FreeStatistics.org

Free Statistics

of Irreproducible Research!

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 computationTue, 19 Nov 2013 06:32:10 -0500
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2013/Nov/19/t13848607407ufct7zg9klxrpy.htm/, Retrieved Fri, 03 May 2024 16:59:08 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=226371, Retrieved Fri, 03 May 2024 16:59:08 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact103

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]
- R PD  [Multiple Regression] [] [2013-11-17 14:13:46] [2710f8dea95e981897be7c03387b4566]
-   PD    [Multiple Regression] [] [2013-11-19 11:20:36] [2710f8dea95e981897be7c03387b4566]
-    D        [Multiple Regression] [] [2013-11-19 11:32:10] [05fc9f73518f9509c56332c989d681e3] [Current]
Feedback Forum

Post a new message

Dataset

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

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time11 seconds
R Server'Sir Maurice George Kendall' @ kendall.wessa.net

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 11 seconds \tabularnewline
R Server & 'Sir Maurice George Kendall' @ kendall.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=226371&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]11 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Sir Maurice George Kendall' @ kendall.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=226371&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=226371&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 time11 seconds
R Server'Sir Maurice George Kendall' @ kendall.wessa.net






Multiple Linear Regression - Estimated Regression Equation
A[t] = + 11.8082958466909 -0.0823701027361202I1[t] -0.124466956938624I2[t] -0.172976683598495E1[t] + 0.104563683563042E2[t] -0.00757350570191653E3[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
A[t] =  +  11.8082958466909 -0.0823701027361202I1[t] -0.124466956938624I2[t] -0.172976683598495E1[t] +  0.104563683563042E2[t] -0.00757350570191653E3[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=226371&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]A[t] =  +  11.8082958466909 -0.0823701027361202I1[t] -0.124466956938624I2[t] -0.172976683598495E1[t] +  0.104563683563042E2[t] -0.00757350570191653E3[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=226371&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=226371&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
A[t] = + 11.8082958466909 -0.0823701027361202I1[t] -0.124466956938624I2[t] -0.172976683598495E1[t] + 0.104563683563042E2[t] -0.00757350570191653E3[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)11.80829584669091.7680656.678700
I1-0.08237010273612020.073292-1.12390.2627980.131399
I2-0.1244669569386240.066481-1.87220.0630490.031524
E1-0.1729766835984950.075267-2.29820.022880.01144
E20.1045636835630420.058151.79820.0740830.037042
E3-0.007573505701916530.060436-0.12530.9004360.450218

\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) & 11.8082958466909 & 1.768065 & 6.6787 & 0 & 0 \tabularnewline
I1 & -0.0823701027361202 & 0.073292 & -1.1239 & 0.262798 & 0.131399 \tabularnewline
I2 & -0.124466956938624 & 0.066481 & -1.8722 & 0.063049 & 0.031524 \tabularnewline
E1 & -0.172976683598495 & 0.075267 & -2.2982 & 0.02288 & 0.01144 \tabularnewline
E2 & 0.104563683563042 & 0.05815 & 1.7982 & 0.074083 & 0.037042 \tabularnewline
E3 & -0.00757350570191653 & 0.060436 & -0.1253 & 0.900436 & 0.450218 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=226371&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]11.8082958466909[/C][C]1.768065[/C][C]6.6787[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]I1[/C][C]-0.0823701027361202[/C][C]0.073292[/C][C]-1.1239[/C][C]0.262798[/C][C]0.131399[/C][/ROW]
[ROW][C]I2[/C][C]-0.124466956938624[/C][C]0.066481[/C][C]-1.8722[/C][C]0.063049[/C][C]0.031524[/C][/ROW]
[ROW][C]E1[/C][C]-0.172976683598495[/C][C]0.075267[/C][C]-2.2982[/C][C]0.02288[/C][C]0.01144[/C][/ROW]
[ROW][C]E2[/C][C]0.104563683563042[/C][C]0.05815[/C][C]1.7982[/C][C]0.074083[/C][C]0.037042[/C][/ROW]
[ROW][C]E3[/C][C]-0.00757350570191653[/C][C]0.060436[/C][C]-0.1253[/C][C]0.900436[/C][C]0.450218[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=226371&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=226371&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)11.80829584669091.7680656.678700
I1-0.08237010273612020.073292-1.12390.2627980.131399
I2-0.1244669569386240.066481-1.87220.0630490.031524
E1-0.1729766835984950.075267-2.29820.022880.01144
E20.1045636835630420.058151.79820.0740830.037042
E3-0.007573505701916530.060436-0.12530.9004360.450218






Multiple Linear Regression - Regression Statistics
Multiple R0.37971819192733
R-squared0.14418590528056
Adjusted R-squared0.116755966347245
F-TEST (value)5.25651572287818
F-TEST (DF numerator)5
F-TEST (DF denominator)156
p-value0.000172500316419022
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.46798912653446
Sum Squared Residuals950.191371276

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.37971819192733 \tabularnewline
R-squared & 0.14418590528056 \tabularnewline
Adjusted R-squared & 0.116755966347245 \tabularnewline
F-TEST (value) & 5.25651572287818 \tabularnewline
F-TEST (DF numerator) & 5 \tabularnewline
F-TEST (DF denominator) & 156 \tabularnewline
p-value & 0.000172500316419022 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 2.46798912653446 \tabularnewline
Sum Squared Residuals & 950.191371276 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=226371&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.37971819192733[/C][/ROW]
[ROW][C]R-squared[/C][C]0.14418590528056[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.116755966347245[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]5.25651572287818[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]5[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]156[/C][/ROW]
[ROW][C]p-value[/C][C]0.000172500316419022[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]2.46798912653446[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]950.191371276[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=226371&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=226371&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 R0.37971819192733
R-squared0.14418590528056
Adjusted R-squared0.116755966347245
F-TEST (value)5.25651572287818
F-TEST (DF numerator)5
F-TEST (DF denominator)156
p-value0.000172500316419022
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.46798912653446
Sum Squared Residuals950.191371276






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
144.67779534650291-0.67779534650291
245.64409458111996-1.64409458111996
365.803580863800240.196419136199755
486.580118732923071.41988126707693
585.927491609001422.07250839099858
644.97709882586314-0.977098825863136
744.60227722612709-0.602277226127092
885.981110570421942.01888942957806
954.634041881814750.365958118185252
1045.73417422570351-1.73417422570351
1146.30129894710337-2.30129894710337
1244.40954195718433-0.409541957184328
1345.25209744178351-1.25209744178351
1443.467163483841090.532836516158908
1544.26957669470373-0.26957669470373
1685.908778244329012.09122175567099
1747.67865132798167-3.67865132798167
1843.799994990061520.200005009938475
1944.24573515527699-0.245735155276992
2086.269293135733771.73070686426623
2146.66009176752096-2.66009176752096
2276.357549174648430.642450825351574
2348.45468462101732-4.45468462101732
2444.67478351570996-0.674783515709956
2556.36855299747342-1.36855299747341
2645.18687191468166-1.18687191468166
2745.18687191468166-1.18687191468166
2846.29555242483964-2.29555242483964
2948.04220901489974-4.04220901489974
3045.64339199777207-1.64339199777206
3145.00753753670211-1.00753753670212
3245.6896114169806-1.6896114169806
33157.143184496654197.85681550334581
34106.663191419007093.33680858099291
3547.32639353827598-3.32639353827598
3685.282847830908692.71715216909131
3747.2556007208342-3.25560072083419
3845.4683006537913-1.4683006537913
3943.896809520853580.10319047914642
4044.62543348597754-0.625433485977537
4175.454720742918091.54527925708191
4245.44490682879627-1.44490682879627
4365.920238433471510.0797615665284938
4454.502819410166420.497180589833582
4544.9744296430052-0.974429643005199
46167.319989317704428.68001068229558
4756.21978117216652-1.21978117216652
48127.004426398537044.99557360146296
4965.249133826442890.750866173557108
5097.832883687869991.16711631213001
5195.93808728096123.0619127190388
5246.08080090078978-2.08080090078978
5355.68899497074678-0.688994970746776
5446.74005783381218-2.74005783381218
5545.44244087427586-1.44244087427586
5656.06760155458495-1.06760155458495
5745.5768112552913-1.5768112552913
5844.67318545614157-0.673185456141573
5946.84112590933572-2.84112590933572
6057.48244782025405-2.48244782025405
6146.55234056366725-2.55234056366724
6266.87637246475702-0.87637246475702
6344.67168218064469-0.671682180644687
6445.42112551893753-1.42112551893753
65186.3369949535752311.6630050464248
6644.52559678892763-0.525596788927626
6765.020530276800120.979469723199881
6845.0411361335236-1.0411361335236
6945.80003176126356-1.80003176126356
7056.88688031034094-1.88688031034094
7144.27155532386887-0.271555323868871
7244.74545586415899-0.745455864158992
7355.77401004791329-0.774010047913293
74105.278725265902664.72127473409734
7555.04153394892387-0.0415339489238739
7687.549400522874460.450599477125543
7787.513249839346870.486750160653131
7855.16304762598682-0.16304762598682
7944.35029018695423-0.350290186954231
8047.05371792546355-3.05371792546355
8144.21855448733293-0.218554487332932
8256.35129469852823-1.35129469852823
8345.58455007259735-1.58455007259735
8445.48092147617835-1.48092147617835
8586.359099024447151.64090097555285
8645.15599072109879-1.15599072109879
8754.940596853125790.0594031468742096
88147.552500174360596.44749982563941
8985.338435090957792.66156490904221
9085.9135035472632.086496452737
9148.09918080821506-4.09918080821506
9244.18390539572309-0.183905395723091
9365.918612782023640.0813872179763552
9445.10821453682457-1.10821453682457
9576.004382242845080.99561775715492
9675.540571061808721.45942893819128
9745.61547779237073-1.61547779237073
9866.22549146127599-0.225491461275987
9946.31012259600483-2.31012259600483
10074.329315790289092.67068420971091
10146.29085301959497-2.29085301959497
10245.22224253456146-1.22224253456146
10386.097037975476851.90296202452315
10445.13676075561249-1.13676075561249
10546.002668770704-2.002668770704
106106.545595384422483.45440461557752
10787.183077180519430.816922819480568
10865.676814947524480.32318505247552
10944.44017862224433-0.440178622244332
11045.15669330444669-1.15669330444669
11145.45633942605929-1.45633942605929
11255.79527549929181-0.795275499291812
11346.80923888337973-2.80923888337973
11466.3089912382189-0.308991238218899
11544.48761384784535-0.487613847845349
11656.07096636309514-1.07096636309514
11777.48512733857693-0.48512733857693
11885.734611652027352.26538834797265
11956.26280109446898-1.26280109446898
12087.643222167795660.356777832204343
121106.277627775700823.72237222429918
12285.902801114616332.09719888538367
12356.61328686111636-1.61328686111636
124127.047704562596624.95229543740338
12543.911956532257410.0880434677425872
12654.619859233013580.380140766986423
12745.45987819313103-1.45987819313103
12865.641248061931180.358751938068822
12946.53573157126922-2.53573157126922
13045.3793403479496-1.3793403479496
13176.373924022099850.626075977900154
13275.415491031477191.58450896852281
133107.020119333570452.97988066642955
13445.8906779530495-1.8906779530495
13555.5195277787614-0.519527778761405
13685.466235892440472.53376410755953
137115.394895510218655.60510448978135
13876.278004967528280.721995032471724
13944.50635648797638-0.506356487976382
14085.684328218730022.31567178126998
14166.83514518908568-0.835145189085682
14275.666198651991891.33380134800811
14357.37741410449633-2.37741410449633
14445.85344749434655-1.85344749434655
14584.110295334551143.88970466544886
14646.48150122003494-2.48150122003494
14786.087611588647421.91238841135258
14864.200328452944171.79967154705583
14945.34844358721397-1.34844358721397
15095.753795091323133.24620490867687
15155.69796330767955-0.697963307679547
15265.62423783393490.375762166065102
15344.89481823308201-0.89481823308201
15444.92348154802176-0.923481548021758
15544.27143991129618-0.271439911296177
15656.41537852745083-1.41537852745083
15766.26324020437196-0.263240204371958
158167.846227717177758.15377228282225
15965.918612782023640.0813872179763552
16066.08703812617808-0.0870381261780793
16146.64795827048918-2.64795827048918
16245.99765072947749-1.99765072947749

