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Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationFri, 12 Dec 2014 15:04:40 +0000
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2014/Dec/12/t1418396730zbipyuwuvsbjdf4.htm/, Retrieved Thu, 16 May 2024 17:30:04 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=266770, Retrieved Thu, 16 May 2024 17:30:04 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact95

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Multiple Regression] [MRzelfvertrouwen] [2014-12-12 15:04:40] [4ce2356216df8db4950cd852fec912aa] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
12 26 50 4 12.9 13
8 57 62 4 12.2 13
11 37 54 5 12.8 11
13 67 71 4 7.4 14
11 43 54 4 6.7 15
10 52 65 9 12.6 14
7 52 73 8 14.8 11
10 43 52 11 13.3 13
15 84 84 4 11.1 16
12 67 42 4 8.2 14
12 49 66 6 11.4 14
10 70 65 4 6.4 15
10 52 78 8 10.6 15
14 58 73 4 12.0 13
6 68 75 4 6.3 14
12 62 72 11 11.3 11
14 43 66 4 11.9 12
11 56 70 4 9.3 14
8 56 61 6 9.6 13
12 74 81 6 10.0 12
15 65 71 4 6.4 15
13 63 69 8 13.8 15
11 58 71 5 10.8 14
12 57 72 4 13.8 14
7 63 68 9 11.7 12
11 53 70 4 10.9 12
7 57 68 7 16.1 12
12 51 61 10 13.4 15
12 64 67 4 9.9 14
13 53 76 4 11.5 16
9 29 70 7 8.3 12
11 54 60 12 11.7 12
12 58 72 7 9.0 14
15 43 69 5 9.7 16
12 51 71 8 10.8 15
6 53 62 5 10.3 12
5 54 70 4 10.4 14
13 56 64 9 12.7 13
11 61 58 7 9.3 14
6 47 76 4 11.8 16
12 39 52 4 5.9 12
10 48 59 4 11.4 14
6 50 68 4 13.0 15
12 35 76 4 10.8 13
11 30 65 7 12.3 16
6 68 67 4 11.3 16
12 49 59 7 11.8 12
12 61 69 4 7.9 12
8 67 76 4 12.7 16
10 47 63 4 12.3 12
11 56 75 4 11.6 15
7 50 63 8 6.7 12
12 43 60 4 10.9 13
13 67 73 4 12.1 12
14 62 63 4 13.3 14
12 57 70 4 10.1 14
6 41 75 7 5.7 11
14 54 66 12 14.3 10
10 45 63 4 8.0 12
12 48 63 4 13.3 11
11 61 64 4 9.3 16
10 56 70 5 12.5 14
7 41 75 15 7.6 14
12 43 61 5 15.9 15
7 53 60 10 9.2 15
12 44 62 9 9.1 14
12 66 73 8 11.1 13
10 58 61 4 13.0 11
10 46 66 5 14.5 16
12 37 64 4 12.2 12
12 51 59 9 12.3 15
12 51 64 4 11.4 14
8 56 60 10 8.8 15
10 66 56 4 14.6 14
5 37 78 4 12.6 13
10 42 67 7 13.0 12
12 38 59 5 12.6 12
11 66 66 4 13.2 14
9 34 68 4 9.9 14
12 53 71 4 7.7 15
11 49 66 4 10.5 11
10 55 73 4 13.4 13
12 49 72 4 10.9 14
10 59 71 6 4.3 16
9 40 59 10 10.3 13
11 58 64 7 11.8 14
12 60 66 4 11.2 16
7 63 78 4 11.4 11
11 56 68 7 8.6 13
12 54 73 4 13.2 13
6 52 62 8 12.6 15
9 34 65 11 5.6 12
15 69 68 6 9.9 13
10 32 65 14 8.8 12
11 48 60 5 7.7 14
12 67 71 4 9.0 14
12 58 65 8 7.3 16
12 57 68 9 11.4 15
11 42 64 4 13.6 14
9 64 74 4 7.9 13
11 58 69 5 10.7 14
12 66 76 4 10.3 15
12 26 68 5 8.3 14
14 61 72 4 9.6 12
8 52 67 4 14.2 7
10 51 63 7 8.5 12
9 55 59 10 13.5 15
10 50 73 4 4.9 12
9 60 66 5 6.4 13
10 56 62 4 9.6 11
12 63 69 4 11.6 14
11 61 66 4 11.1 13

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time6 seconds
R Server'Gwilym Jenkins' @ jenkins.wessa.net

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 6 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ jenkins.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=266770&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]'Gwilym Jenkins' @ jenkins.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=266770&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=266770&T=0

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


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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time6 seconds
R Server'Gwilym Jenkins' @ jenkins.wessa.net






Multiple Linear Regression - Estimated Regression Equation
CONFSOFTTOT[t] = + 9.81372 + 0.0279599AMS.I[t] -0.0307424AMS.E[t] -0.103633AMS.A[t] + 0.0397453TOT[t] + 0.112285STRESSTOT[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
CONFSOFTTOT[t] =  +  9.81372 +  0.0279599AMS.I[t] -0.0307424AMS.E[t] -0.103633AMS.A[t] +  0.0397453TOT[t] +  0.112285STRESSTOT[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=266770&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]CONFSOFTTOT[t] =  +  9.81372 +  0.0279599AMS.I[t] -0.0307424AMS.E[t] -0.103633AMS.A[t] +  0.0397453TOT[t] +  0.112285STRESSTOT[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=266770&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=266770&T=1

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


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

Multiple Linear Regression - Estimated Regression Equation
CONFSOFTTOT[t] = + 9.81372 + 0.0279599AMS.I[t] -0.0307424AMS.E[t] -0.103633AMS.A[t] + 0.0397453TOT[t] + 0.112285STRESSTOT[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)9.813723.136643.1290.002267520.00113376
AMS.I0.02795990.02202271.270.207010.103505
AMS.E-0.03074240.0341976-0.8990.3707090.185354
AMS.A-0.1036330.0888503-1.1660.2460790.12304
TOT0.03974530.08922850.44540.6569150.328458
STRESSTOT0.1122850.1386730.80970.4199230.209962

\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) & 9.81372 & 3.13664 & 3.129 & 0.00226752 & 0.00113376 \tabularnewline
AMS.I & 0.0279599 & 0.0220227 & 1.27 & 0.20701 & 0.103505 \tabularnewline
AMS.E & -0.0307424 & 0.0341976 & -0.899 & 0.370709 & 0.185354 \tabularnewline
AMS.A & -0.103633 & 0.0888503 & -1.166 & 0.246079 & 0.12304 \tabularnewline
TOT & 0.0397453 & 0.0892285 & 0.4454 & 0.656915 & 0.328458 \tabularnewline
STRESSTOT & 0.112285 & 0.138673 & 0.8097 & 0.419923 & 0.209962 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=266770&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]9.81372[/C][C]3.13664[/C][C]3.129[/C][C]0.00226752[/C][C]0.00113376[/C][/ROW]
[ROW][C]AMS.I[/C][C]0.0279599[/C][C]0.0220227[/C][C]1.27[/C][C]0.20701[/C][C]0.103505[/C][/ROW]
[ROW][C]AMS.E[/C][C]-0.0307424[/C][C]0.0341976[/C][C]-0.899[/C][C]0.370709[/C][C]0.185354[/C][/ROW]
[ROW][C]AMS.A[/C][C]-0.103633[/C][C]0.0888503[/C][C]-1.166[/C][C]0.246079[/C][C]0.12304[/C][/ROW]
[ROW][C]TOT[/C][C]0.0397453[/C][C]0.0892285[/C][C]0.4454[/C][C]0.656915[/C][C]0.328458[/C][/ROW]
[ROW][C]STRESSTOT[/C][C]0.112285[/C][C]0.138673[/C][C]0.8097[/C][C]0.419923[/C][C]0.209962[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=266770&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=266770&T=2

