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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 computationThu, 06 Dec 2012 08:50:04 -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/06/t13548018196wgirmfahvdz4ku.htm/, Retrieved Fri, 19 Apr 2024 17:15:15 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=197077, Retrieved Fri, 19 Apr 2024 17:15:15 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact86

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] [2012-12-06 13:50:04] [0ce3a3cc7b36ec2616d0d876d7c7ef2d] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
2	7	41	38	13	12	14	12
2	5	39	32	16	11	18	11
2	5	30	35	19	15	11	14
1	5	31	33	15	6	12	12
2	8	34	37	14	13	16	21
2	6	35	29	13	10	18	12
2	5	39	31	19	12	14	22
2	6	34	36	15	14	14	11
2	5	36	35	14	12	15	10
2	4	37	38	15	6	15	13
1	6	38	31	16	10	17	10
2	5	36	34	16	12	19	8
1	5	38	35	16	12	10	15
2	6	39	38	16	11	16	14
2	7	33	37	17	15	18	10
1	6	32	33	15	12	14	14
1	7	36	32	15	10	14	14
2	6	38	38	20	12	17	11
1	8	39	38	18	11	14	10
2	7	32	32	16	12	16	13
1	5	32	33	16	11	18	7
2	5	31	31	16	12	11	14
2	7	39	38	19	13	14	12
2	7	37	39	16	11	12	14
1	5	39	32	17	9	17	11
2	4	41	32	17	13	9	9
1	10	36	35	16	10	16	11
2	6	33	37	15	14	14	15
2	5	33	33	16	12	15	14
1	5	34	33	14	10	11	13
2	5	31	28	15	12	16	9
1	5	27	32	12	8	13	15
2	6	37	31	14	10	17	10
2	5	34	37	16	12	15	11
1	5	34	30	14	12	14	13
1	5	32	33	7	7	16	8
1	5	29	31	10	6	9	20
1	5	36	33	14	12	15	12
2	5	29	31	16	10	17	10
1	5	35	33	16	10	13	10
1	5	37	32	16	10	15	9
2	7	34	33	14	12	16	14
1	5	38	32	20	15	16	8
1	6	35	33	14	10	12	14
2	7	38	28	14	10	12	11
2	7	37	35	11	12	11	13
2	5	38	39	14	13	15	9
2	5	33	34	15	11	15	11
2	4	36	38	16	11	17	15
1	5	38	32	14	12	13	11
2	4	32	38	16	14	16	10
1	5	32	30	14	10	14	14
1	5	32	33	12	12	11	18
2	7	34	38	16	13	12	14
1	5	32	32	9	5	12	11
2	5	37	32	14	6	15	12
2	6	39	34	16	12	16	13
2	4	29	34	16	12	15	9
1	6	37	36	15	11	12	10
2	6	35	34	16	10	12	15
1	5	30	28	12	7	8	20
1	7	38	34	16	12	13	12
2	6	34	35	16	14	11	12
2	8	31	35	14	11	14	14
2	7	34	31	16	12	15	13
1	5	35	37	17	13	10	11
2	6	36	35	18	14	11	17
1	6	30	27	18	11	12	12
2	5	39	40	12	12	15	13
1	5	35	37	16	12	15	14
1	5	38	36	10	8	14	13
2	5	31	38	14	11	16	15
2	4	34	39	18	14	15	13
1	6	38	41	18	14	15	10
1	6	34	27	16	12	13	11
2	6	39	30	17	9	12	19
2	6	37	37	16	13	17	13
2	7	34	31	16	11	13	17
1	5	28	31	13	12	15	13
1	7	37	27	16	12	13	9
1	6	33	36	16	12	15	11
1	5	37	38	20	12	16	10
2	5	35	37	16	12	15	9
1	4	37	33	15	12	16	12
2	8	32	34	15	11	15	12
2	8	33	31	16	10	14	13
1	5	38	39	14	9	15	13
2	5	33	34	16	12	14	12
2	6	29	32	16	12	13	15
2	4	33	33	15	12	7	22
2	5	31	36	12	9	17	13
2	5	36	32	17	15	13	15
2	5	35	41	16	12	15	13
2	5	32	28	15	12	14	15
2	6	29	30	13	12	13	10
2	6	39	36	16	10	16	11
2	5	37	35	16	13	12	16
2	6	35	31	16	9	14	11
1	5	37	34	16	12	17	11
1	7	32	36	14	10	15	10
2	5	38	36	16	14	17	10
1	6	37	35	16	11	12	16
2	6	36	37	20	15	16	12
1	6	32	28	15	11	11	11
2	4	33	39	16	11	15	16
1	5	40	32	13	12	9	19
2	5	38	35	17	12	16	11
1	7	41	39	16	12	15	16
1	6	36	35	16	11	10	15
2	9	43	42	12	7	10	24
2	6	30	34	16	12	15	14
2	6	31	33	16	14	11	15
2	5	32	41	17	11	13	11
1	6	32	33	13	11	14	15
2	5	37	34	12	10	18	12
1	8	37	32	18	13	16	10
2	7	33	40	14	13	14	14
2	5	34	40	14	8	14	13
2	7	33	35	13	11	14	9
2	6	38	36	16	12	14	15
2	6	33	37	13	11	12	15
2	9	31	27	16	13	14	14
2	7	38	39	13	12	15	11
2	6	37	38	16	14	15	8
2	5	33	31	15	13	15	11
2	5	31	33	16	15	13	11
1	6	39	32	15	10	17	8
2	6	44	39	17	11	17	10
2	7	33	36	15	9	19	11
2	5	35	33	12	11	15	13
1	5	32	33	16	10	13	11
1	5	28	32	10	11	9	20
2	6	40	37	16	8	15	10
1	4	27	30	12	11	15	15
1	5	37	38	14	12	15	12
2	7	32	29	15	12	16	14
1	5	28	22	13	9	11	23
1	7	34	35	15	11	14	14
2	7	30	35	11	10	11	16
2	6	35	34	12	8	15	11
1	5	31	35	8	9	13	12
2	8	32	34	16	8	15	10
1	5	30	34	15	9	16	14
2	5	30	35	17	15	14	12
1	5	31	23	16	11	15	12
2	6	40	31	10	8	16	11
2	4	32	27	18	13	16	12
1	5	36	36	13	12	11	13
1	5	32	31	16	12	12	11
1	7	35	32	13	9	9	19
2	6	38	39	10	7	16	12
2	7	42	37	15	13	13	17
1	10	34	38	16	9	16	9
2	6	35	39	16	6	12	12
2	8	35	34	14	8	9	19
2	4	33	31	10	8	13	18
2	5	36	32	17	15	13	15
2	6	32	37	13	6	14	14
2	7	33	36	15	9	19	11
2	7	34	32	16	11	13	9
2	6	32	35	12	8	12	18
2	6	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 time16 seconds
R Server'Gwilym Jenkins' @ jenkins.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 & 16 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ jenkins.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=197077&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]16 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ jenkins.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=197077&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=197077&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 time16 seconds
R Server'Gwilym Jenkins' @ jenkins.wessa.net






Multiple Linear Regression - Estimated Regression Equation
Software[t] = + 5.37901815846649 + 0.513697841080668Gender[t] -0.164893800687354Age[t] -0.0365316083922087Connected[t] + 0.0204437754472755Separate[t] + 0.518749972935189Learning[t] -0.0657380752961471Happiness[t] -0.0361979552699681Depression[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Software[t] =  +  5.37901815846649 +  0.513697841080668Gender[t] -0.164893800687354Age[t] -0.0365316083922087Connected[t] +  0.0204437754472755Separate[t] +  0.518749972935189Learning[t] -0.0657380752961471Happiness[t] -0.0361979552699681Depression[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=197077&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Software[t] =  +  5.37901815846649 +  0.513697841080668Gender[t] -0.164893800687354Age[t] -0.0365316083922087Connected[t] +  0.0204437754472755Separate[t] +  0.518749972935189Learning[t] -0.0657380752961471Happiness[t] -0.0361979552699681Depression[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=197077&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=197077&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] = + 5.37901815846649 + 0.513697841080668Gender[t] -0.164893800687354Age[t] -0.0365316083922087Connected[t] + 0.0204437754472755Separate[t] + 0.518749972935189Learning[t] -0.0657380752961471Happiness[t] -0.0361979552699681Depression[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)5.379018158466492.3877642.25270.0256880.012844
Gender0.5136978410806680.3120371.64630.1017470.050874
Age-0.1648938006873540.124289-1.32670.1865720.093286
Connected-0.03653160839220870.046655-0.7830.4348220.217411
Separate0.02044377544727550.0443970.46050.6458240.322912
Learning0.5187499729351890.0665197.798500
Happiness-0.06573807529614710.075056-0.87590.3824740.191237
Depression-0.03619795526996810.055281-0.65480.5135710.256786

\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) & 5.37901815846649 & 2.387764 & 2.2527 & 0.025688 & 0.012844 \tabularnewline
Gender & 0.513697841080668 & 0.312037 & 1.6463 & 0.101747 & 0.050874 \tabularnewline
Age & -0.164893800687354 & 0.124289 & -1.3267 & 0.186572 & 0.093286 \tabularnewline
Connected & -0.0365316083922087 & 0.046655 & -0.783 & 0.434822 & 0.217411 \tabularnewline
Separate & 0.0204437754472755 & 0.044397 & 0.4605 & 0.645824 & 0.322912 \tabularnewline
Learning & 0.518749972935189 & 0.066519 & 7.7985 & 0 & 0 \tabularnewline
Happiness & -0.0657380752961471 & 0.075056 & -0.8759 & 0.382474 & 0.191237 \tabularnewline
Depression & -0.0361979552699681 & 0.055281 & -0.6548 & 0.513571 & 0.256786 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=197077&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]5.37901815846649[/C][C]2.387764[/C][C]2.2527[/C][C]0.025688[/C][C]0.012844[/C][/ROW]
[ROW][C]Gender[/C][C]0.513697841080668[/C][C]0.312037[/C][C]1.6463[/C][C]0.101747[/C][C]0.050874[/C][/ROW]
[ROW][C]Age[/C][C]-0.164893800687354[/C][C]0.124289[/C][C]-1.3267[/C][C]0.186572[/C][C]0.093286[/C][/ROW]
[ROW][C]Connected[/C][C]-0.0365316083922087[/C][C]0.046655[/C][C]-0.783[/C][C]0.434822[/C][C]0.217411[/C][/ROW]
[ROW][C]Separate[/C][C]0.0204437754472755[/C][C]0.044397[/C][C]0.4605[/C][C]0.645824[/C][C]0.322912[/C][/ROW]
[ROW][C]Learning[/C][C]0.518749972935189[/C][C]0.066519[/C][C]7.7985[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Happiness[/C][C]-0.0657380752961471[/C][C]0.075056[/C][C]-0.8759[/C][C]0.382474[/C][C]0.191237[/C][/ROW]
[ROW][C]Depression[/C][C]-0.0361979552699681[/C][C]0.055281[/C][C]-0.6548[/C][C]0.513571[/C][C]0.256786[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=197077&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=197077&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)5.379018158466492.3877642.25270.0256880.012844
Gender0.5136978410806680.3120371.64630.1017470.050874
Age-0.1648938006873540.124289-1.32670.1865720.093286
Connected-0.03653160839220870.046655-0.7830.4348220.217411
Separate0.02044377544727550.0443970.46050.6458240.322912
Learning0.5187499729351890.0665197.798500
Happiness-0.06573807529614710.075056-0.87590.3824740.191237
Depression-0.03619795526996810.055281-0.65480.5135710.256786






Multiple Linear Regression - Regression Statistics
Multiple R0.568515474819561
R-squared0.323209845109311
Adjusted R-squared0.292446656250643
F-TEST (value)10.5063830214157
F-TEST (DF numerator)7
F-TEST (DF denominator)154
p-value9.26815291180105e-11
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1.80153168847774
Sum Squared Residuals499.809529386774

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.568515474819561 \tabularnewline
R-squared & 0.323209845109311 \tabularnewline
Adjusted R-squared & 0.292446656250643 \tabularnewline
F-TEST (value) & 10.5063830214157 \tabularnewline
F-TEST (DF numerator) & 7 \tabularnewline
F-TEST (DF denominator) & 154 \tabularnewline
p-value & 9.26815291180105e-11 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 1.80153168847774 \tabularnewline
Sum Squared Residuals & 499.809529386774 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=197077&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.568515474819561[/C][/ROW]
[ROW][C]R-squared[/C][C]0.323209845109311[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.292446656250643[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]10.5063830214157[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]7[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]154[/C][/ROW]
[ROW][C]p-value[/C][C]9.26815291180105e-11[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]1.80153168847774[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]499.809529386774[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=197077&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=197077&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 R0.568515474819561
R-squared0.323209845109311
Adjusted R-squared0.292446656250643
F-TEST (value)10.5063830214157
F-TEST (DF numerator)7
F-TEST (DF denominator)154
p-value9.26815291180105e-11
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1.80153168847774
Sum Squared Residuals499.809529386774






