5817 &  0.8366 &  0.4183 \tabularnewline
69 &  0.588 &  0.824 &  0.412 \tabularnewline
70 &  0.5413 &  0.9174 &  0.4587 \tabularnewline
71 &  0.6028 &  0.7944 &  0.3972 \tabularnewline
72 &  0.5487 &  0.9027 &  0.4513 \tabularnewline
73 &  0.5102 &  0.9797 &  0.4898 \tabularnewline
74 &  0.562 &  0.8761 &  0.438 \tabularnewline
75 &  0.5392 &  0.9217 &  0.4608 \tabularnewline
76 &  0.486 &  0.972 &  0.514 \tabularnewline
77 &  0.4618 &  0.9235 &  0.5382 \tabularnewline
78 &  0.414 &  0.828 &  0.586 \tabularnewline
79 &  0.4071 &  0.8142 &  0.5929 \tabularnewline
80 &  0.3386 &  0.6773 &  0.6614 \tabularnewline
81 &  0.4759 &  0.9518 &  0.5241 \tabularnewline
82 &  0.406 &  0.8119 &  0.594 \tabularnewline
83 &  0.3623 &  0.7245 &  0.6377 \tabularnewline
84 &  0.3138 &  0.6277 &  0.6862 \tabularnewline
85 &  0.2465 &  0.4931 &  0.7535 \tabularnewline
86 &  0.1919 &  0.3838 &  0.8081 \tabularnewline
87 &  0.2248 &  0.4496 &  0.7752 \tabularnewline
88 &  0.2733 &  0.5467 &  0.7267 \tabularnewline
89 &  0.3748 &  0.7496 &  0.6252 \tabularnewline
90 &  0.2865 &  0.573 &  0.7135 \tabularnewline
91 &  0.2366 &  0.4732 &  0.7634 \tabularnewline
92 &  0.3098 &  0.6195 &  0.6902 \tabularnewline
93 &  0.2634 &  0.5268 &  0.7366 \tabularnewline
94 &  0.4209 &  0.8419 &  0.5791 \tabularnewline
95 &  0.2999 &  0.5998 &  0.7001 \tabularnewline
96 &  0.1865 &  0.3731 &  0.8135 \tabularnewline
97 &  0.1467 &  0.2934 &  0.8533 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=280432&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]11[/C][C] 0.7887[/C][C] 0.4226[/C][C] 0.2113[/C][/ROW]
[ROW][C]12[/C][C] 0.9278[/C][C] 0.1444[/C][C] 0.07221[/C][/ROW]
[ROW][C]13[/C][C] 0.8841[/C][C] 0.2318[/C][C] 0.1159[/C][/ROW]
[ROW][C]14[/C][C] 0.814[/C][C] 0.3719[/C][C] 0.186[/C][/ROW]
[ROW][C]15[/C][C] 0.8486[/C][C] 0.3029[/C][C] 0.1514[/C][/ROW]
[ROW][C]16[/C][C] 0.7805[/C][C] 0.4391[/C][C] 0.2195[/C][/ROW]
[ROW][C]17[/C][C] 0.7178[/C][C] 0.5643[/C][C] 0.2822[/C][/ROW]
[ROW][C]18[/C][C] 0.6442[/C][C] 0.7116[/C][C] 0.3558[/C][/ROW]
[ROW][C]19[/C][C] 0.5847[/C][C] 0.8306[/C][C] 0.4153[/C][/ROW]
[ROW][C]20[/C][C] 0.5074[/C][C] 0.9852[/C][C] 0.4926[/C][/ROW]
[ROW][C]21[/C][C] 0.6131[/C][C] 0.7737[/C][C] 0.3869[/C][/ROW]
[ROW][C]22[/C][C] 0.5305[/C][C] 0.939[/C][C] 0.4695[/C][/ROW]
[ROW][C]23[/C][C] 0.4964[/C][C] 0.9928[/C][C] 0.5036[/C][/ROW]
[ROW][C]24[/C][C] 0.4331[/C][C] 0.8663[/C][C] 0.5669[/C][/ROW]
[ROW][C]25[/C][C] 0.4338[/C][C] 0.8677[/C][C] 0.5662[/C][/ROW]
[ROW][C]26[/C][C] 0.5536[/C][C] 0.8929[/C][C] 0.4464[/C][/ROW]
[ROW][C]27[/C][C] 0.4797[/C][C] 0.9594[/C][C] 0.5203[/C][/ROW]