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


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

Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)9.813723.136643.1290.002267520.00113376
AMS.I0.02795990.02202271.270.207010.103505
AMS.E-0.03074240.0341976-0.8990.3707090.185354
AMS.A-0.1036330.0888503-1.1660.2460790.12304
TOT0.03974530.08922850.44540.6569150.328458
STRESSTOT0.1122850.1386730.80970.4199230.209962






Multiple Linear Regression - Regression Statistics
Multiple R0.211642
R-squared0.0447925
Adjusted R-squared-0.000264444
F-TEST (value)0.994131
F-TEST (DF numerator)5
F-TEST (DF denominator)106
p-value0.425011
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.3122
Sum Squared Residuals566.702

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.211642 \tabularnewline
R-squared & 0.0447925 \tabularnewline
Adjusted R-squared & -0.000264444 \tabularnewline
F-TEST (value) & 0.994131 \tabularnewline
F-TEST (DF numerator) & 5 \tabularnewline
F-TEST (DF denominator) & 106 \tabularnewline
p-value & 0.425011 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 2.3122 \tabularnewline
Sum Squared Residuals & 566.702 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=266770&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.211642[/C][/ROW]
[ROW][C]R-squared[/C][C]0.0447925[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]-0.000264444[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]0.994131[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]5[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]106[/C][/ROW]
[ROW][C]p-value[/C][C]0.425011[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]2.3122[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]566.702[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=266770&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=266770&T=3

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


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

Multiple Linear Regression - Regression Statistics
Multiple R0.211642
R-squared0.0447925
Adjusted R-squared-0.000264444
F-TEST (value)0.994131
F-TEST (DF numerator)5
F-TEST (DF denominator)106
p-value0.425011
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.3122
Sum Squared Residuals566.702






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11210.56141.43856
2811.0315-3.03146
31110.41380.58615
41310.95592.04411
51110.89190.108066
61010.4095-0.409454
7710.0177-3.01773
81010.2657-0.265738
91511.40323.59682
101211.87920.120787
111210.5581.44196
121011.2968-1.29676
131010.1462-0.146229
141410.71333.28669
15610.8172-4.81716
16129.878072.12193
171410.39283.60715
181110.75460.245414
19810.7236-2.72364
201210.51571.48432
211510.97254.02749
221310.85772.14235
231110.73570.264251
241210.89991.10008
25710.3644-3.36445
261110.50970.49027
27710.5788-3.57883
281210.54491.45509
291211.09430.90566
301310.79832.20174
3199.42446-0.424457
321110.04780.952153
331210.42621.5738
341510.55874.44132
351210.34141.65858
36610.6282-4.62819
37510.7424-5.74239
381310.44372.55627
391110.95240.0476037
40610.6424-4.64243
411210.47291.52707
421010.9525-0.952539
43610.9077-4.90765
44129.930312.06969
451110.21420.785752
46611.4864-5.48639
471210.46091.53907
481210.64491.35508
49811.2374-3.23739
501010.6128-0.612811
511110.80460.195427
52710.0596-3.05959
531210.64981.35016
541310.85662.14336
551411.29652.70348
561210.81431.18566
5769.39064-3.39064
58149.742164.25784
591010.386-0.385987
601210.56821.43177
611111.3034-0.30341
621010.7781-0.778139
6378.97395-1.97395
641210.93881.06124
65710.4646-3.46464
661210.13891.86111
671210.48671.51332
681010.8974-0.897392
691010.9256-0.925569
701210.29851.7015
711210.66631.33369
721210.88271.11729
73810.5326-2.53263
741011.6752-1.67523
7559.99628-4.99628
761010.067-0.0669653
771210.39241.60757
781111.3122-0.312162
79910.2248-1.2248
801210.68871.31134
811110.39270.607322
821010.6851-0.68507
831210.5611.43902
841010.6263-0.6263
8599.95106-0.951059
861110.78340.216575
871211.28950.710519
88710.451-3.45098
891110.36510.634934
901210.64921.35084
91610.7176-4.7176
9299.19612-0.196124
931510.88384.11615
94108.956491.04351
951110.67110.328894
961211.01950.980521
971210.69481.30523
981210.52161.47838
991110.71850.281492
100910.6874-1.68737
1011110.79330.206741
1021211.00180.99824
103129.83392.1661
1041410.62033.37974
105810.1437-2.14373
1061010.2627-0.26272
107910.7222-1.72221
1081010.0952-0.0951516
109910.6582-1.65822
1101010.6756-0.675596
1111211.07250.927538
1121110.97660.0233875

