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Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationMon, 10 Dec 2012 07:28:39 -0500
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2012/Dec/10/t1355142553gl8xa8wo7nyvm9u.htm/, Retrieved Tue, 30 Apr 2024 07:41:07 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=198107, Retrieved Tue, 30 Apr 2024 07:41:07 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact125

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [] [2010-12-05 18:56:24] [b98453cac15ba1066b407e146608df68]
- R PD    [Multiple Regression] [ws10] [2012-12-10 12:28:39] [2bcb0f1dab9cffb75c9fd882cacbd29a] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
2	41	38	13	12	14	12
2	39	32	16	11	18	11
2	30	35	19	15	11	14
1	31	33	15	6	12	12
2	34	37	14	13	16	21
2	35	29	13	10	18	12
2	39	31	19	12	14	22
2	34	36	15	14	14	11
2	36	35	14	12	15	10
2	37	38	15	6	15	13
1	38	31	16	10	17	10
2	36	34	16	12	19	8
1	38	35	16	12	10	15
2	39	38	16	11	16	14
2	33	37	17	15	18	10
1	32	33	15	12	14	14
1	36	32	15	10	14	14
2	38	38	20	12	17	11
1	39	38	18	11	14	10
2	32	32	16	12	16	13
1	32	33	16	11	18	7
2	31	31	16	12	11	14
2	39	38	19	13	14	12
2	37	39	16	11	12	14
1	39	32	17	9	17	11
2	41	32	17	13	9	9
1	36	35	16	10	16	11
2	33	37	15	14	14	15
2	33	33	16	12	15	14
1	34	33	14	10	11	13
2	31	28	15	12	16	9
1	27	32	12	8	13	15
2	37	31	14	10	17	10
2	34	37	16	12	15	11
1	34	30	14	12	14	13
1	32	33	7	7	16	8
1	29	31	10	6	9	20
1	36	33	14	12	15	12
2	29	31	16	10	17	10
1	35	33	16	10	13	10
1	37	32	16	10	15	9
2	34	33	14	12	16	14
1	38	32	20	15	16	8
1	35	33	14	10	12	14
2	38	28	14	10	12	11
2	37	35	11	12	11	13
2	38	39	14	13	15	9
2	33	34	15	11	15	11
2	36	38	16	11	17	15
1	38	32	14	12	13	11
2	32	38	16	14	16	10
1	32	30	14	10	14	14
1	32	33	12	12	11	18
2	34	38	16	13	12	14
1	32	32	9	5	12	11
2	37	32	14	6	15	12
2	39	34	16	12	16	13
2	29	34	16	12	15	9
1	37	36	15	11	12	10
2	35	34	16	10	12	15
1	30	28	12	7	8	20
1	38	34	16	12	13	12
2	34	35	16	14	11	12
2	31	35	14	11	14	14
2	34	31	16	12	15	13
1	35	37	17	13	10	11
2	36	35	18	14	11	17
1	30	27	18	11	12	12
2	39	40	12	12	15	13
1	35	37	16	12	15	14
1	38	36	10	8	14	13
2	31	38	14	11	16	15
2	34	39	18	14	15	13
1	38	41	18	14	15	10
1	34	27	16	12	13	11
2	39	30	17	9	12	19
2	37	37	16	13	17	13
2	34	31	16	11	13	17
1	28	31	13	12	15	13
1	37	27	16	12	13	9
1	33	36	16	12	15	11
1	37	38	20	12	16	10
2	35	37	16	12	15	9
1	37	33	15	12	16	12
2	32	34	15	11	15	12
2	33	31	16	10	14	13
1	38	39	14	9	15	13
2	33	34	16	12	14	12
2	29	32	16	12	13	15
2	33	33	15	12	7	22
2	31	36	12	9	17	13
2	36	32	17	15	13	15
2	35	41	16	12	15	13
2	32	28	15	12	14	15
2	29	30	13	12	13	10
2	39	36	16	10	16	11
2	37	35	16	13	12	16
2	35	31	16	9	14	11
1	37	34	16	12	17	11
1	32	36	14	10	15	10
2	38	36	16	14	17	10
1	37	35	16	11	12	16
2	36	37	20	15	16	12
1	32	28	15	11	11	11
2	33	39	16	11	15	16
1	40	32	13	12	9	19
2	38	35	17	12	16	11
1	41	39	16	12	15	16
1	36	35	16	11	10	15
2	43	42	12	7	10	24
2	30	34	16	12	15	14
2	31	33	16	14	11	15
2	32	41	17	11	13	11
1	32	33	13	11	14	15
2	37	34	12	10	18	12
1	37	32	18	13	16	10
2	33	40	14	13	14	14
2	34	40	14	8	14	13
2	33	35	13	11	14	9
2	38	36	16	12	14	15
2	33	37	13	11	12	15
2	31	27	16	13	14	14
2	38	39	13	12	15	11
2	37	38	16	14	15	8
2	33	31	15	13	15	11
2	31	33	16	15	13	11
1	39	32	15	10	17	8
2	44	39	17	11	17	10
2	33	36	15	9	19	11
2	35	33	12	11	15	13
1	32	33	16	10	13	11
1	28	32	10	11	9	20
2	40	37	16	8	15	10
1	27	30	12	11	15	15
1	37	38	14	12	15	12
2	32	29	15	12	16	14
1	28	22	13	9	11	23
1	34	35	15	11	14	14
2	30	35	11	10	11	16
2	35	34	12	8	15	11
1	31	35	8	9	13	12
2	32	34	16	8	15	10
1	30	34	15	9	16	14
2	30	35	17	15	14	12
1	31	23	16	11	15	12
2	40	31	10	8	16	11
2	32	27	18	13	16	12
1	36	36	13	12	11	13
1	32	31	16	12	12	11
1	35	32	13	9	9	19
2	38	39	10	7	16	12
2	42	37	15	13	13	17
1	34	38	16	9	16	9
2	35	39	16	6	12	12
2	35	34	14	8	9	19
2	33	31	10	8	13	18
2	36	32	17	15	13	15
2	32	37	13	6	14	14
2	33	36	15	9	19	11
2	34	32	16	11	13	9
2	32	35	12	8	12	18
2	34	36	13	8	13	16

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time10 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 & 10 seconds \tabularnewline
R Server & 'Sir Maurice George Kendall' @ kendall.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=198107&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]10 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=198107&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=198107&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 time10 seconds
R Server'Sir Maurice George Kendall' @ kendall.wessa.net






Multiple Linear Regression - Estimated Regression Equation
Software[t] = + 4.88226007134071 + 0.493032025101953Gender[t] -0.0437482527010791Connected[t] + 0.0178873564985639Separate[t] + 0.516603287827944Learning[t] -0.0664452953070689Happiness[t] -0.0397123919212757depression[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Software[t] =  +  4.88226007134071 +  0.493032025101953Gender[t] -0.0437482527010791Connected[t] +  0.0178873564985639Separate[t] +  0.516603287827944Learning[t] -0.0664452953070689Happiness[t] -0.0397123919212757depression[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=198107&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Software[t] =  +  4.88226007134071 +  0.493032025101953Gender[t] -0.0437482527010791Connected[t] +  0.0178873564985639Separate[t] +  0.516603287827944Learning[t] -0.0664452953070689Happiness[t] -0.0397123919212757depression[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=198107&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=198107&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
Software[t] = + 4.88226007134071 + 0.493032025101953Gender[t] -0.0437482527010791Connected[t] + 0.0178873564985639Separate[t] + 0.516603287827944Learning[t] -0.0664452953070689Happiness[t] -0.0397123919212757depression[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)4.882260071340712.3639992.06530.0405640.020282
Gender0.4930320251019530.3124111.57820.116570.058285
Connected-0.04374825270107910.04645-0.94180.3477470.173874
Separate0.01788735649856390.0444640.40230.6880250.344012
Learning0.5166032878279440.0666627.749500
Happiness-0.06644529530706890.075238-0.88310.3785310.189266
depression-0.03971239192127570.055352-0.71740.4741780.237089

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Ordinary Least Squares \tabularnewline
Variable & Parameter & S.D. & T-STATH0: parameter = 0 & 2-tail p-value & 1-tail p-value \tabularnewline
(Intercept) & 4.88226007134071 & 2.363999 & 2.0653 & 0.040564 & 0.020282 \tabularnewline
Gender & 0.493032025101953 & 0.312411 & 1.5782 & 0.11657 & 0.058285 \tabularnewline
Connected & -0.0437482527010791 & 0.04645 & -0.9418 & 0.347747 & 0.173874 \tabularnewline
Separate & 0.0178873564985639 & 0.044464 & 0.4023 & 0.688025 & 0.344012 \tabularnewline
Learning & 0.516603287827944 & 0.066662 & 7.7495 & 0 & 0 \tabularnewline
Happiness & -0.0664452953070689 & 0.075238 & -0.8831 & 0.378531 & 0.189266 \tabularnewline
depression & -0.0397123919212757 & 0.055352 & -0.7174 & 0.474178 & 0.237089 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=198107&T=2

[TABLE]
[ROW][C]Multiple Linear Regression - Ordinary Least Squares[/C][/ROW]
[ROW][C]Variable[/C][C]Parameter[/C][C]S.D.[/C][C]T-STATH0: parameter = 0[/C][C]2-tail p-value[/C][C]1-tail p-value[/C][/ROW]
[ROW][C](Intercept)[/C][C]4.88226007134071[/C][C]2.363999[/C][C]2.0653[/C][C]0.040564[/C][C]0.020282[/C][/ROW]
[ROW][C]Gender[/C][C]0.493032025101953[/C][C]0.312411[/C][C]1.5782[/C][C]0.11657[/C][C]0.058285[/C][/ROW]
[ROW][C]Connected[/C][C]-0.0437482527010791[/C][C]0.04645[/C][C]-0.9418[/C][C]0.347747[/C][C]0.173874[/C][/ROW]
[ROW][C]Separate[/C][C]0.0178873564985639[/C][C]0.044464[/C][C]0.4023[/C][C]0.688025[/C][C]0.344012[/C][/ROW]
[ROW][C]Learning[/C][C]0.516603287827944[/C][C]0.066662[/C][C]7.7495[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Happiness[/C][C]-0.0664452953070689[/C][C]0.075238[/C][C]-0.8831[/C][C]0.378531[/C][C]0.189266[/C][/ROW]
[ROW][C]depression[/C][C]-0.0397123919212757[/C][C]0.055352[/C][C]-0.7174[/C][C]0.474178[/C][C]0.237089[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=198107&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=198107&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)4.882260071340712.3639992.06530.0405640.020282
Gender0.4930320251019530.3124111.57820.116570.058285
Connected-0.04374825270107910.04645-0.94180.3477470.173874
Separate0.01788735649856390.0444640.40230.6880250.344012
Learning0.5166032878279440.0666627.749500
Happiness-0.06644529530706890.075238-0.88310.3785310.189266
depression-0.03971239192127570.055352-0.71740.4741780.237089






Multiple Linear Regression - Regression Statistics
Multiple R0.561671172642115
R-squared0.315474506177169
Adjusted R-squared0.288976745125962
F-TEST (value)11.9057042429932
F-TEST (DF numerator)6
F-TEST (DF denominator)155
p-value5.73879832543867e-11
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1.80594373003498
Sum Squared Residuals505.522077188161

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.561671172642115 \tabularnewline
R-squared & 0.315474506177169 \tabularnewline
Adjusted R-squared & 0.288976745125962 \tabularnewline
F-TEST (value) & 11.9057042429932 \tabularnewline
F-TEST (DF numerator) & 6 \tabularnewline
F-TEST (DF denominator) & 155 \tabularnewline
p-value & 5.73879832543867e-11 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 1.80594373003498 \tabularnewline
Sum Squared Residuals & 505.522077188161 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=198107&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.561671172642115[/C][/ROW]
[ROW][C]R-squared[/C][C]0.315474506177169[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.288976745125962[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]11.9057042429932[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]6[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]155[/C][/ROW]
[ROW][C]p-value[/C][C]5.73879832543867e-11[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]1.80594373003498[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]505.522077188161[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=198107&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=198107&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 R0.561671172642115
R-squared0.315474506177169
Adjusted R-squared0.288976745125962
F-TEST (value)11.9057042429932
F-TEST (DF numerator)6
F-TEST (DF denominator)155
p-value5.73879832543867e-11
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1.80594373003498
Sum Squared Residuals505.522077188161






