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Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationSat, 20 Nov 2010 14:23:08 +0000
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2010/Nov/20/t12902630713xb6mlhmbqb40oc.htm/, Retrieved Sat, 27 Apr 2024 16:29:19 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=98210, Retrieved Sat, 27 Apr 2024 16:29:19 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact110

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [Competence to learn] [2010-11-17 07:43:53] [b98453cac15ba1066b407e146608df68]
-    D  [Multiple Regression] [] [2010-11-20 13:46:38] [7f2363d2c77d3bf71367965cc53be730]
-   PD      [Multiple Regression] [] [2010-11-20 14:23:08] [4dba6678eac10ee5c3460d144a14bd5c] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
16	24	14	11	12	24	26
19	25	11	7	8	25	23
15	17	6	17	8	30	25
14	18	12	10	8	19	23
13	18	8	12	9	22	19
19	16	10	12	7	22	29
15	20	10	11	4	25	25
14	16	11	11	11	23	21
15	18	16	12	7	17	22
16	17	11	13	7	21	25
16	23	13	14	12	19	24
16	30	12	16	10	19	18
16	23	8	11	10	15	22
17	18	12	10	8	16	15
15	15	11	11	8	23	22
15	12	4	15	4	27	28
20	21	9	9	9	22	20
18	15	8	11	8	14	12
16	20	8	17	7	22	24
16	31	14	17	11	23	20
16	27	15	11	9	23	21
19	34	16	18	11	21	20
16	21	9	14	13	19	21
17	31	14	10	8	18	23
17	19	11	11	8	20	28
16	16	8	15	9	23	24
15	20	9	15	6	25	24
16	21	9	13	9	19	24
14	22	9	16	9	24	23
15	17	9	13	6	22	23
12	24	10	9	6	25	29
14	25	16	18	16	26	24
16	26	11	18	5	29	18
14	25	8	12	7	32	25
7	17	9	17	9	25	21
10	32	16	9	6	29	26
14	33	11	9	6	28	22
16	13	16	12	5	17	22
16	32	12	18	12	28	22
16	25	12	12	7	29	23
14	29	14	18	10	26	30
20	22	9	14	9	25	23
14	18	10	15	8	14	17
14	17	9	16	5	25	23
11	20	10	10	8	26	23
14	15	12	11	8	20	25
15	20	14	14	10	18	24
16	33	14	9	6	32	24
14	29	10	12	8	25	23
16	23	14	17	7	25	21
14	26	16	5	4	23	24
12	18	9	12	8	21	24
16	20	10	12	8	20	28
9	11	6	6	4	15	16
14	28	8	24	20	30	20
16	26	13	12	8	24	29
16	22	10	12	8	26	27
15	17	8	14	6	24	22
16	12	7	7	4	22	28
12	14	15	13	8	14	16
16	17	9	12	9	24	25
16	21	10	13	6	24	24
14	19	12	14	7	24	28
16	18	13	8	9	24	24
17	10	10	11	5	19	23
18	29	11	9	5	31	30
18	31	8	11	8	22	24
12	19	9	13	8	27	21
16	9	13	10	6	19	25
10	20	11	11	8	25	25
14	28	8	12	7	20	22
18	19	9	9	7	21	23
18	30	9	15	9	27	26
16	29	15	18	11	23	23
17	26	9	15	6	25	25
16	23	10	12	8	20	21
16	13	14	13	6	21	25
13	21	12	14	9	22	24
16	19	12	10	8	23	29
16	28	11	13	6	25	22
20	23	14	13	10	25	27
16	18	6	11	8	17	26
15	21	12	13	8	19	22
15	20	8	16	10	25	24
16	23	14	8	5	19	27
14	21	11	16	7	20	24
16	21	10	11	5	26	24
16	15	14	9	8	23	29
15	28	12	16	14	27	22
12	19	10	12	7	17	21
17	26	14	14	8	17	24
16	10	5	8	6	19	24
15	16	11	9	5	17	23
13	22	10	15	6	22	20
16	19	9	11	10	21	27
16	31	10	21	12	32	26
16	31	16	14	9	21	25
16	29	13	18	12	21	21
14	19	9	12	7	18	21
16	22	10	13	8	18	19
16	23	10	15	10	23	21
20	15	7	12	6	19	21
15	20	9	19	10	20	16
16	18	8	15	10	21	22
13	23	14	11	10	20	29
17	25	14	11	5	17	15
16	21	8	10	7	18	17
16	24	9	13	10	19	15
12	25	14	15	11	22	21
16	17	14	12	6	15	21
16	13	8	12	7	14	19
17	28	8	16	12	18	24
13	21	8	9	11	24	20
12	25	7	18	11	35	17
18	9	6	8	11	29	23
14	16	8	13	5	21	24
14	19	6	17	8	25	14
13	17	11	9	6	20	19
16	25	14	15	9	22	24
13	20	11	8	4	13	13
16	29	11	7	4	26	22
13	14	11	12	7	17	16
16	22	14	14	11	25	19
15	15	8	6	6	20	25
16	19	20	8	7	19	25
15	20	11	17	8	21	23
17	15	8	10	4	22	24
15	20	11	11	8	24	26
12	18	10	14	9	21	26
16	33	14	11	8	26	25
10	22	11	13	11	24	18
16	16	9	12	8	16	21
12	17	9	11	5	23	26
14	16	8	9	4	18	23
15	21	10	12	8	16	23
13	26	13	20	10	26	22
15	18	13	12	6	19	20
11	18	12	13	9	21	13
12	17	8	12	9	21	24
8	22	13	12	13	22	15
16	30	14	9	9	23	14
15	30	12	15	10	29	22
17	24	14	24	20	21	10
16	21	15	7	5	21	24
10	21	13	17	11	23	22
18	29	16	11	6	27	24
13	31	9	17	9	25	19
16	20	9	11	7	21	20
13	16	9	12	9	10	13
10	22	8	14	10	20	20
15	20	7	11	9	26	22
16	28	16	16	8	24	24
16	38	11	21	7	29	29
14	22	9	14	6	19	12
10	20	11	20	13	24	20
17	17	9	13	6	19	21
13	28	14	11	8	24	24
15	22	13	15	10	22	22
16	31	16	19	16	17	20

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time9 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 9 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=98210&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]9 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=98210&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=98210&T=0

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


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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time9 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135






Multiple Linear Regression - Estimated Regression Equation
Learning[t] = + 13.2522145863475 + 0.0923250546673155Concern[t] -0.0427279901581466Doubts[t] -0.0293172689010877Expectations[t] -0.0197865743236952Critisism[t] -0.106730229334144Standards[t] + 0.139579173878108Organization[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Learning[t] =  +  13.2522145863475 +  0.0923250546673155Concern[t] -0.0427279901581466Doubts[t] -0.0293172689010877Expectations[t] -0.0197865743236952Critisism[t] -0.106730229334144Standards[t] +  0.139579173878108Organization[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=98210&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Learning[t] =  +  13.2522145863475 +  0.0923250546673155Concern[t] -0.0427279901581466Doubts[t] -0.0293172689010877Expectations[t] -0.0197865743236952Critisism[t] -0.106730229334144Standards[t] +  0.139579173878108Organization[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=98210&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=98210&T=1

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


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

Multiple Linear Regression - Estimated Regression Equation
Learning[t] = + 13.2522145863475 + 0.0923250546673155Concern[t] -0.0427279901581466Doubts[t] -0.0293172689010877Expectations[t] -0.0197865743236952Critisism[t] -0.106730229334144Standards[t] + 0.139579173878108Organization[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)13.25221458634751.5040098.811300
Concern0.09232505466731550.0397742.32120.0216030.010801
Doubts-0.04272799015814660.071763-0.59540.5524560.276228
Expectations-0.02931726890108770.066075-0.44370.6578930.328946
Critisism-0.01978657432369520.083118-0.23810.8121610.40608
Standards-0.1067302293341440.052239-2.04310.0427680.021384
Organization0.1395791738781080.0508942.74250.0068290.003415

\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) & 13.2522145863475 & 1.504009 & 8.8113 & 0 & 0 \tabularnewline
Concern & 0.0923250546673155 & 0.039774 & 2.3212 & 0.021603 & 0.010801 \tabularnewline
Doubts & -0.0427279901581466 & 0.071763 & -0.5954 & 0.552456 & 0.276228 \tabularnewline
Expectations & -0.0293172689010877 & 0.066075 & -0.4437 & 0.657893 & 0.328946 \tabularnewline
Critisism & -0.0197865743236952 & 0.083118 & -0.2381 & 0.812161 & 0.40608 \tabularnewline
Standards & -0.106730229334144 & 0.052239 & -2.0431 & 0.042768 & 0.021384 \tabularnewline
Organization & 0.139579173878108 & 0.050894 & 2.7425 & 0.006829 & 0.003415 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=98210&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]13.2522145863475[/C][C]1.504009[/C][C]8.8113[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Concern[/C][C]0.0923250546673155[/C][C]0.039774[/C][C]2.3212[/C][C]0.021603[/C][C]0.010801[/C][/ROW]
[ROW][C]Doubts[/C][C]-0.0427279901581466[/C][C]0.071763[/C][C]-0.5954[/C][C]0.552456[/C][C]0.276228[/C][/ROW]
[ROW][C]Expectations[/C][C]-0.0293172689010877[/C][C]0.066075[/C][C]-0.4437[/C][C]0.657893[/C][C]0.328946[/C][/ROW]
[ROW][C]Critisism[/C][C]-0.0197865743236952[/C][C]0.083118[/C][C]-0.2381[/C][C]0.812161[/C][C]0.40608[/C][/ROW]
[ROW][C]Standards[/C][C]-0.106730229334144[/C][C]0.052239[/C][C]-2.0431[/C][C]0.042768[/C][C]0.021384[/C][/ROW]
[ROW][C]Organization[/C][C]0.139579173878108[/C][C]0.050894[/C][C]2.7425[/C][C]0.006829[/C][C]0.003415[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=98210&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=98210&T=2

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


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

Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)13.25221458634751.5040098.811300
Concern0.09232505466731550.0397742.32120.0216030.010801
Doubts-0.04272799015814660.071763-0.59540.5524560.276228
Expectations-0.02931726890108770.066075-0.44370.6578930.328946
Critisism-0.01978657432369520.083118-0.23810.8121610.40608
Standards-0.1067302293341440.052239-2.04310.0427680.021384
Organization0.1395791738781080.0508942.74250.0068290.003415






Multiple Linear Regression - Regression Statistics
Multiple R0.28527509482277
R-squared0.0813818797261403
Adjusted R-squared0.0451206381363827
F-TEST (value)2.24432137892177
F-TEST (DF numerator)6
F-TEST (DF denominator)152
p-value0.0419327428945955
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.20293550134255
Sum Squared Residuals737.644573107455

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.28527509482277 \tabularnewline
R-squared & 0.0813818797261403 \tabularnewline
Adjusted R-squared & 0.0451206381363827 \tabularnewline
F-TEST (value) & 2.24432137892177 \tabularnewline
F-TEST (DF numerator) & 6 \tabularnewline
F-TEST (DF denominator) & 152 \tabularnewline
p-value & 0.0419327428945955 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 2.20293550134255 \tabularnewline
Sum Squared Residuals & 737.644573107455 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=98210&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.28527509482277[/C][/ROW]
[ROW][C]R-squared[/C][C]0.0813818797261403[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.0451206381363827[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]2.24432137892177[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]6[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]152[/C][/ROW]
[ROW][C]p-value[/C][C]0.0419327428945955[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]2.20293550134255[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]737.644573107455[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=98210&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=98210&T=3

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


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

Multiple Linear Regression - Regression Statistics
Multiple R0.28527509482277
R-squared0.0813818797261403
Adjusted R-squared0.0451206381363827
F-TEST (value)2.24432137892177
F-TEST (DF numerator)6
F-TEST (DF denominator)152
p-value0.0419327428945955
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.20293550134255
Sum Squared Residuals737.644573107455