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 12 & 10.5614 & 1.43856 \tabularnewline
2 & 8 & 11.0315 & -3.03146 \tabularnewline
3 & 11 & 10.4138 & 0.58615 \tabularnewline
4 & 13 & 10.9559 & 2.04411 \tabularnewline
5 & 11 & 10.8919 & 0.108066 \tabularnewline
6 & 10 & 10.4095 & -0.409454 \tabularnewline
7 & 7 & 10.0177 & -3.01773 \tabularnewline
8 & 10 & 10.2657 & -0.265738 \tabularnewline
9 & 15 & 11.4032 & 3.59682 \tabularnewline
10 & 12 & 11.8792 & 0.120787 \tabularnewline
11 & 12 & 10.558 & 1.44196 \tabularnewline
12 & 10 & 11.2968 & -1.29676 \tabularnewline
13 & 10 & 10.1462 & -0.146229 \tabularnewline
14 & 14 & 10.7133 & 3.28669 \tabularnewline
15 & 6 & 10.8172 & -4.81716 \tabularnewline
16 & 12 & 9.87807 & 2.12193 \tabularnewline
17 & 14 & 10.3928 & 3.60715 \tabularnewline
18 & 11 & 10.7546 & 0.245414 \tabularnewline
19 & 8 & 10.7236 & -2.72364 \tabularnewline
20 & 12 & 10.5157 & 1.48432 \tabularnewline
21 & 15 & 10.9725 & 4.02749 \tabularnewline
22 & 13 & 10.8577 & 2.14235 \tabularnewline
23 & 11 & 10.7357 & 0.264251 \tabularnewline
24 & 12 & 10.8999 & 1.10008 \tabularnewline
25 & 7 & 10.3644 & -3.36445 \tabularnewline
26 & 11 & 10.5097 & 0.49027 \tabularnewline
27 & 7 & 10.5788 & -3.57883 \tabularnewline
28 & 12 & 10.5449 & 1.45509 \tabularnewline
29 & 12 & 11.0943 & 0.90566 \tabularnewline
30 & 13 & 10.7983 & 2.20174 \tabularnewline
31 & 9 & 9.42446 & -0.424457 \tabularnewline
32 & 11 & 10.0478 & 0.952153 \tabularnewline
33 & 12 & 10.4262 & 1.5738 \tabularnewline
34 & 15 & 10.5587 & 4.44132 \tabularnewline
35 & 12 & 10.3414 & 1.65858 \tabularnewline
36 & 6 & 10.6282 & -4.62819 \tabularnewline
37 & 5 & 10.7424 & -5.74239 \tabularnewline
38 & 13 & 10.4437 & 2.55627 \tabularnewline
39 & 11 & 10.9524 & 0.0476037 \tabularnewline
40 & 6 & 10.6424 & -4.64243 \tabularnewline
41 & 12 & 10.4729 & 1.52707 \tabularnewline
42 & 10 & 10.9525 & -0.952539 \tabularnewline
43 & 6 & 10.9077 & -4.90765 \tabularnewline
44 & 12 & 9.93031 & 2.06969 \tabularnewline
45 & 11 & 10.2142 & 0.785752 \tabularnewline
46 & 6 & 11.4864 & -5.48639 \tabularnewline
47 & 12 & 10.4609 & 1.53907 \tabularnewline
48 & 12 & 10.6449 & 1.35508 \tabularnewline
49 & 8 & 11.2374 & -3.23739 \tabularnewline
50 & 10 & 10.6128 & -0.612811 \tabularnewline
51 & 11 & 10.8046 & 0.195427 \tabularnewline
52 & 7 & 10.0596 & -3.05959 \tabularnewline
53 & 12 & 10.6498 & 1.35016 \tabularnewline
54 & 13 & 10.8566 & 2.14336 \tabularnewline
55 & 14 & 11.2965 & 2.70348 \tabularnewline
56 & 12 & 10.8143 & 1.18566 \tabularnewline
57 & 6 & 9.39064 & -3.39064 \tabularnewline
58 & 14 & 9.74216 & 4.25784 \tabularnewline
59 & 10 & 10.386 & -0.385987 \tabularnewline
60 & 12 & 10.5682 & 1.43177 \tabularnewline
61 & 11 & 11.3034 & -0.30341 \tabularnewline
62 & 10 & 10.7781 & -0.778139 \tabularnewline
63 & 7 & 8.97395 & -1.97395 \tabularnewline
64 & 12 & 10.9388 & 1.06124 \tabularnewline
65 & 7 & 10.4646 & -3.46464 \tabularnewline
66 & 12 & 10.1389 & 1.86111 \tabularnewline
67 & 12 & 10.4867 & 1.51332 \tabularnewline
68 & 10 & 10.8974 & -0.897392 \tabularnewline
69 & 10 & 10.9256 & -0.925569 \tabularnewline
70 & 12 & 10.2985 & 1.7015 \tabularnewline
71 & 12 & 10.6663 & 1.33369 \tabularnewline
72 & 12 & 10.8827 & 1.11729 \tabularnewline
73 & 8 & 10.5326 & -2.53263 \tabularnewline
74 & 10 & 11.6752 & -1.67523 \tabularnewline
75 & 5 & 9.99628 & -4.99628 \tabularnewline
76 & 10 & 10.067 & -0.0669653 \tabularnewline
77 & 12 & 10.3924 & 1.60757 \tabularnewline
78 & 11 & 11.3122 & -0.312162 \tabularnewline
79 & 9 & 10.2248 & -1.2248 \tabularnewline
80 & 12 & 10.6887 & 1.31134 \tabularnewline
81 & 11 & 10.3927 & 0.607322 \tabularnewline
82 & 10 & 10.6851 & -0.68507 \tabularnewline
83 & 12 & 10.561 & 1.43902 \tabularnewline
84 & 10 & 10.6263 & -0.6263 \tabularnewline
85 & 9 & 9.95106 & -0.951059 \tabularnewline
86 & 11 & 10.7834 & 0.216575 \tabularnewline
87 & 12 & 11.2895 & 0.710519 \tabularnewline
88 & 7 & 10.451 & -3.45098 \tabularnewline
89 & 11 & 10.3651 & 0.634934 \tabularnewline
90 & 12 & 10.6492 & 1.35084 \tabularnewline
91 & 6 & 10.7176 & -4.7176 \tabularnewline
92 & 9 & 9.19612 & -0.196124 \tabularnewline
93 & 15 & 10.8838 & 4.11615 \tabularnewline
94 & 10 & 8.95649 & 1.04351 \tabularnewline
95 & 11 & 10.6711 & 0.328894 \tabularnewline
96 & 12 & 11.0195 & 0.980521 \tabularnewline
97 & 12 & 10.6948 & 1.30523 \tabularnewline
98 & 12 & 10.5216 & 1.47838 \tabularnewline
99 & 11 & 10.7185 & 0.281492 \tabularnewline
100 & 9 & 10.6874 & -1.68737 \tabularnewline
101 & 11 & 10.7933 & 0.206741 \tabularnewline
102 & 12 & 11.0018 & 0.99824 \tabularnewline
103 & 12 & 9.8339 & 2.1661 \tabularnewline
104 & 14 & 10.6203 & 3.37974 \tabularnewline
105 & 8 & 10.1437 & -2.14373 \tabularnewline
106 & 10 & 10.2627 & -0.26272 \tabularnewline
107 & 9 & 10.7222 & -1.72221 \tabularnewline
108 & 10 & 10.0952 & -0.0951516 \tabularnewline
109 & 9 & 10.6582 & -1.65822 \tabularnewline
110 & 10 & 10.6756 & -0.675596 \tabularnewline
111 & 12 & 11.0725 & 0.927538 \tabularnewline
112 & 11 & 10.9766 & 0.0233875 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=266770&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[/C][C]10.5614[/C][C]1.43856[/C][/ROW]
[ROW][C]2[/C][C]8[/C][C]11.0315[/C][C]-3.03146[/C][/ROW]
[ROW][C]3[/C][C]11[/C][C]10.4138[/C][C]0.58615[/C][/ROW]
[ROW][C]4[/C][C]13[/C][C]10.9559[/C][C]2.04411[/C][/ROW]
[ROW][C]5[/C][C]11[/C][C]10.8919[/C][C]0.108066[/C][/ROW]
[ROW][C]6[/C][C]10[/C][C]10.4095[/C][C]-0.409454[/C][/ROW]
[ROW][C]7[/C][C]7[/C][C]10.0177[/C][C]-3.01773[/C][/ROW]
[ROW][C]8[/C][C]10[/C][C]10.2657[/C][C]-0.265738[/C][/ROW]
[ROW][C]9[/C][C]15[/C][C]11.4032[/C][C]3.59682[/C][/ROW]
[ROW][C]10[/C][C]12[/C][C]11.8792[/C][C]0.120787[/C][/ROW]
[ROW][C]11[/C][C]12[/C][C]10.558[/C][C]1.44196[/C][/ROW]
[ROW][C]12[/C][C]10[/C][C]11.2968[/C][C]-1.29676[/C][/ROW]
[ROW][C]13[/C][C]10[/C][C]10.1462[/C][C]-0.146229[/C][/ROW]