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 4 & 4.67779534650291 & -0.67779534650291 \tabularnewline
2 & 4 & 5.64409458111996 & -1.64409458111996 \tabularnewline
3 & 6 & 5.80358086380024 & 0.196419136199755 \tabularnewline
4 & 8 & 6.58011873292307 & 1.41988126707693 \tabularnewline
5 & 8 & 5.92749160900142 & 2.07250839099858 \tabularnewline
6 & 4 & 4.97709882586314 & -0.977098825863136 \tabularnewline
7 & 4 & 4.60227722612709 & -0.602277226127092 \tabularnewline
8 & 8 & 5.98111057042194 & 2.01888942957806 \tabularnewline
9 & 5 & 4.63404188181475 & 0.365958118185252 \tabularnewline
10 & 4 & 5.73417422570351 & -1.73417422570351 \tabularnewline
11 & 4 & 6.30129894710337 & -2.30129894710337 \tabularnewline
12 & 4 & 4.40954195718433 & -0.409541957184328 \tabularnewline
13 & 4 & 5.25209744178351 & -1.25209744178351 \tabularnewline
14 & 4 & 3.46716348384109 & 0.532836516158908 \tabularnewline
15 & 4 & 4.26957669470373 & -0.26957669470373 \tabularnewline
16 & 8 & 5.90877824432901 & 2.09122175567099 \tabularnewline
17 & 4 & 7.67865132798167 & -3.67865132798167 \tabularnewline
18 & 4 & 3.79999499006152 & 0.200005009938475 \tabularnewline
19 & 4 & 4.24573515527699 & -0.245735155276992 \tabularnewline
20 & 8 & 6.26929313573377 & 1.73070686426623 \tabularnewline
21 & 4 & 6.66009176752096 & -2.66009176752096 \tabularnewline
22 & 7 & 6.35754917464843 & 0.642450825351574 \tabularnewline
23 & 4 & 8.45468462101732 & -4.45468462101732 \tabularnewline
24 & 4 & 4.67478351570996 & -0.674783515709956 \tabularnewline
25 & 5 & 6.36855299747342 & -1.36855299747341 \tabularnewline
26 & 4 & 5.18687191468166 & -1.18687191468166 \tabularnewline
27 & 4 & 5.18687191468166 & -1.18687191468166 \tabularnewline
28 & 4 & 6.29555242483964 & -2.29555242483964 \tabularnewline
29 & 4 & 8.04220901489974 & -4.04220901489974 \tabularnewline
30 & 4 & 5.64339199777207 & -1.64339199777206 \tabularnewline
31 & 4 & 5.00753753670211 & -1.00753753670212 \tabularnewline
32 & 4 & 5.6896114169806 & -1.6896114169806 \tabularnewline
33 & 15 & 7.14318449665419 & 7.85681550334581 \tabularnewline
34 & 10 & 6.66319141900709 & 3.33680858099291 \tabularnewline
35 & 4 & 7.32639353827598 & -3.32639353827598 \tabularnewline
36 & 8 & 5.28284783090869 & 2.71715216909131 \tabularnewline
37 & 4 & 7.2556007208342 & -3.25560072083419 \tabularnewline
38 & 4 & 5.4683006537913 & -1.4683006537913 \tabularnewline
39 & 4 & 3.89680952085358 & 0.10319047914642 \tabularnewline
40 & 4 & 4.62543348597754 & -0.625433485977537 \tabularnewline
41 & 7 & 5.45472074291809 & 1.54527925708191 \tabularnewline
42 & 4 & 5.44490682879627 & -1.44490682879627 \tabularnewline
43 & 6 & 5.92023843347151 & 0.0797615665284938 \tabularnewline
44 & 5 & 4.50281941016642 & 0.497180589833582 \tabularnewline
45 & 4 & 4.9744296430052 & -0.974429643005199 \tabularnewline
46 & 16 & 7.31998931770442 & 8.68001068229558 \tabularnewline
47 & 5 & 6.21978117216652 & -1.21978117216652 \tabularnewline
48 & 12 & 7.00442639853704 & 4.99557360146296 \tabularnewline
49 & 6 & 5.24913382644289 & 0.750866173557108 \tabularnewline
50 & 9 & 7.83288368786999 & 1.16711631213001 \tabularnewline
51 & 9 & 5.9380872809612 & 3.0619127190388 \tabularnewline
52 & 4 & 6.08080090078978 & -2.08080090078978 \tabularnewline
53 & 5 & 5.68899497074678 & -0.688994970746776 \tabularnewline
54 & 4 & 6.74005783381218 & -2.74005783381218 \tabularnewline
55 & 4 & 5.44244087427586 & -1.44244087427586 \tabularnewline
56 & 5 & 6.06760155458495 & -1.06760155458495 \tabularnewline
57 & 4 & 5.5768112552913 & -1.5768112552913 \tabularnewline
58 & 4 & 4.67318545614157 & -0.673185456141573 \tabularnewline
59 & 4 & 6.84112590933572 & -2.84112590933572 \tabularnewline
60 & 5 & 7.48244782025405 & -2.48244782025405 \tabularnewline
61 & 4 & 6.55234056366725 & -2.55234056366724 \tabularnewline
62 & 6 & 6.87637246475702 & -0.87637246475702 \tabularnewline
63 & 4 & 4.67168218064469 & -0.671682180644687 \tabularnewline
64 & 4 & 5.42112551893753 & -1.42112551893753 \tabularnewline
65 & 18 & 6.33699495357523 & 11.6630050464248 \tabularnewline
66 & 4 & 4.52559678892763 & -0.525596788927626 \tabularnewline
67 & 6 & 5.02053027680012 & 0.979469723199881 \tabularnewline
68 & 4 & 5.0411361335236 & -1.0411361335236 \tabularnewline
69 & 4 & 5.80003176126356 & -1.80003176126356 \tabularnewline
70 & 5 & 6.88688031034094 & -1.88688031034094 \tabularnewline
71 & 4 & 4.27155532386887 & -0.271555323868871 \tabularnewline
72 & 4 & 4.74545586415899 & -0.745455864158992 \tabularnewline
73 & 5 & 5.77401004791329 & -0.774010047913293 \tabularnewline
74 & 10 & 5.27872526590266 & 4.72127473409734 \tabularnewline
75 & 5 & 5.04153394892387 & -0.0415339489238739 \tabularnewline
76 & 8 & 7.54940052287446 & 0.450599477125543 \tabularnewline
77 & 8 & 7.51324983934687 & 0.486750160653131 \tabularnewline
78 & 5 & 5.16304762598682 & -0.16304762598682 \tabularnewline
79 & 4 & 4.35029018695423 & -0.350290186954231 \tabularnewline
80 & 4 & 7.05371792546355 & -3.05371792546355 \tabularnewline
81 & 4 & 4.21855448733293 & -0.218554487332932 \tabularnewline
82 & 5 & 6.35129469852823 & -1.35129469852823 \tabularnewline
83 & 4 & 5.58455007259735 & -1.58455007259735 \tabularnewline
84 & 4 & 5.48092147617835 & -1.48092147617835 \tabularnewline
85 & 8 & 6.35909902444715 & 1.64090097555285 \tabularnewline
86 & 4 & 5.15599072109879 & -1.15599072109879 \tabularnewline
87 & 5 & 4.94059685312579 & 0.0594031468742096 \tabularnewline
88 & 14 & 7.55250017436059 & 6.44749982563941 \tabularnewline
89 & 8 & 5.33843509095779 & 2.66156490904221 \tabularnewline
90 & 8 & 5.913503547263 & 2.086496452737 \tabularnewline
91 & 4 & 8.09918080821506 & -4.09918080821506 \tabularnewline
92 & 4 & 4.18390539572309 & -0.183905395723091 \tabularnewline
93 & 6 & 5.91861278202364 & 0.0813872179763552 \tabularnewline
94 & 4 & 5.10821453682457 & -1.10821453682457 \tabularnewline
95 & 7 & 6.00438224284508 & 0.99561775715492 \tabularnewline
96 & 7 & 5.54057106180872 & 1.45942893819128 \tabularnewline
97 & 4 & 5.61547779237073 & -1.61547779237073 \tabularnewline
98 & 6 & 6.22549146127599 & -0.225491461275987 \tabularnewline
99 & 4 & 6.31012259600483 & -2.31012259600483 \tabularnewline
100 & 7 & 4.32931579028909 & 2.67068420971091 \tabularnewline
101 & 4 & 6.29085301959497 & -2.29085301959497 \tabularnewline
102 & 4 & 5.22224253456146 & -1.22224253456146 \tabularnewline
103 & 8 & 6.09703797547685 & 1.90296202452315 \tabularnewline
104 & 4 & 5.13676075561249 & -1.13676075561249 \tabularnewline
105 & 4 & 6.002668770704 & -2.002668770704 \tabularnewline
106 & 10 & 6.54559538442248 & 3.45440461557752 \tabularnewline
107 & 8 & 7.18307718051943 & 0.816922819480568 \tabularnewline
108 & 6 & 5.67681494752448 & 0.32318505247552 \tabularnewline
109 & 4 & 4.44017862224433 & -0.440178622244332 \tabularnewline
110 & 4 & 5.15669330444669 & -1.15669330444669 \tabularnewline
111 & 4 & 5.45633942605929 & -1.45633942605929 \tabularnewline
112 & 5 & 5.79527549929181 & -0.795275499291812 \tabularnewline
113 & 4 & 6.80923888337973 & -2.80923888337973 \tabularnewline
114 & 6 & 6.3089912382189 & -0.308991238218899 \tabularnewline
115 & 4 & 4.48761384784535 & -0.487613847845349 \tabularnewline
116 & 5 & 6.07096636309514 & -1.07096636309514 \tabularnewline
117 & 7 & 7.48512733857693 & -0.48512733857693 \tabularnewline
118 & 8 & 5.73461165202735 & 2.26538834797265 \tabularnewline
119 & 5 & 6.26280109446898 & -1.26280109446898 \tabularnewline
120 & 8 & 7.64322216779566 & 0.356777832204343 \tabularnewline
121 & 10 & 6.27762777570082 & 3.72237222429918 \tabularnewline
122 & 8 & 5.90280111461633 & 2.09719888538367 \tabularnewline
123 & 5 & 6.61328686111636 & -1.61328686111636 \tabularnewline
124 & 12 & 7.04770456259662 & 4.95229543740338 \tabularnewline
125 & 4 & 3.91195653225741 & 0.0880434677425872 \tabularnewline
126 & 5 & 4.61985923301358 & 0.380140766986423 \tabularnewline
127 & 4 & 5.45987819313103 & -1.45987819313103 \tabularnewline
128 & 6 & 5.64124806193118 & 0.358751938068822 \tabularnewline
129 & 4 & 6.53573157126922 & -2.53573157126922 \tabularnewline
130 & 4 & 5.3793403479496 & -1.3793403479496 \tabularnewline
131 & 7 & 6.37392402209985 & 0.626075977900154 \tabularnewline
132 & 7 & 5.41549103147719 & 1.58450896852281 \tabularnewline
133 & 10 & 7.02011933357045 & 2.97988066642955 \tabularnewline
134 & 4 & 5.8906779530495 & -1.8906779530495 \tabularnewline
135 & 5 & 5.5195277787614 & -0.519527778761405 \tabularnewline
136 & 8 & 5.46623589244047 & 2.53376410755953 \tabularnewline
137 & 11 & 5.39489551021865 & 5.60510448978135 \tabularnewline
138 & 7 & 6.27800496752828 & 0.721995032471724 \tabularnewline
139 & 4 & 4.50635648797638 & -0.506356487976382 \tabularnewline
140 & 8 & 5.68432821873002 & 2.31567178126998 \tabularnewline
141 & 6 & 6.83514518908568 & -0.835145189085682 \tabularnewline
142 & 7 & 5.66619865199189 & 1.33380134800811 \tabularnewline
143 & 5 & 7.37741410449633 & -2.37741410449633 \tabularnewline
144 & 4 & 5.85344749434655 & -1.85344749434655 \tabularnewline
145 & 8 & 4.11029533455114 & 3.88970466544886 \tabularnewline
146 & 4 & 6.48150122003494 & -2.48150122003494 \tabularnewline
147 & 8 & 6.08761158864742 & 1.91238841135258 \tabularnewline
148 & 6 & 4.20032845294417 & 1.79967154705583 \tabularnewline
149 & 4 & 5.34844358721397 & -1.34844358721397 \tabularnewline
150 & 9 & 5.75379509132313 & 3.24620490867687 \tabularnewline
151 & 5 & 5.69796330767955 & -0.697963307679547 \tabularnewline
152 & 6 & 5.6242378339349 & 0.375762166065102 \tabularnewline
153 & 4 & 4.89481823308201 & -0.89481823308201 \tabularnewline