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11615.3774282031640.622571796835987
21915.26888485023653.73111514976348
31514.19625887576340.80374112423664
41415.1323110467088-1.13231104670877
51314.3462945117006-1.34629451170062
61915.51155330947823.48844669052183
71515.0910231365047-0.0910231365047432
81414.1956326705673-0.195632670567322
91514.99643240738790.00356759261211682
101615.08024663890800.919753361092036
111615.49437213086620.505627869133822
121615.32683907127210.673160928727901
131615.98329960658790.0167003934120774
141714.33586834368632.66413165631366
151514.30224651274920.697753487250799
161514.69679862747680.303201372523214
172014.80807266612585.1919273338742
181813.99521080844994.00478919155015
191615.12182729456770.878172705432253
201615.13684173281800.86315826718202
211615.07986945992260.920130540077404
221915.51250410627083.48749589372917
231615.04210988620610.957890113793886
241716.35381100640170.646188993598278
251715.82921246268951.17078753731046
261614.66485823571911.33514176428087
271514.83732972853300.162670271466959
281615.56931097403630.430689025963693
291414.9004539014515-0.900453901451533
301514.79960061645760.200399383542408
311216.0377014398412-4.03770143984122
321414.6073112914882-0.607311291488232
331613.97326288323582.02673711676415
341414.8023157929464-0.802315792946436
35714.0236227821235-7.02362278212351
361015.6742754972600-5.67427549725997
371415.5286540365397-1.52865403653973
381614.57438028269871.42561971730130
391615.01102612566230.988973874337695
401614.67243617256011.32756382743994
411416.0182619796846-2.01826197968461
422014.85235820991965.14764179008043
431414.7673567859217-0.767356785921694
441414.4112446960756-0.411244696075594
451114.6553055310207-3.65530553102069
461414.9984467322278-0.99844673222781
471515.3209723546876-0.320972354687619
481615.25270749648490.74729250351507
491415.5343267145585-1.53432671455850
501614.40350630798411.59649369201594
511415.6383904217565-1.63839042175646
521215.1279791945909-3.12797919459086
531615.93494823861390.0650517613860827
54914.3886856780754-5.38868567807539
551413.98582285320500.0141771467949624
561616.0733728526849-0.0733728526849043
571615.33963779806560.660362201934421
581514.45997170516830.540028294831744
591615.33680395488180.663196045118234
601214.1034719803858-2.10347198038577
611614.83525605147551.16474394852448
621615.05229156017850.947708439821472
631415.2913983228153-1.29139832281525
641614.73435904723651.26564095276351
651714.50920904375652.49079095624346
661815.97558309521662.02441690478340
671816.29351993499101.70648006500895
681214.1318680827179-2.1318680827179
691614.57738906094841.42261093905161
701014.9691488490518-4.96914884905182
711415.9413161873238-1.94131618732378
721815.18846345640702.81153654359298
731815.76691844132302.23308155867696
741615.29888388605840.70111611394156
751715.53085923041501.46914076958496
761615.23486918546910.765130814530894
771614.60254902408801.39745097591204
781315.0916190466583-2.09161904665835
791615.63519022730820.364809772691838
801615.26995037560120.73004962439877
812015.29889070388604.70110929611403
821615.99155969905920.00844030094082513
831515.1817552301293-0.181755230129346
841514.77159415249530.228405847504681
851616.1847912960147-0.184791296014749
861415.32874610633-1.32874610633000
871614.91725221363611.08274778636389
881615.20975129722370.790248702776306
891514.76751752548200.232482474518029
901215.2055462291260-3.20554622912597
911716.02122606067300.978773939326954
921614.93059340080491.06940659919505
931515.2925263780727-0.292526378072747
941314.7411258401995-1.74112584019952
951615.62878589114620.371214108853806
961615.04760102278390.952398977216122
971616.0902670359112-0.090267035911169
981615.29885540296310.701144597036892
991415.1415439899500-1.14154398994997
1001615.04752897281280.952471027187227
1011614.78715354211601.21284645788398
1022014.77075609658665.22924390341344
1031514.05793211127980.94206788872024
1041614.76402388164211.23597611835787
1051316.1703347361151-3.17033473611508
1061714.81999997077712.18000002922290
1071614.86923993173251.13076006826751
1081614.57028699881161.42971300118842
1091214.8878353458285-2.88783534582852
1101615.08323119215070.916768807849265
1111614.77808422168461.22191577831542
1121716.21773304652550.782266953474543
1131314.5977670489683-1.59776704896826
1141213.1531697933760-1.15316979337598
1151813.48972601714154.51027398285853
1161415.0160995294844-1.01609952948437
1171413.37918921910950.620810780890483
1181314.4865574749015-1.48655747490150
1191615.34614601611020.65385398388977
1201314.7420596185223-1.74205961852228
1211615.47102196298830.528978037011681
1221314.0032970962407-1.00329709624071
1231614.0408284149691.95917158503099
1241515.3555181860132-0.355518186013225
1251615.2403916399930.759608360007
1261514.94100780522560.0589921947743524
1271714.92478262650992.07521737349013
1281515.2154582522641-0.215458252264068
1291215.2859884400631-3.28598844006306
1301615.93446035991830.0655396400816665
1311014.1654807098006-4.16548070980057
1321615.05824271029260.941757289707381
1331215.1900290208836-3.19002902088364
1341415.3337666935367-1.33376669353674
1351515.7562983412273-0.756298341227266
1361314.6087468770138-1.60874687701377
1371514.65178414576150.348215854238486
1381113.4153204682324-2.41532046823244
1391215.058595555758-3.05859555575799
140813.8644917867719-5.86449178677194
1411614.48115293473811.51884706526189
1421514.84717074234420.152829257655816
1431712.92493501881314.07506498118689
1441615.35453248512040.645467514879553
1451014.5354775240592-4.53547752405919
1461815.27296790636792.72703209363208
1471315.0370151997097-2.03701519970971
1481614.80341645163781.19658354836216
1491314.5622041209489-1.56220412094892
1501014.9902132507904-4.99021325079041
1511514.59480648439220.405193515607761
1521615.31467404655020.685325953449797
1531616.4890094965522-0.489009496552171
1541414.0167283962363-0.0167283962363217
1551014.0151949172672-4.01519491726715
1561714.84063295670382.15936704329619
1571315.5467163713719-2.54671637137193
1581514.81295392018650.187046079813482
1591615.53419971908590.465800280914103