[ROW][C]14[/C][C]14[/C][C]10.7133[/C][C]3.28669[/C][/ROW]
[ROW][C]15[/C][C]6[/C][C]10.8172[/C][C]-4.81716[/C][/ROW]
[ROW][C]16[/C][C]12[/C][C]9.87807[/C][C]2.12193[/C][/ROW]
[ROW][C]17[/C][C]14[/C][C]10.3928[/C][C]3.60715[/C][/ROW]
[ROW][C]18[/C][C]11[/C][C]10.7546[/C][C]0.245414[/C][/ROW]
[ROW][C]19[/C][C]8[/C][C]10.7236[/C][C]-2.72364[/C][/ROW]
[ROW][C]20[/C][C]12[/C][C]10.5157[/C][C]1.48432[/C][/ROW]
[ROW][C]21[/C][C]15[/C][C]10.9725[/C][C]4.02749[/C][/ROW]
[ROW][C]22[/C][C]13[/C][C]10.8577[/C][C]2.14235[/C][/ROW]
[ROW][C]23[/C][C]11[/C][C]10.7357[/C][C]0.264251[/C][/ROW]
[ROW][C]24[/C][C]12[/C][C]10.8999[/C][C]1.10008[/C][/ROW]
[ROW][C]25[/C][C]7[/C][C]10.3644[/C][C]-3.36445[/C][/ROW]
[ROW][C]26[/C][C]11[/C][C]10.5097[/C][C]0.49027[/C][/ROW]
[ROW][C]27[/C][C]7[/C][C]10.5788[/C][C]-3.57883[/C][/ROW]
[ROW][C]28[/C][C]12[/C][C]10.5449[/C][C]1.45509[/C][/ROW]
[ROW][C]29[/C][C]12[/C][C]11.0943[/C][C]0.90566[/C][/ROW]
[ROW][C]30[/C][C]13[/C][C]10.7983[/C][C]2.20174[/C][/ROW]
[ROW][C]31[/C][C]9[/C][C]9.42446[/C][C]-0.424457[/C][/ROW]
[ROW][C]32[/C][C]11[/C][C]10.0478[/C][C]0.952153[/C][/ROW]
[ROW][C]33[/C][C]12[/C][C]10.4262[/C][C]1.5738[/C][/ROW]
[ROW][C]34[/C][C]15[/C][C]10.5587[/C][C]4.44132[/C][/ROW]
[ROW][C]35[/C][C]12[/C][C]10.3414[/C][C]1.65858[/C][/ROW]
[ROW][C]36[/C][C]6[/C][C]10.6282[/C][C]-4.62819[/C][/ROW]
[ROW][C]37[/C][C]5[/C][C]10.7424[/C][C]-5.74239[/C][/ROW]
[ROW][C]38[/C][C]13[/C][C]10.4437[/C][C]2.55627[/C][/ROW]
[ROW][C]39[/C][C]11[/C][C]10.9524[/C][C]0.0476037[/C][/ROW]
[ROW][C]40[/C][C]6[/C][C]10.6424[/C][C]-4.64243[/C][/ROW]
[ROW][C]41[/C][C]12[/C][C]10.4729[/C][C]1.52707[/C][/ROW]
[ROW][C]42[/C][C]10[/C][C]10.9525[/C][C]-0.952539[/C][/ROW]
[ROW][C]43[/C][C]6[/C][C]10.9077[/C][C]-4.90765[/C][/ROW]
[ROW][C]44[/C][C]12[/C][C]9.93031[/C][C]2.06969[/C][/ROW]
[ROW][C]45[/C][C]11[/C][C]10.2142[/C][C]0.785752[/C][/ROW]
[ROW][C]46[/C][C]6[/C][C]11.4864[/C][C]-5.48639[/C][/ROW]
[ROW][C]47[/C][C]12[/C][C]10.4609[/C][C]1.53907[/C][/ROW]
[ROW][C]48[/C][C]12[/C][C]10.6449[/C][C]1.35508[/C][/ROW]
[ROW][C]49[/C][C]8[/C][C]11.2374[/C][C]-3.23739[/C][/ROW]
[ROW][C]50[/C][C]10[/C][C]10.6128[/C][C]-0.612811[/C][/ROW]
[ROW][C]51[/C][C]11[/C][C]10.8046[/C][C]0.195427[/C][/ROW]
[ROW][C]52[/C][C]7[/C][C]10.0596[/C][C]-3.05959[/C][/ROW]
[ROW][C]53[/C][C]12[/C][C]10.6498[/C][C]1.35016[/C][/ROW]
[ROW][C]54[/C][C]13[/C][C]10.8566[/C][C]2.14336[/C][/ROW]
[ROW][C]55[/C][C]14[/C][C]11.2965[/C][C]2.70348[/C][/ROW]
[ROW][C]56[/C][C]12[/C][C]10.8143[/C][C]1.18566[/C][/ROW]
[ROW][C]57[/C][C]6[/C][C]9.39064[/C][C]-3.39064[/C][/ROW]
[ROW][C]58[/C][C]14[/C][C]9.74216[/C][C]4.25784[/C][/ROW]
[ROW][C]59[/C][C]10[/C][C]10.386[/C][C]-0.385987[/C][/ROW]
[ROW][C]60[/C][C]12[/C][C]10.5682[/C][C]1.43177[/C][/ROW]
[ROW][C]61[/C][C]11[/C][C]11.3034[/C][C]-0.30341[/C][/ROW]
[ROW][C]62[/C][C]10[/C][C]10.7781[/C][C]-0.778139[/C][/ROW]
[ROW][C]63[/C][C]7[/C][C]8.97395[/C][C]-1.97395[/C][/ROW]
[ROW][C]64[/C][C]12[/C][C]10.9388[/C][C]1.06124[/C][/ROW]
[ROW][C]65[/C][C]7[/C][C]10.4646[/C][C]-3.46464[/C][/ROW]
[ROW][C]66[/C][C]12[/C][C]10.1389[/C][C]1.86111[/C][/ROW]
[ROW][C]67[/C][C]12[/C][C]10.4867[/C][C]1.51332[/C][/ROW]
[ROW][C]68[/C][C]10[/C][C]10.8974[/C][C]-0.897392[/C][/ROW]
[ROW][C]69[/C][C]10[/C][C]10.9256[/C][C]-0.925569[/C][/ROW]
[ROW][C]70[/C][C]12[/C][C]10.2985[/C][C]1.7015[/C][/ROW]
[ROW][C]71[/C][C]12[/C][C]10.6663[/C][C]1.33369[/C][/ROW]
[ROW][C]72[/C][C]12[/C][C]10.8827[/C][C]1.11729[/C][/ROW]
[ROW][C]73[/C][C]8[/C][C]10.5326[/C][C]-2.53263[/C][/ROW]
[ROW][C]74[/C][C]10[/C][C]11.6752[/C][C]-1.67523[/C][/ROW]
[ROW][C]75[/C][C]5[/C][C]9.99628[/C][C]-4.99628[/C][/ROW]
[ROW][C]76[/C][C]10[/C][C]10.067[/C][C]-0.0669653[/C][/ROW]
[ROW][C]77[/C][C]12[/C][C]10.3924[/C][C]1.60757[/C][/ROW]
[ROW][C]78[/C][C]11[/C][C]11.3122[/C][C]-0.312162[/C][/ROW]
[ROW][C]79[/C][C]9[/C][C]10.2248[/C][C]-1.2248[/C][/ROW]
[ROW][C]80[/C][C]12[/C][C]10.6887[/C][C]1.31134[/C][/ROW]
[ROW][C]81[/C][C]11[/C][C]10.3927[/C][C]0.607322[/C][/ROW]
[ROW][C]82[/C][C]10[/C][C]10.6851[/C][C]-0.68507[/C][/ROW]
[ROW][C]83[/C][C]12[/C][C]10.561[/C][C]1.43902[/C][/ROW]
[ROW][C]84[/C][C]10[/C][C]10.6263[/C][C]-0.6263[/C][/ROW]
[ROW][C]85[/C][C]9[/C][C]9.95106[/C][C]-0.951059[/C][/ROW]
[ROW][C]86[/C][C]11[/C][C]10.7834[/C][C]0.216575[/C][/ROW]
[ROW][C]87[/C][C]12[/C][C]11.2895[/C][C]0.710519[/C][/ROW]
[ROW][C]88[/C][C]7[/C][C]10.451[/C][C]-3.45098[/C][/ROW]
[ROW][C]89[/C][C]11[/C][C]10.3651[/C][C]0.634934[/C][/ROW]
[ROW][C]90[/C][C]12[/C][C]10.6492[/C][C]1.35084[/C][/ROW]
[ROW][C]91[/C][C]6[/C][C]10.7176[/C][C]-4.7176[/C][/ROW]
[ROW][C]92[/C][C]9[/C][C]9.19612[/C][C]-0.196124[/C][/ROW]
[ROW][C]93[/C][C]15[/C][C]10.8838[/C][C]4.11615[/C][/ROW]
[ROW][C]94[/C][C]10[/C][C]8.95649[/C][C]1.04351[/C][/ROW]
[ROW][C]95[/C][C]11[/C][C]10.6711[/C][C]0.328894[/C][/ROW]
[ROW][C]96[/C][C]12[/C][C]11.0195[/C][C]0.980521[/C][/ROW]
[ROW][C]97[/C][C]12[/C][C]10.6948[/C][C]1.30523[/C][/ROW]
[ROW][C]98[/C][C]12[/C][C]10.5216[/C][C]1.47838[/C][/ROW]
[ROW][C]99[/C][C]11[/C][C]10.7185[/C][C]0.281492[/C][/ROW]
[ROW][C]100[/C][C]9[/C][C]10.6874[/C][C]-1.68737[/C][/ROW]
[ROW][C]101[/C][C]11[/C][C]10.7933[/C][C]0.206741[/C][/ROW]
[ROW][C]102[/C][C]12[/C][C]11.0018[/C][C]0.99824[/C][/ROW]
[ROW][C]103[/C][C]12[/C][C]9.8339[/C][C]2.1661[/C][/ROW]
[ROW][C]104[/C][C]14[/C][C]10.6203[/C][C]3.37974[/C][/ROW]
[ROW][C]105[/C][C]8[/C][C]10.1437[/C][C]-2.14373[/C][/ROW]
[ROW][C]106[/C][C]10[/C][C]10.2627[/C][C]-0.26272[/C][/ROW]
[ROW][C]107[/C][C]9[/C][C]10.7222[/C][C]-1.72221[/C][/ROW]
[ROW][C]108[/C][C]10[/C][C]10.0952[/C][C]-0.0951516[/C][/ROW]
[ROW][C]109[/C][C]9[/C][C]10.6582[/C][C]-1.65822[/C][/ROW]
[ROW][C]110[/C][C]10[/C][C]10.6756[/C][C]-0.675596[/C][/ROW]
[ROW][C]111[/C][C]12[/C][C]11.0725[/C][C]0.927538[/C][/ROW]
[ROW][C]112[/C][C]11[/C][C]10.9766[/C][C]0.0233875[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=266770&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=266770&T=4

