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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, 20 Nov 2009 08:10:50 -0700
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2009/Nov/20/t1258729916o5kc3yoyshho06s.htm/, Retrieved Fri, 29 Mar 2024 15:37:04 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=58254, Retrieved Fri, 29 Mar 2024 15:37:04 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywordsSDHW, DSHW
Estimated Impact166
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [Q1 The Seatbeltlaw] [2007-11-14 19:27:43] [8cd6641b921d30ebe00b648d1481bba0]
- RMPD  [Multiple Regression] [Seatbelt] [2009-11-12 13:54:52] [b98453cac15ba1066b407e146608df68]
-    D    [Multiple Regression] [workshop 7 bereke...] [2009-11-19 15:24:52] [eaf42bcf5162b5692bb3c7f9d4636222]
-   PD      [Multiple Regression] [DSHW-WS7-Multiple...] [2009-11-20 13:19:23] [f15cfb7053d35072d573abca87df96a0]
-   P         [Multiple Regression] [DSHW-WS7-Multiple...] [2009-11-20 13:53:17] [f15cfb7053d35072d573abca87df96a0]
-    D          [Multiple Regression] [DSHW-WS7-MultRegr1] [2009-11-20 14:59:26] [f15cfb7053d35072d573abca87df96a0]
-    D              [Multiple Regression] [DSHW-WS7-MultRegr...] [2009-11-20 15:10:50] [36295456a56d4c7dcc9b9537ce63463b] [Current]
-   P                 [Multiple Regression] [DSHW-WS7-MiltRegr.2] [2009-11-20 15:42:03] [f15cfb7053d35072d573abca87df96a0]
-    D                  [Multiple Regression] [review 7] [2009-11-24 20:46:35] [309ee52d0058ff0a6f7eec15e07b2d9f]
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Dataseries X:
1.4	0.0
1.6	0.0
1.7	0.0
2.0	0.0
2.0	0.0
2.1	0.0
2.5	0.0
2.5	0.0
2.6	0.0
2.7	0.0
3.7	0.0
4.0	0.0
5.0	0.0
5.1	0.0
5.1	0.0
5.0	0.0
5.1	0.0
4.7	0.0
4.5	0.0
4.5	0.0
4.6	0.0
4.6	0.0
4.6	0.0
4.6	0.0
5.3	0.0
5.4	0.0
5.3	0.0
5.2	0.0
5.0	0.0
4.2	0.0
4.3	0.0
4.3	0.0
4.3	0.0
4.0	0.0
4.0	0.0
4.1	0.0
4.4	0.0
3.6	0.0
3.7	0.0
3.8	0.0
3.3	0.0
3.3	0.0
3.3	0.0
3.5	0.0
3.3	0.0
3.3	0.0
3.4	0.0
3.4	0.0
5.2	0.0
5.3	0.0
4.8	1.0
5.0	1.0
4.6	1.0
4.6	1.0
3.5	1.0
3.5	1.0




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time5 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 5 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58254&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]5 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58254&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58254&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 Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time5 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135







Multiple Linear Regression - Estimated Regression Equation
IndGez[t] = + 3.888 + 0.445333333333333InvlMex[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
IndGez[t] =  +  3.888 +  0.445333333333333InvlMex[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58254&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]IndGez[t] =  +  3.888 +  0.445333333333333InvlMex[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58254&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58254&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
IndGez[t] = + 3.888 + 0.445333333333333InvlMex[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)3.8880.15200625.577900
InvlMex0.4453333333333330.4643880.9590.3418480.170924

\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) & 3.888 & 0.152006 & 25.5779 & 0 & 0 \tabularnewline
InvlMex & 0.445333333333333 & 0.464388 & 0.959 & 0.341848 & 0.170924 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58254&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]3.888[/C][C]0.152006[/C][C]25.5779[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]InvlMex[/C][C]0.445333333333333[/C][C]0.464388[/C][C]0.959[/C][C]0.341848[/C][C]0.170924[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58254&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58254&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