[ROW][C]28[/C][C] 0.4704[/C][C] 0.9409[/C][C] 0.5296[/C][/ROW]
[ROW][C]29[/C][C] 0.5553[/C][C] 0.8895[/C][C] 0.4447[/C][/ROW]
[ROW][C]30[/C][C] 0.4924[/C][C] 0.9847[/C][C] 0.5076[/C][/ROW]
[ROW][C]31[/C][C] 0.4906[/C][C] 0.9813[/C][C] 0.5094[/C][/ROW]
[ROW][C]32[/C][C] 0.4566[/C][C] 0.9132[/C][C] 0.5434[/C][/ROW]
[ROW][C]33[/C][C] 0.391[/C][C] 0.782[/C][C] 0.609[/C][/ROW]
[ROW][C]34[/C][C] 0.3588[/C][C] 0.7176[/C][C] 0.6412[/C][/ROW]
[ROW][C]35[/C][C] 0.3434[/C][C] 0.6868[/C][C] 0.6566[/C][/ROW]
[ROW][C]36[/C][C] 0.3659[/C][C] 0.7318[/C][C] 0.6341[/C][/ROW]
[ROW][C]37[/C][C] 0.3212[/C][C] 0.6424[/C][C] 0.6788[/C][/ROW]
[ROW][C]38[/C][C] 0.2681[/C][C] 0.5362[/C][C] 0.7319[/C][/ROW]
[ROW][C]39[/C][C] 0.4276[/C][C] 0.8551[/C][C] 0.5724[/C][/ROW]
[ROW][C]40[/C][C] 0.3778[/C][C] 0.7557[/C][C] 0.6222[/C][/ROW]
[ROW][C]41[/C][C] 0.419[/C][C] 0.8381[/C][C] 0.581[/C][/ROW]
[ROW][C]42[/C][C] 0.3653[/C][C] 0.7305[/C][C] 0.6347[/C][/ROW]
[ROW][C]43[/C][C] 0.3848[/C][C] 0.7696[/C][C] 0.6152[/C][/ROW]
[ROW][C]44[/C][C] 0.3369[/C][C] 0.6738[/C][C] 0.6631[/C][/ROW]
[ROW][C]45[/C][C] 0.2853[/C][C] 0.5707[/C][C] 0.7147[/C][/ROW]
[ROW][C]46[/C][C] 0.2878[/C][C] 0.5755[/C][C] 0.7122[/C][/ROW]
[ROW][C]47[/C][C] 0.3342[/C][C] 0.6684[/C][C] 0.6658[/C][/ROW]
[ROW][C]48[/C][C] 0.3317[/C][C] 0.6634[/C][C] 0.6683[/C][/ROW]
[ROW][C]49[/C][C] 0.2869[/C][C] 0.5738[/C][C] 0.7131[/C][/ROW]
[ROW][C]50[/C][C] 0.4144[/C][C] 0.8288[/C][C] 0.5856[/C][/ROW]
[ROW][C]51[/C][C] 0.3643[/C][C] 0.7286[/C][C] 0.6357[/C][/ROW]
[ROW][C]52[/C][C] 0.3311[/C][C] 0.6622[/C][C] 0.6689[/C][/ROW]
[ROW][C]53[/C][C] 0.3211[/C][C] 0.6422[/C][C] 0.6789[/C][/ROW]
[ROW][C]54[/C][C] 0.3239[/C][C] 0.6478[/C][C] 0.6761[/C][/ROW]
[ROW][C]55[/C][C] 0.5337[/C][C] 0.9326[/C][C] 0.4663[/C][/ROW]
[ROW][C]56[/C][C] 0.5755[/C][C] 0.849[/C][C] 0.4245[/C][/ROW]
[ROW][C]57[/C][C] 0.5633[/C][C] 0.8734[/C][C] 0.4367[/C][/ROW]
[ROW][C]58[/C][C] 0.5714[/C][C] 0.8571[/C][C] 0.4286[/C][/ROW]
[ROW][C]59[/C][C] 0.5404[/C][C] 0.9193[/C][C] 0.4596[/C][/ROW]
[ROW][C]60[/C][C] 0.5389[/C][C] 0.9223[/C][C] 0.4611[/C][/ROW]
[ROW][C]61[/C][C] 0.5411[/C][C] 0.9178[/C][C] 0.4589[/C][/ROW]
[ROW][C]62[/C][C] 0.6554[/C][C] 0.6892[/C][C] 0.3446[/C][/ROW]
[ROW][C]63[/C][C] 0.6011[/C][C] 0.7978[/C][C] 0.3989[/C][/ROW]
[ROW][C]64[/C][C] 0.5519[/C][C] 0.8962[/C][C] 0.4481[/C][/ROW]
[ROW][C]65[/C][C] 0.5164[/C][C] 0.9672[/C][C] 0.4836[/C][/ROW]
[ROW][C]66[/C][C] 0.4617[/C][C] 0.9234[/C][C] 0.5383[/C][/ROW]
[ROW][C]67[/C][C] 0.6014[/C][C] 0.7973[/C][C] 0.3986[/C][/ROW]
[ROW][C]68[/C][C] 0.5817[/C][C] 0.8366[/C][C] 0.4183[/C][/ROW]