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
1129.920265889504052.07973411049595
21111.5299496278705-0.529949627870465
31513.82788800981091.17211199018914
4611.1684289529465-5.16842895294647
51310.05214179702812.94785820297194
6109.857403060334580.142596939665416
71212.9305305644437-0.930530564443697
81411.37369129918272.62630870081734
91210.8967880146771.10321198532305
10611.4966376404392-5.4966376404392
111010.9693818496135-0.969381849613493
121211.72328779445540.2767122055446
131211.49522750281310.504772497186862
141111.5106007646492-0.510600764649155
151512.07651848229052.92348151770946
161210.76313148273471.23686851726532
171010.4316674730312-0.431667473031212
181213.6649880552959-1.66498805529588
191111.9808832397363-0.980883239736326
201211.51496352529360.485036474706423
211111.4372086420624-0.437208642062407
221212.153331380824-0.153331380823985
231313.1058289438996-0.105828943899599
241111.7021662573781-0.702166257378083
25911.6007398350211-2.60073983502113
261312.80456877291390.195431227086148
271010.4941850854638-0.494185085463777
281411.28587486194232.71412513805773
291211.85820341374950.14179658625047
301010.5696242748608-0.569624274860832
311211.42554948141610.574450518583925
3289.56352975115641-1.56352975115641
331010.482111353216-0.482111353215991
341212.0120407729563-0.012040772956328
351210.31107872263061.68892127736944
3676.863737080968030.136262919031971
3768.51448533788908-2.51448533788909
381210.26980671216181.73019328783821
391011.9767579669114-1.97675796691139
401011.5477103275566-1.54771032755661
411011.3589251400026-1.35892514000259
421210.38864618281611.61135381718391
431513.3678533033251.63214669667504
441010.2662628352152-0.266262835215155
451010.5118470390054-0.51184703900537
46129.128577321479152.87142267852085
471310.94169785495162.0583021450484
481111.4684910820715-0.468491082071521
491111.8480471606344-0.848047160634373
501210.34397382579241.65602617420764
511412.24090144584921.7590985541508
521010.347943984145-0.347943984145014
53129.424197769425032.57580223057497
541311.79131730710741.20868269289257
5558.03515168676582-3.03515168676582
56610.726529169403-4.72652916940298
571211.465023618130.534976381869979
581212.3706571998028-0.370657199802835
591110.91807273878760.0819272612123745
601011.8017064423435-1.80170644234351
6179.51986042432157-2.51986042432157
621211.05637576591260.943624234087382
631412.0330137672891.96698623271096
641110.50571090879220.494289091207794
651211.4871946083580.512805391641968
661312.30925167289940.690748327100623
671412.81646072002521.18353927997484
681112.4736540267732-1.47365402677324
69129.743318255056422.25668174494358
701211.35321745767350.646782542326453
7188.21261505000463-0.212615050004626
721110.89404953133380.105950468666169
731413.18292615986870.817073840131325
741412.34279570054891.65720429945108
751211.26048752730780.739512472692154
76911.9477630588409-2.94776305884089
771311.53368008596011.46631991403988
781111.4738789378705-0.473878937870453
79129.966224100199762.03377589980024
801211.05839481198380.941605188016197
811211.34953696413320.65046303586676
821213.4546516538609-1.45465165386089
831212.0479050751041-0.0479050751040555
841210.8511808020961.14881919790402
851110.97414333313170.0258566668682996
861011.424570491359-1.42457049135903
87910.2832081927911-1.28320819279106
881212.0167811750329-0.0167811750328891
891211.91427046650610.085729533493938
901211.74066820171110.259331798288873
9199.82231986981269-0.82231986981269
921512.34219298138312.65780701861686
931211.98488835581330.0151116441867151
941211.30330629199640.696693708003649
951210.49812277315581.50187722684422
961011.5783070795645-1.57830707956451
971311.87778284642381.12221715357625
98911.7536907864893-2.75369078648926
991211.19594062976490.804059370235086
1001010.2198727812377-0.219872781237685
1011411.75019236861792.24980763138211
1021111.1991912046557-0.199191204655731
1031513.74714761665921.2528523833408
1041110.96672069719660.0332793028033562
1051112.0733639565806-1.0733639565806
106129.72552929509762.2744707049024
1071212.278038686132-0.278038686131985
1081210.77273184630021.2272681536998
1091111.4033969189102-0.403396918910202
11078.9090170381438-1.90901703814381
1111211.82334821368610.176651786313922
1121411.99312717576122.00687282423879
1131112.8171052150573-1.81710521505733
114119.689433581594331.31056641840567
115109.532702548538710.467297451461291
1161311.79980765324481.2001923467552
1171310.69976036993152.30023963006848
118811.029214318184-3.02921431818399
1191110.25978129610980.740218703890206
1201211.60152301746910.398476982530861
1211110.37985106666420.620148933335816
1221311.2147668503971.78523314960297
1231210.02076437010181.97923562989823
1241411.86658978834952.13341021165047
1251311.40715975572971.59284024427031
1261512.17133664693612.82866335306385
1271010.5069399542733-0.506939954273307
1281111.9461902168543-0.946190216854302
129910.9166387304068-1.91663873040678
130119.746338260494321.25366173950568
1311011.6211071974633-1.62110719746327
132118.571460721728572.42853927827143
133811.6641552771857-3.66415527718569
134119.556059850356921.44394014964308
1351210.3354939810061.66450601899404
1361210.89868427074661.10131572925341
13799.68320286965918-0.683202869659183
1381110.56606201615750.433937983842544
139109.275704714414710.724295285585294
14089.67428414579418-1.67428414579418
14197.512328617998551.48767138200145
142811.5652892166068-3.56528921660683
143910.8900561250614-1.89005612506143
1441512.6655697485922.33443025140802
1451111.2855269455205-0.285526945520462
14688.32705675632479-0.327056756324787
1471312.98112395125960.0188760487403922
1481210.03914241148311.96085758851695
1491211.64595772186490.354042278135134
15099.57839973568394-0.578399735683936
15178.52747222141744-1.52747222141744
1521310.78553875048122.21446124951875
153910.70097553913-1.70097553912996
154612.0125191853898-6.01251918538979
155810.486841299907-2.48684129990699
15688.85639415544362-0.856394155443619
1571512.34219298138312.65780701861686
158610.3211044797341-4.32110447973407
159910.9166387304068-1.91663873040678
1601111.7839063554775-0.783906355477474
16189.74815128541675-1.74815128541675
162810.2209396522586-2.22093965225858