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)3.8880.15200625.577900
InvlMex0.4453333333333330.4643880.9590.3418480.170924







Multiple Linear Regression - Regression Statistics
Multiple R0.129401971531008
R-squared0.0167448702361117
Adjusted R-squared-0.00146355809284926
F-TEST (value)0.919621942849324
F-TEST (DF numerator)1
F-TEST (DF denominator)54
p-value0.341848401095354
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1.07484825228127
Sum Squared Residuals62.3861333333334

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.129401971531008 \tabularnewline
R-squared & 0.0167448702361117 \tabularnewline
Adjusted R-squared & -0.00146355809284926 \tabularnewline
F-TEST (value) & 0.919621942849324 \tabularnewline
F-TEST (DF numerator) & 1 \tabularnewline
F-TEST (DF denominator) & 54 \tabularnewline
p-value & 0.341848401095354 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 1.07484825228127 \tabularnewline
Sum Squared Residuals & 62.3861333333334 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58254&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.129401971531008[/C][/ROW]
[ROW][C]R-squared[/C][C]0.0167448702361117[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]-0.00146355809284926[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]0.919621942849324[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]1[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]54[/C][/ROW]
[ROW][C]p-value[/C][C]0.341848401095354[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]1.07484825228127[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]62.3861333333334[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58254&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58254&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 R0.129401971531008
R-squared0.0167448702361117
Adjusted R-squared-0.00146355809284926
F-TEST (value)0.919621942849324
F-TEST (DF numerator)1
F-TEST (DF denominator)54
p-value0.341848401095354
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1.07484825228127
Sum Squared Residuals62.3861333333334







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11.43.88800000000001-2.48800000000001
21.63.888-2.288
31.73.888-2.188
423.888-1.888
523.888-1.888
62.13.888-1.788
72.53.888-1.388
82.53.888-1.388
92.63.888-1.288
102.73.888-1.188
113.73.888-0.188000000000000
1243.8880.112000000000000
1353.8881.112
145.13.8881.212
155.13.8881.212
1653.8881.112
175.13.8881.212
184.73.8880.812
194.53.8880.612
204.53.8880.612
214.63.8880.712
224.63.8880.712
234.63.8880.712
244.63.8880.712
255.33.8881.412
265.43.8881.512
275.33.8881.412
285.23.8881.312
2953.8881.112
304.23.8880.312000000000000
314.33.8880.412
324.33.8880.412
334.33.8880.412
3443.8880.112000000000000
3543.8880.112000000000000
364.13.8880.212000000000000
374.43.8880.512
383.63.888-0.288
393.73.888-0.188000000000000
403.83.888-0.088
413.33.888-0.588
423.33.888-0.588
433.33.888-0.588
443.53.888-0.388
453.33.888-0.588
463.33.888-0.588
473.43.888-0.488
483.43.888-0.488
495.23.8881.312
505.33.8881.412
514.84.333333333333330.466666666666667
5254.333333333333330.666666666666667
534.64.333333333333330.266666666666666
544.64.333333333333330.266666666666666
553.54.33333333333333-0.833333333333333
563.54.33333333333333-0.833333333333333

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 1.4 & 3.88800000000001 & -2.48800000000001 \tabularnewline
2 & 1.6 & 3.888 & -2.288 \tabularnewline
3 & 1.7 & 3.888 & -2.188 \tabularnewline
4 & 2 & 3.888 & -1.888 \tabularnewline
5 & 2 & 3.888 & -1.888 \tabularnewline
6 & 2.1 & 3.888 & -1.788 \tabularnewline
7 & 2.5 & 3.888 & -1.388 \tabularnewline
8 & 2.5 & 3.888 & -1.388 \tabularnewline
9 & 2.6 & 3.888 & -1.288 \tabularnewline
10 & 2.7 & 3.888 & -1.188 \tabularnewline
11 & 3.7 & 3.888 & -0.188000000000000 \tabularnewline
12 & 4 & 3.888 & 0.112000000000000 \tabularnewline
13 & 5 & 3.888 & 1.112 \tabularnewline
14 & 5.1 & 3.888 & 1.212 \tabularnewline
15 & 5.1 & 3.888 & 1.212 \tabularnewline