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 16 & 15.377428203164 & 0.622571796835987 \tabularnewline
2 & 19 & 15.2688848502365 & 3.73111514976348 \tabularnewline
3 & 15 & 14.1962588757634 & 0.80374112423664 \tabularnewline
4 & 14 & 15.1323110467088 & -1.13231104670877 \tabularnewline
5 & 13 & 14.3462945117006 & -1.34629451170062 \tabularnewline
6 & 19 & 15.5115533094782 & 3.48844669052183 \tabularnewline
7 & 15 & 15.0910231365047 & -0.0910231365047432 \tabularnewline
8 & 14 & 14.1956326705673 & -0.195632670567322 \tabularnewline
9 & 15 & 14.9964324073879 & 0.00356759261211682 \tabularnewline
10 & 16 & 15.0802466389080 & 0.919753361092036 \tabularnewline
11 & 16 & 15.4943721308662 & 0.505627869133822 \tabularnewline
12 & 16 & 15.3268390712721 & 0.673160928727901 \tabularnewline
13 & 16 & 15.9832996065879 & 0.0167003934120774 \tabularnewline
14 & 17 & 14.3358683436863 & 2.66413165631366 \tabularnewline
15 & 15 & 14.3022465127492 & 0.697753487250799 \tabularnewline
16 & 15 & 14.6967986274768 & 0.303201372523214 \tabularnewline
17 & 20 & 14.8080726661258 & 5.1919273338742 \tabularnewline
18 & 18 & 13.9952108084499 & 4.00478919155015 \tabularnewline
19 & 16 & 15.1218272945677 & 0.878172705432253 \tabularnewline
20 & 16 & 15.1368417328180 & 0.86315826718202 \tabularnewline
21 & 16 & 15.0798694599226 & 0.920130540077404 \tabularnewline
22 & 19 & 15.5125041062708 & 3.48749589372917 \tabularnewline
23 & 16 & 15.0421098862061 & 0.957890113793886 \tabularnewline
24 & 17 & 16.3538110064017 & 0.646188993598278 \tabularnewline
25 & 17 & 15.8292124626895 & 1.17078753731046 \tabularnewline
26 & 16 & 14.6648582357191 & 1.33514176428087 \tabularnewline
27 & 15 & 14.8373297285330 & 0.162670271466959 \tabularnewline
28 & 16 & 15.5693109740363 & 0.430689025963693 \tabularnewline
29 & 14 & 14.9004539014515 & -0.900453901451533 \tabularnewline
30 & 15 & 14.7996006164576 & 0.200399383542408 \tabularnewline
31 & 12 & 16.0377014398412 & -4.03770143984122 \tabularnewline
32 & 14 & 14.6073112914882 & -0.607311291488232 \tabularnewline
33 & 16 & 13.9732628832358 & 2.02673711676415 \tabularnewline
34 & 14 & 14.8023157929464 & -0.802315792946436 \tabularnewline
35 & 7 & 14.0236227821235 & -7.02362278212351 \tabularnewline
36 & 10 & 15.6742754972600 & -5.67427549725997 \tabularnewline
37 & 14 & 15.5286540365397 & -1.52865403653973 \tabularnewline
38 & 16 & 14.5743802826987 & 1.42561971730130 \tabularnewline
39 & 16 & 15.0110261256623 & 0.988973874337695 \tabularnewline
40 & 16 & 14.6724361725601 & 1.32756382743994 \tabularnewline
41 & 14 & 16.0182619796846 & -2.01826197968461 \tabularnewline
42 & 20 & 14.8523582099196 & 5.14764179008043 \tabularnewline
43 & 14 & 14.7673567859217 & -0.767356785921694 \tabularnewline
44 & 14 & 14.4112446960756 & -0.411244696075594 \tabularnewline
45 & 11 & 14.6553055310207 & -3.65530553102069 \tabularnewline
46 & 14 & 14.9984467322278 & -0.99844673222781 \tabularnewline
47 & 15 & 15.3209723546876 & -0.320972354687619 \tabularnewline
48 & 16 & 15.2527074964849 & 0.74729250351507 \tabularnewline
49 & 14 & 15.5343267145585 & -1.53432671455850 \tabularnewline
50 & 16 & 14.4035063079841 & 1.59649369201594 \tabularnewline
51 & 14 & 15.6383904217565 & -1.63839042175646 \tabularnewline
52 & 12 & 15.1279791945909 & -3.12797919459086 \tabularnewline
53 & 16 & 15.9349482386139 & 0.0650517613860827 \tabularnewline
54 & 9 & 14.3886856780754 & -5.38868567807539 \tabularnewline
55 & 14 & 13.9858228532050 & 0.0141771467949624 \tabularnewline
56 & 16 & 16.0733728526849 & -0.0733728526849043 \tabularnewline
57 & 16 & 15.3396377980656 & 0.660362201934421 \tabularnewline
58 & 15 & 14.4599717051683 & 0.540028294831744 \tabularnewline
59 & 16 & 15.3368039548818 & 0.663196045118234 \tabularnewline
60 & 12 & 14.1034719803858 & -2.10347198038577 \tabularnewline
61 & 16 & 14.8352560514755 & 1.16474394852448 \tabularnewline
62 & 16 & 15.0522915601785 & 0.947708439821472 \tabularnewline
63 & 14 & 15.2913983228153 & -1.29139832281525 \tabularnewline
64 & 16 & 14.7343590472365 & 1.26564095276351 \tabularnewline
65 & 17 & 14.5092090437565 & 2.49079095624346 \tabularnewline
66 & 18 & 15.9755830952166 & 2.02441690478340 \tabularnewline
67 & 18 & 16.2935199349910 & 1.70648006500895 \tabularnewline
68 & 12 & 14.1318680827179 & -2.1318680827179 \tabularnewline
69 & 16 & 14.5773890609484 & 1.42261093905161 \tabularnewline
70 & 10 & 14.9691488490518 & -4.96914884905182 \tabularnewline
71 & 14 & 15.9413161873238 & -1.94131618732378 \tabularnewline
72 & 18 & 15.1884634564070 & 2.81153654359298 \tabularnewline
73 & 18 & 15.7669184413230 & 2.23308155867696 \tabularnewline
74 & 16 & 15.2988838860584 & 0.70111611394156 \tabularnewline
75 & 17 & 15.5308592304150 & 1.46914076958496 \tabularnewline
76 & 16 & 15.2348691854691 & 0.765130814530894 \tabularnewline
77 & 16 & 14.6025490240880 & 1.39745097591204 \tabularnewline
78 & 13 & 15.0916190466583 & -2.09161904665835 \tabularnewline
79 & 16 & 15.6351902273082 & 0.364809772691838 \tabularnewline
80 & 16 & 15.2699503756012 & 0.73004962439877 \tabularnewline
81 & 20 & 15.2988907038860 & 4.70110929611403 \tabularnewline
82 & 16 & 15.9915596990592 & 0.00844030094082513 \tabularnewline
83 & 15 & 15.1817552301293 & -0.181755230129346 \tabularnewline
84 & 15 & 14.7715941524953 & 0.228405847504681 \tabularnewline
85 & 16 & 16.1847912960147 & -0.184791296014749 \tabularnewline
86 & 14 & 15.32874610633 & -1.32874610633000 \tabularnewline
87 & 16 & 14.9172522136361 & 1.08274778636389 \tabularnewline
88 & 16 & 15.2097512972237 & 0.790248702776306 \tabularnewline
89 & 15 & 14.7675175254820 & 0.232482474518029 \tabularnewline
90 & 12 & 15.2055462291260 & -3.20554622912597 \tabularnewline
91 & 17 & 16.0212260606730 & 0.978773939326954 \tabularnewline
92 & 16 & 14.9305934008049 & 1.06940659919505 \tabularnewline
93 & 15 & 15.2925263780727 & -0.292526378072747 \tabularnewline
94 & 13 & 14.7411258401995 & -1.74112584019952 \tabularnewline
95 & 16 & 15.6287858911462 & 0.371214108853806 \tabularnewline
96 & 16 & 15.0476010227839 & 0.952398977216122 \tabularnewline
97 & 16 & 16.0902670359112 & -0.090267035911169 \tabularnewline
98 & 16 & 15.2988554029631 & 0.701144597036892 \tabularnewline
99 & 14 & 15.1415439899500 & -1.14154398994997 \tabularnewline
100 & 16 & 15.0475289728128 & 0.952471027187227 \tabularnewline
101 & 16 & 14.7871535421160 & 1.21284645788398 \tabularnewline
102 & 20 & 14.7707560965866 & 5.22924390341344 \tabularnewline
103 & 15 & 14.0579321112798 & 0.94206788872024 \tabularnewline
104 & 16 & 14.7640238816421 & 1.23597611835787 \tabularnewline
105 & 13 & 16.1703347361151 & -3.17033473611508 \tabularnewline
106 & 17 & 14.8199999707771 & 2.18000002922290 \tabularnewline
107 & 16 & 14.8692399317325 & 1.13076006826751 \tabularnewline
108 & 16 & 14.5702869988116 & 1.42971300118842 \tabularnewline
109 & 12 & 14.8878353458285 & -2.88783534582852 \tabularnewline
110 & 16 & 15.0832311921507 & 0.916768807849265 \tabularnewline
111 & 16 & 14.7780842216846 & 1.22191577831542 \tabularnewline
112 & 17 & 16.2177330465255 & 0.782266953474543 \tabularnewline
113 & 13 & 14.5977670489683 & -1.59776704896826 \tabularnewline
114 & 12 & 13.1531697933760 & -1.15316979337598 \tabularnewline
115 & 18 & 13.4897260171415 & 4.51027398285853 \tabularnewline
116 & 14 & 15.0160995294844 & -1.01609952948437 \tabularnewline
117 & 14 & 13.3791892191095 & 0.620810780890483 \tabularnewline
118 & 13 & 14.4865574749015 & -1.48655747490150 \tabularnewline
119 & 16 & 15.3461460161102 & 0.65385398388977 \tabularnewline
120 & 13 & 14.7420596185223 & -1.74205961852228 \tabularnewline
121 & 16 & 15.4710219629883 & 0.528978037011681 \tabularnewline
122 & 13 & 14.0032970962407 & -1.00329709624071 \tabularnewline
123 & 16 & 14.040828414969 & 1.95917158503099 \tabularnewline
124 & 15 & 15.3555181860132 & -0.355518186013225 \tabularnewline
125 & 16 & 15.240391639993 & 0.759608360007 \tabularnewline
126 & 15 & 14.9410078052256 & 0.0589921947743524 \tabularnewline
127 & 17 & 14.9247826265099 & 2.07521737349013 \tabularnewline
128 & 15 & 15.2154582522641 & -0.215458252264068 \tabularnewline
129 & 12 & 15.2859884400631 & -3.28598844006306 \tabularnewline
130 & 16 & 15.9344603599183 & 0.0655396400816665 \tabularnewline
131 & 10 & 14.1654807098006 & -4.16548070980057 \tabularnewline
132 & 16 & 15.0582427102926 & 0.941757289707381 \tabularnewline
133 & 12 & 15.1900290208836 & -3.19002902088364 \tabularnewline
134 & 14 & 15.3337666935367 & -1.33376669353674 \tabularnewline
135 & 15 & 15.7562983412273 & -0.756298341227266 \tabularnewline
136 & 13 & 14.6087468770138 & -1.60874687701377 \tabularnewline
137 & 15 & 14.6517841457615 & 0.348215854238486 \tabularnewline
138 & 11 & 13.4153204682324 & -2.41532046823244 \tabularnewline
139 & 12 & 15.058595555758 & -3.05859555575799 \tabularnewline
140 & 8 & 13.8644917867719 & -5.86449178677194 \tabularnewline
141 & 16 & 14.4811529347381 & 1.51884706526189 \tabularnewline
142 & 15 & 14.8471707423442 & 0.152829257655816 \tabularnewline
143 & 17 & 12.9249350188131 & 4.07506498118689 \tabularnewline
144 & 16 & 15.3545324851204 & 0.645467514879553 \tabularnewline
145 & 10 & 14.5354775240592 & -4.53547752405919 \tabularnewline
146 & 18 & 15.2729679063679 & 2.72703209363208 \tabularnewline
147 & 13 & 15.0370151997097 & -2.03701519970971 \tabularnewline
148 & 16 & 14.8034164516378 & 1.19658354836216 \tabularnewline
149 & 13 & 14.5622041209489 & -1.56220412094892 \tabularnewline
150 & 10 & 14.9902132507904 & -4.99021325079041 \tabularnewline
151 & 15 & 14.5948064843922 & 0.405193515607761 \tabularnewline
152 & 16 & 15.3146740465502 & 0.685325953449797 \tabularnewline
153 & 16 & 16.4890094965522 & -0.489009496552171 \tabularnewline
154 & 14 & 14.0167283962363 & -0.0167283962363217 \tabularnewline
155 & 10 & 14.0151949172672 & -4.01519491726715 \tabularnewline
156 & 17 & 14.8406329567038 & 2.15936704329619 \tabularnewline
157 & 13 & 15.5467163713719 & -2.54671637137193 \tabularnewline
158 & 15 & 14.8129539201865 & 0.187046079813482 \tabularnewline
159 & 16 & 15.5341997190859 & 0.465800280914103 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=98210&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]16[/C][C]15.377428203164[/C][C]0.622571796835987[/C][/ROW]
[ROW][C]2[/C][C]19[/C][C]15.2688848502365[/C][C]3.73111514976348[/C][/ROW]
[ROW][C]3[/C][C]15[/C][C]14.1962588757634[/C][C]0.80374112423664[/C][/ROW]
[ROW][C]4[/C][C]14[/C][C]15.1323110467088[/C][C]-1.13231104670877[/C][/ROW]
[ROW][C]5[/C][C]13[/C][C]14.3462945117006[/C][C]-1.34629451170062[/C][/ROW]
[ROW][C]6[/C][C]19[/C][C]15.5115533094782[/C][C]3.48844669052183[/C][/ROW]
[ROW][C]7[/C][C]15[/C][C]15.0910231365047[/C][C]-0.0910231365047432[/C][/ROW]
[ROW][C]8[/C][C]14[/C][C]14.1956326705673[/C][C]-0.195632670567322[/C][/ROW]
[ROW][C]9[/C][C]15[/C][C]14.9964324073879[/C][C]0.00356759261211682[/C][/ROW]
[ROW][C]10[/C][C]16[/C][C]15.0802466389080[/C][C]0.919753361092036[/C][/ROW]
[ROW][C]11[/C][C]16[/C][C]15.4943721308662[/C][C]0.505627869133822[/C][/ROW]
[ROW][C]12[/C][C]16[/C][C]15.3268390712721[/C][C]0.673160928727901[/C][/ROW]
[ROW][C]13[/C][C]16[/C][C]15.9832996065879[/C][C]0.0167003934120774[/C][/ROW]
[ROW][C]14[/C][C]17[/C][C]14.3358683436863[/C][C]2.66413165631366[/C][/ROW]
[ROW][C]15[/C][C]15[/C][C]14.3022465127492[/C][C]0.697753487250799[/C][/ROW]
[ROW][C]16[/C][C]15[/C][C]14.6967986274768[/C][C]0.303201372523214[/C][/ROW]
[ROW][C]17[/C][C]20[/C][C]14.8080726661258[/C][C]5.1919273338742[/C][/ROW]
[ROW][C]18[/C][C]18[/C][C]13.9952108084499[/C][C]4.00478919155015[/C][/ROW]
[ROW][C]19[/C][C]16[/C][C]15.1218272945677[/C][C]0.878172705432253[/C][/ROW]
[ROW][C]20[/C][C]16[/C][C]15.1368417328180[/C][C]0.86315826718202[/C][/ROW]
[ROW][C]21[/C][C]16[/C][C]15.0798694599226[/C][C]0.920130540077404[/C][/ROW]
[ROW][C]22[/C][C]19[/C][C]15.5125041062708[/C][C]3.48749589372917[/C][/ROW]
[ROW][C]23[/C][C]16[/C][C]15.0421098862061[/C][C]0.957890113793886[/C][/ROW]
[ROW][C]24[/C][C]17[/C][C]16.3538110064017[/C][C]0.646188993598278[/C][/ROW]
[ROW][C]25[/C][C]17[/C][C]15.8292124626895[/C][C]1.17078753731046[/C][/ROW]
[ROW][C]26[/C][C]16[/C][C]14.6648582357191[/C][C]1.33514176428087[/C][/ROW]
[ROW][C]27[/C][C]15[/C][C]14.8373297285330[/C][C]0.162670271466959[/C][/ROW]
[ROW][C]28[/C][C]16[/C][C]15.5693109740363[/C][C]0.430689025963693[/C][/ROW]
[ROW][C]29[/C][C]14[/C][C]14.9004539014515[/C][C]-0.900453901451533[/C][/ROW]
[ROW][C]30[/C][C]15[/C][C]14.7996006164576[/C][C]0.200399383542408[/C][/ROW]
[ROW][C]31[/C][C]12[/C][C]16.0377014398412[/C][C]-4.03770143984122[/C][/ROW]
[ROW][C]32[/C][C]14[/C][C]14.6073112914882[/C][C]-0.607311291488232[/C][/ROW]
[ROW][C]33[/C][C]16[/C][C]13.9732628832358[/C][C]2.02673711676415[/C][/ROW]
[ROW][C]34[/C][C]14[/C][C]14.8023157929464[/C][C]-0.802315792946436[/C][/ROW]
[ROW][C]35[/C][C]7[/C][C]14.0236227821235[/C][C]-7.02362278212351[/C][/ROW]
[ROW][C]36[/C][C]10[/C][C]15.6742754972600[/C][C]-5.67427549725997[/C][/ROW]
[ROW][C]37[/C][C]14[/C][C]15.5286540365397[/C][C]-1.52865403653973[/C][/ROW]
[ROW][C]38[/C][C]16[/C][C]14.5743802826987[/C][C]1.42561971730130[/C][/ROW]
[ROW][C]39[/C][C]16[/C][C]15.0110261256623[/C][C]0.988973874337695[/C][/ROW]
[ROW][C]40[/C][C]16[/C][C]14.6724361725601[/C][C]1.32756382743994[/C][/ROW]
[ROW][C]41[/C][C]14[/C][C]16.0182619796846[/C][C]-2.01826197968461[/C][/ROW]
[ROW][C]42[/C][C]20[/C][C]14.8523582099196[/C][C]5.14764179008043[/C][/ROW]
[ROW][C]43[/C][C]14[/C][C]14.7673567859217[/C][C]-0.767356785921694[/C][/ROW]
[ROW][C]44[/C][C]14[/C][C]14.4112446960756[/C][C]-0.411244696075594[/C][/ROW]
[ROW][C]45[/C][C]11[/C][C]14.6553055310207[/C][C]-3.65530553102069[/C][/ROW]
[ROW][C]46[/C][C]14[/C][C]14.9984467322278[/C][C]-0.99844673222781[/C][/ROW]
[ROW][C]47[/C][C]15[/C][C]15.3209723546876[/C][C]-0.320972354687619[/C][/ROW]
[ROW][C]48[/C][C]16[/C][C]15.2527074964849[/C][C]0.74729250351507[/C][/ROW]
[ROW][C]49[/C][C]14[/C][C]15.5343267145585[/C][C]-1.53432671455850[/C][/ROW]
[ROW][C]50[/C][C]16[/C][C]14.4035063079841[/C][C]1.59649369201594[/C][/ROW]
[ROW][C]51[/C][C]14[/C][C]15.6383904217565[/C][C]-1.63839042175646[/C][/ROW]
[ROW][C]52[/C][C]12[/C][C]15.1279791945909[/C][C]-3.12797919459086[/C][/ROW]
[ROW][C]53[/C][C]16[/C][C]15.9349482386139[/C][C]0.0650517613860827[/C][/ROW]
[ROW][C]54[/C][C]9[/C][C]14.3886856780754[/C][C]-5.38868567807539[/C][/ROW]
[ROW][C]55[/C][C]14[/C][C]13.9858228532050[/C][C]0.0141771467949624[/C][/ROW]
[ROW][C]56[/C][C]16[/C][C]16.0733728526849[/C][C]-0.0733728526849043[/C][/ROW]
[ROW][C]57[/C][C]16[/C][C]15.3396377980656[/C][C]0.660362201934421[/C][/ROW]
[ROW][C]58[/C][C]15[/C][C]14.4599717051683[/C][C]0.540028294831744[/C][/ROW]
[ROW][C]59[/C][C]16[/C][C]15.3368039548818[/C][C]0.663196045118234[/C][/ROW]
[ROW][C]60[/C][C]12[/C][C]14.1034719803858[/C][C]-2.10347198038577[/C][/ROW]
[ROW][C]61[/C][C]16[/C][C]14.8352560514755[/C][C]1.16474394852448[/C][/ROW]
[ROW][C]62[/C][C]16[/C][C]15.0522915601785[/C][C]0.947708439821472[/C][/ROW]
[ROW][C]63[/C][C]14[/C][C]15.2913983228153[/C][C]-1.29139832281525[/C][/ROW]
[ROW][C]64[/C][C]16[/C][C]14.7343590472365[/C][C]1.26564095276351[/C][/ROW]
[ROW][C]65[/C][C]17[/C][C]14.5092090437565[/C][C]2.49079095624346[/C][/ROW]
[ROW][C]66[/C][C]18[/C][C]15.9755830952166[/C][C]2.02441690478340[/C][/ROW]
[ROW][C]67[/C][C]18[/C][C]16.2935199349910[/C][C]1.70648006500895[/C][/ROW]
[ROW][C]68[/C][C]12[/C][C]14.1318680827179[/C][C]-2.1318680827179[/C][/ROW]
[ROW][C]69[/C][C]16[/C][C]14.5773890609484[/C][C]1.42261093905161[/C][/ROW]
[ROW][C]70[/C][C]10[/C][C]14.9691488490518[/C][C]-4.96914884905182[/C][/ROW]
[ROW][C]71[/C][C]14[/C][C]15.9413161873238[/C][C]-1.94131618732378[/C][/ROW]
[ROW][C]72[/C][C]18[/C][C]15.1884634564070[/C][C]2.81153654359298[/C][/ROW]
[ROW][C]73[/C][C]18[/C][C]15.7669184413230[/C][C]2.23308155867696[/C][/ROW]
[ROW][C]74[/C][C]16[/C][C]15.2988838860584[/C][C]0.70111611394156[/C][/ROW]
[ROW][C]75[/C][C]17[/C][C]15.5308592304150[/C][C]1.46914076958496[/C][/ROW]
[ROW][C]76[/C][C]16[/C][C]15.2348691854691[/C][C]0.765130814530894[/C][/ROW]
[ROW][C]77[/C][C]16[/C][C]14.6025490240880[/C][C]1.39745097591204[/C][/ROW]
[ROW][C]78[/C][C]13[/C][C]15.0916190466583[/C][C]-2.09161904665835[/C][/ROW]
[ROW][C]79[/C][C]16[/C][C]15.6351902273082[/C][C]0.364809772691838[/C][/ROW]
[ROW][C]80[/C][C]16[/C][C]15.2699503756012[/C][C]0.73004962439877[/C][/ROW]
[ROW][C]81[/C][C]20[/C][C]15.2988907038860[/C][C]4.70110929611403[/C][/ROW]
[ROW][C]82[/C][C]16[/C][C]15.9915596990592[/C][C]0.00844030094082513[/C][/ROW]
[ROW][C]83[/C][C]15[/C][C]15.1817552301293[/C][C]-0.181755230129346[/C][/ROW]
[ROW][C]84[/C][C]15[/C][C]14.7715941524953[/C][C]0.228405847504681[/C][/ROW]
[ROW][C]85[/C][C]16[/C][C]16.1847912960147[/C][C]-0.184791296014749[/C][/ROW]
[ROW][C]86[/C][C]14[/C][C]15.32874610633[/C][C]-1.32874610633000[/C][/ROW]
[ROW][C]87[/C][C]16[/C][C]14.9172522136361[/C][C]1.08274778636389[/C][/ROW]
[ROW][C]88[/C][C]16[/C][C]15.2097512972237[/C][C]0.790248702776306[/C][/ROW]
[ROW][C]89[/C][C]15[/C][C]14.7675175254820[/C][C]0.232482474518029[/C][/ROW]
[ROW][C]90[/C][C]12[/C][C]15.2055462291260[/C][C]-3.20554622912597[/C][/ROW]
[ROW][C]91[/C][C]17[/C][C]16.0212260606730[/C][C]0.978773939326954[/C][/ROW]
[ROW][C]92[/C][C]16[/C][C]14.9305934008049[/C][C]1.06940659919505[/C][/ROW]
[ROW][C]93[/C][C]15[/C][C]15.2925263780727[/C][C]-0.292526378072747[/C][/ROW]
[ROW][C]94[/C][C]13[/C][C]14.7411258401995[/C][C]-1.74112584019952[/C][/ROW]
[ROW][C]95[/C][C]16[/C][C]15.6287858911462[/C][C]0.371214108853806[/C][/ROW]
[ROW][C]96[/C][C]16[/C][C]15.0476010227839[/C][C]0.952398977216122[/C][/ROW]
[ROW][C]97[/C][C]16[/C][C]16.0902670359112[/C][C]-0.090267035911169[/C][/ROW]
[ROW][C]98[/C][C]16[/C][C]15.2988554029631[/C][C]0.701144597036892[/C][/ROW]
[ROW][C]99[/C][C]14[/C][C]15.1415439899500[/C][C]-1.14154398994997[/C][/ROW]
[ROW][C]100[/C][C]16[/C][C]15.0475289728128[/C][C]0.952471027187227[/C][/ROW]
[ROW][C]101[/C][C]16[/C][C]14.7871535421160[/C][C]1.21284645788398[/C][/ROW]
[ROW][C]102[/C][C]20[/C][C]14.7707560965866[/C][C]5.22924390341344[/C][/ROW]
[ROW][C]103[/C][C]15[/C][C]14.0579321112798[/C][C]0.94206788872024[/C][/ROW]
[ROW][C]104[/C][C]16[/C][C]14.7640238816421[/C][C]1.23597611835787[/C][/ROW]
[ROW][C]105[/C][C]13[/C][C]16.1703347361151[/C][C]-3.17033473611508[/C][/ROW]
[ROW][C]106[/C][C]17[/C][C]14.8199999707771[/C][C]2.18000002922290[/C][/ROW]
[ROW][C]107[/C][C]16[/C][C]14.8692399317325[/C][C]1.13076006826751[/C][/ROW]
[ROW][C]108[/C][C]16[/C][C]14.5702869988116[/C][C]1.42971300118842[/C][/ROW]
[ROW][C]109[/C][C]12[/C][C]14.8878353458285[/C][C]-2.88783534582852[/C][/ROW]
[ROW][C]110[/C][C]16[/C][C]15.0832311921507[/C][C]0.916768807849265[/C][/ROW]
[ROW][C]111[/C][C]16[/C][C]14.7780842216846[/C][C]1.22191577831542[/C][/ROW]
[ROW][C]112[/C][C]17[/C][C]16.2177330465255[/C][C]0.782266953474543[/C][/ROW]
[ROW][C]113[/C][C]13[/C][C]14.5977670489683[/C][C]-1.59776704896826[/C][/ROW]
[ROW][C]114[/C][C]12[/C][C]13.1531697933760[/C][C]-1.15316979337598[/C][/ROW]
[ROW][C]115[/C][C]18[/C][C]13.4897260171415[/C][C]4.51027398285853[/C][/ROW]
[ROW][C]116[/C][C]14[/C][C]15.0160995294844[/C][C]-1.01609952948437[/C][/ROW]
[ROW][C]117[/C][C]14[/C][C]13.3791892191095[/C][C]0.620810780890483[/C][/ROW]
[ROW][C]118[/C][C]13[/C][C]14.4865574749015[/C][C]-1.48655747490150[/C][/ROW]
[ROW][C]119[/C][C]16[/C][C]15.3461460161102[/C][C]0.65385398388977[/C][/ROW]
[ROW][C]120[/C][C]13[/C][C]14.7420596185223[/C][C]-1.74205961852228[/C][/ROW]
[ROW][C]121[/C][C]16[/C][C]15.4710219629883[/C][C]0.528978037011681[/C][/ROW]
[ROW][C]122[/C][C]13[/C][C]14.0032970962407[/C][C]-1.00329709624071[/C][/ROW]
[ROW][C]123[/C][C]16[/C][C]14.040828414969[/C][C]1.95917158503099[/C][/ROW]
[ROW][C]124[/C][C]15[/C][C]15.3555181860132[/C][C]-0.355518186013225[/C][/ROW]
[ROW][C]125[/C][C]16[/C][C]15.240391639993[/C][C]0.759608360007[/C][/ROW]
[ROW][C]126[/C][C]15[/C][C]14.9410078052256[/C][C]0.0589921947743524[/C][/ROW]
[ROW][C]127[/C][C]17[/C][C]14.9247826265099[/C][C]2.07521737349013[/C][/ROW]
[ROW][C]128[/C][C]15[/C][C]15.2154582522641[/C][C]-0.215458252264068[/C][/ROW]
[ROW][C]129[/C][C]12[/C][C]15.2859884400631[/C][C]-3.28598844006306[/C][/ROW]
[ROW][C]130[/C][C]16[/C][C]15.9344603599183[/C][C]0.0655396400816665[/C][/ROW]
[ROW][C]131[/C][C]10[/C][C]14.1654807098006[/C][C]-4.16548070980057[/C][/ROW]
[ROW][C]132[/C][C]16[/C][C]15.0582427102926[/C][C]0.941757289707381[/C][/ROW]
[ROW][C]133[/C][C]12[/C][C]15.1900290208836[/C][C]-3.19002902088364[/C][/ROW]
[ROW][C]134[/C][C]14[/C][C]15.3337666935367[/C][C]-1.33376669353674[/C][/ROW]
[ROW][C]135[/C][C]15[/C][C]15.7562983412273[/C][C]-0.756298341227266[/C][/ROW]
[ROW][C]136[/C][C]13[/C][C]14.6087468770138[/C][C]-1.60874687701377[/C][/ROW]
[ROW][C]137[/C][C]15[/C][C]14.6517841457615[/C][C]0.348215854238486[/C][/ROW]
[ROW][C]138[/C][C]11[/C][C]13.4153204682324[/C][C]-2.41532046823244[/C][/ROW]
[ROW][C]139[/C][C]12[/C][C]15.058595555758[/C][C]-3.05859555575799[/C][/ROW]
[ROW][C]140[/C][C]8[/C][C]13.8644917867719[/C][C]-5.86449178677194[/C][/ROW]
[ROW][C]141[/C][C]16[/C][C]14.4811529347381[/C][C]1.51884706526189[/C][/ROW]
[ROW][C]142[/C][C]15[/C][C]14.8471707423442[/C][C]0.152829257655816[/C][/ROW]
[ROW][C]143[/C][C]17[/C][C]12.9249350188131[/C][C]4.07506498118689[/C][/ROW]
[ROW][C]144[/C][C]16[/C][C]15.3545324851204[/C][C]0.645467514879553[/C][/ROW]
[ROW][C]145[/C][C]10[/C][C]14.5354775240592[/C][C]-4.53547752405919[/C][/ROW]
[ROW][C]146[/C][C]18[/C][C]15.2729679063679[/C][C]2.72703209363208[/C][/ROW]
[ROW][C]147[/C][C]13[/C][C]15.0370151997097[/C][C]-2.03701519970971[/C][/ROW]
[ROW][C]148[/C][C]16[/C][C]14.8034164516378[/C][C]1.19658354836216[/C][/ROW]
[ROW][C]149[/C][C]13[/C][C]14.5622041209489[/C][C]-1.56220412094892[/C][/ROW]
[ROW][C]150[/C][C]10[/C][C]14.9902132507904[/C][C]-4.99021325079041[/C][/ROW]
[ROW][C]151[/C][C]15[/C][C]14.5948064843922[/C][C]0.405193515607761[/C][/ROW]
[ROW][C]152[/C][C]16[/C][C]15.3146740465502[/C][C]0.685325953449797[/C][/ROW]
[ROW][C]153[/C][C]16[/C][C]16.4890094965522[/C][C]-0.489009496552171[/C][/ROW]
[ROW][C]154[/C][C]14[/C][C]14.0167283962363[/C][C]-0.0167283962363217[/C][/ROW]
[ROW][C]155[/C][C]10[/C][C]14.0151949172672[/C][C]-4.01519491726715[/C][/ROW]
[ROW][C]156[/C][C]17[/C][C]14.8406329567038[/C][C]2.15936704329619[/C][/ROW]
[ROW][C]157[/C][C]13[/C][C]15.5467163713719[/C][C]-2.54671637137193[/C][/ROW]
[ROW][C]158[/C][C]15[/C][C]14.8129539201865[/C][C]0.187046079813482[/C][/ROW]
[ROW][C]159[/C][C]16[/C][C]15.5341997190859[/C][C]0.465800280914103[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=98210&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=98210&T=4

