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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 computationThu, 24 Nov 2011 10:15:37 -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/2011/Nov/24/t1322147768pc5llx9sps7tk7t.htm/, Retrieved Fri, 29 Mar 2024 08:29:51 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=146951, Retrieved Fri, 29 Mar 2024 08:29:51 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact86
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [Workshop 7 tutorial] [2010-12-02 20:40:56] [2805bc4d0d3810b6cd96238758e5985d]
-    D    [Multiple Regression] [] [2011-11-24 15:15:37] [aedc5b8e4f26bdca34b1a0cf88d6dfa2] [Current]
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Dataseries X:
2	1	3	2	3	2	1	3
2	1	3	2	2	1	1	3
2	2	4	3	3	4	4	5
3	2	2	2	2	2	1	1
3	2	4	2	2	2	2	2
3	2	3	1	1	1	2	1
4	1	3	1	1	2	4	1
4	2	2	1	2	2	2	1
4	1	1	1	1	3	1	1
2	2	3	1	2	2	2	3
4	2	3	1	3	4	1	2
4	2	2	1	1	2	1	2
4	2	4	1	3	4	3	1
3	2	3	2	3	1	1	3
3	2	2	1	2	2	3	2
4	2	2	1	1	2	3	1
4	2	2	1	1	1	1	1
4	2	3	1	1	1	2	3
3	2	2	1	1	1	2	1
4	1	1	1	1	1	1	1
4	2	2	1	1	1	3	1
4	2	1	1	2	2	2	2
3	2	3	1	1	2	3	2
3	1	2	2	1	1	2	2
3	1	4	2	3	3	4	4
4	2	3	1	2	2	1	2
3	1	2	1	2	2	3	2
4	1	3	1	1	3	1	3
4	2	3	1	2	3	3	1
2	1	3	4	3	2	2	3
3	1	3	1	2	2	2	3
3	2	3	2	2	2	3	4
4	2	2	1	1	2	3	3
4	1	2	1	2	2	3	2
3	2	1	1	1	1	2	3
4	1	5	1	4	1	4	1
3	2	2	1	1	3	2	1
3	1	2	1	2	2	1	2
4	2	3	1	2	2	1	1
4	2	2	1	1	3	2	1
3	1	3	1	1	3	3	3
4	1	4	1	2	1	2	3
4	1	4	2	4	3	4	2
4	2	2	1	1	2	3	2
2	2	4	2	1	1	4	1
4	1	2	1	1	1	2	2
4	2	3	1	2	2	3	2
1	2	3	3	2	2	2	2
2	2	4	1	1	3	2	1
3	2	1	1	1	3	3	3
3	2	3	1	2	1	4	3
4	2	1	1	2	2	2	2
4	2	4	1	1	2	1	1
3	2	3	2	3	3	3	1
4	2	4	1	3	4	1	4




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time4 seconds
R Server'Gertrude Mary Cox' @ cox.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 & 4 seconds \tabularnewline
R Server & 'Gertrude Mary Cox' @ cox.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146951&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]4 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gertrude Mary Cox' @ cox.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146951&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146951&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 time4 seconds
R Server'Gertrude Mary Cox' @ cox.wessa.net







Multiple Linear Regression - Estimated Regression Equation
Life[t] = + 4.61327584386358 -0.117117672476001Gender[t] -0.0999050687479881Stress[t] -0.819158374021549Depression[t] + 0.227568905682385Effort[t] + 0.0518301959846751Focus[t] -0.000858554732945232Sleep[t] -0.112410064463305Belong[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Life[t] =  +  4.61327584386358 -0.117117672476001Gender[t] -0.0999050687479881Stress[t] -0.819158374021549Depression[t] +  0.227568905682385Effort[t] +  0.0518301959846751Focus[t] -0.000858554732945232Sleep[t] -0.112410064463305Belong[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146951&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Life[t] =  +  4.61327584386358 -0.117117672476001Gender[t] -0.0999050687479881Stress[t] -0.819158374021549Depression[t] +  0.227568905682385Effort[t] +  0.0518301959846751Focus[t] -0.000858554732945232Sleep[t] -0.112410064463305Belong[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146951&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146951&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
Life[t] = + 4.61327584386358 -0.117117672476001Gender[t] -0.0999050687479881Stress[t] -0.819158374021549Depression[t] + 0.227568905682385Effort[t] + 0.0518301959846751Focus[t] -0.000858554732945232Sleep[t] -0.112410064463305Belong[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)4.613275843863580.4612810.00100
Gender-0.1171176724760010.179048-0.65410.5162260.258113
Stress-0.09990506874798810.102352-0.97610.3340170.167008
Depression-0.8191583740215490.147813-5.54191e-061e-06
Effort0.2275689056823850.1266531.79680.0787970.039398
