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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 computationTue, 13 Dec 2011 12:07:06 -0500
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2011/Dec/13/t1323796137kss0m2byd0rlzz0.htm/, Retrieved Thu, 02 May 2024 20:00:45 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=154551, Retrieved Thu, 02 May 2024 20:00:45 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact77

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]
-   PD    [Multiple Regression] [WS10 - MR] [2011-12-13 17:07:06] [240aada53705cc48eae3e230739818e0] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
2	13	12	30	33	13	16
1	8	8	32	35	11	15
2	14	12	30	35	12	13
2	14	11	33	25	13	14
1	13	11	36	39	12	17
1	16	13	37	37	12	13
1	14	11	31	31	13	12
1	13	10	36	28	12	9
2	15	7	40	38	15	25
1	13	10	31	32	11	13
2	16	12	24	32	13	10
1	20	15	46	46	12	13
1	17	12	40	40	12	9
1	15	15	27	33	12	14
2	16	12	32	25	15	26
1	16	10	41	37	13	12
1	12	10	28	33	13	11
2	9	8	34	33	12	19
2	15	11	31	35	11	12
2	17	14	38	39	11	9
1	12	12	37	36	13	15
1	10	11	34	37	10	15
2	11	6	33	43	12	23
2	16	12	38	27	13	20
1	16	14	27	31	10	0
2	15	11	36	33	12	15
1	13	8	37	35	13	8
2	14	12	35	36	12	12
1	19	15	44	39	11	11
1	16	13	41	31	11	18
2	17	14	29	34	14	19
1	10	12	31	29	12	13
1	15	7	32	37	14	22
1	14	11	35	30	12	12
1	14	7	36	32	12	15
2	16	12	28	31	13	16
1	17	12	34	34	15	16
1	15	12	36	30	12	13
2	17	13	33	33	16	11
2	14	15	35	37	10	16
1	10	9	34	33	13	14
2	14	9	38	28	12	11
2	16	11	35	32	12	20
2	18	14	40	40	16	16
1	15	12	35	39	12	12
1	16	15	32	28	16	17
1	16	12	33	33	13	11
1	10	6	31	36	10	12
2	8	5	32	35	14	14
1	17	13	35	34	13	13
1	14	11	32	35	12	14
1	12	11	26	30	13	19
2	10	6	38	35	16	17
1	14	12	45	37	12	11
1	12	10	36	40	12	12
1	16	6	37	34	13	12
1	16	12	33	37	13	14
1	15	14	35	38	11	15
2	11	6	32	27	14	18
1	16	11	32	27	16	16
2	8	6	32	27	16	16
1	17	14	33	39	14	19
1	16	12	37	37	14	17
1	15	12	40	32	14	15
2	8	8	35	27	14	13
1	13	10	30	35	10	16
1	14	11	36	40	13	17
1	13	7	34	32	14	16
1	16	12	34	36	17	13
2	12	9	37	35	12	15
1	19	13	34	31	12	16
1	19	14	37	34	12	10
1	12	6	43	36	15	19
1	14	12	39	40	10	11
2	15	6	29	33	13	17
1	13	14	41	38	12	19
2	16	12	32	33	13	15
2	10	10	34	35	14	15
1	15	10	34	30	12	17
1	16	12	35	31	13	13
1	15	11	41	42	14	17
2	11	10	32	33	10	12
2	9	7	39	35	12	27
1	16	12	33	33	13	12
1	12	12	30	31	10	15
2	14	12	32	36	13	18
1	14	10	41	32	13	19
1	13	10	24	43	12	21
2	15	12	35	33	12	13
2	17	12	39	34	15	16
2	14	12	32	36	12	13
2	9	9	28	33	16	20
2	11	11	31	32	15	17
1	9	10	36	36	10	10
2	7	5	39	39	13	18
1	13	10	33	30	0	11
2	15	10	36	34	10	18
1	12	12	31	34	12	14
2	15	11	33	36	14	11
2	14	9	33	31	12	14
1	15	15	33	27	13	12
2	9	9	39	28	14	22
1	16	12	35	37	11	12
1	16	16	37	36	11	12
1	14	10	29	31	12	15
2	14	14	34	31	9	13
2	13	10	35	31	13	13
1	14	11	36	34	13	16
2	16	12	29	36	12	12
1	16	14	35	30	14	16
1	13	10	35	37	12	15
2	12	9	36	29	10	19
2	16	12	38	37	11	15
1	16	11	36	38	14	13
1	16	12	37	38	12	9
2	10	7	32	33	13	14
2	14	16	34	34	13	14
2	12	11	29	32	9	12
2	12	12	38	36	13	17
1	12	9	34	30	11	11
1	12	9	33	34	12	17
1	19	15	42	42	13	15
2	14	10	32	24	12	15
1	13	11	31	29	12	11
1	17	14	34	32	11	14
2	16	12	39	31	12	14
1	15	12	38	37	12	14
1	12	12	36	34	13	14
1	8	11	32	35	14	13
1	10	9	37	34	13	14
1	16	11	36	33	12	10
2	10	6	34	31	15	17
2	16	12	34	32	13	11
1	10	12	34	37	14	13
1	18	14	38	39	12	14
1	12	8	33	31	11	14
2	16	15	5	0	12	18
2	10	9	28	30	11	18
2	15	9	33	30	14	18
1	17	11	41	43	13	14
2	16	12	30	31	12	12
2	14	10	31	33	14	16
2	12	11	34	31	13	17
2	11	10	33	38	11	13
2	15	12	37	32	16	16
1	7	11	34	38	13	15

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time6 seconds
R Server'Gertrude Mary Cox' @ cox.wessa.net
R Framework error message
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.

\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 & 6 seconds \tabularnewline
R Server & 'Gertrude Mary Cox' @ cox.wessa.net \tabularnewline
R Framework error message & 
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=154551&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]6 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gertrude Mary Cox' @ cox.wessa.net[/C][/ROW]
[ROW][C]R Framework error message[/C][C]
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=154551&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=154551&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 time6 seconds
R Server'Gertrude Mary Cox' @ cox.wessa.net
R Framework error message
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.






Multiple Linear Regression - Estimated Regression Equation
Learning[t] = + 2.14238735580687 -0.104670400258762Gender[t] + 0.710052774938892Software[t] + 0.098032890551024Connected[t] -0.0181934919894081Separate[t] + 0.145343801095409Happiness[t] -0.0346970786645186`Depression `[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Learning[t] =  +  2.14238735580687 -0.104670400258762Gender[t] +  0.710052774938892Software[t] +  0.098032890551024Connected[t] -0.0181934919894081Separate[t] +  0.145343801095409Happiness[t] -0.0346970786645186`Depression
`[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=154551&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Learning[t] =  +  2.14238735580687 -0.104670400258762Gender[t] +  0.710052774938892Software[t] +  0.098032890551024Connected[t] -0.0181934919894081Separate[t] +  0.145343801095409Happiness[t] -0.0346970786645186`Depression
`[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=154551&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=154551&T=1

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


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

Multiple Linear Regression - Estimated Regression Equation
Learning[t] = + 2.14238735580687 -0.104670400258762Gender[t] + 0.710052774938892Software[t] + 0.098032890551024Connected[t] -0.0181934919894081Separate[t] + 0.145343801095409Happiness[t] -0.0346970786645186`Depression `[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)2.142387355806872.2867250.93690.3504450.175223
Gender-0.1046704002587620.38325-0.27310.7851720.392586
Software0.7100527749388920.0758459.361900
Connected0.0980328905510240.0462352.12030.0357550.017877
Separate-0.01819349198940810.044268-0.4110.6817160.340858
Happiness0.1453438010954090.099051.46740.1445340.072267
`Depression `-0.03469707866451860.054692-0.63440.526860.26343

\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) & 2.14238735580687 & 2.286725 & 0.9369 & 0.350445 & 0.175223 \tabularnewline
Gender & -0.104670400258762 & 0.38325 & -0.2731 & 0.785172 & 0.392586 \tabularnewline
Software & 0.710052774938892 & 0.075845 & 9.3619 & 0 & 0 \tabularnewline
Connected & 0.098032890551024 & 0.046235 & 2.1203 & 0.035755 & 0.017877 \tabularnewline
Separate & -0.0181934919894081 & 0.044268 & -0.411 & 0.681716 & 0.340858 \tabularnewline
Happiness & 0.145343801095409 & 0.09905 & 1.4674 & 0.144534 & 0.072267 \tabularnewline
`Depression
` & -0.0346970786645186 & 0.054692 & -0.6344 & 0.52686 & 0.26343 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=154551&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]2.14238735580687[/C][C]2.286725[/C][C]0.9369[/C][C]0.350445[/C][C]0.175223[/C][/ROW]
[ROW][C]Gender[/C][C]-0.104670400258762[/C][C]0.38325[/C][C]-0.2731[/C][C]0.785172[/C][C]0.392586[/C][/ROW]
[ROW][C]Software[/C][C]0.710052774938892[/C][C]0.075845[/C][C]9.3619[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Connected[/C][C]0.098032890551024[/C][C]0.046235[/C][C]2.1203[/C][C]0.035755[/C][C]0.017877[/C][/ROW]
[ROW][C]Separate[/C][C]-0.0181934919894081[/C][C]0.044268[/C][C]-0.411[/C][C]0.681716[/C][C]0.340858[/C][/ROW]
[ROW][C]Happiness[/C][C]0.145343801095409[/C][C]0.09905[/C][C]1.4674[/C][C]0.144534[/C][C]0.072267[/C][/ROW]
[ROW][C]`Depression
`[/C][C]-0.0346970786645186[/C][C]0.054692[/C][C]-0.6344[/C][C]0.52686[/C][C]0.26343[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=154551&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=154551&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)2.142387355806872.2867250.93690.3504450.175223
Gender-0.1046704002587620.38325-0.27310.7851720.392586
Software0.7100527749388920.0758459.361900
Connected0.0980328905510240.0462352.12030.0357550.017877
Separate-0.01819349198940810.044268-0.4110.6817160.340858
Happiness0.1453438010954090.099051.46740.1445340.072267
`Depression `-0.03469707866451860.054692-0.63440.526860.26343






Multiple Linear Regression - Regression Statistics
Multiple R0.657688431893314
R-squared0.432554073446287
Adjusted R-squared0.408060004674184
F-TEST (value)17.659543519325
F-TEST (DF numerator)6
F-TEST (DF denominator)139
p-value3.88578058618805e-15
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.11585026222332
Sum Squared Residuals622.278304168917

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.657688431893314 \tabularnewline
R-squared & 0.432554073446287 \tabularnewline
Adjusted R-squared & 0.408060004674184 \tabularnewline
F-TEST (value) & 17.659543519325 \tabularnewline
F-TEST (DF numerator) & 6 \tabularnewline
F-TEST (DF denominator) & 139 \tabularnewline
p-value & 3.88578058618805e-15 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 2.11585026222332 \tabularnewline
Sum Squared Residuals & 622.278304168917 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=154551&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.657688431893314[/C][/ROW]
[ROW][C]R-squared[/C][C]0.432554073446287[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.408060004674184[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]17.659543519325[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]6[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]139[/C][/ROW]
[ROW][C]p-value[/C][C]3.88578058618805e-15[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]2.11585026222332[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]622.278304168917[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=154551&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=154551&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 R0.657688431893314
R-squared0.432554073446287
Adjusted R-squared0.408060004674184
F-TEST (value)17.659543519325
F-TEST (DF numerator)6
F-TEST (DF denominator)139
p-value3.88578058618805e-15
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.11585026222332
Sum Squared Residuals622.278304168917