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 12 & 9.92026588950405 & 2.07973411049595 \tabularnewline
2 & 11 & 11.5299496278705 & -0.529949627870465 \tabularnewline
3 & 15 & 13.8278880098109 & 1.17211199018914 \tabularnewline
4 & 6 & 11.1684289529465 & -5.16842895294647 \tabularnewline
5 & 13 & 10.0521417970281 & 2.94785820297194 \tabularnewline
6 & 10 & 9.85740306033458 & 0.142596939665416 \tabularnewline
7 & 12 & 12.9305305644437 & -0.930530564443697 \tabularnewline
8 & 14 & 11.3736912991827 & 2.62630870081734 \tabularnewline
9 & 12 & 10.896788014677 & 1.10321198532305 \tabularnewline
10 & 6 & 11.4966376404392 & -5.4966376404392 \tabularnewline
11 & 10 & 10.9693818496135 & -0.969381849613493 \tabularnewline
12 & 12 & 11.7232877944554 & 0.2767122055446 \tabularnewline
13 & 12 & 11.4952275028131 & 0.504772497186862 \tabularnewline
14 & 11 & 11.5106007646492 & -0.510600764649155 \tabularnewline
15 & 15 & 12.0765184822905 & 2.92348151770946 \tabularnewline
16 & 12 & 10.7631314827347 & 1.23686851726532 \tabularnewline
17 & 10 & 10.4316674730312 & -0.431667473031212 \tabularnewline
18 & 12 & 13.6649880552959 & -1.66498805529588 \tabularnewline
19 & 11 & 11.9808832397363 & -0.980883239736326 \tabularnewline
20 & 12 & 11.5149635252936 & 0.485036474706423 \tabularnewline
21 & 11 & 11.4372086420624 & -0.437208642062407 \tabularnewline
22 & 12 & 12.153331380824 & -0.153331380823985 \tabularnewline
23 & 13 & 13.1058289438996 & -0.105828943899599 \tabularnewline
24 & 11 & 11.7021662573781 & -0.702166257378083 \tabularnewline
25 & 9 & 11.6007398350211 & -2.60073983502113 \tabularnewline
26 & 13 & 12.8045687729139 & 0.195431227086148 \tabularnewline
27 & 10 & 10.4941850854638 & -0.494185085463777 \tabularnewline
28 & 14 & 11.2858748619423 & 2.71412513805773 \tabularnewline
29 & 12 & 11.8582034137495 & 0.14179658625047 \tabularnewline
30 & 10 & 10.5696242748608 & -0.569624274860832 \tabularnewline
31 & 12 & 11.4255494814161 & 0.574450518583925 \tabularnewline
32 & 8 & 9.56352975115641 & -1.56352975115641 \tabularnewline
33 & 10 & 10.482111353216 & -0.482111353215991 \tabularnewline
34 & 12 & 12.0120407729563 & -0.012040772956328 \tabularnewline
35 & 12 & 10.3110787226306 & 1.68892127736944 \tabularnewline
36 & 7 & 6.86373708096803 & 0.136262919031971 \tabularnewline
37 & 6 & 8.51448533788908 & -2.51448533788909 \tabularnewline
38 & 12 & 10.2698067121618 & 1.73019328783821 \tabularnewline
39 & 10 & 11.9767579669114 & -1.97675796691139 \tabularnewline
40 & 10 & 11.5477103275566 & -1.54771032755661 \tabularnewline
41 & 10 & 11.3589251400026 & -1.35892514000259 \tabularnewline
42 & 12 & 10.3886461828161 & 1.61135381718391 \tabularnewline
43 & 15 & 13.367853303325 & 1.63214669667504 \tabularnewline
44 & 10 & 10.2662628352152 & -0.266262835215155 \tabularnewline
45 & 10 & 10.5118470390054 & -0.51184703900537 \tabularnewline
46 & 12 & 9.12857732147915 & 2.87142267852085 \tabularnewline
47 & 13 & 10.9416978549516 & 2.0583021450484 \tabularnewline
48 & 11 & 11.4684910820715 & -0.468491082071521 \tabularnewline
49 & 11 & 11.8480471606344 & -0.848047160634373 \tabularnewline
50 & 12 & 10.3439738257924 & 1.65602617420764 \tabularnewline
51 & 14 & 12.2409014458492 & 1.7590985541508 \tabularnewline
52 & 10 & 10.347943984145 & -0.347943984145014 \tabularnewline
53 & 12 & 9.42419776942503 & 2.57580223057497 \tabularnewline
54 & 13 & 11.7913173071074 & 1.20868269289257 \tabularnewline
55 & 5 & 8.03515168676582 & -3.03515168676582 \tabularnewline
56 & 6 & 10.726529169403 & -4.72652916940298 \tabularnewline
57 & 12 & 11.46502361813 & 0.534976381869979 \tabularnewline
58 & 12 & 12.3706571998028 & -0.370657199802835 \tabularnewline
59 & 11 & 10.9180727387876 & 0.0819272612123745 \tabularnewline
60 & 10 & 11.8017064423435 & -1.80170644234351 \tabularnewline
61 & 7 & 9.51986042432157 & -2.51986042432157 \tabularnewline
62 & 12 & 11.0563757659126 & 0.943624234087382 \tabularnewline
63 & 14 & 12.033013767289 & 1.96698623271096 \tabularnewline
64 & 11 & 10.5057109087922 & 0.494289091207794 \tabularnewline
65 & 12 & 11.487194608358 & 0.512805391641968 \tabularnewline
66 & 13 & 12.3092516728994 & 0.690748327100623 \tabularnewline
67 & 14 & 12.8164607200252 & 1.18353927997484 \tabularnewline
68 & 11 & 12.4736540267732 & -1.47365402677324 \tabularnewline
69 & 12 & 9.74331825505642 & 2.25668174494358 \tabularnewline
70 & 12 & 11.3532174576735 & 0.646782542326453 \tabularnewline
71 & 8 & 8.21261505000463 & -0.212615050004626 \tabularnewline
72 & 11 & 10.8940495313338 & 0.105950468666169 \tabularnewline
73 & 14 & 13.1829261598687 & 0.817073840131325 \tabularnewline
74 & 14 & 12.3427957005489 & 1.65720429945108 \tabularnewline
75 & 12 & 11.2604875273078 & 0.739512472692154 \tabularnewline
76 & 9 & 11.9477630588409 & -2.94776305884089 \tabularnewline
77 & 13 & 11.5336800859601 & 1.46631991403988 \tabularnewline
78 & 11 & 11.4738789378705 & -0.473878937870453 \tabularnewline
79 & 12 & 9.96622410019976 & 2.03377589980024 \tabularnewline
80 & 12 & 11.0583948119838 & 0.941605188016197 \tabularnewline
81 & 12 & 11.3495369641332 & 0.65046303586676 \tabularnewline
82 & 12 & 13.4546516538609 & -1.45465165386089 \tabularnewline
83 & 12 & 12.0479050751041 & -0.0479050751040555 \tabularnewline
84 & 12 & 10.851180802096 & 1.14881919790402 \tabularnewline
85 & 11 & 10.9741433331317 & 0.0258566668682996 \tabularnewline
86 & 10 & 11.424570491359 & -1.42457049135903 \tabularnewline
87 & 9 & 10.2832081927911 & -1.28320819279106 \tabularnewline
88 & 12 & 12.0167811750329 & -0.0167811750328891 \tabularnewline
89 & 12 & 11.9142704665061 & 0.085729533493938 \tabularnewline
90 & 12 & 11.7406682017111 & 0.259331798288873 \tabularnewline
91 & 9 & 9.82231986981269 & -0.82231986981269 \tabularnewline
92 & 15 & 12.3421929813831 & 2.65780701861686 \tabularnewline
93 & 12 & 11.9848883558133 & 0.0151116441867151 \tabularnewline
94 & 12 & 11.3033062919964 & 0.696693708003649 \tabularnewline
95 & 12 & 10.4981227731558 & 1.50187722684422 \tabularnewline
96 & 10 & 11.5783070795645 & -1.57830707956451 \tabularnewline
97 & 13 & 11.8777828464238 & 1.12221715357625 \tabularnewline
98 & 9 & 11.7536907864893 & -2.75369078648926 \tabularnewline
99 & 12 & 11.1959406297649 & 0.804059370235086 \tabularnewline
100 & 10 & 10.2198727812377 & -0.219872781237685 \tabularnewline
101 & 14 & 11.7501923686179 & 2.24980763138211 \tabularnewline
102 & 11 & 11.1991912046557 & -0.199191204655731 \tabularnewline
103 & 15 & 13.7471476166592 & 1.2528523833408 \tabularnewline
104 & 11 & 10.9667206971966 & 0.0332793028033562 \tabularnewline
105 & 11 & 12.0733639565806 & -1.0733639565806 \tabularnewline
106 & 12 & 9.7255292950976 & 2.2744707049024 \tabularnewline
107 & 12 & 12.278038686132 & -0.278038686131985 \tabularnewline
108 & 12 & 10.7727318463002 & 1.2272681536998 \tabularnewline
109 & 11 & 11.4033969189102 & -0.403396918910202 \tabularnewline
110 & 7 & 8.9090170381438 & -1.90901703814381 \tabularnewline
111 & 12 & 11.8233482136861 & 0.176651786313922 \tabularnewline
112 & 14 & 11.9931271757612 & 2.00687282423879 \tabularnewline
113 & 11 & 12.8171052150573 & -1.81710521505733 \tabularnewline
114 & 11 & 9.68943358159433 & 1.31056641840567 \tabularnewline
115 & 10 & 9.53270254853871 & 0.467297451461291 \tabularnewline
116 & 13 & 11.7998076532448 & 1.2001923467552 \tabularnewline
117 & 13 & 10.6997603699315 & 2.30023963006848 \tabularnewline
118 & 8 & 11.029214318184 & -3.02921431818399 \tabularnewline
119 & 11 & 10.2597812961098 & 0.740218703890206 \tabularnewline
120 & 12 & 11.6015230174691 & 0.398476982530861 \tabularnewline
121 & 11 & 10.3798510666642 & 0.620148933335816 \tabularnewline
122 & 13 & 11.214766850397 & 1.78523314960297 \tabularnewline
123 & 12 & 10.0207643701018 & 1.97923562989823 \tabularnewline
124 & 14 & 11.8665897883495 & 2.13341021165047 \tabularnewline
125 & 13 & 11.4071597557297 & 1.59284024427031 \tabularnewline
126 & 15 & 12.1713366469361 & 2.82866335306385 \tabularnewline
127 & 10 & 10.5069399542733 & -0.506939954273307 \tabularnewline
128 & 11 & 11.9461902168543 & -0.946190216854302 \tabularnewline
129 & 9 & 10.9166387304068 & -1.91663873040678 \tabularnewline
130 & 11 & 9.74633826049432 & 1.25366173950568 \tabularnewline
131 & 10 & 11.6211071974633 & -1.62110719746327 \tabularnewline
132 & 11 & 8.57146072172857 & 2.42853927827143 \tabularnewline
133 & 8 & 11.6641552771857 & -3.66415527718569 \tabularnewline
134 & 11 & 9.55605985035692 & 1.44394014964308 \tabularnewline
135 & 12 & 10.335493981006 & 1.66450601899404 \tabularnewline
136 & 12 & 10.8986842707466 & 1.10131572925341 \tabularnewline
137 & 9 & 9.68320286965918 & -0.683202869659183 \tabularnewline
138 & 11 & 10.5660620161575 & 0.433937983842544 \tabularnewline
139 & 10 & 9.27570471441471 & 0.724295285585294 \tabularnewline
140 & 8 & 9.67428414579418 & -1.67428414579418 \tabularnewline
141 & 9 & 7.51232861799855 & 1.48767138200145 \tabularnewline
142 & 8 & 11.5652892166068 & -3.56528921660683 \tabularnewline
143 & 9 & 10.8900561250614 & -1.89005612506143 \tabularnewline
144 & 15 & 12.665569748592 & 2.33443025140802 \tabularnewline
145 & 11 & 11.2855269455205 & -0.285526945520462 \tabularnewline
146 & 8 & 8.32705675632479 & -0.327056756324787 \tabularnewline
147 & 13 & 12.9811239512596 & 0.0188760487403922 \tabularnewline
148 & 12 & 10.0391424114831 & 1.96085758851695 \tabularnewline
149 & 12 & 11.6459577218649 & 0.354042278135134 \tabularnewline
150 & 9 & 9.57839973568394 & -0.578399735683936 \tabularnewline
151 & 7 & 8.52747222141744 & -1.52747222141744 \tabularnewline
152 & 13 & 10.7855387504812 & 2.21446124951875 \tabularnewline
153 & 9 & 10.70097553913 & -1.70097553912996 \tabularnewline
154 & 6 & 12.0125191853898 & -6.01251918538979 \tabularnewline
155 & 8 & 10.486841299907 & -2.48684129990699 \tabularnewline
156 & 8 & 8.85639415544362 & -0.856394155443619 \tabularnewline
157 & 15 & 12.3421929813831 & 2.65780701861686 \tabularnewline
158 & 6 & 10.3211044797341 & -4.32110447973407 \tabularnewline
159 & 9 & 10.9166387304068 & -1.91663873040678 \tabularnewline
160 & 11 & 11.7839063554775 & -0.783906355477474 \tabularnewline
161 & 8 & 9.74815128541675 & -1.74815128541675 \tabularnewline
162 & 8 & 10.2209396522586 & -2.22093965225858 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=197077&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]9.92026588950405[/C][C]2.07973411049595[/C][/ROW]
[ROW][C]2[/C][C]11[/C][C]11.5299496278705[/C][C]-0.529949627870465[/C][/ROW]
[ROW][C]3[/C][C]15[/C][C]13.8278880098109[/C][C]1.17211199018914[/C][/ROW]
[ROW][C]4[/C][C]6[/C][C]11.1684289529465[/C][C]-5.16842895294647[/C][/ROW]
[ROW][C]5[/C][C]13[/C][C]10.0521417970281[/C][C]2.94785820297194[/C][/ROW]
[ROW][C]6[/C][C]10[/C][C]9.85740306033458[/C][C]0.142596939665416[/C][/ROW]
[ROW][C]7[/C][C]12[/C][C]12.9305305644437[/C][C]-0.930530564443697[/C][/ROW]
[ROW][C]8[/C][C]14[/C][C]11.3736912991827[/C][C]2.62630870081734[/C][/ROW]
[ROW][C]9[/C][C]12[/C][C]10.896788014677[/C][C]1.10321198532305[/C][/ROW]
[ROW][C]10[/C][C]6[/C][C]11.4966376404392[/C][C]-5.4966376404392[/C][/ROW]
[ROW][C]11[/C][C]10[/C][C]10.9693818496135[/C][C]-0.969381849613493[/C][/ROW]
[ROW][C]12[/C][C]12[/C][C]11.7232877944554[/C][C]0.2767122055446[/C][/ROW]
[ROW][C]13[/C][C]12[/C][C]11.4952275028131[/C][C]0.504772497186862[/C][/ROW]
[ROW][C]14[/C][C]11[/C][C]11.5106007646492[/C][C]-0.510600764649155[/C][/ROW]
[ROW][C]15[/C][C]15[/C][C]12.0765184822905[/C][C]2.92348151770946[/C][/ROW]
[ROW][C]16[/C][C]12[/C][C]10.7631314827347[/C][C]1.23686851726532[/C][/ROW]
[ROW][C]17[/C][C]10[/C][C]10.4316674730312[/C][C]-0.431667473031212[/C][/ROW]
[ROW][C]18[/C][C]12[/C][C]13.6649880552959[/C][C]-1.66498805529588[/C][/ROW]
[ROW][C]19[/C][C]11[/C][C]11.9808832397363[/C][C]-0.980883239736326[/C][/ROW]
[ROW][C]20[/C][C]12[/C][C]11.5149635252936[/C][C]0.485036474706423[/C][/ROW]
[ROW][C]21[/C][C]11[/C][C]11.4372086420624[/C][C]-0.437208642062407[/C][/ROW]
[ROW][C]22[/C][C]12[/C][C]12.153331380824[/C][C]-0.153331380823985[/C][/ROW]
[ROW][C]23[/C][C]13[/C][C]13.1058289438996[/C][C]-0.105828943899599[/C][/ROW]
[ROW][C]24[/C][C]11[/C][C]11.7021662573781[/C][C]-0.702166257378083[/C][/ROW]
[ROW][C]25[/C][C]9[/C][C]11.6007398350211[/C][C]-2.60073983502113[/C][/ROW]
[ROW][C]26[/C][C]13[/C][C]12.8045687729139[/C][C]0.195431227086148[/C][/ROW]
[ROW][C]27[/C][C]10[/C][C]10.4941850854638[/C][C]-0.494185085463777[/C][/ROW]
[ROW][C]28[/C][C]14[/C][C]11.2858748619423[/C][C]2.71412513805773[/C][/ROW]
[ROW][C]29[/C][C]12[/C][C]11.8582034137495[/C][C]0.14179658625047[/C][/ROW]
[ROW][C]30[/C][C]10[/C][C]10.5696242748608[/C][C]-0.569624274860832[/C][/ROW]
[ROW][C]31[/C][C]12[/C][C]11.4255494814161[/C][C]0.574450518583925[/C][/ROW]
[ROW][C]32[/C][C]8[/C][C]9.56352975115641[/C][C]-1.56352975115641[/C][/ROW]
[ROW][C]33[/C][C]10[/C][C]10.482111353216[/C][C]-0.482111353215991[/C][/ROW]
[ROW][C]34[/C][C]12[/C][C]12.0120407729563[/C][C]-0.012040772956328[/C][/ROW]
[ROW][C]35[/C][C]12[/C][C]10.3110787226306[/C][C]1.68892127736944[/C][/ROW]
[ROW][C]36[/C][C]7[/C][C]6.86373708096803[/C][C]0.136262919031971[/C][/ROW]
[ROW][C]37[/C][C]6[/C][C]8.51448533788908[/C][C]-2.51448533788909[/C][/ROW]
[ROW][C]38[/C][C]12[/C][C]10.2698067121618[/C][C]1.73019328783821[/C][/ROW]
[ROW][C]39[/C][C]10[/C][C]11.9767579669114[/C][C]-1.97675796691139[/C][/ROW]
[ROW][C]40[/C][C]10[/C][C]11.5477103275566[/C][C]-1.54771032755661[/C][/ROW]
[ROW][C]41[/C][C]10[/C][C]11.3589251400026[/C][C]-1.35892514000259[/C][/ROW]
[ROW][C]42[/C][C]12[/C][C]10.3886461828161[/C][C]1.61135381718391[/C][/ROW]
[ROW][C]43[/C][C]15[/C][C]13.367853303325[/C][C]1.63214669667504[/C][/ROW]
[ROW][C]44[/C][C]10[/C][C]10.2662628352152[/C][C]-0.266262835215155[/C][/ROW]
[ROW][C]45[/C][C]10[/C][C]10.5118470390054[/C][C]-0.51184703900537[/C][/ROW]
[ROW][C]46[/C][C]12[/C][C]9.12857732147915[/C][C]2.87142267852085[/C][/ROW]
[ROW][C]47[/C][C]13[/C][C]10.9416978549516[/C][C]2.0583021450484[/C][/ROW]
[ROW][C]48[/C][C]11[/C][C]11.4684910820715[/C][C]-0.468491082071521[/C][/ROW]
[ROW][C]49[/C][C]11[/C][C]11.8480471606344[/C][C]-0.848047160634373[/C][/ROW]
[ROW][C]50[/C][C]12[/C][C]10.3439738257924[/C][C]1.65602617420764[/C][/ROW]
[ROW][C]51[/C][C]14[/C][C]12.2409014458492[/C][C]1.7590985541508[/C][/ROW]
[ROW][C]52[/C][C]10[/C][C]10.347943984145[/C][C]-0.347943984145014[/C][/ROW]
[ROW][C]53[/C][C]12[/C][C]9.42419776942503[/C][C]2.57580223057497[/C][/ROW]
[ROW][C]54[/C][C]13[/C][C]11.7913173071074[/C][C]1.20868269289257[/C][/ROW]
[ROW][C]55[/C][C]5[/C][C]8.03515168676582[/C][C]-3.03515168676582[/C][/ROW]