154 & 4 & 4.92348154802176 & -0.923481548021758 \tabularnewline
155 & 4 & 4.27143991129618 & -0.271439911296177 \tabularnewline
156 & 5 & 6.41537852745083 & -1.41537852745083 \tabularnewline
157 & 6 & 6.26324020437196 & -0.263240204371958 \tabularnewline
158 & 16 & 7.84622771717775 & 8.15377228282225 \tabularnewline
159 & 6 & 5.91861278202364 & 0.0813872179763552 \tabularnewline
160 & 6 & 6.08703812617808 & -0.0870381261780793 \tabularnewline
161 & 4 & 6.64795827048918 & -2.64795827048918 \tabularnewline
162 & 4 & 5.99765072947749 & -1.99765072947749 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=226371&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]4[/C][C]4.67779534650291[/C][C]-0.67779534650291[/C][/ROW]
[ROW][C]2[/C][C]4[/C][C]5.64409458111996[/C][C]-1.64409458111996[/C][/ROW]
[ROW][C]3[/C][C]6[/C][C]5.80358086380024[/C][C]0.196419136199755[/C][/ROW]
[ROW][C]4[/C][C]8[/C][C]6.58011873292307[/C][C]1.41988126707693[/C][/ROW]
[ROW][C]5[/C][C]8[/C][C]5.92749160900142[/C][C]2.07250839099858[/C][/ROW]
[ROW][C]6[/C][C]4[/C][C]4.97709882586314[/C][C]-0.977098825863136[/C][/ROW]
[ROW][C]7[/C][C]4[/C][C]4.60227722612709[/C][C]-0.602277226127092[/C][/ROW]
[ROW][C]8[/C][C]8[/C][C]5.98111057042194[/C][C]2.01888942957806[/C][/ROW]
[ROW][C]9[/C][C]5[/C][C]4.63404188181475[/C][C]0.365958118185252[/C][/ROW]
[ROW][C]10[/C][C]4[/C][C]5.73417422570351[/C][C]-1.73417422570351[/C][/ROW]
[ROW][C]11[/C][C]4[/C][C]6.30129894710337[/C][C]-2.30129894710337[/C][/ROW]
[ROW][C]12[/C][C]4[/C][C]4.40954195718433[/C][C]-0.409541957184328[/C][/ROW]
[ROW][C]13[/C][C]4[/C][C]5.25209744178351[/C][C]-1.25209744178351[/C][/ROW]
[ROW][C]14[/C][C]4[/C][C]3.46716348384109[/C][C]0.532836516158908[/C][/ROW]
[ROW][C]15[/C][C]4[/C][C]4.26957669470373[/C][C]-0.26957669470373[/C][/ROW]
[ROW][C]16[/C][C]8[/C][C]5.90877824432901[/C][C]2.09122175567099[/C][/ROW]
[ROW][C]17[/C][C]4[/C][C]7.67865132798167[/C][C]-3.67865132798167[/C][/ROW]
[ROW][C]18[/C][C]4[/C][C]3.79999499006152[/C][C]0.200005009938475[/C][/ROW]
[ROW][C]19[/C][C]4[/C][C]4.24573515527699[/C][C]-0.245735155276992[/C][/ROW]
[ROW][C]20[/C][C]8[/C][C]6.26929313573377[/C][C]1.73070686426623[/C][/ROW]
[ROW][C]21[/C][C]4[/C][C]6.66009176752096[/C][C]-2.66009176752096[/C][/ROW]
[ROW][C]22[/C][C]7[/C][C]6.35754917464843[/C][C]0.642450825351574[/C][/ROW]
[ROW][C]23[/C][C]4[/C][C]8.45468462101732[/C][C]-4.45468462101732[/C][/ROW]
[ROW][C]24[/C][C]4[/C][C]4.67478351570996[/C][C]-0.674783515709956[/C][/ROW]
[ROW][C]25[/C][C]5[/C][C]6.36855299747342[/C][C]-1.36855299747341[/C][/ROW]
[ROW][C]26[/C][C]4[/C][C]5.18687191468166[/C][C]-1.18687191468166[/C][/ROW]
[ROW][C]27[/C][C]4[/C][C]5.18687191468166[/C][C]-1.18687191468166[/C][/ROW]
[ROW][C]28[/C][C]4[/C][C]6.29555242483964[/C][C]-2.29555242483964[/C][/ROW]
[ROW][C]29[/C][C]4[/C][C]8.04220901489974[/C][C]-4.04220901489974[/C][/ROW]
[ROW][C]30[/C][C]4[/C][C]5.64339199777207[/C][C]-1.64339199777206[/C][/ROW]
[ROW][C]31[/C][C]4[/C][C]5.00753753670211[/C][C]-1.00753753670212[/C][/ROW]
[ROW][C]32[/C][C]4[/C][C]5.6896114169806[/C][C]-1.6896114169806[/C][/ROW]
[ROW][C]33[/C][C]15[/C][C]7.14318449665419[/C][C]7.85681550334581[/C][/ROW]
[ROW][C]34[/C][C]10[/C][C]6.66319141900709[/C][C]3.33680858099291[/C][/ROW]
[ROW][C]35[/C][C]4[/C][C]7.32639353827598[/C][C]-3.32639353827598[/C][/ROW]
[ROW][C]36[/C][C]8[/C][C]5.28284783090869[/C][C]2.71715216909131[/C][/ROW]
[ROW][C]37[/C][C]4[/C][C]7.2556007208342[/C][C]-3.25560072083419[/C][/ROW]
[ROW][C]38[/C][C]4[/C][C]5.4683006537913[/C][C]-1.4683006537913[/C][/ROW]
[ROW][C]39[/C][C]4[/C][C]3.89680952085358[/C][C]0.10319047914642[/C][/ROW]
[ROW][C]40[/C][C]4[/C][C]4.62543348597754[/C][C]-0.625433485977537[/C][/ROW]
[ROW][C]41[/C][C]7[/C][C]5.45472074291809[/C][C]1.54527925708191[/C][/ROW]
[ROW][C]42[/C][C]4[/C][C]5.44490682879627[/C][C]-1.44490682879627[/C][/ROW]
[ROW][C]43[/C][C]6[/C][C]5.92023843347151[/C][C]0.0797615665284938[/C][/ROW]
[ROW][C]44[/C][C]5[/C][C]4.50281941016642[/C][C]0.497180589833582[/C][/ROW]
[ROW][C]45[/C][C]4[/C][C]4.9744296430052[/C][C]-0.974429643005199[/C][/ROW]
[ROW][C]46[/C][C]16[/C][C]7.31998931770442[/C][C]8.68001068229558[/C][/ROW]
[ROW][C]47[/C][C]5[/C][C]6.21978117216652[/C][C]-1.21978117216652[/C][/ROW]
[ROW][C]48[/C][C]12[/C][C]7.00442639853704[/C][C]4.99557360146296[/C][/ROW]
[ROW][C]49[/C][C]6[/C][C]5.24913382644289[/C][C]0.750866173557108[/C][/ROW]
[ROW][C]50[/C][C]9[/C][C]7.83288368786999[/C][C]1.16711631213001[/C][/ROW]
[ROW][C]51[/C][C]9[/C][C]5.9380872809612[/C][C]3.0619127190388[/C][/ROW]
[ROW][C]52[/C][C]4[/C][C]6.08080090078978[/C][C]-2.08080090078978[/C][/ROW]
[ROW][C]53[/C][C]5[/C][C]5.68899497074678[/C][C]-0.688994970746776[/C][/ROW]
[ROW][C]54[/C][C]4[/C][C]6.74005783381218[/C][C]-2.74005783381218[/C][/ROW]
[ROW][C]55[/C][C]4[/C][C]5.44244087427586[/C][C]-1.44244087427586[/C][/ROW]
[ROW][C]56[/C][C]5[/C][C]6.06760155458495[/C][C]-1.06760155458495[/C][/ROW]
[ROW][C]57[/C][C]4[/C][C]5.5768112552913[/C][C]-1.5768112552913[/C][/ROW]
[ROW][C]58[/C][C]4[/C][C]4.67318545614157[/C][C]-0.673185456141573[/C][/ROW]
[ROW][C]59[/C][C]4[/C][C]6.84112590933572[/C][C]-2.84112590933572[/C][/ROW]
[ROW][C]60[/C][C]5[/C][C]7.48244782025405[/C][C]-2.48244782025405[/C][/ROW]
[ROW][C]61[/C][C]4[/C][C]6.55234056366725[/C][C]-2.55234056366724[/C][/ROW]
[ROW][C]62[/C][C]6[/C][C]6.87637246475702[/C][C]-0.87637246475702[/C][/ROW]
[ROW][C]63[/C][C]4[/C][C]4.67168218064469[/C][C]-0.671682180644687[/C][/ROW]
[ROW][C]64[/C][C]4[/C][C]5.42112551893753[/C][C]-1.42112551893753[/C][/ROW]
[ROW][C]65[/C][C]18[/C][C]6.33699495357523[/C][C]11.6630050464248[/C][/ROW]
[ROW][C]66[/C][C]4[/C][C]4.52559678892763[/C][C]-0.525596788927626[/C][/ROW]
[ROW][C]67[/C][C]6[/C][C]5.02053027680012[/C][C]0.979469723199881[/C][/ROW]
[ROW][C]68[/C][C]4[/C][C]5.0411361335236[/C][C]-1.0411361335236[/C][/ROW]
[ROW][C]69[/C][C]4[/C][C]5.80003176126356[/C][C]-1.80003176126356[/C][/ROW]
[ROW][C]70[/C][C]5[/C][C]6.88688031034094[/C][C]-1.88688031034094[/C][/ROW]
[ROW][C]71[/C][C]4[/C][C]4.27155532386887[/C][C]-0.271555323868871[/C][/ROW]
[ROW][C]72[/C][C]4[/C][C]4.74545586415899[/C][C]-0.745455864158992[/C][/ROW]
[ROW][C]73[/C][C]5[/C][C]5.77401004791329[/C][C]-0.774010047913293[/C][/ROW]
[ROW][C]74[/C][C]10[/C][C]5.27872526590266[/C][C]4.72127473409734[/C][/ROW]
[ROW][C]75[/C][C]5[/C][C]5.04153394892387[/C][C]-0.0415339489238739[/C][/ROW]
[ROW][C]76[/C][C]8[/C][C]7.54940052287446[/C][C]0.450599477125543[/C][/ROW]
[ROW][C]77[/C][C]8[/C][C]7.51324983934687[/C][C]0.486750160653131[/C][/ROW]
[ROW][C]78[/C][C]5[/C][C]5.16304762598682[/C][C]-0.16304762598682[/C][/ROW]
[ROW][C]79[/C][C]4[/C][C]4.35029018695423[/C][C]-0.350290186954231[/C][/ROW]
[ROW][C]80[/C][C]4[/C][C]7.05371792546355[/C][C]-3.05371792546355[/C][/ROW]
[ROW][C]81[/C][C]4[/C][C]4.21855448733293[/C][C]-0.218554487332932[/C][/ROW]
[ROW][C]82[/C][C]5[/C][C]6.35129469852823[/C][C]-1.35129469852823[/C][/ROW]
[ROW][C]83[/C][C]4[/C][C]5.58455007259735[/C][C]-1.58455007259735[/C][/ROW]
[ROW][C]84[/C][C]4[/C][C]5.48092147617835[/C][C]-1.48092147617835[/C][/ROW]
[ROW][C]85[/C][C]8[/C][C]6.35909902444715[/C][C]1.64090097555285[/C][/ROW]
[ROW][C]86[/C][C]4[/C][C]5.15599072109879[/C][C]-1.15599072109879[/C][/ROW]
[ROW][C]87[/C][C]5[/C][C]4.94059685312579[/C][C]0.0594031468742096[/C][/ROW]
[ROW][C]88[/C][C]14[/C][C]7.55250017436059[/C][C]6.44749982563941[/C][/ROW]
[ROW][C]89[/C][C]8[/C][C]5.33843509095779[/C][C]2.66156490904221[/C][/ROW]
[ROW][C]90[/C][C]8[/C][C]5.913503547263[/C][C]2.086496452737[/C][/ROW]
[ROW][C]91[/C][C]4[/C][C]8.09918080821506[/C][C]-4.09918080821506[/C][/ROW]
[ROW][C]92[/C][C]4[/C][C]4.18390539572309[/C][C]-0.183905395723091[/C][/ROW]
[ROW][C]93[/C][C]6[/C][C]5.91861278202364[/C][C]0.0813872179763552[/C][/ROW]
[ROW][C]94[/C][C]4[/C][C]5.10821453682457[/C][C]-1.10821453682457[/C][/ROW]
[ROW][C]95[/C][C]7[/C][C]6.00438224284508[/C][C]0.99561775715492[/C][/ROW]
[ROW][C]96[/C][C]7[/C][C]5.54057106180872[/C][C]1.45942893819128[/C][/ROW]
[ROW][C]97[/C][C]4[/C][C]5.61547779237073[/C][C]-1.61547779237073[/C][/ROW]
[ROW][C]98[/C][C]6[/C][C]6.22549146127599[/C][C]-0.225491461275987[/C][/ROW]
[ROW][C]99[/C][C]4[/C][C]6.31012259600483[/C][C]-2.31012259600483[/C][/ROW]
[ROW][C]100[/C][C]7[/C][C]4.32931579028909[/C][C]2.67068420971091[/C][/ROW]
[ROW][C]101[/C][C]4[/C][C]6.29085301959497[/C][C]-2.29085301959497[/C][/ROW]
[ROW][C]102[/C][C]4[/C][C]5.22224253456146[/C][C]-1.22224253456146[/C][/ROW]
[ROW][C]103[/C][C]8[/C][C]6.09703797547685[/C][C]1.90296202452315[/C][/ROW]
[ROW][C]104[/C][C]4[/C][C]5.13676075561249[/C][C]-1.13676075561249[/C][/ROW]
[ROW][C]105[/C][C]4[/C][C]6.002668770704[/C][C]-2.002668770704[/C][/ROW]
[ROW][C]106[/C][C]10[/C][C]6.54559538442248[/C][C]3.45440461557752[/C][/ROW]
[ROW][C]107[/C][C]8[/C][C]7.18307718051943[/C][C]0.816922819480568[/C][/ROW]
[ROW][C]108[/C][C]6[/C][C]5.67681494752448[/C][C]0.32318505247552[/C][/ROW]
[ROW][C]109[/C][C]4[/C][C]4.44017862224433[/C][C]-0.440178622244332[/C][/ROW]
[ROW][C]110[/C][C]4[/C][C]5.15669330444669[/C][C]-1.15669330444669[/C][/ROW]
[ROW][C]111[/C][C]4[/C][C]5.45633942605929[/C][C]-1.45633942605929[/C][/ROW]
[ROW][C]112[/C][C]5[/C][C]5.79527549929181[/C][C]-0.795275499291812[/C][/ROW]
[ROW][C]113[/C][C]4[/C][C]6.80923888337973[/C][C]-2.80923888337973[/C][/ROW]
[ROW][C]114[/C][C]6[/C][C]6.3089912382189[/C][C]-0.308991238218899[/C][/ROW]
[ROW][C]115[/C][C]4[/C][C]4.48761384784535[/C][C]-0.487613847845349[/C][/ROW]
[ROW][C]116[/C][C]5[/C][C]6.07096636309514[/C][C]-1.07096636309514[/C][/ROW]
[ROW][C]117[/C][C]7[/C][C]7.48512733857693[/C][C]-0.48512733857693[/C][/ROW]
[ROW][C]118[/C][C]8[/C][C]5.73461165202735[/C][C]2.26538834797265[/C][/ROW]