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


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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11210.56141.43856
2811.0315-3.03146
31110.41380.58615
41310.95592.04411
51110.89190.108066
61010.4095-0.409454
7710.0177-3.01773
81010.2657-0.265738
91511.40323.59682
101211.87920.120787
111210.5581.44196
121011.2968-1.29676
131010.1462-0.146229
141410.71333.28669
15610.8172-4.81716
16129.878072.12193
171410.39283.60715
181110.75460.245414
19810.7236-2.72364
201210.51571.48432
211510.97254.02749
221310.85772.14235
231110.73570.264251
241210.89991.10008
25710.3644-3.36445
261110.50970.49027
27710.5788-3.57883
281210.54491.45509
291211.09430.90566
301310.79832.20174
3199.42446-0.424457
321110.04780.952153
331210.42621.5738
341510.55874.44132
351210.34141.65858
36610.6282-4.62819
37510.7424-5.74239
381310.44372.55627
391110.95240.0476037
40610.6424-4.64243
411210.47291.52707
421010.9525-0.952539
43610.9077-4.90765
44129.930312.06969
451110.21420.785752
46611.4864-5.48639
471210.46091.53907
481210.64491.35508
49811.2374-3.23739
501010.6128-0.612811
511110.80460.195427
52710.0596-3.05959
531210.64981.35016
541310.85662.14336
551411.29652.70348
561210.81431.18566
5769.39064-3.39064
58149.742164.25784
591010.386-0.385987
601210.56821.43177
611111.3034-0.30341
621010.7781-0.778139
6378.97395-1.97395
641210.93881.06124
65710.4646-3.46464
661210.13891.86111
671210.48671.51332
681010.8974-0.897392
691010.9256-0.925569
701210.29851.7015
711210.66631.33369
721210.88271.11729
73810.5326-2.53263
741011.6752-1.67523
7559.99628-4.99628
761010.067-0.0669653
771210.39241.60757
781111.3122-0.312162
79910.2248-1.2248
801210.68871.31134
811110.39270.607322
821010.6851-0.68507
831210.5611.43902
841010.6263-0.6263
8599.95106-0.951059
861110.78340.216575
871211.28950.710519
88710.451-3.45098
891110.36510.634934
901210.64921.35084
91610.7176-4.7176
9299.19612-0.196124
931510.88384.11615
94108.956491.04351
951110.67110.328894
961211.01950.980521
971210.69481.30523
981210.52161.47838
991110.71850.281492
100910.6874-1.68737
1011110.79330.206741
1021211.00180.99824
103129.83392.1661
1041410.62033.37974
105810.1437-2.14373
1061010.2627-0.26272
107910.7222-1.72221
1081010.0952-0.0951516
109910.6582-1.65822
1101010.6756-0.675596
1111211.07250.927538
1121110.97660.0233875






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
90.748480.503040.25152
100.6064820.7870370.393518
110.4722570.9445150.527743
120.4489270.8978540.551073
130.3529020.7058040.647098
140.4051170.8102340.594883
150.5626020.8747970.437398
160.7838870.4322270.216113
170.8292390.3415210.170761
180.7686070.4627860.231393
190.7649990.4700010.235001
200.7252290.5495430.274771
210.8173550.365290.182645
220.7789830.4420350.221017
230.7242640.5514720.275736
240.6686120.6627760.331388
250.7000870.5998250.299913
260.6364060.7271880.363594
270.72350.5530010.2765
280.6777930.6444130.322207
290.6197920.7604150.380208
300.5790740.8418530.420926
310.5174030.9651930.482597
320.4960290.9920580.503971
330.4516370.9032740.548363
340.5157680.9684630.484232
350.4704890.9409780.529511
360.6263270.7473460.373673
370.8920620.2158760.107938
380.8963760.2072490.103624
390.8668560.2662890.133144
400.9591210.0817590.0408795
410.9520460.09590710.0479536
420.9387850.122430.0612148
430.9776310.04473740.0223687
440.9761220.04775650.0238783
450.9682260.06354730.0317737
460.9929040.0141930.00709648
470.9911030.01779330.00889665
480.9884260.02314790.011574
490.9911090.0177810.0088905
500.9873660.02526740.0126337
510.9820270.03594690.0179734
520.9886810.02263790.0113189
530.9856640.02867110.0143355
540.9855290.02894140.0144707
550.987760.02448020.0122401
560.9840260.03194860.0159743
570.9897810.02043870.0102193
580.997050.005900190.0029501
590.9956430.008714450.00435722
600.9945430.01091420.00545712
610.9921130.01577320.0078866
620.9887110.02257790.0112889
630.9862210.02755890.0137795
640.98230.03540090.0177004
650.9896660.02066820.0103341
660.988320.023360.01168
670.9867760.02644850.0132242
680.9816720.03665660.0183283
690.974840.05031920.0251596
700.9715960.05680880.0284044
710.9661380.06772410.0338621
720.9564640.0870730.0435365
730.9624720.07505530.0375276
740.9578670.08426550.0421327
750.9903410.01931890.00965945
760.9852280.02954460.0147723
770.9845270.03094570.0154728
780.9767910.04641880.0232094
790.973240.05352090.0267605
800.9625130.07497460.0374873
810.9491530.1016940.0508471
820.9321280.1357440.067872
830.9110190.1779620.0889809
840.9036190.1927620.0963812
850.870990.258020.12901
860.8296270.3407460.170373
870.7793520.4412970.220648
880.8918630.2162730.108137
890.8511990.2976020.148801
900.8041120.3917760.195888
910.9546560.09068820.0453441
920.9311970.1376050.0688025
930.9906160.01876890.00938446
940.9835450.03291040.0164552
950.9707390.05852130.0292607
960.950570.09886040.0494302
970.9286590.1426810.0713407
980.9066770.1866450.0933226
990.8670710.2658580.132929
1000.8758920.2482150.124108
1010.7909750.418050.209025
1020.7259830.5480330.274017
1030.6013710.7972580.398629