[ROW][C]56[/C][C]6[/C][C]10.726529169403[/C][C]-4.72652916940298[/C][/ROW]
[ROW][C]57[/C][C]12[/C][C]11.46502361813[/C][C]0.534976381869979[/C][/ROW]
[ROW][C]58[/C][C]12[/C][C]12.3706571998028[/C][C]-0.370657199802835[/C][/ROW]
[ROW][C]59[/C][C]11[/C][C]10.9180727387876[/C][C]0.0819272612123745[/C][/ROW]
[ROW][C]60[/C][C]10[/C][C]11.8017064423435[/C][C]-1.80170644234351[/C][/ROW]
[ROW][C]61[/C][C]7[/C][C]9.51986042432157[/C][C]-2.51986042432157[/C][/ROW]
[ROW][C]62[/C][C]12[/C][C]11.0563757659126[/C][C]0.943624234087382[/C][/ROW]
[ROW][C]63[/C][C]14[/C][C]12.033013767289[/C][C]1.96698623271096[/C][/ROW]
[ROW][C]64[/C][C]11[/C][C]10.5057109087922[/C][C]0.494289091207794[/C][/ROW]
[ROW][C]65[/C][C]12[/C][C]11.487194608358[/C][C]0.512805391641968[/C][/ROW]
[ROW][C]66[/C][C]13[/C][C]12.3092516728994[/C][C]0.690748327100623[/C][/ROW]
[ROW][C]67[/C][C]14[/C][C]12.8164607200252[/C][C]1.18353927997484[/C][/ROW]
[ROW][C]68[/C][C]11[/C][C]12.4736540267732[/C][C]-1.47365402677324[/C][/ROW]
[ROW][C]69[/C][C]12[/C][C]9.74331825505642[/C][C]2.25668174494358[/C][/ROW]
[ROW][C]70[/C][C]12[/C][C]11.3532174576735[/C][C]0.646782542326453[/C][/ROW]
[ROW][C]71[/C][C]8[/C][C]8.21261505000463[/C][C]-0.212615050004626[/C][/ROW]
[ROW][C]72[/C][C]11[/C][C]10.8940495313338[/C][C]0.105950468666169[/C][/ROW]
[ROW][C]73[/C][C]14[/C][C]13.1829261598687[/C][C]0.817073840131325[/C][/ROW]
[ROW][C]74[/C][C]14[/C][C]12.3427957005489[/C][C]1.65720429945108[/C][/ROW]
[ROW][C]75[/C][C]12[/C][C]11.2604875273078[/C][C]0.739512472692154[/C][/ROW]
[ROW][C]76[/C][C]9[/C][C]11.9477630588409[/C][C]-2.94776305884089[/C][/ROW]
[ROW][C]77[/C][C]13[/C][C]11.5336800859601[/C][C]1.46631991403988[/C][/ROW]
[ROW][C]78[/C][C]11[/C][C]11.4738789378705[/C][C]-0.473878937870453[/C][/ROW]
[ROW][C]79[/C][C]12[/C][C]9.96622410019976[/C][C]2.03377589980024[/C][/ROW]
[ROW][C]80[/C][C]12[/C][C]11.0583948119838[/C][C]0.941605188016197[/C][/ROW]
[ROW][C]81[/C][C]12[/C][C]11.3495369641332[/C][C]0.65046303586676[/C][/ROW]
[ROW][C]82[/C][C]12[/C][C]13.4546516538609[/C][C]-1.45465165386089[/C][/ROW]
[ROW][C]83[/C][C]12[/C][C]12.0479050751041[/C][C]-0.0479050751040555[/C][/ROW]
[ROW][C]84[/C][C]12[/C][C]10.851180802096[/C][C]1.14881919790402[/C][/ROW]
[ROW][C]85[/C][C]11[/C][C]10.9741433331317[/C][C]0.0258566668682996[/C][/ROW]
[ROW][C]86[/C][C]10[/C][C]11.424570491359[/C][C]-1.42457049135903[/C][/ROW]
[ROW][C]87[/C][C]9[/C][C]10.2832081927911[/C][C]-1.28320819279106[/C][/ROW]
[ROW][C]88[/C][C]12[/C][C]12.0167811750329[/C][C]-0.0167811750328891[/C][/ROW]
[ROW][C]89[/C][C]12[/C][C]11.9142704665061[/C][C]0.085729533493938[/C][/ROW]
[ROW][C]90[/C][C]12[/C][C]11.7406682017111[/C][C]0.259331798288873[/C][/ROW]
[ROW][C]91[/C][C]9[/C][C]9.82231986981269[/C][C]-0.82231986981269[/C][/ROW]
[ROW][C]92[/C][C]15[/C][C]12.3421929813831[/C][C]2.65780701861686[/C][/ROW]
[ROW][C]93[/C][C]12[/C][C]11.9848883558133[/C][C]0.0151116441867151[/C][/ROW]
[ROW][C]94[/C][C]12[/C][C]11.3033062919964[/C][C]0.696693708003649[/C][/ROW]
[ROW][C]95[/C][C]12[/C][C]10.4981227731558[/C][C]1.50187722684422[/C][/ROW]
[ROW][C]96[/C][C]10[/C][C]11.5783070795645[/C][C]-1.57830707956451[/C][/ROW]
[ROW][C]97[/C][C]13[/C][C]11.8777828464238[/C][C]1.12221715357625[/C][/ROW]
[ROW][C]98[/C][C]9[/C][C]11.7536907864893[/C][C]-2.75369078648926[/C][/ROW]
[ROW][C]99[/C][C]12[/C][C]11.1959406297649[/C][C]0.804059370235086[/C][/ROW]
[ROW][C]100[/C][C]10[/C][C]10.2198727812377[/C][C]-0.219872781237685[/C][/ROW]
[ROW][C]101[/C][C]14[/C][C]11.7501923686179[/C][C]2.24980763138211[/C][/ROW]
[ROW][C]102[/C][C]11[/C][C]11.1991912046557[/C][C]-0.199191204655731[/C][/ROW]
[ROW][C]103[/C][C]15[/C][C]13.7471476166592[/C][C]1.2528523833408[/C][/ROW]
[ROW][C]104[/C][C]11[/C][C]10.9667206971966[/C][C]0.0332793028033562[/C][/ROW]
[ROW][C]105[/C][C]11[/C][C]12.0733639565806[/C][C]-1.0733639565806[/C][/ROW]
[ROW][C]106[/C][C]12[/C][C]9.7255292950976[/C][C]2.2744707049024[/C][/ROW]
[ROW][C]107[/C][C]12[/C][C]12.278038686132[/C][C]-0.278038686131985[/C][/ROW]
[ROW][C]108[/C][C]12[/C][C]10.7727318463002[/C][C]1.2272681536998[/C][/ROW]
[ROW][C]109[/C][C]11[/C][C]11.4033969189102[/C][C]-0.403396918910202[/C][/ROW]
[ROW][C]110[/C][C]7[/C][C]8.9090170381438[/C][C]-1.90901703814381[/C][/ROW]
[ROW][C]111[/C][C]12[/C][C]11.8233482136861[/C][C]0.176651786313922[/C][/ROW]
[ROW][C]112[/C][C]14[/C][C]11.9931271757612[/C][C]2.00687282423879[/C][/ROW]
[ROW][C]113[/C][C]11[/C][C]12.8171052150573[/C][C]-1.81710521505733[/C][/ROW]
[ROW][C]114[/C][C]11[/C][C]9.68943358159433[/C][C]1.31056641840567[/C][/ROW]
[ROW][C]115[/C][C]10[/C][C]9.53270254853871[/C][C]0.467297451461291[/C][/ROW]
[ROW][C]116[/C][C]13[/C][C]11.7998076532448[/C][C]1.2001923467552[/C][/ROW]
[ROW][C]117[/C][C]13[/C][C]10.6997603699315[/C][C]2.30023963006848[/C][/ROW]
[ROW][C]118[/C][C]8[/C][C]11.029214318184[/C][C]-3.02921431818399[/C][/ROW]
[ROW][C]119[/C][C]11[/C][C]10.2597812961098[/C][C]0.740218703890206[/C][/ROW]
[ROW][C]120[/C][C]12[/C][C]11.6015230174691[/C][C]0.398476982530861[/C][/ROW]
[ROW][C]121[/C][C]11[/C][C]10.3798510666642[/C][C]0.620148933335816[/C][/ROW]
[ROW][C]122[/C][C]13[/C][C]11.214766850397[/C][C]1.78523314960297[/C][/ROW]
[ROW][C]123[/C][C]12[/C][C]10.0207643701018[/C][C]1.97923562989823[/C][/ROW]
[ROW][C]124[/C][C]14[/C][C]11.8665897883495[/C][C]2.13341021165047[/C][/ROW]
[ROW][C]125[/C][C]13[/C][C]11.4071597557297[/C][C]1.59284024427031[/C][/ROW]
[ROW][C]126[/C][C]15[/C][C]12.1713366469361[/C][C]2.82866335306385[/C][/ROW]
[ROW][C]127[/C][C]10[/C][C]10.5069399542733[/C][C]-0.506939954273307[/C][/ROW]
[ROW][C]128[/C][C]11[/C][C]11.9461902168543[/C][C]-0.946190216854302[/C][/ROW]
[ROW][C]129[/C][C]9[/C][C]10.9166387304068[/C][C]-1.91663873040678[/C][/ROW]
[ROW][C]130[/C][C]11[/C][C]9.74633826049432[/C][C]1.25366173950568[/C][/ROW]
[ROW][C]131[/C][C]10[/C][C]11.6211071974633[/C][C]-1.62110719746327[/C][/ROW]
[ROW][C]132[/C][C]11[/C][C]8.57146072172857[/C][C]2.42853927827143[/C][/ROW]
[ROW][C]133[/C][C]8[/C][C]11.6641552771857[/C][C]-3.66415527718569[/C][/ROW]
[ROW][C]134[/C][C]11[/C][C]9.55605985035692[/C][C]1.44394014964308[/C][/ROW]
[ROW][C]135[/C][C]12[/C][C]10.335493981006[/C][C]1.66450601899404[/C][/ROW]
[ROW][C]136[/C][C]12[/C][C]10.8986842707466[/C][C]1.10131572925341[/C][/ROW]
[ROW][C]137[/C][C]9[/C][C]9.68320286965918[/C][C]-0.683202869659183[/C][/ROW]
[ROW][C]138[/C][C]11[/C][C]10.5660620161575[/C][C]0.433937983842544[/C][/ROW]
[ROW][C]139[/C][C]10[/C][C]9.27570471441471[/C][C]0.724295285585294[/C][/ROW]
[ROW][C]140[/C][C]8[/C][C]9.67428414579418[/C][C]-1.67428414579418[/C][/ROW]
[ROW][C]141[/C][C]9[/C][C]7.51232861799855[/C][C]1.48767138200145[/C][/ROW]
[ROW][C]142[/C][C]8[/C][C]11.5652892166068[/C][C]-3.56528921660683[/C][/ROW]
[ROW][C]143[/C][C]9[/C][C]10.8900561250614[/C][C]-1.89005612506143[/C][/ROW]
[ROW][C]144[/C][C]15[/C][C]12.665569748592[/C][C]2.33443025140802[/C][/ROW]
[ROW][C]145[/C][C]11[/C][C]11.2855269455205[/C][C]-0.285526945520462[/C][/ROW]
[ROW][C]146[/C][C]8[/C][C]8.32705675632479[/C][C]-0.327056756324787[/C][/ROW]
[ROW][C]147[/C][C]13[/C][C]12.9811239512596[/C][C]0.0188760487403922[/C][/ROW]
[ROW][C]148[/C][C]12[/C][C]10.0391424114831[/C][C]1.96085758851695[/C][/ROW]
[ROW][C]149[/C][C]12[/C][C]11.6459577218649[/C][C]0.354042278135134[/C][/ROW]
[ROW][C]150[/C][C]9[/C][C]9.57839973568394[/C][C]-0.578399735683936[/C][/ROW]
[ROW][C]151[/C][C]7[/C][C]8.52747222141744[/C][C]-1.52747222141744[/C][/ROW]
[ROW][C]152[/C][C]13[/C][C]10.7855387504812[/C][C]2.21446124951875[/C][/ROW]
[ROW][C]153[/C][C]9[/C][C]10.70097553913[/C][C]-1.70097553912996[/C][/ROW]
[ROW][C]154[/C][C]6[/C][C]12.0125191853898[/C][C]-6.01251918538979[/C][/ROW]
[ROW][C]155[/C][C]8[/C][C]10.486841299907[/C][C]-2.48684129990699[/C][/ROW]
[ROW][C]156[/C][C]8[/C][C]8.85639415544362[/C][C]-0.856394155443619[/C][/ROW]
[ROW][C]157[/C][C]15[/C][C]12.3421929813831[/C][C]2.65780701861686[/C][/ROW]
[ROW][C]158[/C][C]6[/C][C]10.3211044797341[/C][C]-4.32110447973407[/C][/ROW]
[ROW][C]159[/C][C]9[/C][C]10.9166387304068[/C][C]-1.91663873040678[/C][/ROW]
[ROW][C]160[/C][C]11[/C][C]11.7839063554775[/C][C]-0.783906355477474[/C][/ROW]
[ROW][C]161[/C][C]8[/C][C]9.74815128541675[/C][C]-1.74815128541675[/C][/ROW]
[ROW][C]162[/C][C]8[/C][C]10.2209396522586[/C][C]-2.22093965225858[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=197077&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=197077&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
1129.920265889504052.07973411049595
21111.5299496278705-0.529949627870465
31513.82788800981091.17211199018914
4611.1684289529465-5.16842895294647
51310.05214179702812.94785820297194
6109.857403060334580.142596939665416
71212.9305305644437-0.930530564443697
81411.37369129918272.62630870081734
91210.8967880146771.10321198532305
10611.4966376404392-5.4966376404392
111010.9693818496135-0.969381849613493
121211.72328779445540.2767122055446
131211.49522750281310.504772497186862
141111.5106007646492-0.510600764649155
151512.07651848229052.92348151770946
161210.76313148273471.23686851726532
171010.4316674730312-0.431667473031212
181213.6649880552959-1.66498805529588
191111.9808832397363-0.980883239736326
201211.51496352529360.485036474706423
211111.4372086420624-0.437208642062407
221212.153331380824-0.153331380823985
231313.1058289438996-0.105828943899599
241111.7021662573781-0.702166257378083
25911.6007398350211-2.60073983502113
261312.80456877291390.195431227086148
271010.4941850854638-0.494185085463777
281411.28587486194232.71412513805773
291211.85820341374950.14179658625047
301010.5696242748608-0.569624274860832
311211.42554948141610.574450518583925
3289.56352975115641-1.56352975115641
331010.482111353216-0.482111353215991
341212.0120407729563-0.012040772956328
351210.31107872263061.68892127736944
3676.863737080968030.136262919031971
3768.51448533788908-2.51448533788909
381210.26980671216181.73019328783821
391011.9767579669114-1.97675796691139
401011.5477103275566-1.54771032755661
411011.3589251400026-1.35892514000259
421210.38864618281611.61135381718391
431513.3678533033251.63214669667504
441010.2662628352152-0.266262835215155
451010.5118470390054-0.51184703900537
46129.128577321479152.87142267852085
471310.94169785495162.0583021450484
481111.4684910820715-0.468491082071521
491111.8480471606344-0.848047160634373
501210.34397382579241.65602617420764
511412.24090144584921.7590985541508
521010.347943984145-0.347943984145014
53129.424197769425032.57580223057497
541311.79131730710741.20868269289257
5558.03515168676582-3.03515168676582
56610.726529169403-4.72652916940298
571211.465023618130.534976381869979
581212.3706571998028-0.370657199802835
591110.91807273878760.0819272612123745
601011.8017064423435-1.80170644234351
6179.51986042432157-2.51986042432157
621211.05637576591260.943624234087382
631412.0330137672891.96698623271096
641110.50571090879220.494289091207794
651211.4871946083580.512805391641968
661312.30925167289940.690748327100623
671412.81646072002521.18353927997484
681112.4736540267732-1.47365402677324
69129.743318255056422.25668174494358
701211.35321745767350.646782542326453
7188.21261505000463-0.212615050004626
721110.89404953133380.105950468666169
731413.18292615986870.817073840131325
741412.34279570054891.65720429945108
751211.26048752730780.739512472692154
76911.9477630588409-2.94776305884089
771311.53368008596011.46631991403988
781111.4738789378705-0.473878937870453
79129.966224100199762.03377589980024
801211.05839481198380.941605188016197
811211.34953696413320.65046303586676
821213.4546516538609-1.45465165386089
831212.0479050751041-0.0479050751040555
841210.8511808020961.14881919790402
851110.97414333313170.0258566668682996
861011.424570491359-1.42457049135903
87910.2832081927911-1.28320819279106
881212.0167811750329-0.0167811750328891
891211.91427046650610.085729533493938
901211.74066820171110.259331798288873
9199.82231986981269-0.82231986981269
921512.34219298138312.65780701861686
931211.98488835581330.0151116441867151
941211.30330629199640.696693708003649
951210.49812277315581.50187722684422
961011.5783070795645-1.57830707956451
971311.87778284642381.12221715357625
98911.7536907864893-2.75369078648926
991211.19594062976490.804059370235086
1001010.2198727812377-0.219872781237685
1011411.75019236861792.24980763138211
1021111.1991912046557-0.199191204655731
1031513.74714761665921.2528523833408
1041110.96672069719660.0332793028033562
1051112.0733639565806-1.0733639565806
106129.72552929509762.2744707049024
1071212.278038686132-0.278038686131985
1081210.77273184630021.2272681536998
1091111.4033969189102-0.403396918910202
11078.9090170381438-1.90901703814381
1111211.82334821368610.176651786313922
1121411.99312717576122.00687282423879
1131112.8171052150573-1.81710521505733
114119.689433581594331.31056641840567
115109.532702548538710.467297451461291
1161311.79980765324481.2001923467552
1171310.69976036993152.30023963006848
118811.029214318184-3.02921431818399
1191110.25978129610980.740218703890206
1201211.60152301746910.398476982530861
1211110.37985106666420.620148933335816
1221311.2147668503971.78523314960297
1231210.02076437010181.97923562989823
1241411.86658978834952.13341021165047
1251311.40715975572971.59284024427031
1261512.17133664693612.82866335306385
1271010.5069399542733-0.506939954273307
1281111.9461902168543-0.946190216854302
129910.9166387304068-1.91663873040678
130119.746338260494321.25366173950568
1311011.6211071974633-1.62110719746327
132118.571460721728572.42853927827143
133811.6641552771857-3.66415527718569
134119.556059850356921.44394014964308
1351210.3354939810061.66450601899404
1361210.89868427074661.10131572925341
13799.68320286965918-0.683202869659183
1381110.56606201615750.433937983842544
139109.275704714414710.724295285585294
14089.67428414579418-1.67428414579418
14197.512328617998551.48767138200145
142811.5652892166068-3.56528921660683
143910.8900561250614-1.89005612506143
1441512.6655697485922.33443025140802
1451111.2855269455205-0.285526945520462
14688.32705675632479-0.327056756324787
1471312.98112395125960.0188760487403922
1481210.03914241148311.96085758851695
1491211.64595772186490.354042278135134
15099.57839973568394-0.578399735683936
15178.52747222141744-1.52747222141744
1521310.78553875048122.21446124951875
153910.70097553913-1.70097553912996
154612.0125191853898-6.01251918538979
155810.486841299907-2.48684129990699
15688.85639415544362-0.856394155443619
1571512.34219298138312.65780701861686
158610.3211044797341-4.32110447973407
159910.9166387304068-1.91663873040678
1601111.7839063554775-0.783906355477474
16189.74815128541675-1.74815128541675
162810.2209396522586-2.22093965225858