[ROW][C]119[/C][C]5[/C][C]6.26280109446898[/C][C]-1.26280109446898[/C][/ROW]
[ROW][C]120[/C][C]8[/C][C]7.64322216779566[/C][C]0.356777832204343[/C][/ROW]
[ROW][C]121[/C][C]10[/C][C]6.27762777570082[/C][C]3.72237222429918[/C][/ROW]
[ROW][C]122[/C][C]8[/C][C]5.90280111461633[/C][C]2.09719888538367[/C][/ROW]
[ROW][C]123[/C][C]5[/C][C]6.61328686111636[/C][C]-1.61328686111636[/C][/ROW]
[ROW][C]124[/C][C]12[/C][C]7.04770456259662[/C][C]4.95229543740338[/C][/ROW]
[ROW][C]125[/C][C]4[/C][C]3.91195653225741[/C][C]0.0880434677425872[/C][/ROW]
[ROW][C]126[/C][C]5[/C][C]4.61985923301358[/C][C]0.380140766986423[/C][/ROW]
[ROW][C]127[/C][C]4[/C][C]5.45987819313103[/C][C]-1.45987819313103[/C][/ROW]
[ROW][C]128[/C][C]6[/C][C]5.64124806193118[/C][C]0.358751938068822[/C][/ROW]
[ROW][C]129[/C][C]4[/C][C]6.53573157126922[/C][C]-2.53573157126922[/C][/ROW]
[ROW][C]130[/C][C]4[/C][C]5.3793403479496[/C][C]-1.3793403479496[/C][/ROW]
[ROW][C]131[/C][C]7[/C][C]6.37392402209985[/C][C]0.626075977900154[/C][/ROW]
[ROW][C]132[/C][C]7[/C][C]5.41549103147719[/C][C]1.58450896852281[/C][/ROW]
[ROW][C]133[/C][C]10[/C][C]7.02011933357045[/C][C]2.97988066642955[/C][/ROW]
[ROW][C]134[/C][C]4[/C][C]5.8906779530495[/C][C]-1.8906779530495[/C][/ROW]
[ROW][C]135[/C][C]5[/C][C]5.5195277787614[/C][C]-0.519527778761405[/C][/ROW]
[ROW][C]136[/C][C]8[/C][C]5.46623589244047[/C][C]2.53376410755953[/C][/ROW]
[ROW][C]137[/C][C]11[/C][C]5.39489551021865[/C][C]5.60510448978135[/C][/ROW]
[ROW][C]138[/C][C]7[/C][C]6.27800496752828[/C][C]0.721995032471724[/C][/ROW]
[ROW][C]139[/C][C]4[/C][C]4.50635648797638[/C][C]-0.506356487976382[/C][/ROW]
[ROW][C]140[/C][C]8[/C][C]5.68432821873002[/C][C]2.31567178126998[/C][/ROW]
[ROW][C]141[/C][C]6[/C][C]6.83514518908568[/C][C]-0.835145189085682[/C][/ROW]
[ROW][C]142[/C][C]7[/C][C]5.66619865199189[/C][C]1.33380134800811[/C][/ROW]
[ROW][C]143[/C][C]5[/C][C]7.37741410449633[/C][C]-2.37741410449633[/C][/ROW]
[ROW][C]144[/C][C]4[/C][C]5.85344749434655[/C][C]-1.85344749434655[/C][/ROW]
[ROW][C]145[/C][C]8[/C][C]4.11029533455114[/C][C]3.88970466544886[/C][/ROW]
[ROW][C]146[/C][C]4[/C][C]6.48150122003494[/C][C]-2.48150122003494[/C][/ROW]
[ROW][C]147[/C][C]8[/C][C]6.08761158864742[/C][C]1.91238841135258[/C][/ROW]
[ROW][C]148[/C][C]6[/C][C]4.20032845294417[/C][C]1.79967154705583[/C][/ROW]
[ROW][C]149[/C][C]4[/C][C]5.34844358721397[/C][C]-1.34844358721397[/C][/ROW]
[ROW][C]150[/C][C]9[/C][C]5.75379509132313[/C][C]3.24620490867687[/C][/ROW]
[ROW][C]151[/C][C]5[/C][C]5.69796330767955[/C][C]-0.697963307679547[/C][/ROW]
[ROW][C]152[/C][C]6[/C][C]5.6242378339349[/C][C]0.375762166065102[/C][/ROW]
[ROW][C]153[/C][C]4[/C][C]4.89481823308201[/C][C]-0.89481823308201[/C][/ROW]
[ROW][C]154[/C][C]4[/C][C]4.92348154802176[/C][C]-0.923481548021758[/C][/ROW]
[ROW][C]155[/C][C]4[/C][C]4.27143991129618[/C][C]-0.271439911296177[/C][/ROW]
[ROW][C]156[/C][C]5[/C][C]6.41537852745083[/C][C]-1.41537852745083[/C][/ROW]
[ROW][C]157[/C][C]6[/C][C]6.26324020437196[/C][C]-0.263240204371958[/C][/ROW]
[ROW][C]158[/C][C]16[/C][C]7.84622771717775[/C][C]8.15377228282225[/C][/ROW]
[ROW][C]159[/C][C]6[/C][C]5.91861278202364[/C][C]0.0813872179763552[/C][/ROW]
[ROW][C]160[/C][C]6[/C][C]6.08703812617808[/C][C]-0.0870381261780793[/C][/ROW]
[ROW][C]161[/C][C]4[/C][C]6.64795827048918[/C][C]-2.64795827048918[/C][/ROW]
[ROW][C]162[/C][C]4[/C][C]5.99765072947749[/C][C]-1.99765072947749[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=226371&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=226371&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
144.67779534650291-0.67779534650291
245.64409458111996-1.64409458111996
365.803580863800240.196419136199755
486.580118732923071.41988126707693
585.927491609001422.07250839099858
644.97709882586314-0.977098825863136
744.60227722612709-0.602277226127092
885.981110570421942.01888942957806
954.634041881814750.365958118185252
1045.73417422570351-1.73417422570351
1146.30129894710337-2.30129894710337
1244.40954195718433-0.409541957184328
1345.25209744178351-1.25209744178351
1443.467163483841090.532836516158908
1544.26957669470373-0.26957669470373
1685.908778244329012.09122175567099
1747.67865132798167-3.67865132798167
1843.799994990061520.200005009938475
1944.24573515527699-0.245735155276992
2086.269293135733771.73070686426623
2146.66009176752096-2.66009176752096
2276.357549174648430.642450825351574
2348.45468462101732-4.45468462101732
2444.67478351570996-0.674783515709956
2556.36855299747342-1.36855299747341
2645.18687191468166-1.18687191468166
2745.18687191468166-1.18687191468166
2846.29555242483964-2.29555242483964
2948.04220901489974-4.04220901489974
3045.64339199777207-1.64339199777206
3145.00753753670211-1.00753753670212
3245.6896114169806-1.6896114169806
33157.143184496654197.85681550334581
34106.663191419007093.33680858099291
3547.32639353827598-3.32639353827598
3685.282847830908692.71715216909131
3747.2556007208342-3.25560072083419
3845.4683006537913-1.4683006537913
3943.896809520853580.10319047914642
4044.62543348597754-0.625433485977537
4175.454720742918091.54527925708191
4245.44490682879627-1.44490682879627
4365.920238433471510.0797615665284938
4454.502819410166420.497180589833582
4544.9744296430052-0.974429643005199
46167.319989317704428.68001068229558
4756.21978117216652-1.21978117216652
48127.004426398537044.99557360146296
4965.249133826442890.750866173557108
5097.832883687869991.16711631213001
5195.93808728096123.0619127190388
5246.08080090078978-2.08080090078978
5355.68899497074678-0.688994970746776
5446.74005783381218-2.74005783381218
5545.44244087427586-1.44244087427586
5656.06760155458495-1.06760155458495
5745.5768112552913-1.5768112552913
5844.67318545614157-0.673185456141573
5946.84112590933572-2.84112590933572
6057.48244782025405-2.48244782025405
6146.55234056366725-2.55234056366724
6266.87637246475702-0.87637246475702
6344.67168218064469-0.671682180644687
6445.42112551893753-1.42112551893753
65186.3369949535752311.6630050464248
6644.52559678892763-0.525596788927626
6765.020530276800120.979469723199881
6845.0411361335236-1.0411361335236
6945.80003176126356-1.80003176126356
7056.88688031034094-1.88688031034094
7144.27155532386887-0.271555323868871
7244.74545586415899-0.745455864158992
7355.77401004791329-0.774010047913293
74105.278725265902664.72127473409734
7555.04153394892387-0.0415339489238739
7687.549400522874460.450599477125543
7787.513249839346870.486750160653131
7855.16304762598682-0.16304762598682
7944.35029018695423-0.350290186954231
8047.05371792546355-3.05371792546355
8144.21855448733293-0.218554487332932
8256.35129469852823-1.35129469852823
8345.58455007259735-1.58455007259735
8445.48092147617835-1.48092147617835
8586.359099024447151.64090097555285
8645.15599072109879-1.15599072109879
8754.940596853125790.0594031468742096
88147.552500174360596.44749982563941
8985.338435090957792.66156490904221
9085.9135035472632.086496452737
9148.09918080821506-4.09918080821506
9244.18390539572309-0.183905395723091
9365.918612782023640.0813872179763552
9445.10821453682457-1.10821453682457
9576.004382242845080.99561775715492
9675.540571061808721.45942893819128
9745.61547779237073-1.61547779237073
9866.22549146127599-0.225491461275987
9946.31012259600483-2.31012259600483
10074.329315790289092.67068420971091
10146.29085301959497-2.29085301959497
10245.22224253456146-1.22224253456146
10386.097037975476851.90296202452315
10445.13676075561249-1.13676075561249
10546.002668770704-2.002668770704
106106.545595384422483.45440461557752
10787.183077180519430.816922819480568
10865.676814947524480.32318505247552
10944.44017862224433-0.440178622244332
11045.15669330444669-1.15669330444669
11145.45633942605929-1.45633942605929
11255.79527549929181-0.795275499291812
11346.80923888337973-2.80923888337973
11466.3089912382189-0.308991238218899
11544.48761384784535-0.487613847845349
11656.07096636309514-1.07096636309514
11777.48512733857693-0.48512733857693
11885.734611652027352.26538834797265
11956.26280109446898-1.26280109446898
12087.643222167795660.356777832204343
121106.277627775700823.72237222429918
12285.902801114616332.09719888538367
12356.61328686111636-1.61328686111636
124127.047704562596624.95229543740338
12543.911956532257410.0880434677425872
12654.619859233013580.380140766986423
12745.45987819313103-1.45987819313103
12865.641248061931180.358751938068822
12946.53573157126922-2.53573157126922
13045.3793403479496-1.3793403479496
13176.373924022099850.626075977900154
13275.415491031477191.58450896852281
133107.020119333570452.97988066642955
13445.8906779530495-1.8906779530495
13555.5195277787614-0.519527778761405
13685.466235892440472.53376410755953
137115.394895510218655.60510448978135
13876.278004967528280.721995032471724
13944.50635648797638-0.506356487976382
14085.684328218730022.31567178126998
14166.83514518908568-0.835145189085682
14275.666198651991891.33380134800811
14357.37741410449633-2.37741410449633
14445.85344749434655-1.85344749434655
14584.110295334551143.88970466544886
14646.48150122003494-2.48150122003494
14786.087611588647421.91238841135258
14864.200328452944171.79967154705583
14945.34844358721397-1.34844358721397
15095.753795091323133.24620490867687
15155.69796330767955-0.697963307679547
15265.62423783393490.375762166065102
15344.89481823308201-0.89481823308201
15444.92348154802176-0.923481548021758
15544.27143991129618-0.271439911296177
15656.41537852745083-1.41537852745083
15766.26324020437196-0.263240204371958
158167.846227717177758.15377228282225
15965.918612782023640.0813872179763552
16066.08703812617808-0.0870381261780793
16146.64795827048918-2.64795827048918
16245.99765072947749-1.99765072947749