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


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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11615.3774282031640.622571796835987
21915.26888485023653.73111514976348
31514.19625887576340.80374112423664
41415.1323110467088-1.13231104670877
51314.3462945117006-1.34629451170062
61915.51155330947823.48844669052183
71515.0910231365047-0.0910231365047432
81414.1956326705673-0.195632670567322
91514.99643240738790.00356759261211682
101615.08024663890800.919753361092036
111615.49437213086620.505627869133822
121615.32683907127210.673160928727901
131615.98329960658790.0167003934120774
141714.33586834368632.66413165631366
151514.30224651274920.697753487250799
161514.69679862747680.303201372523214
172014.80807266612585.1919273338742
181813.99521080844994.00478919155015
191615.12182729456770.878172705432253
201615.13684173281800.86315826718202
211615.07986945992260.920130540077404
221915.51250410627083.48749589372917
231615.04210988620610.957890113793886
241716.35381100640170.646188993598278
251715.82921246268951.17078753731046
261614.66485823571911.33514176428087
271514.83732972853300.162670271466959
281615.56931097403630.430689025963693
291414.9004539014515-0.900453901451533
301514.79960061645760.200399383542408
311216.0377014398412-4.03770143984122
321414.6073112914882-0.607311291488232
331613.97326288323582.02673711676415
341414.8023157929464-0.802315792946436
35714.0236227821235-7.02362278212351
361015.6742754972600-5.67427549725997
371415.5286540365397-1.52865403653973
381614.57438028269871.42561971730130
391615.01102612566230.988973874337695
401614.67243617256011.32756382743994
411416.0182619796846-2.01826197968461
422014.85235820991965.14764179008043
431414.7673567859217-0.767356785921694
441414.4112446960756-0.411244696075594
451114.6553055310207-3.65530553102069
461414.9984467322278-0.99844673222781
471515.3209723546876-0.320972354687619
481615.25270749648490.74729250351507
491415.5343267145585-1.53432671455850
501614.40350630798411.59649369201594
511415.6383904217565-1.63839042175646
521215.1279791945909-3.12797919459086
531615.93494823861390.0650517613860827
54914.3886856780754-5.38868567807539
551413.98582285320500.0141771467949624
561616.0733728526849-0.0733728526849043
571615.33963779806560.660362201934421
581514.45997170516830.540028294831744
591615.33680395488180.663196045118234
601214.1034719803858-2.10347198038577
611614.83525605147551.16474394852448
621615.05229156017850.947708439821472
631415.2913983228153-1.29139832281525
641614.73435904723651.26564095276351
651714.50920904375652.49079095624346
661815.97558309521662.02441690478340
671816.29351993499101.70648006500895
681214.1318680827179-2.1318680827179
691614.57738906094841.42261093905161
701014.9691488490518-4.96914884905182
711415.9413161873238-1.94131618732378
721815.18846345640702.81153654359298
731815.76691844132302.23308155867696
741615.29888388605840.70111611394156
751715.53085923041501.46914076958496
761615.23486918546910.765130814530894
771614.60254902408801.39745097591204
781315.0916190466583-2.09161904665835
791615.63519022730820.364809772691838
801615.26995037560120.73004962439877
812015.29889070388604.70110929611403
821615.99155969905920.00844030094082513
831515.1817552301293-0.181755230129346
841514.77159415249530.228405847504681
851616.1847912960147-0.184791296014749
861415.32874610633-1.32874610633000
871614.91725221363611.08274778636389
881615.20975129722370.790248702776306
891514.76751752548200.232482474518029
901215.2055462291260-3.20554622912597
911716.02122606067300.978773939326954
921614.93059340080491.06940659919505
931515.2925263780727-0.292526378072747
941314.7411258401995-1.74112584019952
951615.62878589114620.371214108853806
961615.04760102278390.952398977216122
971616.0902670359112-0.090267035911169
981615.29885540296310.701144597036892
991415.1415439899500-1.14154398994997
1001615.04752897281280.952471027187227
1011614.78715354211601.21284645788398
1022014.77075609658665.22924390341344
1031514.05793211127980.94206788872024
1041614.76402388164211.23597611835787
1051316.1703347361151-3.17033473611508
1061714.81999997077712.18000002922290
1071614.86923993173251.13076006826751
1081614.57028699881161.42971300118842
1091214.8878353458285-2.88783534582852
1101615.08323119215070.916768807849265
1111614.77808422168461.22191577831542
1121716.21773304652550.782266953474543
1131314.5977670489683-1.59776704896826
1141213.1531697933760-1.15316979337598
1151813.48972601714154.51027398285853
1161415.0160995294844-1.01609952948437
1171413.37918921910950.620810780890483
1181314.4865574749015-1.48655747490150
1191615.34614601611020.65385398388977
1201314.7420596185223-1.74205961852228
1211615.47102196298830.528978037011681
1221314.0032970962407-1.00329709624071
1231614.0408284149691.95917158503099
1241515.3555181860132-0.355518186013225
1251615.2403916399930.759608360007
1261514.94100780522560.0589921947743524
1271714.92478262650992.07521737349013
1281515.2154582522641-0.215458252264068
1291215.2859884400631-3.28598844006306
1301615.93446035991830.0655396400816665
1311014.1654807098006-4.16548070980057
1321615.05824271029260.941757289707381
1331215.1900290208836-3.19002902088364
1341415.3337666935367-1.33376669353674
1351515.7562983412273-0.756298341227266
1361314.6087468770138-1.60874687701377
1371514.65178414576150.348215854238486
1381113.4153204682324-2.41532046823244
1391215.058595555758-3.05859555575799
140813.8644917867719-5.86449178677194
1411614.48115293473811.51884706526189
1421514.84717074234420.152829257655816
1431712.92493501881314.07506498118689
1441615.35453248512040.645467514879553
1451014.5354775240592-4.53547752405919
1461815.27296790636792.72703209363208
1471315.0370151997097-2.03701519970971
1481614.80341645163781.19658354836216
1491314.5622041209489-1.56220412094892
1501014.9902132507904-4.99021325079041
1511514.59480648439220.405193515607761
1521615.31467404655020.685325953449797
1531616.4890094965522-0.489009496552171
1541414.0167283962363-0.0167283962363217
1551014.0151949172672-4.01519491726715
1561714.84063295670382.15936704329619
1571315.5467163713719-2.54671637137193
1581514.81295392018650.187046079813482
1591615.53419971908590.465800280914103