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
9 & 0.74848 & 0.50304 & 0.25152 \tabularnewline
10 & 0.606482 & 0.787037 & 0.393518 \tabularnewline
11 & 0.472257 & 0.944515 & 0.527743 \tabularnewline
12 & 0.448927 & 0.897854 & 0.551073 \tabularnewline
13 & 0.352902 & 0.705804 & 0.647098 \tabularnewline
14 & 0.405117 & 0.810234 & 0.594883 \tabularnewline
15 & 0.562602 & 0.874797 & 0.437398 \tabularnewline
16 & 0.783887 & 0.432227 & 0.216113 \tabularnewline
17 & 0.829239 & 0.341521 & 0.170761 \tabularnewline
18 & 0.768607 & 0.462786 & 0.231393 \tabularnewline
19 & 0.764999 & 0.470001 & 0.235001 \tabularnewline
20 & 0.725229 & 0.549543 & 0.274771 \tabularnewline
21 & 0.817355 & 0.36529 & 0.182645 \tabularnewline
22 & 0.778983 & 0.442035 & 0.221017 \tabularnewline
23 & 0.724264 & 0.551472 & 0.275736 \tabularnewline
24 & 0.668612 & 0.662776 & 0.331388 \tabularnewline
25 & 0.700087 & 0.599825 & 0.299913 \tabularnewline
26 & 0.636406 & 0.727188 & 0.363594 \tabularnewline
27 & 0.7235 & 0.553001 & 0.2765 \tabularnewline
28 & 0.677793 & 0.644413 & 0.322207 \tabularnewline
29 & 0.619792 & 0.760415 & 0.380208 \tabularnewline
30 & 0.579074 & 0.841853 & 0.420926 \tabularnewline
31 & 0.517403 & 0.965193 & 0.482597 \tabularnewline
32 & 0.496029 & 0.992058 & 0.503971 \tabularnewline
33 & 0.451637 & 0.903274 & 0.548363 \tabularnewline
34 & 0.515768 & 0.968463 & 0.484232 \tabularnewline
35 & 0.470489 & 0.940978 & 0.529511 \tabularnewline
36 & 0.626327 & 0.747346 & 0.373673 \tabularnewline
37 & 0.892062 & 0.215876 & 0.107938 \tabularnewline
38 & 0.896376 & 0.207249 & 0.103624 \tabularnewline
39 & 0.866856 & 0.266289 & 0.133144 \tabularnewline
40 & 0.959121 & 0.081759 & 0.0408795 \tabularnewline
41 & 0.952046 & 0.0959071 & 0.0479536 \tabularnewline
42 & 0.938785 & 0.12243 & 0.0612148 \tabularnewline
43 & 0.977631 & 0.0447374 & 0.0223687 \tabularnewline
44 & 0.976122 & 0.0477565 & 0.0238783 \tabularnewline
45 & 0.968226 & 0.0635473 & 0.0317737 \tabularnewline
46 & 0.992904 & 0.014193 & 0.00709648 \tabularnewline
47 & 0.991103 & 0.0177933 & 0.00889665 \tabularnewline
48 & 0.988426 & 0.0231479 & 0.011574 \tabularnewline
49 & 0.991109 & 0.017781 & 0.0088905 \tabularnewline
50 & 0.987366 & 0.0252674 & 0.0126337 \tabularnewline
51 & 0.982027 & 0.0359469 & 0.0179734 \tabularnewline
52 & 0.988681 & 0.0226379 & 0.0113189 \tabularnewline
53 & 0.985664 & 0.0286711 & 0.0143355 \tabularnewline
54 & 0.985529 & 0.0289414 & 0.0144707 \tabularnewline
55 & 0.98776 & 0.0244802 & 0.0122401 \tabularnewline
56 & 0.984026 & 0.0319486 & 0.0159743 \tabularnewline
57 & 0.989781 & 0.0204387 & 0.0102193 \tabularnewline
58 & 0.99705 & 0.00590019 & 0.0029501 \tabularnewline
59 & 0.995643 & 0.00871445 & 0.00435722 \tabularnewline
60 & 0.994543 & 0.0109142 & 0.00545712 \tabularnewline
61 & 0.992113 & 0.0157732 & 0.0078866 \tabularnewline
62 & 0.988711 & 0.0225779 & 0.0112889 \tabularnewline
63 & 0.986221 & 0.0275589 & 0.0137795 \tabularnewline
64 & 0.9823 & 0.0354009 & 0.0177004 \tabularnewline
65 & 0.989666 & 0.0206682 & 0.0103341 \tabularnewline
66 & 0.98832 & 0.02336 & 0.01168 \tabularnewline
67 & 0.986776 & 0.0264485 & 0.0132242 \tabularnewline
68 & 0.981672 & 0.0366566 & 0.0183283 \tabularnewline
69 & 0.97484 & 0.0503192 & 0.0251596 \tabularnewline
70 & 0.971596 & 0.0568088 & 0.0284044 \tabularnewline
71 & 0.966138 & 0.0677241 & 0.0338621 \tabularnewline
72 & 0.956464 & 0.087073 & 0.0435365 \tabularnewline
73 & 0.962472 & 0.0750553 & 0.0375276 \tabularnewline
74 & 0.957867 & 0.0842655 & 0.0421327 \tabularnewline
75 & 0.990341 & 0.0193189 & 0.00965945 \tabularnewline
76 & 0.985228 & 0.0295446 & 0.0147723 \tabularnewline
77 & 0.984527 & 0.0309457 & 0.0154728 \tabularnewline
78 & 0.976791 & 0.0464188 & 0.0232094 \tabularnewline
79 & 0.97324 & 0.0535209 & 0.0267605 \tabularnewline
80 & 0.962513 & 0.0749746 & 0.0374873 \tabularnewline
81 & 0.949153 & 0.101694 & 0.0508471 \tabularnewline
82 & 0.932128 & 0.135744 & 0.067872 \tabularnewline
83 & 0.911019 & 0.177962 & 0.0889809 \tabularnewline
84 & 0.903619 & 0.192762 & 0.0963812 \tabularnewline
85 & 0.87099 & 0.25802 & 0.12901 \tabularnewline
86 & 0.829627 & 0.340746 & 0.170373 \tabularnewline
87 & 0.779352 & 0.441297 & 0.220648 \tabularnewline
88 & 0.891863 & 0.216273 & 0.108137 \tabularnewline
89 & 0.851199 & 0.297602 & 0.148801 \tabularnewline
90 & 0.804112 & 0.391776 & 0.195888 \tabularnewline
91 & 0.954656 & 0.0906882 & 0.0453441 \tabularnewline
92 & 0.931197 & 0.137605 & 0.0688025 \tabularnewline
93 & 0.990616 & 0.0187689 & 0.00938446 \tabularnewline
94 & 0.983545 & 0.0329104 & 0.0164552 \tabularnewline
95 & 0.970739 & 0.0585213 & 0.0292607 \tabularnewline
96 & 0.95057 & 0.0988604 & 0.0494302 \tabularnewline
97 & 0.928659 & 0.142681 & 0.0713407 \tabularnewline
98 & 0.906677 & 0.186645 & 0.0933226 \tabularnewline
99 & 0.867071 & 0.265858 & 0.132929 \tabularnewline
100 & 0.875892 & 0.248215 & 0.124108 \tabularnewline
101 & 0.790975 & 0.41805 & 0.209025 \tabularnewline
102 & 0.725983 & 0.548033 & 0.274017 \tabularnewline
103 & 0.601371 & 0.797258 & 0.398629 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=266770&T=5

