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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 computationMon, 05 Nov 2012 13:28:43 -0500
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2012/Nov/05/t1352140222p5yzvp5opg9s7xe.htm/, Retrieved Fri, 03 Feb 2023 10:25:54 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=186202, Retrieved Fri, 03 Feb 2023 10:25:54 +0000
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User-defined keywords
Estimated Impact148
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Multiple Regression] [] [2012-11-05 18:28:43] [1fa8d8a5ead94b4e7273b6802fa6471c] [Current]
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- RMPD    [Notched Boxplots] [] [2012-12-19 15:17:30] [0208c1f08efc8e25366206e954253d87]
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Dataseries X:
2,5	4	3,5	0,2
2,3	1,4	0,7	0,8
1,4	0,3	5,2	1,2
0,5	-1	2,9	4,5
-2,3	-1,9	1	0,4
-3,7	-1,5	-5,5	5,9
-3,5	-0,2	-7,1	6,5
-0,2	3,4	-7,9	12,8
0,2	3	-8,9	4,2
-0,1	4,1	-7	-3,3
-1	3,4	-5,6	-12,5
-0,9	3,2	-7,3	-16,3
2,2	6,1	-7,5	-10,5
1,9	5,8	-3,5	-11,8
2,4	6,2	2,3	-11,4
2,3	5,8	3,1	-17,7
2,3	5,9	4,1	-17,3
3,8	6,7	4,2	-18,6
3	5,9	6	-17,9
2,4	3,8	5,5	-21,4
0,7	1,7	5,1	-19,4
1,4	1,4	5,6	-15,5
2,5	1,8	1	-7,7
2,9	3	1,6	-0,7
3,8	3,6	1,3	-1,6
2,9	4,8	1,7	1,4
3	4,3	-8,6	0,7
5,1	4,2	-11,2	9,5
3,4	2,9	-12,7	1,4
3,8	4,9	-3,1	4,1
2,7	7,2	-1	6,6
4,7	8,7	0,3	18,4
4,8	9,1	-2,9	16,9
5,5	8,9	-2,3	9,2
5,1	9	3,2	-4,3
7,7	11,6	5,9	-5,9
5,4	9,6	13,2	-7,7
4,8	9,1	7,2	-5,4
4,7	9,2	6,9	-2,3
5,3	10,8	8,2	-4,8
7,5	11	11,8	2,3
5,7	8,5	9,3	-5,2
3,6	6,5	5,8	-10
2,8	7,2	8,8	-17,1
3,4	7,8	14,6	-14,4
3,8	8,7	28,2	-3,9
1,5	7,8	19,8	3,7
0,3	7,5	16,5	6,5
0,4	7,7	-5,3	0,9
0,3	7,5	-2,4	-4,1
1,2	8,3	1,9	-7
0,9	7,9	1,6	-12,2
2,8	10,4	-0,1	-2,5
2,9	11,5	-2	4,4
4,9	14	3,4	13,7
2,3	11,9	3,3	12,3
4	11,9	3,3	13,4
2,3	10,3	-9,8	2,2
5	11,3	-4,6	1,7
2,6	9,9	-6,1	-7,2
1,7	8,9	10,6	-4,8
4,3	9,2	8,9	-2,9
4	8,8	10,7	-2,4
3,8	6,7	11,7	-2,5
2,5	7,1	13,5	-5,3
3,2	6,6	14,6	-7,1
4	7,2	14,1	-8
4,1	5	11,1	-8,9
3,3	5,3	9,2	-7,7
4,3	6,3	13	-1,1
5,8	8	14,4	4
8,1	7,6	16,5	9,6
6,8	7	11,7	10,9
5,3	6,9	11,8	13
4,8	6,8	10,4	14,9
5,5	7,5	12,2	20,1
5,2	6,4	14,7	10,8
6	8	15	11
4	6,4	10,3	3,8
6,2	9,6	11,9	10,8
3,7	7,5	13,1	7,6
5,2	9	15,5	10,2
2,7	7,8	10,3	2,2
0,8	7,8	5,2	-0,1
2,9	8,7	5,4	-1,7
0,2	4,3	4,3	-4,8
-2,6	-0,4	6,6	-9,9
-6,7	-4,9	4,2	-13,5
-12,5	-10,1	-3,3	-18,1
-14,4	-13,4	-6,6	-18
-16	-15,8	-8	-15,7




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time8 seconds
R Server'Sir Maurice George Kendall' @ kendall.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 & 8 seconds \tabularnewline
R Server & 'Sir Maurice George Kendall' @ kendall.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186202&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]8 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Sir Maurice George Kendall' @ kendall.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186202&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186202&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 time8 seconds
R Server'Sir Maurice George Kendall' @ kendall.wessa.net







Multiple Linear Regression - Estimated Regression Equation
IndProd[t] = -1.48358856264544 + 0.618539905255023TotOmzet[t] + 0.0730223253351926Invest[t] + 0.0459292845305723RegWag[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
IndProd[t] =  -1.48358856264544 +  0.618539905255023TotOmzet[t] +  0.0730223253351926Invest[t] +  0.0459292845305723RegWag[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186202&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]IndProd[t] =  -1.48358856264544 +  0.618539905255023TotOmzet[t] +  0.0730223253351926Invest[t] +  0.0459292845305723RegWag[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186202&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186202&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
IndProd[t] = -1.48358856264544 + 0.618539905255023TotOmzet[t] + 0.0730223253351926Invest[t] + 0.0459292845305723RegWag[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)-1.483588562645440.345152-4.29844.5e-052.2e-05
TotOmzet0.6185399052550230.04821812.827900
Invest0.07302232533519260.0273452.67040.0090410.004521
RegWag0.04592928453057230.022412.04950.0434290.021715

\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) & -1.48358856264544 & 0.345152 & -4.2984 & 4.5e-05 & 2.2e-05 \tabularnewline
TotOmzet & 0.618539905255023 & 0.048218 & 12.8279 & 0 & 0 \tabularnewline
Invest & 0.0730223253351926 & 0.027345 & 2.6704 & 0.009041 & 0.004521 \tabularnewline
RegWag & 0.0459292845305723 & 0.02241 & 2.0495 & 0.043429 & 0.021715 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186202&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]-1.48358856264544[/C][C]0.345152[/C][C]-4.2984[/C][C]4.5e-05[/C][C]2.2e-05[/C][/ROW]
[ROW][C]TotOmzet[/C][C]0.618539905255023[/C][C]0.048218[/C][C]12.8279[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Invest[/C][C]0.0730223253351926[/C][C]0.027345[/C][C]2.6704[/C][C]0.009041[/C][C]0.004521[/C][/ROW]
[ROW][C]RegWag[/C][C]0.0459292845305723[/C][C]0.02241[/C][C]2.0495[/C][C]0.043429[/C][C]0.021715[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186202&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186202&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)-1.483588562645440.345152-4.29844.5e-052.2e-05
TotOmzet0.6185399052550230.04821812.827900
Invest0.07302232533519260.0273452.67040.0090410.004521
RegWag0.04592928453057230.022412.04950.0434290.021715







Multiple Linear Regression - Regression Statistics
Multiple R0.875141180691189
R-squared0.765872086141567
Adjusted R-squared0.757798709801621
F-TEST (value)94.8639149090744
F-TEST (DF numerator)3
F-TEST (DF denominator)87
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1.96123953070687
Sum Squared Residuals334.642063222235

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.875141180691189 \tabularnewline
R-squared & 0.765872086141567 \tabularnewline
Adjusted R-squared & 0.757798709801621 \tabularnewline
F-TEST (value) & 94.8639149090744 \tabularnewline
F-TEST (DF numerator) & 3 \tabularnewline
F-TEST (DF denominator) & 87 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 1.96123953070687 \tabularnewline
Sum Squared Residuals & 334.642063222235 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186202&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.875141180691189[/C][/ROW]
[ROW][C]R-squared[/C][C]0.765872086141567[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.757798709801621[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]94.8639149090744[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]3[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]87[/C][/ROW]
[ROW][C]p-value[/C][C]0[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]1.96123953070687[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]334.642063222235[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186202&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186202&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 R0.875141180691189
R-squared0.765872086141567
Adjusted R-squared0.757798709801621
F-TEST (value)94.8639149090744
F-TEST (DF numerator)3
F-TEST (DF denominator)87
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1.96123953070687
Sum Squared Residuals334.642063222235