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
100.4885564177155880.9771128354311770.511443582284411
110.3299624246123570.6599248492247150.670037575387643
120.2449547132643100.4899094265286210.75504528673569
130.1654462290310460.3308924580620920.834553770968954
140.3607045146342530.7214090292685070.639295485365747
150.2594132504584490.5188265009168990.74058674954155
160.1791032871774240.3582065743548470.820896712822576
170.3534655511567410.7069311023134820.646534448843259
180.3980761275001650.796152255000330.601923872499835
190.3245869500546110.6491739001092220.675413049945389
200.2559108473373150.511821694674630.744089152662685
210.1958439070682080.3916878141364170.804156092931792
220.2532996404906570.5065992809813150.746700359509343
230.1953711816027380.3907423632054770.804628818397262
240.1672580035811730.3345160071623470.832741996418827
250.1295456074315740.2590912148631470.870454392568426
260.09892227424463790.1978445484892760.901077725755362
270.07458325899695230.1491665179939050.925416741003048
280.05378588344680160.1075717668936030.946214116553198
290.0491834222824240.0983668445648480.950816577717576
300.03534037048799670.07068074097599340.964659629512003
310.1321687666053200.2643375332106410.86783123339468
320.1065275609373180.2130551218746350.893472439062682
330.0832339135551060.1664678271102120.916766086444894
340.06810922003810220.1362184400762040.931890779961898
350.5936980832121790.8126038335756430.406301916787821
360.8256672926781990.3486654146436030.174332707321801
370.8008456913954420.3983086172091170.199154308604558
380.7657035512930740.4685928974138510.234296448706926
390.735752148380480.5284957032390390.264247851619519
400.7184218855481840.5631562289036320.281578114451816
410.6872037232156760.6255925535686480.312796276784324
420.8446395828666870.3107208342666250.155360417133313
430.8506183824832360.2987632350335270.149381617516764
440.819296385924710.361407228150580.18070361407529
450.876732792034720.2465344159305590.123267207965279
460.8547171953456960.2905656093086080.145282804654304
470.8248918523013150.350216295397370.175108147698685
480.8022585175986530.3954829648026950.197741482401347
490.7873314259876530.4253371480246930.212668574012347
500.7652854669723740.4694290660552520.234714533027626
510.7420669443294380.5158661113411250.257933055670562
520.7865065720572390.4269868558855220.213493427942761
530.7494617777498270.5010764445003460.250538222250173
540.9164394675097710.1671210649804580.0835605324902288
550.9033232399969050.1933535200061900.0966767600030949
560.881117968389370.237764063221260.11888203161063
570.8598944714034940.2802110571930110.140105528596506
580.8322689447475290.3354621105049430.167731055252471
590.8104136994803580.3791726010392840.189586300519642
600.8161993424732880.3676013150534230.183800657526712
610.7925141501193880.4149716997612240.207485849880612
620.7626174712624550.4747650574750890.237382528737545
630.7353441887441650.529311622511670.264655811255835
640.7108288242107250.5783423515785490.289171175789275
650.7202670414412440.5594659171175130.279732958558756
660.719200072116880.5615998557662390.280799927883119
670.6984237357654470.6031525284691050.301576264234553
680.69650083864790.6069983227041990.303499161352099
690.6726308326637630.6547383346724740.327369167336237
700.8180901922696890.3638196154606230.181909807730311
710.8139437289409540.3721125421180920.186056271059046
720.830091133172270.3398177336554610.169908866827730
730.8283690056599640.3432619886800730.171630994340036
740.7998979398680010.4002041202639980.200102060131999
750.7789532638823480.4420934722353030.221046736117652
760.7469457480193250.506108503961350.253054251980675
770.7221774634711150.5556450730577690.277822536528885
780.7185317382474020.5629365235051960.281468261752598
790.6788238654900280.6423522690199430.321176134509972
800.639432472177950.72113505564410.36056752782205
810.7798514194337080.4402971611325830.220148580566292
820.7449158028825770.5101683942348460.255084197117423
830.7067407008172220.5865185983655550.293259299182778
840.6662024371296210.6675951257407580.333797562870379
850.6224807587256190.7550384825487620.377519241274381
860.5937447723615770.8125104552768450.406255227638423
870.5583758779231860.8832482441536290.441624122076814
880.5199884259516280.9600231480967440.480011574048372
890.4761741008576890.9523482017153770.523825899142311
900.5250792032131060.9498415935737880.474920796786894
910.4898849412194150.979769882438830.510115058780585
920.4555022835212370.9110045670424740.544497716478763
930.4089229968706660.8178459937413310.591077003129334
940.3920091198185210.7840182396370420.607990880181479
950.3534645185363530.7069290370727060.646535481463647
960.3231914599684260.6463829199368510.676808540031574
970.2829818799569850.5659637599139710.717018120043015
980.2528511527892220.5057023055784440.747148847210778
990.2237070329885890.4474140659771770.776292967011411
1000.1961272533729900.3922545067459800.80387274662701
1010.1766934291315540.3533868582631070.823306570868446
1020.3597770434804550.7195540869609110.640222956519545
1030.3263236222300010.6526472444600020.673676377769999
1040.3107931921534890.6215863843069790.68920680784651
1050.3310572753105860.6621145506211720.668942724689414
1060.3220813856265160.6441627712530310.677918614373484
1070.2951562553321090.5903125106642190.70484374466789
1080.2826324114389680.5652648228779370.717367588561032
1090.3036163521645210.6072327043290430.696383647835479
1100.2701660113651030.5403320227302070.729833988634897
1110.2594058987931140.5188117975862280.740594101206886
1120.2626563865075630.5253127730151250.737343613492437
1130.2338853666436560.4677707332873120.766114633356344
1140.2095126404433920.4190252808867850.790487359556608
1150.4281821985145530.8563643970291050.571817801485447
1160.3799955276172880.7599910552345770.620004472382712
1170.3541685034470410.7083370068940810.645831496552959
1180.3187143063412840.6374286126825680.681285693658716
1190.2769154746339580.5538309492679160.723084525366042
1200.2783561253796730.5567122507593450.721643874620327
1210.2353386165215560.4706772330431110.764661383478444
1220.2016719133285530.4033438266571050.798328086671448
1230.2119201581320450.423840316264090.788079841867955
1240.1897258531175460.3794517062350930.810274146882454
1250.1537897323166920.3075794646333840.846210267683308
1260.1232407411483660.2464814822967320.876759258851634
1270.1638892778540820.3277785557081630.836110722145919
1280.1489764751245140.2979529502490280.851023524875486
1290.1367579788925670.2735159577851340.863242021107433
1300.1057135538486200.2114271076972400.89428644615138
1310.1278039803034340.2556079606068680.872196019696566
1320.1292801387533740.2585602775067480.870719861246626
1330.1142094328395980.2284188656791970.885790567160402
1340.08650454972829740.1730090994565950.913495450271703
1350.06457292410761090.1291458482152220.935427075892389
1360.05166844132109050.1033368826421810.94833155867891
1370.03561593016910330.07123186033820670.964384069830897
1380.03852422606614480.07704845213228960.961475773933855
1390.02856213303508410.05712426607016830.971437866964916
1400.1617791875442350.323558375088470.838220812455765
1410.1273764405742700.2547528811485390.87262355942573
1420.0877779713165090.1755559426330180.912222028683491
1430.2540377183719090.5080754367438180.745962281628091
1440.2095677485176250.4191354970352510.790432251482375
1450.2933376209085690.5866752418171380.706662379091431
1460.2266177756719760.4532355513439510.773382224328024
1470.1492242026279370.2984484052558740.850775797372063
1480.1086858275847110.2173716551694210.89131417241529
1490.06775264458974720.1355052891794940.932247355410253