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
110.5060760863721750.9878478272556490.493923913627825
120.338897113026370.6777942260527410.66110288697363
130.7924287288298920.4151425423402150.207571271170108
140.7382692151883870.5234615696232270.261730784811613
150.6543180318035050.6913639363929890.345681968196495
160.7513480403477620.4973039193044770.248651959652238
170.7572067557852990.4855864884294010.242793244214701
180.8149234681943390.3701530636113220.185076531805661
190.8418783254982280.3162433490035430.158121674501772
200.8645064160269240.2709871679461530.135493583973076
210.8668206518074070.2663586963851860.133179348192593
220.828792866489920.342414267020160.17120713351008
230.7889930366655380.4220139266689240.211006963334462
240.7782034722085080.4435930555829830.221796527791491
250.7415668247506780.5168663504986430.258433175249322
260.7080173352745270.5839653294509450.291982664725473
270.7408912973108360.5182174053783270.259108702689164
280.7642465688498930.4715068623002150.235753431150108
290.7098967727676830.5802064544646340.290103227232317
300.6571892849193310.6856214301613380.342810715080669
310.5984064476888450.803187104622310.401593552311155
320.5641128741750680.8717742516498650.435887125824932
330.5267960357585390.9464079284829220.473203964241461
340.4648829764308410.9297659528616810.535117023569159
350.5413029007314570.9173941985370860.458697099268543
360.4814639866492180.9629279732984360.518536013350782
370.5294028989298540.9411942021402910.470597101070146
380.6155979942349090.7688040115301830.384402005765091
390.6350495222841420.7299009554317150.364950477715858
400.5961186792757590.8077626414484830.403881320724241
410.5541836155223640.8916327689552710.445816384477636
420.5164147375195520.9671705249608970.483585262480448
430.5992245122046070.8015509755907860.400775487795393
440.5470257336374640.9059485327250730.452974266362536
450.5285392889563650.942921422087270.471460711043635
460.5536768345842010.8926463308315990.446323165415799
470.5565232308509910.8869535382980180.443476769149009
480.5091998169282240.9816003661435520.490800183071776
490.462419069145330.924838138290660.53758093085467
500.4835810432793330.9671620865586660.516418956720667
510.4889158487239690.9778316974479390.511084151276031
520.4437278686443680.8874557372887360.556272131355632
530.5194341894265180.9611316211469630.480565810573482
540.4810590958118920.9621181916237850.518940904188108
550.5827561065259680.8344877869480630.417243893474032
560.8291699858083460.3416600283833080.170830014191654
570.7980643667621110.4038712664757780.201935633237889
580.7639574760506740.4720850478986530.236042523949326
590.7250087502770640.5499824994458710.274991249722936
600.7316470273776870.5367059452446260.268352972622313
610.7555072017176660.4889855965646680.244492798282334
620.7258189953486390.5483620093027220.274181004651361
630.7236737008777690.5526525982444620.276326299122231
640.6959445783628060.6081108432743890.304055421637195
650.656515948794870.686968102410260.34348405120513
660.6198975842753430.7602048314493150.380102415724657
670.5939309013759560.8121381972480880.406069098624044
680.5755094449812150.8489811100375690.424490555018785
690.5870010013970050.825997997205990.412998998602995
700.5486310169431920.9027379661136160.451368983056808
710.5044790945544980.9910418108910030.495520905445502
720.4595266575356970.9190533150713950.540473342464303
730.4220229395558840.8440458791117680.577977060444116
740.4061588022735530.8123176045471060.593841197726447
750.3865292874013770.7730585748027550.613470712598623
760.4549125489698640.9098250979397270.545087451030136
770.4407191480486780.8814382960973560.559280851951322
780.3972254096835420.7944508193670850.602774590316458
790.4221637383748990.8443274767497970.577836261625101
800.3940159989283650.788031997856730.605984001071635
810.3544927799569370.7089855599138740.645507220043063
820.341512545180170.683025090360340.65848745481983
830.3014533938582220.6029067877164430.698546606141778
840.2898214166998540.5796428333997070.710178583300146
850.2618414457962420.5236828915924850.738158554203758
860.2515594431893280.5031188863786560.748440556810672
870.2406576276588070.4813152553176150.759342372341193
880.2055530703060220.4111061406120440.794446929693978
890.1739745986722420.3479491973444850.826025401327758
900.1515120831695250.3030241663390490.848487916830476
910.1319185742273610.2638371484547220.868081425772639
920.1667000130056560.3334000260113110.833299986994344
930.1420492282488290.2840984564976590.857950771751171
940.1233557242316850.246711448463370.876644275768315
950.114933644439940.2298672888798810.88506635556006
960.1118069783022530.2236139566045060.888193021697747
970.09767903795361290.1953580759072260.902320962046387
980.1315421525712760.2630843051425520.868457847428724
990.1123758932420260.2247517864840520.887624106757974
1000.09264150284385560.1852830056877110.907358497156144
1010.1028182970600990.2056365941201990.897181702939901
1020.08372615857562110.1674523171512420.916273841424379
1030.07809242963444060.1561848592688810.921907570365559
1040.06503975001592440.1300795000318490.934960249984076
1050.05392308071775740.1078461614355150.946076919282243
1060.05610182465810590.1122036493162120.943898175341894
1070.04368487316649860.08736974633299710.956315126833501
1080.03988762731362450.0797752546272490.960112372686376
1090.03121259091252590.06242518182505180.968787409087474
1100.0330880848379190.06617616967583790.966911915162081
1110.02557694182969220.05115388365938430.974423058170308
1120.02721560890026140.05443121780052270.972784391099739
1130.0255803367191480.0511606734382960.974419663280852
1140.02188732848988380.04377465697976760.978112671510116
1150.01654245822096830.03308491644193650.983457541779032
1160.0140886391408560.0281772782817120.985911360859144
1170.02356143586484910.04712287172969810.976438564135151
1180.03260377649936660.06520755299873320.967396223500633
1190.02596310124204160.05192620248408310.974036898757958
1200.02004189339967760.04008378679935520.979958106600322
1210.01576746467646880.03153492935293770.984232535323531
1220.02186892042200640.04373784084401290.978131079577994
1230.0306003360715430.06120067214308610.969399663928457
1240.04174208378075960.08348416756151920.95825791621924
1250.04038475512345860.08076951024691710.959615244876541
1260.08001926290466230.1600385258093250.919980737095338
1270.06342796485150660.1268559297030130.936572035148493
1280.04914400359862190.09828800719724370.950855996401378
1290.04162883466839860.08325766933679720.958371165331601
1300.03763333232972160.07526666465944330.962366667670278
1310.03601689403923850.0720337880784770.963983105960762
1320.03913729643540540.07827459287081080.960862703564595
1330.07890492620141170.1578098524028230.921095073798588
1340.0701089251589440.1402178503178880.929891074841056
1350.05708676589020560.1141735317804110.942913234109794
1360.07021631327936570.1404326265587310.929783686720634
1370.05439653023389110.1087930604677820.945603469766109
1380.0404788161045620.08095763220912410.959521183895438
1390.08686718105358680.1737343621071740.913132818946413
1400.06576290707846130.1315258141569230.934237092921539
1410.1211602703574840.2423205407149670.878839729642516
1420.1087415639347230.2174831278694460.891258436065277
1430.09961662707158370.1992332541431670.900383372928416
1440.4014143176649150.8028286353298290.598585682335085
1450.470997201464160.941994402928320.52900279853584
1460.5537562822963250.892487435407350.446243717703675
1470.6029489508507820.7941020982984370.397051049149218
1480.7765719195932690.4468561608134630.223428080406731
1490.6927874228133520.6144251543732950.307212577186648
1500.6368650673171660.7262698653656680.363134932682834
1510.5462314360330860.9075371279338280.453768563966914