Focus0.05183019598467510.1007290.51460.6092780.304639
Sleep-0.0008585547329452320.084797-0.01010.9919650.495982
Belong-0.1124100644633050.088923-1.26410.2124180.106209

\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) & 4.61327584386358 & 0.46128 & 10.001 & 0 & 0 \tabularnewline
Gender & -0.117117672476001 & 0.179048 & -0.6541 & 0.516226 & 0.258113 \tabularnewline
Stress & -0.0999050687479881 & 0.102352 & -0.9761 & 0.334017 & 0.167008 \tabularnewline
Depression & -0.819158374021549 & 0.147813 & -5.5419 & 1e-06 & 1e-06 \tabularnewline
Effort & 0.227568905682385 & 0.126653 & 1.7968 & 0.078797 & 0.039398 \tabularnewline
Focus & 0.0518301959846751 & 0.100729 & 0.5146 & 0.609278 & 0.304639 \tabularnewline
Sleep & -0.000858554732945232 & 0.084797 & -0.0101 & 0.991965 & 0.495982 \tabularnewline
Belong & -0.112410064463305 & 0.088923 & -1.2641 & 0.212418 & 0.106209 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146951&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]4.61327584386358[/C][C]0.46128[/C][C]10.001[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Gender[/C][C]-0.117117672476001[/C][C]0.179048[/C][C]-0.6541[/C][C]0.516226[/C][C]0.258113[/C][/ROW]
[ROW][C]Stress[/C][C]-0.0999050687479881[/C][C]0.102352[/C][C]-0.9761[/C][C]0.334017[/C][C]0.167008[/C][/ROW]
[ROW][C]Depression[/C][C]-0.819158374021549[/C][C]0.147813[/C][C]-5.5419[/C][C]1e-06[/C][C]1e-06[/C][/ROW]
[ROW][C]Effort[/C][C]0.227568905682385[/C][C]0.126653[/C][C]1.7968[/C][C]0.078797[/C][C]0.039398[/C][/ROW]
[ROW][C]Focus[/C][C]0.0518301959846751[/C][C]0.100729[/C][C]0.5146[/C][C]0.609278[/C][C]0.304639[/C][/ROW]
[ROW][C]Sleep[/C][C]-0.000858554732945232[/C][C]0.084797[/C][C]-0.0101[/C][C]0.991965[/C][C]0.495982[/C][/ROW]
[ROW][C]Belong[/C][C]-0.112410064463305[/C][C]0.088923[/C][C]-1.2641[/C][C]0.212418[/C][C]0.106209[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146951&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146951&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)4.613275843863580.4612810.00100
Gender-0.1171176724760010.179048-0.65410.5162260.258113
Stress-0.09990506874798810.102352-0.97610.3340170.167008
Depression-0.8191583740215490.147813-5.54191e-061e-06
Effort0.2275689056823850.1266531.79680.0787970.039398
Focus0.05183019598467510.1007290.51460.6092780.304639
Sleep-0.0008585547329452320.084797-0.01010.9919650.495982
Belong-0.1124100644633050.088923-1.26410.2124180.106209







Multiple Linear Regression - Regression Statistics
Multiple R0.690977874552319
R-squared0.477450423120841
Adjusted R-squared0.399623890394157
F-TEST (value)6.13480270022545
F-TEST (DF numerator)7
F-TEST (DF denominator)47
p-value4.03512907229953e-05
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.600524736742799
Sum Squared Residuals16.9496080936804

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.690977874552319 \tabularnewline
R-squared & 0.477450423120841 \tabularnewline
Adjusted R-squared & 0.399623890394157 \tabularnewline
F-TEST (value) & 6.13480270022545 \tabularnewline
F-TEST (DF numerator) & 7 \tabularnewline
F-TEST (DF denominator) & 47 \tabularnewline
p-value & 4.03512907229953e-05 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 0.600524736742799 \tabularnewline
Sum Squared Residuals & 16.9496080936804 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146951&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.690977874552319[/C][/ROW]
[ROW][C]R-squared[/C][C]0.477450423120841[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.399623890394157[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]6.13480270022545[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]7[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]47[/C][/ROW]