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
12.51.255335053953941.24466494604606
22.3-0.5297736399293132.82977363992931
31.4-0.8631953578892422.26319535788924
40.5-1.683681944040832.18368194404083
5-2.3-2.567420343482560.26742034348256
6-3.7-2.54203843114115-1.15796156885885
7-3.5-1.82721470412759-1.67278529587241
8-0.20.630465587064943-0.830465587064943
90.2-0.08496454733518030.28496454733518
10-0.10.389702132602918-0.489702132602918
11-1-0.363593963287592-0.636406036712408
12-0.9-0.785971178624599-0.114028821375401
132.21.259579931825250.940420068174753
141.91.306399191699770.593600808300233
152.41.995716354558120.404283645441877
162.31.517363760181660.782636239818338
172.31.670611789854590.629388210145414
183.82.113037876702381.68696212329762
1931.781796637273111.21820336272689
202.40.2855991777129622.11440082228704
210.7-0.9506849843955181.65068498439552
221.4-0.9206115836351982.3206115836352
232.5-0.650849898736613.15084989873661
242.90.4567163744845382.44328362551546
253.80.7645972639594793.03540273604052
262.91.67384193399131.2261580660087
2730.5802915312399042.4197084687601
285.10.7327571987119374.36724280128806
293.4-0.5529053708200173.95290537082002
303.81.509197831140422.29080216885958
312.73.20000970775731-0.50000970775731
324.74.76471414603635-0.0647141460363467
334.84.709564740269880.0904352597301186
345.54.276014663534591.22398533646541
355.14.119446102240920.980553897759077
367.75.851323279060091.84867672093991
375.45.064633731341920.335366268658083
384.84.422867181123570.377132818876434
394.74.605195256093280.0948047439067162
405.35.57496491611064-0.274964916110641
417.56.28765118853541.2123488114646
425.74.214275978080571.48572402191943
433.62.50115746315061.0988425368494
442.82.82710445266764-0.0271044526676357
453.43.74576695099731-0.345766950997311
463.85.77781397785646-1.97781397785646
471.54.95680309274367-3.45680309274367
480.34.65886944424663-4.35886944424663
490.42.93348673961923-2.53348673961923
500.32.79189707938742-2.49189707938742
511.23.46753007739411-2.26753007739411
520.92.95937513813257-2.05937513813257
532.84.82710100814685-2.02710100814685
542.95.68566454905146-2.78566454905146
554.98.05347721513338-3.15347721513337
562.36.68294018322151-4.38294018322151
5746.73346239620514-2.73346239620514
582.34.27279809916367-1.97279809916367
5955.24808945389641-0.248089453896406
602.63.86382946621449-1.26382946621449
611.74.57499267693056-2.87499267693056
624.34.72368233604533-0.423682336045326
6344.63067120181195-0.63067120181195
643.83.400166797658540.399833202341462
652.53.65042094867829-1.15042094867829
663.23.33880284176446-0.138802841764461
6743.632079266172360.367920733827636
684.12.010888142528222.08911185747178
693.32.112822837404551.18717716259545
704.33.311980856835080.98801914316492
715.84.699969302343811.10003069765619
728.14.863104216816913.23689578318309
736.84.201181181944712.59881881805529
745.34.243080921466931.05691907853307
754.84.166261316080250.633738683919753
765.54.969511714921080.530488285078915
775.24.044531286344221.15546871365578
7865.065287689258930.934712310741072
7943.401728063155370.598271936844634
806.25.819396472221750.380603527778247
813.74.46111575109061-0.761115751090605
825.25.68359532955709-0.483595329557089
832.74.19419707526348-1.49419707526348
840.83.71614586163368-2.91614586163368
852.94.21394938618133-1.31394938618133
860.21.26966846314574-1.06966846314574
87-2.6-1.70375709438784-0.89624290561216
88-6.7-4.82778567314997-1.87221432685003
89-12.5-8.80313532933066-3.69686467066934
90-14.4-11.0806977618253-3.31930223817469
91-16-12.5617874354863-3.43821256451368