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
11 & 0.506076086372175 & 0.987847827255649 & 0.493923913627825 \tabularnewline
12 & 0.33889711302637 & 0.677794226052741 & 0.66110288697363 \tabularnewline
13 & 0.792428728829892 & 0.415142542340215 & 0.207571271170108 \tabularnewline
14 & 0.738269215188387 & 0.523461569623227 & 0.261730784811613 \tabularnewline
15 & 0.654318031803505 & 0.691363936392989 & 0.345681968196495 \tabularnewline
16 & 0.751348040347762 & 0.497303919304477 & 0.248651959652238 \tabularnewline
17 & 0.757206755785299 & 0.485586488429401 & 0.242793244214701 \tabularnewline
18 & 0.814923468194339 & 0.370153063611322 & 0.185076531805661 \tabularnewline
19 & 0.841878325498228 & 0.316243349003543 & 0.158121674501772 \tabularnewline
20 & 0.864506416026924 & 0.270987167946153 & 0.135493583973076 \tabularnewline
21 & 0.866820651807407 & 0.266358696385186 & 0.133179348192593 \tabularnewline
22 & 0.82879286648992 & 0.34241426702016 & 0.17120713351008 \tabularnewline
23 & 0.788993036665538 & 0.422013926668924 & 0.211006963334462 \tabularnewline
24 & 0.778203472208508 & 0.443593055582983 & 0.221796527791491 \tabularnewline
25 & 0.741566824750678 & 0.516866350498643 & 0.258433175249322 \tabularnewline
26 & 0.708017335274527 & 0.583965329450945 & 0.291982664725473 \tabularnewline
27 & 0.740891297310836 & 0.518217405378327 & 0.259108702689164 \tabularnewline
28 & 0.764246568849893 & 0.471506862300215 & 0.235753431150108 \tabularnewline
29 & 0.709896772767683 & 0.580206454464634 & 0.290103227232317 \tabularnewline
30 & 0.657189284919331 & 0.685621430161338 & 0.342810715080669 \tabularnewline
31 & 0.598406447688845 & 0.80318710462231 & 0.401593552311155 \tabularnewline
32 & 0.564112874175068 & 0.871774251649865 & 0.435887125824932 \tabularnewline
33 & 0.526796035758539 & 0.946407928482922 & 0.473203964241461 \tabularnewline
34 & 0.464882976430841 & 0.929765952861681 & 0.535117023569159 \tabularnewline
35 & 0.541302900731457 & 0.917394198537086 & 0.458697099268543 \tabularnewline
36 & 0.481463986649218 & 0.962927973298436 & 0.518536013350782 \tabularnewline
37 & 0.529402898929854 & 0.941194202140291 & 0.470597101070146 \tabularnewline
38 & 0.615597994234909 & 0.768804011530183 & 0.384402005765091 \tabularnewline
39 & 0.635049522284142 & 0.729900955431715 & 0.364950477715858 \tabularnewline
40 & 0.596118679275759 & 0.807762641448483 & 0.403881320724241 \tabularnewline
41 & 0.554183615522364 & 0.891632768955271 & 0.445816384477636 \tabularnewline
42 & 0.516414737519552 & 0.967170524960897 & 0.483585262480448 \tabularnewline
43 & 0.599224512204607 & 0.801550975590786 & 0.400775487795393 \tabularnewline
44 & 0.547025733637464 & 0.905948532725073 & 0.452974266362536 \tabularnewline
45 & 0.528539288956365 & 0.94292142208727 & 0.471460711043635 \tabularnewline
46 & 0.553676834584201 & 0.892646330831599 & 0.446323165415799 \tabularnewline
47 & 0.556523230850991 & 0.886953538298018 & 0.443476769149009 \tabularnewline
48 & 0.509199816928224 & 0.981600366143552 & 0.490800183071776 \tabularnewline
49 & 0.46241906914533 & 0.92483813829066 & 0.53758093085467 \tabularnewline
50 & 0.483581043279333 & 0.967162086558666 & 0.516418956720667 \tabularnewline
51 & 0.488915848723969 & 0.977831697447939 & 0.511084151276031 \tabularnewline
52 & 0.443727868644368 & 0.887455737288736 & 0.556272131355632 \tabularnewline
53 & 0.519434189426518 & 0.961131621146963 & 0.480565810573482 \tabularnewline
54 & 0.481059095811892 & 0.962118191623785 & 0.518940904188108 \tabularnewline
55 & 0.582756106525968 & 0.834487786948063 & 0.417243893474032 \tabularnewline
56 & 0.829169985808346 & 0.341660028383308 & 0.170830014191654 \tabularnewline
57 & 0.798064366762111 & 0.403871266475778 & 0.201935633237889 \tabularnewline
58 & 0.763957476050674 & 0.472085047898653 & 0.236042523949326 \tabularnewline
59 & 0.725008750277064 & 0.549982499445871 & 0.274991249722936 \tabularnewline
60 & 0.731647027377687 & 0.536705945244626 & 0.268352972622313 \tabularnewline
61 & 0.755507201717666 & 0.488985596564668 & 0.244492798282334 \tabularnewline
62 & 0.725818995348639 & 0.548362009302722 & 0.274181004651361 \tabularnewline
63 & 0.723673700877769 & 0.552652598244462 & 0.276326299122231 \tabularnewline
64 & 0.695944578362806 & 0.608110843274389 & 0.304055421637195 \tabularnewline
65 & 0.65651594879487 & 0.68696810241026 & 0.34348405120513 \tabularnewline
66 & 0.619897584275343 & 0.760204831449315 & 0.380102415724657 \tabularnewline
67 & 0.593930901375956 & 0.812138197248088 & 0.406069098624044 \tabularnewline
68 & 0.575509444981215 & 0.848981110037569 & 0.424490555018785 \tabularnewline
69 & 0.587001001397005 & 0.82599799720599 & 0.412998998602995 \tabularnewline
70 & 0.548631016943192 & 0.902737966113616 & 0.451368983056808 \tabularnewline
71 & 0.504479094554498 & 0.991041810891003 & 0.495520905445502 \tabularnewline
72 & 0.459526657535697 & 0.919053315071395 & 0.540473342464303 \tabularnewline
73 & 0.422022939555884 & 0.844045879111768 & 0.577977060444116 \tabularnewline
74 & 0.406158802273553 & 0.812317604547106 & 0.593841197726447 \tabularnewline
75 & 0.386529287401377 & 0.773058574802755 & 0.613470712598623 \tabularnewline
76 & 0.454912548969864 & 0.909825097939727 & 0.545087451030136 \tabularnewline
77 & 0.440719148048678 & 0.881438296097356 & 0.559280851951322 \tabularnewline
78 & 0.397225409683542 & 0.794450819367085 & 0.602774590316458 \tabularnewline
79 & 0.422163738374899 & 0.844327476749797 & 0.577836261625101 \tabularnewline
80 & 0.394015998928365 & 0.78803199785673 & 0.605984001071635 \tabularnewline
81 & 0.354492779956937 & 0.708985559913874 & 0.645507220043063 \tabularnewline
82 & 0.34151254518017 & 0.68302509036034 & 0.65848745481983 \tabularnewline
83 & 0.301453393858222 & 0.602906787716443 & 0.698546606141778 \tabularnewline
84 & 0.289821416699854 & 0.579642833399707 & 0.710178583300146 \tabularnewline
85 & 0.261841445796242 & 0.523682891592485 & 0.738158554203758 \tabularnewline
86 & 0.251559443189328 & 0.503118886378656 & 0.748440556810672 \tabularnewline
87 & 0.240657627658807 & 0.481315255317615 & 0.759342372341193 \tabularnewline
88 & 0.205553070306022 & 0.411106140612044 & 0.794446929693978 \tabularnewline
89 & 0.173974598672242 & 0.347949197344485 & 0.826025401327758 \tabularnewline
90 & 0.151512083169525 & 0.303024166339049 & 0.848487916830476 \tabularnewline
91 & 0.131918574227361 & 0.263837148454722 & 0.868081425772639 \tabularnewline
92 & 0.166700013005656 & 0.333400026011311 & 0.833299986994344 \tabularnewline
93 & 0.142049228248829 & 0.284098456497659 & 0.857950771751171 \tabularnewline
94 & 0.123355724231685 & 0.24671144846337 & 0.876644275768315 \tabularnewline
95 & 0.11493364443994 & 0.229867288879881 & 0.88506635556006 \tabularnewline
96 & 0.111806978302253 & 0.223613956604506 & 0.888193021697747 \tabularnewline
97 & 0.0976790379536129 & 0.195358075907226 & 0.902320962046387 \tabularnewline
98 & 0.131542152571276 & 0.263084305142552 & 0.868457847428724 \tabularnewline
99 & 0.112375893242026 & 0.224751786484052 & 0.887624106757974 \tabularnewline
100 & 0.0926415028438556 & 0.185283005687711 & 0.907358497156144 \tabularnewline
101 & 0.102818297060099 & 0.205636594120199 & 0.897181702939901 \tabularnewline
102 & 0.0837261585756211 & 0.167452317151242 & 0.916273841424379 \tabularnewline
103 & 0.0780924296344406 & 0.156184859268881 & 0.921907570365559 \tabularnewline
104 & 0.0650397500159244 & 0.130079500031849 & 0.934960249984076 \tabularnewline
105 & 0.0539230807177574 & 0.107846161435515 & 0.946076919282243 \tabularnewline
106 & 0.0561018246581059 & 0.112203649316212 & 0.943898175341894 \tabularnewline
107 & 0.0436848731664986 & 0.0873697463329971 & 0.956315126833501 \tabularnewline
108 & 0.0398876273136245 & 0.079775254627249 & 0.960112372686376 \tabularnewline
109 & 0.0312125909125259 & 0.0624251818250518 & 0.968787409087474 \tabularnewline
110 & 0.033088084837919 & 0.0661761696758379 & 0.966911915162081 \tabularnewline
111 & 0.0255769418296922 & 0.0511538836593843 & 0.974423058170308 \tabularnewline
112 & 0.0272156089002614 & 0.0544312178005227 & 0.972784391099739 \tabularnewline
113 & 0.025580336719148 & 0.051160673438296 & 0.974419663280852 \tabularnewline
114 & 0.0218873284898838 & 0.0437746569797676 & 0.978112671510116 \tabularnewline
115 & 0.0165424582209683 & 0.0330849164419365 & 0.983457541779032 \tabularnewline
116 & 0.014088639140856 & 0.028177278281712 & 0.985911360859144 \tabularnewline
117 & 0.0235614358648491 & 0.0471228717296981 & 0.976438564135151 \tabularnewline
118 & 0.0326037764993666 & 0.0652075529987332 & 0.967396223500633 \tabularnewline
119 & 0.0259631012420416 & 0.0519262024840831 & 0.974036898757958 \tabularnewline
120 & 0.0200418933996776 & 0.0400837867993552 & 0.979958106600322 \tabularnewline
121 & 0.0157674646764688 & 0.0315349293529377 & 0.984232535323531 \tabularnewline
122 & 0.0218689204220064 & 0.0437378408440129 & 0.978131079577994 \tabularnewline
123 & 0.030600336071543 & 0.0612006721430861 & 0.969399663928457 \tabularnewline
124 & 0.0417420837807596 & 0.0834841675615192 & 0.95825791621924 \tabularnewline
125 & 0.0403847551234586 & 0.0807695102469171 & 0.959615244876541 \tabularnewline
126 & 0.0800192629046623 & 0.160038525809325 & 0.919980737095338 \tabularnewline
127 & 0.0634279648515066 & 0.126855929703013 & 0.936572035148493 \tabularnewline
128 & 0.0491440035986219 & 0.0982880071972437 & 0.950855996401378 \tabularnewline
129 & 0.0416288346683986 & 0.0832576693367972 & 0.958371165331601 \tabularnewline
130 & 0.0376333323297216 & 0.0752666646594433 & 0.962366667670278 \tabularnewline
131 & 0.0360168940392385 & 0.072033788078477 & 0.963983105960762 \tabularnewline
132 & 0.0391372964354054 & 0.0782745928708108 & 0.960862703564595 \tabularnewline
133 & 0.0789049262014117 & 0.157809852402823 & 0.921095073798588 \tabularnewline
134 & 0.070108925158944 & 0.140217850317888 & 0.929891074841056 \tabularnewline
135 & 0.0570867658902056 & 0.114173531780411 & 0.942913234109794 \tabularnewline
136 & 0.0702163132793657 & 0.140432626558731 & 0.929783686720634 \tabularnewline
137 & 0.0543965302338911 & 0.108793060467782 & 0.945603469766109 \tabularnewline
138 & 0.040478816104562 & 0.0809576322091241 & 0.959521183895438 \tabularnewline
139 & 0.0868671810535868 & 0.173734362107174 & 0.913132818946413 \tabularnewline
140 & 0.0657629070784613 & 0.131525814156923 & 0.934237092921539 \tabularnewline
141 & 0.121160270357484 & 0.242320540714967 & 0.878839729642516 \tabularnewline
142 & 0.108741563934723 & 0.217483127869446 & 0.891258436065277 \tabularnewline
143 & 0.0996166270715837 & 0.199233254143167 & 0.900383372928416 \tabularnewline
144 & 0.401414317664915 & 0.802828635329829 & 0.598585682335085 \tabularnewline
145 & 0.47099720146416 & 0.94199440292832 & 0.52900279853584 \tabularnewline
146 & 0.553756282296325 & 0.89248743540735 & 0.446243717703675 \tabularnewline
147 & 0.602948950850782 & 0.794102098298437 & 0.397051049149218 \tabularnewline
148 & 0.776571919593269 & 0.446856160813463 & 0.223428080406731 \tabularnewline
149 & 0.692787422813352 & 0.614425154373295 & 0.307212577186648 \tabularnewline
150 & 0.636865067317166 & 0.726269865365668 & 0.363134932682834 \tabularnewline
151 & 0.546231436033086 & 0.907537127933828 & 0.453768563966914 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=197077&T=5