[ROW][C]p-value[/C][C]4.03512907229953e-05[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]0.600524736742799[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]16.9496080936804[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146951&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146951&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 R0.690977874552319
R-squared0.477450423120841
Adjusted R-squared0.399623890394157
F-TEST (value)6.13480270022545
F-TEST (DF numerator)7
F-TEST (DF denominator)47
p-value4.03512907229953e-05
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.600524736742799
Sum Squared Residuals16.9496080936804







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
123.00640457799416-1.00640457799416
222.7270054763271-0.727005476327104
321.846488061592530.153511938407469
432.986443197510370.0135568024896252
532.673364440818150.326635559181851
633.42543884638393-0.425438846383931
743.592669605378720.407330394621283
843.804743016798980.195256983201021
943.84688560305820.153114396941796
1023.48001781912438-1.48001781912438
1143.924515735972370.0754842640276336
1243.465622601386230.534377398613765
1343.935303622221790.0646963777782074
1432.837456709533490.162543290466512
1533.69147439760273-0.691474397602729
1643.576315556383650.423684443616351
1743.526202469864860.473797530135135
1843.200618717457320.799381282542678
1933.52534391513192-0.525343915131919
2043.743225211088850.256774788911146
2143.524485360398970.475514639601026
2243.792238021083660.207761978916337
2333.36400042317236-0.364000423172357
2432.710893149123070.289106850876933
2532.843343976568710.15665602343129
2643.593286438320630.406713561679368
2733.80859207007873-0.80859207007873
2843.422255336635620.577744663364382
2943.755809589302720.244190410697279
3021.367229275218120.632770724781879
3133.59713549160038-0.597135491600383
3232.547590825906580.452409174093418
3343.351495427457040.64850457254296
3443.808592070078730.19140792992127
3533.4004288549533-0.400428854953298
3644.02373598894522-0.02373598894522
3733.62900430710127-0.62900430710127
3833.81030917954462-0.810309179544621
3943.705696502783940.294303497216064
4043.629004307101270.37099569289873
4133.42053822716973-0.420538227169728
4243.445400226867720.55459977313228
4343.29573301117770.704266988822296
4443.463905491920340.536094508079656
4522.5046582941485-0.504658294148504
4643.530051523144620.469948476855384
4743.591569328854740.408430671145259
4811.95411113554459-0.954111135544589
4923.42919416960529-1.42919416960529
5033.5032306921897-0.503230692189703
5133.42647051367382-0.426470513673816
5243.792238021083660.207761978916337
5343.378222528353560.621777471646436
5433.16422012096356-0.164220120963557
5543.599790538297770.400209461702231

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 2 & 3.00640457799416 & -1.00640457799416 \tabularnewline
2 & 2 & 2.7270054763271 & -0.727005476327104 \tabularnewline
3 & 2 & 1.84648806159253 & 0.153511938407469 \tabularnewline
4 & 3 & 2.98644319751037 & 0.0135568024896252 \tabularnewline
5 & 3 & 2.67336444081815 & 0.326635559181851 \tabularnewline
6 & 3 & 3.42543884638393 & -0.425438846383931 \tabularnewline
7 & 4 & 3.59266960537872 & 0.407330394621283 \tabularnewline
8 & 4 & 3.80474301679898 & 0.195256983201021 \tabularnewline
9 & 4 & 3.8468856030582 & 0.153114396941796 \tabularnewline
10 & 2 & 3.48001781912438 & -1.48001781912438 \tabularnewline
11 & 4 & 3.92451573597237 & 0.0754842640276336 \tabularnewline
12 & 4 & 3.46562260138623 & 0.534377398613765 \tabularnewline
13 & 4 & 3.93530362222179 & 0.0646963777782074 \tabularnewline
14 & 3 & 2.83745670953349 & 0.162543290466512 \tabularnewline
15 & 3 & 3.69147439760273 & -0.691474397602729 \tabularnewline
16 & 4 & 3.57631555638365 & 0.423684443616351 \tabularnewline
17 & 4 & 3.52620246986486 & 0.473797530135135 \tabularnewline
18 & 4 & 3.20061871745732 & 0.799381282542678 \tabularnewline
19 & 3 & 3.52534391513192 & -0.525343915131919 \tabularnewline
20 & 4 & 3.74322521108885 & 0.256774788911146 \tabularnewline
21 & 4 & 3.52448536039897 & 0.475514639601026 \tabularnewline