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
10 & 0.488556417715588 & 0.977112835431177 & 0.511443582284411 \tabularnewline
11 & 0.329962424612357 & 0.659924849224715 & 0.670037575387643 \tabularnewline
12 & 0.244954713264310 & 0.489909426528621 & 0.75504528673569 \tabularnewline
13 & 0.165446229031046 & 0.330892458062092 & 0.834553770968954 \tabularnewline
14 & 0.360704514634253 & 0.721409029268507 & 0.639295485365747 \tabularnewline
15 & 0.259413250458449 & 0.518826500916899 & 0.74058674954155 \tabularnewline
16 & 0.179103287177424 & 0.358206574354847 & 0.820896712822576 \tabularnewline
17 & 0.353465551156741 & 0.706931102313482 & 0.646534448843259 \tabularnewline
18 & 0.398076127500165 & 0.79615225500033 & 0.601923872499835 \tabularnewline
19 & 0.324586950054611 & 0.649173900109222 & 0.675413049945389 \tabularnewline
20 & 0.255910847337315 & 0.51182169467463 & 0.744089152662685 \tabularnewline
21 & 0.195843907068208 & 0.391687814136417 & 0.804156092931792 \tabularnewline
22 & 0.253299640490657 & 0.506599280981315 & 0.746700359509343 \tabularnewline
23 & 0.195371181602738 & 0.390742363205477 & 0.804628818397262 \tabularnewline
24 & 0.167258003581173 & 0.334516007162347 & 0.832741996418827 \tabularnewline
25 & 0.129545607431574 & 0.259091214863147 & 0.870454392568426 \tabularnewline
26 & 0.0989222742446379 & 0.197844548489276 & 0.901077725755362 \tabularnewline
27 & 0.0745832589969523 & 0.149166517993905 & 0.925416741003048 \tabularnewline
28 & 0.0537858834468016 & 0.107571766893603 & 0.946214116553198 \tabularnewline
29 & 0.049183422282424 & 0.098366844564848 & 0.950816577717576 \tabularnewline
30 & 0.0353403704879967 & 0.0706807409759934 & 0.964659629512003 \tabularnewline
31 & 0.132168766605320 & 0.264337533210641 & 0.86783123339468 \tabularnewline
32 & 0.106527560937318 & 0.213055121874635 & 0.893472439062682 \tabularnewline
33 & 0.083233913555106 & 0.166467827110212 & 0.916766086444894 \tabularnewline
34 & 0.0681092200381022 & 0.136218440076204 & 0.931890779961898 \tabularnewline
35 & 0.593698083212179 & 0.812603833575643 & 0.406301916787821 \tabularnewline
36 & 0.825667292678199 & 0.348665414643603 & 0.174332707321801 \tabularnewline
37 & 0.800845691395442 & 0.398308617209117 & 0.199154308604558 \tabularnewline
38 & 0.765703551293074 & 0.468592897413851 & 0.234296448706926 \tabularnewline
39 & 0.73575214838048 & 0.528495703239039 & 0.264247851619519 \tabularnewline
40 & 0.718421885548184 & 0.563156228903632 & 0.281578114451816 \tabularnewline
41 & 0.687203723215676 & 0.625592553568648 & 0.312796276784324 \tabularnewline
42 & 0.844639582866687 & 0.310720834266625 & 0.155360417133313 \tabularnewline
43 & 0.850618382483236 & 0.298763235033527 & 0.149381617516764 \tabularnewline
44 & 0.81929638592471 & 0.36140722815058 & 0.18070361407529 \tabularnewline
45 & 0.87673279203472 & 0.246534415930559 & 0.123267207965279 \tabularnewline
46 & 0.854717195345696 & 0.290565609308608 & 0.145282804654304 \tabularnewline
47 & 0.824891852301315 & 0.35021629539737 & 0.175108147698685 \tabularnewline
48 & 0.802258517598653 & 0.395482964802695 & 0.197741482401347 \tabularnewline
49 & 0.787331425987653 & 0.425337148024693 & 0.212668574012347 \tabularnewline
50 & 0.765285466972374 & 0.469429066055252 & 0.234714533027626 \tabularnewline
51 & 0.742066944329438 & 0.515866111341125 & 0.257933055670562 \tabularnewline
52 & 0.786506572057239 & 0.426986855885522 & 0.213493427942761 \tabularnewline
53 & 0.749461777749827 & 0.501076444500346 & 0.250538222250173 \tabularnewline
54 & 0.916439467509771 & 0.167121064980458 & 0.0835605324902288 \tabularnewline
55 & 0.903323239996905 & 0.193353520006190 & 0.0966767600030949 \tabularnewline
56 & 0.88111796838937 & 0.23776406322126 & 0.11888203161063 \tabularnewline
57 & 0.859894471403494 & 0.280211057193011 & 0.140105528596506 \tabularnewline
58 & 0.832268944747529 & 0.335462110504943 & 0.167731055252471 \tabularnewline
59 & 0.810413699480358 & 0.379172601039284 & 0.189586300519642 \tabularnewline
60 & 0.816199342473288 & 0.367601315053423 & 0.183800657526712 \tabularnewline
61 & 0.792514150119388 & 0.414971699761224 & 0.207485849880612 \tabularnewline
62 & 0.762617471262455 & 0.474765057475089 & 0.237382528737545 \tabularnewline
63 & 0.735344188744165 & 0.52931162251167 & 0.264655811255835 \tabularnewline
64 & 0.710828824210725 & 0.578342351578549 & 0.289171175789275 \tabularnewline
65 & 0.720267041441244 & 0.559465917117513 & 0.279732958558756 \tabularnewline
66 & 0.71920007211688 & 0.561599855766239 & 0.280799927883119 \tabularnewline
67 & 0.698423735765447 & 0.603152528469105 & 0.301576264234553 \tabularnewline
68 & 0.6965008386479 & 0.606998322704199 & 0.303499161352099 \tabularnewline
69 & 0.672630832663763 & 0.654738334672474 & 0.327369167336237 \tabularnewline
70 & 0.818090192269689 & 0.363819615460623 & 0.181909807730311 \tabularnewline
71 & 0.813943728940954 & 0.372112542118092 & 0.186056271059046 \tabularnewline
72 & 0.83009113317227 & 0.339817733655461 & 0.169908866827730 \tabularnewline
73 & 0.828369005659964 & 0.343261988680073 & 0.171630994340036 \tabularnewline
74 & 0.799897939868001 & 0.400204120263998 & 0.200102060131999 \tabularnewline
75 & 0.778953263882348 & 0.442093472235303 & 0.221046736117652 \tabularnewline
76 & 0.746945748019325 & 0.50610850396135 & 0.253054251980675 \tabularnewline
77 & 0.722177463471115 & 0.555645073057769 & 0.277822536528885 \tabularnewline
78 & 0.718531738247402 & 0.562936523505196 & 0.281468261752598 \tabularnewline
79 & 0.678823865490028 & 0.642352269019943 & 0.321176134509972 \tabularnewline
80 & 0.63943247217795 & 0.7211350556441 & 0.36056752782205 \tabularnewline
81 & 0.779851419433708 & 0.440297161132583 & 0.220148580566292 \tabularnewline
82 & 0.744915802882577 & 0.510168394234846 & 0.255084197117423 \tabularnewline
83 & 0.706740700817222 & 0.586518598365555 & 0.293259299182778 \tabularnewline
84 & 0.666202437129621 & 0.667595125740758 & 0.333797562870379 \tabularnewline
85 & 0.622480758725619 & 0.755038482548762 & 0.377519241274381 \tabularnewline
86 & 0.593744772361577 & 0.812510455276845 & 0.406255227638423 \tabularnewline
87 & 0.558375877923186 & 0.883248244153629 & 0.441624122076814 \tabularnewline
88 & 0.519988425951628 & 0.960023148096744 & 0.480011574048372 \tabularnewline
89 & 0.476174100857689 & 0.952348201715377 & 0.523825899142311 \tabularnewline
90 & 0.525079203213106 & 0.949841593573788 & 0.474920796786894 \tabularnewline
91 & 0.489884941219415 & 0.97976988243883 & 0.510115058780585 \tabularnewline
92 & 0.455502283521237 & 0.911004567042474 & 0.544497716478763 \tabularnewline
93 & 0.408922996870666 & 0.817845993741331 & 0.591077003129334 \tabularnewline
94 & 0.392009119818521 & 0.784018239637042 & 0.607990880181479 \tabularnewline
95 & 0.353464518536353 & 0.706929037072706 & 0.646535481463647 \tabularnewline
96 & 0.323191459968426 & 0.646382919936851 & 0.676808540031574 \tabularnewline
97 & 0.282981879956985 & 0.565963759913971 & 0.717018120043015 \tabularnewline
98 & 0.252851152789222 & 0.505702305578444 & 0.747148847210778 \tabularnewline
99 & 0.223707032988589 & 0.447414065977177 & 0.776292967011411 \tabularnewline
100 & 0.196127253372990 & 0.392254506745980 & 0.80387274662701 \tabularnewline
101 & 0.176693429131554 & 0.353386858263107 & 0.823306570868446 \tabularnewline
102 & 0.359777043480455 & 0.719554086960911 & 0.640222956519545 \tabularnewline
103 & 0.326323622230001 & 0.652647244460002 & 0.673676377769999 \tabularnewline
104 & 0.310793192153489 & 0.621586384306979 & 0.68920680784651 \tabularnewline
105 & 0.331057275310586 & 0.662114550621172 & 0.668942724689414 \tabularnewline
106 & 0.322081385626516 & 0.644162771253031 & 0.677918614373484 \tabularnewline
107 & 0.295156255332109 & 0.590312510664219 & 0.70484374466789 \tabularnewline
108 & 0.282632411438968 & 0.565264822877937 & 0.717367588561032 \tabularnewline
109 & 0.303616352164521 & 0.607232704329043 & 0.696383647835479 \tabularnewline
110 & 0.270166011365103 & 0.540332022730207 & 0.729833988634897 \tabularnewline
111 & 0.259405898793114 & 0.518811797586228 & 0.740594101206886 \tabularnewline
112 & 0.262656386507563 & 0.525312773015125 & 0.737343613492437 \tabularnewline
113 & 0.233885366643656 & 0.467770733287312 & 0.766114633356344 \tabularnewline
114 & 0.209512640443392 & 0.419025280886785 & 0.790487359556608 \tabularnewline
115 & 0.428182198514553 & 0.856364397029105 & 0.571817801485447 \tabularnewline
116 & 0.379995527617288 & 0.759991055234577 & 0.620004472382712 \tabularnewline
117 & 0.354168503447041 & 0.708337006894081 & 0.645831496552959 \tabularnewline
118 & 0.318714306341284 & 0.637428612682568 & 0.681285693658716 \tabularnewline
119 & 0.276915474633958 & 0.553830949267916 & 0.723084525366042 \tabularnewline
120 & 0.278356125379673 & 0.556712250759345 & 0.721643874620327 \tabularnewline
121 & 0.235338616521556 & 0.470677233043111 & 0.764661383478444 \tabularnewline
122 & 0.201671913328553 & 0.403343826657105 & 0.798328086671448 \tabularnewline
123 & 0.211920158132045 & 0.42384031626409 & 0.788079841867955 \tabularnewline
124 & 0.189725853117546 & 0.379451706235093 & 0.810274146882454 \tabularnewline
125 & 0.153789732316692 & 0.307579464633384 & 0.846210267683308 \tabularnewline
126 & 0.123240741148366 & 0.246481482296732 & 0.876759258851634 \tabularnewline
127 & 0.163889277854082 & 0.327778555708163 & 0.836110722145919 \tabularnewline
128 & 0.148976475124514 & 0.297952950249028 & 0.851023524875486 \tabularnewline
129 & 0.136757978892567 & 0.273515957785134 & 0.863242021107433 \tabularnewline
130 & 0.105713553848620 & 0.211427107697240 & 0.89428644615138 \tabularnewline
131 & 0.127803980303434 & 0.255607960606868 & 0.872196019696566 \tabularnewline
132 & 0.129280138753374 & 0.258560277506748 & 0.870719861246626 \tabularnewline
133 & 0.114209432839598 & 0.228418865679197 & 0.885790567160402 \tabularnewline
134 & 0.0865045497282974 & 0.173009099456595 & 0.913495450271703 \tabularnewline
135 & 0.0645729241076109 & 0.129145848215222 & 0.935427075892389 \tabularnewline
136 & 0.0516684413210905 & 0.103336882642181 & 0.94833155867891 \tabularnewline
137 & 0.0356159301691033 & 0.0712318603382067 & 0.964384069830897 \tabularnewline
138 & 0.0385242260661448 & 0.0770484521322896 & 0.961475773933855 \tabularnewline
139 & 0.0285621330350841 & 0.0571242660701683 & 0.971437866964916 \tabularnewline
140 & 0.161779187544235 & 0.32355837508847 & 0.838220812455765 \tabularnewline
141 & 0.127376440574270 & 0.254752881148539 & 0.87262355942573 \tabularnewline
142 & 0.087777971316509 & 0.175555942633018 & 0.912222028683491 \tabularnewline
143 & 0.254037718371909 & 0.508075436743818 & 0.745962281628091 \tabularnewline
144 & 0.209567748517625 & 0.419135497035251 & 0.790432251482375 \tabularnewline
145 & 0.293337620908569 & 0.586675241817138 & 0.706662379091431 \tabularnewline
146 & 0.226617775671976 & 0.453235551343951 & 0.773382224328024 \tabularnewline
147 & 0.149224202627937 & 0.298448405255874 & 0.850775797372063 \tabularnewline
148 & 0.108685827584711 & 0.217371655169421 & 0.89131417241529 \tabularnewline
149 & 0.0677526445897472 & 0.135505289179494 & 0.932247355410253 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=98210&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]10[/C][C]0.488556417715588[/C][C]0.977112835431177[/C][C]0.511443582284411[/C][/ROW]
[ROW][C]11[/C][C]0.329962424612357[/C][C]0.659924849224715[/C][C]0.670037575387643[/C][/ROW]
[ROW][C]12[/C][C]0.244954713264310[/C][C]0.489909426528621[/C][C]0.75504528673569[/C][/ROW]
[ROW][C]13[/C][C]0.165446229031046[/C][C]0.330892458062092[/C][C]0.834553770968954[/C][/ROW]
[ROW][C]14[/C][C]0.360704514634253[/C][C]0.721409029268507[/C][C]0.639295485365747[/C][/ROW]
[ROW][C]15[/C][C]0.259413250458449[/C][C]0.518826500916899[/C][C]0.74058674954155[/C][/ROW]
[ROW][C]16[/C][C]0.179103287177424[/C][C]0.358206574354847[/C][C]0.820896712822576[/C][/ROW]
[ROW][C]17[/C][C]0.353465551156741[/C][C]0.706931102313482[/C][C]0.646534448843259[/C][/ROW]
[ROW][C]18[/C][C]0.398076127500165[/C][C]0.79615225500033[/C][C]0.601923872499835[/C][/ROW]
[ROW][C]19[/C][C]0.324586950054611[/C][C]0.649173900109222[/C][C]0.675413049945389[/C][/ROW]