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11314.1285974910443-1.12859749104432
2811.2967450651444-3.29674506514445
31414.0509579419637-0.0509579419636536
41413.92758548100280.072414518997194
51313.822210627974-0.822210627973962
61615.51552436703970.484475632960338
71413.79642330555210.203576694447891
81313.5898628942347-0.589862894234708
91511.44610895612323.55389104387679
101312.74279235776850.257207642231528
111613.76677611171472.2332238882853
122017.6541845039722.34581549602801
131715.18377810244371.81622189755631
141515.9933779007003-0.99337790070032
151614.41392802360731.58607197639273
161613.9575384841872.04246151581299
171212.7905819536458-0.790581953645846
18911.4310829164039-2.43108291640389
191513.32829133514491.6717086648551
201716.17599716185470.824002838145335
211214.8996147278565-2.89961472785655
221013.4412383859889-3.44123838598895
23119.592221241422921.40777875857708
241614.88323325273091.11676674726911
251615.51478360885270.485216391147313
261513.89609533698071.10390466301931
271312.3204766707420.679523329257982
281414.5576259813939-0.557625981393886
291917.50952352302941.49047647697057
301615.69798768676220.302012313237795
311715.47372922348351.52627077651647
321014.3628221847099-4.36282218470989
331510.74345715886134.25654284113866
341414.0614045586502-0.0614045586502041
351411.17874812947332.82125187052671
361613.96891869392112.03108130607891
371714.89789356370862.10210643629141
381514.83479314547560.165206854524398
391715.74226573424511.25773426575489
401416.2401148973723-2.24011489737226
411012.5646352860195-2.56463528601954
421412.90181134281011.09818865718994
431613.64277054509652.35722945490352
441816.83770890579271.16229109420728
451514.60771590568440.392284094315577
461617.0517937817906-1.05179378179056
471614.70085195627881.29914804372125
48109.719160567624380.28083943237562
4989.53264482201975-1.53264482201975
501715.51938286300121.48061713699875
511413.60694426972110.393055730278946
521213.0815727941348-1.08157279413477
531011.0174913064621-1.01749130646205
541415.659128873838-1.659128873838
551213.2674497543683-1.26744975436825
561610.77977629819565.22022370180443
571614.52398675232761.47601324767244
581515.7965799104627-0.796579910462652
591110.24945721821580.750542781784161
601614.26447325268891.73552674731108
61810.6095389777357-2.60953897773569
621715.87956372599931.12043627400065
631614.95737087963351.04262912036649
641515.4118311685627-0.411831168562662
65812.1371468330693-4.13714683306929
661312.34074395416030.659256045839741
671413.949360937080.0506390629200376
681311.23867287189751.76132712810246
691615.25628541791410.74371458208585
701212.5376356936751-0.537635693675109
711915.22649541132953.77350458867052
721916.38424885394032.61575114605967
731211.37939470906210.620605290937919
741414.7256634523728-0.725663452372812
75159.735550872195435.26444912780457
761316.3913327402061-3.39133274020613
771614.35936035081091.64063964918911
781013.2442773991517-3.24427739915175
791513.07983349983771.92016650016231
801614.86391056403061.13608943596942
811514.54848220695170.451517793048325
821112.6073146336404-1.60731463364044
83910.8972309809251-1.89723098092515
841614.66615487761421.33384512238577
851213.8683205506602-1.86832055066019
861414.2006886388491-0.20068863884911
871413.80562639348240.194373606517582
881311.72420088380711.27579911619293
891514.57750937869760.422490621302409
901715.28338761620491.71661238379505
911414.2288302310763-0.228830231076295
92912.0996164737538-3.09961647375375
931113.7907616221722-2.79076162217216
94913.1189302774641-4.11893027746411
9579.86196897216581-2.86196897216581
961311.44585746812891.55414253187112
971512.7730702318682.22692976813199
981214.2371576460983-2.23715764609833
991513.97689210620831.02310789379172
1001412.25297517809321.74702482190684
1011516.9054741543674-1.90547415436736
102912.9088639702422-3.9088639702422
1031614.49875908856781.50124091143217
1041617.5532294614149-1.55322946141486
1051412.64086971242221.3591302875778
1061415.4999376187169-1.49993761871694
1071313.339134613894-0.339134613894031
1081414.0932189676809-0.0932189676809299
1091613.96942863808772.03057136191226
1101616.3434621709996-0.343462170999625
1111313.1199061037919-0.119906103791899
1121212.1192878382116-0.119287838211641
1131614.58409612396861.41590387603141
1141614.26988003681231.73011996318774
1151614.92606641476941.07393358523056
1161010.8437935547809-0.843793554780948
1171417.4121408183436-3.41214081834362
1181212.8961184278203-0.896118427820255
1191214.8235830608198-2.82358306081977
1201212.4326193957905-0.432619395790505
1211212.1989738663901-0.198973866390145
1221917.41077655349191.5892234465081
1231412.95765242774241.04234757225763
1241313.7221635671-0.722163567100035
1251715.84240505051261.1575949494874
1261614.9713308462161.02866915378401
1271514.86880740398730.131192596012726
1281214.8726658999489-2.87266589994886
129813.9323289505764-5.93232895057639
1301012.8405404656832-2.84054046568321
1311614.1742511305621.82574886943796
1321010.5527899111202-0.552789911120181
1331614.71240793856041.28759206143958
1341014.8020605226385-4.80206052263851
1351816.25252596988621.74747403011376
1361211.50224900231760.497750997682375
1371614.19358082931141.80641917068858
1381011.496872101574-1.49687210157397
1391512.42306795761532.57693204238468
1401714.48903614986042.51096385013959
1411614.15842898858581.84157101141419
1421412.9518686328131.04813136718703
1431213.8123661836238-1.81236618362383
1441112.7250267866753-1.72502678667531
1451515.2690526201771-0.269052620177127
146713.8590762972858-6.85907629728577