22 & 4 & 3.79223802108366 & 0.207761978916337 \tabularnewline
23 & 3 & 3.36400042317236 & -0.364000423172357 \tabularnewline
24 & 3 & 2.71089314912307 & 0.289106850876933 \tabularnewline
25 & 3 & 2.84334397656871 & 0.15665602343129 \tabularnewline
26 & 4 & 3.59328643832063 & 0.406713561679368 \tabularnewline
27 & 3 & 3.80859207007873 & -0.80859207007873 \tabularnewline
28 & 4 & 3.42225533663562 & 0.577744663364382 \tabularnewline
29 & 4 & 3.75580958930272 & 0.244190410697279 \tabularnewline
30 & 2 & 1.36722927521812 & 0.632770724781879 \tabularnewline
31 & 3 & 3.59713549160038 & -0.597135491600383 \tabularnewline
32 & 3 & 2.54759082590658 & 0.452409174093418 \tabularnewline
33 & 4 & 3.35149542745704 & 0.64850457254296 \tabularnewline
34 & 4 & 3.80859207007873 & 0.19140792992127 \tabularnewline
35 & 3 & 3.4004288549533 & -0.400428854953298 \tabularnewline
36 & 4 & 4.02373598894522 & -0.02373598894522 \tabularnewline
37 & 3 & 3.62900430710127 & -0.62900430710127 \tabularnewline
38 & 3 & 3.81030917954462 & -0.810309179544621 \tabularnewline
39 & 4 & 3.70569650278394 & 0.294303497216064 \tabularnewline
40 & 4 & 3.62900430710127 & 0.37099569289873 \tabularnewline
41 & 3 & 3.42053822716973 & -0.420538227169728 \tabularnewline
42 & 4 & 3.44540022686772 & 0.55459977313228 \tabularnewline
43 & 4 & 3.2957330111777 & 0.704266988822296 \tabularnewline
44 & 4 & 3.46390549192034 & 0.536094508079656 \tabularnewline
45 & 2 & 2.5046582941485 & -0.504658294148504 \tabularnewline
46 & 4 & 3.53005152314462 & 0.469948476855384 \tabularnewline
47 & 4 & 3.59156932885474 & 0.408430671145259 \tabularnewline
48 & 1 & 1.95411113554459 & -0.954111135544589 \tabularnewline
49 & 2 & 3.42919416960529 & -1.42919416960529 \tabularnewline
50 & 3 & 3.5032306921897 & -0.503230692189703 \tabularnewline
51 & 3 & 3.42647051367382 & -0.426470513673816 \tabularnewline
52 & 4 & 3.79223802108366 & 0.207761978916337 \tabularnewline
53 & 4 & 3.37822252835356 & 0.621777471646436 \tabularnewline
54 & 3 & 3.16422012096356 & -0.164220120963557 \tabularnewline
55 & 4 & 3.59979053829777 & 0.400209461702231 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146951&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[/C][C]3.00640457799416[/C][C]-1.00640457799416[/C][/ROW]
[ROW][C]2[/C][C]2[/C][C]2.7270054763271[/C][C]-0.727005476327104[/C][/ROW]
[ROW][C]3[/C][C]2[/C][C]1.84648806159253[/C][C]0.153511938407469[/C][/ROW]
[ROW][C]4[/C][C]3[/C][C]2.98644319751037[/C][C]0.0135568024896252[/C][/ROW]
[ROW][C]5[/C][C]3[/C][C]2.67336444081815[/C][C]0.326635559181851[/C][/ROW]
[ROW][C]6[/C][C]3[/C][C]3.42543884638393[/C][C]-0.425438846383931[/C][/ROW]
[ROW][C]7[/C][C]4[/C][C]3.59266960537872[/C][C]0.407330394621283[/C][/ROW]
[ROW][C]8[/C][C]4[/C][C]3.80474301679898[/C][C]0.195256983201021[/C][/ROW]
[ROW][C]9[/C][C]4[/C][C]3.8468856030582[/C][C]0.153114396941796[/C][/ROW]
[ROW][C]10[/C][C]2[/C][C]3.48001781912438[/C][C]-1.48001781912438[/C][/ROW]
[ROW][C]11[/C][C]4[/C][C]3.92451573597237[/C][C]0.0754842640276336[/C][/ROW]
[ROW][C]12[/C][C]4[/C][C]3.46562260138623[/C][C]0.534377398613765[/C][/ROW]
[ROW][C]13[/C][C]4[/C][C]3.93530362222179[/C][C]0.0646963777782074[/C][/ROW]
[ROW][C]14[/C][C]3[/C][C]2.83745670953349[/C][C]0.162543290466512[/C][/ROW]
[ROW][C]15[/C][C]3[/C][C]3.69147439760273[/C][C]-0.691474397602729[/C][/ROW]
[ROW][C]16[/C][C]4[/C][C]3.57631555638365[/C][C]0.423684443616351[/C][/ROW]
[ROW][C]17[/C][C]4[/C][C]3.52620246986486[/C][C]0.473797530135135[/C][/ROW]
[ROW][C]18[/C][C]4[/C][C]3.20061871745732[/C][C]0.799381282542678[/C][/ROW]
[ROW][C]19[/C][C]3[/C][C]3.52534391513192[/C][C]-0.525343915131919[/C][/ROW]
[ROW][C]20[/C][C]4[/C][C]3.74322521108885[/C][C]0.256774788911146[/C][/ROW]
[ROW][C]21[/C][C]4[/C][C]3.52448536039897[/C][C]0.475514639601026[/C][/ROW]
[ROW][C]22[/C][C]4[/C][C]3.79223802108366[/C][C]0.207761978916337[/C][/ROW]