16 & 5 & 3.888 & 1.112 \tabularnewline
17 & 5.1 & 3.888 & 1.212 \tabularnewline
18 & 4.7 & 3.888 & 0.812 \tabularnewline
19 & 4.5 & 3.888 & 0.612 \tabularnewline
20 & 4.5 & 3.888 & 0.612 \tabularnewline
21 & 4.6 & 3.888 & 0.712 \tabularnewline
22 & 4.6 & 3.888 & 0.712 \tabularnewline
23 & 4.6 & 3.888 & 0.712 \tabularnewline
24 & 4.6 & 3.888 & 0.712 \tabularnewline
25 & 5.3 & 3.888 & 1.412 \tabularnewline
26 & 5.4 & 3.888 & 1.512 \tabularnewline
27 & 5.3 & 3.888 & 1.412 \tabularnewline
28 & 5.2 & 3.888 & 1.312 \tabularnewline
29 & 5 & 3.888 & 1.112 \tabularnewline
30 & 4.2 & 3.888 & 0.312000000000000 \tabularnewline
31 & 4.3 & 3.888 & 0.412 \tabularnewline
32 & 4.3 & 3.888 & 0.412 \tabularnewline
33 & 4.3 & 3.888 & 0.412 \tabularnewline
34 & 4 & 3.888 & 0.112000000000000 \tabularnewline
35 & 4 & 3.888 & 0.112000000000000 \tabularnewline
36 & 4.1 & 3.888 & 0.212000000000000 \tabularnewline
37 & 4.4 & 3.888 & 0.512 \tabularnewline
38 & 3.6 & 3.888 & -0.288 \tabularnewline
39 & 3.7 & 3.888 & -0.188000000000000 \tabularnewline
40 & 3.8 & 3.888 & -0.088 \tabularnewline
41 & 3.3 & 3.888 & -0.588 \tabularnewline
42 & 3.3 & 3.888 & -0.588 \tabularnewline
43 & 3.3 & 3.888 & -0.588 \tabularnewline
44 & 3.5 & 3.888 & -0.388 \tabularnewline
45 & 3.3 & 3.888 & -0.588 \tabularnewline
46 & 3.3 & 3.888 & -0.588 \tabularnewline
47 & 3.4 & 3.888 & -0.488 \tabularnewline
48 & 3.4 & 3.888 & -0.488 \tabularnewline
49 & 5.2 & 3.888 & 1.312 \tabularnewline
50 & 5.3 & 3.888 & 1.412 \tabularnewline
51 & 4.8 & 4.33333333333333 & 0.466666666666667 \tabularnewline
52 & 5 & 4.33333333333333 & 0.666666666666667 \tabularnewline
53 & 4.6 & 4.33333333333333 & 0.266666666666666 \tabularnewline
54 & 4.6 & 4.33333333333333 & 0.266666666666666 \tabularnewline
55 & 3.5 & 4.33333333333333 & -0.833333333333333 \tabularnewline
56 & 3.5 & 4.33333333333333 & -0.833333333333333 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58254&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]1.4[/C][C]3.88800000000001[/C][C]-2.48800000000001[/C][/ROW]
[ROW][C]2[/C][C]1.6[/C][C]3.888[/C][C]-2.288[/C][/ROW]
[ROW][C]3[/C][C]1.7[/C][C]3.888[/C][C]-2.188[/C][/ROW]
[ROW][C]4[/C][C]2[/C][C]3.888[/C][C]-1.888[/C][/ROW]
[ROW][C]5[/C][C]2[/C][C]3.888[/C][C]-1.888[/C][/ROW]
[ROW][C]6[/C][C]2.1[/C][C]3.888[/C][C]-1.788[/C][/ROW]
[ROW][C]7[/C][C]2.5[/C][C]3.888[/C][C]-1.388[/C][/ROW]
[ROW][C]8[/C][C]2.5[/C][C]3.888[/C][C]-1.388[/C][/ROW]
[ROW][C]9[/C][C]2.6[/C][C]3.888[/C][C]-1.288[/C][/ROW]
[ROW][C]10[/C][C]2.7[/C][C]3.888[/C][C]-1.188[/C][/ROW]
[ROW][C]11[/C][C]3.7[/C][C]3.888[/C][C]-0.188000000000000[/C][/ROW]
[ROW][C]12[/C][C]4[/C][C]3.888[/C][C]0.112000000000000[/C][/ROW]
[ROW][C]13[/C][C]5[/C][C]3.888[/C][C]1.112[/C][/ROW]
[ROW][C]14[/C][C]5.1[/C][C]3.888[/C][C]1.212[/C][/ROW]
[ROW][C]15[/C][C]5.1[/C][C]3.888[/C][C]1.212[/C][/ROW]
[ROW][C]16[/C][C]5[/C][C]3.888[/C][C]1.112[/C][/ROW]
[ROW][C]17[/C][C]5.1[/C][C]3.888[/C][C]1.212[/C][/ROW]
[ROW][C]18[/C][C]4.7[/C][C]3.888[/C][C]0.812[/C][/ROW]
[ROW][C]19[/C][C]4.5[/C][C]3.888[/C][C]0.612[/C][/ROW]
[ROW][C]20[/C][C]4.5[/C][C]3.888[/C][C]0.612[/C][/ROW]
[ROW][C]21[/C][C]4.6[/C][C]3.888[/C][C]0.712[/C][/ROW]
[ROW][C]22[/C][C]4.6[/C][C]3.888[/C][C]0.712[/C][/ROW]
[ROW][C]23[/C][C]4.6[/C][C]3.888[/C][C]0.712[/C][/ROW]
[ROW][C]24[/C][C]4.6[/C][C]3.888[/C][C]0.712[/C][/ROW]
[ROW][C]25[/C][C]5.3[/C][C]3.888[/C][C]1.412[/C][/ROW]
[ROW][C]26[/C][C]5.4[/C][C]3.888[/C][C]1.512[/C][/ROW]
[ROW][C]27[/C][C]5.3[/C][C]3.888[/C][C]1.412[/C][/ROW]
[ROW][C]28[/C][C]5.2[/C][C]3.888[/C][C]1.312[/C][/ROW]
[ROW][C]29[/C][C]5[/C][C]3.888[/C][C]1.112[/C][/ROW]
[ROW][C]30[/C][C]4.2[/C][C]3.888[/C][C]0.312000000000000[/C][/ROW]
[ROW][C]31[/C][C]4.3[/C][C]3.888[/C][C]0.412[/C][/ROW]
[ROW][C]32[/C][C]4.3[/C][C]3.888[/C][C]0.412[/C][/ROW]
[ROW][C]33[/C][C]4.3[/C][C]3.888[/C][C]0.412[/C][/ROW]
[ROW][C]34[/C][C]4[/C][C]3.888[/C][C]0.112000000000000[/C][/ROW]
[ROW][C]35[/C][C]4[/C][C]3.888[/C][C]0.112000000000000[/C][/ROW]