[TABLE]
[ROW][C]Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]p-values[/C][C]Alternative Hypothesis[/C][/ROW]
[ROW][C]breakpoint index[/C][C]greater[/C][C]2-sided[/C][C]less[/C][/ROW]
[ROW][C]11[/C][C]0.506076086372175[/C][C]0.987847827255649[/C][C]0.493923913627825[/C][/ROW]
[ROW][C]12[/C][C]0.33889711302637[/C][C]0.677794226052741[/C][C]0.66110288697363[/C][/ROW]
[ROW][C]13[/C][C]0.792428728829892[/C][C]0.415142542340215[/C][C]0.207571271170108[/C][/ROW]
[ROW][C]14[/C][C]0.738269215188387[/C][C]0.523461569623227[/C][C]0.261730784811613[/C][/ROW]
[ROW][C]15[/C][C]0.654318031803505[/C][C]0.691363936392989[/C][C]0.345681968196495[/C][/ROW]
[ROW][C]16[/C][C]0.751348040347762[/C][C]0.497303919304477[/C][C]0.248651959652238[/C][/ROW]
[ROW][C]17[/C][C]0.757206755785299[/C][C]0.485586488429401[/C][C]0.242793244214701[/C][/ROW]
[ROW][C]18[/C][C]0.814923468194339[/C][C]0.370153063611322[/C][C]0.185076531805661[/C][/ROW]
[ROW][C]19[/C][C]0.841878325498228[/C][C]0.316243349003543[/C][C]0.158121674501772[/C][/ROW]
[ROW][C]20[/C][C]0.864506416026924[/C][C]0.270987167946153[/C][C]0.135493583973076[/C][/ROW]
[ROW][C]21[/C][C]0.866820651807407[/C][C]0.266358696385186[/C][C]0.133179348192593[/C][/ROW]
[ROW][C]22[/C][C]0.82879286648992[/C][C]0.34241426702016[/C][C]0.17120713351008[/C][/ROW]
[ROW][C]23[/C][C]0.788993036665538[/C][C]0.422013926668924[/C][C]0.211006963334462[/C][/ROW]
[ROW][C]24[/C][C]0.778203472208508[/C][C]0.443593055582983[/C][C]0.221796527791491[/C][/ROW]
[ROW][C]25[/C][C]0.741566824750678[/C][C]0.516866350498643[/C][C]0.258433175249322[/C][/ROW]
[ROW][C]26[/C][C]0.708017335274527[/C][C]0.583965329450945[/C][C]0.291982664725473[/C][/ROW]
[ROW][C]27[/C][C]0.740891297310836[/C][C]0.518217405378327[/C][C]0.259108702689164[/C][/ROW]
[ROW][C]28[/C][C]0.764246568849893[/C][C]0.471506862300215[/C][C]0.235753431150108[/C][/ROW]
[ROW][C]29[/C][C]0.709896772767683[/C][C]0.580206454464634[/C][C]0.290103227232317[/C][/ROW]
[ROW][C]30[/C][C]0.657189284919331[/C][C]0.685621430161338[/C][C]0.342810715080669[/C][/ROW]
[ROW][C]31[/C][C]0.598406447688845[/C][C]0.80318710462231[/C][C]0.401593552311155[/C][/ROW]
[ROW][C]32[/C][C]0.564112874175068[/C][C]0.871774251649865[/C][C]0.435887125824932[/C][/ROW]
[ROW][C]33[/C][C]0.526796035758539[/C][C]0.946407928482922[/C][C]0.473203964241461[/C][/ROW]
[ROW][C]34[/C][C]0.464882976430841[/C][C]0.929765952861681[/C][C]0.535117023569159[/C][/ROW]
[ROW][C]35[/C][C]0.541302900731457[/C][C]0.917394198537086[/C][C]0.458697099268543[/C][/ROW]
[ROW][C]36[/C][C]0.481463986649218[/C][C]0.962927973298436[/C][C]0.518536013350782[/C][/ROW]
[ROW][C]37[/C][C]0.529402898929854[/C][C]0.941194202140291[/C][C]0.470597101070146[/C][/ROW]
[ROW][C]38[/C][C]0.615597994234909[/C][C]0.768804011530183[/C][C]0.384402005765091[/C][/ROW]
[ROW][C]39[/C][C]0.635049522284142[/C][C]0.729900955431715[/C][C]0.364950477715858[/C][/ROW]
[ROW][C]40[/C][C]0.596118679275759[/C][C]0.807762641448483[/C][C]0.403881320724241[/C][/ROW]
[ROW][C]41[/C][C]0.554183615522364[/C][C]0.891632768955271[/C][C]0.445816384477636[/C][/ROW]
[ROW][C]42[/C][C]0.516414737519552[/C][C]0.967170524960897[/C][C]0.483585262480448[/C][/ROW]
[ROW][C]43[/C][C]0.599224512204607[/C][C]0.801550975590786[/C][C]0.400775487795393[/C][/ROW]
[ROW][C]44[/C][C]0.547025733637464[/C][C]0.905948532725073[/C][C]0.452974266362536[/C][/ROW]
[ROW][C]45[/C][C]0.528539288956365[/C][C]0.94292142208727[/C][C]0.471460711043635[/C][/ROW]
[ROW][C]46[/C][C]0.553676834584201[/C][C]0.892646330831599[/C][C]0.446323165415799[/C][/ROW]
[ROW][C]47[/C][C]0.556523230850991[/C][C]0.886953538298018[/C][C]0.443476769149009[/C][/ROW]
[ROW][C]48[/C][C]0.509199816928224[/C][C]0.981600366143552[/C][C]0.490800183071776[/C][/ROW]
[ROW][C]49[/C][C]0.46241906914533[/C][C]0.92483813829066[/C][C]0.53758093085467[/C][/ROW]
[ROW][C]50[/C][C]0.483581043279333[/C][C]0.967162086558666[/C][C]0.516418956720667[/C][/ROW]
[ROW][C]51[/C][C]0.488915848723969[/C][C]0.977831697447939[/C][C]0.511084151276031[/C][/ROW]
[ROW][C]52[/C][C]0.443727868644368[/C][C]0.887455737288736[/C][C]0.556272131355632[/C][/ROW]
[ROW][C]53[/C][C]0.519434189426518[/C][C]0.961131621146963[/C][C]0.480565810573482[/C][/ROW]
[ROW][C]54[/C][C]0.481059095811892[/C][C]0.962118191623785[/C][C]0.518940904188108[/C][/ROW]
[ROW][C]55[/C][C]0.582756106525968[/C][C]0.834487786948063[/C][C]0.417243893474032[/C][/ROW]
[ROW][C]56[/C][C]0.829169985808346[/C][C]0.341660028383308[/C][C]0.170830014191654[/C][/ROW]
[ROW][C]57[/C][C]0.798064366762111[/C][C]0.403871266475778[/C][C]0.201935633237889[/C][/ROW]
[ROW][C]58[/C][C]0.763957476050674[/C][C]0.472085047898653[/C][C]0.236042523949326[/C][/ROW]
[ROW][C]59[/C][C]0.725008750277064[/C][C]0.549982499445871[/C][C]0.274991249722936[/C][/ROW]
[ROW][C]60[/C][C]0.731647027377687[/C][C]0.536705945244626[/C][C]0.268352972622313[/C][/ROW]
[ROW][C]61[/C][C]0.755507201717666[/C][C]0.488985596564668[/C][C]0.244492798282334[/C][/ROW]
[ROW][C]62[/C][C]0.725818995348639[/C][C]0.548362009302722[/C][C]0.274181004651361[/C][/ROW]
[ROW][C]63[/C][C]0.723673700877769[/C][C]0.552652598244462[/C][C]0.276326299122231[/C][/ROW]
[ROW][C]64[/C][C]0.695944578362806[/C][C]0.608110843274389[/C][C]0.304055421637195[/C][/ROW]
[ROW][C]65[/C][C]0.65651594879487[/C][C]0.68696810241026[/C][C]0.34348405120513[/C][/ROW]
[ROW][C]66[/C][C]0.619897584275343[/C][C]0.760204831449315[/C][C]0.380102415724657[/C][/ROW]
[ROW][C]67[/C][C]0.593930901375956[/C][C]0.812138197248088[/C][C]0.406069098624044[/C][/ROW]
[ROW][C]68[/C][C]0.575509444981215[/C][C]0.848981110037569[/C][C]0.424490555018785[/C][/ROW]
[ROW][C]69[/C][C]0.587001001397005[/C][C]0.82599799720599[/C][C]0.412998998602995[/C][/ROW]
[ROW][C]70[/C][C]0.548631016943192[/C][C]0.902737966113616[/C][C]0.451368983056808[/C][/ROW]
[ROW][C]71[/C][C]0.504479094554498[/C][C]0.991041810891003[/C][C]0.495520905445502[/C][/ROW]
[ROW][C]72[/C][C]0.459526657535697[/C][C]0.919053315071395[/C][C]0.540473342464303[/C][/ROW]
[ROW][C]73[/C][C]0.422022939555884[/C][C]0.844045879111768[/C][C]0.577977060444116[/C][/ROW]
[ROW][C]74[/C][C]0.406158802273553[/C][C]0.812317604547106[/C][C]0.593841197726447[/C][/ROW]
[ROW][C]75[/C][C]0.386529287401377[/C][C]0.773058574802755[/C][C]0.613470712598623[/C][/ROW]
[ROW][C]76[/C][C]0.454912548969864[/C][C]0.909825097939727[/C][C]0.545087451030136[/C][/ROW]
[ROW][C]77[/C][C]0.440719148048678[/C][C]0.881438296097356[/C][C]0.559280851951322[/C][/ROW]
[ROW][C]78[/C][C]0.397225409683542[/C][C]0.794450819367085[/C][C]0.602774590316458[/C][/ROW]
[ROW][C]79[/C][C]0.422163738374899[/C][C]0.844327476749797[/C][C]0.577836261625101[/C][/ROW]
[ROW][C]80[/C][C]0.394015998928365[/C][C]0.78803199785673[/C][C]0.605984001071635[/C][/ROW]
[ROW][C]81[/C][C]0.354492779956937[/C][C]0.708985559913874[/C][C]0.645507220043063[/C][/ROW]
[ROW][C]82[/C][C]0.34151254518017[/C][C]0.68302509036034[/C][C]0.65848745481983[/C][/ROW]
[ROW][C]83[/C][C]0.301453393858222[/C][C]0.602906787716443[/C][C]0.698546606141778[/C][/ROW]
[ROW][C]84[/C][C]0.289821416699854[/C][C]0.579642833399707[/C][C]0.710178583300146[/C][/ROW]
[ROW][C]85[/C][C]0.261841445796242[/C][C]0.523682891592485[/C][C]0.738158554203758[/C][/ROW]
[ROW][C]86[/C][C]0.251559443189328[/C][C]0.503118886378656[/C][C]0.748440556810672[/C][/ROW]
[ROW][C]87[/C][C]0.240657627658807[/C][C]0.481315255317615[/C][C]0.759342372341193[/C][/ROW]
[ROW][C]88[/C][C]0.205553070306022[/C][C]0.411106140612044[/C][C]0.794446929693978[/C][/ROW]
[ROW][C]89[/C][C]0.173974598672242[/C][C]0.347949197344485[/C][C]0.826025401327758[/C][/ROW]
[ROW][C]90[/C][C]0.151512083169525[/C][C]0.303024166339049[/C][C]0.848487916830476[/C][/ROW]
[ROW][C]91[/C][C]0.131918574227361[/C][C]0.263837148454722[/C][C]0.868081425772639[/C][/ROW]
[ROW][C]92[/C][C]0.166700013005656[/C][C]0.333400026011311[/C][C]0.833299986994344[/C][/ROW]
[ROW][C]93[/C][C]0.142049228248829[/C][C]0.284098456497659[/C][C]0.857950771751171[/C][/ROW]
[ROW][C]94[/C][C]0.123355724231685[/C][C]0.24671144846337[/C][C]0.876644275768315[/C][/ROW]
[ROW][C]95[/C][C]0.11493364443994[/C][C]0.229867288879881[/C][C]0.88506635556006[/C][/ROW]
[ROW][C]96[/C][C]0.111806978302253[/C][C]0.223613956604506[/C][C]0.888193021697747[/C][/ROW]
[ROW][C]97[/C][C]0.0976790379536129[/C][C]0.195358075907226[/C][C]0.902320962046387[/C][/ROW]
[ROW][C]98[/C][C]0.131542152571276[/C][C]0.263084305142552[/C][C]0.868457847428724[/C][/ROW]
[ROW][C]99[/C][C]0.112375893242026[/C][C]0.224751786484052[/C][C]0.887624106757974[/C][/ROW]
[ROW][C]100[/C][C]0.0926415028438556[/C][C]0.185283005687711[/C][C]0.907358497156144[/C][/ROW]
[ROW][C]101[/C][C]0.102818297060099[/C][C]0.205636594120199[/C][C]0.897181702939901[/C][/ROW]
[ROW][C]102[/C][C]0.0837261585756211[/C][C]0.167452317151242[/C][C]0.916273841424379[/C][/ROW]
[ROW][C]103[/C][C]0.0780924296344406[/C][C]0.156184859268881[/C][C]0.921907570365559[/C][/ROW]
[ROW][C]104[/C][C]0.0650397500159244[/C][C]0.130079500031849[/C][C]0.934960249984076[/C][/ROW]
[ROW][C]105[/C][C]0.0539230807177574[/C][C]0.107846161435515[/C][C]0.946076919282243[/C][/ROW]
[ROW][C]106[/C][C]0.0561018246581059[/C][C]0.112203649316212[/C][C]0.943898175341894[/C][/ROW]
[ROW][C]107[/C][C]0.0436848731664986[/C][C]0.0873697463329971[/C][C]0.956315126833501[/C][/ROW]
[ROW][C]108[/C][C]0.0398876273136245[/C][C]0.079775254627249[/C][C]0.960112372686376[/C][/ROW]
[ROW][C]109[/C][C]0.0312125909125259[/C][C]0.0624251818250518[/C][C]0.968787409087474[/C][/ROW]
[ROW][C]110[/C][C]0.033088084837919[/C][C]0.0661761696758379[/C][C]0.966911915162081[/C][/ROW]
[ROW][C]111[/C][C]0.0255769418296922[/C][C]0.0511538836593843[/C][C]0.974423058170308[/C][/ROW]
[ROW][C]112[/C][C]0.0272156089002614[/C][C]0.0544312178005227[/C][C]0.972784391099739[/C][/ROW]
[ROW][C]113[/C][C]0.025580336719148[/C][C]0.051160673438296[/C][C]0.974419663280852[/C][/ROW]
[ROW][C]114[/C][C]0.0218873284898838[/C][C]0.0437746569797676[/C][C]0.978112671510116[/C][/ROW]
[ROW][C]115[/C][C]0.0165424582209683[/C][C]0.0330849164419365[/C][C]0.983457541779032[/C][/ROW]
[ROW][C]116[/C][C]0.014088639140856[/C][C]0.028177278281712[/C][C]0.985911360859144[/C][/ROW]
[ROW][C]117[/C][C]0.0235614358648491[/C][C]0.0471228717296981[/C][C]0.976438564135151[/C][/ROW]
[ROW][C]118[/C][C]0.0326037764993666[/C][C]0.0652075529987332[/C][C]0.967396223500633[/C][/ROW]
[ROW][C]119[/C][C]0.0259631012420416[/C][C]0.0519262024840831[/C][C]0.974036898757958[/C][/ROW]
[ROW][C]120[/C][C]0.0200418933996776[/C][C]0.0400837867993552[/C][C]0.979958106600322[/C][/ROW]
[ROW][C]121[/C][C]0.0157674646764688[/C][C]0.0315349293529377[/C][C]0.984232535323531[/C][/ROW]
[ROW][C]122[/C][C]0.0218689204220064[/C][C]0.0437378408440129[/C][C]0.978131079577994[/C][/ROW]
[ROW][C]123[/C][C]0.030600336071543[/C][C]0.0612006721430861[/C][C]0.969399663928457[/C][/ROW]
[ROW][C]124[/C][C]0.0417420837807596[/C][C]0.0834841675615192[/C][C]0.95825791621924[/C][/ROW]
[ROW][C]125[/C][C]0.0403847551234586[/C][C]0.0807695102469171[/C][C]0.959615244876541[/C][/ROW]
[ROW][C]126[/C][C]0.0800192629046623[/C][C]0.160038525809325[/C][C]0.919980737095338[/C][/ROW]
[ROW][C]127[/C][C]0.0634279648515066[/C][C]0.126855929703013[/C][C]0.936572035148493[/C][/ROW]
[ROW][C]128[/C][C]0.0491440035986219[/C][C]0.0982880071972437[/C][C]0.950855996401378[/C][/ROW]
[ROW][C]129[/C][C]0.0416288346683986[/C][C]0.0832576693367972[/C][C]0.958371165331601[/C][/ROW]
[ROW][C]130[/C][C]0.0376333323297216[/C][C]0.0752666646594433[/C][C]0.962366667670278[/C][/ROW]
[ROW][C]131[/C][C]0.0360168940392385[/C][C]0.072033788078477[/C][C]0.963983105960762[/C][/ROW]
[ROW][C]132[/C][C]0.0391372964354054[/C][C]0.0782745928708108[/C][C]0.960862703564595[/C][/ROW]
[ROW][C]133[/C][C]0.0789049262014117[/C][C]0.157809852402823[/C][C]0.921095073798588[/C][/ROW]
[ROW][C]134[/C][C]0.070108925158944[/C][C]0.140217850317888[/C][C]0.929891074841056[/C][/ROW]
[ROW][C]135[/C][C]0.0570867658902056[/C][C]0.114173531780411[/C][C]0.942913234109794[/C][/ROW]
[ROW][C]136[/C][C]0.0702163132793657[/C][C]0.140432626558731[/C][C]0.929783686720634[/C][/ROW]
[ROW][C]137[/C][C]0.0543965302338911[/C][C]0.108793060467782[/C][C]0.945603469766109[/C][/ROW]
[ROW][C]138[/C][C]0.040478816104562[/C][C]0.0809576322091241[/C][C]0.959521183895438[/C][/ROW]
[ROW][C]139[/C][C]0.0868671810535868[/C][C]0.173734362107174[/C][C]0.913132818946413[/C][/ROW]
[ROW][C]140[/C][C]0.0657629070784613[/C][C]0.131525814156923[/C][C]0.934237092921539[/C][/ROW]
[ROW][C]141[/C][C]0.121160270357484[/C][C]0.242320540714967[/C][C]0.878839729642516[/C][/ROW]
[ROW][C]142[/C][C]0.108741563934723[/C][C]0.217483127869446[/C][C]0.891258436065277[/C][/ROW]
[ROW][C]143[/C][C]0.0996166270715837[/C][C]0.199233254143167[/C][C]0.900383372928416[/C][/ROW]
[ROW][C]144[/C][C]0.401414317664915[/C][C]0.802828635329829[/C][C]0.598585682335085[/C][/ROW]
[ROW][C]145[/C][C]0.47099720146416[/C][C]0.94199440292832[/C][C]0.52900279853584[/C][/ROW]
[ROW][C]146[/C][C]0.553756282296325[/C][C]0.89248743540735[/C][C]0.446243717703675[/C][/ROW]
[ROW][C]147[/C][C]0.602948950850782[/C][C]0.794102098298437[/C][C]0.397051049149218[/C][/ROW]
[ROW][C]148[/C][C]0.776571919593269[/C][C]0.446856160813463[/C][C]0.223428080406731[/C][/ROW]
[ROW][C]149[/C][C]0.692787422813352[/C][C]0.614425154373295[/C][C]0.307212577186648[/C][/ROW]
[ROW][C]150[/C][C]0.636865067317166[/C][C]0.726269865365668[/C][C]0.363134932682834[/C][/ROW]
[ROW][C]151[/C][C]0.546231436033086[/C][C]0.907537127933828[/C][C]0.453768563966914[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=197077&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=197077&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
110.5060760863721750.9878478272556490.493923913627825
120.338897113026370.6777942260527410.66110288697363
130.7924287288298920.4151425423402150.207571271170108
140.7382692151883870.5234615696232270.261730784811613
150.6543180318035050.6913639363929890.345681968196495
160.7513480403477620.4973039193044770.248651959652238
170.7572067557852990.4855864884294010.242793244214701
180.8149234681943390.3701530636113220.185076531805661
190.8418783254982280.3162433490035430.158121674501772
200.8645064160269240.2709871679461530.135493583973076
210.8668206518074070.2663586963851860.133179348192593
220.828792866489920.342414267020160.17120713351008
230.7889930366655380.4220139266689240.211006963334462
240.7782034722085080.4435930555829830.221796527791491
250.7415668247506780.5168663504986430.258433175249322
260.7080173352745270.5839653294509450.291982664725473
270.7408912973108360.5182174053783270.259108702689164
280.7642465688498930.4715068623002150.235753431150108
290.7098967727676830.5802064544646340.290103227232317
300.6571892849193310.6856214301613380.342810715080669
310.5984064476888450.803187104622310.401593552311155
320.5641128741750680.8717742516498650.435887125824932
330.5267960357585390.9464079284829220.473203964241461
340.4648829764308410.9297659528616810.535117023569159
350.5413029007314570.9173941985370860.458697099268543
360.4814639866492180.9629279732984360.518536013350782
370.5294028989298540.9411942021402910.470597101070146
380.6155979942349090.7688040115301830.384402005765091
390.6350495222841420.7299009554317150.364950477715858
400.5961186792757590.8077626414484830.403881320724241
410.5541836155223640.8916327689552710.445816384477636
420.5164147375195520.9671705249608970.483585262480448
430.5992245122046070.8015509755907860.400775487795393
440.5470257336374640.9059485327250730.452974266362536
450.5285392889563650.942921422087270.471460711043635
460.5536768345842010.8926463308315990.446323165415799
470.5565232308509910.8869535382980180.443476769149009
480.5091998169282240.9816003661435520.490800183071776
490.462419069145330.924838138290660.53758093085467
500.4835810432793330.9671620865586660.516418956720667
510.4889158487239690.9778316974479390.511084151276031
520.4437278686443680.8874557372887360.556272131355632
530.5194341894265180.9611316211469630.480565810573482
540.4810590958118920.9621181916237850.518940904188108
550.5827561065259680.8344877869480630.417243893474032
560.8291699858083460.3416600283833080.170830014191654
570.7980643667621110.4038712664757780.201935633237889
580.7639574760506740.4720850478986530.236042523949326
590.7250087502770640.5499824994458710.274991249722936
600.7316470273776870.5367059452446260.268352972622313
610.7555072017176660.4889855965646680.244492798282334
620.7258189953486390.5483620093027220.274181004651361
630.7236737008777690.5526525982444620.276326299122231
640.6959445783628060.6081108432743890.304055421637195
650.656515948794870.686968102410260.34348405120513
660.6198975842753430.7602048314493150.380102415724657
670.5939309013759560.8121381972480880.406069098624044
680.5755094449812150.8489811100375690.424490555018785
690.5870010013970050.825997997205990.412998998602995
700.5486310169431920.9027379661136160.451368983056808
710.5044790945544980.9910418108910030.495520905445502
720.4595266575356970.9190533150713950.540473342464303
730.4220229395558840.8440458791117680.577977060444116
740.4061588022735530.8123176045471060.593841197726447
750.3865292874013770.7730585748027550.613470712598623
760.4549125489698640.9098250979397270.545087451030136
770.4407191480486780.8814382960973560.559280851951322
780.3972254096835420.7944508193670850.602774590316458
790.4221637383748990.8443274767497970.577836261625101
800.3940159989283650.788031997856730.605984001071635
810.3544927799569370.7089855599138740.645507220043063
820.341512545180170.683025090360340.65848745481983
830.3014533938582220.6029067877164430.698546606141778
840.2898214166998540.5796428333997070.710178583300146
850.2618414457962420.5236828915924850.738158554203758
860.2515594431893280.5031188863786560.748440556810672
870.2406576276588070.4813152553176150.759342372341193
880.2055530703060220.4111061406120440.794446929693978
890.1739745986722420.3479491973444850.826025401327758
900.1515120831695250.3030241663390490.848487916830476
910.1319185742273610.2638371484547220.868081425772639
920.1667000130056560.3334000260113110.833299986994344
930.1420492282488290.2840984564976590.857950771751171
940.1233557242316850.246711448463370.876644275768315
950.114933644439940.2298672888798810.88506635556006
960.1118069783022530.2236139566045060.888193021697747
970.09767903795361290.1953580759072260.902320962046387
980.1315421525712760.2630843051425520.868457847428724
990.1123758932420260.2247517864840520.887624106757974
1000.09264150284385560.1852830056877110.907358497156144
1010.1028182970600990.2056365941201990.897181702939901
1020.08372615857562110.1674523171512420.916273841424379
1030.07809242963444060.1561848592688810.921907570365559
1040.06503975001592440.1300795000318490.934960249984076
1050.05392308071775740.1078461614355150.946076919282243
1060.05610182465810590.1122036493162120.943898175341894
1070.04368487316649860.08736974633299710.956315126833501
1080.03988762731362450.0797752546272490.960112372686376
1090.03121259091252590.06242518182505180.968787409087474
1100.0330880848379190.06617616967583790.966911915162081
1110.02557694182969220.05115388365938430.974423058170308
1120.02721560890026140.05443121780052270.972784391099739
1130.0255803367191480.0511606734382960.974419663280852
1140.02188732848988380.04377465697976760.978112671510116
1150.01654245822096830.03308491644193650.983457541779032
1160.0140886391408560.0281772782817120.985911360859144
1170.02356143586484910.04712287172969810.976438564135151
1180.03260377649936660.06520755299873320.967396223500633
1190.02596310124204160.05192620248408310.974036898757958
1200.02004189339967760.04008378679935520.979958106600322
1210.01576746467646880.03153492935293770.984232535323531
1220.02186892042200640.04373784084401290.978131079577994
1230.0306003360715430.06120067214308610.969399663928457
1240.04174208378075960.08348416756151920.95825791621924
1250.04038475512345860.08076951024691710.959615244876541
1260.08001926290466230.1600385258093250.919980737095338
1270.06342796485150660.1268559297030130.936572035148493
1280.04914400359862190.09828800719724370.950855996401378
1290.04162883466839860.08325766933679720.958371165331601
1300.03763333232972160.07526666465944330.962366667670278
1310.03601689403923850.0720337880784770.963983105960762
1320.03913729643540540.07827459287081080.960862703564595
1330.07890492620141170.1578098524028230.921095073798588
1340.0701089251589440.1402178503178880.929891074841056
1350.05708676589020560.1141735317804110.942913234109794
1360.07021631327936570.1404326265587310.929783686720634
1370.05439653023389110.1087930604677820.945603469766109
1380.0404788161045620.08095763220912410.959521183895438
1390.08686718105358680.1737343621071740.913132818946413
1400.06576290707846130.1315258141569230.934237092921539
1410.1211602703574840.2423205407149670.878839729642516
1420.1087415639347230.2174831278694460.891258436065277
1430.09961662707158370.1992332541431670.900383372928416
1440.4014143176649150.8028286353298290.598585682335085
1450.470997201464160.941994402928320.52900279853584
1460.5537562822963250.892487435407350.446243717703675
1470.6029489508507820.7941020982984370.397051049149218
1480.7765719195932690.4468561608134630.223428080406731
1490.6927874228133520.6144251543732950.307212577186648
1500.6368650673171660.7262698653656680.363134932682834
1510.5462314360330860.9075371279338280.453768563966914