[ROW][C]23[/C][C]3[/C][C]3.36400042317236[/C][C]-0.364000423172357[/C][/ROW]
[ROW][C]24[/C][C]3[/C][C]2.71089314912307[/C][C]0.289106850876933[/C][/ROW]
[ROW][C]25[/C][C]3[/C][C]2.84334397656871[/C][C]0.15665602343129[/C][/ROW]
[ROW][C]26[/C][C]4[/C][C]3.59328643832063[/C][C]0.406713561679368[/C][/ROW]
[ROW][C]27[/C][C]3[/C][C]3.80859207007873[/C][C]-0.80859207007873[/C][/ROW]
[ROW][C]28[/C][C]4[/C][C]3.42225533663562[/C][C]0.577744663364382[/C][/ROW]
[ROW][C]29[/C][C]4[/C][C]3.75580958930272[/C][C]0.244190410697279[/C][/ROW]
[ROW][C]30[/C][C]2[/C][C]1.36722927521812[/C][C]0.632770724781879[/C][/ROW]
[ROW][C]31[/C][C]3[/C][C]3.59713549160038[/C][C]-0.597135491600383[/C][/ROW]
[ROW][C]32[/C][C]3[/C][C]2.54759082590658[/C][C]0.452409174093418[/C][/ROW]
[ROW][C]33[/C][C]4[/C][C]3.35149542745704[/C][C]0.64850457254296[/C][/ROW]
[ROW][C]34[/C][C]4[/C][C]3.80859207007873[/C][C]0.19140792992127[/C][/ROW]
[ROW][C]35[/C][C]3[/C][C]3.4004288549533[/C][C]-0.400428854953298[/C][/ROW]
[ROW][C]36[/C][C]4[/C][C]4.02373598894522[/C][C]-0.02373598894522[/C][/ROW]
[ROW][C]37[/C][C]3[/C][C]3.62900430710127[/C][C]-0.62900430710127[/C][/ROW]
[ROW][C]38[/C][C]3[/C][C]3.81030917954462[/C][C]-0.810309179544621[/C][/ROW]
[ROW][C]39[/C][C]4[/C][C]3.70569650278394[/C][C]0.294303497216064[/C][/ROW]
[ROW][C]40[/C][C]4[/C][C]3.62900430710127[/C][C]0.37099569289873[/C][/ROW]
[ROW][C]41[/C][C]3[/C][C]3.42053822716973[/C][C]-0.420538227169728[/C][/ROW]
[ROW][C]42[/C][C]4[/C][C]3.44540022686772[/C][C]0.55459977313228[/C][/ROW]
[ROW][C]43[/C][C]4[/C][C]3.2957330111777[/C][C]0.704266988822296[/C][/ROW]
[ROW][C]44[/C][C]4[/C][C]3.46390549192034[/C][C]0.536094508079656[/C][/ROW]
[ROW][C]45[/C][C]2[/C][C]2.5046582941485[/C][C]-0.504658294148504[/C][/ROW]
[ROW][C]46[/C][C]4[/C][C]3.53005152314462[/C][C]0.469948476855384[/C][/ROW]
[ROW][C]47[/C][C]4[/C][C]3.59156932885474[/C][C]0.408430671145259[/C][/ROW]
[ROW][C]48[/C][C]1[/C][C]1.95411113554459[/C][C]-0.954111135544589[/C][/ROW]
[ROW][C]49[/C][C]2[/C][C]3.42919416960529[/C][C]-1.42919416960529[/C][/ROW]
[ROW][C]50[/C][C]3[/C][C]3.5032306921897[/C][C]-0.503230692189703[/C][/ROW]
[ROW][C]51[/C][C]3[/C][C]3.42647051367382[/C][C]-0.426470513673816[/C][/ROW]
[ROW][C]52[/C][C]4[/C][C]3.79223802108366[/C][C]0.207761978916337[/C][/ROW]
[ROW][C]53[/C][C]4[/C][C]3.37822252835356[/C][C]0.621777471646436[/C][/ROW]
[ROW][C]54[/C][C]3[/C][C]3.16422012096356[/C][C]-0.164220120963557[/C][/ROW]
[ROW][C]55[/C][C]4[/C][C]3.59979053829777[/C][C]0.400209461702231[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146951&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146951&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
123.00640457799416-1.00640457799416
222.7270054763271-0.727005476327104
321.846488061592530.153511938407469
432.986443197510370.0135568024896252
532.673364440818150.326635559181851
633.42543884638393-0.425438846383931
743.592669605378720.407330394621283
843.804743016798980.195256983201021
943.84688560305820.153114396941796
1023.48001781912438-1.48001781912438
1143.924515735972370.0754842640276336
1243.465622601386230.534377398613765
1343.935303622221790.0646963777782074
1432.837456709533490.162543290466512
1533.69147439760273-0.691474397602729
1643.576315556383650.423684443616351
1743.526202469864860.473797530135135
1843.200618717457320.799381282542678
1933.52534391513192-0.525343915131919
2043.743225211088850.256774788911146
2143.524485360398970.475514639601026
2243.792238021083660.207761978916337
2333.36400042317236-0.364000423172357
2432.710893149123070.289106850876933
2532.843343976568710.15665602343129
2643.593286438320630.406713561679368
2733.80859207007873-0.80859207007873
2843.422255336635620.577744663364382
2943.755809589302720.244190410697279
3021.367229275218120.632770724781879
3133.59713549160038-0.597135491600383
3232.547590825906580.452409174093418
3343.351495427457040.64850457254296
3443.808592070078730.19140792992127