[ROW][C]69[/C][C] 0.588[/C][C] 0.824[/C][C] 0.412[/C][/ROW]
[ROW][C]70[/C][C] 0.5413[/C][C] 0.9174[/C][C] 0.4587[/C][/ROW]
[ROW][C]71[/C][C] 0.6028[/C][C] 0.7944[/C][C] 0.3972[/C][/ROW]
[ROW][C]72[/C][C] 0.5487[/C][C] 0.9027[/C][C] 0.4513[/C][/ROW]
[ROW][C]73[/C][C] 0.5102[/C][C] 0.9797[/C][C] 0.4898[/C][/ROW]
[ROW][C]74[/C][C] 0.562[/C][C] 0.8761[/C][C] 0.438[/C][/ROW]
[ROW][C]75[/C][C] 0.5392[/C][C] 0.9217[/C][C] 0.4608[/C][/ROW]
[ROW][C]76[/C][C] 0.486[/C][C] 0.972[/C][C] 0.514[/C][/ROW]
[ROW][C]77[/C][C] 0.4618[/C][C] 0.9235[/C][C] 0.5382[/C][/ROW]
[ROW][C]78[/C][C] 0.414[/C][C] 0.828[/C][C] 0.586[/C][/ROW]
[ROW][C]79[/C][C] 0.4071[/C][C] 0.8142[/C][C] 0.5929[/C][/ROW]
[ROW][C]80[/C][C] 0.3386[/C][C] 0.6773[/C][C] 0.6614[/C][/ROW]
[ROW][C]81[/C][C] 0.4759[/C][C] 0.9518[/C][C] 0.5241[/C][/ROW]
[ROW][C]82[/C][C] 0.406[/C][C] 0.8119[/C][C] 0.594[/C][/ROW]
[ROW][C]83[/C][C] 0.3623[/C][C] 0.7245[/C][C] 0.6377[/C][/ROW]
[ROW][C]84[/C][C] 0.3138[/C][C] 0.6277[/C][C] 0.6862[/C][/ROW]
[ROW][C]85[/C][C] 0.2465[/C][C] 0.4931[/C][C] 0.7535[/C][/ROW]
[ROW][C]86[/C][C] 0.1919[/C][C] 0.3838[/C][C] 0.8081[/C][/ROW]
[ROW][C]87[/C][C] 0.2248[/C][C] 0.4496[/C][C] 0.7752[/C][/ROW]
[ROW][C]88[/C][C] 0.2733[/C][C] 0.5467[/C][C] 0.7267[/C][/ROW]
[ROW][C]89[/C][C] 0.3748[/C][C] 0.7496[/C][C] 0.6252[/C][/ROW]
[ROW][C]90[/C][C] 0.2865[/C][C] 0.573[/C][C] 0.7135[/C][/ROW]
[ROW][C]91[/C][C] 0.2366[/C][C] 0.4732[/C][C] 0.7634[/C][/ROW]
[ROW][C]92[/C][C] 0.3098[/C][C] 0.6195[/C][C] 0.6902[/C][/ROW]
[ROW][C]93[/C][C] 0.2634[/C][C] 0.5268[/C][C] 0.7366[/C][/ROW]
[ROW][C]94[/C][C] 0.4209[/C][C] 0.8419[/C][C] 0.5791[/C][/ROW]
[ROW][C]95[/C][C] 0.2999[/C][C] 0.5998[/C][C] 0.7001[/C][/ROW]
[ROW][C]96[/C][C] 0.1865[/C][C] 0.3731[/C][C] 0.8135[/C][/ROW]
[ROW][C]97[/C][C] 0.1467[/C][C] 0.2934[/C][C] 0.8533[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=280432&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=280432&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
11	0.7887	0.4226	0.2113
12	0.9278	0.1444	0.07221
13	0.8841	0.2318	0.1159
14	0.814	0.3719	0.186
15	0.8486	0.3029	0.1514
16	0.7805	0.4391	0.2195
17	0.7178	0.5643	0.2822
18	0.6442	0.7116	0.3558
19	0.5847	0.8306	0.4153
20	0.5074	0.9852	0.4926
21	0.6131	0.7737	0.3869
22	0.5305	0.939	0.4695
23	0.4964	0.9928	0.5036
24	0.4331	0.8663	0.5669
25	0.4338	0.8677	0.5662
26	0.5536	0.8929	0.4464
27	0.4797	0.9594	0.5203
28	0.4704	0.9409	0.5296
29	0.5553	0.8895	0.4447
30	0.4924	0.9847	0.5076
31	0.4906	0.9813	0.5094
32	0.4566	0.9132	0.5434
33	0.391	0.782	0.609
34	0.3588	0.7176	0.6412