[TABLE]
[ROW][C]Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]p-values[/C][C]Alternative Hypothesis[/C][/ROW]
[ROW][C]breakpoint index[/C][C]greater[/C][C]2-sided[/C][C]less[/C][/ROW]
[ROW][C]9[/C][C]0.74848[/C][C]0.50304[/C][C]0.25152[/C][/ROW]
[ROW][C]10[/C][C]0.606482[/C][C]0.787037[/C][C]0.393518[/C][/ROW]
[ROW][C]11[/C][C]0.472257[/C][C]0.944515[/C][C]0.527743[/C][/ROW]
[ROW][C]12[/C][C]0.448927[/C][C]0.897854[/C][C]0.551073[/C][/ROW]
[ROW][C]13[/C][C]0.352902[/C][C]0.705804[/C][C]0.647098[/C][/ROW]
[ROW][C]14[/C][C]0.405117[/C][C]0.810234[/C][C]0.594883[/C][/ROW]
[ROW][C]15[/C][C]0.562602[/C][C]0.874797[/C][C]0.437398[/C][/ROW]
[ROW][C]16[/C][C]0.783887[/C][C]0.432227[/C][C]0.216113[/C][/ROW]
[ROW][C]17[/C][C]0.829239[/C][C]0.341521[/C][C]0.170761[/C][/ROW]
[ROW][C]18[/C][C]0.768607[/C][C]0.462786[/C][C]0.231393[/C][/ROW]
[ROW][C]19[/C][C]0.764999[/C][C]0.470001[/C][C]0.235001[/C][/ROW]
[ROW][C]20[/C][C]0.725229[/C][C]0.549543[/C][C]0.274771[/C][/ROW]
[ROW][C]21[/C][C]0.817355[/C][C]0.36529[/C][C]0.182645[/C][/ROW]
[ROW][C]22[/C][C]0.778983[/C][C]0.442035[/C][C]0.221017[/C][/ROW]
[ROW][C]23[/C][C]0.724264[/C][C]0.551472[/C][C]0.275736[/C][/ROW]
[ROW][C]24[/C][C]0.668612[/C][C]0.662776[/C][C]0.331388[/C][/ROW]
[ROW][C]25[/C][C]0.700087[/C][C]0.599825[/C][C]0.299913[/C][/ROW]
[ROW][C]26[/C][C]0.636406[/C][C]0.727188[/C][C]0.363594[/C][/ROW]
[ROW][C]27[/C][C]0.7235[/C][C]0.553001[/C][C]0.2765[/C][/ROW]
[ROW][C]28[/C][C]0.677793[/C][C]0.644413[/C][C]0.322207[/C][/ROW]
[ROW][C]29[/C][C]0.619792[/C][C]0.760415[/C][C]0.380208[/C][/ROW]
[ROW][C]30[/C][C]0.579074[/C][C]0.841853[/C][C]0.420926[/C][/ROW]
[ROW][C]31[/C][C]0.517403[/C][C]0.965193[/C][C]0.482597[/C][/ROW]
[ROW][C]32[/C][C]0.496029[/C][C]0.992058[/C][C]0.503971[/C][/ROW]
[ROW][C]33[/C][C]0.451637[/C][C]0.903274[/C][C]0.548363[/C][/ROW]
[ROW][C]34[/C][C]0.515768[/C][C]0.968463[/C][C]0.484232[/C][/ROW]
[ROW][C]35[/C][C]0.470489[/C][C]0.940978[/C][C]0.529511[/C][/ROW]
[ROW][C]36[/C][C]0.626327[/C][C]0.747346[/C][C]0.373673[/C][/ROW]
[ROW][C]37[/C][C]0.892062[/C][C]0.215876[/C][C]0.107938[/C][/ROW]
[ROW][C]38[/C][C]0.896376[/C][C]0.207249[/C][C]0.103624[/C][/ROW]
[ROW][C]39[/C][C]0.866856[/C][C]0.266289[/C][C]0.133144[/C][/ROW]
[ROW][C]40[/C][C]0.959121[/C][C]0.081759[/C][C]0.0408795[/C][/ROW]
[ROW][C]41[/C][C]0.952046[/C][C]0.0959071[/C][C]0.0479536[/C][/ROW]
[ROW][C]42[/C][C]0.938785[/C][C]0.12243[/C][C]0.0612148[/C][/ROW]
[ROW][C]43[/C][C]0.977631[/C][C]0.0447374[/C][C]0.0223687[/C][/ROW]
[ROW][C]44[/C][C]0.976122[/C][C]0.0477565[/C][C]0.0238783[/C][/ROW]
[ROW][C]45[/C][C]0.968226[/C][C]0.0635473[/C][C]0.0317737[/C][/ROW]
[ROW][C]46[/C][C]0.992904[/C][C]0.014193[/C][C]0.00709648[/C][/ROW]
[ROW][C]47[/C][C]0.991103[/C][C]0.0177933[/C][C]0.00889665[/C][/ROW]
[ROW][C]48[/C][C]0.988426[/C][C]0.0231479[/C][C]0.011574[/C][/ROW]
[ROW][C]49[/C][C]0.991109[/C][C]0.017781[/C][C]0.0088905[/C][/ROW]
[ROW][C]50[/C][C]0.987366[/C][C]0.0252674[/C][C]0.0126337[/C][/ROW]
[ROW][C]51[/C][C]0.982027[/C][C]0.0359469[/C][C]0.0179734[/C][/ROW]
[ROW][C]52[/C][C]0.988681[/C][C]0.0226379[/C][C]0.0113189[/C][/ROW]
[ROW][C]53[/C][C]0.985664[/C][C]0.0286711[/C][C]0.0143355[/C][/ROW]
[ROW][C]54[/C][C]0.985529[/C][C]0.0289414[/C][C]0.0144707[/C][/ROW]
[ROW][C]55[/C][C]0.98776[/C][C]0.0244802[/C][C]0.0122401[/C][/ROW]
[ROW][C]56[/C][C]0.984026[/C][C]0.0319486[/C][C]0.0159743[/C][/ROW]
[ROW][C]57[/C][C]0.989781[/C][C]0.0204387[/C][C]0.0102193[/C][/ROW]
[ROW][C]58[/C][C]0.99705[/C][C]0.00590019[/C][C]0.0029501[/C][/ROW]
[ROW][C]59[/C][C]0.995643[/C][C]0.00871445[/C][C]0.00435722[/C][/ROW]
[ROW][C]60[/C][C]0.994543[/C][C]0.0109142[/C][C]0.00545712[/C][/ROW]
[ROW][C]61[/C][C]0.992113[/C][C]0.0157732[/C][C]0.0078866[/C][/ROW]
[ROW][C]62[/C][C]0.988711[/C][C]0.0225779[/C][C]0.0112889[/C][/ROW]
[ROW][C]63[/C][C]0.986221[/C][C]0.0275589[/C][C]0.0137795[/C][/ROW]
[ROW][C]64[/C][C]0.9823[/C][C]0.0354009[/C][C]0.0177004[/C][/ROW]
[ROW][C]65[/C][C]0.989666[/C][C]0.0206682[/C][C]0.0103341[/C][/ROW]
[ROW][C]66[/C][C]0.98832[/C][C]0.02336[/C][C]0.01168[/C][/ROW]
[ROW][C]67[/C][C]0.986776[/C][C]0.0264485[/C][C]0.0132242[/C][/ROW]
[ROW][C]68[/C][C]0.981672[/C][C]0.0366566[/C][C]0.0183283[/C][/ROW]
[ROW][C]69[/C][C]0.97484[/C][C]0.0503192[/C][C]0.0251596[/C][/ROW]
[ROW][C]70[/C][C]0.971596[/C][C]0.0568088[/C][C]0.0284044[/C][/ROW]
[ROW][C]71[/C][C]0.966138[/C][C]0.0677241[/C][C]0.0338621[/C][/ROW]
[ROW][C]72[/C][C]0.956464[/C][C]0.087073[/C][C]0.0435365[/C][/ROW]
[ROW][C]73[/C][C]0.962472[/C][C]0.0750553[/C][C]0.0375276[/C][/ROW]
[ROW][C]74[/C][C]0.957867[/C][C]0.0842655[/C][C]0.0421327[/C][/ROW]
[ROW][C]75[/C][C]0.990341[/C][C]0.0193189[/C][C]0.00965945[/C][/ROW]
[ROW][C]76[/C][C]0.985228[/C][C]0.0295446[/C][C]0.0147723[/C][/ROW]
[ROW][C]77[/C][C]0.984527[/C][C]0.0309457[/C][C]0.0154728[/C][/ROW]
[ROW][C]78[/C][C]0.976791[/C][C]0.0464188[/C][C]0.0232094[/C][/ROW]
[ROW][C]79[/C][C]0.97324[/C][C]0.0535209[/C][C]0.0267605[/C][/ROW]
[ROW][C]80[/C][C]0.962513[/C][C]0.0749746[/C][C]0.0374873[/C][/ROW]
[ROW][C]81[/C][C]0.949153[/C][C]0.101694[/C][C]0.0508471[/C][/ROW]
[ROW][C]82[/C][C]0.932128[/C][C]0.135744[/C][C]0.067872[/C][/ROW]
[ROW][C]83[/C][C]0.911019[/C][C]0.177962[/C][C]0.0889809[/C][/ROW]
[ROW][C]84[/C][C]0.903619[/C][C]0.192762[/C][C]0.0963812[/C][/ROW]
[ROW][C]85[/C][C]0.87099[/C][C]0.25802[/C][C]0.12901[/C][/ROW]
[ROW][C]86[/C][C]0.829627[/C][C]0.340746[/C][C]0.170373[/C][/ROW]
[ROW][C]87[/C][C]0.779352[/C][C]0.441297[/C][C]0.220648[/C][/ROW]
[ROW][C]88[/C][C]0.891863[/C][C]0.216273[/C][C]0.108137[/C][/ROW]
[ROW][C]89[/C][C]0.851199[/C][C]0.297602[/C][C]0.148801[/C][/ROW]
[ROW][C]90[/C][C]0.804112[/C][C]0.391776[/C][C]0.195888[/C][/ROW]
[ROW][C]91[/C][C]0.954656[/C][C]0.0906882[/C][C]0.0453441[/C][/ROW]
[ROW][C]92[/C][C]0.931197[/C][C]0.137605[/C][C]0.0688025[/C][/ROW]
[ROW][C]93[/C][C]0.990616[/C][C]0.0187689[/C][C]0.00938446[/C][/ROW]
[ROW][C]94[/C][C]0.983545[/C][C]0.0329104[/C][C]0.0164552[/C][/ROW]
[ROW][C]95[/C][C]0.970739[/C][C]0.0585213[/C][C]0.0292607[/C][/ROW]
[ROW][C]96[/C][C]0.95057[/C][C]0.0988604[/C][C]0.0494302[/C][/ROW]
[ROW][C]97[/C][C]0.928659[/C][C]0.142681[/C][C]0.0713407[/C][/ROW]
[ROW][C]98[/C][C]0.906677[/C][C]0.186645[/C][C]0.0933226[/C][/ROW]
[ROW][C]99[/C][C]0.867071[/C][C]0.265858[/C][C]0.132929[/C][/ROW]
[ROW][C]100[/C][C]0.875892[/C][C]0.248215[/C][C]0.124108[/C][/ROW]
[ROW][C]101[/C][C]0.790975[/C][C]0.41805[/C][C]0.209025[/C][/ROW]
[ROW][C]102[/C][C]0.725983[/C][C]0.548033[/C][C]0.274017[/C][/ROW]
[ROW][C]103[/C][C]0.601371[/C][C]0.797258[/C][C]0.398629[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=266770&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=266770&T=5