[ROW][C]36[/C][C]4.1[/C][C]3.888[/C][C]0.212000000000000[/C][/ROW]
[ROW][C]37[/C][C]4.4[/C][C]3.888[/C][C]0.512[/C][/ROW]
[ROW][C]38[/C][C]3.6[/C][C]3.888[/C][C]-0.288[/C][/ROW]
[ROW][C]39[/C][C]3.7[/C][C]3.888[/C][C]-0.188000000000000[/C][/ROW]
[ROW][C]40[/C][C]3.8[/C][C]3.888[/C][C]-0.088[/C][/ROW]
[ROW][C]41[/C][C]3.3[/C][C]3.888[/C][C]-0.588[/C][/ROW]
[ROW][C]42[/C][C]3.3[/C][C]3.888[/C][C]-0.588[/C][/ROW]
[ROW][C]43[/C][C]3.3[/C][C]3.888[/C][C]-0.588[/C][/ROW]
[ROW][C]44[/C][C]3.5[/C][C]3.888[/C][C]-0.388[/C][/ROW]
[ROW][C]45[/C][C]3.3[/C][C]3.888[/C][C]-0.588[/C][/ROW]
[ROW][C]46[/C][C]3.3[/C][C]3.888[/C][C]-0.588[/C][/ROW]
[ROW][C]47[/C][C]3.4[/C][C]3.888[/C][C]-0.488[/C][/ROW]
[ROW][C]48[/C][C]3.4[/C][C]3.888[/C][C]-0.488[/C][/ROW]
[ROW][C]49[/C][C]5.2[/C][C]3.888[/C][C]1.312[/C][/ROW]
[ROW][C]50[/C][C]5.3[/C][C]3.888[/C][C]1.412[/C][/ROW]
[ROW][C]51[/C][C]4.8[/C][C]4.33333333333333[/C][C]0.466666666666667[/C][/ROW]
[ROW][C]52[/C][C]5[/C][C]4.33333333333333[/C][C]0.666666666666667[/C][/ROW]
[ROW][C]53[/C][C]4.6[/C][C]4.33333333333333[/C][C]0.266666666666666[/C][/ROW]
[ROW][C]54[/C][C]4.6[/C][C]4.33333333333333[/C][C]0.266666666666666[/C][/ROW]
[ROW][C]55[/C][C]3.5[/C][C]4.33333333333333[/C][C]-0.833333333333333[/C][/ROW]
[ROW][C]56[/C][C]3.5[/C][C]4.33333333333333[/C][C]-0.833333333333333[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58254&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58254&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 IndexActualsInterpolationForecastResidualsPrediction Error
11.43.88800000000001-2.48800000000001
21.63.888-2.288
31.73.888-2.188
423.888-1.888
523.888-1.888
62.13.888-1.788
72.53.888-1.388
82.53.888-1.388
92.63.888-1.288
102.73.888-1.188
113.73.888-0.188000000000000
1243.8880.112000000000000
1353.8881.112
145.13.8881.212
155.13.8881.212
1653.8881.112
175.13.8881.212
184.73.8880.812
194.53.8880.612
204.53.8880.612
214.63.8880.712
224.63.8880.712
234.63.8880.712
244.63.8880.712
255.33.8881.412
265.43.8881.512
275.33.8881.412
285.23.8881.312
2953.8881.112
304.23.8880.312000000000000
314.33.8880.412
324.33.8880.412
334.33.8880.412
3443.8880.112000000000000
3543.8880.112000000000000
364.13.8880.212000000000000
374.43.8880.512
383.63.888-0.288
393.73.888-0.188000000000000
403.83.888-0.088
413.33.888-0.588
423.33.888-0.588
433.33.888-0.588
443.53.888-0.388
453.33.888-0.588
463.33.888-0.588
473.43.888-0.488
483.43.888-0.488
495.23.8881.312
505.33.8881.412
514.84.333333333333330.466666666666667
5254.333333333333330.666666666666667
534.64.333333333333330.266666666666666
544.64.333333333333330.266666666666666
553.54.33333333333333-0.833333333333333
563.54.33333333333333-0.833333333333333







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
50.05316148256839170.1063229651367830.946838517431608
60.03401792293861920.06803584587723830.96598207706138
70.0651457491646750.130291498329350.934854250835325
80.07873219903813230.1574643980762650.921267800961868
90.1014641593996840.2029283187993690.898535840600316
100.1389295547289510.2778591094579020.861070445271049
110.5156145090303990.9687709819392020.484385490969601
120.7961165513045220.4077668973909560.203883448695478
130.9813044272956350.03739114540872930.0186955727043646
140.9975236493183410.004952701363316890.00247635068165845
150.9994339137927080.001132172414584690.000566086207292343
160.9997637287167360.0004725425665276830.000236271283263841
170.9998960283228680.0002079433542644500.000103971677132225
180.999890519972340.0002189600553205410.000109480027660271
190.9998434200664730.0003131598670533880.000156579933526694
200.9997675048424020.0004649903151965210.000232495157598261
210.9996757995400660.0006484009198680670.000324200459934034
220.9995362240416030.0009275519167932960.000463775958396648
230.9993257103148880.001348579370223390.000674289685111693
240.9990109434734120.001978113053176740.000989056526588368
250.9994259661942340.001148067611531310.000574033805765657