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 13 & 14.1285974910443 & -1.12859749104432 \tabularnewline
2 & 8 & 11.2967450651444 & -3.29674506514445 \tabularnewline
3 & 14 & 14.0509579419637 & -0.0509579419636536 \tabularnewline
4 & 14 & 13.9275854810028 & 0.072414518997194 \tabularnewline
5 & 13 & 13.822210627974 & -0.822210627973962 \tabularnewline
6 & 16 & 15.5155243670397 & 0.484475632960338 \tabularnewline
7 & 14 & 13.7964233055521 & 0.203576694447891 \tabularnewline
8 & 13 & 13.5898628942347 & -0.589862894234708 \tabularnewline
9 & 15 & 11.4461089561232 & 3.55389104387679 \tabularnewline
10 & 13 & 12.7427923577685 & 0.257207642231528 \tabularnewline
11 & 16 & 13.7667761117147 & 2.2332238882853 \tabularnewline
12 & 20 & 17.654184503972 & 2.34581549602801 \tabularnewline
13 & 17 & 15.1837781024437 & 1.81622189755631 \tabularnewline
14 & 15 & 15.9933779007003 & -0.99337790070032 \tabularnewline
15 & 16 & 14.4139280236073 & 1.58607197639273 \tabularnewline
16 & 16 & 13.957538484187 & 2.04246151581299 \tabularnewline
17 & 12 & 12.7905819536458 & -0.790581953645846 \tabularnewline
18 & 9 & 11.4310829164039 & -2.43108291640389 \tabularnewline
19 & 15 & 13.3282913351449 & 1.6717086648551 \tabularnewline
20 & 17 & 16.1759971618547 & 0.824002838145335 \tabularnewline
21 & 12 & 14.8996147278565 & -2.89961472785655 \tabularnewline
22 & 10 & 13.4412383859889 & -3.44123838598895 \tabularnewline
23 & 11 & 9.59222124142292 & 1.40777875857708 \tabularnewline
24 & 16 & 14.8832332527309 & 1.11676674726911 \tabularnewline
25 & 16 & 15.5147836088527 & 0.485216391147313 \tabularnewline
26 & 15 & 13.8960953369807 & 1.10390466301931 \tabularnewline
27 & 13 & 12.320476670742 & 0.679523329257982 \tabularnewline
28 & 14 & 14.5576259813939 & -0.557625981393886 \tabularnewline
29 & 19 & 17.5095235230294 & 1.49047647697057 \tabularnewline
30 & 16 & 15.6979876867622 & 0.302012313237795 \tabularnewline
31 & 17 & 15.4737292234835 & 1.52627077651647 \tabularnewline
32 & 10 & 14.3628221847099 & -4.36282218470989 \tabularnewline
33 & 15 & 10.7434571588613 & 4.25654284113866 \tabularnewline
34 & 14 & 14.0614045586502 & -0.0614045586502041 \tabularnewline
35 & 14 & 11.1787481294733 & 2.82125187052671 \tabularnewline
36 & 16 & 13.9689186939211 & 2.03108130607891 \tabularnewline
37 & 17 & 14.8978935637086 & 2.10210643629141 \tabularnewline
38 & 15 & 14.8347931454756 & 0.165206854524398 \tabularnewline
39 & 17 & 15.7422657342451 & 1.25773426575489 \tabularnewline
40 & 14 & 16.2401148973723 & -2.24011489737226 \tabularnewline
41 & 10 & 12.5646352860195 & -2.56463528601954 \tabularnewline
42 & 14 & 12.9018113428101 & 1.09818865718994 \tabularnewline
43 & 16 & 13.6427705450965 & 2.35722945490352 \tabularnewline
44 & 18 & 16.8377089057927 & 1.16229109420728 \tabularnewline
45 & 15 & 14.6077159056844 & 0.392284094315577 \tabularnewline
46 & 16 & 17.0517937817906 & -1.05179378179056 \tabularnewline
47 & 16 & 14.7008519562788 & 1.29914804372125 \tabularnewline
48 & 10 & 9.71916056762438 & 0.28083943237562 \tabularnewline
49 & 8 & 9.53264482201975 & -1.53264482201975 \tabularnewline
50 & 17 & 15.5193828630012 & 1.48061713699875 \tabularnewline
51 & 14 & 13.6069442697211 & 0.393055730278946 \tabularnewline
52 & 12 & 13.0815727941348 & -1.08157279413477 \tabularnewline
53 & 10 & 11.0174913064621 & -1.01749130646205 \tabularnewline
54 & 14 & 15.659128873838 & -1.659128873838 \tabularnewline
55 & 12 & 13.2674497543683 & -1.26744975436825 \tabularnewline
56 & 16 & 10.7797762981956 & 5.22022370180443 \tabularnewline
57 & 16 & 14.5239867523276 & 1.47601324767244 \tabularnewline
58 & 15 & 15.7965799104627 & -0.796579910462652 \tabularnewline
59 & 11 & 10.2494572182158 & 0.750542781784161 \tabularnewline
60 & 16 & 14.2644732526889 & 1.73552674731108 \tabularnewline
61 & 8 & 10.6095389777357 & -2.60953897773569 \tabularnewline
62 & 17 & 15.8795637259993 & 1.12043627400065 \tabularnewline
63 & 16 & 14.9573708796335 & 1.04262912036649 \tabularnewline
64 & 15 & 15.4118311685627 & -0.411831168562662 \tabularnewline
65 & 8 & 12.1371468330693 & -4.13714683306929 \tabularnewline
66 & 13 & 12.3407439541603 & 0.659256045839741 \tabularnewline
67 & 14 & 13.94936093708 & 0.0506390629200376 \tabularnewline
68 & 13 & 11.2386728718975 & 1.76132712810246 \tabularnewline
69 & 16 & 15.2562854179141 & 0.74371458208585 \tabularnewline
70 & 12 & 12.5376356936751 & -0.537635693675109 \tabularnewline
71 & 19 & 15.2264954113295 & 3.77350458867052 \tabularnewline
72 & 19 & 16.3842488539403 & 2.61575114605967 \tabularnewline
73 & 12 & 11.3793947090621 & 0.620605290937919 \tabularnewline
74 & 14 & 14.7256634523728 & -0.725663452372812 \tabularnewline
75 & 15 & 9.73555087219543 & 5.26444912780457 \tabularnewline
76 & 13 & 16.3913327402061 & -3.39133274020613 \tabularnewline
77 & 16 & 14.3593603508109 & 1.64063964918911 \tabularnewline
78 & 10 & 13.2442773991517 & -3.24427739915175 \tabularnewline
79 & 15 & 13.0798334998377 & 1.92016650016231 \tabularnewline
80 & 16 & 14.8639105640306 & 1.13608943596942 \tabularnewline
81 & 15 & 14.5484822069517 & 0.451517793048325 \tabularnewline
82 & 11 & 12.6073146336404 & -1.60731463364044 \tabularnewline
83 & 9 & 10.8972309809251 & -1.89723098092515 \tabularnewline
84 & 16 & 14.6661548776142 & 1.33384512238577 \tabularnewline
85 & 12 & 13.8683205506602 & -1.86832055066019 \tabularnewline
86 & 14 & 14.2006886388491 & -0.20068863884911 \tabularnewline
87 & 14 & 13.8056263934824 & 0.194373606517582 \tabularnewline
88 & 13 & 11.7242008838071 & 1.27579911619293 \tabularnewline
89 & 15 & 14.5775093786976 & 0.422490621302409 \tabularnewline
90 & 17 & 15.2833876162049 & 1.71661238379505 \tabularnewline
91 & 14 & 14.2288302310763 & -0.228830231076295 \tabularnewline
92 & 9 & 12.0996164737538 & -3.09961647375375 \tabularnewline
93 & 11 & 13.7907616221722 & -2.79076162217216 \tabularnewline
94 & 9 & 13.1189302774641 & -4.11893027746411 \tabularnewline
95 & 7 & 9.86196897216581 & -2.86196897216581 \tabularnewline
96 & 13 & 11.4458574681289 & 1.55414253187112 \tabularnewline
97 & 15 & 12.773070231868 & 2.22692976813199 \tabularnewline
98 & 12 & 14.2371576460983 & -2.23715764609833 \tabularnewline
99 & 15 & 13.9768921062083 & 1.02310789379172 \tabularnewline
100 & 14 & 12.2529751780932 & 1.74702482190684 \tabularnewline
101 & 15 & 16.9054741543674 & -1.90547415436736 \tabularnewline
102 & 9 & 12.9088639702422 & -3.9088639702422 \tabularnewline
103 & 16 & 14.4987590885678 & 1.50124091143217 \tabularnewline
104 & 16 & 17.5532294614149 & -1.55322946141486 \tabularnewline
105 & 14 & 12.6408697124222 & 1.3591302875778 \tabularnewline
106 & 14 & 15.4999376187169 & -1.49993761871694 \tabularnewline
107 & 13 & 13.339134613894 & -0.339134613894031 \tabularnewline
108 & 14 & 14.0932189676809 & -0.0932189676809299 \tabularnewline
109 & 16 & 13.9694286380877 & 2.03057136191226 \tabularnewline
110 & 16 & 16.3434621709996 & -0.343462170999625 \tabularnewline
111 & 13 & 13.1199061037919 & -0.119906103791899 \tabularnewline
112 & 12 & 12.1192878382116 & -0.119287838211641 \tabularnewline
113 & 16 & 14.5840961239686 & 1.41590387603141 \tabularnewline
114 & 16 & 14.2698800368123 & 1.73011996318774 \tabularnewline
115 & 16 & 14.9260664147694 & 1.07393358523056 \tabularnewline
116 & 10 & 10.8437935547809 & -0.843793554780948 \tabularnewline
117 & 14 & 17.4121408183436 & -3.41214081834362 \tabularnewline
118 & 12 & 12.8961184278203 & -0.896118427820255 \tabularnewline
119 & 12 & 14.8235830608198 & -2.82358306081977 \tabularnewline
120 & 12 & 12.4326193957905 & -0.432619395790505 \tabularnewline
121 & 12 & 12.1989738663901 & -0.198973866390145 \tabularnewline
122 & 19 & 17.4107765534919 & 1.5892234465081 \tabularnewline
123 & 14 & 12.9576524277424 & 1.04234757225763 \tabularnewline
124 & 13 & 13.7221635671 & -0.722163567100035 \tabularnewline
125 & 17 & 15.8424050505126 & 1.1575949494874 \tabularnewline
126 & 16 & 14.971330846216 & 1.02866915378401 \tabularnewline
127 & 15 & 14.8688074039873 & 0.131192596012726 \tabularnewline
128 & 12 & 14.8726658999489 & -2.87266589994886 \tabularnewline
129 & 8 & 13.9323289505764 & -5.93232895057639 \tabularnewline
130 & 10 & 12.8405404656832 & -2.84054046568321 \tabularnewline
131 & 16 & 14.174251130562 & 1.82574886943796 \tabularnewline
132 & 10 & 10.5527899111202 & -0.552789911120181 \tabularnewline
133 & 16 & 14.7124079385604 & 1.28759206143958 \tabularnewline
134 & 10 & 14.8020605226385 & -4.80206052263851 \tabularnewline
135 & 18 & 16.2525259698862 & 1.74747403011376 \tabularnewline
136 & 12 & 11.5022490023176 & 0.497750997682375 \tabularnewline
137 & 16 & 14.1935808293114 & 1.80641917068858 \tabularnewline
138 & 10 & 11.496872101574 & -1.49687210157397 \tabularnewline
139 & 15 & 12.4230679576153 & 2.57693204238468 \tabularnewline
140 & 17 & 14.4890361498604 & 2.51096385013959 \tabularnewline
141 & 16 & 14.1584289885858 & 1.84157101141419 \tabularnewline
142 & 14 & 12.951868632813 & 1.04813136718703 \tabularnewline
143 & 12 & 13.8123661836238 & -1.81236618362383 \tabularnewline
144 & 11 & 12.7250267866753 & -1.72502678667531 \tabularnewline
145 & 15 & 15.2690526201771 & -0.269052620177127 \tabularnewline
146 & 7 & 13.8590762972858 & -6.85907629728577 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=154551&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]13[/C][C]14.1285974910443[/C][C]-1.12859749104432[/C][/ROW]
[ROW][C]2[/C][C]8[/C][C]11.2967450651444[/C][C]-3.29674506514445[/C][/ROW]
[ROW][C]3[/C][C]14[/C][C]14.0509579419637[/C][C]-0.0509579419636536[/C][/ROW]
[ROW][C]4[/C][C]14[/C][C]13.9275854810028[/C][C]0.072414518997194[/C][/ROW]
[ROW][C]5[/C][C]13[/C][C]13.822210627974[/C][C]-0.822210627973962[/C][/ROW]
[ROW][C]6[/C][C]16[/C][C]15.5155243670397[/C][C]0.484475632960338[/C][/ROW]
[ROW][C]7[/C][C]14[/C][C]13.7964233055521[/C][C]0.203576694447891[/C][/ROW]
[ROW][C]8[/C][C]13[/C][C]13.5898628942347[/C][C]-0.589862894234708[/C][/ROW]
[ROW][C]9[/C][C]15[/C][C]11.4461089561232[/C][C]3.55389104387679[/C][/ROW]
[ROW][C]10[/C][C]13[/C][C]12.7427923577685[/C][C]0.257207642231528[/C][/ROW]
[ROW][C]11[/C][C]16[/C][C]13.7667761117147[/C][C]2.2332238882853[/C][/ROW]
[ROW][C]12[/C][C]20[/C][C]17.654184503972[/C][C]2.34581549602801[/C][/ROW]
[ROW][C]13[/C][C]17[/C][C]15.1837781024437[/C][C]1.81622189755631[/C][/ROW]
[ROW][C]14[/C][C]15[/C][C]15.9933779007003[/C][C]-0.99337790070032[/C][/ROW]
[ROW][C]15[/C][C]16[/C][C]14.4139280236073[/C][C]1.58607197639273[/C][/ROW]
[ROW][C]16[/C][C]16[/C][C]13.957538484187[/C][C]2.04246151581299[/C][/ROW]
[ROW][C]17[/C][C]12[/C][C]12.7905819536458[/C][C]-0.790581953645846[/C][/ROW]
[ROW][C]18[/C][C]9[/C][C]11.4310829164039[/C][C]-2.43108291640389[/C][/ROW]
[ROW][C]19[/C][C]15[/C][C]13.3282913351449[/C][C]1.6717086648551[/C][/ROW]
[ROW][C]20[/C][C]17[/C][C]16.1759971618547[/C][C]0.824002838145335[/C][/ROW]
[ROW][C]21[/C][C]12[/C][C]14.8996147278565[/C][C]-2.89961472785655[/C][/ROW]
[ROW][C]22[/C][C]10[/C][C]13.4412383859889[/C][C]-3.44123838598895[/C][/ROW]
[ROW][C]23[/C][C]11[/C][C]9.59222124142292[/C][C]1.40777875857708[/C][/ROW]
[ROW][C]24[/C][C]16[/C][C]14.8832332527309[/C][C]1.11676674726911[/C][/ROW]
[ROW][C]25[/C][C]16[/C][C]15.5147836088527[/C][C]0.485216391147313[/C][/ROW]
[ROW][C]26[/C][C]15[/C][C]13.8960953369807[/C][C]1.10390466301931[/C][/ROW]
[ROW][C]27[/C][C]13[/C][C]12.320476670742[/C][C]0.679523329257982[/C][/ROW]
[ROW][C]28[/C][C]14[/C][C]14.5576259813939[/C][C]-0.557625981393886[/C][/ROW]
[ROW][C]29[/C][C]19[/C][C]17.5095235230294[/C][C]1.49047647697057[/C][/ROW]
[ROW][C]30[/C][C]16[/C][C]15.6979876867622[/C][C]0.302012313237795[/C][/ROW]
[ROW][C]31[/C][C]17[/C][C]15.4737292234835[/C][C]1.52627077651647[/C][/ROW]
[ROW][C]32[/C][C]10[/C][C]14.3628221847099[/C][C]-4.36282218470989[/C][/ROW]
[ROW][C]33[/C][C]15[/C][C]10.7434571588613[/C][C]4.25654284113866[/C][/ROW]
[ROW][C]34[/C][C]14[/C][C]14.0614045586502[/C][C]-0.0614045586502041[/C][/ROW]
[ROW][C]35[/C][C]14[/C][C]11.1787481294733[/C][C]2.82125187052671[/C][/ROW]
[ROW][C]36[/C][C]16[/C][C]13.9689186939211[/C][C]2.03108130607891[/C][/ROW]
[ROW][C]37[/C][C]17[/C][C]14.8978935637086[/C][C]2.10210643629141[/C][/ROW]
[ROW][C]38[/C][C]15[/C][C]14.8347931454756[/C][C]0.165206854524398[/C][/ROW]
[ROW][C]39[/C][C]17[/C][C]15.7422657342451[/C][C]1.25773426575489[/C][/ROW]
[ROW][C]40[/C][C]14[/C][C]16.2401148973723[/C][C]-2.24011489737226[/C][/ROW]
[ROW][C]41[/C][C]10[/C][C]12.5646352860195[/C][C]-2.56463528601954[/C][/ROW]
[ROW][C]42[/C][C]14[/C][C]12.9018113428101[/C][C]1.09818865718994[/C][/ROW]
[ROW][C]43[/C][C]16[/C][C]13.6427705450965[/C][C]2.35722945490352[/C][/ROW]
[ROW][C]44[/C][C]18[/C][C]16.8377089057927[/C][C]1.16229109420728[/C][/ROW]
[ROW][C]45[/C][C]15[/C][C]14.6077159056844[/C][C]0.392284094315577[/C][/ROW]
[ROW][C]46[/C][C]16[/C][C]17.0517937817906[/C][C]-1.05179378179056[/C][/ROW]
[ROW][C]47[/C][C]16[/C][C]14.7008519562788[/C][C]1.29914804372125[/C][/ROW]
[ROW][C]48[/C][C]10[/C][C]9.71916056762438[/C][C]0.28083943237562[/C][/ROW]
[ROW][C]49[/C][C]8[/C][C]9.53264482201975[/C][C]-1.53264482201975[/C][/ROW]
[ROW][C]50[/C][C]17[/C][C]15.5193828630012[/C][C]1.48061713699875[/C][/ROW]