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


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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
90.748480.503040.25152
100.6064820.7870370.393518
110.4722570.9445150.527743
120.4489270.8978540.551073
130.3529020.7058040.647098
140.4051170.8102340.594883
150.5626020.8747970.437398
160.7838870.4322270.216113
170.8292390.3415210.170761
180.7686070.4627860.231393
190.7649990.4700010.235001
200.7252290.5495430.274771
210.8173550.365290.182645
220.7789830.4420350.221017
230.7242640.5514720.275736
240.6686120.6627760.331388
250.7000870.5998250.299913
260.6364060.7271880.363594
270.72350.5530010.2765
280.6777930.6444130.322207
290.6197920.7604150.380208
300.5790740.8418530.420926
310.5174030.9651930.482597
320.4960290.9920580.503971
330.4516370.9032740.548363
340.5157680.9684630.484232
350.4704890.9409780.529511
360.6263270.7473460.373673
370.8920620.2158760.107938
380.8963760.2072490.103624
390.8668560.2662890.133144
400.9591210.0817590.0408795
410.9520460.09590710.0479536
420.9387850.122430.0612148
430.9776310.04473740.0223687
440.9761220.04775650.0238783
450.9682260.06354730.0317737
460.9929040.0141930.00709648
470.9911030.01779330.00889665
480.9884260.02314790.011574
490.9911090.0177810.0088905
500.9873660.02526740.0126337
510.9820270.03594690.0179734
520.9886810.02263790.0113189
530.9856640.02867110.0143355
540.9855290.02894140.0144707
550.987760.02448020.0122401
560.9840260.03194860.0159743
570.9897810.02043870.0102193
580.997050.005900190.0029501
590.9956430.008714450.00435722
600.9945430.01091420.00545712
610.9921130.01577320.0078866
620.9887110.02257790.0112889
630.9862210.02755890.0137795
640.98230.03540090.0177004
650.9896660.02066820.0103341
660.988320.023360.01168
670.9867760.02644850.0132242
680.9816720.03665660.0183283
690.974840.05031920.0251596
700.9715960.05680880.0284044
710.9661380.06772410.0338621
720.9564640.0870730.0435365
730.9624720.07505530.0375276
740.9578670.08426550.0421327
750.9903410.01931890.00965945
760.9852280.02954460.0147723
770.9845270.03094570.0154728
780.9767910.04641880.0232094
790.973240.05352090.0267605
800.9625130.07497460.0374873
810.9491530.1016940.0508471
820.9321280.1357440.067872
830.9110190.1779620.0889809
840.9036190.1927620.0963812
850.870990.258020.12901
860.8296270.3407460.170373
870.7793520.4412970.220648
880.8918630.2162730.108137
890.8511990.2976020.148801
900.8041120.3917760.195888
910.9546560.09068820.0453441
920.9311970.1376050.0688025
930.9906160.01876890.00938446
940.9835450.03291040.0164552
950.9707390.05852130.0292607
960.950570.09886040.0494302
970.9286590.1426810.0713407
980.9066770.1866450.0933226
990.8670710.2658580.132929
1000.8758920.2482150.124108
1010.7909750.418050.209025
1020.7259830.5480330.274017
1030.6013710.7972580.398629






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level20.0210526NOK
5% type I error level310.326316NOK
10% type I error level450.473684NOK

\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 & 2 & 0.0210526 & NOK \tabularnewline
5% type I error level & 31 & 0.326316 & NOK \tabularnewline
10% type I error level & 45 & 0.473684 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=266770&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]2[/C][C]0.0210526[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]31[/C][C]0.326316[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]45[/C][C]0.473684[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=266770&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=266770&T=6

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

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level20.0210526NOK
5% type I error level310.326316NOK
10% type I error level450.473684NOK


Figures (Output of Computation)

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Input Parameters & R Code

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):
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')
}