260.9997482316336850.0005035367326295990.000251768366314800
270.9998809920884730.0002380158230548260.000119007911527413
280.9999405932192280.000118813561543995.9406780771995e-05
290.9999599915437428.00169125161963e-054.00084562580981e-05
300.999914019172570.0001719616548601968.59808274300981e-05
310.9998348629239940.0003302741520128330.000165137076006416
320.9996938692760040.000612261447992820.00030613072399641
330.9994530448658910.001093910268217260.000546955134108631
340.998866396400230.002267207199538960.00113360359976948
350.9977369504853370.004526099029326930.00226304951466347
360.9958235875446360.008352824910727460.00417641245536373
370.994089142874910.01182171425017950.00591085712508976
380.9890361797870070.02192764042598590.0109638202129930
390.980108380195630.03978323960874140.0198916198043707
400.965275428800990.06944914239802220.0347245711990111
410.9484719316855610.1030561366288770.0515280683144387
420.9265248493470660.1469503013058680.0734751506529342
430.8999024943085440.2001950113829130.100097505691456
440.8566517003140540.2866965993718910.143348299685946
450.8256077877211350.3487844245577300.174392212278865
460.810126904813370.379746190373260.18987309518663
470.8198510193097180.3602979613805640.180148980690282
480.934102142798110.1317957144037810.0658978572018906
490.8767728375293410.2464543249413180.123227162470659
500.781023132473770.4379537350524590.218976867526230
510.6676585280219190.6646829439561630.332341471978081

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
5 & 0.0531614825683917 & 0.106322965136783 & 0.946838517431608 \tabularnewline
6 & 0.0340179229386192 & 0.0680358458772383 & 0.96598207706138 \tabularnewline
7 & 0.065145749164675 & 0.13029149832935 & 0.934854250835325 \tabularnewline
8 & 0.0787321990381323 & 0.157464398076265 & 0.921267800961868 \tabularnewline
9 & 0.101464159399684 & 0.202928318799369 & 0.898535840600316 \tabularnewline
10 & 0.138929554728951 & 0.277859109457902 & 0.861070445271049 \tabularnewline
11 & 0.515614509030399 & 0.968770981939202 & 0.484385490969601 \tabularnewline
12 & 0.796116551304522 & 0.407766897390956 & 0.203883448695478 \tabularnewline
13 & 0.981304427295635 & 0.0373911454087293 & 0.0186955727043646 \tabularnewline
14 & 0.997523649318341 & 0.00495270136331689 & 0.00247635068165845 \tabularnewline
15 & 0.999433913792708 & 0.00113217241458469 & 0.000566086207292343 \tabularnewline
16 & 0.999763728716736 & 0.000472542566527683 & 0.000236271283263841 \tabularnewline
17 & 0.999896028322868 & 0.000207943354264450 & 0.000103971677132225 \tabularnewline
18 & 0.99989051997234 & 0.000218960055320541 & 0.000109480027660271 \tabularnewline
19 & 0.999843420066473 & 0.000313159867053388 & 0.000156579933526694 \tabularnewline
20 & 0.999767504842402 & 0.000464990315196521 & 0.000232495157598261 \tabularnewline
21 & 0.999675799540066 & 0.000648400919868067 & 0.000324200459934034 \tabularnewline
22 & 0.999536224041603 & 0.000927551916793296 & 0.000463775958396648 \tabularnewline
23 & 0.999325710314888 & 0.00134857937022339 & 0.000674289685111693 \tabularnewline
24 & 0.999010943473412 & 0.00197811305317674 & 0.000989056526588368 \tabularnewline
25 & 0.999425966194234 & 0.00114806761153131 & 0.000574033805765657 \tabularnewline
26 & 0.999748231633685 & 0.000503536732629599 & 0.000251768366314800 \tabularnewline
27 & 0.999880992088473 & 0.000238015823054826 & 0.000119007911527413 \tabularnewline
28 & 0.999940593219228 & 0.00011881356154399 & 5.9406780771995e-05 \tabularnewline
29 & 0.999959991543742 & 8.00169125161963e-05 & 4.00084562580981e-05 \tabularnewline
30 & 0.99991401917257 & 0.000171961654860196 & 8.59808274300981e-05 \tabularnewline
31 & 0.999834862923994 & 0.000330274152012833 & 0.000165137076006416 \tabularnewline
32 & 0.999693869276004 & 0.00061226144799282 & 0.00030613072399641 \tabularnewline
33 & 0.999453044865891 & 0.00109391026821726 & 0.000546955134108631 \tabularnewline
34 & 0.99886639640023 & 0.00226720719953896 & 0.00113360359976948 \tabularnewline