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 2.5 & 1.25533505395394 & 1.24466494604606 \tabularnewline
2 & 2.3 & -0.529773639929313 & 2.82977363992931 \tabularnewline
3 & 1.4 & -0.863195357889242 & 2.26319535788924 \tabularnewline
4 & 0.5 & -1.68368194404083 & 2.18368194404083 \tabularnewline
5 & -2.3 & -2.56742034348256 & 0.26742034348256 \tabularnewline
6 & -3.7 & -2.54203843114115 & -1.15796156885885 \tabularnewline
7 & -3.5 & -1.82721470412759 & -1.67278529587241 \tabularnewline
8 & -0.2 & 0.630465587064943 & -0.830465587064943 \tabularnewline
9 & 0.2 & -0.0849645473351803 & 0.28496454733518 \tabularnewline
10 & -0.1 & 0.389702132602918 & -0.489702132602918 \tabularnewline
11 & -1 & -0.363593963287592 & -0.636406036712408 \tabularnewline
12 & -0.9 & -0.785971178624599 & -0.114028821375401 \tabularnewline
13 & 2.2 & 1.25957993182525 & 0.940420068174753 \tabularnewline
14 & 1.9 & 1.30639919169977 & 0.593600808300233 \tabularnewline
15 & 2.4 & 1.99571635455812 & 0.404283645441877 \tabularnewline
16 & 2.3 & 1.51736376018166 & 0.782636239818338 \tabularnewline
17 & 2.3 & 1.67061178985459 & 0.629388210145414 \tabularnewline
18 & 3.8 & 2.11303787670238 & 1.68696212329762 \tabularnewline
19 & 3 & 1.78179663727311 & 1.21820336272689 \tabularnewline
20 & 2.4 & 0.285599177712962 & 2.11440082228704 \tabularnewline
21 & 0.7 & -0.950684984395518 & 1.65068498439552 \tabularnewline
22 & 1.4 & -0.920611583635198 & 2.3206115836352 \tabularnewline
23 & 2.5 & -0.65084989873661 & 3.15084989873661 \tabularnewline
24 & 2.9 & 0.456716374484538 & 2.44328362551546 \tabularnewline
25 & 3.8 & 0.764597263959479 & 3.03540273604052 \tabularnewline
26 & 2.9 & 1.6738419339913 & 1.2261580660087 \tabularnewline
27 & 3 & 0.580291531239904 & 2.4197084687601 \tabularnewline
28 & 5.1 & 0.732757198711937 & 4.36724280128806 \tabularnewline
29 & 3.4 & -0.552905370820017 & 3.95290537082002 \tabularnewline
30 & 3.8 & 1.50919783114042 & 2.29080216885958 \tabularnewline
31 & 2.7 & 3.20000970775731 & -0.50000970775731 \tabularnewline
32 & 4.7 & 4.76471414603635 & -0.0647141460363467 \tabularnewline
33 & 4.8 & 4.70956474026988 & 0.0904352597301186 \tabularnewline
34 & 5.5 & 4.27601466353459 & 1.22398533646541 \tabularnewline
35 & 5.1 & 4.11944610224092 & 0.980553897759077 \tabularnewline
36 & 7.7 & 5.85132327906009 & 1.84867672093991 \tabularnewline
37 & 5.4 & 5.06463373134192 & 0.335366268658083 \tabularnewline
38 & 4.8 & 4.42286718112357 & 0.377132818876434 \tabularnewline
39 & 4.7 & 4.60519525609328 & 0.0948047439067162 \tabularnewline
40 & 5.3 & 5.57496491611064 & -0.274964916110641 \tabularnewline
41 & 7.5 & 6.2876511885354 & 1.2123488114646 \tabularnewline
42 & 5.7 & 4.21427597808057 & 1.48572402191943 \tabularnewline
43 & 3.6 & 2.5011574631506 & 1.0988425368494 \tabularnewline
44 & 2.8 & 2.82710445266764 & -0.0271044526676357 \tabularnewline
45 & 3.4 & 3.74576695099731 & -0.345766950997311 \tabularnewline
46 & 3.8 & 5.77781397785646 & -1.97781397785646 \tabularnewline
47 & 1.5 & 4.95680309274367 & -3.45680309274367 \tabularnewline
48 & 0.3 & 4.65886944424663 & -4.35886944424663 \tabularnewline
49 & 0.4 & 2.93348673961923 & -2.53348673961923 \tabularnewline
50 & 0.3 & 2.79189707938742 & -2.49189707938742 \tabularnewline
51 & 1.2 & 3.46753007739411 & -2.26753007739411 \tabularnewline
52 & 0.9 & 2.95937513813257 & -2.05937513813257 \tabularnewline
53 & 2.8 & 4.82710100814685 & -2.02710100814685 \tabularnewline
54 & 2.9 & 5.68566454905146 & -2.78566454905146 \tabularnewline
55 & 4.9 & 8.05347721513338 & -3.15347721513337 \tabularnewline
56 & 2.3 & 6.68294018322151 & -4.38294018322151 \tabularnewline
57 & 4 & 6.73346239620514 & -2.73346239620514 \tabularnewline
58 & 2.3 & 4.27279809916367 & -1.97279809916367 \tabularnewline
59 & 5 & 5.24808945389641 & -0.248089453896406 \tabularnewline
60 & 2.6 & 3.86382946621449 & -1.26382946621449 \tabularnewline
61 & 1.7 & 4.57499267693056 & -2.87499267693056 \tabularnewline
62 & 4.3 & 4.72368233604533 & -0.423682336045326 \tabularnewline
63 & 4 & 4.63067120181195 & -0.63067120181195 \tabularnewline
64 & 3.8 & 3.40016679765854 & 0.399833202341462 \tabularnewline
65 & 2.5 & 3.65042094867829 & -1.15042094867829 \tabularnewline
66 & 3.2 & 3.33880284176446 & -0.138802841764461 \tabularnewline
67 & 4 & 3.63207926617236 & 0.367920733827636 \tabularnewline
68 & 4.1 & 2.01088814252822 & 2.08911185747178 \tabularnewline
69 & 3.3 & 2.11282283740455 & 1.18717716259545 \tabularnewline
70 & 4.3 & 3.31198085683508 & 0.98801914316492 \tabularnewline
71 & 5.8 & 4.69996930234381 & 1.10003069765619 \tabularnewline
72 & 8.1 & 4.86310421681691 & 3.23689578318309 \tabularnewline
73 & 6.8 & 4.20118118194471 & 2.59881881805529 \tabularnewline
74 & 5.3 & 4.24308092146693 & 1.05691907853307 \tabularnewline
75 & 4.8 & 4.16626131608025 & 0.633738683919753 \tabularnewline
76 & 5.5 & 4.96951171492108 & 0.530488285078915 \tabularnewline
77 & 5.2 & 4.04453128634422 & 1.15546871365578 \tabularnewline
78 & 6 & 5.06528768925893 & 0.934712310741072 \tabularnewline
79 & 4 & 3.40172806315537 & 0.598271936844634 \tabularnewline
80 & 6.2 & 5.81939647222175 & 0.380603527778247 \tabularnewline
81 & 3.7 & 4.46111575109061 & -0.761115751090605 \tabularnewline
82 & 5.2 & 5.68359532955709 & -0.483595329557089 \tabularnewline
83 & 2.7 & 4.19419707526348 & -1.49419707526348 \tabularnewline
84 & 0.8 & 3.71614586163368 & -2.91614586163368 \tabularnewline
85 & 2.9 & 4.21394938618133 & -1.31394938618133 \tabularnewline
86 & 0.2 & 1.26966846314574 & -1.06966846314574 \tabularnewline
87 & -2.6 & -1.70375709438784 & -0.89624290561216 \tabularnewline
88 & -6.7 & -4.82778567314997 & -1.87221432685003 \tabularnewline
89 & -12.5 & -8.80313532933066 & -3.69686467066934 \tabularnewline
90 & -14.4 & -11.0806977618253 & -3.31930223817469 \tabularnewline
91 & -16 & -12.5617874354863 & -3.43821256451368 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186202&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]2.5[/C][C]1.25533505395394[/C][C]1.24466494604606[/C][/ROW]
[ROW][C]2[/C][C]2.3[/C][C]-0.529773639929313[/C][C]2.82977363992931[/C][/ROW]
[ROW][C]3[/C][C]1.4[/C][C]-0.863195357889242[/C][C]2.26319535788924[/C][/ROW]
[ROW][C]4[/C][C]0.5[/C][C]-1.68368194404083[/C][C]2.18368194404083[/C][/ROW]
[ROW][C]5[/C][C]-2.3[/C][C]-2.56742034348256[/C][C]0.26742034348256[/C][/ROW]
[ROW][C]6[/C][C]-3.7[/C][C]-2.54203843114115[/C][C]-1.15796156885885[/C][/ROW]
[ROW][C]7[/C][C]-3.5[/C][C]-1.82721470412759[/C][C]-1.67278529587241[/C][/ROW]
[ROW][C]8[/C][C]-0.2[/C][C]0.630465587064943[/C][C]-0.830465587064943[/C][/ROW]
[ROW][C]9[/C][C]0.2[/C][C]-0.0849645473351803[/C][C]0.28496454733518[/C][/ROW]
[ROW][C]10[/C][C]-0.1[/C][C]0.389702132602918[/C][C]-0.489702132602918[/C][/ROW]
[ROW][C]11[/C][C]-1[/C][C]-0.363593963287592[/C][C]-0.636406036712408[/C][/ROW]
[ROW][C]12[/C][C]-0.9[/C][C]-0.785971178624599[/C][C]-0.114028821375401[/C][/ROW]
[ROW][C]13[/C][C]2.2[/C][C]1.25957993182525[/C][C]0.940420068174753[/C][/ROW]
[ROW][C]14[/C][C]1.9[/C][C]1.30639919169977[/C][C]0.593600808300233[/C][/ROW]
[ROW][C]15[/C][C]2.4[/C][C]1.99571635455812[/C][C]0.404283645441877[/C][/ROW]
[ROW][C]16[/C][C]2.3[/C][C]1.51736376018166[/C][C]0.782636239818338[/C][/ROW]
[ROW][C]17[/C][C]2.3[/C][C]1.67061178985459[/C][C]0.629388210145414[/C][/ROW]
[ROW][C]18[/C][C]3.8[/C][C]2.11303787670238[/C][C]1.68696212329762[/C][/ROW]
[ROW][C]19[/C][C]3[/C][C]1.78179663727311[/C][C]1.21820336272689[/C][/ROW]
[ROW][C]20[/C][C]2.4[/C][C]0.285599177712962[/C][C]2.11440082228704[/C][/ROW]
[ROW][C]21[/C][C]0.7[/C][C]-0.950684984395518[/C][C]1.65068498439552[/C][/ROW]
[ROW][C]22[/C][C]1.4[/C][C]-0.920611583635198[/C][C]2.3206115836352[/C][/ROW]
[ROW][C]23[/C][C]2.5[/C][C]-0.65084989873661[/C][C]3.15084989873661[/C][/ROW]
[ROW][C]24[/C][C]2.9[/C][C]0.456716374484538[/C][C]2.44328362551546[/C][/ROW]
[ROW][C]25[/C][C]3.8[/C][C]0.764597263959479[/C][C]3.03540273604052[/C][/ROW]
[ROW][C]26[/C][C]2.9[/C][C]1.6738419339913[/C][C]1.2261580660087[/C][/ROW]
[ROW][C]27[/C][C]3[/C][C]0.580291531239904[/C][C]2.4197084687601[/C][/ROW]
[ROW][C]28[/C][C]5.1[/C][C]0.732757198711937[/C][C]4.36724280128806[/C][/ROW]
[ROW][C]29[/C][C]3.4[/C][C]-0.552905370820017[/C][C]3.95290537082002[/C][/ROW]
[ROW][C]30[/C][C]3.8[/C][C]1.50919783114042[/C][C]2.29080216885958[/C][/ROW]
[ROW][C]31[/C][C]2.7[/C][C]3.20000970775731[/C][C]-0.50000970775731[/C][/ROW]