[ROW][C]20[/C][C]0.255910847337315[/C][C]0.51182169467463[/C][C]0.744089152662685[/C][/ROW]
[ROW][C]21[/C][C]0.195843907068208[/C][C]0.391687814136417[/C][C]0.804156092931792[/C][/ROW]
[ROW][C]22[/C][C]0.253299640490657[/C][C]0.506599280981315[/C][C]0.746700359509343[/C][/ROW]
[ROW][C]23[/C][C]0.195371181602738[/C][C]0.390742363205477[/C][C]0.804628818397262[/C][/ROW]
[ROW][C]24[/C][C]0.167258003581173[/C][C]0.334516007162347[/C][C]0.832741996418827[/C][/ROW]
[ROW][C]25[/C][C]0.129545607431574[/C][C]0.259091214863147[/C][C]0.870454392568426[/C][/ROW]
[ROW][C]26[/C][C]0.0989222742446379[/C][C]0.197844548489276[/C][C]0.901077725755362[/C][/ROW]
[ROW][C]27[/C][C]0.0745832589969523[/C][C]0.149166517993905[/C][C]0.925416741003048[/C][/ROW]
[ROW][C]28[/C][C]0.0537858834468016[/C][C]0.107571766893603[/C][C]0.946214116553198[/C][/ROW]
[ROW][C]29[/C][C]0.049183422282424[/C][C]0.098366844564848[/C][C]0.950816577717576[/C][/ROW]
[ROW][C]30[/C][C]0.0353403704879967[/C][C]0.0706807409759934[/C][C]0.964659629512003[/C][/ROW]
[ROW][C]31[/C][C]0.132168766605320[/C][C]0.264337533210641[/C][C]0.86783123339468[/C][/ROW]
[ROW][C]32[/C][C]0.106527560937318[/C][C]0.213055121874635[/C][C]0.893472439062682[/C][/ROW]
[ROW][C]33[/C][C]0.083233913555106[/C][C]0.166467827110212[/C][C]0.916766086444894[/C][/ROW]
[ROW][C]34[/C][C]0.0681092200381022[/C][C]0.136218440076204[/C][C]0.931890779961898[/C][/ROW]
[ROW][C]35[/C][C]0.593698083212179[/C][C]0.812603833575643[/C][C]0.406301916787821[/C][/ROW]
[ROW][C]36[/C][C]0.825667292678199[/C][C]0.348665414643603[/C][C]0.174332707321801[/C][/ROW]
[ROW][C]37[/C][C]0.800845691395442[/C][C]0.398308617209117[/C][C]0.199154308604558[/C][/ROW]
[ROW][C]38[/C][C]0.765703551293074[/C][C]0.468592897413851[/C][C]0.234296448706926[/C][/ROW]
[ROW][C]39[/C][C]0.73575214838048[/C][C]0.528495703239039[/C][C]0.264247851619519[/C][/ROW]
[ROW][C]40[/C][C]0.718421885548184[/C][C]0.563156228903632[/C][C]0.281578114451816[/C][/ROW]
[ROW][C]41[/C][C]0.687203723215676[/C][C]0.625592553568648[/C][C]0.312796276784324[/C][/ROW]
[ROW][C]42[/C][C]0.844639582866687[/C][C]0.310720834266625[/C][C]0.155360417133313[/C][/ROW]
[ROW][C]43[/C][C]0.850618382483236[/C][C]0.298763235033527[/C][C]0.149381617516764[/C][/ROW]
[ROW][C]44[/C][C]0.81929638592471[/C][C]0.36140722815058[/C][C]0.18070361407529[/C][/ROW]
[ROW][C]45[/C][C]0.87673279203472[/C][C]0.246534415930559[/C][C]0.123267207965279[/C][/ROW]
[ROW][C]46[/C][C]0.854717195345696[/C][C]0.290565609308608[/C][C]0.145282804654304[/C][/ROW]
[ROW][C]47[/C][C]0.824891852301315[/C][C]0.35021629539737[/C][C]0.175108147698685[/C][/ROW]
[ROW][C]48[/C][C]0.802258517598653[/C][C]0.395482964802695[/C][C]0.197741482401347[/C][/ROW]
[ROW][C]49[/C][C]0.787331425987653[/C][C]0.425337148024693[/C][C]0.212668574012347[/C][/ROW]
[ROW][C]50[/C][C]0.765285466972374[/C][C]0.469429066055252[/C][C]0.234714533027626[/C][/ROW]
[ROW][C]51[/C][C]0.742066944329438[/C][C]0.515866111341125[/C][C]0.257933055670562[/C][/ROW]
[ROW][C]52[/C][C]0.786506572057239[/C][C]0.426986855885522[/C][C]0.213493427942761[/C][/ROW]
[ROW][C]53[/C][C]0.749461777749827[/C][C]0.501076444500346[/C][C]0.250538222250173[/C][/ROW]
[ROW][C]54[/C][C]0.916439467509771[/C][C]0.167121064980458[/C][C]0.0835605324902288[/C][/ROW]
[ROW][C]55[/C][C]0.903323239996905[/C][C]0.193353520006190[/C][C]0.0966767600030949[/C][/ROW]
[ROW][C]56[/C][C]0.88111796838937[/C][C]0.23776406322126[/C][C]0.11888203161063[/C][/ROW]
[ROW][C]57[/C][C]0.859894471403494[/C][C]0.280211057193011[/C][C]0.140105528596506[/C][/ROW]
[ROW][C]58[/C][C]0.832268944747529[/C][C]0.335462110504943[/C][C]0.167731055252471[/C][/ROW]
[ROW][C]59[/C][C]0.810413699480358[/C][C]0.379172601039284[/C][C]0.189586300519642[/C][/ROW]
[ROW][C]60[/C][C]0.816199342473288[/C][C]0.367601315053423[/C][C]0.183800657526712[/C][/ROW]
[ROW][C]61[/C][C]0.792514150119388[/C][C]0.414971699761224[/C][C]0.207485849880612[/C][/ROW]
[ROW][C]62[/C][C]0.762617471262455[/C][C]0.474765057475089[/C][C]0.237382528737545[/C][/ROW]
[ROW][C]63[/C][C]0.735344188744165[/C][C]0.52931162251167[/C][C]0.264655811255835[/C][/ROW]
[ROW][C]64[/C][C]0.710828824210725[/C][C]0.578342351578549[/C][C]0.289171175789275[/C][/ROW]
[ROW][C]65[/C][C]0.720267041441244[/C][C]0.559465917117513[/C][C]0.279732958558756[/C][/ROW]
[ROW][C]66[/C][C]0.71920007211688[/C][C]0.561599855766239[/C][C]0.280799927883119[/C][/ROW]
[ROW][C]67[/C][C]0.698423735765447[/C][C]0.603152528469105[/C][C]0.301576264234553[/C][/ROW]
[ROW][C]68[/C][C]0.6965008386479[/C][C]0.606998322704199[/C][C]0.303499161352099[/C][/ROW]
[ROW][C]69[/C][C]0.672630832663763[/C][C]0.654738334672474[/C][C]0.327369167336237[/C][/ROW]
[ROW][C]70[/C][C]0.818090192269689[/C][C]0.363819615460623[/C][C]0.181909807730311[/C][/ROW]
[ROW][C]71[/C][C]0.813943728940954[/C][C]0.372112542118092[/C][C]0.186056271059046[/C][/ROW]
[ROW][C]72[/C][C]0.83009113317227[/C][C]0.339817733655461[/C][C]0.169908866827730[/C][/ROW]
[ROW][C]73[/C][C]0.828369005659964[/C][C]0.343261988680073[/C][C]0.171630994340036[/C][/ROW]
[ROW][C]74[/C][C]0.799897939868001[/C][C]0.400204120263998[/C][C]0.200102060131999[/C][/ROW]
[ROW][C]75[/C][C]0.778953263882348[/C][C]0.442093472235303[/C][C]0.221046736117652[/C][/ROW]
[ROW][C]76[/C][C]0.746945748019325[/C][C]0.50610850396135[/C][C]0.253054251980675[/C][/ROW]
[ROW][C]77[/C][C]0.722177463471115[/C][C]0.555645073057769[/C][C]0.277822536528885[/C][/ROW]
[ROW][C]78[/C][C]0.718531738247402[/C][C]0.562936523505196[/C][C]0.281468261752598[/C][/ROW]
[ROW][C]79[/C][C]0.678823865490028[/C][C]0.642352269019943[/C][C]0.321176134509972[/C][/ROW]
[ROW][C]80[/C][C]0.63943247217795[/C][C]0.7211350556441[/C][C]0.36056752782205[/C][/ROW]
[ROW][C]81[/C][C]0.779851419433708[/C][C]0.440297161132583[/C][C]0.220148580566292[/C][/ROW]
[ROW][C]82[/C][C]0.744915802882577[/C][C]0.510168394234846[/C][C]0.255084197117423[/C][/ROW]
[ROW][C]83[/C][C]0.706740700817222[/C][C]0.586518598365555[/C][C]0.293259299182778[/C][/ROW]
[ROW][C]84[/C][C]0.666202437129621[/C][C]0.667595125740758[/C][C]0.333797562870379[/C][/ROW]
[ROW][C]85[/C][C]0.622480758725619[/C][C]0.755038482548762[/C][C]0.377519241274381[/C][/ROW]
[ROW][C]86[/C][C]0.593744772361577[/C][C]0.812510455276845[/C][C]0.406255227638423[/C][/ROW]
[ROW][C]87[/C][C]0.558375877923186[/C][C]0.883248244153629[/C][C]0.441624122076814[/C][/ROW]
[ROW][C]88[/C][C]0.519988425951628[/C][C]0.960023148096744[/C][C]0.480011574048372[/C][/ROW]
[ROW][C]89[/C][C]0.476174100857689[/C][C]0.952348201715377[/C][C]0.523825899142311[/C][/ROW]
[ROW][C]90[/C][C]0.525079203213106[/C][C]0.949841593573788[/C][C]0.474920796786894[/C][/ROW]
[ROW][C]91[/C][C]0.489884941219415[/C][C]0.97976988243883[/C][C]0.510115058780585[/C][/ROW]
[ROW][C]92[/C][C]0.455502283521237[/C][C]0.911004567042474[/C][C]0.544497716478763[/C][/ROW]
[ROW][C]93[/C][C]0.408922996870666[/C][C]0.817845993741331[/C][C]0.591077003129334[/C][/ROW]
[ROW][C]94[/C][C]0.392009119818521[/C][C]0.784018239637042[/C][C]0.607990880181479[/C][/ROW]
[ROW][C]95[/C][C]0.353464518536353[/C][C]0.706929037072706[/C][C]0.646535481463647[/C][/ROW]
[ROW][C]96[/C][C]0.323191459968426[/C][C]0.646382919936851[/C][C]0.676808540031574[/C][/ROW]
[ROW][C]97[/C][C]0.282981879956985[/C][C]0.565963759913971[/C][C]0.717018120043015[/C][/ROW]
[ROW][C]98[/C][C]0.252851152789222[/C][C]0.505702305578444[/C][C]0.747148847210778[/C][/ROW]
[ROW][C]99[/C][C]0.223707032988589[/C][C]0.447414065977177[/C][C]0.776292967011411[/C][/ROW]
[ROW][C]100[/C][C]0.196127253372990[/C][C]0.392254506745980[/C][C]0.80387274662701[/C][/ROW]
[ROW][C]101[/C][C]0.176693429131554[/C][C]0.353386858263107[/C][C]0.823306570868446[/C][/ROW]
[ROW][C]102[/C][C]0.359777043480455[/C][C]0.719554086960911[/C][C]0.640222956519545[/C][/ROW]
[ROW][C]103[/C][C]0.326323622230001[/C][C]0.652647244460002[/C][C]0.673676377769999[/C][/ROW]
[ROW][C]104[/C][C]0.310793192153489[/C][C]0.621586384306979[/C][C]0.68920680784651[/C][/ROW]
[ROW][C]105[/C][C]0.331057275310586[/C][C]0.662114550621172[/C][C]0.668942724689414[/C][/ROW]
[ROW][C]106[/C][C]0.322081385626516[/C][C]0.644162771253031[/C][C]0.677918614373484[/C][/ROW]
[ROW][C]107[/C][C]0.295156255332109[/C][C]0.590312510664219[/C][C]0.70484374466789[/C][/ROW]
[ROW][C]108[/C][C]0.282632411438968[/C][C]0.565264822877937[/C][C]0.717367588561032[/C][/ROW]
[ROW][C]109[/C][C]0.303616352164521[/C][C]0.607232704329043[/C][C]0.696383647835479[/C][/ROW]
[ROW][C]110[/C][C]0.270166011365103[/C][C]0.540332022730207[/C][C]0.729833988634897[/C][/ROW]
[ROW][C]111[/C][C]0.259405898793114[/C][C]0.518811797586228[/C][C]0.740594101206886[/C][/ROW]
[ROW][C]112[/C][C]0.262656386507563[/C][C]0.525312773015125[/C][C]0.737343613492437[/C][/ROW]
[ROW][C]113[/C][C]0.233885366643656[/C][C]0.467770733287312[/C][C]0.766114633356344[/C][/ROW]
[ROW][C]114[/C][C]0.209512640443392[/C][C]0.419025280886785[/C][C]0.790487359556608[/C][/ROW]
[ROW][C]115[/C][C]0.428182198514553[/C][C]0.856364397029105[/C][C]0.571817801485447[/C][/ROW]
[ROW][C]116[/C][C]0.379995527617288[/C][C]0.759991055234577[/C][C]0.620004472382712[/C][/ROW]
[ROW][C]117[/C][C]0.354168503447041[/C][C]0.708337006894081[/C][C]0.645831496552959[/C][/ROW]
[ROW][C]118[/C][C]0.318714306341284[/C][C]0.637428612682568[/C][C]0.681285693658716[/C][/ROW]
[ROW][C]119[/C][C]0.276915474633958[/C][C]0.553830949267916[/C][C]0.723084525366042[/C][/ROW]
[ROW][C]120[/C][C]0.278356125379673[/C][C]0.556712250759345[/C][C]0.721643874620327[/C][/ROW]
[ROW][C]121[/C][C]0.235338616521556[/C][C]0.470677233043111[/C][C]0.764661383478444[/C][/ROW]
[ROW][C]122[/C][C]0.201671913328553[/C][C]0.403343826657105[/C][C]0.798328086671448[/C][/ROW]
[ROW][C]123[/C][C]0.211920158132045[/C][C]0.42384031626409[/C][C]0.788079841867955[/C][/ROW]
[ROW][C]124[/C][C]0.189725853117546[/C][C]0.379451706235093[/C][C]0.810274146882454[/C][/ROW]
[ROW][C]125[/C][C]0.153789732316692[/C][C]0.307579464633384[/C][C]0.846210267683308[/C][/ROW]
[ROW][C]126[/C][C]0.123240741148366[/C][C]0.246481482296732[/C][C]0.876759258851634[/C][/ROW]
[ROW][C]127[/C][C]0.163889277854082[/C][C]0.327778555708163[/C][C]0.836110722145919[/C][/ROW]
[ROW][C]128[/C][C]0.148976475124514[/C][C]0.297952950249028[/C][C]0.851023524875486[/C][/ROW]
[ROW][C]129[/C][C]0.136757978892567[/C][C]0.273515957785134[/C][C]0.863242021107433[/C][/ROW]
[ROW][C]130[/C][C]0.105713553848620[/C][C]0.211427107697240[/C][C]0.89428644615138[/C][/ROW]
[ROW][C]131[/C][C]0.127803980303434[/C][C]0.255607960606868[/C][C]0.872196019696566[/C][/ROW]
[ROW][C]132[/C][C]0.129280138753374[/C][C]0.258560277506748[/C][C]0.870719861246626[/C][/ROW]
[ROW][C]133[/C][C]0.114209432839598[/C][C]0.228418865679197[/C][C]0.885790567160402[/C][/ROW]
[ROW][C]134[/C][C]0.0865045497282974[/C][C]0.173009099456595[/C][C]0.913495450271703[/C][/ROW]
[ROW][C]135[/C][C]0.0645729241076109[/C][C]0.129145848215222[/C][C]0.935427075892389[/C][/ROW]
[ROW][C]136[/C][C]0.0516684413210905[/C][C]0.103336882642181[/C][C]0.94833155867891[/C][/ROW]
[ROW][C]137[/C][C]0.0356159301691033[/C][C]0.0712318603382067[/C][C]0.964384069830897[/C][/ROW]
[ROW][C]138[/C][C]0.0385242260661448[/C][C]0.0770484521322896[/C][C]0.961475773933855[/C][/ROW]
[ROW][C]139[/C][C]0.0285621330350841[/C][C]0.0571242660701683[/C][C]0.971437866964916[/C][/ROW]
[ROW][C]140[/C][C]0.161779187544235[/C][C]0.32355837508847[/C][C]0.838220812455765[/C][/ROW]
[ROW][C]141[/C][C]0.127376440574270[/C][C]0.254752881148539[/C][C]0.87262355942573[/C][/ROW]
[ROW][C]142[/C][C]0.087777971316509[/C][C]0.175555942633018[/C][C]0.912222028683491[/C][/ROW]
[ROW][C]143[/C][C]0.254037718371909[/C][C]0.508075436743818[/C][C]0.745962281628091[/C][/ROW]
[ROW][C]144[/C][C]0.209567748517625[/C][C]0.419135497035251[/C][C]0.790432251482375[/C][/ROW]
[ROW][C]145[/C][C]0.293337620908569[/C][C]0.586675241817138[/C][C]0.706662379091431[/C][/ROW]
[ROW][C]146[/C][C]0.226617775671976[/C][C]0.453235551343951[/C][C]0.773382224328024[/C][/ROW]
[ROW][C]147[/C][C]0.149224202627937[/C][C]0.298448405255874[/C][C]0.850775797372063[/C][/ROW]
[ROW][C]148[/C][C]0.108685827584711[/C][C]0.217371655169421[/C][C]0.89131417241529[/C][/ROW]
[ROW][C]149[/C][C]0.0677526445897472[/C][C]0.135505289179494[/C][C]0.932247355410253[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=98210&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=98210&T=5