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
90.06431423862258140.1286284772451630.935685761377419
100.2120305966806160.4240611933612320.787969403319384
110.1940115049469240.3880230098938490.805988495053076
120.1610472840683160.3220945681366330.838952715931684
130.1020229680531250.204045936106250.897977031946875
140.08894014902010460.1778802980402090.911059850979895
150.05387919299801640.1077583859960330.946120807001984
160.05192299175555590.1038459835111120.948077008244444
170.05061306352834350.1012261270566870.949386936471657
180.03412273038455610.06824546076911230.965877269615444
190.02148757986657890.04297515973315770.978512420133421
200.02314104962552130.04628209925104270.976858950374479
210.02027380636899250.04054761273798510.979726193631007
220.01257598133707150.02515196267414290.987424018662929
230.01040075274592820.02080150549185640.989599247254072
240.006192369851985120.01238473970397020.993807630148015
250.003506380276660820.007012760553321650.996493619723339
260.003144411496276890.006288822992553780.996855588503723
270.002100076483077730.004200152966155470.997899923516922
280.002197377794428380.004394755588856760.997802622205572
290.002905951197941990.005811902395883970.997094048802058
300.002190259951571970.004380519903143930.997809740048428
310.001259567321918490.002519134643836980.998740432678082
320.0008221032242860560.001644206448572110.999177896775714
330.170408182058890.340816364117780.82959181794111
340.3109283798394650.621856759678930.689071620160535
350.3291394848449720.6582789696899440.670860515155028
360.3336284039339180.6672568078678360.666371596066082
370.3226970280852520.6453940561705050.677302971914748
380.2847997413116830.5695994826233660.715200258688317
390.2385488977183180.4770977954366350.761451102281682
400.1982462712736320.3964925425472630.801753728726368
410.2134956522160010.4269913044320010.786504347783999
420.1801495614280340.3602991228560680.819850438571966
430.1483698395802680.2967396791605350.851630160419732
440.1196334082676450.2392668165352910.880366591732355
450.1009944886572830.2019889773145660.899005511342717
460.7368747827993290.5262504344013420.263125217200671
470.7043892194661040.5912215610677920.295610780533896
480.8264529762884880.3470940474230250.173547023711512
490.7957936475649070.4084127048701860.204206352435093
500.7765347484412060.4469305031175890.223465251558795
510.7973388609300640.4053222781398720.202661139069936
520.7842341198853890.4315317602292220.215765880114611
530.7483629716901730.5032740566196540.251637028309827
540.7598715761892040.4802568476215930.240128423810796
550.7345806881276150.5308386237447710.265419311872385
560.6993304089933250.601339182013350.300669591006675
570.6711461756808410.6577076486383170.328853824319159
580.6292214914594340.7415570170811320.370778508540566
590.6334283652200030.7331432695599950.366571634779997
600.6286387206799510.7427225586400990.371361279320049
610.6277752641335150.7444494717329690.372224735866484
620.5874332128587950.8251335742824110.412566787141205
630.5433381774272090.9133236451455830.456661822572791
640.5108514686450170.9782970627099670.489148531354983
650.9940666852337190.01186662953256280.00593331476628138
660.9918717468784560.01625650624308880.0081282531215444
670.9894200580713360.02115988385732850.0105799419286643
680.9863665236872140.02726695262557150.0136334763127857
690.9846002489199480.03079950216010430.0153997510800521
700.9829055075202030.0341889849595940.017094492479797
710.9778115557572320.0443768884855360.022188444242768
720.9719949964926360.05601000701472750.0280050035073638
730.964789571192060.07042085761587950.0352104288079398
740.98446469390730.03107061218540070.0155353060927003
750.9795217818925830.04095643621483340.0204782181074167
760.9733278253108720.05334434937825580.0266721746891279
770.9656387752886760.06872244942264820.0343612247113241
780.9561545945865980.0876908108268030.0438454054134015
790.945116146661480.109767706677040.05488385333852
800.9489758964649390.1020482070701210.0510241035350606
810.9388833053024630.1222333893950740.0611166946975369
820.9291234549632620.1417530900734760.0708765450367378
830.9211076403604680.1577847192790630.0788923596395317
840.9090621547076970.1818756905846060.0909378452923032
850.8985480265563990.2029039468872020.101451973443601
860.8826526111970950.2346947776058110.117347388802905
870.8599569758355550.280086048328890.140043024164445
880.9602748116893730.07945037662125340.0397251883106267
890.9587103063316480.08257938733670310.0412896936683516
900.9532145956432070.09357080871358570.0467854043567928
910.9662920792818230.06741584143635470.0337079207181773
920.9564555976354650.08708880472907040.0435444023645352
930.9444021876771790.1111956246456410.0555978123228207
940.9322620994184590.1354758011630830.0677379005815414
950.9177810300302570.1644379399394860.0822189699697429
960.9039061255460640.1921877489078710.0960938744539357
970.8920291122311790.2159417755376420.107970887768821
980.868292091837290.2634158163254190.13170790816271
990.8653149423128140.2693701153743730.134685057687186
1000.8676292892452980.2647414215094050.132370710754702
1010.8681109584718040.2637780830563910.131889041528196
1020.846227709491830.307544581016340.15377229050817
1030.8310702932874290.3378594134251430.168929706712571
1040.807492947541580.3850141049168410.19250705245842
1050.799640143026930.400719713946140.20035985697307
1060.8252445507529190.3495108984941620.174755449247081
1070.7942645423701250.4114709152597490.205735457629875
1080.7574234179996530.4851531640006930.242576582000347
1090.7184995041276450.5630009917447110.281500495872355
1100.6880637965928230.6238724068143530.311936203407177
1110.6643310334173180.6713379331653630.335668966582681
1120.6253757142357060.7492485715285890.374624285764294
1130.6431179294578690.7137641410842610.356882070542131
1140.5967247298854650.8065505402290710.403275270114536
1150.550581549586850.89883690082630.44941845041315
1160.5120171949911760.9759656100176480.487982805008824
1170.4727162015215990.9454324030431980.527283798478401
1180.4515816112005310.9031632224010620.548418388799469
1190.4218854363283610.8437708726567220.578114563671639
1200.3743054113410290.7486108226820580.625694588658971
1210.4241479356528720.8482958713057440.575852064347128
1220.3923793972652810.7847587945305620.607620602734719
1230.3738417943686790.7476835887373580.626158205631321
1240.5763220104791230.8473559790417550.423677989520877
1250.5241448126959140.9517103746081730.475855187304086
1260.4667478088649670.9334956177299330.533252191135033
1270.4450417264151090.8900834528302180.554958273584891
1280.3884031114339270.7768062228678550.611596888566073
1290.3853846515254850.770769303050970.614615348474515
1300.3529263389924830.7058526779849660.647073661007517
1310.3004753793937820.6009507587875630.699524620606218
1320.2579864390794080.5159728781588170.742013560920592
1330.324968888869720.6499377777394410.67503111113028
1340.2898885026815430.5797770053630860.710111497318457
1350.291352701593330.582705403186660.70864729840667
1360.4089228509491510.8178457018983030.591077149050849
1370.5451712672176420.9096574655647160.454828732782358
1380.481563231812350.96312646362470.51843676818765
1390.4476116073176210.8952232146352410.552388392682379
1400.3894707423258360.7789414846516710.610529257674164
1410.3371574316471910.6743148632943830.662842568352809
1420.3030000845004180.6060001690008360.696999915499582
1430.4859565454870490.9719130909740980.514043454512951
1440.5452043050446020.9095913899107970.454795694955398
1450.5682842213191750.8634315573616510.431715778680825
1460.5312635194868930.9374729610262150.468736480513107
1470.472576827968680.9451536559373610.52742317203132
1480.7794681465108920.4410637069782160.220531853489108
1490.6886308044973890.6227383910052220.311369195502611
1500.8294106468580490.3411787062839020.170589353141951
1510.7362602995886470.5274794008227060.263739700411353
1520.6469864476509470.7060271046981070.353013552349053
1530.8130513628567410.3738972742865170.186948637143259