[ROW][C]32[/C][C]4.7[/C][C]4.76471414603635[/C][C]-0.0647141460363467[/C][/ROW]
[ROW][C]33[/C][C]4.8[/C][C]4.70956474026988[/C][C]0.0904352597301186[/C][/ROW]
[ROW][C]34[/C][C]5.5[/C][C]4.27601466353459[/C][C]1.22398533646541[/C][/ROW]
[ROW][C]35[/C][C]5.1[/C][C]4.11944610224092[/C][C]0.980553897759077[/C][/ROW]
[ROW][C]36[/C][C]7.7[/C][C]5.85132327906009[/C][C]1.84867672093991[/C][/ROW]
[ROW][C]37[/C][C]5.4[/C][C]5.06463373134192[/C][C]0.335366268658083[/C][/ROW]
[ROW][C]38[/C][C]4.8[/C][C]4.42286718112357[/C][C]0.377132818876434[/C][/ROW]
[ROW][C]39[/C][C]4.7[/C][C]4.60519525609328[/C][C]0.0948047439067162[/C][/ROW]
[ROW][C]40[/C][C]5.3[/C][C]5.57496491611064[/C][C]-0.274964916110641[/C][/ROW]
[ROW][C]41[/C][C]7.5[/C][C]6.2876511885354[/C][C]1.2123488114646[/C][/ROW]
[ROW][C]42[/C][C]5.7[/C][C]4.21427597808057[/C][C]1.48572402191943[/C][/ROW]
[ROW][C]43[/C][C]3.6[/C][C]2.5011574631506[/C][C]1.0988425368494[/C][/ROW]
[ROW][C]44[/C][C]2.8[/C][C]2.82710445266764[/C][C]-0.0271044526676357[/C][/ROW]
[ROW][C]45[/C][C]3.4[/C][C]3.74576695099731[/C][C]-0.345766950997311[/C][/ROW]
[ROW][C]46[/C][C]3.8[/C][C]5.77781397785646[/C][C]-1.97781397785646[/C][/ROW]
[ROW][C]47[/C][C]1.5[/C][C]4.95680309274367[/C][C]-3.45680309274367[/C][/ROW]
[ROW][C]48[/C][C]0.3[/C][C]4.65886944424663[/C][C]-4.35886944424663[/C][/ROW]
[ROW][C]49[/C][C]0.4[/C][C]2.93348673961923[/C][C]-2.53348673961923[/C][/ROW]
[ROW][C]50[/C][C]0.3[/C][C]2.79189707938742[/C][C]-2.49189707938742[/C][/ROW]
[ROW][C]51[/C][C]1.2[/C][C]3.46753007739411[/C][C]-2.26753007739411[/C][/ROW]
[ROW][C]52[/C][C]0.9[/C][C]2.95937513813257[/C][C]-2.05937513813257[/C][/ROW]
[ROW][C]53[/C][C]2.8[/C][C]4.82710100814685[/C][C]-2.02710100814685[/C][/ROW]
[ROW][C]54[/C][C]2.9[/C][C]5.68566454905146[/C][C]-2.78566454905146[/C][/ROW]
[ROW][C]55[/C][C]4.9[/C][C]8.05347721513338[/C][C]-3.15347721513337[/C][/ROW]
[ROW][C]56[/C][C]2.3[/C][C]6.68294018322151[/C][C]-4.38294018322151[/C][/ROW]
[ROW][C]57[/C][C]4[/C][C]6.73346239620514[/C][C]-2.73346239620514[/C][/ROW]
[ROW][C]58[/C][C]2.3[/C][C]4.27279809916367[/C][C]-1.97279809916367[/C][/ROW]
[ROW][C]59[/C][C]5[/C][C]5.24808945389641[/C][C]-0.248089453896406[/C][/ROW]
[ROW][C]60[/C][C]2.6[/C][C]3.86382946621449[/C][C]-1.26382946621449[/C][/ROW]
[ROW][C]61[/C][C]1.7[/C][C]4.57499267693056[/C][C]-2.87499267693056[/C][/ROW]
[ROW][C]62[/C][C]4.3[/C][C]4.72368233604533[/C][C]-0.423682336045326[/C][/ROW]
[ROW][C]63[/C][C]4[/C][C]4.63067120181195[/C][C]-0.63067120181195[/C][/ROW]
[ROW][C]64[/C][C]3.8[/C][C]3.40016679765854[/C][C]0.399833202341462[/C][/ROW]
[ROW][C]65[/C][C]2.5[/C][C]3.65042094867829[/C][C]-1.15042094867829[/C][/ROW]
[ROW][C]66[/C][C]3.2[/C][C]3.33880284176446[/C][C]-0.138802841764461[/C][/ROW]
[ROW][C]67[/C][C]4[/C][C]3.63207926617236[/C][C]0.367920733827636[/C][/ROW]
[ROW][C]68[/C][C]4.1[/C][C]2.01088814252822[/C][C]2.08911185747178[/C][/ROW]
[ROW][C]69[/C][C]3.3[/C][C]2.11282283740455[/C][C]1.18717716259545[/C][/ROW]
[ROW][C]70[/C][C]4.3[/C][C]3.31198085683508[/C][C]0.98801914316492[/C][/ROW]
[ROW][C]71[/C][C]5.8[/C][C]4.69996930234381[/C][C]1.10003069765619[/C][/ROW]
[ROW][C]72[/C][C]8.1[/C][C]4.86310421681691[/C][C]3.23689578318309[/C][/ROW]
[ROW][C]73[/C][C]6.8[/C][C]4.20118118194471[/C][C]2.59881881805529[/C][/ROW]
[ROW][C]74[/C][C]5.3[/C][C]4.24308092146693[/C][C]1.05691907853307[/C][/ROW]
[ROW][C]75[/C][C]4.8[/C][C]4.16626131608025[/C][C]0.633738683919753[/C][/ROW]
[ROW][C]76[/C][C]5.5[/C][C]4.96951171492108[/C][C]0.530488285078915[/C][/ROW]
[ROW][C]77[/C][C]5.2[/C][C]4.04453128634422[/C][C]1.15546871365578[/C][/ROW]
[ROW][C]78[/C][C]6[/C][C]5.06528768925893[/C][C]0.934712310741072[/C][/ROW]
[ROW][C]79[/C][C]4[/C][C]3.40172806315537[/C][C]0.598271936844634[/C][/ROW]
[ROW][C]80[/C][C]6.2[/C][C]5.81939647222175[/C][C]0.380603527778247[/C][/ROW]
[ROW][C]81[/C][C]3.7[/C][C]4.46111575109061[/C][C]-0.761115751090605[/C][/ROW]
[ROW][C]82[/C][C]5.2[/C][C]5.68359532955709[/C][C]-0.483595329557089[/C][/ROW]
[ROW][C]83[/C][C]2.7[/C][C]4.19419707526348[/C][C]-1.49419707526348[/C][/ROW]
[ROW][C]84[/C][C]0.8[/C][C]3.71614586163368[/C][C]-2.91614586163368[/C][/ROW]
[ROW][C]85[/C][C]2.9[/C][C]4.21394938618133[/C][C]-1.31394938618133[/C][/ROW]
[ROW][C]86[/C][C]0.2[/C][C]1.26966846314574[/C][C]-1.06966846314574[/C][/ROW]
[ROW][C]87[/C][C]-2.6[/C][C]-1.70375709438784[/C][C]-0.89624290561216[/C][/ROW]
[ROW][C]88[/C][C]-6.7[/C][C]-4.82778567314997[/C][C]-1.87221432685003[/C][/ROW]
[ROW][C]89[/C][C]-12.5[/C][C]-8.80313532933066[/C][C]-3.69686467066934[/C][/ROW]
[ROW][C]90[/C][C]-14.4[/C][C]-11.0806977618253[/C][C]-3.31930223817469[/C][/ROW]
[ROW][C]91[/C][C]-16[/C][C]-12.5617874354863[/C][C]-3.43821256451368[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186202&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186202&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
12.51.255335053953941.24466494604606
22.3-0.5297736399293132.82977363992931
31.4-0.8631953578892422.26319535788924
40.5-1.683681944040832.18368194404083
5-2.3-2.567420343482560.26742034348256
6-3.7-2.54203843114115-1.15796156885885
7-3.5-1.82721470412759-1.67278529587241
8-0.20.630465587064943-0.830465587064943
90.2-0.08496454733518030.28496454733518
10-0.10.389702132602918-0.489702132602918
11-1-0.363593963287592-0.636406036712408
12-0.9-0.785971178624599-0.114028821375401
132.21.259579931825250.940420068174753
141.91.306399191699770.593600808300233
152.41.995716354558120.404283645441877
162.31.517363760181660.782636239818338
172.31.670611789854590.629388210145414
183.82.113037876702381.68696212329762
1931.781796637273111.21820336272689
202.40.2855991777129622.11440082228704
210.7-0.9506849843955181.65068498439552
221.4-0.9206115836351982.3206115836352
232.5-0.650849898736613.15084989873661
242.90.4567163744845382.44328362551546
253.80.7645972639594793.03540273604052
262.91.67384193399131.2261580660087
2730.5802915312399042.4197084687601
285.10.7327571987119374.36724280128806
293.4-0.5529053708200173.95290537082002
303.81.509197831140422.29080216885958
312.73.20000970775731-0.50000970775731
324.74.76471414603635-0.0647141460363467
334.84.709564740269880.0904352597301186
345.54.276014663534591.22398533646541
355.14.119446102240920.980553897759077
367.75.851323279060091.84867672093991
375.45.064633731341920.335366268658083
384.84.422867181123570.377132818876434
394.74.605195256093280.0948047439067162
405.35.57496491611064-0.274964916110641
417.56.28765118853541.2123488114646
425.74.214275978080571.48572402191943
433.62.50115746315061.0988425368494
442.82.82710445266764-0.0271044526676357
453.43.74576695099731-0.345766950997311
463.85.77781397785646-1.97781397785646
471.54.95680309274367-3.45680309274367
480.34.65886944424663-4.35886944424663
490.42.93348673961923-2.53348673961923
500.32.79189707938742-2.49189707938742
511.23.46753007739411-2.26753007739411
520.92.95937513813257-2.05937513813257
532.84.82710100814685-2.02710100814685
542.95.68566454905146-2.78566454905146
554.98.05347721513338-3.15347721513337
562.36.68294018322151-4.38294018322151
5746.73346239620514-2.73346239620514
582.34.27279809916367-1.97279809916367
5955.24808945389641-0.248089453896406
602.63.86382946621449-1.26382946621449
611.74.57499267693056-2.87499267693056
624.34.72368233604533-0.423682336045326
6344.63067120181195-0.63067120181195
643.83.400166797658540.399833202341462
652.53.65042094867829-1.15042094867829
663.23.33880284176446-0.138802841764461
6743.632079266172360.367920733827636
684.12.010888142528222.08911185747178
693.32.112822837404551.18717716259545
704.33.311980856835080.98801914316492
715.84.699969302343811.10003069765619
728.14.863104216816913.23689578318309
736.84.201181181944712.59881881805529
745.34.243080921466931.05691907853307
754.84.166261316080250.633738683919753
765.54.969511714921080.530488285078915
775.24.044531286344221.15546871365578
7865.065287689258930.934712310741072
7943.401728063155370.598271936844634
806.25.819396472221750.380603527778247
813.74.46111575109061-0.761115751090605
825.25.68359532955709-0.483595329557089
832.74.19419707526348-1.49419707526348
840.83.71614586163368-2.91614586163368
852.94.21394938618133-1.31394938618133
860.21.26966846314574-1.06966846314574
87-2.6-1.70375709438784-0.89624290561216
88-6.7-4.82778567314997-1.87221432685003
89-12.5-8.80313532933066-3.69686467066934
90-14.4-11.0806977618253-3.31930223817469
91-16-12.5617874354863-3.43821256451368