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11210.06342521215481.9365747878452
21111.3673386527424-0.367338652742406
31513.71052475141731.28947524858271
4611.0845360978408-5.08453609784084
51310.37807679448612.62192320551388
6109.899147338645920.100852661354075
71212.7282060298219-0.728206029821916
81411.40680723564242.59319276435761
91210.75808718252791.24191281747207
10611.1654671113867-5.16546711138666
111011.0063252110713-1.00632521107131
121211.58705000429950.41294999570047
131211.34442974460870.655570255391328
141111.4884162065841-0.488416206584101
151512.27558063119092.72441936880908
161210.82847247068311.17152752931692
171010.6355921033802-0.635592103380195
181213.6512694910537-1.65126949105371
191112.3203309154373-1.32033091543727
201211.72704222842150.272957771578454
211111.3572813207317-0.357281320731673
221212.0454172092381-0.0454172092381301
231313.2505414445246-0.25054144452462
241111.8595812497131-0.859581249713098
25911.4573552107755-2.45735521077546
261312.47387787677440.526122123225637
271011.1921040458535-1.19210404585352
281411.30959327715692.69040672284307
291211.72791423560480.272085764395176
301010.4634209552955-0.463420955295456
311211.34148733498550.658512665014471
3289.50624941759187-1.50624941759187
331010.5098989132185-0.509898913218459
341211.87485258466180.125147415338172
351210.21042299987861.78957700012144
3676.801029928973040.198970071026955
3768.43487820165716-2.43487820165716
381210.14985566058631.8501443394137
391011.893091510483-1.89309151048298
401011.4391258634-1.43912586339995
411011.2405638028064-1.24056380280637
421210.58451411194081.41548588805921
431513.23649579803131.76350420196873
441010.313515015366-0.313515015366032
451010.7050026756358-0.705002675635756
46129.311173071807472.68882692819253
471310.78185249504132.2181475049587
481111.3483354800393-0.348335480039272
491111.513503277459-0.513503277458993
501210.2170747812211.78292521877901
511411.95350354317682.04649645682324
521010.2582071133594-0.25820711335944
53129.319148925435352.68085107456465
541311.97293865131781.02706134868223
5557.96299315359482-2.96299315359482
56610.5812520764886-4.58125207648861
571211.45657917251110.543420827488879
581212.1193565625141-0.119356562514083
591110.95513343497260.0448665650273877
601011.8179285807012-1.81792858070116
6179.43711975042334-2.43711975042334
621211.24634367795270.753656322047272
631412.06514666097171.9348533390283
641110.88442417365530.115575826344708
651211.68810366182790.311896338172107
661312.18690207122210.813097928777917
671412.8122947716191.18770522838095
681112.5707700750344-1.57077007503437
69129.56393545549782.4360645445022
701211.2189351310950.781064868905032
7188.07634097675385-0.076340976753851
721110.76548326061560.234516739384429
731412.86440908947231.13559091052771
741412.3512959423271.64870405767302
751211.33583758518840.664162414811627
76911.9291398640454-2.92913986404543
771311.53129245210191.4687075478981
781111.6621446847569-0.662144684756928
79129.907751289448582.09224871055142
801211.28401761092770.715982389072312
811211.40768145576240.592318544237611
821213.3081434058812-1.30814340588118
831211.91052911580330.0894708841966996
841210.55626540040611.44373459959391
851111.3523713408191-0.352371340819075
861011.798297209836-1.79829720983604
87910.1299709022542-1.12997090225425
881211.8916716712530.108328328746991
891211.97819808860340.0218019113965614
901211.42517417486320.574825825136774
9199.70948146049804-0.709481460498039
921512.18856360752382.81143639247617
931211.82322897411250.176771025887547
941211.19235532137090.807644678629067
951210.59117547172891.40882452827114
961011.5717786693508-1.5717786693508
971311.70860703987631.29139296012371
98911.7902254882764-2.79022548827643
991211.06402314134680.935976858653192
1001010.4579355247289-0.457935524728858
1011411.58879401866612.41120598133391
1021111.2155750147743-0.215575014774337
1031513.74761154334311.2523884566569
1041111.0575087498753-0.0575087498752895
1051111.7558135907537-0.755813590753656
106129.561057033848962.43894296615104
1071212.1142428533813-0.114242853381258
1081210.91279554404311.08720445595693
1091111.4319262500108-0.43192625001083
11079.32010732309192-2.32010732309192
1111211.87704635020660.122953649793375
1121412.0414795303141.95852046968602
1131112.6833923945003-1.68339239450032
114119.755553503105911.24444649689409
115109.384484327908640.615515672091357
1161312.16761269123390.832387308766088
1171310.8863644507462.11363554925405
118810.8823285899661-2.88232858996615
1191110.47888634003160.521113659968431
1201211.58956794498090.410432055019086
1211110.40927729211520.590722707884819
1221311.77453189732271.22546810267733
1231210.18582442337081.81417557662919
1241411.8806323588212.11936764117902
1251311.29467341054361.70532658945642
1261512.06743850738492.93256149261505
1271010.5432858108834-0.543285810883406
1281111.8965698597832-0.896569859783247
129911.1183290118081-2.11832901180812
130119.613716970812171.38628302918783
1311011.5306582295819-1.53065822958191
132118.49651381085682.5034861891432
133811.6520754603766-3.65207546037663
134119.33758411398061.6624158860194
1351210.1955441903781.80445580962196
1361211.11706447917660.882935520823365
13799.61542234297703-0.615422342977027
1381110.7767506782780.223249321721955
139109.49827366495120.501726335048804
14089.71102911115328-1.71102911115328
14197.43764250073531.5623574992647
142811.9483994124896-3.94839941248957
143910.8009657419697-1.80096574196966
1441512.55740707368282.44259292631725
1451111.2229299347619-0.222929934761941
14688.33897390718924-0.338973907189241
1471312.71052441350590.289475586494111
148129.912983231561052.08701676843895
1491211.56132881189190.438671188108145
15099.77979829735435-0.779798297354353
15178.52985687265863-1.52985687265863
1521310.90287951431172.09712048568826
153911.4126874045939-2.41268740459392
154612.0265025389578-6.02650253895781
155810.8252083232814-2.82520832328138
15688.56656081856907-0.566560818569071
1571512.18856360752382.81143639247617
158610.3598473461234-4.3598473461234
159911.1183290118081-2.11832901180812
1601111.9977311766257-0.997731176625698
16189.78151036822736-1.78151036822736
162810.2414839956872-2.24148399568719