35 & 0.997736950485337 & 0.00452609902932693 & 0.00226304951466347 \tabularnewline
36 & 0.995823587544636 & 0.00835282491072746 & 0.00417641245536373 \tabularnewline
37 & 0.99408914287491 & 0.0118217142501795 & 0.00591085712508976 \tabularnewline
38 & 0.989036179787007 & 0.0219276404259859 & 0.0109638202129930 \tabularnewline
39 & 0.98010838019563 & 0.0397832396087414 & 0.0198916198043707 \tabularnewline
40 & 0.96527542880099 & 0.0694491423980222 & 0.0347245711990111 \tabularnewline
41 & 0.948471931685561 & 0.103056136628877 & 0.0515280683144387 \tabularnewline
42 & 0.926524849347066 & 0.146950301305868 & 0.0734751506529342 \tabularnewline
43 & 0.899902494308544 & 0.200195011382913 & 0.100097505691456 \tabularnewline
44 & 0.856651700314054 & 0.286696599371891 & 0.143348299685946 \tabularnewline
45 & 0.825607787721135 & 0.348784424557730 & 0.174392212278865 \tabularnewline
46 & 0.81012690481337 & 0.37974619037326 & 0.18987309518663 \tabularnewline
47 & 0.819851019309718 & 0.360297961380564 & 0.180148980690282 \tabularnewline
48 & 0.93410214279811 & 0.131795714403781 & 0.0658978572018906 \tabularnewline
49 & 0.876772837529341 & 0.246454324941318 & 0.123227162470659 \tabularnewline
50 & 0.78102313247377 & 0.437953735052459 & 0.218976867526230 \tabularnewline
51 & 0.667658528021919 & 0.664682943956163 & 0.332341471978081 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58254&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]5[/C][C]0.0531614825683917[/C][C]0.106322965136783[/C][C]0.946838517431608[/C][/ROW]
[ROW][C]6[/C][C]0.0340179229386192[/C][C]0.0680358458772383[/C][C]0.96598207706138[/C][/ROW]
[ROW][C]7[/C][C]0.065145749164675[/C][C]0.13029149832935[/C][C]0.934854250835325[/C][/ROW]
[ROW][C]8[/C][C]0.0787321990381323[/C][C]0.157464398076265[/C][C]0.921267800961868[/C][/ROW]
[ROW][C]9[/C][C]0.101464159399684[/C][C]0.202928318799369[/C][C]0.898535840600316[/C][/ROW]
[ROW][C]10[/C][C]0.138929554728951[/C][C]0.277859109457902[/C][C]0.861070445271049[/C][/ROW]
[ROW][C]11[/C][C]0.515614509030399[/C][C]0.968770981939202[/C][C]0.484385490969601[/C][/ROW]
[ROW][C]12[/C][C]0.796116551304522[/C][C]0.407766897390956[/C][C]0.203883448695478[/C][/ROW]
[ROW][C]13[/C][C]0.981304427295635[/C][C]0.0373911454087293[/C][C]0.0186955727043646[/C][/ROW]
[ROW][C]14[/C][C]0.997523649318341[/C][C]0.00495270136331689[/C][C]0.00247635068165845[/C][/ROW]
[ROW][C]15[/C][C]0.999433913792708[/C][C]0.00113217241458469[/C][C]0.000566086207292343[/C][/ROW]
[ROW][C]16[/C][C]0.999763728716736[/C][C]0.000472542566527683[/C][C]0.000236271283263841[/C][/ROW]
[ROW][C]17[/C][C]0.999896028322868[/C][C]0.000207943354264450[/C][C]0.000103971677132225[/C][/ROW]
[ROW][C]18[/C][C]0.99989051997234[/C][C]0.000218960055320541[/C][C]0.000109480027660271[/C][/ROW]
[ROW][C]19[/C][C]0.999843420066473[/C][C]0.000313159867053388[/C][C]0.000156579933526694[/C][/ROW]
[ROW][C]20[/C][C]0.999767504842402[/C][C]0.000464990315196521[/C][C]0.000232495157598261[/C][/ROW]
[ROW][C]21[/C][C]0.999675799540066[/C][C]0.000648400919868067[/C][C]0.000324200459934034[/C][/ROW]
[ROW][C]22[/C][C]0.999536224041603[/C][C]0.000927551916793296[/C][C]0.000463775958396648[/C][/ROW]
[ROW][C]23[/C][C]0.999325710314888[/C][C]0.00134857937022339[/C][C]0.000674289685111693[/C][/ROW]
[ROW][C]24[/C][C]0.999010943473412[/C][C]0.00197811305317674[/C][C]0.000989056526588368[/C][/ROW]
[ROW][C]25[/C][C]0.999425966194234[/C][C]0.00114806761153131[/C][C]0.000574033805765657[/C][/ROW]
[ROW][C]26[/C][C]0.999748231633685[/C][C]0.000503536732629599[/C][C]0.000251768366314800[/C][/ROW]
[ROW][C]27[/C][C]0.999880992088473[/C][C]0.000238015823054826[/C][C]0.000119007911527413[/C][/ROW]
[ROW][C]28[/C][C]0.999940593219228[/C][C]0.00011881356154399[/C][C]5.9406780771995e-05[/C][/ROW]
[ROW][C]29[/C][C]0.999959991543742[/C][C]8.00169125161963e-05[/C][C]4.00084562580981e-05[/C][/ROW]
[ROW][C]30[/C][C]0.99991401917257[/C][C]0.000171961654860196[/C][C]8.59808274300981e-05[/C][/ROW]