35	0.3434	0.6868	0.6566
36	0.3659	0.7318	0.6341
37	0.3212	0.6424	0.6788
38	0.2681	0.5362	0.7319
39	0.4276	0.8551	0.5724
40	0.3778	0.7557	0.6222
41	0.419	0.8381	0.581
42	0.3653	0.7305	0.6347
43	0.3848	0.7696	0.6152
44	0.3369	0.6738	0.6631
45	0.2853	0.5707	0.7147
46	0.2878	0.5755	0.7122
47	0.3342	0.6684	0.6658
48	0.3317	0.6634	0.6683
49	0.2869	0.5738	0.7131
50	0.4144	0.8288	0.5856
51	0.3643	0.7286	0.6357
52	0.3311	0.6622	0.6689
53	0.3211	0.6422	0.6789
54	0.3239	0.6478	0.6761
55	0.5337	0.9326	0.4663
56	0.5755	0.849	0.4245
57	0.5633	0.8734	0.4367
58	0.5714	0.8571	0.4286
59	0.5404	0.9193	0.4596
60	0.5389	0.9223	0.4611
61	0.5411	0.9178	0.4589
62	0.6554	0.6892	0.3446
63	0.6011	0.7978	0.3989
64	0.5519	0.8962	0.4481
65	0.5164	0.9672	0.4836
66	0.4617	0.9234	0.5383
67	0.6014	0.7973	0.3986
68	0.5817	0.8366	0.4183
69	0.588	0.824	0.412
70	0.5413	0.9174	0.4587
71	0.6028	0.7944	0.3972
72	0.5487	0.9027	0.4513
73	0.5102	0.9797	0.4898
74	0.562	0.8761	0.438
75	0.5392	0.9217	0.4608
76	0.486	0.972	0.514
77	0.4618	0.9235	0.5382
78	0.414	0.828	0.586
79	0.4071	0.8142	0.5929
80	0.3386	0.6773	0.6614
81	0.4759	0.9518	0.5241
82	0.406	0.8119	0.594
83	0.3623	0.7245	0.6377
84	0.3138	0.6277	0.6862
85	0.2465	0.4931	0.7535
86	0.1919	0.3838	0.8081
87	0.2248	0.4496	0.7752
88	0.2733	0.5467	0.7267
89	0.3748	0.7496	0.6252
90	0.2865	0.573	0.7135
91	0.2366	0.4732	0.7634
92	0.3098	0.6195	0.6902
93	0.2634	0.5268	0.7366
94	0.4209	0.8419	0.5791
95	0.2999	0.5998	0.7001
96	0.1865	0.3731	0.8135
97	0.1467	0.2934	0.8533

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=280432&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=280432&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=280432&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 2

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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):

Parameters (R input):

par1 = 8 ; 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
mywarning <- ''
par1 <- as.numeric(par1)
if(is.na(par1)) {
par1 <- 1
mywarning = 'Warning: you did not specify the column number of the endogenous series! The first column was selected by default.'
}
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$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
}
}
myeq <- paste(myeq, ' + e[t]')
a<-table.row.start(a)
a<-table.element(a, myeq)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, mywarning)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable1.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,hyperlink('ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Variable',header=TRUE)