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 12 & 10.0634252121548 & 1.9365747878452 \tabularnewline
2 & 11 & 11.3673386527424 & -0.367338652742406 \tabularnewline
3 & 15 & 13.7105247514173 & 1.28947524858271 \tabularnewline
4 & 6 & 11.0845360978408 & -5.08453609784084 \tabularnewline
5 & 13 & 10.3780767944861 & 2.62192320551388 \tabularnewline
6 & 10 & 9.89914733864592 & 0.100852661354075 \tabularnewline
7 & 12 & 12.7282060298219 & -0.728206029821916 \tabularnewline
8 & 14 & 11.4068072356424 & 2.59319276435761 \tabularnewline
9 & 12 & 10.7580871825279 & 1.24191281747207 \tabularnewline
10 & 6 & 11.1654671113867 & -5.16546711138666 \tabularnewline
11 & 10 & 11.0063252110713 & -1.00632521107131 \tabularnewline
12 & 12 & 11.5870500042995 & 0.41294999570047 \tabularnewline
13 & 12 & 11.3444297446087 & 0.655570255391328 \tabularnewline
14 & 11 & 11.4884162065841 & -0.488416206584101 \tabularnewline
15 & 15 & 12.2755806311909 & 2.72441936880908 \tabularnewline
16 & 12 & 10.8284724706831 & 1.17152752931692 \tabularnewline
17 & 10 & 10.6355921033802 & -0.635592103380195 \tabularnewline
18 & 12 & 13.6512694910537 & -1.65126949105371 \tabularnewline
19 & 11 & 12.3203309154373 & -1.32033091543727 \tabularnewline
20 & 12 & 11.7270422284215 & 0.272957771578454 \tabularnewline
21 & 11 & 11.3572813207317 & -0.357281320731673 \tabularnewline
22 & 12 & 12.0454172092381 & -0.0454172092381301 \tabularnewline
23 & 13 & 13.2505414445246 & -0.25054144452462 \tabularnewline
24 & 11 & 11.8595812497131 & -0.859581249713098 \tabularnewline
25 & 9 & 11.4573552107755 & -2.45735521077546 \tabularnewline
26 & 13 & 12.4738778767744 & 0.526122123225637 \tabularnewline
27 & 10 & 11.1921040458535 & -1.19210404585352 \tabularnewline
28 & 14 & 11.3095932771569 & 2.69040672284307 \tabularnewline
29 & 12 & 11.7279142356048 & 0.272085764395176 \tabularnewline
30 & 10 & 10.4634209552955 & -0.463420955295456 \tabularnewline
31 & 12 & 11.3414873349855 & 0.658512665014471 \tabularnewline
32 & 8 & 9.50624941759187 & -1.50624941759187 \tabularnewline
33 & 10 & 10.5098989132185 & -0.509898913218459 \tabularnewline
34 & 12 & 11.8748525846618 & 0.125147415338172 \tabularnewline
35 & 12 & 10.2104229998786 & 1.78957700012144 \tabularnewline
36 & 7 & 6.80102992897304 & 0.198970071026955 \tabularnewline
37 & 6 & 8.43487820165716 & -2.43487820165716 \tabularnewline
38 & 12 & 10.1498556605863 & 1.8501443394137 \tabularnewline
39 & 10 & 11.893091510483 & -1.89309151048298 \tabularnewline
40 & 10 & 11.4391258634 & -1.43912586339995 \tabularnewline
41 & 10 & 11.2405638028064 & -1.24056380280637 \tabularnewline
42 & 12 & 10.5845141119408 & 1.41548588805921 \tabularnewline
43 & 15 & 13.2364957980313 & 1.76350420196873 \tabularnewline
44 & 10 & 10.313515015366 & -0.313515015366032 \tabularnewline
45 & 10 & 10.7050026756358 & -0.705002675635756 \tabularnewline
46 & 12 & 9.31117307180747 & 2.68882692819253 \tabularnewline
47 & 13 & 10.7818524950413 & 2.2181475049587 \tabularnewline
48 & 11 & 11.3483354800393 & -0.348335480039272 \tabularnewline
49 & 11 & 11.513503277459 & -0.513503277458993 \tabularnewline
50 & 12 & 10.217074781221 & 1.78292521877901 \tabularnewline
51 & 14 & 11.9535035431768 & 2.04649645682324 \tabularnewline
52 & 10 & 10.2582071133594 & -0.25820711335944 \tabularnewline
53 & 12 & 9.31914892543535 & 2.68085107456465 \tabularnewline
54 & 13 & 11.9729386513178 & 1.02706134868223 \tabularnewline
55 & 5 & 7.96299315359482 & -2.96299315359482 \tabularnewline
56 & 6 & 10.5812520764886 & -4.58125207648861 \tabularnewline
57 & 12 & 11.4565791725111 & 0.543420827488879 \tabularnewline
58 & 12 & 12.1193565625141 & -0.119356562514083 \tabularnewline
59 & 11 & 10.9551334349726 & 0.0448665650273877 \tabularnewline
60 & 10 & 11.8179285807012 & -1.81792858070116 \tabularnewline
61 & 7 & 9.43711975042334 & -2.43711975042334 \tabularnewline
62 & 12 & 11.2463436779527 & 0.753656322047272 \tabularnewline
63 & 14 & 12.0651466609717 & 1.9348533390283 \tabularnewline
64 & 11 & 10.8844241736553 & 0.115575826344708 \tabularnewline
65 & 12 & 11.6881036618279 & 0.311896338172107 \tabularnewline
66 & 13 & 12.1869020712221 & 0.813097928777917 \tabularnewline
67 & 14 & 12.812294771619 & 1.18770522838095 \tabularnewline
68 & 11 & 12.5707700750344 & -1.57077007503437 \tabularnewline
69 & 12 & 9.5639354554978 & 2.4360645445022 \tabularnewline
70 & 12 & 11.218935131095 & 0.781064868905032 \tabularnewline
71 & 8 & 8.07634097675385 & -0.076340976753851 \tabularnewline
72 & 11 & 10.7654832606156 & 0.234516739384429 \tabularnewline
73 & 14 & 12.8644090894723 & 1.13559091052771 \tabularnewline
74 & 14 & 12.351295942327 & 1.64870405767302 \tabularnewline
75 & 12 & 11.3358375851884 & 0.664162414811627 \tabularnewline
76 & 9 & 11.9291398640454 & -2.92913986404543 \tabularnewline
77 & 13 & 11.5312924521019 & 1.4687075478981 \tabularnewline
78 & 11 & 11.6621446847569 & -0.662144684756928 \tabularnewline
79 & 12 & 9.90775128944858 & 2.09224871055142 \tabularnewline
80 & 12 & 11.2840176109277 & 0.715982389072312 \tabularnewline
81 & 12 & 11.4076814557624 & 0.592318544237611 \tabularnewline
82 & 12 & 13.3081434058812 & -1.30814340588118 \tabularnewline
83 & 12 & 11.9105291158033 & 0.0894708841966996 \tabularnewline
84 & 12 & 10.5562654004061 & 1.44373459959391 \tabularnewline
85 & 11 & 11.3523713408191 & -0.352371340819075 \tabularnewline
86 & 10 & 11.798297209836 & -1.79829720983604 \tabularnewline
87 & 9 & 10.1299709022542 & -1.12997090225425 \tabularnewline
88 & 12 & 11.891671671253 & 0.108328328746991 \tabularnewline
89 & 12 & 11.9781980886034 & 0.0218019113965614 \tabularnewline
90 & 12 & 11.4251741748632 & 0.574825825136774 \tabularnewline
91 & 9 & 9.70948146049804 & -0.709481460498039 \tabularnewline
92 & 15 & 12.1885636075238 & 2.81143639247617 \tabularnewline
93 & 12 & 11.8232289741125 & 0.176771025887547 \tabularnewline
94 & 12 & 11.1923553213709 & 0.807644678629067 \tabularnewline
95 & 12 & 10.5911754717289 & 1.40882452827114 \tabularnewline
96 & 10 & 11.5717786693508 & -1.5717786693508 \tabularnewline
97 & 13 & 11.7086070398763 & 1.29139296012371 \tabularnewline
98 & 9 & 11.7902254882764 & -2.79022548827643 \tabularnewline
99 & 12 & 11.0640231413468 & 0.935976858653192 \tabularnewline
100 & 10 & 10.4579355247289 & -0.457935524728858 \tabularnewline
101 & 14 & 11.5887940186661 & 2.41120598133391 \tabularnewline
102 & 11 & 11.2155750147743 & -0.215575014774337 \tabularnewline
103 & 15 & 13.7476115433431 & 1.2523884566569 \tabularnewline
104 & 11 & 11.0575087498753 & -0.0575087498752895 \tabularnewline
105 & 11 & 11.7558135907537 & -0.755813590753656 \tabularnewline
106 & 12 & 9.56105703384896 & 2.43894296615104 \tabularnewline
107 & 12 & 12.1142428533813 & -0.114242853381258 \tabularnewline
108 & 12 & 10.9127955440431 & 1.08720445595693 \tabularnewline
109 & 11 & 11.4319262500108 & -0.43192625001083 \tabularnewline
110 & 7 & 9.32010732309192 & -2.32010732309192 \tabularnewline
111 & 12 & 11.8770463502066 & 0.122953649793375 \tabularnewline
112 & 14 & 12.041479530314 & 1.95852046968602 \tabularnewline
113 & 11 & 12.6833923945003 & -1.68339239450032 \tabularnewline
114 & 11 & 9.75555350310591 & 1.24444649689409 \tabularnewline
115 & 10 & 9.38448432790864 & 0.615515672091357 \tabularnewline
116 & 13 & 12.1676126912339 & 0.832387308766088 \tabularnewline
117 & 13 & 10.886364450746 & 2.11363554925405 \tabularnewline
118 & 8 & 10.8823285899661 & -2.88232858996615 \tabularnewline
119 & 11 & 10.4788863400316 & 0.521113659968431 \tabularnewline
120 & 12 & 11.5895679449809 & 0.410432055019086 \tabularnewline
121 & 11 & 10.4092772921152 & 0.590722707884819 \tabularnewline
122 & 13 & 11.7745318973227 & 1.22546810267733 \tabularnewline
123 & 12 & 10.1858244233708 & 1.81417557662919 \tabularnewline
124 & 14 & 11.880632358821 & 2.11936764117902 \tabularnewline
125 & 13 & 11.2946734105436 & 1.70532658945642 \tabularnewline
126 & 15 & 12.0674385073849 & 2.93256149261505 \tabularnewline
127 & 10 & 10.5432858108834 & -0.543285810883406 \tabularnewline
128 & 11 & 11.8965698597832 & -0.896569859783247 \tabularnewline
129 & 9 & 11.1183290118081 & -2.11832901180812 \tabularnewline
130 & 11 & 9.61371697081217 & 1.38628302918783 \tabularnewline
131 & 10 & 11.5306582295819 & -1.53065822958191 \tabularnewline
132 & 11 & 8.4965138108568 & 2.5034861891432 \tabularnewline
133 & 8 & 11.6520754603766 & -3.65207546037663 \tabularnewline
134 & 11 & 9.3375841139806 & 1.6624158860194 \tabularnewline
135 & 12 & 10.195544190378 & 1.80445580962196 \tabularnewline
136 & 12 & 11.1170644791766 & 0.882935520823365 \tabularnewline
137 & 9 & 9.61542234297703 & -0.615422342977027 \tabularnewline
138 & 11 & 10.776750678278 & 0.223249321721955 \tabularnewline
139 & 10 & 9.4982736649512 & 0.501726335048804 \tabularnewline
140 & 8 & 9.71102911115328 & -1.71102911115328 \tabularnewline
141 & 9 & 7.4376425007353 & 1.5623574992647 \tabularnewline
142 & 8 & 11.9483994124896 & -3.94839941248957 \tabularnewline
143 & 9 & 10.8009657419697 & -1.80096574196966 \tabularnewline
144 & 15 & 12.5574070736828 & 2.44259292631725 \tabularnewline
145 & 11 & 11.2229299347619 & -0.222929934761941 \tabularnewline
146 & 8 & 8.33897390718924 & -0.338973907189241 \tabularnewline
147 & 13 & 12.7105244135059 & 0.289475586494111 \tabularnewline
148 & 12 & 9.91298323156105 & 2.08701676843895 \tabularnewline
149 & 12 & 11.5613288118919 & 0.438671188108145 \tabularnewline
150 & 9 & 9.77979829735435 & -0.779798297354353 \tabularnewline
151 & 7 & 8.52985687265863 & -1.52985687265863 \tabularnewline
152 & 13 & 10.9028795143117 & 2.09712048568826 \tabularnewline
153 & 9 & 11.4126874045939 & -2.41268740459392 \tabularnewline
154 & 6 & 12.0265025389578 & -6.02650253895781 \tabularnewline
155 & 8 & 10.8252083232814 & -2.82520832328138 \tabularnewline
156 & 8 & 8.56656081856907 & -0.566560818569071 \tabularnewline
157 & 15 & 12.1885636075238 & 2.81143639247617 \tabularnewline
158 & 6 & 10.3598473461234 & -4.3598473461234 \tabularnewline
159 & 9 & 11.1183290118081 & -2.11832901180812 \tabularnewline
160 & 11 & 11.9977311766257 & -0.997731176625698 \tabularnewline
161 & 8 & 9.78151036822736 & -1.78151036822736 \tabularnewline
162 & 8 & 10.2414839956872 & -2.24148399568719 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=198107&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]12[/C][C]10.0634252121548[/C][C]1.9365747878452[/C][/ROW]
[ROW][C]2[/C][C]11[/C][C]11.3673386527424[/C][C]-0.367338652742406[/C][/ROW]
[ROW][C]3[/C][C]15[/C][C]13.7105247514173[/C][C]1.28947524858271[/C][/ROW]
[ROW][C]4[/C][C]6[/C][C]11.0845360978408[/C][C]-5.08453609784084[/C][/ROW]
[ROW][C]5[/C][C]13[/C][C]10.3780767944861[/C][C]2.62192320551388[/C][/ROW]
[ROW][C]6[/C][C]10[/C][C]9.89914733864592[/C][C]0.100852661354075[/C][/ROW]
[ROW][C]7[/C][C]12[/C][C]12.7282060298219[/C][C]-0.728206029821916[/C][/ROW]
[ROW][C]8[/C][C]14[/C][C]11.4068072356424[/C][C]2.59319276435761[/C][/ROW]
[ROW][C]9[/C][C]12[/C][C]10.7580871825279[/C][C]1.24191281747207[/C][/ROW]
[ROW][C]10[/C][C]6[/C][C]11.1654671113867[/C][C]-5.16546711138666[/C][/ROW]
[ROW][C]11[/C][C]10[/C][C]11.0063252110713[/C][C]-1.00632521107131[/C][/ROW]
[ROW][C]12[/C][C]12[/C][C]11.5870500042995[/C][C]0.41294999570047[/C][/ROW]
[ROW][C]13[/C][C]12[/C][C]11.3444297446087[/C][C]0.655570255391328[/C][/ROW]
[ROW][C]14[/C][C]11[/C][C]11.4884162065841[/C][C]-0.488416206584101[/C][/ROW]
[ROW][C]15[/C][C]15[/C][C]12.2755806311909[/C][C]2.72441936880908[/C][/ROW]
[ROW][C]16[/C][C]12[/C][C]10.8284724706831[/C][C]1.17152752931692[/C][/ROW]
[ROW][C]17[/C][C]10[/C][C]10.6355921033802[/C][C]-0.635592103380195[/C][/ROW]
[ROW][C]18[/C][C]12[/C][C]13.6512694910537[/C][C]-1.65126949105371[/C][/ROW]
[ROW][C]19[/C][C]11[/C][C]12.3203309154373[/C][C]-1.32033091543727[/C][/ROW]
[ROW][C]20[/C][C]12[/C][C]11.7270422284215[/C][C]0.272957771578454[/C][/ROW]
[ROW][C]21[/C][C]11[/C][C]11.3572813207317[/C][C]-0.357281320731673[/C][/ROW]
[ROW][C]22[/C][C]12[/C][C]12.0454172092381[/C][C]-0.0454172092381301[/C][/ROW]
[ROW][C]23[/C][C]13[/C][C]13.2505414445246[/C][C]-0.25054144452462[/C][/ROW]
[ROW][C]24[/C][C]11[/C][C]11.8595812497131[/C][C]-0.859581249713098[/C][/ROW]
[ROW][C]25[/C][C]9[/C][C]11.4573552107755[/C][C]-2.45735521077546[/C][/ROW]
[ROW][C]26[/C][C]13[/C][C]12.4738778767744[/C][C]0.526122123225637[/C][/ROW]
[ROW][C]27[/C][C]10[/C][C]11.1921040458535[/C][C]-1.19210404585352[/C][/ROW]
[ROW][C]28[/C][C]14[/C][C]11.3095932771569[/C][C]2.69040672284307[/C][/ROW]
[ROW][C]29[/C][C]12[/C][C]11.7279142356048[/C][C]0.272085764395176[/C][/ROW]
[ROW][C]30[/C][C]10[/C][C]10.4634209552955[/C][C]-0.463420955295456[/C][/ROW]
[ROW][C]31[/C][C]12[/C][C]11.3414873349855[/C][C]0.658512665014471[/C][/ROW]
[ROW][C]32[/C][C]8[/C][C]9.50624941759187[/C][C]-1.50624941759187[/C][/ROW]
[ROW][C]33[/C][C]10[/C][C]10.5098989132185[/C][C]-0.509898913218459[/C][/ROW]
[ROW][C]34[/C][C]12[/C][C]11.8748525846618[/C][C]0.125147415338172[/C][/ROW]
[ROW][C]35[/C][C]12[/C][C]10.2104229998786[/C][C]1.78957700012144[/C][/ROW]
[ROW][C]36[/C][C]7[/C][C]6.80102992897304[/C][C]0.198970071026955[/C][/ROW]
[ROW][C]37[/C][C]6[/C][C]8.43487820165716[/C][C]-2.43487820165716[/C][/ROW]
[ROW][C]38[/C][C]12[/C][C]10.1498556605863[/C][C]1.8501443394137[/C][/ROW]
[ROW][C]39[/C][C]10[/C][C]11.893091510483[/C][C]-1.89309151048298[/C][/ROW]
[ROW][C]40[/C][C]10[/C][C]11.4391258634[/C][C]-1.43912586339995[/C][/ROW]
[ROW][C]41[/C][C]10[/C][C]11.2405638028064[/C][C]-1.24056380280637[/C][/ROW]
[ROW][C]42[/C][C]12[/C][C]10.5845141119408[/C][C]1.41548588805921[/C][/ROW]
[ROW][C]43[/C][C]15[/C][C]13.2364957980313[/C][C]1.76350420196873[/C][/ROW]
[ROW][C]44[/C][C]10[/C][C]10.313515015366[/C][C]-0.313515015366032[/C][/ROW]
[ROW][C]45[/C][C]10[/C][C]10.7050026756358[/C][C]-0.705002675635756[/C][/ROW]
[ROW][C]46[/C][C]12[/C][C]9.31117307180747[/C][C]2.68882692819253[/C][/ROW]
[ROW][C]47[/C][C]13[/C][C]10.7818524950413[/C][C]2.2181475049587[/C][/ROW]
[ROW][C]48[/C][C]11[/C][C]11.3483354800393[/C][C]-0.348335480039272[/C][/ROW]
[ROW][C]49[/C][C]11[/C][C]11.513503277459[/C][C]-0.513503277458993[/C][/ROW]
[ROW][C]50[/C][C]12[/C][C]10.217074781221[/C][C]1.78292521877901[/C][/ROW]
[ROW][C]51[/C][C]14[/C][C]11.9535035431768[/C][C]2.04649645682324[/C][/ROW]
[ROW][C]52[/C][C]10[/C][C]10.2582071133594[/C][C]-0.25820711335944[/C][/ROW]
[ROW][C]53[/C][C]12[/C][C]9.31914892543535[/C][C]2.68085107456465[/C][/ROW]
[ROW][C]54[/C][C]13[/C][C]11.9729386513178[/C][C]1.02706134868223[/C][/ROW]
[ROW][C]55[/C][C]5[/C][C]7.96299315359482[/C][C]-2.96299315359482[/C][/ROW]