[ROW][C]31[/C][C]0.999834862923994[/C][C]0.000330274152012833[/C][C]0.000165137076006416[/C][/ROW]
[ROW][C]32[/C][C]0.999693869276004[/C][C]0.00061226144799282[/C][C]0.00030613072399641[/C][/ROW]
[ROW][C]33[/C][C]0.999453044865891[/C][C]0.00109391026821726[/C][C]0.000546955134108631[/C][/ROW]
[ROW][C]34[/C][C]0.99886639640023[/C][C]0.00226720719953896[/C][C]0.00113360359976948[/C][/ROW]
[ROW][C]35[/C][C]0.997736950485337[/C][C]0.00452609902932693[/C][C]0.00226304951466347[/C][/ROW]
[ROW][C]36[/C][C]0.995823587544636[/C][C]0.00835282491072746[/C][C]0.00417641245536373[/C][/ROW]
[ROW][C]37[/C][C]0.99408914287491[/C][C]0.0118217142501795[/C][C]0.00591085712508976[/C][/ROW]
[ROW][C]38[/C][C]0.989036179787007[/C][C]0.0219276404259859[/C][C]0.0109638202129930[/C][/ROW]
[ROW][C]39[/C][C]0.98010838019563[/C][C]0.0397832396087414[/C][C]0.0198916198043707[/C][/ROW]
[ROW][C]40[/C][C]0.96527542880099[/C][C]0.0694491423980222[/C][C]0.0347245711990111[/C][/ROW]
[ROW][C]41[/C][C]0.948471931685561[/C][C]0.103056136628877[/C][C]0.0515280683144387[/C][/ROW]
[ROW][C]42[/C][C]0.926524849347066[/C][C]0.146950301305868[/C][C]0.0734751506529342[/C][/ROW]
[ROW][C]43[/C][C]0.899902494308544[/C][C]0.200195011382913[/C][C]0.100097505691456[/C][/ROW]
[ROW][C]44[/C][C]0.856651700314054[/C][C]0.286696599371891[/C][C]0.143348299685946[/C][/ROW]
[ROW][C]45[/C][C]0.825607787721135[/C][C]0.348784424557730[/C][C]0.174392212278865[/C][/ROW]
[ROW][C]46[/C][C]0.81012690481337[/C][C]0.37974619037326[/C][C]0.18987309518663[/C][/ROW]
[ROW][C]47[/C][C]0.819851019309718[/C][C]0.360297961380564[/C][C]0.180148980690282[/C][/ROW]
[ROW][C]48[/C][C]0.93410214279811[/C][C]0.131795714403781[/C][C]0.0658978572018906[/C][/ROW]
[ROW][C]49[/C][C]0.876772837529341[/C][C]0.246454324941318[/C][C]0.123227162470659[/C][/ROW]
[ROW][C]50[/C][C]0.78102313247377[/C][C]0.437953735052459[/C][C]0.218976867526230[/C][/ROW]
[ROW][C]51[/C][C]0.667658528021919[/C][C]0.664682943956163[/C][C]0.332341471978081[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58254&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58254&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-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
50.05316148256839170.1063229651367830.946838517431608
60.03401792293861920.06803584587723830.96598207706138
70.0651457491646750.130291498329350.934854250835325
80.07873219903813230.1574643980762650.921267800961868
90.1014641593996840.2029283187993690.898535840600316
100.1389295547289510.2778591094579020.861070445271049
110.5156145090303990.9687709819392020.484385490969601
120.7961165513045220.4077668973909560.203883448695478
130.9813044272956350.03739114540872930.0186955727043646
140.9975236493183410.004952701363316890.00247635068165845
150.9994339137927080.001132172414584690.000566086207292343
160.9997637287167360.0004725425665276830.000236271283263841
170.9998960283228680.0002079433542644500.000103971677132225
180.999890519972340.0002189600553205410.000109480027660271
190.9998434200664730.0003131598670533880.000156579933526694
200.9997675048424020.0004649903151965210.000232495157598261
210.9996757995400660.0006484009198680670.000324200459934034
220.9995362240416030.0009275519167932960.000463775958396648
230.9993257103148880.001348579370223390.000674289685111693
240.9990109434734120.001978113053176740.000989056526588368
250.9994259661942340.001148067611531310.000574033805765657
260.9997482316336850.0005035367326295990.000251768366314800
270.9998809920884730.0002380158230548260.000119007911527413
280.9999405932192280.000118813561543995.9406780771995e-05
290.9999599915437428.00169125161963e-054.00084562580981e-05
300.999914019172570.0001719616548601968.59808274300981e-05
310.9998348629239940.0003302741520128330.000165137076006416
320.9996938692760040.000612261447992820.00030613072399641
330.9994530448658910.001093910268217260.000546955134108631
340.998866396400230.002267207199538960.00113360359976948