3533.4004288549533-0.400428854953298
3644.02373598894522-0.02373598894522
3733.62900430710127-0.62900430710127
3833.81030917954462-0.810309179544621
3943.705696502783940.294303497216064
4043.629004307101270.37099569289873
4133.42053822716973-0.420538227169728
4243.445400226867720.55459977313228
4343.29573301117770.704266988822296
4443.463905491920340.536094508079656
4522.5046582941485-0.504658294148504
4643.530051523144620.469948476855384
4743.591569328854740.408430671145259
4811.95411113554459-0.954111135544589
4923.42919416960529-1.42919416960529
5033.5032306921897-0.503230692189703
5133.42647051367382-0.426470513673816
5243.792238021083660.207761978916337
5343.378222528353560.621777471646436
5433.16422012096356-0.164220120963557
5543.599790538297770.400209461702231







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
110.3421691867588950.684338373517790.657830813241105
120.6079121495944630.7841757008110730.392087850405537
130.4864693376871890.9729386753743780.513530662312811
140.6759057609655680.6481884780688640.324094239034432
150.6268627496730290.7462745006539410.373137250326971
160.5443114220853460.9113771558293070.455688577914654
170.4837630369590070.9675260739180140.516236963040993
180.6346415539280430.7307168921439140.365358446071957
190.6339905095426550.7320189809146910.366009490457345
200.5876922178248720.8246155643502550.412307782175127
210.5479155640021190.9041688719957620.452084435997881
220.4781017633076470.9562035266152930.521898236692353
230.4388147759033760.8776295518067530.561185224096624
240.3653262261408360.7306524522816720.634673773859164
250.335051412251610.670102824503220.66494858774839
260.300737061205610.6014741224112190.69926293879439
270.3011947159394840.6023894318789690.698805284060515
280.2718958552785050.543791710557010.728104144721495
290.2140490593052710.4280981186105430.785950940694729
300.2078119235378530.4156238470757060.792188076462147
310.1878088397349770.3756176794699530.812191160265023
320.1602779613451990.3205559226903970.839722038654801
330.1656221672604540.3312443345209070.834377832739546
340.1343608376457260.2687216752914520.865639162354274
350.1096350314722370.2192700629444740.890364968527763
360.1450140563377680.2900281126755360.854985943662232
370.1506246731578560.3012493463157120.849375326842144
380.3657308811948380.7314617623896750.634269118805162
390.2966629243140970.5933258486281940.703337075685903
400.2509406773524820.5018813547049650.749059322647518
410.1748243489222620.3496486978445230.825175651077738
420.1329753720679870.2659507441359730.867024627932013
430.1056327816183760.2112655632367530.894367218381624
440.1292002122062320.2584004244124640.870799787793768

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
11 & 0.342169186758895 & 0.68433837351779 & 0.657830813241105 \tabularnewline
12 & 0.607912149594463 & 0.784175700811073 & 0.392087850405537 \tabularnewline
13 & 0.486469337687189 & 0.972938675374378 & 0.513530662312811 \tabularnewline
14 & 0.675905760965568 & 0.648188478068864 & 0.324094239034432 \tabularnewline
15 & 0.626862749673029 & 0.746274500653941 & 0.373137250326971 \tabularnewline
16 & 0.544311422085346 & 0.911377155829307 & 0.455688577914654 \tabularnewline
17 & 0.483763036959007 & 0.967526073918014 & 0.516236963040993 \tabularnewline
18 & 0.634641553928043 & 0.730716892143914 & 0.365358446071957 \tabularnewline
19 & 0.633990509542655 & 0.732018980914691 & 0.366009490457345 \tabularnewline
20 & 0.587692217824872 & 0.824615564350255 & 0.412307782175127 \tabularnewline
21 & 0.547915564002119 & 0.904168871995762 & 0.452084435997881 \tabularnewline
22 & 0.478101763307647 & 0.956203526615293 & 0.521898236692353 \tabularnewline
23 & 0.438814775903376 & 0.877629551806753 & 0.561185224096624 \tabularnewline
24 & 0.365326226140836 & 0.730652452281672 & 0.634673773859164 \tabularnewline
25 & 0.33505141225161 & 0.67010282450322 & 0.66494858774839 \tabularnewline