[ROW][C]56[/C][C]6[/C][C]10.5812520764886[/C][C]-4.58125207648861[/C][/ROW]
[ROW][C]57[/C][C]12[/C][C]11.4565791725111[/C][C]0.543420827488879[/C][/ROW]
[ROW][C]58[/C][C]12[/C][C]12.1193565625141[/C][C]-0.119356562514083[/C][/ROW]
[ROW][C]59[/C][C]11[/C][C]10.9551334349726[/C][C]0.0448665650273877[/C][/ROW]
[ROW][C]60[/C][C]10[/C][C]11.8179285807012[/C][C]-1.81792858070116[/C][/ROW]
[ROW][C]61[/C][C]7[/C][C]9.43711975042334[/C][C]-2.43711975042334[/C][/ROW]
[ROW][C]62[/C][C]12[/C][C]11.2463436779527[/C][C]0.753656322047272[/C][/ROW]
[ROW][C]63[/C][C]14[/C][C]12.0651466609717[/C][C]1.9348533390283[/C][/ROW]
[ROW][C]64[/C][C]11[/C][C]10.8844241736553[/C][C]0.115575826344708[/C][/ROW]
[ROW][C]65[/C][C]12[/C][C]11.6881036618279[/C][C]0.311896338172107[/C][/ROW]
[ROW][C]66[/C][C]13[/C][C]12.1869020712221[/C][C]0.813097928777917[/C][/ROW]
[ROW][C]67[/C][C]14[/C][C]12.812294771619[/C][C]1.18770522838095[/C][/ROW]
[ROW][C]68[/C][C]11[/C][C]12.5707700750344[/C][C]-1.57077007503437[/C][/ROW]
[ROW][C]69[/C][C]12[/C][C]9.5639354554978[/C][C]2.4360645445022[/C][/ROW]
[ROW][C]70[/C][C]12[/C][C]11.218935131095[/C][C]0.781064868905032[/C][/ROW]
[ROW][C]71[/C][C]8[/C][C]8.07634097675385[/C][C]-0.076340976753851[/C][/ROW]
[ROW][C]72[/C][C]11[/C][C]10.7654832606156[/C][C]0.234516739384429[/C][/ROW]
[ROW][C]73[/C][C]14[/C][C]12.8644090894723[/C][C]1.13559091052771[/C][/ROW]
[ROW][C]74[/C][C]14[/C][C]12.351295942327[/C][C]1.64870405767302[/C][/ROW]
[ROW][C]75[/C][C]12[/C][C]11.3358375851884[/C][C]0.664162414811627[/C][/ROW]
[ROW][C]76[/C][C]9[/C][C]11.9291398640454[/C][C]-2.92913986404543[/C][/ROW]
[ROW][C]77[/C][C]13[/C][C]11.5312924521019[/C][C]1.4687075478981[/C][/ROW]
[ROW][C]78[/C][C]11[/C][C]11.6621446847569[/C][C]-0.662144684756928[/C][/ROW]
[ROW][C]79[/C][C]12[/C][C]9.90775128944858[/C][C]2.09224871055142[/C][/ROW]
[ROW][C]80[/C][C]12[/C][C]11.2840176109277[/C][C]0.715982389072312[/C][/ROW]
[ROW][C]81[/C][C]12[/C][C]11.4076814557624[/C][C]0.592318544237611[/C][/ROW]
[ROW][C]82[/C][C]12[/C][C]13.3081434058812[/C][C]-1.30814340588118[/C][/ROW]
[ROW][C]83[/C][C]12[/C][C]11.9105291158033[/C][C]0.0894708841966996[/C][/ROW]
[ROW][C]84[/C][C]12[/C][C]10.5562654004061[/C][C]1.44373459959391[/C][/ROW]
[ROW][C]85[/C][C]11[/C][C]11.3523713408191[/C][C]-0.352371340819075[/C][/ROW]
[ROW][C]86[/C][C]10[/C][C]11.798297209836[/C][C]-1.79829720983604[/C][/ROW]
[ROW][C]87[/C][C]9[/C][C]10.1299709022542[/C][C]-1.12997090225425[/C][/ROW]
[ROW][C]88[/C][C]12[/C][C]11.891671671253[/C][C]0.108328328746991[/C][/ROW]
[ROW][C]89[/C][C]12[/C][C]11.9781980886034[/C][C]0.0218019113965614[/C][/ROW]
[ROW][C]90[/C][C]12[/C][C]11.4251741748632[/C][C]0.574825825136774[/C][/ROW]
[ROW][C]91[/C][C]9[/C][C]9.70948146049804[/C][C]-0.709481460498039[/C][/ROW]
[ROW][C]92[/C][C]15[/C][C]12.1885636075238[/C][C]2.81143639247617[/C][/ROW]
[ROW][C]93[/C][C]12[/C][C]11.8232289741125[/C][C]0.176771025887547[/C][/ROW]
[ROW][C]94[/C][C]12[/C][C]11.1923553213709[/C][C]0.807644678629067[/C][/ROW]
[ROW][C]95[/C][C]12[/C][C]10.5911754717289[/C][C]1.40882452827114[/C][/ROW]
[ROW][C]96[/C][C]10[/C][C]11.5717786693508[/C][C]-1.5717786693508[/C][/ROW]
[ROW][C]97[/C][C]13[/C][C]11.7086070398763[/C][C]1.29139296012371[/C][/ROW]
[ROW][C]98[/C][C]9[/C][C]11.7902254882764[/C][C]-2.79022548827643[/C][/ROW]
[ROW][C]99[/C][C]12[/C][C]11.0640231413468[/C][C]0.935976858653192[/C][/ROW]
[ROW][C]100[/C][C]10[/C][C]10.4579355247289[/C][C]-0.457935524728858[/C][/ROW]
[ROW][C]101[/C][C]14[/C][C]11.5887940186661[/C][C]2.41120598133391[/C][/ROW]
[ROW][C]102[/C][C]11[/C][C]11.2155750147743[/C][C]-0.215575014774337[/C][/ROW]
[ROW][C]103[/C][C]15[/C][C]13.7476115433431[/C][C]1.2523884566569[/C][/ROW]
[ROW][C]104[/C][C]11[/C][C]11.0575087498753[/C][C]-0.0575087498752895[/C][/ROW]
[ROW][C]105[/C][C]11[/C][C]11.7558135907537[/C][C]-0.755813590753656[/C][/ROW]
[ROW][C]106[/C][C]12[/C][C]9.56105703384896[/C][C]2.43894296615104[/C][/ROW]
[ROW][C]107[/C][C]12[/C][C]12.1142428533813[/C][C]-0.114242853381258[/C][/ROW]
[ROW][C]108[/C][C]12[/C][C]10.9127955440431[/C][C]1.08720445595693[/C][/ROW]
[ROW][C]109[/C][C]11[/C][C]11.4319262500108[/C][C]-0.43192625001083[/C][/ROW]
[ROW][C]110[/C][C]7[/C][C]9.32010732309192[/C][C]-2.32010732309192[/C][/ROW]
[ROW][C]111[/C][C]12[/C][C]11.8770463502066[/C][C]0.122953649793375[/C][/ROW]
[ROW][C]112[/C][C]14[/C][C]12.041479530314[/C][C]1.95852046968602[/C][/ROW]
[ROW][C]113[/C][C]11[/C][C]12.6833923945003[/C][C]-1.68339239450032[/C][/ROW]
[ROW][C]114[/C][C]11[/C][C]9.75555350310591[/C][C]1.24444649689409[/C][/ROW]
[ROW][C]115[/C][C]10[/C][C]9.38448432790864[/C][C]0.615515672091357[/C][/ROW]
[ROW][C]116[/C][C]13[/C][C]12.1676126912339[/C][C]0.832387308766088[/C][/ROW]
[ROW][C]117[/C][C]13[/C][C]10.886364450746[/C][C]2.11363554925405[/C][/ROW]
[ROW][C]118[/C][C]8[/C][C]10.8823285899661[/C][C]-2.88232858996615[/C][/ROW]
[ROW][C]119[/C][C]11[/C][C]10.4788863400316[/C][C]0.521113659968431[/C][/ROW]
[ROW][C]120[/C][C]12[/C][C]11.5895679449809[/C][C]0.410432055019086[/C][/ROW]
[ROW][C]121[/C][C]11[/C][C]10.4092772921152[/C][C]0.590722707884819[/C][/ROW]
[ROW][C]122[/C][C]13[/C][C]11.7745318973227[/C][C]1.22546810267733[/C][/ROW]
[ROW][C]123[/C][C]12[/C][C]10.1858244233708[/C][C]1.81417557662919[/C][/ROW]
[ROW][C]124[/C][C]14[/C][C]11.880632358821[/C][C]2.11936764117902[/C][/ROW]
[ROW][C]125[/C][C]13[/C][C]11.2946734105436[/C][C]1.70532658945642[/C][/ROW]
[ROW][C]126[/C][C]15[/C][C]12.0674385073849[/C][C]2.93256149261505[/C][/ROW]
[ROW][C]127[/C][C]10[/C][C]10.5432858108834[/C][C]-0.543285810883406[/C][/ROW]
[ROW][C]128[/C][C]11[/C][C]11.8965698597832[/C][C]-0.896569859783247[/C][/ROW]
[ROW][C]129[/C][C]9[/C][C]11.1183290118081[/C][C]-2.11832901180812[/C][/ROW]
[ROW][C]130[/C][C]11[/C][C]9.61371697081217[/C][C]1.38628302918783[/C][/ROW]
[ROW][C]131[/C][C]10[/C][C]11.5306582295819[/C][C]-1.53065822958191[/C][/ROW]
[ROW][C]132[/C][C]11[/C][C]8.4965138108568[/C][C]2.5034861891432[/C][/ROW]
[ROW][C]133[/C][C]8[/C][C]11.6520754603766[/C][C]-3.65207546037663[/C][/ROW]
[ROW][C]134[/C][C]11[/C][C]9.3375841139806[/C][C]1.6624158860194[/C][/ROW]
[ROW][C]135[/C][C]12[/C][C]10.195544190378[/C][C]1.80445580962196[/C][/ROW]
[ROW][C]136[/C][C]12[/C][C]11.1170644791766[/C][C]0.882935520823365[/C][/ROW]
[ROW][C]137[/C][C]9[/C][C]9.61542234297703[/C][C]-0.615422342977027[/C][/ROW]
[ROW][C]138[/C][C]11[/C][C]10.776750678278[/C][C]0.223249321721955[/C][/ROW]
[ROW][C]139[/C][C]10[/C][C]9.4982736649512[/C][C]0.501726335048804[/C][/ROW]
[ROW][C]140[/C][C]8[/C][C]9.71102911115328[/C][C]-1.71102911115328[/C][/ROW]
[ROW][C]141[/C][C]9[/C][C]7.4376425007353[/C][C]1.5623574992647[/C][/ROW]
[ROW][C]142[/C][C]8[/C][C]11.9483994124896[/C][C]-3.94839941248957[/C][/ROW]
[ROW][C]143[/C][C]9[/C][C]10.8009657419697[/C][C]-1.80096574196966[/C][/ROW]
[ROW][C]144[/C][C]15[/C][C]12.5574070736828[/C][C]2.44259292631725[/C][/ROW]
[ROW][C]145[/C][C]11[/C][C]11.2229299347619[/C][C]-0.222929934761941[/C][/ROW]
[ROW][C]146[/C][C]8[/C][C]8.33897390718924[/C][C]-0.338973907189241[/C][/ROW]
[ROW][C]147[/C][C]13[/C][C]12.7105244135059[/C][C]0.289475586494111[/C][/ROW]
[ROW][C]148[/C][C]12[/C][C]9.91298323156105[/C][C]2.08701676843895[/C][/ROW]
[ROW][C]149[/C][C]12[/C][C]11.5613288118919[/C][C]0.438671188108145[/C][/ROW]
[ROW][C]150[/C][C]9[/C][C]9.77979829735435[/C][C]-0.779798297354353[/C][/ROW]
[ROW][C]151[/C][C]7[/C][C]8.52985687265863[/C][C]-1.52985687265863[/C][/ROW]
[ROW][C]152[/C][C]13[/C][C]10.9028795143117[/C][C]2.09712048568826[/C][/ROW]
[ROW][C]153[/C][C]9[/C][C]11.4126874045939[/C][C]-2.41268740459392[/C][/ROW]
[ROW][C]154[/C][C]6[/C][C]12.0265025389578[/C][C]-6.02650253895781[/C][/ROW]
[ROW][C]155[/C][C]8[/C][C]10.8252083232814[/C][C]-2.82520832328138[/C][/ROW]
[ROW][C]156[/C][C]8[/C][C]8.56656081856907[/C][C]-0.566560818569071[/C][/ROW]
[ROW][C]157[/C][C]15[/C][C]12.1885636075238[/C][C]2.81143639247617[/C][/ROW]
[ROW][C]158[/C][C]6[/C][C]10.3598473461234[/C][C]-4.3598473461234[/C][/ROW]
[ROW][C]159[/C][C]9[/C][C]11.1183290118081[/C][C]-2.11832901180812[/C][/ROW]
[ROW][C]160[/C][C]11[/C][C]11.9977311766257[/C][C]-0.997731176625698[/C][/ROW]
[ROW][C]161[/C][C]8[/C][C]9.78151036822736[/C][C]-1.78151036822736[/C][/ROW]
[ROW][C]162[/C][C]8[/C][C]10.2414839956872[/C][C]-2.24148399568719[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=198107&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=198107&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
11210.06342521215481.9365747878452
21111.3673386527424-0.367338652742406
31513.71052475141731.28947524858271
4611.0845360978408-5.08453609784084
51310.37807679448612.62192320551388
6109.899147338645920.100852661354075
71212.7282060298219-0.728206029821916
81411.40680723564242.59319276435761
91210.75808718252791.24191281747207
10611.1654671113867-5.16546711138666
111011.0063252110713-1.00632521107131
121211.58705000429950.41294999570047
131211.34442974460870.655570255391328
141111.4884162065841-0.488416206584101
151512.27558063119092.72441936880908
161210.82847247068311.17152752931692
171010.6355921033802-0.635592103380195
181213.6512694910537-1.65126949105371
191112.3203309154373-1.32033091543727
201211.72704222842150.272957771578454
211111.3572813207317-0.357281320731673
221212.0454172092381-0.0454172092381301
231313.2505414445246-0.25054144452462
241111.8595812497131-0.859581249713098
25911.4573552107755-2.45735521077546
261312.47387787677440.526122123225637
271011.1921040458535-1.19210404585352
281411.30959327715692.69040672284307
291211.72791423560480.272085764395176
301010.4634209552955-0.463420955295456
311211.34148733498550.658512665014471
3289.50624941759187-1.50624941759187
331010.5098989132185-0.509898913218459
341211.87485258466180.125147415338172
351210.21042299987861.78957700012144
3676.801029928973040.198970071026955
3768.43487820165716-2.43487820165716
381210.14985566058631.8501443394137
391011.893091510483-1.89309151048298
401011.4391258634-1.43912586339995
411011.2405638028064-1.24056380280637
421210.58451411194081.41548588805921
431513.23649579803131.76350420196873
441010.313515015366-0.313515015366032
451010.7050026756358-0.705002675635756
46129.311173071807472.68882692819253
471310.78185249504132.2181475049587
481111.3483354800393-0.348335480039272
491111.513503277459-0.513503277458993
501210.2170747812211.78292521877901
511411.95350354317682.04649645682324
521010.2582071133594-0.25820711335944
53129.319148925435352.68085107456465
541311.97293865131781.02706134868223
5557.96299315359482-2.96299315359482
56610.5812520764886-4.58125207648861
571211.45657917251110.543420827488879
581212.1193565625141-0.119356562514083
591110.95513343497260.0448665650273877
601011.8179285807012-1.81792858070116
6179.43711975042334-2.43711975042334
621211.24634367795270.753656322047272
631412.06514666097171.9348533390283
641110.88442417365530.115575826344708
651211.68810366182790.311896338172107
661312.18690207122210.813097928777917
671412.8122947716191.18770522838095
681112.5707700750344-1.57077007503437
69129.56393545549782.4360645445022
701211.2189351310950.781064868905032
7188.07634097675385-0.076340976753851
721110.76548326061560.234516739384429
731412.86440908947231.13559091052771
741412.3512959423271.64870405767302
751211.33583758518840.664162414811627
76911.9291398640454-2.92913986404543
771311.53129245210191.4687075478981
781111.6621446847569-0.662144684756928
79129.907751289448582.09224871055142
801211.28401761092770.715982389072312
811211.40768145576240.592318544237611
821213.3081434058812-1.30814340588118
831211.91052911580330.0894708841966996
841210.55626540040611.44373459959391
851111.3523713408191-0.352371340819075
861011.798297209836-1.79829720983604
87910.1299709022542-1.12997090225425
881211.8916716712530.108328328746991
891211.97819808860340.0218019113965614
901211.42517417486320.574825825136774
9199.70948146049804-0.709481460498039
921512.18856360752382.81143639247617
931211.82322897411250.176771025887547
941211.19235532137090.807644678629067
951210.59117547172891.40882452827114
961011.5717786693508-1.5717786693508
971311.70860703987631.29139296012371
98911.7902254882764-2.79022548827643
991211.06402314134680.935976858653192
1001010.4579355247289-0.457935524728858
1011411.58879401866612.41120598133391
1021111.2155750147743-0.215575014774337
1031513.74761154334311.2523884566569
1041111.0575087498753-0.0575087498752895
1051111.7558135907537-0.755813590753656
106129.561057033848962.43894296615104
1071212.1142428533813-0.114242853381258
1081210.91279554404311.08720445595693
1091111.4319262500108-0.43192625001083
11079.32010732309192-2.32010732309192
1111211.87704635020660.122953649793375
1121412.0414795303141.95852046968602
1131112.6833923945003-1.68339239450032
114119.755553503105911.24444649689409
115109.384484327908640.615515672091357
1161312.16761269123390.832387308766088
1171310.8863644507462.11363554925405
118810.8823285899661-2.88232858996615
1191110.47888634003160.521113659968431
1201211.58956794498090.410432055019086
1211110.40927729211520.590722707884819
1221311.77453189732271.22546810267733
1231210.18582442337081.81417557662919
1241411.8806323588212.11936764117902
1251311.29467341054361.70532658945642
1261512.06743850738492.93256149261505
1271010.5432858108834-0.543285810883406
1281111.8965698597832-0.896569859783247
129911.1183290118081-2.11832901180812
130119.613716970812171.38628302918783
1311011.5306582295819-1.53065822958191
132118.49651381085682.5034861891432
133811.6520754603766-3.65207546037663
134119.33758411398061.6624158860194
1351210.1955441903781.80445580962196
1361211.11706447917660.882935520823365
13799.61542234297703-0.615422342977027
1381110.7767506782780.223249321721955
139109.49827366495120.501726335048804
14089.71102911115328-1.71102911115328
14197.43764250073531.5623574992647
142811.9483994124896-3.94839941248957
143910.8009657419697-1.80096574196966
1441512.55740707368282.44259292631725
1451111.2229299347619-0.222929934761941
14688.33897390718924-0.338973907189241
1471312.71052441350590.289475586494111
148129.912983231561052.08701676843895
1491211.56132881189190.438671188108145
15099.77979829735435-0.779798297354353
15178.52985687265863-1.52985687265863
1521310.90287951431172.09712048568826
153911.4126874045939-2.41268740459392
154612.0265025389578-6.02650253895781
155810.8252083232814-2.82520832328138
15688.56656081856907-0.566560818569071
1571512.18856360752382.81143639247617
158610.3598473461234-4.3598473461234
159911.1183290118081-2.11832901180812
1601111.9977311766257-0.997731176625698
16189.78151036822736-1.78151036822736
162810.2414839956872-2.24148399568719