[ROW][C]51[/C][C]14[/C][C]13.6069442697211[/C][C]0.393055730278946[/C][/ROW]
[ROW][C]52[/C][C]12[/C][C]13.0815727941348[/C][C]-1.08157279413477[/C][/ROW]
[ROW][C]53[/C][C]10[/C][C]11.0174913064621[/C][C]-1.01749130646205[/C][/ROW]
[ROW][C]54[/C][C]14[/C][C]15.659128873838[/C][C]-1.659128873838[/C][/ROW]
[ROW][C]55[/C][C]12[/C][C]13.2674497543683[/C][C]-1.26744975436825[/C][/ROW]
[ROW][C]56[/C][C]16[/C][C]10.7797762981956[/C][C]5.22022370180443[/C][/ROW]
[ROW][C]57[/C][C]16[/C][C]14.5239867523276[/C][C]1.47601324767244[/C][/ROW]
[ROW][C]58[/C][C]15[/C][C]15.7965799104627[/C][C]-0.796579910462652[/C][/ROW]
[ROW][C]59[/C][C]11[/C][C]10.2494572182158[/C][C]0.750542781784161[/C][/ROW]
[ROW][C]60[/C][C]16[/C][C]14.2644732526889[/C][C]1.73552674731108[/C][/ROW]
[ROW][C]61[/C][C]8[/C][C]10.6095389777357[/C][C]-2.60953897773569[/C][/ROW]
[ROW][C]62[/C][C]17[/C][C]15.8795637259993[/C][C]1.12043627400065[/C][/ROW]
[ROW][C]63[/C][C]16[/C][C]14.9573708796335[/C][C]1.04262912036649[/C][/ROW]
[ROW][C]64[/C][C]15[/C][C]15.4118311685627[/C][C]-0.411831168562662[/C][/ROW]
[ROW][C]65[/C][C]8[/C][C]12.1371468330693[/C][C]-4.13714683306929[/C][/ROW]
[ROW][C]66[/C][C]13[/C][C]12.3407439541603[/C][C]0.659256045839741[/C][/ROW]
[ROW][C]67[/C][C]14[/C][C]13.94936093708[/C][C]0.0506390629200376[/C][/ROW]
[ROW][C]68[/C][C]13[/C][C]11.2386728718975[/C][C]1.76132712810246[/C][/ROW]
[ROW][C]69[/C][C]16[/C][C]15.2562854179141[/C][C]0.74371458208585[/C][/ROW]
[ROW][C]70[/C][C]12[/C][C]12.5376356936751[/C][C]-0.537635693675109[/C][/ROW]
[ROW][C]71[/C][C]19[/C][C]15.2264954113295[/C][C]3.77350458867052[/C][/ROW]
[ROW][C]72[/C][C]19[/C][C]16.3842488539403[/C][C]2.61575114605967[/C][/ROW]
[ROW][C]73[/C][C]12[/C][C]11.3793947090621[/C][C]0.620605290937919[/C][/ROW]
[ROW][C]74[/C][C]14[/C][C]14.7256634523728[/C][C]-0.725663452372812[/C][/ROW]
[ROW][C]75[/C][C]15[/C][C]9.73555087219543[/C][C]5.26444912780457[/C][/ROW]
[ROW][C]76[/C][C]13[/C][C]16.3913327402061[/C][C]-3.39133274020613[/C][/ROW]
[ROW][C]77[/C][C]16[/C][C]14.3593603508109[/C][C]1.64063964918911[/C][/ROW]
[ROW][C]78[/C][C]10[/C][C]13.2442773991517[/C][C]-3.24427739915175[/C][/ROW]
[ROW][C]79[/C][C]15[/C][C]13.0798334998377[/C][C]1.92016650016231[/C][/ROW]
[ROW][C]80[/C][C]16[/C][C]14.8639105640306[/C][C]1.13608943596942[/C][/ROW]
[ROW][C]81[/C][C]15[/C][C]14.5484822069517[/C][C]0.451517793048325[/C][/ROW]
[ROW][C]82[/C][C]11[/C][C]12.6073146336404[/C][C]-1.60731463364044[/C][/ROW]
[ROW][C]83[/C][C]9[/C][C]10.8972309809251[/C][C]-1.89723098092515[/C][/ROW]
[ROW][C]84[/C][C]16[/C][C]14.6661548776142[/C][C]1.33384512238577[/C][/ROW]
[ROW][C]85[/C][C]12[/C][C]13.8683205506602[/C][C]-1.86832055066019[/C][/ROW]
[ROW][C]86[/C][C]14[/C][C]14.2006886388491[/C][C]-0.20068863884911[/C][/ROW]
[ROW][C]87[/C][C]14[/C][C]13.8056263934824[/C][C]0.194373606517582[/C][/ROW]
[ROW][C]88[/C][C]13[/C][C]11.7242008838071[/C][C]1.27579911619293[/C][/ROW]
[ROW][C]89[/C][C]15[/C][C]14.5775093786976[/C][C]0.422490621302409[/C][/ROW]
[ROW][C]90[/C][C]17[/C][C]15.2833876162049[/C][C]1.71661238379505[/C][/ROW]
[ROW][C]91[/C][C]14[/C][C]14.2288302310763[/C][C]-0.228830231076295[/C][/ROW]
[ROW][C]92[/C][C]9[/C][C]12.0996164737538[/C][C]-3.09961647375375[/C][/ROW]
[ROW][C]93[/C][C]11[/C][C]13.7907616221722[/C][C]-2.79076162217216[/C][/ROW]
[ROW][C]94[/C][C]9[/C][C]13.1189302774641[/C][C]-4.11893027746411[/C][/ROW]
[ROW][C]95[/C][C]7[/C][C]9.86196897216581[/C][C]-2.86196897216581[/C][/ROW]
[ROW][C]96[/C][C]13[/C][C]11.4458574681289[/C][C]1.55414253187112[/C][/ROW]
[ROW][C]97[/C][C]15[/C][C]12.773070231868[/C][C]2.22692976813199[/C][/ROW]
[ROW][C]98[/C][C]12[/C][C]14.2371576460983[/C][C]-2.23715764609833[/C][/ROW]
[ROW][C]99[/C][C]15[/C][C]13.9768921062083[/C][C]1.02310789379172[/C][/ROW]
[ROW][C]100[/C][C]14[/C][C]12.2529751780932[/C][C]1.74702482190684[/C][/ROW]
[ROW][C]101[/C][C]15[/C][C]16.9054741543674[/C][C]-1.90547415436736[/C][/ROW]
[ROW][C]102[/C][C]9[/C][C]12.9088639702422[/C][C]-3.9088639702422[/C][/ROW]
[ROW][C]103[/C][C]16[/C][C]14.4987590885678[/C][C]1.50124091143217[/C][/ROW]
[ROW][C]104[/C][C]16[/C][C]17.5532294614149[/C][C]-1.55322946141486[/C][/ROW]
[ROW][C]105[/C][C]14[/C][C]12.6408697124222[/C][C]1.3591302875778[/C][/ROW]
[ROW][C]106[/C][C]14[/C][C]15.4999376187169[/C][C]-1.49993761871694[/C][/ROW]
[ROW][C]107[/C][C]13[/C][C]13.339134613894[/C][C]-0.339134613894031[/C][/ROW]
[ROW][C]108[/C][C]14[/C][C]14.0932189676809[/C][C]-0.0932189676809299[/C][/ROW]
[ROW][C]109[/C][C]16[/C][C]13.9694286380877[/C][C]2.03057136191226[/C][/ROW]
[ROW][C]110[/C][C]16[/C][C]16.3434621709996[/C][C]-0.343462170999625[/C][/ROW]
[ROW][C]111[/C][C]13[/C][C]13.1199061037919[/C][C]-0.119906103791899[/C][/ROW]
[ROW][C]112[/C][C]12[/C][C]12.1192878382116[/C][C]-0.119287838211641[/C][/ROW]
[ROW][C]113[/C][C]16[/C][C]14.5840961239686[/C][C]1.41590387603141[/C][/ROW]
[ROW][C]114[/C][C]16[/C][C]14.2698800368123[/C][C]1.73011996318774[/C][/ROW]
[ROW][C]115[/C][C]16[/C][C]14.9260664147694[/C][C]1.07393358523056[/C][/ROW]
[ROW][C]116[/C][C]10[/C][C]10.8437935547809[/C][C]-0.843793554780948[/C][/ROW]
[ROW][C]117[/C][C]14[/C][C]17.4121408183436[/C][C]-3.41214081834362[/C][/ROW]
[ROW][C]118[/C][C]12[/C][C]12.8961184278203[/C][C]-0.896118427820255[/C][/ROW]
[ROW][C]119[/C][C]12[/C][C]14.8235830608198[/C][C]-2.82358306081977[/C][/ROW]
[ROW][C]120[/C][C]12[/C][C]12.4326193957905[/C][C]-0.432619395790505[/C][/ROW]
[ROW][C]121[/C][C]12[/C][C]12.1989738663901[/C][C]-0.198973866390145[/C][/ROW]
[ROW][C]122[/C][C]19[/C][C]17.4107765534919[/C][C]1.5892234465081[/C][/ROW]
[ROW][C]123[/C][C]14[/C][C]12.9576524277424[/C][C]1.04234757225763[/C][/ROW]
[ROW][C]124[/C][C]13[/C][C]13.7221635671[/C][C]-0.722163567100035[/C][/ROW]
[ROW][C]125[/C][C]17[/C][C]15.8424050505126[/C][C]1.1575949494874[/C][/ROW]
[ROW][C]126[/C][C]16[/C][C]14.971330846216[/C][C]1.02866915378401[/C][/ROW]
[ROW][C]127[/C][C]15[/C][C]14.8688074039873[/C][C]0.131192596012726[/C][/ROW]
[ROW][C]128[/C][C]12[/C][C]14.8726658999489[/C][C]-2.87266589994886[/C][/ROW]
[ROW][C]129[/C][C]8[/C][C]13.9323289505764[/C][C]-5.93232895057639[/C][/ROW]
[ROW][C]130[/C][C]10[/C][C]12.8405404656832[/C][C]-2.84054046568321[/C][/ROW]
[ROW][C]131[/C][C]16[/C][C]14.174251130562[/C][C]1.82574886943796[/C][/ROW]
[ROW][C]132[/C][C]10[/C][C]10.5527899111202[/C][C]-0.552789911120181[/C][/ROW]
[ROW][C]133[/C][C]16[/C][C]14.7124079385604[/C][C]1.28759206143958[/C][/ROW]
[ROW][C]134[/C][C]10[/C][C]14.8020605226385[/C][C]-4.80206052263851[/C][/ROW]
[ROW][C]135[/C][C]18[/C][C]16.2525259698862[/C][C]1.74747403011376[/C][/ROW]
[ROW][C]136[/C][C]12[/C][C]11.5022490023176[/C][C]0.497750997682375[/C][/ROW]
[ROW][C]137[/C][C]16[/C][C]14.1935808293114[/C][C]1.80641917068858[/C][/ROW]
[ROW][C]138[/C][C]10[/C][C]11.496872101574[/C][C]-1.49687210157397[/C][/ROW]
[ROW][C]139[/C][C]15[/C][C]12.4230679576153[/C][C]2.57693204238468[/C][/ROW]
[ROW][C]140[/C][C]17[/C][C]14.4890361498604[/C][C]2.51096385013959[/C][/ROW]
[ROW][C]141[/C][C]16[/C][C]14.1584289885858[/C][C]1.84157101141419[/C][/ROW]
[ROW][C]142[/C][C]14[/C][C]12.951868632813[/C][C]1.04813136718703[/C][/ROW]
[ROW][C]143[/C][C]12[/C][C]13.8123661836238[/C][C]-1.81236618362383[/C][/ROW]
[ROW][C]144[/C][C]11[/C][C]12.7250267866753[/C][C]-1.72502678667531[/C][/ROW]
[ROW][C]145[/C][C]15[/C][C]15.2690526201771[/C][C]-0.269052620177127[/C][/ROW]
[ROW][C]146[/C][C]7[/C][C]13.8590762972858[/C][C]-6.85907629728577[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=154551&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=154551&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
11314.1285974910443-1.12859749104432
2811.2967450651444-3.29674506514445
31414.0509579419637-0.0509579419636536
41413.92758548100280.072414518997194
51313.822210627974-0.822210627973962
61615.51552436703970.484475632960338
71413.79642330555210.203576694447891
81313.5898628942347-0.589862894234708
91511.44610895612323.55389104387679
101312.74279235776850.257207642231528
111613.76677611171472.2332238882853
122017.6541845039722.34581549602801
131715.18377810244371.81622189755631
141515.9933779007003-0.99337790070032
151614.41392802360731.58607197639273
161613.9575384841872.04246151581299
171212.7905819536458-0.790581953645846
18911.4310829164039-2.43108291640389
191513.32829133514491.6717086648551
201716.17599716185470.824002838145335
211214.8996147278565-2.89961472785655
221013.4412383859889-3.44123838598895
23119.592221241422921.40777875857708
241614.88323325273091.11676674726911
251615.51478360885270.485216391147313
261513.89609533698071.10390466301931
271312.3204766707420.679523329257982
281414.5576259813939-0.557625981393886
291917.50952352302941.49047647697057
301615.69798768676220.302012313237795
311715.47372922348351.52627077651647
321014.3628221847099-4.36282218470989
331510.74345715886134.25654284113866
341414.0614045586502-0.0614045586502041
351411.17874812947332.82125187052671
361613.96891869392112.03108130607891
371714.89789356370862.10210643629141
381514.83479314547560.165206854524398
391715.74226573424511.25773426575489
401416.2401148973723-2.24011489737226
411012.5646352860195-2.56463528601954
421412.90181134281011.09818865718994
431613.64277054509652.35722945490352
441816.83770890579271.16229109420728
451514.60771590568440.392284094315577
461617.0517937817906-1.05179378179056
471614.70085195627881.29914804372125
48109.719160567624380.28083943237562
4989.53264482201975-1.53264482201975
501715.51938286300121.48061713699875
511413.60694426972110.393055730278946
521213.0815727941348-1.08157279413477
531011.0174913064621-1.01749130646205
541415.659128873838-1.659128873838
551213.2674497543683-1.26744975436825
561610.77977629819565.22022370180443
571614.52398675232761.47601324767244
581515.7965799104627-0.796579910462652
591110.24945721821580.750542781784161
601614.26447325268891.73552674731108
61810.6095389777357-2.60953897773569
621715.87956372599931.12043627400065
631614.95737087963351.04262912036649
641515.4118311685627-0.411831168562662
65812.1371468330693-4.13714683306929
661312.34074395416030.659256045839741
671413.949360937080.0506390629200376
681311.23867287189751.76132712810246
691615.25628541791410.74371458208585
701212.5376356936751-0.537635693675109
711915.22649541132953.77350458867052
721916.38424885394032.61575114605967
731211.37939470906210.620605290937919
741414.7256634523728-0.725663452372812
75159.735550872195435.26444912780457
761316.3913327402061-3.39133274020613
771614.35936035081091.64063964918911
781013.2442773991517-3.24427739915175
791513.07983349983771.92016650016231
801614.86391056403061.13608943596942
811514.54848220695170.451517793048325
821112.6073146336404-1.60731463364044
83910.8972309809251-1.89723098092515
841614.66615487761421.33384512238577
851213.8683205506602-1.86832055066019
861414.2006886388491-0.20068863884911
871413.80562639348240.194373606517582
881311.72420088380711.27579911619293
891514.57750937869760.422490621302409
901715.28338761620491.71661238379505
911414.2288302310763-0.228830231076295
92912.0996164737538-3.09961647375375
931113.7907616221722-2.79076162217216
94913.1189302774641-4.11893027746411
9579.86196897216581-2.86196897216581
961311.44585746812891.55414253187112
971512.7730702318682.22692976813199
981214.2371576460983-2.23715764609833
991513.97689210620831.02310789379172
1001412.25297517809321.74702482190684
1011516.9054741543674-1.90547415436736
102912.9088639702422-3.9088639702422
1031614.49875908856781.50124091143217
1041617.5532294614149-1.55322946141486
1051412.64086971242221.3591302875778
1061415.4999376187169-1.49993761871694
1071313.339134613894-0.339134613894031
1081414.0932189676809-0.0932189676809299
1091613.96942863808772.03057136191226
1101616.3434621709996-0.343462170999625
1111313.1199061037919-0.119906103791899
1121212.1192878382116-0.119287838211641
1131614.58409612396861.41590387603141
1141614.26988003681231.73011996318774
1151614.92606641476941.07393358523056
1161010.8437935547809-0.843793554780948
1171417.4121408183436-3.41214081834362
1181212.8961184278203-0.896118427820255
1191214.8235830608198-2.82358306081977
1201212.4326193957905-0.432619395790505
1211212.1989738663901-0.198973866390145
1221917.41077655349191.5892234465081
1231412.95765242774241.04234757225763
1241313.7221635671-0.722163567100035
1251715.84240505051261.1575949494874
1261614.9713308462161.02866915378401
1271514.86880740398730.131192596012726
1281214.8726658999489-2.87266589994886
129813.9323289505764-5.93232895057639
1301012.8405404656832-2.84054046568321
1311614.1742511305621.82574886943796
1321010.5527899111202-0.552789911120181
1331614.71240793856041.28759206143958
1341014.8020605226385-4.80206052263851
1351816.25252596988621.74747403011376
1361211.50224900231760.497750997682375
1371614.19358082931141.80641917068858
1381011.496872101574-1.49687210157397
1391512.42306795761532.57693204238468
1401714.48903614986042.51096385013959
1411614.15842898858581.84157101141419
1421412.9518686328131.04813136718703
1431213.8123661836238-1.81236618362383
1441112.7250267866753-1.72502678667531
1451515.2690526201771-0.269052620177127
146713.8590762972858-6.85907629728577