26 & 0.30073706120561 & 0.601474122411219 & 0.69926293879439 \tabularnewline
27 & 0.301194715939484 & 0.602389431878969 & 0.698805284060515 \tabularnewline
28 & 0.271895855278505 & 0.54379171055701 & 0.728104144721495 \tabularnewline
29 & 0.214049059305271 & 0.428098118610543 & 0.785950940694729 \tabularnewline
30 & 0.207811923537853 & 0.415623847075706 & 0.792188076462147 \tabularnewline
31 & 0.187808839734977 & 0.375617679469953 & 0.812191160265023 \tabularnewline
32 & 0.160277961345199 & 0.320555922690397 & 0.839722038654801 \tabularnewline
33 & 0.165622167260454 & 0.331244334520907 & 0.834377832739546 \tabularnewline
34 & 0.134360837645726 & 0.268721675291452 & 0.865639162354274 \tabularnewline
35 & 0.109635031472237 & 0.219270062944474 & 0.890364968527763 \tabularnewline
36 & 0.145014056337768 & 0.290028112675536 & 0.854985943662232 \tabularnewline
37 & 0.150624673157856 & 0.301249346315712 & 0.849375326842144 \tabularnewline
38 & 0.365730881194838 & 0.731461762389675 & 0.634269118805162 \tabularnewline
39 & 0.296662924314097 & 0.593325848628194 & 0.703337075685903 \tabularnewline
40 & 0.250940677352482 & 0.501881354704965 & 0.749059322647518 \tabularnewline
41 & 0.174824348922262 & 0.349648697844523 & 0.825175651077738 \tabularnewline
42 & 0.132975372067987 & 0.265950744135973 & 0.867024627932013 \tabularnewline
43 & 0.105632781618376 & 0.211265563236753 & 0.894367218381624 \tabularnewline
44 & 0.129200212206232 & 0.258400424412464 & 0.870799787793768 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146951&T=5

[TABLE]
[ROW][C]Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]p-values[/C][C]Alternative Hypothesis[/C][/ROW]
[ROW][C]breakpoint index[/C][C]greater[/C][C]2-sided[/C][C]less[/C][/ROW]
[ROW][C]11[/C][C]0.342169186758895[/C][C]0.68433837351779[/C][C]0.657830813241105[/C][/ROW]
[ROW][C]12[/C][C]0.607912149594463[/C][C]0.784175700811073[/C][C]0.392087850405537[/C][/ROW]
[ROW][C]13[/C][C]0.486469337687189[/C][C]0.972938675374378[/C][C]0.513530662312811[/C][/ROW]
[ROW][C]14[/C][C]0.675905760965568[/C][C]0.648188478068864[/C][C]0.324094239034432[/C][/ROW]
[ROW][C]15[/C][C]0.626862749673029[/C][C]0.746274500653941[/C][C]0.373137250326971[/C][/ROW]
[ROW][C]16[/C][C]0.544311422085346[/C][C]0.911377155829307[/C][C]0.455688577914654[/C][/ROW]
[ROW][C]17[/C][C]0.483763036959007[/C][C]0.967526073918014[/C][C]0.516236963040993[/C][/ROW]
[ROW][C]18[/C][C]0.634641553928043[/C][C]0.730716892143914[/C][C]0.365358446071957[/C][/ROW]
[ROW][C]19[/C][C]0.633990509542655[/C][C]0.732018980914691[/C][C]0.366009490457345[/C][/ROW]
[ROW][C]20[/C][C]0.587692217824872[/C][C]0.824615564350255[/C][C]0.412307782175127[/C][/ROW]
[ROW][C]21[/C][C]0.547915564002119[/C][C]0.904168871995762[/C][C]0.452084435997881[/C][/ROW]
[ROW][C]22[/C][C]0.478101763307647[/C][C]0.956203526615293[/C][C]0.521898236692353[/C][/ROW]
[ROW][C]23[/C][C]0.438814775903376[/C][C]0.877629551806753[/C][C]0.561185224096624[/C][/ROW]
[ROW][C]24[/C][C]0.365326226140836[/C][C]0.730652452281672[/C][C]0.634673773859164[/C][/ROW]
[ROW][C]25[/C][C]0.33505141225161[/C][C]0.67010282450322[/C][C]0.66494858774839[/C][/ROW]
[ROW][C]26[/C][C]0.30073706120561[/C][C]0.601474122411219[/C][C]0.69926293879439[/C][/ROW]
[ROW][C]27[/C][C]0.301194715939484[/C][C]0.602389431878969[/C][C]0.698805284060515[/C][/ROW]
[ROW][C]28[/C][C]0.271895855278505[/C][C]0.54379171055701[/C][C]0.728104144721495[/C][/ROW]
[ROW][C]29[/C][C]0.214049059305271[/C][C]0.428098118610543[/C][C]0.785950940694729[/C][/ROW]
[ROW][C]30[/C][C]0.207811923537853[/C][C]0.415623847075706[/C][C]0.792188076462147[/C][/ROW]
[ROW][C]31[/C][C]0.187808839734977[/C][C]0.375617679469953[/C][C]0.812191160265023[/C][/ROW]
[ROW][C]32[/C][C]0.160277961345199[/C][C]0.320555922690397[/C][C]0.839722038654801[/C][/ROW]
[ROW][C]33[/C][C]0.165622167260454[/C][C]0.331244334520907[/C][C]0.834377832739546[/C][/ROW]