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
100.9872944365665310.02541112686693860.0127055634334693
110.998158069361480.003683861277039180.00184193063851959
120.9956038905115860.008792218976827020.00439610948841351
130.9963173863303330.007365227339333410.0036826136696667
140.992863945180280.01427210963943910.00713605481971955
150.9945792591370060.01084148172598880.00542074086299438
160.9942556401335960.01148871973280820.00574435986640412
170.9899977103757160.02000457924856750.0100022896242838
180.9864628970167790.02707420596644260.0135371029832213
190.9790913585232250.04181728295354910.0209086414767745
200.9675404546978660.06491909060426820.0324595453021341
210.9520587823262320.09588243534753640.0479412176737682
220.9311597374571280.1376805250857430.0688402625428715
230.9042080573897740.1915838852204520.0957919426102259
240.8804125081502590.2391749836994820.119587491849741
250.861857306760060.2762853864798790.13814269323994
260.8388296301783470.3223407396433060.161170369821653
270.7988811059839630.4022377880320750.201118894016037
280.8042839267923430.3914321464153150.195716073207657
290.7577840009834080.4844319980331840.242215999016592
300.7061092910526330.5877814178947350.293890708947368
310.650984733765180.698030532469640.34901526623482
320.6397625518937330.7204748962125350.360237448106267
330.5914597174902090.8170805650195830.408540282509791
340.5338044117172880.9323911765654250.466195588282712
350.5806124908246280.8387750183507440.419387509175372
360.5226189763499440.9547620473001130.477381023650056
370.5660846561424480.8678306877151050.433915343857552
380.6127870989238690.7744258021522620.387212901076131
390.6308826562675320.7382346874649360.369117343732468
400.5904889259356160.8190221481287670.409511074064384
410.5458537435569230.9082925128861550.454146256443077
420.5108200432611390.9783599134777220.489179956738861
430.57308782975230.85382434049540.4269121702477
440.5222760224058010.9554479551883980.477723977594199
450.4780403497803250.956080699560650.521959650219675
460.5103464409906760.9793071180186490.489653559009324
470.5042863388322180.9914273223355650.495713661167783
480.4587516628722050.9175033257444090.541248337127795
490.4192626370620720.8385252741241450.580737362937928
500.4326793394477290.8653586788954580.567320660552271
510.4307881731912540.8615763463825090.569211826808746
520.384695596163220.769391192326440.61530440383678
530.451195065724420.902390131448840.54880493427558
540.4105277893444440.8210555786888890.589472210655556
550.5055999330251210.9888001339497590.49440006697488
560.7619436613084310.4761126773831380.238056338691569
570.7249232292405020.5501535415189970.275076770759498
580.6833063726616720.6333872546766560.316693627338328
590.6394457321376760.7211085357246480.360554267862324
600.6418570216085290.7162859567829410.358142978391471
610.6587504225148050.6824991549703890.341249577485195
620.6263575477076810.7472849045846380.373642452292319
630.6280669576704420.7438660846591160.371933042329558
640.5827610477315610.8344779045368790.417238952268439
650.5383650840455030.9232698319089940.461634915954497
660.499676131564860.999352263129720.50032386843514
670.4725545634338140.9451091268676270.527445436566186
680.4524565676826380.9049131353652750.547543432317362
690.4695349306006350.939069861201270.530465069399365
700.4295631597184630.8591263194369260.570436840281537
710.3851572322853960.7703144645707920.614842767714604
720.345643663042260.6912873260845190.65435633695774
730.319549500184340.639099000368680.68045049981566
740.3052114643734060.6104229287468110.694788535626594
750.2906674589920890.5813349179841790.709332541007911
760.3491587827224260.6983175654448520.650841217277574
770.3345669594127820.6691339188255640.665433040587218
780.297694343282270.595388686564540.70230565671773
790.3204315464160320.6408630928320640.679568453583968
800.3020419092281410.6040838184562810.697958090771859
810.2662227146640550.532445429328110.733777285335945
820.2512858210151430.5025716420302860.748714178984857
830.2196018537155310.4392037074310630.780398146284469
840.2077284540340470.4154569080680930.792271545965953
850.1778803587539240.3557607175078470.822119641246077
860.1768627406829920.3537254813659850.823137259317008
870.1641122218619990.3282244437239990.835887778138001
880.137075457115060.2741509142301210.86292454288494
890.1131202490437680.2262404980875370.886879750956232
900.09477199143597350.1895439828719470.905228008564026
910.08106397671283070.1621279534256610.918936023287169
920.1100213693487590.2200427386975180.889978630651241
930.09475014374394510.189500287487890.905249856256055
940.08100904810935760.1620180962187150.918990951890642
950.07362895881795430.1472579176359090.926371041182046
960.07049999048002360.1409999809600470.929500009519976
970.06317674766856460.1263534953371290.936823252331435
980.08699226166568510.173984523331370.913007738334315
990.07383069149775550.1476613829955110.926169308502245
1000.05961126730278590.1192225346055720.940388732697214
1010.07249079518754130.1449815903750830.927509204812459
1020.05779383489837610.1155876697967520.942206165101624
1030.0550553942729130.1101107885458260.944944605727087
1040.04585430526755620.09170861053511250.954145694732444
1050.0380775757564870.0761551515129740.961922424243513
1060.04168280674297350.08336561348594710.958317193257026
1070.032278295088640.06455659017728010.96772170491136
1080.02952223472485060.05904446944970110.970477765275149
1090.02263844060297060.04527688120594120.977361559397029
1100.02518013438050170.05036026876100340.974819865619498
1110.01930650070057760.03861300140115530.980693499299422
1120.02066948421489580.04133896842979160.979330515785104
1130.01873543199909050.0374708639981810.98126456800091
1140.01572154234003210.03144308468006430.984278457659968
1150.01209669223535770.02419338447071530.987903307764642
1160.009356466951564360.01871293390312870.990643533048436
1170.01442304763345310.02884609526690630.985576952366547
1180.01773402763160170.03546805526320340.982265972368398
1190.01314581833750760.02629163667501520.986854181662492
1200.01028834343195090.02057668686390170.989711656568049
1210.008085633212786240.01617126642557250.991914366787214
1220.006534006752155860.01306801350431170.993465993247844
1230.007997789516211730.01599557903242350.992002210483788
1240.01276318813485380.02552637626970770.987236811865146
1250.01348937565198210.02697875130396410.986510624348018
1260.03868064747317330.07736129494634660.961319352526827
1270.03031274977617120.06062549955234240.969687250223829
1280.02319423157540410.04638846315080820.976805768424596
1290.01950909968233920.03901819936467850.980490900317661
1300.0193124047954240.03862480959084810.980687595204576
1310.01603940185832750.03207880371665490.983960598141673
1320.01835232478067260.03670464956134520.981647675219327
1330.03098466161824050.06196932323648090.96901533838176
1340.03175335583827730.06350671167655460.968246644161723
1350.03191683857834530.06383367715669060.968083161421655
1360.0279050120771660.05581002415433190.972094987922834
1370.02447156547699730.04894313095399450.975528434523003
1380.016850816158950.03370163231789990.98314918384105
1390.01940495388094480.03880990776188960.980595046119055
1400.01395239908908660.02790479817817320.986047600910913
1410.03435950833827860.06871901667655720.965640491661721
1420.04481874308407590.08963748616815170.955181256915924
1430.03199284729584690.06398569459169380.968007152704153
1440.2623534960009750.524706992001950.737646503999025
1450.3324363863191890.6648727726383790.667563613680811
1460.5211100781380970.9577798437238070.478889921861903
1470.4985404705089270.9970809410178530.501459529491073
1480.8260952323096680.3478095353806640.173904767690332
1490.7959483739191190.4081032521617620.204051626080881
1500.740126605733330.519746788533340.25987339426667
1510.6750219040859090.6499561918281820.324978095914091
1520.6584280143145730.6831439713708540.341571985685427