a<-table.element(a,'Parameter',header=TRUE)
a<-table.element(a,'S.D.',header=TRUE)
a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
a<-table.element(a,'2-tail p-value',header=TRUE)
a<-table.element(a,'1-tail p-value',header=TRUE)
a<-table.row.end(a)
for (i in 1:k){
a<-table.row.start(a)
a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
a<-table.element(a,formatC(signif(mysum$coefficients[i,1],5),format='g',flag='+'))
a<-table.element(a,formatC(signif(mysum$coefficients[i,2],5),format='g',flag=' '))
a<-table.element(a,formatC(signif(mysum$coefficients[i,3],4),format='e',flag='+'))
a<-table.element(a,formatC(signif(mysum$coefficients[i,4],4),format='g',flag=' '))
a<-table.element(a,formatC(signif(mysum$coefficients[i,4]/2,4),format='g',flag=' '))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable2.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple R',1,TRUE)
a<-table.element(a,formatC(signif(sqrt(mysum$r.squared),6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'R-squared',1,TRUE)
a<-table.element(a,formatC(signif(mysum$r.squared,6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Adjusted R-squared',1,TRUE)
a<-table.element(a,formatC(signif(mysum$adj.r.squared,6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (value)',1,TRUE)
a<-table.element(a,formatC(signif(mysum$fstatistic[1],6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
a<-table.element(a, signif(mysum$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$fstatistic[3],6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'p-value',1,TRUE)
a<-table.element(a,formatC(signif(1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]),6),format='g',flag=' '))
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,formatC(signif(mysum$sigma,6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
a<-table.element(a,formatC(signif(sum(myerror*myerror),6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,formatC(signif(x[i],6),format='g',flag=' '))
a<-table.element(a,formatC(signif(x[i]-mysum$resid[i],6),format='g',flag=' '))
a<-table.element(a,formatC(signif(mysum$resid[i],6),format='g',flag=' '))
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,formatC(signif(gqarr[mypoint-kp3+1,1],6),format='g',flag=' '))
a<-table.element(a,formatC(signif(gqarr[mypoint-kp3+1,2],6),format='g',flag=' '))
a<-table.element(a,formatC(signif(gqarr[mypoint-kp3+1,3],6),format='g',flag=' '))
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,formatC(signif(numsignificant1/numgqtests,6),format='g',flag=' '))
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