[ROW][C]34[/C][C]0.134360837645726[/C][C]0.268721675291452[/C][C]0.865639162354274[/C][/ROW]
[ROW][C]35[/C][C]0.109635031472237[/C][C]0.219270062944474[/C][C]0.890364968527763[/C][/ROW]
[ROW][C]36[/C][C]0.145014056337768[/C][C]0.290028112675536[/C][C]0.854985943662232[/C][/ROW]
[ROW][C]37[/C][C]0.150624673157856[/C][C]0.301249346315712[/C][C]0.849375326842144[/C][/ROW]
[ROW][C]38[/C][C]0.365730881194838[/C][C]0.731461762389675[/C][C]0.634269118805162[/C][/ROW]
[ROW][C]39[/C][C]0.296662924314097[/C][C]0.593325848628194[/C][C]0.703337075685903[/C][/ROW]
[ROW][C]40[/C][C]0.250940677352482[/C][C]0.501881354704965[/C][C]0.749059322647518[/C][/ROW]
[ROW][C]41[/C][C]0.174824348922262[/C][C]0.349648697844523[/C][C]0.825175651077738[/C][/ROW]
[ROW][C]42[/C][C]0.132975372067987[/C][C]0.265950744135973[/C][C]0.867024627932013[/C][/ROW]
[ROW][C]43[/C][C]0.105632781618376[/C][C]0.211265563236753[/C][C]0.894367218381624[/C][/ROW]
[ROW][C]44[/C][C]0.129200212206232[/C][C]0.258400424412464[/C][C]0.870799787793768[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146951&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146951&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
110.3421691867588950.684338373517790.657830813241105
120.6079121495944630.7841757008110730.392087850405537
130.4864693376871890.9729386753743780.513530662312811
140.6759057609655680.6481884780688640.324094239034432
150.6268627496730290.7462745006539410.373137250326971
160.5443114220853460.9113771558293070.455688577914654
170.4837630369590070.9675260739180140.516236963040993
180.6346415539280430.7307168921439140.365358446071957
190.6339905095426550.7320189809146910.366009490457345
200.5876922178248720.8246155643502550.412307782175127
210.5479155640021190.9041688719957620.452084435997881
220.4781017633076470.9562035266152930.521898236692353
230.4388147759033760.8776295518067530.561185224096624
240.3653262261408360.7306524522816720.634673773859164
250.335051412251610.670102824503220.66494858774839
260.300737061205610.6014741224112190.69926293879439
270.3011947159394840.6023894318789690.698805284060515
280.2718958552785050.543791710557010.728104144721495
290.2140490593052710.4280981186105430.785950940694729
300.2078119235378530.4156238470757060.792188076462147
310.1878088397349770.3756176794699530.812191160265023
320.1602779613451990.3205559226903970.839722038654801
330.1656221672604540.3312443345209070.834377832739546
340.1343608376457260.2687216752914520.865639162354274
350.1096350314722370.2192700629444740.890364968527763
360.1450140563377680.2900281126755360.854985943662232
370.1506246731578560.3012493463157120.849375326842144
380.3657308811948380.7314617623896750.634269118805162
390.2966629243140970.5933258486281940.703337075685903
400.2509406773524820.5018813547049650.749059322647518
410.1748243489222620.3496486978445230.825175651077738
420.1329753720679870.2659507441359730.867024627932013
430.1056327816183760.2112655632367530.894367218381624
440.1292002122062320.2584004244124640.870799787793768







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level00OK
10% type I error level00OK

\begin{tabular}{lllllllll}
\hline
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
Description & # significant tests & % significant tests & OK/NOK \tabularnewline
1% type I error level & 0 & 0 & OK \tabularnewline
5% type I error level & 0 & 0 & OK \tabularnewline
10% type I error level & 0 & 0 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146951&T=6

[TABLE]
[ROW][C]Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]Description[/C][C]# significant tests[/C][C]% significant tests[/C][C]OK/NOK[/C][/ROW]
[ROW][C]1% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146951&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146951&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 level00OK
5% type I error level00OK
10% type I error level00OK



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')
}