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
10 & 0.987294436566531 & 0.0254111268669386 & 0.0127055634334693 \tabularnewline
11 & 0.99815806936148 & 0.00368386127703918 & 0.00184193063851959 \tabularnewline
12 & 0.995603890511586 & 0.00879221897682702 & 0.00439610948841351 \tabularnewline
13 & 0.996317386330333 & 0.00736522733933341 & 0.0036826136696667 \tabularnewline
14 & 0.99286394518028 & 0.0142721096394391 & 0.00713605481971955 \tabularnewline
15 & 0.994579259137006 & 0.0108414817259888 & 0.00542074086299438 \tabularnewline
16 & 0.994255640133596 & 0.0114887197328082 & 0.00574435986640412 \tabularnewline
17 & 0.989997710375716 & 0.0200045792485675 & 0.0100022896242838 \tabularnewline
18 & 0.986462897016779 & 0.0270742059664426 & 0.0135371029832213 \tabularnewline
19 & 0.979091358523225 & 0.0418172829535491 & 0.0209086414767745 \tabularnewline
20 & 0.967540454697866 & 0.0649190906042682 & 0.0324595453021341 \tabularnewline
21 & 0.952058782326232 & 0.0958824353475364 & 0.0479412176737682 \tabularnewline
22 & 0.931159737457128 & 0.137680525085743 & 0.0688402625428715 \tabularnewline
23 & 0.904208057389774 & 0.191583885220452 & 0.0957919426102259 \tabularnewline
24 & 0.880412508150259 & 0.239174983699482 & 0.119587491849741 \tabularnewline
25 & 0.86185730676006 & 0.276285386479879 & 0.13814269323994 \tabularnewline
26 & 0.838829630178347 & 0.322340739643306 & 0.161170369821653 \tabularnewline
27 & 0.798881105983963 & 0.402237788032075 & 0.201118894016037 \tabularnewline
28 & 0.804283926792343 & 0.391432146415315 & 0.195716073207657 \tabularnewline
29 & 0.757784000983408 & 0.484431998033184 & 0.242215999016592 \tabularnewline
30 & 0.706109291052633 & 0.587781417894735 & 0.293890708947368 \tabularnewline
31 & 0.65098473376518 & 0.69803053246964 & 0.34901526623482 \tabularnewline
32 & 0.639762551893733 & 0.720474896212535 & 0.360237448106267 \tabularnewline
33 & 0.591459717490209 & 0.817080565019583 & 0.408540282509791 \tabularnewline
34 & 0.533804411717288 & 0.932391176565425 & 0.466195588282712 \tabularnewline
35 & 0.580612490824628 & 0.838775018350744 & 0.419387509175372 \tabularnewline
36 & 0.522618976349944 & 0.954762047300113 & 0.477381023650056 \tabularnewline
37 & 0.566084656142448 & 0.867830687715105 & 0.433915343857552 \tabularnewline
38 & 0.612787098923869 & 0.774425802152262 & 0.387212901076131 \tabularnewline
39 & 0.630882656267532 & 0.738234687464936 & 0.369117343732468 \tabularnewline
40 & 0.590488925935616 & 0.819022148128767 & 0.409511074064384 \tabularnewline
41 & 0.545853743556923 & 0.908292512886155 & 0.454146256443077 \tabularnewline
42 & 0.510820043261139 & 0.978359913477722 & 0.489179956738861 \tabularnewline
43 & 0.5730878297523 & 0.8538243404954 & 0.4269121702477 \tabularnewline
44 & 0.522276022405801 & 0.955447955188398 & 0.477723977594199 \tabularnewline
45 & 0.478040349780325 & 0.95608069956065 & 0.521959650219675 \tabularnewline
46 & 0.510346440990676 & 0.979307118018649 & 0.489653559009324 \tabularnewline
47 & 0.504286338832218 & 0.991427322335565 & 0.495713661167783 \tabularnewline
48 & 0.458751662872205 & 0.917503325744409 & 0.541248337127795 \tabularnewline
49 & 0.419262637062072 & 0.838525274124145 & 0.580737362937928 \tabularnewline
50 & 0.432679339447729 & 0.865358678895458 & 0.567320660552271 \tabularnewline
51 & 0.430788173191254 & 0.861576346382509 & 0.569211826808746 \tabularnewline
52 & 0.38469559616322 & 0.76939119232644 & 0.61530440383678 \tabularnewline
53 & 0.45119506572442 & 0.90239013144884 & 0.54880493427558 \tabularnewline
54 & 0.410527789344444 & 0.821055578688889 & 0.589472210655556 \tabularnewline
55 & 0.505599933025121 & 0.988800133949759 & 0.49440006697488 \tabularnewline
56 & 0.761943661308431 & 0.476112677383138 & 0.238056338691569 \tabularnewline
57 & 0.724923229240502 & 0.550153541518997 & 0.275076770759498 \tabularnewline
58 & 0.683306372661672 & 0.633387254676656 & 0.316693627338328 \tabularnewline
59 & 0.639445732137676 & 0.721108535724648 & 0.360554267862324 \tabularnewline
60 & 0.641857021608529 & 0.716285956782941 & 0.358142978391471 \tabularnewline
61 & 0.658750422514805 & 0.682499154970389 & 0.341249577485195 \tabularnewline
62 & 0.626357547707681 & 0.747284904584638 & 0.373642452292319 \tabularnewline
63 & 0.628066957670442 & 0.743866084659116 & 0.371933042329558 \tabularnewline
64 & 0.582761047731561 & 0.834477904536879 & 0.417238952268439 \tabularnewline
65 & 0.538365084045503 & 0.923269831908994 & 0.461634915954497 \tabularnewline
66 & 0.49967613156486 & 0.99935226312972 & 0.50032386843514 \tabularnewline
67 & 0.472554563433814 & 0.945109126867627 & 0.527445436566186 \tabularnewline
68 & 0.452456567682638 & 0.904913135365275 & 0.547543432317362 \tabularnewline
69 & 0.469534930600635 & 0.93906986120127 & 0.530465069399365 \tabularnewline
70 & 0.429563159718463 & 0.859126319436926 & 0.570436840281537 \tabularnewline
71 & 0.385157232285396 & 0.770314464570792 & 0.614842767714604 \tabularnewline
72 & 0.34564366304226 & 0.691287326084519 & 0.65435633695774 \tabularnewline
73 & 0.31954950018434 & 0.63909900036868 & 0.68045049981566 \tabularnewline
74 & 0.305211464373406 & 0.610422928746811 & 0.694788535626594 \tabularnewline
75 & 0.290667458992089 & 0.581334917984179 & 0.709332541007911 \tabularnewline
76 & 0.349158782722426 & 0.698317565444852 & 0.650841217277574 \tabularnewline
77 & 0.334566959412782 & 0.669133918825564 & 0.665433040587218 \tabularnewline
78 & 0.29769434328227 & 0.59538868656454 & 0.70230565671773 \tabularnewline
79 & 0.320431546416032 & 0.640863092832064 & 0.679568453583968 \tabularnewline
80 & 0.302041909228141 & 0.604083818456281 & 0.697958090771859 \tabularnewline
81 & 0.266222714664055 & 0.53244542932811 & 0.733777285335945 \tabularnewline
82 & 0.251285821015143 & 0.502571642030286 & 0.748714178984857 \tabularnewline
83 & 0.219601853715531 & 0.439203707431063 & 0.780398146284469 \tabularnewline
84 & 0.207728454034047 & 0.415456908068093 & 0.792271545965953 \tabularnewline
85 & 0.177880358753924 & 0.355760717507847 & 0.822119641246077 \tabularnewline
86 & 0.176862740682992 & 0.353725481365985 & 0.823137259317008 \tabularnewline
87 & 0.164112221861999 & 0.328224443723999 & 0.835887778138001 \tabularnewline
88 & 0.13707545711506 & 0.274150914230121 & 0.86292454288494 \tabularnewline
89 & 0.113120249043768 & 0.226240498087537 & 0.886879750956232 \tabularnewline
90 & 0.0947719914359735 & 0.189543982871947 & 0.905228008564026 \tabularnewline
91 & 0.0810639767128307 & 0.162127953425661 & 0.918936023287169 \tabularnewline
92 & 0.110021369348759 & 0.220042738697518 & 0.889978630651241 \tabularnewline
93 & 0.0947501437439451 & 0.18950028748789 & 0.905249856256055 \tabularnewline
94 & 0.0810090481093576 & 0.162018096218715 & 0.918990951890642 \tabularnewline
95 & 0.0736289588179543 & 0.147257917635909 & 0.926371041182046 \tabularnewline
96 & 0.0704999904800236 & 0.140999980960047 & 0.929500009519976 \tabularnewline
97 & 0.0631767476685646 & 0.126353495337129 & 0.936823252331435 \tabularnewline
98 & 0.0869922616656851 & 0.17398452333137 & 0.913007738334315 \tabularnewline
99 & 0.0738306914977555 & 0.147661382995511 & 0.926169308502245 \tabularnewline
100 & 0.0596112673027859 & 0.119222534605572 & 0.940388732697214 \tabularnewline
101 & 0.0724907951875413 & 0.144981590375083 & 0.927509204812459 \tabularnewline
102 & 0.0577938348983761 & 0.115587669796752 & 0.942206165101624 \tabularnewline
103 & 0.055055394272913 & 0.110110788545826 & 0.944944605727087 \tabularnewline
104 & 0.0458543052675562 & 0.0917086105351125 & 0.954145694732444 \tabularnewline
105 & 0.038077575756487 & 0.076155151512974 & 0.961922424243513 \tabularnewline
106 & 0.0416828067429735 & 0.0833656134859471 & 0.958317193257026 \tabularnewline
107 & 0.03227829508864 & 0.0645565901772801 & 0.96772170491136 \tabularnewline
108 & 0.0295222347248506 & 0.0590444694497011 & 0.970477765275149 \tabularnewline
109 & 0.0226384406029706 & 0.0452768812059412 & 0.977361559397029 \tabularnewline
110 & 0.0251801343805017 & 0.0503602687610034 & 0.974819865619498 \tabularnewline
111 & 0.0193065007005776 & 0.0386130014011553 & 0.980693499299422 \tabularnewline
112 & 0.0206694842148958 & 0.0413389684297916 & 0.979330515785104 \tabularnewline
113 & 0.0187354319990905 & 0.037470863998181 & 0.98126456800091 \tabularnewline
114 & 0.0157215423400321 & 0.0314430846800643 & 0.984278457659968 \tabularnewline
115 & 0.0120966922353577 & 0.0241933844707153 & 0.987903307764642 \tabularnewline
116 & 0.00935646695156436 & 0.0187129339031287 & 0.990643533048436 \tabularnewline
117 & 0.0144230476334531 & 0.0288460952669063 & 0.985576952366547 \tabularnewline
118 & 0.0177340276316017 & 0.0354680552632034 & 0.982265972368398 \tabularnewline
119 & 0.0131458183375076 & 0.0262916366750152 & 0.986854181662492 \tabularnewline
120 & 0.0102883434319509 & 0.0205766868639017 & 0.989711656568049 \tabularnewline
121 & 0.00808563321278624 & 0.0161712664255725 & 0.991914366787214 \tabularnewline
122 & 0.00653400675215586 & 0.0130680135043117 & 0.993465993247844 \tabularnewline
123 & 0.00799778951621173 & 0.0159955790324235 & 0.992002210483788 \tabularnewline
124 & 0.0127631881348538 & 0.0255263762697077 & 0.987236811865146 \tabularnewline
125 & 0.0134893756519821 & 0.0269787513039641 & 0.986510624348018 \tabularnewline
126 & 0.0386806474731733 & 0.0773612949463466 & 0.961319352526827 \tabularnewline
127 & 0.0303127497761712 & 0.0606254995523424 & 0.969687250223829 \tabularnewline
128 & 0.0231942315754041 & 0.0463884631508082 & 0.976805768424596 \tabularnewline
129 & 0.0195090996823392 & 0.0390181993646785 & 0.980490900317661 \tabularnewline
130 & 0.019312404795424 & 0.0386248095908481 & 0.980687595204576 \tabularnewline
131 & 0.0160394018583275 & 0.0320788037166549 & 0.983960598141673 \tabularnewline
132 & 0.0183523247806726 & 0.0367046495613452 & 0.981647675219327 \tabularnewline
133 & 0.0309846616182405 & 0.0619693232364809 & 0.96901533838176 \tabularnewline
134 & 0.0317533558382773 & 0.0635067116765546 & 0.968246644161723 \tabularnewline
135 & 0.0319168385783453 & 0.0638336771566906 & 0.968083161421655 \tabularnewline
136 & 0.027905012077166 & 0.0558100241543319 & 0.972094987922834 \tabularnewline
137 & 0.0244715654769973 & 0.0489431309539945 & 0.975528434523003 \tabularnewline
138 & 0.01685081615895 & 0.0337016323178999 & 0.98314918384105 \tabularnewline
139 & 0.0194049538809448 & 0.0388099077618896 & 0.980595046119055 \tabularnewline
140 & 0.0139523990890866 & 0.0279047981781732 & 0.986047600910913 \tabularnewline
141 & 0.0343595083382786 & 0.0687190166765572 & 0.965640491661721 \tabularnewline
142 & 0.0448187430840759 & 0.0896374861681517 & 0.955181256915924 \tabularnewline
143 & 0.0319928472958469 & 0.0639856945916938 & 0.968007152704153 \tabularnewline
144 & 0.262353496000975 & 0.52470699200195 & 0.737646503999025 \tabularnewline
145 & 0.332436386319189 & 0.664872772638379 & 0.667563613680811 \tabularnewline
146 & 0.521110078138097 & 0.957779843723807 & 0.478889921861903 \tabularnewline
147 & 0.498540470508927 & 0.997080941017853 & 0.501459529491073 \tabularnewline
148 & 0.826095232309668 & 0.347809535380664 & 0.173904767690332 \tabularnewline
149 & 0.795948373919119 & 0.408103252161762 & 0.204051626080881 \tabularnewline
150 & 0.74012660573333 & 0.51974678853334 & 0.25987339426667 \tabularnewline
151 & 0.675021904085909 & 0.649956191828182 & 0.324978095914091 \tabularnewline
152 & 0.658428014314573 & 0.683143971370854 & 0.341571985685427 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=198107&T=5