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
100.4490490162555960.8980980325111930.550950983744404
110.3726823044048840.7453646088097680.627317695595116
120.2450164790022150.4900329580044290.754983520997785
130.1501573671625710.3003147343251420.849842632837429
140.0868487637474220.1736975274948440.913151236252578
150.0567817486193710.1135634972387420.943218251380629
160.0306509119740080.0613018239480160.969349088025992
170.02481058205860070.04962116411720140.975189417941399
180.02011010608291020.04022021216582050.97988989391709
190.04099023869209970.08198047738419930.9590097613079
200.02602526943749110.05205053887498220.973974730562509
210.1059988118411310.2119976236822610.894001188158869
220.0817314232272960.1634628464545920.918268576772704
230.07084579622888170.1416915924577630.929154203771118
240.05364359492347980.107287189846960.94635640507652
250.04982833266532320.09965666533064640.950171667334677
260.03386861346823450.0677372269364690.966131386531766
270.02308145578273910.04616291156547820.976918544217261
280.02423195491670360.04846390983340720.975768045083296
290.01900985556360510.03801971112721020.980990144436395
300.01692934118393920.03385868236787850.983070658816061
310.01123918087008940.02247836174017880.988760819129911
320.03048264959208650.0609652991841730.969517350407914
330.1101211671833760.2202423343667530.889878832816623
340.0847819561553810.1695639123107620.915218043844619
350.1313141242093010.2626282484186020.868685875790699
360.1240057783159780.2480115566319570.875994221684022
370.10097270716540.2019454143308010.8990272928346
380.07799314500419220.1559862900083840.922006854995808
390.08285846709987170.1657169341997430.917141532900128
400.07307409568914450.1461481913782890.926925904310856
410.1055072398580770.2110144797161540.894492760141923
420.08360912409685740.1672182481937150.916390875903143
430.09311633776510910.1862326755302180.906883662234891
440.1009726351316660.2019452702633320.899027364868334
450.07855010203692260.1571002040738450.921449897963077
460.07004766889602530.1400953377920510.929952331103975
470.05895103544577350.1179020708915470.941048964554226
480.04585216065068280.09170432130136550.954147839349317
490.06674283261022410.1334856652204480.933257167389776
500.05732320949869360.1146464189973870.942676790501306
510.04423511880655380.08847023761310750.955764881193446
520.03421982413704450.06843964827408890.965780175862956
530.04395106340909610.08790212681819210.956048936590904
540.0455014598084210.09100291961684190.954498540191579
550.03994129071291620.07988258142583240.960058709287084
560.1285343169702240.2570686339404480.871465683029776
570.1130682507174250.2261365014348490.886931749282575
580.09197361613878090.1839472322775620.908026383861219
590.07451640632604120.1490328126520820.925483593673959
600.0650307705353560.1300615410707120.934969229464644
610.09648697466399730.1929739493279950.903513025336003
620.08032870882646880.1606574176529380.919671291173531
630.06625198901680530.1325039780336110.933748010983195
640.0540470032360350.108094006472070.945952996763965
650.1148179546350020.2296359092700030.885182045364998
660.09673454758976230.1934690951795250.903265452410238
670.07986114255522550.1597222851104510.920138857444775
680.0727959759395780.1455919518791560.927204024060422
690.06037658607246710.1207531721449340.939623413927533
700.04814151049186830.09628302098373660.951858489508132
710.09051340838117380.1810268167623480.909486591618826
720.1025040314823940.2050080629647880.897495968517606
730.08795710986580880.1759142197316180.912042890134191
740.07230676552862940.1446135310572590.927693234471371
750.2195634650476020.4391269300952050.780436534952398
760.2831325613467370.5662651226934740.716867438653263
770.2676224672466510.5352449344933010.732377532753349
780.3331114058378030.6662228116756060.666888594162197
790.3382223167554590.6764446335109190.661777683244541
800.311668026990290.6233360539805810.68833197300971
810.2833964512396960.5667929024793910.716603548760304
820.2675586100870410.5351172201740820.732441389912959
830.2498805917401120.4997611834802230.750119408259888
840.2332777834292520.4665555668585050.766722216570748
850.2167204658553360.4334409317106720.783279534144664
860.1829720019018110.3659440038036220.817027998098189
870.1581033536960670.3162067073921340.841896646303933
880.1744765641090360.3489531282180720.825523435890964
890.1446285273370780.2892570546741560.855371472662922
900.1374114176531760.2748228353063530.862588582346824
910.11178666826790.2235733365357990.8882133317321
920.1294644406002590.2589288812005190.870535559399741
930.1397315349490060.2794630698980120.860268465050994
940.2354646838296040.4709293676592080.764535316170396
950.2499443757557180.4998887515114350.750055624244282
960.2458865296878590.4917730593757170.754113470312141
970.2529622588098320.5059245176196640.747037741190168
980.2427499438856720.4854998877713430.757250056114328
990.2115196095083990.4230392190167970.788480390491601
1000.1962954290488050.3925908580976110.803704570951195
1010.1897567348644680.3795134697289360.810243265135532
1020.250957097600360.501914195200720.74904290239964
1030.2363509074421850.472701814884370.763649092557815
1040.2154665965812640.4309331931625280.784533403418736
1050.2111501275035580.4223002550071160.788849872496442
1060.2220107313411460.4440214626822920.777989268658854
1070.1863313603587620.3726627207175240.813668639641238
1080.1538799720081370.3077599440162750.846120027991863
1090.1801139671391220.3602279342782430.819886032860878
1100.1449573933845290.2899147867690590.85504260661547
1110.1213760595992840.2427521191985680.878623940400716
1120.1052263491487530.2104526982975070.894773650851247
1130.0858439069326820.1716878138653640.914156093067318
1140.1194216315055280.2388432630110560.880578368494472
1150.1108881698943910.2217763397887820.889111830105609
1160.0864946139019180.1729892278038360.913505386098082
1170.1281227167503830.2562454335007670.871877283249617
1180.1080708257703180.2161416515406350.891929174229682
1190.1718367534931410.3436735069862810.828163246506859
1200.1331368738343650.266273747668730.866863126165635
1210.1153435830417970.2306871660835940.884656416958203
1220.1019912580784010.2039825161568030.898008741921599
1230.08843940609338510.176878812186770.911560593906615
1240.06307254203495450.1261450840699090.936927457965046
1250.04530192290680730.09060384581361450.954698077093193
1260.07379812942121420.1475962588424280.926201870578786
1270.04985734482224110.09971468964448230.950142655177759
1280.05412075471619440.1082415094323890.945879245283806
1290.06575341680700410.1315068336140080.934246583192996
1300.07772330989722320.1554466197944460.922276690102777
1310.05038208632724640.1007641726544930.949617913672754
1320.02983779290545150.05967558581090310.970162207094548
1330.01794026987151490.03588053974302970.982059730128485
1340.01955831737633120.03911663475266230.980441682623669
1350.01886188833090990.03772377666181980.98113811166909
1360.02966144232509480.05932288465018970.970338557674905