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


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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
100.4885564177155880.9771128354311770.511443582284411
110.3299624246123570.6599248492247150.670037575387643
120.2449547132643100.4899094265286210.75504528673569
130.1654462290310460.3308924580620920.834553770968954
140.3607045146342530.7214090292685070.639295485365747
150.2594132504584490.5188265009168990.74058674954155
160.1791032871774240.3582065743548470.820896712822576
170.3534655511567410.7069311023134820.646534448843259
180.3980761275001650.796152255000330.601923872499835
190.3245869500546110.6491739001092220.675413049945389
200.2559108473373150.511821694674630.744089152662685
210.1958439070682080.3916878141364170.804156092931792
220.2532996404906570.5065992809813150.746700359509343
230.1953711816027380.3907423632054770.804628818397262
240.1672580035811730.3345160071623470.832741996418827
250.1295456074315740.2590912148631470.870454392568426
260.09892227424463790.1978445484892760.901077725755362
270.07458325899695230.1491665179939050.925416741003048
280.05378588344680160.1075717668936030.946214116553198
290.0491834222824240.0983668445648480.950816577717576
300.03534037048799670.07068074097599340.964659629512003
310.1321687666053200.2643375332106410.86783123339468
320.1065275609373180.2130551218746350.893472439062682
330.0832339135551060.1664678271102120.916766086444894
340.06810922003810220.1362184400762040.931890779961898
350.5936980832121790.8126038335756430.406301916787821
360.8256672926781990.3486654146436030.174332707321801
370.8008456913954420.3983086172091170.199154308604558
380.7657035512930740.4685928974138510.234296448706926
390.735752148380480.5284957032390390.264247851619519
400.7184218855481840.5631562289036320.281578114451816
410.6872037232156760.6255925535686480.312796276784324
420.8446395828666870.3107208342666250.155360417133313
430.8506183824832360.2987632350335270.149381617516764
440.819296385924710.361407228150580.18070361407529
450.876732792034720.2465344159305590.123267207965279
460.8547171953456960.2905656093086080.145282804654304
470.8248918523013150.350216295397370.175108147698685
480.8022585175986530.3954829648026950.197741482401347
490.7873314259876530.4253371480246930.212668574012347
500.7652854669723740.4694290660552520.234714533027626
510.7420669443294380.5158661113411250.257933055670562
520.7865065720572390.4269868558855220.213493427942761
530.7494617777498270.5010764445003460.250538222250173
540.9164394675097710.1671210649804580.0835605324902288
550.9033232399969050.1933535200061900.0966767600030949
560.881117968389370.237764063221260.11888203161063
570.8598944714034940.2802110571930110.140105528596506
580.8322689447475290.3354621105049430.167731055252471
590.8104136994803580.3791726010392840.189586300519642
600.8161993424732880.3676013150534230.183800657526712
610.7925141501193880.4149716997612240.207485849880612
620.7626174712624550.4747650574750890.237382528737545
630.7353441887441650.529311622511670.264655811255835
640.7108288242107250.5783423515785490.289171175789275
650.7202670414412440.5594659171175130.279732958558756
660.719200072116880.5615998557662390.280799927883119
670.6984237357654470.6031525284691050.301576264234553
680.69650083864790.6069983227041990.303499161352099
690.6726308326637630.6547383346724740.327369167336237
700.8180901922696890.3638196154606230.181909807730311
710.8139437289409540.3721125421180920.186056271059046
720.830091133172270.3398177336554610.169908866827730
730.8283690056599640.3432619886800730.171630994340036
740.7998979398680010.4002041202639980.200102060131999
750.7789532638823480.4420934722353030.221046736117652
760.7469457480193250.506108503961350.253054251980675
770.7221774634711150.5556450730577690.277822536528885
780.7185317382474020.5629365235051960.281468261752598
790.6788238654900280.6423522690199430.321176134509972
800.639432472177950.72113505564410.36056752782205
810.7798514194337080.4402971611325830.220148580566292
820.7449158028825770.5101683942348460.255084197117423
830.7067407008172220.5865185983655550.293259299182778
840.6662024371296210.6675951257407580.333797562870379
850.6224807587256190.7550384825487620.377519241274381
860.5937447723615770.8125104552768450.406255227638423
870.5583758779231860.8832482441536290.441624122076814
880.5199884259516280.9600231480967440.480011574048372
890.4761741008576890.9523482017153770.523825899142311
900.5250792032131060.9498415935737880.474920796786894
910.4898849412194150.979769882438830.510115058780585
920.4555022835212370.9110045670424740.544497716478763
930.4089229968706660.8178459937413310.591077003129334
940.3920091198185210.7840182396370420.607990880181479
950.3534645185363530.7069290370727060.646535481463647
960.3231914599684260.6463829199368510.676808540031574
970.2829818799569850.5659637599139710.717018120043015
980.2528511527892220.5057023055784440.747148847210778
990.2237070329885890.4474140659771770.776292967011411
1000.1961272533729900.3922545067459800.80387274662701
1010.1766934291315540.3533868582631070.823306570868446
1020.3597770434804550.7195540869609110.640222956519545
1030.3263236222300010.6526472444600020.673676377769999
1040.3107931921534890.6215863843069790.68920680784651
1050.3310572753105860.6621145506211720.668942724689414
1060.3220813856265160.6441627712530310.677918614373484
1070.2951562553321090.5903125106642190.70484374466789
1080.2826324114389680.5652648228779370.717367588561032
1090.3036163521645210.6072327043290430.696383647835479
1100.2701660113651030.5403320227302070.729833988634897
1110.2594058987931140.5188117975862280.740594101206886
1120.2626563865075630.5253127730151250.737343613492437
1130.2338853666436560.4677707332873120.766114633356344
1140.2095126404433920.4190252808867850.790487359556608
1150.4281821985145530.8563643970291050.571817801485447
1160.3799955276172880.7599910552345770.620004472382712
1170.3541685034470410.7083370068940810.645831496552959
1180.3187143063412840.6374286126825680.681285693658716
1190.2769154746339580.5538309492679160.723084525366042
1200.2783561253796730.5567122507593450.721643874620327
1210.2353386165215560.4706772330431110.764661383478444
1220.2016719133285530.4033438266571050.798328086671448
1230.2119201581320450.423840316264090.788079841867955
1240.1897258531175460.3794517062350930.810274146882454
1250.1537897323166920.3075794646333840.846210267683308
1260.1232407411483660.2464814822967320.876759258851634
1270.1638892778540820.3277785557081630.836110722145919
1280.1489764751245140.2979529502490280.851023524875486
1290.1367579788925670.2735159577851340.863242021107433
1300.1057135538486200.2114271076972400.89428644615138
1310.1278039803034340.2556079606068680.872196019696566
1320.1292801387533740.2585602775067480.870719861246626
1330.1142094328395980.2284188656791970.885790567160402
1340.08650454972829740.1730090994565950.913495450271703
1350.06457292410761090.1291458482152220.935427075892389
1360.05166844132109050.1033368826421810.94833155867891
1370.03561593016910330.07123186033820670.964384069830897
1380.03852422606614480.07704845213228960.961475773933855
1390.02856213303508410.05712426607016830.971437866964916
1400.1617791875442350.323558375088470.838220812455765
1410.1273764405742700.2547528811485390.87262355942573
1420.0877779713165090.1755559426330180.912222028683491
1430.2540377183719090.5080754367438180.745962281628091
1440.2095677485176250.4191354970352510.790432251482375
1450.2933376209085690.5866752418171380.706662379091431
1460.2266177756719760.4532355513439510.773382224328024
1470.1492242026279370.2984484052558740.850775797372063
1480.1086858275847110.2173716551694210.89131417241529
1490.06775264458974720.1355052891794940.932247355410253






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 level50.0357142857142857OK

\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 & 5 & 0.0357142857142857 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=98210&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]5[/C][C]0.0357142857142857[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=98210&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=98210&T=6

As an alternative you can also use a QR Code:  
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 level50.0357142857142857OK


Figures (Output of Computation)

Figure 1
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Figure 2
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Figure 5
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Figure 6
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Figure 7
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Figure 8
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Figure 9
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Figure 10
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Input Parameters & R Code

Parameters (Session):
par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
Parameters (R input):
par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
R code (references can be found in the software module):
library(lattice)
library(lmtest)
n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
par1 <- as.numeric(par1)
x <- t(y)
k <- length(x[1,])
n <- length(x[,1])
x1 <- cbind(x[,par1], x[,1:k!=par1])
mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
colnames(x1) <- mycolnames #colnames(x)[par1]
x <- x1
if (par3 == 'First Differences'){
x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
for (i in 1:n-1) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
}
if (par2 == 'Include Monthly Dummies'){
x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
for (i in 1:11){
x2[seq(i,n,12),i] <- 1
}
x <- cbind(x, x2)
}
if (par2 == 'Include Quarterly Dummies'){
x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
for (i in 1:3){
x2[seq(i,n,4),i] <- 1
}
x <- cbind(x, x2)
}
k <- length(x[1,])
if (par3 == 'Linear Trend'){
x <- cbind(x, c(1:n))
colnames(x)[k+1] <- 't'
}
x
k <- length(x[1,])
df <- as.data.frame(x)
(mylm <- lm(df))
(mysum <- summary(mylm))
if (n > n25) {
kp3 <- k + 3
nmkm3 <- n - k - 3
gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
numgqtests <- 0
numsignificant1 <- 0
numsignificant5 <- 0
numsignificant10 <- 0
for (mypoint in kp3:nmkm3) {
j <- 0
numgqtests <- numgqtests + 1
for (myalt in c('greater', 'two.sided', 'less')) {
j <- j + 1
gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
}
if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
}
gqarr
}
bitmap(file='test0.png')
plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
points(x[,1]-mysum$resid)
grid()
dev.off()
bitmap(file='test1.png')
plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
grid()
dev.off()
bitmap(file='test2.png')
hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
grid()
dev.off()
bitmap(file='test3.png')
densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
dev.off()
bitmap(file='test4.png')
qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
qqline(mysum$resid)
grid()
dev.off()
(myerror <- as.ts(mysum$resid))
bitmap(file='test5.png')
dum <- cbind(lag(myerror,k=1),myerror)
dum
dum1 <- dum[2:length(myerror),]
dum1
z <- as.data.frame(dum1)
z
plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
lines(lowess(z))
abline(lm(z))
grid()
dev.off()
bitmap(file='test6.png')
acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
grid()
dev.off()
bitmap(file='test7.png')
pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
grid()
dev.off()
bitmap(file='test8.png')
opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
plot(mylm, las = 1, sub='Residual Diagnostics')
par(opar)
dev.off()
if (n > n25) {
bitmap(file='test9.png')
plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
grid()
dev.off()
}
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
a<-table.row.end(a)
myeq <- colnames(x)[1]
myeq <- paste(myeq, '[t] = ', sep='')
for (i in 1:k){
if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
myeq <- paste(myeq, mysum$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')
}