[TABLE]
[ROW][C]Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]p-values[/C][C]Alternative Hypothesis[/C][/ROW]
[ROW][C]breakpoint index[/C][C]greater[/C][C]2-sided[/C][C]less[/C][/ROW]
[ROW][C]10[/C][C]0.987294436566531[/C][C]0.0254111268669386[/C][C]0.0127055634334693[/C][/ROW]
[ROW][C]11[/C][C]0.99815806936148[/C][C]0.00368386127703918[/C][C]0.00184193063851959[/C][/ROW]
[ROW][C]12[/C][C]0.995603890511586[/C][C]0.00879221897682702[/C][C]0.00439610948841351[/C][/ROW]
[ROW][C]13[/C][C]0.996317386330333[/C][C]0.00736522733933341[/C][C]0.0036826136696667[/C][/ROW]
[ROW][C]14[/C][C]0.99286394518028[/C][C]0.0142721096394391[/C][C]0.00713605481971955[/C][/ROW]
[ROW][C]15[/C][C]0.994579259137006[/C][C]0.0108414817259888[/C][C]0.00542074086299438[/C][/ROW]
[ROW][C]16[/C][C]0.994255640133596[/C][C]0.0114887197328082[/C][C]0.00574435986640412[/C][/ROW]
[ROW][C]17[/C][C]0.989997710375716[/C][C]0.0200045792485675[/C][C]0.0100022896242838[/C][/ROW]
[ROW][C]18[/C][C]0.986462897016779[/C][C]0.0270742059664426[/C][C]0.0135371029832213[/C][/ROW]
[ROW][C]19[/C][C]0.979091358523225[/C][C]0.0418172829535491[/C][C]0.0209086414767745[/C][/ROW]
[ROW][C]20[/C][C]0.967540454697866[/C][C]0.0649190906042682[/C][C]0.0324595453021341[/C][/ROW]
[ROW][C]21[/C][C]0.952058782326232[/C][C]0.0958824353475364[/C][C]0.0479412176737682[/C][/ROW]
[ROW][C]22[/C][C]0.931159737457128[/C][C]0.137680525085743[/C][C]0.0688402625428715[/C][/ROW]
[ROW][C]23[/C][C]0.904208057389774[/C][C]0.191583885220452[/C][C]0.0957919426102259[/C][/ROW]
[ROW][C]24[/C][C]0.880412508150259[/C][C]0.239174983699482[/C][C]0.119587491849741[/C][/ROW]
[ROW][C]25[/C][C]0.86185730676006[/C][C]0.276285386479879[/C][C]0.13814269323994[/C][/ROW]
[ROW][C]26[/C][C]0.838829630178347[/C][C]0.322340739643306[/C][C]0.161170369821653[/C][/ROW]
[ROW][C]27[/C][C]0.798881105983963[/C][C]0.402237788032075[/C][C]0.201118894016037[/C][/ROW]
[ROW][C]28[/C][C]0.804283926792343[/C][C]0.391432146415315[/C][C]0.195716073207657[/C][/ROW]
[ROW][C]29[/C][C]0.757784000983408[/C][C]0.484431998033184[/C][C]0.242215999016592[/C][/ROW]
[ROW][C]30[/C][C]0.706109291052633[/C][C]0.587781417894735[/C][C]0.293890708947368[/C][/ROW]
[ROW][C]31[/C][C]0.65098473376518[/C][C]0.69803053246964[/C][C]0.34901526623482[/C][/ROW]
[ROW][C]32[/C][C]0.639762551893733[/C][C]0.720474896212535[/C][C]0.360237448106267[/C][/ROW]
[ROW][C]33[/C][C]0.591459717490209[/C][C]0.817080565019583[/C][C]0.408540282509791[/C][/ROW]
[ROW][C]34[/C][C]0.533804411717288[/C][C]0.932391176565425[/C][C]0.466195588282712[/C][/ROW]
[ROW][C]35[/C][C]0.580612490824628[/C][C]0.838775018350744[/C][C]0.419387509175372[/C][/ROW]
[ROW][C]36[/C][C]0.522618976349944[/C][C]0.954762047300113[/C][C]0.477381023650056[/C][/ROW]
[ROW][C]37[/C][C]0.566084656142448[/C][C]0.867830687715105[/C][C]0.433915343857552[/C][/ROW]
[ROW][C]38[/C][C]0.612787098923869[/C][C]0.774425802152262[/C][C]0.387212901076131[/C][/ROW]
[ROW][C]39[/C][C]0.630882656267532[/C][C]0.738234687464936[/C][C]0.369117343732468[/C][/ROW]
[ROW][C]40[/C][C]0.590488925935616[/C][C]0.819022148128767[/C][C]0.409511074064384[/C][/ROW]
[ROW][C]41[/C][C]0.545853743556923[/C][C]0.908292512886155[/C][C]0.454146256443077[/C][/ROW]
[ROW][C]42[/C][C]0.510820043261139[/C][C]0.978359913477722[/C][C]0.489179956738861[/C][/ROW]
[ROW][C]43[/C][C]0.5730878297523[/C][C]0.8538243404954[/C][C]0.4269121702477[/C][/ROW]
[ROW][C]44[/C][C]0.522276022405801[/C][C]0.955447955188398[/C][C]0.477723977594199[/C][/ROW]
[ROW][C]45[/C][C]0.478040349780325[/C][C]0.95608069956065[/C][C]0.521959650219675[/C][/ROW]
[ROW][C]46[/C][C]0.510346440990676[/C][C]0.979307118018649[/C][C]0.489653559009324[/C][/ROW]
[ROW][C]47[/C][C]0.504286338832218[/C][C]0.991427322335565[/C][C]0.495713661167783[/C][/ROW]
[ROW][C]48[/C][C]0.458751662872205[/C][C]0.917503325744409[/C][C]0.541248337127795[/C][/ROW]
[ROW][C]49[/C][C]0.419262637062072[/C][C]0.838525274124145[/C][C]0.580737362937928[/C][/ROW]
[ROW][C]50[/C][C]0.432679339447729[/C][C]0.865358678895458[/C][C]0.567320660552271[/C][/ROW]
[ROW][C]51[/C][C]0.430788173191254[/C][C]0.861576346382509[/C][C]0.569211826808746[/C][/ROW]
[ROW][C]52[/C][C]0.38469559616322[/C][C]0.76939119232644[/C][C]0.61530440383678[/C][/ROW]
[ROW][C]53[/C][C]0.45119506572442[/C][C]0.90239013144884[/C][C]0.54880493427558[/C][/ROW]
[ROW][C]54[/C][C]0.410527789344444[/C][C]0.821055578688889[/C][C]0.589472210655556[/C][/ROW]
[ROW][C]55[/C][C]0.505599933025121[/C][C]0.988800133949759[/C][C]0.49440006697488[/C][/ROW]
[ROW][C]56[/C][C]0.761943661308431[/C][C]0.476112677383138[/C][C]0.238056338691569[/C][/ROW]
[ROW][C]57[/C][C]0.724923229240502[/C][C]0.550153541518997[/C][C]0.275076770759498[/C][/ROW]
[ROW][C]58[/C][C]0.683306372661672[/C][C]0.633387254676656[/C][C]0.316693627338328[/C][/ROW]
[ROW][C]59[/C][C]0.639445732137676[/C][C]0.721108535724648[/C][C]0.360554267862324[/C][/ROW]
[ROW][C]60[/C][C]0.641857021608529[/C][C]0.716285956782941[/C][C]0.358142978391471[/C][/ROW]
[ROW][C]61[/C][C]0.658750422514805[/C][C]0.682499154970389[/C][C]0.341249577485195[/C][/ROW]
[ROW][C]62[/C][C]0.626357547707681[/C][C]0.747284904584638[/C][C]0.373642452292319[/C][/ROW]
[ROW][C]63[/C][C]0.628066957670442[/C][C]0.743866084659116[/C][C]0.371933042329558[/C][/ROW]
[ROW][C]64[/C][C]0.582761047731561[/C][C]0.834477904536879[/C][C]0.417238952268439[/C][/ROW]
[ROW][C]65[/C][C]0.538365084045503[/C][C]0.923269831908994[/C][C]0.461634915954497[/C][/ROW]
[ROW][C]66[/C][C]0.49967613156486[/C][C]0.99935226312972[/C][C]0.50032386843514[/C][/ROW]
[ROW][C]67[/C][C]0.472554563433814[/C][C]0.945109126867627[/C][C]0.527445436566186[/C][/ROW]
[ROW][C]68[/C][C]0.452456567682638[/C][C]0.904913135365275[/C][C]0.547543432317362[/C][/ROW]
[ROW][C]69[/C][C]0.469534930600635[/C][C]0.93906986120127[/C][C]0.530465069399365[/C][/ROW]
[ROW][C]70[/C][C]0.429563159718463[/C][C]0.859126319436926[/C][C]0.570436840281537[/C][/ROW]
[ROW][C]71[/C][C]0.385157232285396[/C][C]0.770314464570792[/C][C]0.614842767714604[/C][/ROW]
[ROW][C]72[/C][C]0.34564366304226[/C][C]0.691287326084519[/C][C]0.65435633695774[/C][/ROW]
[ROW][C]73[/C][C]0.31954950018434[/C][C]0.63909900036868[/C][C]0.68045049981566[/C][/ROW]
[ROW][C]74[/C][C]0.305211464373406[/C][C]0.610422928746811[/C][C]0.694788535626594[/C][/ROW]
[ROW][C]75[/C][C]0.290667458992089[/C][C]0.581334917984179[/C][C]0.709332541007911[/C][/ROW]
[ROW][C]76[/C][C]0.349158782722426[/C][C]0.698317565444852[/C][C]0.650841217277574[/C][/ROW]
[ROW][C]77[/C][C]0.334566959412782[/C][C]0.669133918825564[/C][C]0.665433040587218[/C][/ROW]
[ROW][C]78[/C][C]0.29769434328227[/C][C]0.59538868656454[/C][C]0.70230565671773[/C][/ROW]
[ROW][C]79[/C][C]0.320431546416032[/C][C]0.640863092832064[/C][C]0.679568453583968[/C][/ROW]
[ROW][C]80[/C][C]0.302041909228141[/C][C]0.604083818456281[/C][C]0.697958090771859[/C][/ROW]
[ROW][C]81[/C][C]0.266222714664055[/C][C]0.53244542932811[/C][C]0.733777285335945[/C][/ROW]
[ROW][C]82[/C][C]0.251285821015143[/C][C]0.502571642030286[/C][C]0.748714178984857[/C][/ROW]
[ROW][C]83[/C][C]0.219601853715531[/C][C]0.439203707431063[/C][C]0.780398146284469[/C][/ROW]
[ROW][C]84[/C][C]0.207728454034047[/C][C]0.415456908068093[/C][C]0.792271545965953[/C][/ROW]
[ROW][C]85[/C][C]0.177880358753924[/C][C]0.355760717507847[/C][C]0.822119641246077[/C][/ROW]
[ROW][C]86[/C][C]0.176862740682992[/C][C]0.353725481365985[/C][C]0.823137259317008[/C][/ROW]
[ROW][C]87[/C][C]0.164112221861999[/C][C]0.328224443723999[/C][C]0.835887778138001[/C][/ROW]
[ROW][C]88[/C][C]0.13707545711506[/C][C]0.274150914230121[/C][C]0.86292454288494[/C][/ROW]
[ROW][C]89[/C][C]0.113120249043768[/C][C]0.226240498087537[/C][C]0.886879750956232[/C][/ROW]
[ROW][C]90[/C][C]0.0947719914359735[/C][C]0.189543982871947[/C][C]0.905228008564026[/C][/ROW]
[ROW][C]91[/C][C]0.0810639767128307[/C][C]0.162127953425661[/C][C]0.918936023287169[/C][/ROW]
[ROW][C]92[/C][C]0.110021369348759[/C][C]0.220042738697518[/C][C]0.889978630651241[/C][/ROW]
[ROW][C]93[/C][C]0.0947501437439451[/C][C]0.18950028748789[/C][C]0.905249856256055[/C][/ROW]
[ROW][C]94[/C][C]0.0810090481093576[/C][C]0.162018096218715[/C][C]0.918990951890642[/C][/ROW]
[ROW][C]95[/C][C]0.0736289588179543[/C][C]0.147257917635909[/C][C]0.926371041182046[/C][/ROW]
[ROW][C]96[/C][C]0.0704999904800236[/C][C]0.140999980960047[/C][C]0.929500009519976[/C][/ROW]
[ROW][C]97[/C][C]0.0631767476685646[/C][C]0.126353495337129[/C][C]0.936823252331435[/C][/ROW]
[ROW][C]98[/C][C]0.0869922616656851[/C][C]0.17398452333137[/C][C]0.913007738334315[/C][/ROW]
[ROW][C]99[/C][C]0.0738306914977555[/C][C]0.147661382995511[/C][C]0.926169308502245[/C][/ROW]
[ROW][C]100[/C][C]0.0596112673027859[/C][C]0.119222534605572[/C][C]0.940388732697214[/C][/ROW]
[ROW][C]101[/C][C]0.0724907951875413[/C][C]0.144981590375083[/C][C]0.927509204812459[/C][/ROW]
[ROW][C]102[/C][C]0.0577938348983761[/C][C]0.115587669796752[/C][C]0.942206165101624[/C][/ROW]
[ROW][C]103[/C][C]0.055055394272913[/C][C]0.110110788545826[/C][C]0.944944605727087[/C][/ROW]
[ROW][C]104[/C][C]0.0458543052675562[/C][C]0.0917086105351125[/C][C]0.954145694732444[/C][/ROW]
[ROW][C]105[/C][C]0.038077575756487[/C][C]0.076155151512974[/C][C]0.961922424243513[/C][/ROW]
[ROW][C]106[/C][C]0.0416828067429735[/C][C]0.0833656134859471[/C][C]0.958317193257026[/C][/ROW]
[ROW][C]107[/C][C]0.03227829508864[/C][C]0.0645565901772801[/C][C]0.96772170491136[/C][/ROW]
[ROW][C]108[/C][C]0.0295222347248506[/C][C]0.0590444694497011[/C][C]0.970477765275149[/C][/ROW]
[ROW][C]109[/C][C]0.0226384406029706[/C][C]0.0452768812059412[/C][C]0.977361559397029[/C][/ROW]
[ROW][C]110[/C][C]0.0251801343805017[/C][C]0.0503602687610034[/C][C]0.974819865619498[/C][/ROW]
[ROW][C]111[/C][C]0.0193065007005776[/C][C]0.0386130014011553[/C][C]0.980693499299422[/C][/ROW]
[ROW][C]112[/C][C]0.0206694842148958[/C][C]0.0413389684297916[/C][C]0.979330515785104[/C][/ROW]
[ROW][C]113[/C][C]0.0187354319990905[/C][C]0.037470863998181[/C][C]0.98126456800091[/C][/ROW]
[ROW][C]114[/C][C]0.0157215423400321[/C][C]0.0314430846800643[/C][C]0.984278457659968[/C][/ROW]
[ROW][C]115[/C][C]0.0120966922353577[/C][C]0.0241933844707153[/C][C]0.987903307764642[/C][/ROW]
[ROW][C]116[/C][C]0.00935646695156436[/C][C]0.0187129339031287[/C][C]0.990643533048436[/C][/ROW]
[ROW][C]117[/C][C]0.0144230476334531[/C][C]0.0288460952669063[/C][C]0.985576952366547[/C][/ROW]
[ROW][C]118[/C][C]0.0177340276316017[/C][C]0.0354680552632034[/C][C]0.982265972368398[/C][/ROW]
[ROW][C]119[/C][C]0.0131458183375076[/C][C]0.0262916366750152[/C][C]0.986854181662492[/C][/ROW]
[ROW][C]120[/C][C]0.0102883434319509[/C][C]0.0205766868639017[/C][C]0.989711656568049[/C][/ROW]
[ROW][C]121[/C][C]0.00808563321278624[/C][C]0.0161712664255725[/C][C]0.991914366787214[/C][/ROW]
[ROW][C]122[/C][C]0.00653400675215586[/C][C]0.0130680135043117[/C][C]0.993465993247844[/C][/ROW]
[ROW][C]123[/C][C]0.00799778951621173[/C][C]0.0159955790324235[/C][C]0.992002210483788[/C][/ROW]
[ROW][C]124[/C][C]0.0127631881348538[/C][C]0.0255263762697077[/C][C]0.987236811865146[/C][/ROW]
[ROW][C]125[/C][C]0.0134893756519821[/C][C]0.0269787513039641[/C][C]0.986510624348018[/C][/ROW]
[ROW][C]126[/C][C]0.0386806474731733[/C][C]0.0773612949463466[/C][C]0.961319352526827[/C][/ROW]
[ROW][C]127[/C][C]0.0303127497761712[/C][C]0.0606254995523424[/C][C]0.969687250223829[/C][/ROW]
[ROW][C]128[/C][C]0.0231942315754041[/C][C]0.0463884631508082[/C][C]0.976805768424596[/C][/ROW]
[ROW][C]129[/C][C]0.0195090996823392[/C][C]0.0390181993646785[/C][C]0.980490900317661[/C][/ROW]
[ROW][C]130[/C][C]0.019312404795424[/C][C]0.0386248095908481[/C][C]0.980687595204576[/C][/ROW]
[ROW][C]131[/C][C]0.0160394018583275[/C][C]0.0320788037166549[/C][C]0.983960598141673[/C][/ROW]
[ROW][C]132[/C][C]0.0183523247806726[/C][C]0.0367046495613452[/C][C]0.981647675219327[/C][/ROW]
[ROW][C]133[/C][C]0.0309846616182405[/C][C]0.0619693232364809[/C][C]0.96901533838176[/C][/ROW]
[ROW][C]134[/C][C]0.0317533558382773[/C][C]0.0635067116765546[/C][C]0.968246644161723[/C][/ROW]
[ROW][C]135[/C][C]0.0319168385783453[/C][C]0.0638336771566906[/C][C]0.968083161421655[/C][/ROW]
[ROW][C]136[/C][C]0.027905012077166[/C][C]0.0558100241543319[/C][C]0.972094987922834[/C][/ROW]
[ROW][C]137[/C][C]0.0244715654769973[/C][C]0.0489431309539945[/C][C]0.975528434523003[/C][/ROW]
[ROW][C]138[/C][C]0.01685081615895[/C][C]0.0337016323178999[/C][C]0.98314918384105[/C][/ROW]
[ROW][C]139[/C][C]0.0194049538809448[/C][C]0.0388099077618896[/C][C]0.980595046119055[/C][/ROW]
[ROW][C]140[/C][C]0.0139523990890866[/C][C]0.0279047981781732[/C][C]0.986047600910913[/C][/ROW]
[ROW][C]141[/C][C]0.0343595083382786[/C][C]0.0687190166765572[/C][C]0.965640491661721[/C][/ROW]
[ROW][C]142[/C][C]0.0448187430840759[/C][C]0.0896374861681517[/C][C]0.955181256915924[/C][/ROW]
[ROW][C]143[/C][C]0.0319928472958469[/C][C]0.0639856945916938[/C][C]0.968007152704153[/C][/ROW]
[ROW][C]144[/C][C]0.262353496000975[/C][C]0.52470699200195[/C][C]0.737646503999025[/C][/ROW]
[ROW][C]145[/C][C]0.332436386319189[/C][C]0.664872772638379[/C][C]0.667563613680811[/C][/ROW]
[ROW][C]146[/C][C]0.521110078138097[/C][C]0.957779843723807[/C][C]0.478889921861903[/C][/ROW]
[ROW][C]147[/C][C]0.498540470508927[/C][C]0.997080941017853[/C][C]0.501459529491073[/C][/ROW]
[ROW][C]148[/C][C]0.826095232309668[/C][C]0.347809535380664[/C][C]0.173904767690332[/C][/ROW]
[ROW][C]149[/C][C]0.795948373919119[/C][C]0.408103252161762[/C][C]0.204051626080881[/C][/ROW]
[ROW][C]150[/C][C]0.74012660573333[/C][C]0.51974678853334[/C][C]0.25987339426667[/C][/ROW]
[ROW][C]151[/C][C]0.675021904085909[/C][C]0.649956191828182[/C][C]0.324978095914091[/C][/ROW]
[ROW][C]152[/C][C]0.658428014314573[/C][C]0.683143971370854[/C][C]0.341571985685427[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=198107&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=198107&T=5