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
10 & 0.449049016255596 & 0.898098032511193 & 0.550950983744404 \tabularnewline
11 & 0.372682304404884 & 0.745364608809768 & 0.627317695595116 \tabularnewline
12 & 0.245016479002215 & 0.490032958004429 & 0.754983520997785 \tabularnewline
13 & 0.150157367162571 & 0.300314734325142 & 0.849842632837429 \tabularnewline
14 & 0.086848763747422 & 0.173697527494844 & 0.913151236252578 \tabularnewline
15 & 0.056781748619371 & 0.113563497238742 & 0.943218251380629 \tabularnewline
16 & 0.030650911974008 & 0.061301823948016 & 0.969349088025992 \tabularnewline
17 & 0.0248105820586007 & 0.0496211641172014 & 0.975189417941399 \tabularnewline
18 & 0.0201101060829102 & 0.0402202121658205 & 0.97988989391709 \tabularnewline
19 & 0.0409902386920997 & 0.0819804773841993 & 0.9590097613079 \tabularnewline
20 & 0.0260252694374911 & 0.0520505388749822 & 0.973974730562509 \tabularnewline
21 & 0.105998811841131 & 0.211997623682261 & 0.894001188158869 \tabularnewline
22 & 0.081731423227296 & 0.163462846454592 & 0.918268576772704 \tabularnewline
23 & 0.0708457962288817 & 0.141691592457763 & 0.929154203771118 \tabularnewline
24 & 0.0536435949234798 & 0.10728718984696 & 0.94635640507652 \tabularnewline
25 & 0.0498283326653232 & 0.0996566653306464 & 0.950171667334677 \tabularnewline
26 & 0.0338686134682345 & 0.067737226936469 & 0.966131386531766 \tabularnewline
27 & 0.0230814557827391 & 0.0461629115654782 & 0.976918544217261 \tabularnewline
28 & 0.0242319549167036 & 0.0484639098334072 & 0.975768045083296 \tabularnewline
29 & 0.0190098555636051 & 0.0380197111272102 & 0.980990144436395 \tabularnewline
30 & 0.0169293411839392 & 0.0338586823678785 & 0.983070658816061 \tabularnewline
31 & 0.0112391808700894 & 0.0224783617401788 & 0.988760819129911 \tabularnewline
32 & 0.0304826495920865 & 0.060965299184173 & 0.969517350407914 \tabularnewline
33 & 0.110121167183376 & 0.220242334366753 & 0.889878832816623 \tabularnewline
34 & 0.084781956155381 & 0.169563912310762 & 0.915218043844619 \tabularnewline
35 & 0.131314124209301 & 0.262628248418602 & 0.868685875790699 \tabularnewline
36 & 0.124005778315978 & 0.248011556631957 & 0.875994221684022 \tabularnewline
37 & 0.1009727071654 & 0.201945414330801 & 0.8990272928346 \tabularnewline
38 & 0.0779931450041922 & 0.155986290008384 & 0.922006854995808 \tabularnewline
39 & 0.0828584670998717 & 0.165716934199743 & 0.917141532900128 \tabularnewline
40 & 0.0730740956891445 & 0.146148191378289 & 0.926925904310856 \tabularnewline
41 & 0.105507239858077 & 0.211014479716154 & 0.894492760141923 \tabularnewline
42 & 0.0836091240968574 & 0.167218248193715 & 0.916390875903143 \tabularnewline
43 & 0.0931163377651091 & 0.186232675530218 & 0.906883662234891 \tabularnewline
44 & 0.100972635131666 & 0.201945270263332 & 0.899027364868334 \tabularnewline
45 & 0.0785501020369226 & 0.157100204073845 & 0.921449897963077 \tabularnewline
46 & 0.0700476688960253 & 0.140095337792051 & 0.929952331103975 \tabularnewline
47 & 0.0589510354457735 & 0.117902070891547 & 0.941048964554226 \tabularnewline
48 & 0.0458521606506828 & 0.0917043213013655 & 0.954147839349317 \tabularnewline
49 & 0.0667428326102241 & 0.133485665220448 & 0.933257167389776 \tabularnewline
50 & 0.0573232094986936 & 0.114646418997387 & 0.942676790501306 \tabularnewline
51 & 0.0442351188065538 & 0.0884702376131075 & 0.955764881193446 \tabularnewline
52 & 0.0342198241370445 & 0.0684396482740889 & 0.965780175862956 \tabularnewline
53 & 0.0439510634090961 & 0.0879021268181921 & 0.956048936590904 \tabularnewline
54 & 0.045501459808421 & 0.0910029196168419 & 0.954498540191579 \tabularnewline
55 & 0.0399412907129162 & 0.0798825814258324 & 0.960058709287084 \tabularnewline
56 & 0.128534316970224 & 0.257068633940448 & 0.871465683029776 \tabularnewline
57 & 0.113068250717425 & 0.226136501434849 & 0.886931749282575 \tabularnewline
58 & 0.0919736161387809 & 0.183947232277562 & 0.908026383861219 \tabularnewline
59 & 0.0745164063260412 & 0.149032812652082 & 0.925483593673959 \tabularnewline
60 & 0.065030770535356 & 0.130061541070712 & 0.934969229464644 \tabularnewline
61 & 0.0964869746639973 & 0.192973949327995 & 0.903513025336003 \tabularnewline
62 & 0.0803287088264688 & 0.160657417652938 & 0.919671291173531 \tabularnewline
63 & 0.0662519890168053 & 0.132503978033611 & 0.933748010983195 \tabularnewline
64 & 0.054047003236035 & 0.10809400647207 & 0.945952996763965 \tabularnewline
65 & 0.114817954635002 & 0.229635909270003 & 0.885182045364998 \tabularnewline
66 & 0.0967345475897623 & 0.193469095179525 & 0.903265452410238 \tabularnewline
67 & 0.0798611425552255 & 0.159722285110451 & 0.920138857444775 \tabularnewline
68 & 0.072795975939578 & 0.145591951879156 & 0.927204024060422 \tabularnewline
69 & 0.0603765860724671 & 0.120753172144934 & 0.939623413927533 \tabularnewline
70 & 0.0481415104918683 & 0.0962830209837366 & 0.951858489508132 \tabularnewline
71 & 0.0905134083811738 & 0.181026816762348 & 0.909486591618826 \tabularnewline
72 & 0.102504031482394 & 0.205008062964788 & 0.897495968517606 \tabularnewline
73 & 0.0879571098658088 & 0.175914219731618 & 0.912042890134191 \tabularnewline
74 & 0.0723067655286294 & 0.144613531057259 & 0.927693234471371 \tabularnewline
75 & 0.219563465047602 & 0.439126930095205 & 0.780436534952398 \tabularnewline
76 & 0.283132561346737 & 0.566265122693474 & 0.716867438653263 \tabularnewline
77 & 0.267622467246651 & 0.535244934493301 & 0.732377532753349 \tabularnewline
78 & 0.333111405837803 & 0.666222811675606 & 0.666888594162197 \tabularnewline
79 & 0.338222316755459 & 0.676444633510919 & 0.661777683244541 \tabularnewline
80 & 0.31166802699029 & 0.623336053980581 & 0.68833197300971 \tabularnewline
81 & 0.283396451239696 & 0.566792902479391 & 0.716603548760304 \tabularnewline
82 & 0.267558610087041 & 0.535117220174082 & 0.732441389912959 \tabularnewline
83 & 0.249880591740112 & 0.499761183480223 & 0.750119408259888 \tabularnewline
84 & 0.233277783429252 & 0.466555566858505 & 0.766722216570748 \tabularnewline
85 & 0.216720465855336 & 0.433440931710672 & 0.783279534144664 \tabularnewline
86 & 0.182972001901811 & 0.365944003803622 & 0.817027998098189 \tabularnewline
87 & 0.158103353696067 & 0.316206707392134 & 0.841896646303933 \tabularnewline
88 & 0.174476564109036 & 0.348953128218072 & 0.825523435890964 \tabularnewline
89 & 0.144628527337078 & 0.289257054674156 & 0.855371472662922 \tabularnewline
90 & 0.137411417653176 & 0.274822835306353 & 0.862588582346824 \tabularnewline
91 & 0.1117866682679 & 0.223573336535799 & 0.8882133317321 \tabularnewline
92 & 0.129464440600259 & 0.258928881200519 & 0.870535559399741 \tabularnewline
93 & 0.139731534949006 & 0.279463069898012 & 0.860268465050994 \tabularnewline
94 & 0.235464683829604 & 0.470929367659208 & 0.764535316170396 \tabularnewline
95 & 0.249944375755718 & 0.499888751511435 & 0.750055624244282 \tabularnewline
96 & 0.245886529687859 & 0.491773059375717 & 0.754113470312141 \tabularnewline
97 & 0.252962258809832 & 0.505924517619664 & 0.747037741190168 \tabularnewline
98 & 0.242749943885672 & 0.485499887771343 & 0.757250056114328 \tabularnewline
99 & 0.211519609508399 & 0.423039219016797 & 0.788480390491601 \tabularnewline
100 & 0.196295429048805 & 0.392590858097611 & 0.803704570951195 \tabularnewline
101 & 0.189756734864468 & 0.379513469728936 & 0.810243265135532 \tabularnewline
102 & 0.25095709760036 & 0.50191419520072 & 0.74904290239964 \tabularnewline
103 & 0.236350907442185 & 0.47270181488437 & 0.763649092557815 \tabularnewline
104 & 0.215466596581264 & 0.430933193162528 & 0.784533403418736 \tabularnewline
105 & 0.211150127503558 & 0.422300255007116 & 0.788849872496442 \tabularnewline
106 & 0.222010731341146 & 0.444021462682292 & 0.777989268658854 \tabularnewline
107 & 0.186331360358762 & 0.372662720717524 & 0.813668639641238 \tabularnewline
108 & 0.153879972008137 & 0.307759944016275 & 0.846120027991863 \tabularnewline
109 & 0.180113967139122 & 0.360227934278243 & 0.819886032860878 \tabularnewline
110 & 0.144957393384529 & 0.289914786769059 & 0.85504260661547 \tabularnewline
111 & 0.121376059599284 & 0.242752119198568 & 0.878623940400716 \tabularnewline
112 & 0.105226349148753 & 0.210452698297507 & 0.894773650851247 \tabularnewline
113 & 0.085843906932682 & 0.171687813865364 & 0.914156093067318 \tabularnewline
114 & 0.119421631505528 & 0.238843263011056 & 0.880578368494472 \tabularnewline
115 & 0.110888169894391 & 0.221776339788782 & 0.889111830105609 \tabularnewline
116 & 0.086494613901918 & 0.172989227803836 & 0.913505386098082 \tabularnewline
117 & 0.128122716750383 & 0.256245433500767 & 0.871877283249617 \tabularnewline
118 & 0.108070825770318 & 0.216141651540635 & 0.891929174229682 \tabularnewline
119 & 0.171836753493141 & 0.343673506986281 & 0.828163246506859 \tabularnewline
120 & 0.133136873834365 & 0.26627374766873 & 0.866863126165635 \tabularnewline
121 & 0.115343583041797 & 0.230687166083594 & 0.884656416958203 \tabularnewline
122 & 0.101991258078401 & 0.203982516156803 & 0.898008741921599 \tabularnewline
123 & 0.0884394060933851 & 0.17687881218677 & 0.911560593906615 \tabularnewline
124 & 0.0630725420349545 & 0.126145084069909 & 0.936927457965046 \tabularnewline
125 & 0.0453019229068073 & 0.0906038458136145 & 0.954698077093193 \tabularnewline
126 & 0.0737981294212142 & 0.147596258842428 & 0.926201870578786 \tabularnewline
127 & 0.0498573448222411 & 0.0997146896444823 & 0.950142655177759 \tabularnewline
128 & 0.0541207547161944 & 0.108241509432389 & 0.945879245283806 \tabularnewline
129 & 0.0657534168070041 & 0.131506833614008 & 0.934246583192996 \tabularnewline
130 & 0.0777233098972232 & 0.155446619794446 & 0.922276690102777 \tabularnewline
131 & 0.0503820863272464 & 0.100764172654493 & 0.949617913672754 \tabularnewline
132 & 0.0298377929054515 & 0.0596755858109031 & 0.970162207094548 \tabularnewline
133 & 0.0179402698715149 & 0.0358805397430297 & 0.982059730128485 \tabularnewline
134 & 0.0195583173763312 & 0.0391166347526623 & 0.980441682623669 \tabularnewline
135 & 0.0188618883309099 & 0.0377237766618198 & 0.98113811166909 \tabularnewline
136 & 0.0296614423250948 & 0.0593228846501897 & 0.970338557674905 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=154551&T=5