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level70.049645390070922OK
10% type I error level250.177304964539007NOK

\begin{tabular}{lllllllll}
\hline
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
Description & # significant tests & % significant tests & OK/NOK \tabularnewline
1% type I error level & 0 & 0 & OK \tabularnewline
5% type I error level & 7 & 0.049645390070922 & OK \tabularnewline
10% type I error level & 25 & 0.177304964539007 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=197077&T=6

[TABLE]
[ROW][C]Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]Description[/C][C]# significant tests[/C][C]% significant tests[/C][C]OK/NOK[/C][/ROW]
[ROW][C]1% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]7[/C][C]0.049645390070922[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]25[/C][C]0.177304964539007[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=197077&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=197077&T=6

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


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

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level70.049645390070922OK
10% type I error level250.177304964539007NOK


Figures (Output of Computation)

Figure 1
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Figure 2
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Figure 4
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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 = 6 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
Parameters (R input):
par1 = 6 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
R code (references can be found in the software module):
library(lattice)
library(lmtest)
n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
par1 <- as.numeric(par1)
x <- t(y)
k <- length(x[1,])
n <- length(x[,1])
x1 <- cbind(x[,par1], x[,1:k!=par1])
mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
colnames(x1) <- mycolnames #colnames(x)[par1]
x <- x1
if (par3 == 'First Differences'){
x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
for (i in 1:n-1) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
}
if (par2 == 'Include Monthly Dummies'){
x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
for (i in 1:11){
x2[seq(i,n,12),i] <- 1
}
x <- cbind(x, x2)
}
if (par2 == 'Include Quarterly Dummies'){
x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
for (i in 1:3){
x2[seq(i,n,4),i] <- 1
}
x <- cbind(x, x2)
}
k <- length(x[1,])
if (par3 == 'Linear Trend'){
x <- cbind(x, c(1:n))
colnames(x)[k+1] <- 't'
}
x
k <- length(x[1,])
df <- as.data.frame(x)
(mylm <- lm(df))
(mysum <- summary(mylm))
if (n > n25) {
kp3 <- k + 3
nmkm3 <- n - k - 3
gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
numgqtests <- 0
numsignificant1 <- 0
numsignificant5 <- 0
numsignificant10 <- 0
for (mypoint in kp3:nmkm3) {
j <- 0
numgqtests <- numgqtests + 1
for (myalt in c('greater', 'two.sided', 'less')) {
j <- j + 1
gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
}
if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
}
gqarr
}
bitmap(file='test0.png')
plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
points(x[,1]-mysum$resid)
grid()
dev.off()
bitmap(file='test1.png')
plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
grid()
dev.off()
bitmap(file='test2.png')
hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
grid()
dev.off()
bitmap(file='test3.png')
densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
dev.off()
bitmap(file='test4.png')
qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
qqline(mysum$resid)
grid()
dev.off()
(myerror <- as.ts(mysum$resid))
bitmap(file='test5.png')
dum <- cbind(lag(myerror,k=1),myerror)
dum
dum1 <- dum[2:length(myerror),]
dum1
z <- as.data.frame(dum1)
z
plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
lines(lowess(z))
abline(lm(z))
grid()
dev.off()
bitmap(file='test6.png')
acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
grid()
dev.off()
bitmap(file='test7.png')
pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
grid()
dev.off()
bitmap(file='test8.png')
opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
plot(mylm, las = 1, sub='Residual Diagnostics')
par(opar)
dev.off()
if (n > n25) {
bitmap(file='test9.png')
plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
grid()
dev.off()
}
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
a<-table.row.end(a)
myeq <- colnames(x)[1]
myeq <- paste(myeq, '[t] = ', sep='')
for (i in 1:k){
if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ')
if (rownames(mysum$coefficients)[i] != '(Intercept)') {
myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
}
}
myeq <- paste(myeq, ' + e[t]')
a<-table.row.start(a)
a<-table.element(a, myeq)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable1.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,hyperlink('ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Variable',header=TRUE)
a<-table.element(a,'Parameter',header=TRUE)
a<-table.element(a,'S.D.',header=TRUE)
a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
a<-table.element(a,'2-tail p-value',header=TRUE)
a<-table.element(a,'1-tail p-value',header=TRUE)
a<-table.row.end(a)
for (i in 1:k){
a<-table.row.start(a)
a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
a<-table.element(a,mysum$coefficients[i,1])
a<-table.element(a, round(mysum$coefficients[i,2],6))
a<-table.element(a, round(mysum$coefficients[i,3],4))
a<-table.element(a, round(mysum$coefficients[i,4],6))
a<-table.element(a, round(mysum$coefficients[i,4]/2,6))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable2.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple R',1,TRUE)
a<-table.element(a, sqrt(mysum$r.squared))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'R-squared',1,TRUE)
a<-table.element(a, mysum$r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Adjusted R-squared',1,TRUE)
a<-table.element(a, mysum$adj.r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (value)',1,TRUE)
a<-table.element(a, mysum$fstatistic[1])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[2])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[3])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'p-value',1,TRUE)
a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
a<-table.element(a, mysum$sigma)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
a<-table.element(a, sum(myerror*myerror))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,x[i])
a<-table.element(a,x[i]-mysum$resid[i])
a<-table.element(a,mysum$resid[i])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable4.tab')
if (n > n25) {
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'p-values',header=TRUE)
a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'breakpoint index',header=TRUE)
a<-table.element(a,'greater',header=TRUE)
a<-table.element(a,'2-sided',header=TRUE)
a<-table.element(a,'less',header=TRUE)
a<-table.row.end(a)
for (mypoint in kp3:nmkm3) {
a<-table.row.start(a)
a<-table.element(a,mypoint,header=TRUE)
a<-table.element(a,gqarr[mypoint-kp3+1,1])
a<-table.element(a,gqarr[mypoint-kp3+1,2])
a<-table.element(a,gqarr[mypoint-kp3+1,3])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable5.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Description',header=TRUE)
a<-table.element(a,'# significant tests',header=TRUE)
a<-table.element(a,'% significant tests',header=TRUE)
a<-table.element(a,'OK/NOK',header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'1% type I error level',header=TRUE)
a<-table.element(a,numsignificant1)
a<-table.element(a,numsignificant1/numgqtests)
if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'5% type I error level',header=TRUE)
a<-table.element(a,numsignificant5)
a<-table.element(a,numsignificant5/numgqtests)
if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'10% type I error level',header=TRUE)
a<-table.element(a,numsignificant10)
a<-table.element(a,numsignificant10/numgqtests)
if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable6.tab')
}