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
70.2486581683202570.4973163366405150.751341831679743
80.1230831243021460.2461662486042920.876916875697854
90.1039282499348790.2078564998697570.896071750065122
100.05623809705418540.1124761941083710.943761902945815
110.02793726510692680.05587453021385350.972062734893073
120.01416185726579810.02832371453159620.985838142734202
130.008565134996288860.01713026999257770.991434865003711
140.004075814050923390.008151628101846790.995924185949077
150.006160914830604920.01232182966120980.993839085169395
160.003874866736923630.007749733473847250.996125133263076
170.002507230112479310.005014460224958610.997492769887521
180.001188296300684640.002376592601369280.998811703699315
190.0006019582236359880.001203916447271980.999398041776364
200.0003470924582435060.0006941849164870110.999652907541757
210.0001645015970910040.0003290031941820090.999835498402909
220.0001042579316724790.0002085158633449570.999895742068328
230.000462223858838850.00092444771767770.999537776141161
240.0004516024618386940.0009032049236773880.999548397538161
250.0008374219009599640.001674843801919930.99916257809904
260.000466778074396450.00093355614879290.999533221925604
270.002517864338510090.005035728677020190.99748213566149
280.05801405896413620.1160281179282720.941985941035864
290.2662258932167720.5324517864335440.733774106783228
300.2879002481506370.5758004963012740.712099751849363
310.3246773869985020.6493547739970050.675322613001498
320.3030837755589490.6061675511178980.696916224441051
330.2666453696099540.5332907392199090.733354630390046
340.2618623949903190.5237247899806380.738137605009681
350.2358211930845370.4716423861690750.764178806915463
360.2355372962275410.4710745924550830.764462703772459
370.2049409175246820.4098818350493640.795059082475318
380.1762529342850940.3525058685701890.823747065714906
390.150398912429360.3007978248587190.84960108757064
400.1276469682741920.2552939365483840.872353031725808
410.1092173666268020.2184347332536040.890782633373198
420.1038644397430010.2077288794860030.896135560256999
430.1007145681807060.2014291363614120.899285431819294
440.09107262252180110.1821452450436020.908927377478199
450.07963930747597050.1592786149519410.920360692524029
460.1354089723594140.2708179447188280.864591027640586
470.3737091804299290.7474183608598580.626290819570071
480.8325550891688940.3348898216622110.167444910831106
490.8793159327825260.2413681344349480.120684067217474
500.9021768220098720.1956463559802560.0978231779901281
510.9071028027794820.1857943944410370.0928971972205183
520.9066181129118430.1867637741763140.093381887088157
530.89283158368010.21433683263980.1071684163199
540.8845265418396610.2309469163206780.115473458160339
550.8986734472701680.2026531054596640.101326552729832
560.9737109015918820.05257819681623590.0262890984081179
570.9843062555658720.03138748886825580.0156937444341279
580.9781155674401140.04376886511977260.0218844325598863
590.9777830705271320.04443385894573680.0222169294728684
600.9895133723522340.02097325529553110.0104866276477656
610.9957981365263750.00840372694724890.00420186347362445
620.9932066517814390.01358669643712240.00679334821856118
630.9890282648739560.02194347025208780.0109717351260439
640.9829206617222170.03415867655556570.0170793382777829
650.9855850750448180.02882984991036370.0144149249551818
660.9834620194154550.03307596116909020.0165379805845451
670.9769671512803530.04606569743929390.023032848719647
680.9844038478594360.03119230428112890.0155961521405645
690.9905815465894150.01883690682117030.00941845341058516
700.9865497874398960.02690042512020820.0134502125601041
710.9802723341121450.03945533177570960.0197276658878548
720.9931582700327330.01368345993453380.00684172996726692
730.9991663475917230.001667304816554430.000833652408277217
740.9986639016293810.002672196741238730.00133609837061937
750.9974604186411290.005079162717742440.00253958135887122
760.9943165795662730.01136684086745310.00568342043372656
770.9901958574819310.01960828503613850.00980414251806923
780.983351078191920.03329784361616040.0166489218080802
790.9884595565306180.02308088693876310.0115404434693815
800.9912978359940630.01740432801187440.00870216400593722
810.9776084484463040.04478310310739150.0223915515536958
820.9460717298386950.1078565403226090.0539282701613047
830.8957801673602150.208439665279570.104219832639785
840.9848774231003920.03024515379921540.0151225768996077