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


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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
100.9872944365665310.02541112686693860.0127055634334693
110.998158069361480.003683861277039180.00184193063851959
120.9956038905115860.008792218976827020.00439610948841351
130.9963173863303330.007365227339333410.0036826136696667
140.992863945180280.01427210963943910.00713605481971955
150.9945792591370060.01084148172598880.00542074086299438
160.9942556401335960.01148871973280820.00574435986640412
170.9899977103757160.02000457924856750.0100022896242838
180.9864628970167790.02707420596644260.0135371029832213
190.9790913585232250.04181728295354910.0209086414767745
200.9675404546978660.06491909060426820.0324595453021341
210.9520587823262320.09588243534753640.0479412176737682
220.9311597374571280.1376805250857430.0688402625428715
230.9042080573897740.1915838852204520.0957919426102259
240.8804125081502590.2391749836994820.119587491849741
250.861857306760060.2762853864798790.13814269323994
260.8388296301783470.3223407396433060.161170369821653
270.7988811059839630.4022377880320750.201118894016037
280.8042839267923430.3914321464153150.195716073207657
290.7577840009834080.4844319980331840.242215999016592
300.7061092910526330.5877814178947350.293890708947368
310.650984733765180.698030532469640.34901526623482
320.6397625518937330.7204748962125350.360237448106267
330.5914597174902090.8170805650195830.408540282509791
340.5338044117172880.9323911765654250.466195588282712
350.5806124908246280.8387750183507440.419387509175372
360.5226189763499440.9547620473001130.477381023650056
370.5660846561424480.8678306877151050.433915343857552
380.6127870989238690.7744258021522620.387212901076131
390.6308826562675320.7382346874649360.369117343732468
400.5904889259356160.8190221481287670.409511074064384
410.5458537435569230.9082925128861550.454146256443077
420.5108200432611390.9783599134777220.489179956738861
430.57308782975230.85382434049540.4269121702477
440.5222760224058010.9554479551883980.477723977594199
450.4780403497803250.956080699560650.521959650219675
460.5103464409906760.9793071180186490.489653559009324
470.5042863388322180.9914273223355650.495713661167783
480.4587516628722050.9175033257444090.541248337127795
490.4192626370620720.8385252741241450.580737362937928
500.4326793394477290.8653586788954580.567320660552271
510.4307881731912540.8615763463825090.569211826808746
520.384695596163220.769391192326440.61530440383678
530.451195065724420.902390131448840.54880493427558
540.4105277893444440.8210555786888890.589472210655556
550.5055999330251210.9888001339497590.49440006697488
560.7619436613084310.4761126773831380.238056338691569
570.7249232292405020.5501535415189970.275076770759498
580.6833063726616720.6333872546766560.316693627338328
590.6394457321376760.7211085357246480.360554267862324
600.6418570216085290.7162859567829410.358142978391471
610.6587504225148050.6824991549703890.341249577485195
620.6263575477076810.7472849045846380.373642452292319
630.6280669576704420.7438660846591160.371933042329558
640.5827610477315610.8344779045368790.417238952268439
650.5383650840455030.9232698319089940.461634915954497
660.499676131564860.999352263129720.50032386843514
670.4725545634338140.9451091268676270.527445436566186
680.4524565676826380.9049131353652750.547543432317362
690.4695349306006350.939069861201270.530465069399365
700.4295631597184630.8591263194369260.570436840281537
710.3851572322853960.7703144645707920.614842767714604
720.345643663042260.6912873260845190.65435633695774
730.319549500184340.639099000368680.68045049981566
740.3052114643734060.6104229287468110.694788535626594
750.2906674589920890.5813349179841790.709332541007911
760.3491587827224260.6983175654448520.650841217277574
770.3345669594127820.6691339188255640.665433040587218
780.297694343282270.595388686564540.70230565671773
790.3204315464160320.6408630928320640.679568453583968
800.3020419092281410.6040838184562810.697958090771859
810.2662227146640550.532445429328110.733777285335945
820.2512858210151430.5025716420302860.748714178984857
830.2196018537155310.4392037074310630.780398146284469
840.2077284540340470.4154569080680930.792271545965953
850.1778803587539240.3557607175078470.822119641246077
860.1768627406829920.3537254813659850.823137259317008
870.1641122218619990.3282244437239990.835887778138001
880.137075457115060.2741509142301210.86292454288494
890.1131202490437680.2262404980875370.886879750956232
900.09477199143597350.1895439828719470.905228008564026
910.08106397671283070.1621279534256610.918936023287169
920.1100213693487590.2200427386975180.889978630651241
930.09475014374394510.189500287487890.905249856256055
940.08100904810935760.1620180962187150.918990951890642
950.07362895881795430.1472579176359090.926371041182046
960.07049999048002360.1409999809600470.929500009519976
970.06317674766856460.1263534953371290.936823252331435
980.08699226166568510.173984523331370.913007738334315
990.07383069149775550.1476613829955110.926169308502245
1000.05961126730278590.1192225346055720.940388732697214
1010.07249079518754130.1449815903750830.927509204812459
1020.05779383489837610.1155876697967520.942206165101624
1030.0550553942729130.1101107885458260.944944605727087
1040.04585430526755620.09170861053511250.954145694732444
1050.0380775757564870.0761551515129740.961922424243513
1060.04168280674297350.08336561348594710.958317193257026
1070.032278295088640.06455659017728010.96772170491136
1080.02952223472485060.05904446944970110.970477765275149
1090.02263844060297060.04527688120594120.977361559397029
1100.02518013438050170.05036026876100340.974819865619498
1110.01930650070057760.03861300140115530.980693499299422
1120.02066948421489580.04133896842979160.979330515785104
1130.01873543199909050.0374708639981810.98126456800091
1140.01572154234003210.03144308468006430.984278457659968
1150.01209669223535770.02419338447071530.987903307764642
1160.009356466951564360.01871293390312870.990643533048436
1170.01442304763345310.02884609526690630.985576952366547
1180.01773402763160170.03546805526320340.982265972368398
1190.01314581833750760.02629163667501520.986854181662492
1200.01028834343195090.02057668686390170.989711656568049
1210.008085633212786240.01617126642557250.991914366787214
1220.006534006752155860.01306801350431170.993465993247844
1230.007997789516211730.01599557903242350.992002210483788
1240.01276318813485380.02552637626970770.987236811865146
1250.01348937565198210.02697875130396410.986510624348018
1260.03868064747317330.07736129494634660.961319352526827
1270.03031274977617120.06062549955234240.969687250223829
1280.02319423157540410.04638846315080820.976805768424596
1290.01950909968233920.03901819936467850.980490900317661
1300.0193124047954240.03862480959084810.980687595204576
1310.01603940185832750.03207880371665490.983960598141673
1320.01835232478067260.03670464956134520.981647675219327
1330.03098466161824050.06196932323648090.96901533838176
1340.03175335583827730.06350671167655460.968246644161723
1350.03191683857834530.06383367715669060.968083161421655
1360.0279050120771660.05581002415433190.972094987922834
1370.02447156547699730.04894313095399450.975528434523003
1380.016850816158950.03370163231789990.98314918384105
1390.01940495388094480.03880990776188960.980595046119055
1400.01395239908908660.02790479817817320.986047600910913
1410.03435950833827860.06871901667655720.965640491661721
1420.04481874308407590.08963748616815170.955181256915924
1430.03199284729584690.06398569459169380.968007152704153
1440.2623534960009750.524706992001950.737646503999025
1450.3324363863191890.6648727726383790.667563613680811
1460.5211100781380970.9577798437238070.478889921861903
1470.4985404705089270.9970809410178530.501459529491073
1480.8260952323096680.3478095353806640.173904767690332
1490.7959483739191190.4081032521617620.204051626080881
1500.740126605733330.519746788533340.25987339426667
1510.6750219040859090.6499561918281820.324978095914091
1520.6584280143145730.6831439713708540.341571985685427






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level30.020979020979021NOK
5% type I error level350.244755244755245NOK
10% type I error level520.363636363636364NOK

\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 & 3 & 0.020979020979021 & NOK \tabularnewline
5% type I error level & 35 & 0.244755244755245 & NOK \tabularnewline
10% type I error level & 52 & 0.363636363636364 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=198107&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]3[/C][C]0.020979020979021[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]35[/C][C]0.244755244755245[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]52[/C][C]0.363636363636364[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=198107&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=198107&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 level30.020979020979021NOK
5% type I error level350.244755244755245NOK
10% type I error level520.363636363636364NOK


Figures (Output of Computation)

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

Parameters (Session):
par1 = 5 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
Parameters (R input):
par1 = 5 ; 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')
}