[TABLE]
[ROW][C]Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]p-values[/C][C]Alternative Hypothesis[/C][/ROW]
[ROW][C]breakpoint index[/C][C]greater[/C][C]2-sided[/C][C]less[/C][/ROW]
[ROW][C]10[/C][C]0.449049016255596[/C][C]0.898098032511193[/C][C]0.550950983744404[/C][/ROW]
[ROW][C]11[/C][C]0.372682304404884[/C][C]0.745364608809768[/C][C]0.627317695595116[/C][/ROW]
[ROW][C]12[/C][C]0.245016479002215[/C][C]0.490032958004429[/C][C]0.754983520997785[/C][/ROW]
[ROW][C]13[/C][C]0.150157367162571[/C][C]0.300314734325142[/C][C]0.849842632837429[/C][/ROW]
[ROW][C]14[/C][C]0.086848763747422[/C][C]0.173697527494844[/C][C]0.913151236252578[/C][/ROW]
[ROW][C]15[/C][C]0.056781748619371[/C][C]0.113563497238742[/C][C]0.943218251380629[/C][/ROW]
[ROW][C]16[/C][C]0.030650911974008[/C][C]0.061301823948016[/C][C]0.969349088025992[/C][/ROW]
[ROW][C]17[/C][C]0.0248105820586007[/C][C]0.0496211641172014[/C][C]0.975189417941399[/C][/ROW]
[ROW][C]18[/C][C]0.0201101060829102[/C][C]0.0402202121658205[/C][C]0.97988989391709[/C][/ROW]
[ROW][C]19[/C][C]0.0409902386920997[/C][C]0.0819804773841993[/C][C]0.9590097613079[/C][/ROW]
[ROW][C]20[/C][C]0.0260252694374911[/C][C]0.0520505388749822[/C][C]0.973974730562509[/C][/ROW]
[ROW][C]21[/C][C]0.105998811841131[/C][C]0.211997623682261[/C][C]0.894001188158869[/C][/ROW]
[ROW][C]22[/C][C]0.081731423227296[/C][C]0.163462846454592[/C][C]0.918268576772704[/C][/ROW]
[ROW][C]23[/C][C]0.0708457962288817[/C][C]0.141691592457763[/C][C]0.929154203771118[/C][/ROW]
[ROW][C]24[/C][C]0.0536435949234798[/C][C]0.10728718984696[/C][C]0.94635640507652[/C][/ROW]
[ROW][C]25[/C][C]0.0498283326653232[/C][C]0.0996566653306464[/C][C]0.950171667334677[/C][/ROW]
[ROW][C]26[/C][C]0.0338686134682345[/C][C]0.067737226936469[/C][C]0.966131386531766[/C][/ROW]
[ROW][C]27[/C][C]0.0230814557827391[/C][C]0.0461629115654782[/C][C]0.976918544217261[/C][/ROW]
[ROW][C]28[/C][C]0.0242319549167036[/C][C]0.0484639098334072[/C][C]0.975768045083296[/C][/ROW]
[ROW][C]29[/C][C]0.0190098555636051[/C][C]0.0380197111272102[/C][C]0.980990144436395[/C][/ROW]
[ROW][C]30[/C][C]0.0169293411839392[/C][C]0.0338586823678785[/C][C]0.983070658816061[/C][/ROW]
[ROW][C]31[/C][C]0.0112391808700894[/C][C]0.0224783617401788[/C][C]0.988760819129911[/C][/ROW]
[ROW][C]32[/C][C]0.0304826495920865[/C][C]0.060965299184173[/C][C]0.969517350407914[/C][/ROW]
[ROW][C]33[/C][C]0.110121167183376[/C][C]0.220242334366753[/C][C]0.889878832816623[/C][/ROW]
[ROW][C]34[/C][C]0.084781956155381[/C][C]0.169563912310762[/C][C]0.915218043844619[/C][/ROW]
[ROW][C]35[/C][C]0.131314124209301[/C][C]0.262628248418602[/C][C]0.868685875790699[/C][/ROW]
[ROW][C]36[/C][C]0.124005778315978[/C][C]0.248011556631957[/C][C]0.875994221684022[/C][/ROW]
[ROW][C]37[/C][C]0.1009727071654[/C][C]0.201945414330801[/C][C]0.8990272928346[/C][/ROW]
[ROW][C]38[/C][C]0.0779931450041922[/C][C]0.155986290008384[/C][C]0.922006854995808[/C][/ROW]
[ROW][C]39[/C][C]0.0828584670998717[/C][C]0.165716934199743[/C][C]0.917141532900128[/C][/ROW]
[ROW][C]40[/C][C]0.0730740956891445[/C][C]0.146148191378289[/C][C]0.926925904310856[/C][/ROW]
[ROW][C]41[/C][C]0.105507239858077[/C][C]0.211014479716154[/C][C]0.894492760141923[/C][/ROW]
[ROW][C]42[/C][C]0.0836091240968574[/C][C]0.167218248193715[/C][C]0.916390875903143[/C][/ROW]
[ROW][C]43[/C][C]0.0931163377651091[/C][C]0.186232675530218[/C][C]0.906883662234891[/C][/ROW]
[ROW][C]44[/C][C]0.100972635131666[/C][C]0.201945270263332[/C][C]0.899027364868334[/C][/ROW]
[ROW][C]45[/C][C]0.0785501020369226[/C][C]0.157100204073845[/C][C]0.921449897963077[/C][/ROW]
[ROW][C]46[/C][C]0.0700476688960253[/C][C]0.140095337792051[/C][C]0.929952331103975[/C][/ROW]
[ROW][C]47[/C][C]0.0589510354457735[/C][C]0.117902070891547[/C][C]0.941048964554226[/C][/ROW]
[ROW][C]48[/C][C]0.0458521606506828[/C][C]0.0917043213013655[/C][C]0.954147839349317[/C][/ROW]
[ROW][C]49[/C][C]0.0667428326102241[/C][C]0.133485665220448[/C][C]0.933257167389776[/C][/ROW]
[ROW][C]50[/C][C]0.0573232094986936[/C][C]0.114646418997387[/C][C]0.942676790501306[/C][/ROW]
[ROW][C]51[/C][C]0.0442351188065538[/C][C]0.0884702376131075[/C][C]0.955764881193446[/C][/ROW]
[ROW][C]52[/C][C]0.0342198241370445[/C][C]0.0684396482740889[/C][C]0.965780175862956[/C][/ROW]
[ROW][C]53[/C][C]0.0439510634090961[/C][C]0.0879021268181921[/C][C]0.956048936590904[/C][/ROW]
[ROW][C]54[/C][C]0.045501459808421[/C][C]0.0910029196168419[/C][C]0.954498540191579[/C][/ROW]
[ROW][C]55[/C][C]0.0399412907129162[/C][C]0.0798825814258324[/C][C]0.960058709287084[/C][/ROW]
[ROW][C]56[/C][C]0.128534316970224[/C][C]0.257068633940448[/C][C]0.871465683029776[/C][/ROW]
[ROW][C]57[/C][C]0.113068250717425[/C][C]0.226136501434849[/C][C]0.886931749282575[/C][/ROW]
[ROW][C]58[/C][C]0.0919736161387809[/C][C]0.183947232277562[/C][C]0.908026383861219[/C][/ROW]
[ROW][C]59[/C][C]0.0745164063260412[/C][C]0.149032812652082[/C][C]0.925483593673959[/C][/ROW]
[ROW][C]60[/C][C]0.065030770535356[/C][C]0.130061541070712[/C][C]0.934969229464644[/C][/ROW]
[ROW][C]61[/C][C]0.0964869746639973[/C][C]0.192973949327995[/C][C]0.903513025336003[/C][/ROW]
[ROW][C]62[/C][C]0.0803287088264688[/C][C]0.160657417652938[/C][C]0.919671291173531[/C][/ROW]
[ROW][C]63[/C][C]0.0662519890168053[/C][C]0.132503978033611[/C][C]0.933748010983195[/C][/ROW]
[ROW][C]64[/C][C]0.054047003236035[/C][C]0.10809400647207[/C][C]0.945952996763965[/C][/ROW]
[ROW][C]65[/C][C]0.114817954635002[/C][C]0.229635909270003[/C][C]0.885182045364998[/C][/ROW]
[ROW][C]66[/C][C]0.0967345475897623[/C][C]0.193469095179525[/C][C]0.903265452410238[/C][/ROW]
[ROW][C]67[/C][C]0.0798611425552255[/C][C]0.159722285110451[/C][C]0.920138857444775[/C][/ROW]
[ROW][C]68[/C][C]0.072795975939578[/C][C]0.145591951879156[/C][C]0.927204024060422[/C][/ROW]
[ROW][C]69[/C][C]0.0603765860724671[/C][C]0.120753172144934[/C][C]0.939623413927533[/C][/ROW]
[ROW][C]70[/C][C]0.0481415104918683[/C][C]0.0962830209837366[/C][C]0.951858489508132[/C][/ROW]
[ROW][C]71[/C][C]0.0905134083811738[/C][C]0.181026816762348[/C][C]0.909486591618826[/C][/ROW]
[ROW][C]72[/C][C]0.102504031482394[/C][C]0.205008062964788[/C][C]0.897495968517606[/C][/ROW]
[ROW][C]73[/C][C]0.0879571098658088[/C][C]0.175914219731618[/C][C]0.912042890134191[/C][/ROW]
[ROW][C]74[/C][C]0.0723067655286294[/C][C]0.144613531057259[/C][C]0.927693234471371[/C][/ROW]
[ROW][C]75[/C][C]0.219563465047602[/C][C]0.439126930095205[/C][C]0.780436534952398[/C][/ROW]
[ROW][C]76[/C][C]0.283132561346737[/C][C]0.566265122693474[/C][C]0.716867438653263[/C][/ROW]
[ROW][C]77[/C][C]0.267622467246651[/C][C]0.535244934493301[/C][C]0.732377532753349[/C][/ROW]
[ROW][C]78[/C][C]0.333111405837803[/C][C]0.666222811675606[/C][C]0.666888594162197[/C][/ROW]
[ROW][C]79[/C][C]0.338222316755459[/C][C]0.676444633510919[/C][C]0.661777683244541[/C][/ROW]
[ROW][C]80[/C][C]0.31166802699029[/C][C]0.623336053980581[/C][C]0.68833197300971[/C][/ROW]
[ROW][C]81[/C][C]0.283396451239696[/C][C]0.566792902479391[/C][C]0.716603548760304[/C][/ROW]
[ROW][C]82[/C][C]0.267558610087041[/C][C]0.535117220174082[/C][C]0.732441389912959[/C][/ROW]
[ROW][C]83[/C][C]0.249880591740112[/C][C]0.499761183480223[/C][C]0.750119408259888[/C][/ROW]
[ROW][C]84[/C][C]0.233277783429252[/C][C]0.466555566858505[/C][C]0.766722216570748[/C][/ROW]
[ROW][C]85[/C][C]0.216720465855336[/C][C]0.433440931710672[/C][C]0.783279534144664[/C][/ROW]
[ROW][C]86[/C][C]0.182972001901811[/C][C]0.365944003803622[/C][C]0.817027998098189[/C][/ROW]
[ROW][C]87[/C][C]0.158103353696067[/C][C]0.316206707392134[/C][C]0.841896646303933[/C][/ROW]
[ROW][C]88[/C][C]0.174476564109036[/C][C]0.348953128218072[/C][C]0.825523435890964[/C][/ROW]
[ROW][C]89[/C][C]0.144628527337078[/C][C]0.289257054674156[/C][C]0.855371472662922[/C][/ROW]
[ROW][C]90[/C][C]0.137411417653176[/C][C]0.274822835306353[/C][C]0.862588582346824[/C][/ROW]
[ROW][C]91[/C][C]0.1117866682679[/C][C]0.223573336535799[/C][C]0.8882133317321[/C][/ROW]
[ROW][C]92[/C][C]0.129464440600259[/C][C]0.258928881200519[/C][C]0.870535559399741[/C][/ROW]
[ROW][C]93[/C][C]0.139731534949006[/C][C]0.279463069898012[/C][C]0.860268465050994[/C][/ROW]
[ROW][C]94[/C][C]0.235464683829604[/C][C]0.470929367659208[/C][C]0.764535316170396[/C][/ROW]
[ROW][C]95[/C][C]0.249944375755718[/C][C]0.499888751511435[/C][C]0.750055624244282[/C][/ROW]
[ROW][C]96[/C][C]0.245886529687859[/C][C]0.491773059375717[/C][C]0.754113470312141[/C][/ROW]
[ROW][C]97[/C][C]0.252962258809832[/C][C]0.505924517619664[/C][C]0.747037741190168[/C][/ROW]
[ROW][C]98[/C][C]0.242749943885672[/C][C]0.485499887771343[/C][C]0.757250056114328[/C][/ROW]
[ROW][C]99[/C][C]0.211519609508399[/C][C]0.423039219016797[/C][C]0.788480390491601[/C][/ROW]
[ROW][C]100[/C][C]0.196295429048805[/C][C]0.392590858097611[/C][C]0.803704570951195[/C][/ROW]
[ROW][C]101[/C][C]0.189756734864468[/C][C]0.379513469728936[/C][C]0.810243265135532[/C][/ROW]
[ROW][C]102[/C][C]0.25095709760036[/C][C]0.50191419520072[/C][C]0.74904290239964[/C][/ROW]
[ROW][C]103[/C][C]0.236350907442185[/C][C]0.47270181488437[/C][C]0.763649092557815[/C][/ROW]
[ROW][C]104[/C][C]0.215466596581264[/C][C]0.430933193162528[/C][C]0.784533403418736[/C][/ROW]
[ROW][C]105[/C][C]0.211150127503558[/C][C]0.422300255007116[/C][C]0.788849872496442[/C][/ROW]
[ROW][C]106[/C][C]0.222010731341146[/C][C]0.444021462682292[/C][C]0.777989268658854[/C][/ROW]
[ROW][C]107[/C][C]0.186331360358762[/C][C]0.372662720717524[/C][C]0.813668639641238[/C][/ROW]
[ROW][C]108[/C][C]0.153879972008137[/C][C]0.307759944016275[/C][C]0.846120027991863[/C][/ROW]
[ROW][C]109[/C][C]0.180113967139122[/C][C]0.360227934278243[/C][C]0.819886032860878[/C][/ROW]
[ROW][C]110[/C][C]0.144957393384529[/C][C]0.289914786769059[/C][C]0.85504260661547[/C][/ROW]
[ROW][C]111[/C][C]0.121376059599284[/C][C]0.242752119198568[/C][C]0.878623940400716[/C][/ROW]
[ROW][C]112[/C][C]0.105226349148753[/C][C]0.210452698297507[/C][C]0.894773650851247[/C][/ROW]
[ROW][C]113[/C][C]0.085843906932682[/C][C]0.171687813865364[/C][C]0.914156093067318[/C][/ROW]
[ROW][C]114[/C][C]0.119421631505528[/C][C]0.238843263011056[/C][C]0.880578368494472[/C][/ROW]
[ROW][C]115[/C][C]0.110888169894391[/C][C]0.221776339788782[/C][C]0.889111830105609[/C][/ROW]
[ROW][C]116[/C][C]0.086494613901918[/C][C]0.172989227803836[/C][C]0.913505386098082[/C][/ROW]
[ROW][C]117[/C][C]0.128122716750383[/C][C]0.256245433500767[/C][C]0.871877283249617[/C][/ROW]
[ROW][C]118[/C][C]0.108070825770318[/C][C]0.216141651540635[/C][C]0.891929174229682[/C][/ROW]
[ROW][C]119[/C][C]0.171836753493141[/C][C]0.343673506986281[/C][C]0.828163246506859[/C][/ROW]
[ROW][C]120[/C][C]0.133136873834365[/C][C]0.26627374766873[/C][C]0.866863126165635[/C][/ROW]
[ROW][C]121[/C][C]0.115343583041797[/C][C]0.230687166083594[/C][C]0.884656416958203[/C][/ROW]
[ROW][C]122[/C][C]0.101991258078401[/C][C]0.203982516156803[/C][C]0.898008741921599[/C][/ROW]
[ROW][C]123[/C][C]0.0884394060933851[/C][C]0.17687881218677[/C][C]0.911560593906615[/C][/ROW]
[ROW][C]124[/C][C]0.0630725420349545[/C][C]0.126145084069909[/C][C]0.936927457965046[/C][/ROW]
[ROW][C]125[/C][C]0.0453019229068073[/C][C]0.0906038458136145[/C][C]0.954698077093193[/C][/ROW]
[ROW][C]126[/C][C]0.0737981294212142[/C][C]0.147596258842428[/C][C]0.926201870578786[/C][/ROW]
[ROW][C]127[/C][C]0.0498573448222411[/C][C]0.0997146896444823[/C][C]0.950142655177759[/C][/ROW]
[ROW][C]128[/C][C]0.0541207547161944[/C][C]0.108241509432389[/C][C]0.945879245283806[/C][/ROW]
[ROW][C]129[/C][C]0.0657534168070041[/C][C]0.131506833614008[/C][C]0.934246583192996[/C][/ROW]
[ROW][C]130[/C][C]0.0777233098972232[/C][C]0.155446619794446[/C][C]0.922276690102777[/C][/ROW]
[ROW][C]131[/C][C]0.0503820863272464[/C][C]0.100764172654493[/C][C]0.949617913672754[/C][/ROW]
[ROW][C]132[/C][C]0.0298377929054515[/C][C]0.0596755858109031[/C][C]0.970162207094548[/C][/ROW]
[ROW][C]133[/C][C]0.0179402698715149[/C][C]0.0358805397430297[/C][C]0.982059730128485[/C][/ROW]
[ROW][C]134[/C][C]0.0195583173763312[/C][C]0.0391166347526623[/C][C]0.980441682623669[/C][/ROW]
[ROW][C]135[/C][C]0.0188618883309099[/C][C]0.0377237766618198[/C][C]0.98113811166909[/C][/ROW]
[ROW][C]136[/C][C]0.0296614423250948[/C][C]0.0593228846501897[/C][C]0.970338557674905[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=154551&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=154551&T=5