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
7 & 0.248658168320257 & 0.497316336640515 & 0.751341831679743 \tabularnewline
8 & 0.123083124302146 & 0.246166248604292 & 0.876916875697854 \tabularnewline
9 & 0.103928249934879 & 0.207856499869757 & 0.896071750065122 \tabularnewline
10 & 0.0562380970541854 & 0.112476194108371 & 0.943761902945815 \tabularnewline
11 & 0.0279372651069268 & 0.0558745302138535 & 0.972062734893073 \tabularnewline
12 & 0.0141618572657981 & 0.0283237145315962 & 0.985838142734202 \tabularnewline
13 & 0.00856513499628886 & 0.0171302699925777 & 0.991434865003711 \tabularnewline
14 & 0.00407581405092339 & 0.00815162810184679 & 0.995924185949077 \tabularnewline
15 & 0.00616091483060492 & 0.0123218296612098 & 0.993839085169395 \tabularnewline
16 & 0.00387486673692363 & 0.00774973347384725 & 0.996125133263076 \tabularnewline
17 & 0.00250723011247931 & 0.00501446022495861 & 0.997492769887521 \tabularnewline
18 & 0.00118829630068464 & 0.00237659260136928 & 0.998811703699315 \tabularnewline
19 & 0.000601958223635988 & 0.00120391644727198 & 0.999398041776364 \tabularnewline
20 & 0.000347092458243506 & 0.000694184916487011 & 0.999652907541757 \tabularnewline
21 & 0.000164501597091004 & 0.000329003194182009 & 0.999835498402909 \tabularnewline
22 & 0.000104257931672479 & 0.000208515863344957 & 0.999895742068328 \tabularnewline
23 & 0.00046222385883885 & 0.0009244477176777 & 0.999537776141161 \tabularnewline
24 & 0.000451602461838694 & 0.000903204923677388 & 0.999548397538161 \tabularnewline
25 & 0.000837421900959964 & 0.00167484380191993 & 0.99916257809904 \tabularnewline
26 & 0.00046677807439645 & 0.0009335561487929 & 0.999533221925604 \tabularnewline
27 & 0.00251786433851009 & 0.00503572867702019 & 0.99748213566149 \tabularnewline
28 & 0.0580140589641362 & 0.116028117928272 & 0.941985941035864 \tabularnewline
29 & 0.266225893216772 & 0.532451786433544 & 0.733774106783228 \tabularnewline
30 & 0.287900248150637 & 0.575800496301274 & 0.712099751849363 \tabularnewline
31 & 0.324677386998502 & 0.649354773997005 & 0.675322613001498 \tabularnewline
32 & 0.303083775558949 & 0.606167551117898 & 0.696916224441051 \tabularnewline
33 & 0.266645369609954 & 0.533290739219909 & 0.733354630390046 \tabularnewline
34 & 0.261862394990319 & 0.523724789980638 & 0.738137605009681 \tabularnewline
35 & 0.235821193084537 & 0.471642386169075 & 0.764178806915463 \tabularnewline
36 & 0.235537296227541 & 0.471074592455083 & 0.764462703772459 \tabularnewline
37 & 0.204940917524682 & 0.409881835049364 & 0.795059082475318 \tabularnewline
38 & 0.176252934285094 & 0.352505868570189 & 0.823747065714906 \tabularnewline
39 & 0.15039891242936 & 0.300797824858719 & 0.84960108757064 \tabularnewline
40 & 0.127646968274192 & 0.255293936548384 & 0.872353031725808 \tabularnewline
41 & 0.109217366626802 & 0.218434733253604 & 0.890782633373198 \tabularnewline
42 & 0.103864439743001 & 0.207728879486003 & 0.896135560256999 \tabularnewline
43 & 0.100714568180706 & 0.201429136361412 & 0.899285431819294 \tabularnewline
44 & 0.0910726225218011 & 0.182145245043602 & 0.908927377478199 \tabularnewline
45 & 0.0796393074759705 & 0.159278614951941 & 0.920360692524029 \tabularnewline
46 & 0.135408972359414 & 0.270817944718828 & 0.864591027640586 \tabularnewline
47 & 0.373709180429929 & 0.747418360859858 & 0.626290819570071 \tabularnewline
48 & 0.832555089168894 & 0.334889821662211 & 0.167444910831106 \tabularnewline
49 & 0.879315932782526 & 0.241368134434948 & 0.120684067217474 \tabularnewline
50 & 0.902176822009872 & 0.195646355980256 & 0.0978231779901281 \tabularnewline
51 & 0.907102802779482 & 0.185794394441037 & 0.0928971972205183 \tabularnewline
52 & 0.906618112911843 & 0.186763774176314 & 0.093381887088157 \tabularnewline
53 & 0.8928315836801 & 0.2143368326398 & 0.1071684163199 \tabularnewline
54 & 0.884526541839661 & 0.230946916320678 & 0.115473458160339 \tabularnewline
55 & 0.898673447270168 & 0.202653105459664 & 0.101326552729832 \tabularnewline
56 & 0.973710901591882 & 0.0525781968162359 & 0.0262890984081179 \tabularnewline
57 & 0.984306255565872 & 0.0313874888682558 & 0.0156937444341279 \tabularnewline
58 & 0.978115567440114 & 0.0437688651197726 & 0.0218844325598863 \tabularnewline
59 & 0.977783070527132 & 0.0444338589457368 & 0.0222169294728684 \tabularnewline
60 & 0.989513372352234 & 0.0209732552955311 & 0.0104866276477656 \tabularnewline
61 & 0.995798136526375 & 0.0084037269472489 & 0.00420186347362445 \tabularnewline
62 & 0.993206651781439 & 0.0135866964371224 & 0.00679334821856118 \tabularnewline
63 & 0.989028264873956 & 0.0219434702520878 & 0.0109717351260439 \tabularnewline
64 & 0.982920661722217 & 0.0341586765555657 & 0.0170793382777829 \tabularnewline
65 & 0.985585075044818 & 0.0288298499103637 & 0.0144149249551818 \tabularnewline
66 & 0.983462019415455 & 0.0330759611690902 & 0.0165379805845451 \tabularnewline
67 & 0.976967151280353 & 0.0460656974392939 & 0.023032848719647 \tabularnewline
68 & 0.984403847859436 & 0.0311923042811289 & 0.0155961521405645 \tabularnewline
69 & 0.990581546589415 & 0.0188369068211703 & 0.00941845341058516 \tabularnewline
70 & 0.986549787439896 & 0.0269004251202082 & 0.0134502125601041 \tabularnewline
71 & 0.980272334112145 & 0.0394553317757096 & 0.0197276658878548 \tabularnewline
72 & 0.993158270032733 & 0.0136834599345338 & 0.00684172996726692 \tabularnewline
73 & 0.999166347591723 & 0.00166730481655443 & 0.000833652408277217 \tabularnewline
74 & 0.998663901629381 & 0.00267219674123873 & 0.00133609837061937 \tabularnewline
75 & 0.997460418641129 & 0.00507916271774244 & 0.00253958135887122 \tabularnewline
76 & 0.994316579566273 & 0.0113668408674531 & 0.00568342043372656 \tabularnewline
77 & 0.990195857481931 & 0.0196082850361385 & 0.00980414251806923 \tabularnewline
78 & 0.98335107819192 & 0.0332978436161604 & 0.0166489218080802 \tabularnewline
79 & 0.988459556530618 & 0.0230808869387631 & 0.0115404434693815 \tabularnewline
80 & 0.991297835994063 & 0.0174043280118744 & 0.00870216400593722 \tabularnewline
81 & 0.977608448446304 & 0.0447831031073915 & 0.0223915515536958 \tabularnewline
82 & 0.946071729838695 & 0.107856540322609 & 0.0539282701613047 \tabularnewline
83 & 0.895780167360215 & 0.20843966527957 & 0.104219832639785 \tabularnewline
84 & 0.984877423100392 & 0.0302451537992154 & 0.0151225768996077 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186202&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]7[/C][C]0.248658168320257[/C][C]0.497316336640515[/C][C]0.751341831679743[/C][/ROW]