350.9977369504853370.004526099029326930.00226304951466347
360.9958235875446360.008352824910727460.00417641245536373
370.994089142874910.01182171425017950.00591085712508976
380.9890361797870070.02192764042598590.0109638202129930
390.980108380195630.03978323960874140.0198916198043707
400.965275428800990.06944914239802220.0347245711990111
410.9484719316855610.1030561366288770.0515280683144387
420.9265248493470660.1469503013058680.0734751506529342
430.8999024943085440.2001950113829130.100097505691456
440.8566517003140540.2866965993718910.143348299685946
450.8256077877211350.3487844245577300.174392212278865
460.810126904813370.379746190373260.18987309518663
470.8198510193097180.3602979613805640.180148980690282
480.934102142798110.1317957144037810.0658978572018906
490.8767728375293410.2464543249413180.123227162470659
500.781023132473770.4379537350524590.218976867526230
510.6676585280219190.6646829439561630.332341471978081







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level230.489361702127660NOK
5% type I error level270.574468085106383NOK
10% type I error level290.617021276595745NOK

\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 & 23 & 0.489361702127660 & NOK \tabularnewline
5% type I error level & 27 & 0.574468085106383 & NOK \tabularnewline
10% type I error level & 29 & 0.617021276595745 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58254&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]23[/C][C]0.489361702127660[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]27[/C][C]0.574468085106383[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]29[/C][C]0.617021276595745[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58254&T=6

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Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level230.489361702127660NOK
5% type I error level270.574468085106383NOK
10% type I error level290.617021276595745NOK



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$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
myeq <- paste(myeq, mysum$coefficients[i,1], 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,mysum$coefficients[i,1])
a<-table.element(a, round(mysum$coefficients[i,2],6))
a<-table.element(a, round(mysum$coefficients[i,3],4))
a<-table.element(a, round(mysum$coefficients[i,4],6))
a<-table.element(a, round(mysum$coefficients[i,4]/2,6))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable2.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple R',1,TRUE)
a<-table.element(a, sqrt(mysum$r.squared))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'R-squared',1,TRUE)
a<-table.element(a, mysum$r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Adjusted R-squared',1,TRUE)
a<-table.element(a, mysum$adj.r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (value)',1,TRUE)
a<-table.element(a, mysum$fstatistic[1])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[2])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[3])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'p-value',1,TRUE)
a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]))
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, mysum$sigma)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
a<-table.element(a, sum(myerror*myerror))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,x[i])
a<-table.element(a,x[i]-mysum$resid[i])
a<-table.element(a,mysum$resid[i])
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,gqarr[mypoint-kp3+1,1])
a<-table.element(a,gqarr[mypoint-kp3+1,2])
a<-table.element(a,gqarr[mypoint-kp3+1,3])
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,numsignificant1)
a<-table.element(a,numsignificant1/numgqtests)
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,numsignificant5)
a<-table.element(a,numsignificant5/numgqtests)
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,numsignificant10)
a<-table.element(a,numsignificant10/numgqtests)
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')
}