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


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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
100.4490490162555960.8980980325111930.550950983744404
110.3726823044048840.7453646088097680.627317695595116
120.2450164790022150.4900329580044290.754983520997785
130.1501573671625710.3003147343251420.849842632837429
140.0868487637474220.1736975274948440.913151236252578
150.0567817486193710.1135634972387420.943218251380629
160.0306509119740080.0613018239480160.969349088025992
170.02481058205860070.04962116411720140.975189417941399
180.02011010608291020.04022021216582050.97988989391709
190.04099023869209970.08198047738419930.9590097613079
200.02602526943749110.05205053887498220.973974730562509
210.1059988118411310.2119976236822610.894001188158869
220.0817314232272960.1634628464545920.918268576772704
230.07084579622888170.1416915924577630.929154203771118
240.05364359492347980.107287189846960.94635640507652
250.04982833266532320.09965666533064640.950171667334677
260.03386861346823450.0677372269364690.966131386531766
270.02308145578273910.04616291156547820.976918544217261
280.02423195491670360.04846390983340720.975768045083296
290.01900985556360510.03801971112721020.980990144436395
300.01692934118393920.03385868236787850.983070658816061
310.01123918087008940.02247836174017880.988760819129911
320.03048264959208650.0609652991841730.969517350407914
330.1101211671833760.2202423343667530.889878832816623
340.0847819561553810.1695639123107620.915218043844619
350.1313141242093010.2626282484186020.868685875790699
360.1240057783159780.2480115566319570.875994221684022
370.10097270716540.2019454143308010.8990272928346
380.07799314500419220.1559862900083840.922006854995808
390.08285846709987170.1657169341997430.917141532900128
400.07307409568914450.1461481913782890.926925904310856
410.1055072398580770.2110144797161540.894492760141923
420.08360912409685740.1672182481937150.916390875903143
430.09311633776510910.1862326755302180.906883662234891
440.1009726351316660.2019452702633320.899027364868334
450.07855010203692260.1571002040738450.921449897963077
460.07004766889602530.1400953377920510.929952331103975
470.05895103544577350.1179020708915470.941048964554226
480.04585216065068280.09170432130136550.954147839349317
490.06674283261022410.1334856652204480.933257167389776
500.05732320949869360.1146464189973870.942676790501306
510.04423511880655380.08847023761310750.955764881193446
520.03421982413704450.06843964827408890.965780175862956
530.04395106340909610.08790212681819210.956048936590904
540.0455014598084210.09100291961684190.954498540191579
550.03994129071291620.07988258142583240.960058709287084
560.1285343169702240.2570686339404480.871465683029776
570.1130682507174250.2261365014348490.886931749282575
580.09197361613878090.1839472322775620.908026383861219
590.07451640632604120.1490328126520820.925483593673959
600.0650307705353560.1300615410707120.934969229464644
610.09648697466399730.1929739493279950.903513025336003
620.08032870882646880.1606574176529380.919671291173531
630.06625198901680530.1325039780336110.933748010983195
640.0540470032360350.108094006472070.945952996763965
650.1148179546350020.2296359092700030.885182045364998
660.09673454758976230.1934690951795250.903265452410238
670.07986114255522550.1597222851104510.920138857444775
680.0727959759395780.1455919518791560.927204024060422
690.06037658607246710.1207531721449340.939623413927533
700.04814151049186830.09628302098373660.951858489508132
710.09051340838117380.1810268167623480.909486591618826
720.1025040314823940.2050080629647880.897495968517606
730.08795710986580880.1759142197316180.912042890134191
740.07230676552862940.1446135310572590.927693234471371
750.2195634650476020.4391269300952050.780436534952398
760.2831325613467370.5662651226934740.716867438653263
770.2676224672466510.5352449344933010.732377532753349
780.3331114058378030.6662228116756060.666888594162197
790.3382223167554590.6764446335109190.661777683244541
800.311668026990290.6233360539805810.68833197300971
810.2833964512396960.5667929024793910.716603548760304
820.2675586100870410.5351172201740820.732441389912959
830.2498805917401120.4997611834802230.750119408259888
840.2332777834292520.4665555668585050.766722216570748
850.2167204658553360.4334409317106720.783279534144664
860.1829720019018110.3659440038036220.817027998098189
870.1581033536960670.3162067073921340.841896646303933
880.1744765641090360.3489531282180720.825523435890964
890.1446285273370780.2892570546741560.855371472662922
900.1374114176531760.2748228353063530.862588582346824
910.11178666826790.2235733365357990.8882133317321
920.1294644406002590.2589288812005190.870535559399741
930.1397315349490060.2794630698980120.860268465050994
940.2354646838296040.4709293676592080.764535316170396
950.2499443757557180.4998887515114350.750055624244282
960.2458865296878590.4917730593757170.754113470312141
970.2529622588098320.5059245176196640.747037741190168
980.2427499438856720.4854998877713430.757250056114328
990.2115196095083990.4230392190167970.788480390491601
1000.1962954290488050.3925908580976110.803704570951195
1010.1897567348644680.3795134697289360.810243265135532
1020.250957097600360.501914195200720.74904290239964
1030.2363509074421850.472701814884370.763649092557815
1040.2154665965812640.4309331931625280.784533403418736
1050.2111501275035580.4223002550071160.788849872496442
1060.2220107313411460.4440214626822920.777989268658854
1070.1863313603587620.3726627207175240.813668639641238
1080.1538799720081370.3077599440162750.846120027991863
1090.1801139671391220.3602279342782430.819886032860878
1100.1449573933845290.2899147867690590.85504260661547
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1120.1052263491487530.2104526982975070.894773650851247
1130.0858439069326820.1716878138653640.914156093067318
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1150.1108881698943910.2217763397887820.889111830105609
1160.0864946139019180.1729892278038360.913505386098082
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1180.1080708257703180.2161416515406350.891929174229682
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1270.04985734482224110.09971468964448230.950142655177759
1280.05412075471619440.1082415094323890.945879245283806
1290.06575341680700410.1315068336140080.934246583192996
1300.07772330989722320.1554466197944460.922276690102777
1310.05038208632724640.1007641726544930.949617913672754
1320.02983779290545150.05967558581090310.970162207094548
1330.01794026987151490.03588053974302970.982059730128485
1340.01955831737633120.03911663475266230.980441682623669
1350.01886188833090990.03772377666181980.98113811166909
1360.02966144232509480.05932288465018970.970338557674905






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level100.078740157480315NOK
10% type I error level270.21259842519685NOK

\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 & 10 & 0.078740157480315 & NOK \tabularnewline
10% type I error level & 27 & 0.21259842519685 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=154551&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]10[/C][C]0.078740157480315[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]27[/C][C]0.21259842519685[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=154551&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=154551&T=6

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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 level100.078740157480315NOK
10% type I error level270.21259842519685NOK


Figures (Output of Computation)

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Figure 10
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Input Parameters & R Code

Parameters (Session):
par1 = 2 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
Parameters (R input):
par1 = 2 ; 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')
}