[ROW][C]8[/C][C]0.123083124302146[/C][C]0.246166248604292[/C][C]0.876916875697854[/C][/ROW]
[ROW][C]9[/C][C]0.103928249934879[/C][C]0.207856499869757[/C][C]0.896071750065122[/C][/ROW]
[ROW][C]10[/C][C]0.0562380970541854[/C][C]0.112476194108371[/C][C]0.943761902945815[/C][/ROW]
[ROW][C]11[/C][C]0.0279372651069268[/C][C]0.0558745302138535[/C][C]0.972062734893073[/C][/ROW]
[ROW][C]12[/C][C]0.0141618572657981[/C][C]0.0283237145315962[/C][C]0.985838142734202[/C][/ROW]
[ROW][C]13[/C][C]0.00856513499628886[/C][C]0.0171302699925777[/C][C]0.991434865003711[/C][/ROW]
[ROW][C]14[/C][C]0.00407581405092339[/C][C]0.00815162810184679[/C][C]0.995924185949077[/C][/ROW]
[ROW][C]15[/C][C]0.00616091483060492[/C][C]0.0123218296612098[/C][C]0.993839085169395[/C][/ROW]
[ROW][C]16[/C][C]0.00387486673692363[/C][C]0.00774973347384725[/C][C]0.996125133263076[/C][/ROW]
[ROW][C]17[/C][C]0.00250723011247931[/C][C]0.00501446022495861[/C][C]0.997492769887521[/C][/ROW]
[ROW][C]18[/C][C]0.00118829630068464[/C][C]0.00237659260136928[/C][C]0.998811703699315[/C][/ROW]
[ROW][C]19[/C][C]0.000601958223635988[/C][C]0.00120391644727198[/C][C]0.999398041776364[/C][/ROW]
[ROW][C]20[/C][C]0.000347092458243506[/C][C]0.000694184916487011[/C][C]0.999652907541757[/C][/ROW]
[ROW][C]21[/C][C]0.000164501597091004[/C][C]0.000329003194182009[/C][C]0.999835498402909[/C][/ROW]
[ROW][C]22[/C][C]0.000104257931672479[/C][C]0.000208515863344957[/C][C]0.999895742068328[/C][/ROW]
[ROW][C]23[/C][C]0.00046222385883885[/C][C]0.0009244477176777[/C][C]0.999537776141161[/C][/ROW]
[ROW][C]24[/C][C]0.000451602461838694[/C][C]0.000903204923677388[/C][C]0.999548397538161[/C][/ROW]
[ROW][C]25[/C][C]0.000837421900959964[/C][C]0.00167484380191993[/C][C]0.99916257809904[/C][/ROW]
[ROW][C]26[/C][C]0.00046677807439645[/C][C]0.0009335561487929[/C][C]0.999533221925604[/C][/ROW]
[ROW][C]27[/C][C]0.00251786433851009[/C][C]0.00503572867702019[/C][C]0.99748213566149[/C][/ROW]
[ROW][C]28[/C][C]0.0580140589641362[/C][C]0.116028117928272[/C][C]0.941985941035864[/C][/ROW]
[ROW][C]29[/C][C]0.266225893216772[/C][C]0.532451786433544[/C][C]0.733774106783228[/C][/ROW]
[ROW][C]30[/C][C]0.287900248150637[/C][C]0.575800496301274[/C][C]0.712099751849363[/C][/ROW]
[ROW][C]31[/C][C]0.324677386998502[/C][C]0.649354773997005[/C][C]0.675322613001498[/C][/ROW]
[ROW][C]32[/C][C]0.303083775558949[/C][C]0.606167551117898[/C][C]0.696916224441051[/C][/ROW]
[ROW][C]33[/C][C]0.266645369609954[/C][C]0.533290739219909[/C][C]0.733354630390046[/C][/ROW]
[ROW][C]34[/C][C]0.261862394990319[/C][C]0.523724789980638[/C][C]0.738137605009681[/C][/ROW]
[ROW][C]35[/C][C]0.235821193084537[/C][C]0.471642386169075[/C][C]0.764178806915463[/C][/ROW]
[ROW][C]36[/C][C]0.235537296227541[/C][C]0.471074592455083[/C][C]0.764462703772459[/C][/ROW]
[ROW][C]37[/C][C]0.204940917524682[/C][C]0.409881835049364[/C][C]0.795059082475318[/C][/ROW]
[ROW][C]38[/C][C]0.176252934285094[/C][C]0.352505868570189[/C][C]0.823747065714906[/C][/ROW]
[ROW][C]39[/C][C]0.15039891242936[/C][C]0.300797824858719[/C][C]0.84960108757064[/C][/ROW]
[ROW][C]40[/C][C]0.127646968274192[/C][C]0.255293936548384[/C][C]0.872353031725808[/C][/ROW]
[ROW][C]41[/C][C]0.109217366626802[/C][C]0.218434733253604[/C][C]0.890782633373198[/C][/ROW]
[ROW][C]42[/C][C]0.103864439743001[/C][C]0.207728879486003[/C][C]0.896135560256999[/C][/ROW]
[ROW][C]43[/C][C]0.100714568180706[/C][C]0.201429136361412[/C][C]0.899285431819294[/C][/ROW]
[ROW][C]44[/C][C]0.0910726225218011[/C][C]0.182145245043602[/C][C]0.908927377478199[/C][/ROW]
[ROW][C]45[/C][C]0.0796393074759705[/C][C]0.159278614951941[/C][C]0.920360692524029[/C][/ROW]
[ROW][C]46[/C][C]0.135408972359414[/C][C]0.270817944718828[/C][C]0.864591027640586[/C][/ROW]
[ROW][C]47[/C][C]0.373709180429929[/C][C]0.747418360859858[/C][C]0.626290819570071[/C][/ROW]
[ROW][C]48[/C][C]0.832555089168894[/C][C]0.334889821662211[/C][C]0.167444910831106[/C][/ROW]
[ROW][C]49[/C][C]0.879315932782526[/C][C]0.241368134434948[/C][C]0.120684067217474[/C][/ROW]
[ROW][C]50[/C][C]0.902176822009872[/C][C]0.195646355980256[/C][C]0.0978231779901281[/C][/ROW]
[ROW][C]51[/C][C]0.907102802779482[/C][C]0.185794394441037[/C][C]0.0928971972205183[/C][/ROW]
[ROW][C]52[/C][C]0.906618112911843[/C][C]0.186763774176314[/C][C]0.093381887088157[/C][/ROW]
[ROW][C]53[/C][C]0.8928315836801[/C][C]0.2143368326398[/C][C]0.1071684163199[/C][/ROW]
[ROW][C]54[/C][C]0.884526541839661[/C][C]0.230946916320678[/C][C]0.115473458160339[/C][/ROW]
[ROW][C]55[/C][C]0.898673447270168[/C][C]0.202653105459664[/C][C]0.101326552729832[/C][/ROW]
[ROW][C]56[/C][C]0.973710901591882[/C][C]0.0525781968162359[/C][C]0.0262890984081179[/C][/ROW]
[ROW][C]57[/C][C]0.984306255565872[/C][C]0.0313874888682558[/C][C]0.0156937444341279[/C][/ROW]
[ROW][C]58[/C][C]0.978115567440114[/C][C]0.0437688651197726[/C][C]0.0218844325598863[/C][/ROW]
[ROW][C]59[/C][C]0.977783070527132[/C][C]0.0444338589457368[/C][C]0.0222169294728684[/C][/ROW]
[ROW][C]60[/C][C]0.989513372352234[/C][C]0.0209732552955311[/C][C]0.0104866276477656[/C][/ROW]
[ROW][C]61[/C][C]0.995798136526375[/C][C]0.0084037269472489[/C][C]0.00420186347362445[/C][/ROW]
[ROW][C]62[/C][C]0.993206651781439[/C][C]0.0135866964371224[/C][C]0.00679334821856118[/C][/ROW]
[ROW][C]63[/C][C]0.989028264873956[/C][C]0.0219434702520878[/C][C]0.0109717351260439[/C][/ROW]
[ROW][C]64[/C][C]0.982920661722217[/C][C]0.0341586765555657[/C][C]0.0170793382777829[/C][/ROW]
[ROW][C]65[/C][C]0.985585075044818[/C][C]0.0288298499103637[/C][C]0.0144149249551818[/C][/ROW]
[ROW][C]66[/C][C]0.983462019415455[/C][C]0.0330759611690902[/C][C]0.0165379805845451[/C][/ROW]
[ROW][C]67[/C][C]0.976967151280353[/C][C]0.0460656974392939[/C][C]0.023032848719647[/C][/ROW]
[ROW][C]68[/C][C]0.984403847859436[/C][C]0.0311923042811289[/C][C]0.0155961521405645[/C][/ROW]
[ROW][C]69[/C][C]0.990581546589415[/C][C]0.0188369068211703[/C][C]0.00941845341058516[/C][/ROW]
[ROW][C]70[/C][C]0.986549787439896[/C][C]0.0269004251202082[/C][C]0.0134502125601041[/C][/ROW]
[ROW][C]71[/C][C]0.980272334112145[/C][C]0.0394553317757096[/C][C]0.0197276658878548[/C][/ROW]
[ROW][C]72[/C][C]0.993158270032733[/C][C]0.0136834599345338[/C][C]0.00684172996726692[/C][/ROW]
[ROW][C]73[/C][C]0.999166347591723[/C][C]0.00166730481655443[/C][C]0.000833652408277217[/C][/ROW]
[ROW][C]74[/C][C]0.998663901629381[/C][C]0.00267219674123873[/C][C]0.00133609837061937[/C][/ROW]
[ROW][C]75[/C][C]0.997460418641129[/C][C]0.00507916271774244[/C][C]0.00253958135887122[/C][/ROW]