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
9 & 0.0643142386225814 & 0.128628477245163 & 0.935685761377419 \tabularnewline
10 & 0.212030596680616 & 0.424061193361232 & 0.787969403319384 \tabularnewline
11 & 0.194011504946924 & 0.388023009893849 & 0.805988495053076 \tabularnewline
12 & 0.161047284068316 & 0.322094568136633 & 0.838952715931684 \tabularnewline
13 & 0.102022968053125 & 0.20404593610625 & 0.897977031946875 \tabularnewline
14 & 0.0889401490201046 & 0.177880298040209 & 0.911059850979895 \tabularnewline
15 & 0.0538791929980164 & 0.107758385996033 & 0.946120807001984 \tabularnewline
16 & 0.0519229917555559 & 0.103845983511112 & 0.948077008244444 \tabularnewline
17 & 0.0506130635283435 & 0.101226127056687 & 0.949386936471657 \tabularnewline
18 & 0.0341227303845561 & 0.0682454607691123 & 0.965877269615444 \tabularnewline
19 & 0.0214875798665789 & 0.0429751597331577 & 0.978512420133421 \tabularnewline
20 & 0.0231410496255213 & 0.0462820992510427 & 0.976858950374479 \tabularnewline
21 & 0.0202738063689925 & 0.0405476127379851 & 0.979726193631007 \tabularnewline
22 & 0.0125759813370715 & 0.0251519626741429 & 0.987424018662929 \tabularnewline
23 & 0.0104007527459282 & 0.0208015054918564 & 0.989599247254072 \tabularnewline
24 & 0.00619236985198512 & 0.0123847397039702 & 0.993807630148015 \tabularnewline
25 & 0.00350638027666082 & 0.00701276055332165 & 0.996493619723339 \tabularnewline
26 & 0.00314441149627689 & 0.00628882299255378 & 0.996855588503723 \tabularnewline
27 & 0.00210007648307773 & 0.00420015296615547 & 0.997899923516922 \tabularnewline
28 & 0.00219737779442838 & 0.00439475558885676 & 0.997802622205572 \tabularnewline
29 & 0.00290595119794199 & 0.00581190239588397 & 0.997094048802058 \tabularnewline
30 & 0.00219025995157197 & 0.00438051990314393 & 0.997809740048428 \tabularnewline
31 & 0.00125956732191849 & 0.00251913464383698 & 0.998740432678082 \tabularnewline
32 & 0.000822103224286056 & 0.00164420644857211 & 0.999177896775714 \tabularnewline
33 & 0.17040818205889 & 0.34081636411778 & 0.82959181794111 \tabularnewline
34 & 0.310928379839465 & 0.62185675967893 & 0.689071620160535 \tabularnewline
35 & 0.329139484844972 & 0.658278969689944 & 0.670860515155028 \tabularnewline
36 & 0.333628403933918 & 0.667256807867836 & 0.666371596066082 \tabularnewline
37 & 0.322697028085252 & 0.645394056170505 & 0.677302971914748 \tabularnewline
38 & 0.284799741311683 & 0.569599482623366 & 0.715200258688317 \tabularnewline
39 & 0.238548897718318 & 0.477097795436635 & 0.761451102281682 \tabularnewline
40 & 0.198246271273632 & 0.396492542547263 & 0.801753728726368 \tabularnewline
41 & 0.213495652216001 & 0.426991304432001 & 0.786504347783999 \tabularnewline
42 & 0.180149561428034 & 0.360299122856068 & 0.819850438571966 \tabularnewline
43 & 0.148369839580268 & 0.296739679160535 & 0.851630160419732 \tabularnewline
44 & 0.119633408267645 & 0.239266816535291 & 0.880366591732355 \tabularnewline
45 & 0.100994488657283 & 0.201988977314566 & 0.899005511342717 \tabularnewline
46 & 0.736874782799329 & 0.526250434401342 & 0.263125217200671 \tabularnewline
47 & 0.704389219466104 & 0.591221561067792 & 0.295610780533896 \tabularnewline
48 & 0.826452976288488 & 0.347094047423025 & 0.173547023711512 \tabularnewline
49 & 0.795793647564907 & 0.408412704870186 & 0.204206352435093 \tabularnewline
50 & 0.776534748441206 & 0.446930503117589 & 0.223465251558795 \tabularnewline
51 & 0.797338860930064 & 0.405322278139872 & 0.202661139069936 \tabularnewline
52 & 0.784234119885389 & 0.431531760229222 & 0.215765880114611 \tabularnewline
53 & 0.748362971690173 & 0.503274056619654 & 0.251637028309827 \tabularnewline
54 & 0.759871576189204 & 0.480256847621593 & 0.240128423810796 \tabularnewline
55 & 0.734580688127615 & 0.530838623744771 & 0.265419311872385 \tabularnewline
56 & 0.699330408993325 & 0.60133918201335 & 0.300669591006675 \tabularnewline
57 & 0.671146175680841 & 0.657707648638317 & 0.328853824319159 \tabularnewline
58 & 0.629221491459434 & 0.741557017081132 & 0.370778508540566 \tabularnewline
59 & 0.633428365220003 & 0.733143269559995 & 0.366571634779997 \tabularnewline
60 & 0.628638720679951 & 0.742722558640099 & 0.371361279320049 \tabularnewline
61 & 0.627775264133515 & 0.744449471732969 & 0.372224735866484 \tabularnewline
62 & 0.587433212858795 & 0.825133574282411 & 0.412566787141205 \tabularnewline
63 & 0.543338177427209 & 0.913323645145583 & 0.456661822572791 \tabularnewline
64 & 0.510851468645017 & 0.978297062709967 & 0.489148531354983 \tabularnewline
65 & 0.994066685233719 & 0.0118666295325628 & 0.00593331476628138 \tabularnewline
66 & 0.991871746878456 & 0.0162565062430888 & 0.0081282531215444 \tabularnewline
67 & 0.989420058071336 & 0.0211598838573285 & 0.0105799419286643 \tabularnewline
68 & 0.986366523687214 & 0.0272669526255715 & 0.0136334763127857 \tabularnewline
69 & 0.984600248919948 & 0.0307995021601043 & 0.0153997510800521 \tabularnewline
70 & 0.982905507520203 & 0.034188984959594 & 0.017094492479797 \tabularnewline
71 & 0.977811555757232 & 0.044376888485536 & 0.022188444242768 \tabularnewline
72 & 0.971994996492636 & 0.0560100070147275 & 0.0280050035073638 \tabularnewline
73 & 0.96478957119206 & 0.0704208576158795 & 0.0352104288079398 \tabularnewline
74 & 0.9844646939073 & 0.0310706121854007 & 0.0155353060927003 \tabularnewline
75 & 0.979521781892583 & 0.0409564362148334 & 0.0204782181074167 \tabularnewline
76 & 0.973327825310872 & 0.0533443493782558 & 0.0266721746891279 \tabularnewline
77 & 0.965638775288676 & 0.0687224494226482 & 0.0343612247113241 \tabularnewline
78 & 0.956154594586598 & 0.087690810826803 & 0.0438454054134015 \tabularnewline
79 & 0.94511614666148 & 0.10976770667704 & 0.05488385333852 \tabularnewline
80 & 0.948975896464939 & 0.102048207070121 & 0.0510241035350606 \tabularnewline
81 & 0.938883305302463 & 0.122233389395074 & 0.0611166946975369 \tabularnewline
82 & 0.929123454963262 & 0.141753090073476 & 0.0708765450367378 \tabularnewline
83 & 0.921107640360468 & 0.157784719279063 & 0.0788923596395317 \tabularnewline
84 & 0.909062154707697 & 0.181875690584606 & 0.0909378452923032 \tabularnewline
85 & 0.898548026556399 & 0.202903946887202 & 0.101451973443601 \tabularnewline
86 & 0.882652611197095 & 0.234694777605811 & 0.117347388802905 \tabularnewline
87 & 0.859956975835555 & 0.28008604832889 & 0.140043024164445 \tabularnewline
88 & 0.960274811689373 & 0.0794503766212534 & 0.0397251883106267 \tabularnewline
89 & 0.958710306331648 & 0.0825793873367031 & 0.0412896936683516 \tabularnewline
90 & 0.953214595643207 & 0.0935708087135857 & 0.0467854043567928 \tabularnewline
91 & 0.966292079281823 & 0.0674158414363547 & 0.0337079207181773 \tabularnewline
92 & 0.956455597635465 & 0.0870888047290704 & 0.0435444023645352 \tabularnewline
93 & 0.944402187677179 & 0.111195624645641 & 0.0555978123228207 \tabularnewline
94 & 0.932262099418459 & 0.135475801163083 & 0.0677379005815414 \tabularnewline
95 & 0.917781030030257 & 0.164437939939486 & 0.0822189699697429 \tabularnewline
96 & 0.903906125546064 & 0.192187748907871 & 0.0960938744539357 \tabularnewline
97 & 0.892029112231179 & 0.215941775537642 & 0.107970887768821 \tabularnewline
98 & 0.86829209183729 & 0.263415816325419 & 0.13170790816271 \tabularnewline
99 & 0.865314942312814 & 0.269370115374373 & 0.134685057687186 \tabularnewline
100 & 0.867629289245298 & 0.264741421509405 & 0.132370710754702 \tabularnewline
101 & 0.868110958471804 & 0.263778083056391 & 0.131889041528196 \tabularnewline
102 & 0.84622770949183 & 0.30754458101634 & 0.15377229050817 \tabularnewline
103 & 0.831070293287429 & 0.337859413425143 & 0.168929706712571 \tabularnewline
104 & 0.80749294754158 & 0.385014104916841 & 0.19250705245842 \tabularnewline
105 & 0.79964014302693 & 0.40071971394614 & 0.20035985697307 \tabularnewline
106 & 0.825244550752919 & 0.349510898494162 & 0.174755449247081 \tabularnewline
107 & 0.794264542370125 & 0.411470915259749 & 0.205735457629875 \tabularnewline
108 & 0.757423417999653 & 0.485153164000693 & 0.242576582000347 \tabularnewline
109 & 0.718499504127645 & 0.563000991744711 & 0.281500495872355 \tabularnewline
110 & 0.688063796592823 & 0.623872406814353 & 0.311936203407177 \tabularnewline
111 & 0.664331033417318 & 0.671337933165363 & 0.335668966582681 \tabularnewline
112 & 0.625375714235706 & 0.749248571528589 & 0.374624285764294 \tabularnewline
113 & 0.643117929457869 & 0.713764141084261 & 0.356882070542131 \tabularnewline
114 & 0.596724729885465 & 0.806550540229071 & 0.403275270114536 \tabularnewline
115 & 0.55058154958685 & 0.8988369008263 & 0.44941845041315 \tabularnewline
116 & 0.512017194991176 & 0.975965610017648 & 0.487982805008824 \tabularnewline
117 & 0.472716201521599 & 0.945432403043198 & 0.527283798478401 \tabularnewline
118 & 0.451581611200531 & 0.903163222401062 & 0.548418388799469 \tabularnewline
119 & 0.421885436328361 & 0.843770872656722 & 0.578114563671639 \tabularnewline
120 & 0.374305411341029 & 0.748610822682058 & 0.625694588658971 \tabularnewline
121 & 0.424147935652872 & 0.848295871305744 & 0.575852064347128 \tabularnewline
122 & 0.392379397265281 & 0.784758794530562 & 0.607620602734719 \tabularnewline
123 & 0.373841794368679 & 0.747683588737358 & 0.626158205631321 \tabularnewline
124 & 0.576322010479123 & 0.847355979041755 & 0.423677989520877 \tabularnewline
125 & 0.524144812695914 & 0.951710374608173 & 0.475855187304086 \tabularnewline
126 & 0.466747808864967 & 0.933495617729933 & 0.533252191135033 \tabularnewline
127 & 0.445041726415109 & 0.890083452830218 & 0.554958273584891 \tabularnewline
128 & 0.388403111433927 & 0.776806222867855 & 0.611596888566073 \tabularnewline
129 & 0.385384651525485 & 0.77076930305097 & 0.614615348474515 \tabularnewline
130 & 0.352926338992483 & 0.705852677984966 & 0.647073661007517 \tabularnewline
131 & 0.300475379393782 & 0.600950758787563 & 0.699524620606218 \tabularnewline
132 & 0.257986439079408 & 0.515972878158817 & 0.742013560920592 \tabularnewline
133 & 0.32496888886972 & 0.649937777739441 & 0.67503111113028 \tabularnewline
134 & 0.289888502681543 & 0.579777005363086 & 0.710111497318457 \tabularnewline
135 & 0.29135270159333 & 0.58270540318666 & 0.70864729840667 \tabularnewline
136 & 0.408922850949151 & 0.817845701898303 & 0.591077149050849 \tabularnewline
137 & 0.545171267217642 & 0.909657465564716 & 0.454828732782358 \tabularnewline
138 & 0.48156323181235 & 0.9631264636247 & 0.51843676818765 \tabularnewline
139 & 0.447611607317621 & 0.895223214635241 & 0.552388392682379 \tabularnewline
140 & 0.389470742325836 & 0.778941484651671 & 0.610529257674164 \tabularnewline
141 & 0.337157431647191 & 0.674314863294383 & 0.662842568352809 \tabularnewline
142 & 0.303000084500418 & 0.606000169000836 & 0.696999915499582 \tabularnewline
143 & 0.485956545487049 & 0.971913090974098 & 0.514043454512951 \tabularnewline
144 & 0.545204305044602 & 0.909591389910797 & 0.454795694955398 \tabularnewline
145 & 0.568284221319175 & 0.863431557361651 & 0.431715778680825 \tabularnewline
146 & 0.531263519486893 & 0.937472961026215 & 0.468736480513107 \tabularnewline
147 & 0.47257682796868 & 0.945153655937361 & 0.52742317203132 \tabularnewline
148 & 0.779468146510892 & 0.441063706978216 & 0.220531853489108 \tabularnewline
149 & 0.688630804497389 & 0.622738391005222 & 0.311369195502611 \tabularnewline
150 & 0.829410646858049 & 0.341178706283902 & 0.170589353141951 \tabularnewline
151 & 0.736260299588647 & 0.527479400822706 & 0.263739700411353 \tabularnewline
152 & 0.646986447650947 & 0.706027104698107 & 0.353013552349053 \tabularnewline
153 & 0.813051362856741 & 0.373897274286517 & 0.186948637143259 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=226371&T=5