[ROW][C]76[/C][C]0.994316579566273[/C][C]0.0113668408674531[/C][C]0.00568342043372656[/C][/ROW]
[ROW][C]77[/C][C]0.990195857481931[/C][C]0.0196082850361385[/C][C]0.00980414251806923[/C][/ROW]
[ROW][C]78[/C][C]0.98335107819192[/C][C]0.0332978436161604[/C][C]0.0166489218080802[/C][/ROW]
[ROW][C]79[/C][C]0.988459556530618[/C][C]0.0230808869387631[/C][C]0.0115404434693815[/C][/ROW]
[ROW][C]80[/C][C]0.991297835994063[/C][C]0.0174043280118744[/C][C]0.00870216400593722[/C][/ROW]
[ROW][C]81[/C][C]0.977608448446304[/C][C]0.0447831031073915[/C][C]0.0223915515536958[/C][/ROW]
[ROW][C]82[/C][C]0.946071729838695[/C][C]0.107856540322609[/C][C]0.0539282701613047[/C][/ROW]
[ROW][C]83[/C][C]0.895780167360215[/C][C]0.20843966527957[/C][C]0.104219832639785[/C][/ROW]
[ROW][C]84[/C][C]0.984877423100392[/C][C]0.0302451537992154[/C][C]0.0151225768996077[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186202&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186202&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
70.2486581683202570.4973163366405150.751341831679743
80.1230831243021460.2461662486042920.876916875697854
90.1039282499348790.2078564998697570.896071750065122
100.05623809705418540.1124761941083710.943761902945815
110.02793726510692680.05587453021385350.972062734893073
120.01416185726579810.02832371453159620.985838142734202
130.008565134996288860.01713026999257770.991434865003711
140.004075814050923390.008151628101846790.995924185949077
150.006160914830604920.01232182966120980.993839085169395
160.003874866736923630.007749733473847250.996125133263076
170.002507230112479310.005014460224958610.997492769887521
180.001188296300684640.002376592601369280.998811703699315
190.0006019582236359880.001203916447271980.999398041776364
200.0003470924582435060.0006941849164870110.999652907541757
210.0001645015970910040.0003290031941820090.999835498402909
220.0001042579316724790.0002085158633449570.999895742068328
230.000462223858838850.00092444771767770.999537776141161
240.0004516024618386940.0009032049236773880.999548397538161
250.0008374219009599640.001674843801919930.99916257809904
260.000466778074396450.00093355614879290.999533221925604
270.002517864338510090.005035728677020190.99748213566149
280.05801405896413620.1160281179282720.941985941035864
290.2662258932167720.5324517864335440.733774106783228
300.2879002481506370.5758004963012740.712099751849363
310.3246773869985020.6493547739970050.675322613001498
320.3030837755589490.6061675511178980.696916224441051
330.2666453696099540.5332907392199090.733354630390046
340.2618623949903190.5237247899806380.738137605009681
350.2358211930845370.4716423861690750.764178806915463
360.2355372962275410.4710745924550830.764462703772459
370.2049409175246820.4098818350493640.795059082475318
380.1762529342850940.3525058685701890.823747065714906
390.150398912429360.3007978248587190.84960108757064
400.1276469682741920.2552939365483840.872353031725808
410.1092173666268020.2184347332536040.890782633373198
420.1038644397430010.2077288794860030.896135560256999
430.1007145681807060.2014291363614120.899285431819294
440.09107262252180110.1821452450436020.908927377478199
450.07963930747597050.1592786149519410.920360692524029
460.1354089723594140.2708179447188280.864591027640586
470.3737091804299290.7474183608598580.626290819570071
480.8325550891688940.3348898216622110.167444910831106
490.8793159327825260.2413681344349480.120684067217474
500.9021768220098720.1956463559802560.0978231779901281
510.9071028027794820.1857943944410370.0928971972205183
520.9066181129118430.1867637741763140.093381887088157
530.89283158368010.21433683263980.1071684163199
540.8845265418396610.2309469163206780.115473458160339
550.8986734472701680.2026531054596640.101326552729832
560.9737109015918820.05257819681623590.0262890984081179
570.9843062555658720.03138748886825580.0156937444341279
580.9781155674401140.04376886511977260.0218844325598863
590.9777830705271320.04443385894573680.0222169294728684
600.9895133723522340.02097325529553110.0104866276477656
610.9957981365263750.00840372694724890.00420186347362445
620.9932066517814390.01358669643712240.00679334821856118
630.9890282648739560.02194347025208780.0109717351260439
640.9829206617222170.03415867655556570.0170793382777829
650.9855850750448180.02882984991036370.0144149249551818
660.9834620194154550.03307596116909020.0165379805845451
670.9769671512803530.04606569743929390.023032848719647
680.9844038478594360.03119230428112890.0155961521405645
690.9905815465894150.01883690682117030.00941845341058516
700.9865497874398960.02690042512020820.0134502125601041
710.9802723341121450.03945533177570960.0197276658878548
720.9931582700327330.01368345993453380.00684172996726692
730.9991663475917230.001667304816554430.000833652408277217
740.9986639016293810.002672196741238730.00133609837061937
750.9974604186411290.005079162717742440.00253958135887122
760.9943165795662730.01136684086745310.00568342043372656
770.9901958574819310.01960828503613850.00980414251806923
780.983351078191920.03329784361616040.0166489218080802
790.9884595565306180.02308088693876310.0115404434693815
800.9912978359940630.01740432801187440.00870216400593722
810.9776084484463040.04478310310739150.0223915515536958
820.9460717298386950.1078565403226090.0539282701613047
830.8957801673602150.208439665279570.104219832639785
840.9848774231003920.03024515379921540.0151225768996077







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level170.217948717948718NOK
5% type I error level420.538461538461538NOK
10% type I error level440.564102564102564NOK

\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 & 17 & 0.217948717948718 & NOK \tabularnewline
5% type I error level & 42 & 0.538461538461538 & NOK \tabularnewline
10% type I error level & 44 & 0.564102564102564 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186202&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]17[/C][C]0.217948717948718[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]42[/C][C]0.538461538461538[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]44[/C][C]0.564102564102564[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186202&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186202&T=6

As an alternative you can also use a QR Code:  

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

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level170.217948717948718NOK
5% type I error level420.538461538461538NOK
10% type I error level440.564102564102564NOK



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')
}