[TABLE]
[ROW][C]Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]p-values[/C][C]Alternative Hypothesis[/C][/ROW]
[ROW][C]breakpoint index[/C][C]greater[/C][C]2-sided[/C][C]less[/C][/ROW]
[ROW][C]9[/C][C]0.0643142386225814[/C][C]0.128628477245163[/C][C]0.935685761377419[/C][/ROW]
[ROW][C]10[/C][C]0.212030596680616[/C][C]0.424061193361232[/C][C]0.787969403319384[/C][/ROW]
[ROW][C]11[/C][C]0.194011504946924[/C][C]0.388023009893849[/C][C]0.805988495053076[/C][/ROW]
[ROW][C]12[/C][C]0.161047284068316[/C][C]0.322094568136633[/C][C]0.838952715931684[/C][/ROW]
[ROW][C]13[/C][C]0.102022968053125[/C][C]0.20404593610625[/C][C]0.897977031946875[/C][/ROW]
[ROW][C]14[/C][C]0.0889401490201046[/C][C]0.177880298040209[/C][C]0.911059850979895[/C][/ROW]
[ROW][C]15[/C][C]0.0538791929980164[/C][C]0.107758385996033[/C][C]0.946120807001984[/C][/ROW]
[ROW][C]16[/C][C]0.0519229917555559[/C][C]0.103845983511112[/C][C]0.948077008244444[/C][/ROW]
[ROW][C]17[/C][C]0.0506130635283435[/C][C]0.101226127056687[/C][C]0.949386936471657[/C][/ROW]
[ROW][C]18[/C][C]0.0341227303845561[/C][C]0.0682454607691123[/C][C]0.965877269615444[/C][/ROW]
[ROW][C]19[/C][C]0.0214875798665789[/C][C]0.0429751597331577[/C][C]0.978512420133421[/C][/ROW]
[ROW][C]20[/C][C]0.0231410496255213[/C][C]0.0462820992510427[/C][C]0.976858950374479[/C][/ROW]
[ROW][C]21[/C][C]0.0202738063689925[/C][C]0.0405476127379851[/C][C]0.979726193631007[/C][/ROW]
[ROW][C]22[/C][C]0.0125759813370715[/C][C]0.0251519626741429[/C][C]0.987424018662929[/C][/ROW]
[ROW][C]23[/C][C]0.0104007527459282[/C][C]0.0208015054918564[/C][C]0.989599247254072[/C][/ROW]
[ROW][C]24[/C][C]0.00619236985198512[/C][C]0.0123847397039702[/C][C]0.993807630148015[/C][/ROW]
[ROW][C]25[/C][C]0.00350638027666082[/C][C]0.00701276055332165[/C][C]0.996493619723339[/C][/ROW]
[ROW][C]26[/C][C]0.00314441149627689[/C][C]0.00628882299255378[/C][C]0.996855588503723[/C][/ROW]
[ROW][C]27[/C][C]0.00210007648307773[/C][C]0.00420015296615547[/C][C]0.997899923516922[/C][/ROW]
[ROW][C]28[/C][C]0.00219737779442838[/C][C]0.00439475558885676[/C][C]0.997802622205572[/C][/ROW]
[ROW][C]29[/C][C]0.00290595119794199[/C][C]0.00581190239588397[/C][C]0.997094048802058[/C][/ROW]
[ROW][C]30[/C][C]0.00219025995157197[/C][C]0.00438051990314393[/C][C]0.997809740048428[/C][/ROW]
[ROW][C]31[/C][C]0.00125956732191849[/C][C]0.00251913464383698[/C][C]0.998740432678082[/C][/ROW]
[ROW][C]32[/C][C]0.000822103224286056[/C][C]0.00164420644857211[/C][C]0.999177896775714[/C][/ROW]
[ROW][C]33[/C][C]0.17040818205889[/C][C]0.34081636411778[/C][C]0.82959181794111[/C][/ROW]
[ROW][C]34[/C][C]0.310928379839465[/C][C]0.62185675967893[/C][C]0.689071620160535[/C][/ROW]
[ROW][C]35[/C][C]0.329139484844972[/C][C]0.658278969689944[/C][C]0.670860515155028[/C][/ROW]
[ROW][C]36[/C][C]0.333628403933918[/C][C]0.667256807867836[/C][C]0.666371596066082[/C][/ROW]
[ROW][C]37[/C][C]0.322697028085252[/C][C]0.645394056170505[/C][C]0.677302971914748[/C][/ROW]
[ROW][C]38[/C][C]0.284799741311683[/C][C]0.569599482623366[/C][C]0.715200258688317[/C][/ROW]
[ROW][C]39[/C][C]0.238548897718318[/C][C]0.477097795436635[/C][C]0.761451102281682[/C][/ROW]
[ROW][C]40[/C][C]0.198246271273632[/C][C]0.396492542547263[/C][C]0.801753728726368[/C][/ROW]
[ROW][C]41[/C][C]0.213495652216001[/C][C]0.426991304432001[/C][C]0.786504347783999[/C][/ROW]
[ROW][C]42[/C][C]0.180149561428034[/C][C]0.360299122856068[/C][C]0.819850438571966[/C][/ROW]
[ROW][C]43[/C][C]0.148369839580268[/C][C]0.296739679160535[/C][C]0.851630160419732[/C][/ROW]
[ROW][C]44[/C][C]0.119633408267645[/C][C]0.239266816535291[/C][C]0.880366591732355[/C][/ROW]
[ROW][C]45[/C][C]0.100994488657283[/C][C]0.201988977314566[/C][C]0.899005511342717[/C][/ROW]
[ROW][C]46[/C][C]0.736874782799329[/C][C]0.526250434401342[/C][C]0.263125217200671[/C][/ROW]
[ROW][C]47[/C][C]0.704389219466104[/C][C]0.591221561067792[/C][C]0.295610780533896[/C][/ROW]
[ROW][C]48[/C][C]0.826452976288488[/C][C]0.347094047423025[/C][C]0.173547023711512[/C][/ROW]
[ROW][C]49[/C][C]0.795793647564907[/C][C]0.408412704870186[/C][C]0.204206352435093[/C][/ROW]
[ROW][C]50[/C][C]0.776534748441206[/C][C]0.446930503117589[/C][C]0.223465251558795[/C][/ROW]
[ROW][C]51[/C][C]0.797338860930064[/C][C]0.405322278139872[/C][C]0.202661139069936[/C][/ROW]
[ROW][C]52[/C][C]0.784234119885389[/C][C]0.431531760229222[/C][C]0.215765880114611[/C][/ROW]
[ROW][C]53[/C][C]0.748362971690173[/C][C]0.503274056619654[/C][C]0.251637028309827[/C][/ROW]
[ROW][C]54[/C][C]0.759871576189204[/C][C]0.480256847621593[/C][C]0.240128423810796[/C][/ROW]
[ROW][C]55[/C][C]0.734580688127615[/C][C]0.530838623744771[/C][C]0.265419311872385[/C][/ROW]
[ROW][C]56[/C][C]0.699330408993325[/C][C]0.60133918201335[/C][C]0.300669591006675[/C][/ROW]
[ROW][C]57[/C][C]0.671146175680841[/C][C]0.657707648638317[/C][C]0.328853824319159[/C][/ROW]
[ROW][C]58[/C][C]0.629221491459434[/C][C]0.741557017081132[/C][C]0.370778508540566[/C][/ROW]
[ROW][C]59[/C][C]0.633428365220003[/C][C]0.733143269559995[/C][C]0.366571634779997[/C][/ROW]
[ROW][C]60[/C][C]0.628638720679951[/C][C]0.742722558640099[/C][C]0.371361279320049[/C][/ROW]
[ROW][C]61[/C][C]0.627775264133515[/C][C]0.744449471732969[/C][C]0.372224735866484[/C][/ROW]
[ROW][C]62[/C][C]0.587433212858795[/C][C]0.825133574282411[/C][C]0.412566787141205[/C][/ROW]
[ROW][C]63[/C][C]0.543338177427209[/C][C]0.913323645145583[/C][C]0.456661822572791[/C][/ROW]
[ROW][C]64[/C][C]0.510851468645017[/C][C]0.978297062709967[/C][C]0.489148531354983[/C][/ROW]
[ROW][C]65[/C][C]0.994066685233719[/C][C]0.0118666295325628[/C][C]0.00593331476628138[/C][/ROW]
[ROW][C]66[/C][C]0.991871746878456[/C][C]0.0162565062430888[/C][C]0.0081282531215444[/C][/ROW]
[ROW][C]67[/C][C]0.989420058071336[/C][C]0.0211598838573285[/C][C]0.0105799419286643[/C][/ROW]
[ROW][C]68[/C][C]0.986366523687214[/C][C]0.0272669526255715[/C][C]0.0136334763127857[/C][/ROW]
[ROW][C]69[/C][C]0.984600248919948[/C][C]0.0307995021601043[/C][C]0.0153997510800521[/C][/ROW]
[ROW][C]70[/C][C]0.982905507520203[/C][C]0.034188984959594[/C][C]0.017094492479797[/C][/ROW]
[ROW][C]71[/C][C]0.977811555757232[/C][C]0.044376888485536[/C][C]0.022188444242768[/C][/ROW]
[ROW][C]72[/C][C]0.971994996492636[/C][C]0.0560100070147275[/C][C]0.0280050035073638[/C][/ROW]
[ROW][C]73[/C][C]0.96478957119206[/C][C]0.0704208576158795[/C][C]0.0352104288079398[/C][/ROW]
[ROW][C]74[/C][C]0.9844646939073[/C][C]0.0310706121854007[/C][C]0.0155353060927003[/C][/ROW]
[ROW][C]75[/C][C]0.979521781892583[/C][C]0.0409564362148334[/C][C]0.0204782181074167[/C][/ROW]
[ROW][C]76[/C][C]0.973327825310872[/C][C]0.0533443493782558[/C][C]0.0266721746891279[/C][/ROW]
[ROW][C]77[/C][C]0.965638775288676[/C][C]0.0687224494226482[/C][C]0.0343612247113241[/C][/ROW]
[ROW][C]78[/C][C]0.956154594586598[/C][C]0.087690810826803[/C][C]0.0438454054134015[/C][/ROW]
[ROW][C]79[/C][C]0.94511614666148[/C][C]0.10976770667704[/C][C]0.05488385333852[/C][/ROW]
[ROW][C]80[/C][C]0.948975896464939[/C][C]0.102048207070121[/C][C]0.0510241035350606[/C][/ROW]
[ROW][C]81[/C][C]0.938883305302463[/C][C]0.122233389395074[/C][C]0.0611166946975369[/C][/ROW]
[ROW][C]82[/C][C]0.929123454963262[/C][C]0.141753090073476[/C][C]0.0708765450367378[/C][/ROW]
[ROW][C]83[/C][C]0.921107640360468[/C][C]0.157784719279063[/C][C]0.0788923596395317[/C][/ROW]
[ROW][C]84[/C][C]0.909062154707697[/C][C]0.181875690584606[/C][C]0.0909378452923032[/C][/ROW]
[ROW][C]85[/C][C]0.898548026556399[/C][C]0.202903946887202[/C][C]0.101451973443601[/C][/ROW]
[ROW][C]86[/C][C]0.882652611197095[/C][C]0.234694777605811[/C][C]0.117347388802905[/C][/ROW]
[ROW][C]87[/C][C]0.859956975835555[/C][C]0.28008604832889[/C][C]0.140043024164445[/C][/ROW]
[ROW][C]88[/C][C]0.960274811689373[/C][C]0.0794503766212534[/C][C]0.0397251883106267[/C][/ROW]
[ROW][C]89[/C][C]0.958710306331648[/C][C]0.0825793873367031[/C][C]0.0412896936683516[/C][/ROW]
[ROW][C]90[/C][C]0.953214595643207[/C][C]0.0935708087135857[/C][C]0.0467854043567928[/C][/ROW]
[ROW][C]91[/C][C]0.966292079281823[/C][C]0.0674158414363547[/C][C]0.0337079207181773[/C][/ROW]
[ROW][C]92[/C][C]0.956455597635465[/C][C]0.0870888047290704[/C][C]0.0435444023645352[/C][/ROW]
[ROW][C]93[/C][C]0.944402187677179[/C][C]0.111195624645641[/C][C]0.0555978123228207[/C][/ROW]
[ROW][C]94[/C][C]0.932262099418459[/C][C]0.135475801163083[/C][C]0.0677379005815414[/C][/ROW]
[ROW][C]95[/C][C]0.917781030030257[/C][C]0.164437939939486[/C][C]0.0822189699697429[/C][/ROW]
[ROW][C]96[/C][C]0.903906125546064[/C][C]0.192187748907871[/C][C]0.0960938744539357[/C][/ROW]
[ROW][C]97[/C][C]0.892029112231179[/C][C]0.215941775537642[/C][C]0.107970887768821[/C][/ROW]
[ROW][C]98[/C][C]0.86829209183729[/C][C]0.263415816325419[/C][C]0.13170790816271[/C][/ROW]
[ROW][C]99[/C][C]0.865314942312814[/C][C]0.269370115374373[/C][C]0.134685057687186[/C][/ROW]
[ROW][C]100[/C][C]0.867629289245298[/C][C]0.264741421509405[/C][C]0.132370710754702[/C][/ROW]
[ROW][C]101[/C][C]0.868110958471804[/C][C]0.263778083056391[/C][C]0.131889041528196[/C][/ROW]
[ROW][C]102[/C][C]0.84622770949183[/C][C]0.30754458101634[/C][C]0.15377229050817[/C][/ROW]
[ROW][C]103[/C][C]0.831070293287429[/C][C]0.337859413425143[/C][C]0.168929706712571[/C][/ROW]
[ROW][C]104[/C][C]0.80749294754158[/C][C]0.385014104916841[/C][C]0.19250705245842[/C][/ROW]
[ROW][C]105[/C][C]0.79964014302693[/C][C]0.40071971394614[/C][C]0.20035985697307[/C][/ROW]
[ROW][C]106[/C][C]0.825244550752919[/C][C]0.349510898494162[/C][C]0.174755449247081[/C][/ROW]
[ROW][C]107[/C][C]0.794264542370125[/C][C]0.411470915259749[/C][C]0.205735457629875[/C][/ROW]
[ROW][C]108[/C][C]0.757423417999653[/C][C]0.485153164000693[/C][C]0.242576582000347[/C][/ROW]
[ROW][C]109[/C][C]0.718499504127645[/C][C]0.563000991744711[/C][C]0.281500495872355[/C][/ROW]
[ROW][C]110[/C][C]0.688063796592823[/C][C]0.623872406814353[/C][C]0.311936203407177[/C][/ROW]
[ROW][C]111[/C][C]0.664331033417318[/C][C]0.671337933165363[/C][C]0.335668966582681[/C][/ROW]
[ROW][C]112[/C][C]0.625375714235706[/C][C]0.749248571528589[/C][C]0.374624285764294[/C][/ROW]
[ROW][C]113[/C][C]0.643117929457869[/C][C]0.713764141084261[/C][C]0.356882070542131[/C][/ROW]
[ROW][C]114[/C][C]0.596724729885465[/C][C]0.806550540229071[/C][C]0.403275270114536[/C][/ROW]
[ROW][C]115[/C][C]0.55058154958685[/C][C]0.8988369008263[/C][C]0.44941845041315[/C][/ROW]
[ROW][C]116[/C][C]0.512017194991176[/C][C]0.975965610017648[/C][C]0.487982805008824[/C][/ROW]
[ROW][C]117[/C][C]0.472716201521599[/C][C]0.945432403043198[/C][C]0.527283798478401[/C][/ROW]
[ROW][C]118[/C][C]0.451581611200531[/C][C]0.903163222401062[/C][C]0.548418388799469[/C][/ROW]
[ROW][C]119[/C][C]0.421885436328361[/C][C]0.843770872656722[/C][C]0.578114563671639[/C][/ROW]
[ROW][C]120[/C][C]0.374305411341029[/C][C]0.748610822682058[/C][C]0.625694588658971[/C][/ROW]
[ROW][C]121[/C][C]0.424147935652872[/C][C]0.848295871305744[/C][C]0.575852064347128[/C][/ROW]
[ROW][C]122[/C][C]0.392379397265281[/C][C]0.784758794530562[/C][C]0.607620602734719[/C][/ROW]
[ROW][C]123[/C][C]0.373841794368679[/C][C]0.747683588737358[/C][C]0.626158205631321[/C][/ROW]
[ROW][C]124[/C][C]0.576322010479123[/C][C]0.847355979041755[/C][C]0.423677989520877[/C][/ROW]
[ROW][C]125[/C][C]0.524144812695914[/C][C]0.951710374608173[/C][C]0.475855187304086[/C][/ROW]
[ROW][C]126[/C][C]0.466747808864967[/C][C]0.933495617729933[/C][C]0.533252191135033[/C][/ROW]
[ROW][C]127[/C][C]0.445041726415109[/C][C]0.890083452830218[/C][C]0.554958273584891[/C][/ROW]
[ROW][C]128[/C][C]0.388403111433927[/C][C]0.776806222867855[/C][C]0.611596888566073[/C][/ROW]
[ROW][C]129[/C][C]0.385384651525485[/C][C]0.77076930305097[/C][C]0.614615348474515[/C][/ROW]
[ROW][C]130[/C][C]0.352926338992483[/C][C]0.705852677984966[/C][C]0.647073661007517[/C][/ROW]
[ROW][C]131[/C][C]0.300475379393782[/C][C]0.600950758787563[/C][C]0.699524620606218[/C][/ROW]
[ROW][C]132[/C][C]0.257986439079408[/C][C]0.515972878158817[/C][C]0.742013560920592[/C][/ROW]
[ROW][C]133[/C][C]0.32496888886972[/C][C]0.649937777739441[/C][C]0.67503111113028[/C][/ROW]
[ROW][C]134[/C][C]0.289888502681543[/C][C]0.579777005363086[/C][C]0.710111497318457[/C][/ROW]
[ROW][C]135[/C][C]0.29135270159333[/C][C]0.58270540318666[/C][C]0.70864729840667[/C][/ROW]
[ROW][C]136[/C][C]0.408922850949151[/C][C]0.817845701898303[/C][C]0.591077149050849[/C][/ROW]
[ROW][C]137[/C][C]0.545171267217642[/C][C]0.909657465564716[/C][C]0.454828732782358[/C][/ROW]
[ROW][C]138[/C][C]0.48156323181235[/C][C]0.9631264636247[/C][C]0.51843676818765[/C][/ROW]
[ROW][C]139[/C][C]0.447611607317621[/C][C]0.895223214635241[/C][C]0.552388392682379[/C][/ROW]
[ROW][C]140[/C][C]0.389470742325836[/C][C]0.778941484651671[/C][C]0.610529257674164[/C][/ROW]
[ROW][C]141[/C][C]0.337157431647191[/C][C]0.674314863294383[/C][C]0.662842568352809[/C][/ROW]
[ROW][C]142[/C][C]0.303000084500418[/C][C]0.606000169000836[/C][C]0.696999915499582[/C][/ROW]
[ROW][C]143[/C][C]0.485956545487049[/C][C]0.971913090974098[/C][C]0.514043454512951[/C][/ROW]
[ROW][C]144[/C][C]0.545204305044602[/C][C]0.909591389910797[/C][C]0.454795694955398[/C][/ROW]
[ROW][C]145[/C][C]0.568284221319175[/C][C]0.863431557361651[/C][C]0.431715778680825[/C][/ROW]
[ROW][C]146[/C][C]0.531263519486893[/C][C]0.937472961026215[/C][C]0.468736480513107[/C][/ROW]
[ROW][C]147[/C][C]0.47257682796868[/C][C]0.945153655937361[/C][C]0.52742317203132[/C][/ROW]
[ROW][C]148[/C][C]0.779468146510892[/C][C]0.441063706978216[/C][C]0.220531853489108[/C][/ROW]
[ROW][C]149[/C][C]0.688630804497389[/C][C]0.622738391005222[/C][C]0.311369195502611[/C][/ROW]
[ROW][C]150[/C][C]0.829410646858049[/C][C]0.341178706283902[/C][C]0.170589353141951[/C][/ROW]
[ROW][C]151[/C][C]0.736260299588647[/C][C]0.527479400822706[/C][C]0.263739700411353[/C][/ROW]
[ROW][C]152[/C][C]0.646986447650947[/C][C]0.706027104698107[/C][C]0.353013552349053[/C][/ROW]
[ROW][C]153[/C][C]0.813051362856741[/C][C]0.373897274286517[/C][C]0.186948637143259[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=226371&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=226371&T=5

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


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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
90.06431423862258140.1286284772451630.935685761377419
100.2120305966806160.4240611933612320.787969403319384
110.1940115049469240.3880230098938490.805988495053076
120.1610472840683160.3220945681366330.838952715931684
130.1020229680531250.204045936106250.897977031946875
140.08894014902010460.1778802980402090.911059850979895
150.05387919299801640.1077583859960330.946120807001984
160.05192299175555590.1038459835111120.948077008244444
170.05061306352834350.1012261270566870.949386936471657
180.03412273038455610.06824546076911230.965877269615444
190.02148757986657890.04297515973315770.978512420133421
200.02314104962552130.04628209925104270.976858950374479
210.02027380636899250.04054761273798510.979726193631007
220.01257598133707150.02515196267414290.987424018662929
230.01040075274592820.02080150549185640.989599247254072
240.006192369851985120.01238473970397020.993807630148015
250.003506380276660820.007012760553321650.996493619723339
260.003144411496276890.006288822992553780.996855588503723
270.002100076483077730.004200152966155470.997899923516922
280.002197377794428380.004394755588856760.997802622205572
290.002905951197941990.005811902395883970.997094048802058
300.002190259951571970.004380519903143930.997809740048428
310.001259567321918490.002519134643836980.998740432678082
320.0008221032242860560.001644206448572110.999177896775714
330.170408182058890.340816364117780.82959181794111
340.3109283798394650.621856759678930.689071620160535
350.3291394848449720.6582789696899440.670860515155028
360.3336284039339180.6672568078678360.666371596066082
370.3226970280852520.6453940561705050.677302971914748
380.2847997413116830.5695994826233660.715200258688317
390.2385488977183180.4770977954366350.761451102281682
400.1982462712736320.3964925425472630.801753728726368
410.2134956522160010.4269913044320010.786504347783999
420.1801495614280340.3602991228560680.819850438571966
430.1483698395802680.2967396791605350.851630160419732
440.1196334082676450.2392668165352910.880366591732355
450.1009944886572830.2019889773145660.899005511342717
460.7368747827993290.5262504344013420.263125217200671
470.7043892194661040.5912215610677920.295610780533896
480.8264529762884880.3470940474230250.173547023711512
490.7957936475649070.4084127048701860.204206352435093
500.7765347484412060.4469305031175890.223465251558795
510.7973388609300640.4053222781398720.202661139069936
520.7842341198853890.4315317602292220.215765880114611
530.7483629716901730.5032740566196540.251637028309827
540.7598715761892040.4802568476215930.240128423810796
550.7345806881276150.5308386237447710.265419311872385
560.6993304089933250.601339182013350.300669591006675
570.6711461756808410.6577076486383170.328853824319159
580.6292214914594340.7415570170811320.370778508540566
590.6334283652200030.7331432695599950.366571634779997
600.6286387206799510.7427225586400990.371361279320049
610.6277752641335150.7444494717329690.372224735866484
620.5874332128587950.8251335742824110.412566787141205
630.5433381774272090.9133236451455830.456661822572791
640.5108514686450170.9782970627099670.489148531354983
650.9940666852337190.01186662953256280.00593331476628138
660.9918717468784560.01625650624308880.0081282531215444
670.9894200580713360.02115988385732850.0105799419286643
680.9863665236872140.02726695262557150.0136334763127857
690.9846002489199480.03079950216010430.0153997510800521
700.9829055075202030.0341889849595940.017094492479797
710.9778115557572320.0443768884855360.022188444242768
720.9719949964926360.05601000701472750.0280050035073638
730.964789571192060.07042085761587950.0352104288079398
740.98446469390730.03107061218540070.0155353060927003
750.9795217818925830.04095643621483340.0204782181074167
760.9733278253108720.05334434937825580.0266721746891279
770.9656387752886760.06872244942264820.0343612247113241
780.9561545945865980.0876908108268030.0438454054134015
790.945116146661480.109767706677040.05488385333852
800.9489758964649390.1020482070701210.0510241035350606
810.9388833053024630.1222333893950740.0611166946975369
820.9291234549632620.1417530900734760.0708765450367378
830.9211076403604680.1577847192790630.0788923596395317
840.9090621547076970.1818756905846060.0909378452923032
850.8985480265563990.2029039468872020.101451973443601
860.8826526111970950.2346947776058110.117347388802905
870.8599569758355550.280086048328890.140043024164445
880.9602748116893730.07945037662125340.0397251883106267
890.9587103063316480.08257938733670310.0412896936683516
900.9532145956432070.09357080871358570.0467854043567928
910.9662920792818230.06741584143635470.0337079207181773
920.9564555976354650.08708880472907040.0435444023645352
930.9444021876771790.1111956246456410.0555978123228207
940.9322620994184590.1354758011630830.0677379005815414
950.9177810300302570.1644379399394860.0822189699697429
960.9039061255460640.1921877489078710.0960938744539357
970.8920291122311790.2159417755376420.107970887768821
980.868292091837290.2634158163254190.13170790816271
990.8653149423128140.2693701153743730.134685057687186
1000.8676292892452980.2647414215094050.132370710754702
1010.8681109584718040.2637780830563910.131889041528196
1020.846227709491830.307544581016340.15377229050817
1030.8310702932874290.3378594134251430.168929706712571
1040.807492947541580.3850141049168410.19250705245842
1050.799640143026930.400719713946140.20035985697307
1060.8252445507529190.3495108984941620.174755449247081
1070.7942645423701250.4114709152597490.205735457629875
1080.7574234179996530.4851531640006930.242576582000347
1090.7184995041276450.5630009917447110.281500495872355
1100.6880637965928230.6238724068143530.311936203407177
1110.6643310334173180.6713379331653630.335668966582681
1120.6253757142357060.7492485715285890.374624285764294
1130.6431179294578690.7137641410842610.356882070542131
1140.5967247298854650.8065505402290710.403275270114536
1150.550581549586850.89883690082630.44941845041315
1160.5120171949911760.9759656100176480.487982805008824
1170.4727162015215990.9454324030431980.527283798478401
1180.4515816112005310.9031632224010620.548418388799469
1190.4218854363283610.8437708726567220.578114563671639
1200.3743054113410290.7486108226820580.625694588658971
1210.4241479356528720.8482958713057440.575852064347128
1220.3923793972652810.7847587945305620.607620602734719
1230.3738417943686790.7476835887373580.626158205631321
1240.5763220104791230.8473559790417550.423677989520877
1250.5241448126959140.9517103746081730.475855187304086
1260.4667478088649670.9334956177299330.533252191135033
1270.4450417264151090.8900834528302180.554958273584891
1280.3884031114339270.7768062228678550.611596888566073
1290.3853846515254850.770769303050970.614615348474515
1300.3529263389924830.7058526779849660.647073661007517
1310.3004753793937820.6009507587875630.699524620606218
1320.2579864390794080.5159728781588170.742013560920592
1330.324968888869720.6499377777394410.67503111113028
1340.2898885026815430.5797770053630860.710111497318457
1350.291352701593330.582705403186660.70864729840667
1360.4089228509491510.8178457018983030.591077149050849
1370.5451712672176420.9096574655647160.454828732782358
1380.481563231812350.96312646362470.51843676818765
1390.4476116073176210.8952232146352410.552388392682379
1400.3894707423258360.7789414846516710.610529257674164
1410.3371574316471910.6743148632943830.662842568352809
1420.3030000845004180.6060001690008360.696999915499582
1430.4859565454870490.9719130909740980.514043454512951
1440.5452043050446020.9095913899107970.454795694955398
1450.5682842213191750.8634315573616510.431715778680825
1460.5312635194868930.9374729610262150.468736480513107
1470.472576827968680.9451536559373610.52742317203132
1480.7794681465108920.4410637069782160.220531853489108
1490.6886308044973890.6227383910052220.311369195502611
1500.8294106468580490.3411787062839020.170589353141951
1510.7362602995886470.5274794008227060.263739700411353
1520.6469864476509470.7060271046981070.353013552349053
1530.8130513628567410.3738972742865170.186948637143259






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level80.0551724137931034NOK
5% type I error level230.158620689655172NOK
10% type I error level340.23448275862069NOK

\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 & 8 & 0.0551724137931034 & NOK \tabularnewline
5% type I error level & 23 & 0.158620689655172 & NOK \tabularnewline
10% type I error level & 34 & 0.23448275862069 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=226371&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]8[/C][C]0.0551724137931034[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]23[/C][C]0.158620689655172[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]34[/C][C]0.23448275862069[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=226371&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=226371&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 level80.0551724137931034NOK
5% type I error level230.158620689655172NOK
10% type I error level340.23448275862069NOK


Figures (Output of Computation)

Figure 1
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 2
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 3
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 4
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 5
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 6
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 7
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 8
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 9
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 10
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Input Parameters & R Code

Parameters (Session):
par1 = 6 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
Parameters (R input):
par1 = 6 ; 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')
}