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Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationThu, 24 Nov 2011 15:33:56 -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/Nov/24/t1322166870v809e6004ul4xbq.htm/, Retrieved Thu, 31 Oct 2024 23:05:44 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=147198, Retrieved Thu, 31 Oct 2024 23:05:44 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact158

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Multiple Regression] [] [2011-11-24 20:33:56] [1118fb1265e4c78f2f623b6bb1012fba] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
41	38	13	12	14	12	53	32
39	32	16	11	18	11	86	51
30	35	19	15	11	14	66	42
31	33	15	6	12	12	67	41
34	37	14	13	16	21	76	46
35	29	13	10	18	12	78	47
39	31	19	12	14	22	53	37
34	36	15	14	14	11	80	49
36	35	14	12	15	10	74	45
37	38	15	6	15	13	76	47
38	31	16	10	17	10	79	49
36	34	16	12	19	8	54	33
38	35	16	12	10	15	67	42
39	38	16	11	16	14	54	33
33	37	17	15	18	10	87	53
32	33	15	12	14	14	58	36
36	32	15	10	14	14	75	45
38	38	20	12	17	11	88	54
39	38	18	11	14	10	64	41
32	32	16	12	16	13	57	36
32	33	16	11	18	7	66	41
31	31	16	12	11	14	68	44
39	38	19	13	14	12	54	33
37	39	16	11	12	14	56	37
39	32	17	9	17	11	86	52
41	32	17	13	9	9	80	47
36	35	16	10	16	11	76	43
33	37	15	14	14	15	69	44
33	33	16	12	15	14	78	45
34	33	14	10	11	13	67	44
31	28	15	12	16	9	80	49
27	32	12	8	13	15	54	33
37	31	14	10	17	10	71	43
34	37	16	12	15	11	84	54
34	30	14	12	14	13	74	42
32	33	7	7	16	8	71	44
29	31	10	6	9	20	63	37
36	33	14	12	15	12	71	43
29	31	16	10	17	10	76	46
35	33	16	10	13	10	69	42
37	32	16	10	15	9	74	45
34	33	14	12	16	14	75	44
38	32	20	15	16	8	54	33
35	33	14	10	12	14	52	31
38	28	14	10	12	11	69	42
37	35	11	12	11	13	68	40
38	39	14	13	15	9	65	43
33	34	15	11	15	11	75	46
36	38	16	11	17	15	74	42
38	32	14	12	13	11	75	45
32	38	16	14	16	10	72	44
32	30	14	10	14	14	67	40
32	33	12	12	11	18	63	37
34	38	16	13	12	14	62	46
32	32	9	5	12	11	63	36
37	32	14	6	15	12	76	47
39	34	16	12	16	13	74	45
29	34	16	12	15	9	67	42
37	36	15	11	12	10	73	43
35	34	16	10	12	15	70	43
30	28	12	7	8	20	53	32
38	34	16	12	13	12	77	45
34	35	16	14	11	12	77	45
31	35	14	11	14	14	52	31
34	31	16	12	15	13	54	33
35	37	17	13	10	11	80	49
36	35	18	14	11	17	66	42
30	27	18	11	12	12	73	41
39	40	12	12	15	13	63	38
35	37	16	12	15	14	69	42
38	36	10	8	14	13	67	44
31	38	14	11	16	15	54	33
34	39	18	14	15	13	81	48
38	41	18	14	15	10	69	40
34	27	16	12	13	11	84	50
39	30	17	9	12	19	80	49
37	37	16	13	17	13	70	43
34	31	16	11	13	17	69	44
28	31	13	12	15	13	77	47
37	27	16	12	13	9	54	33
33	36	16	12	15	11	79	46
37	38	20	12	16	10	30	0
35	37	16	12	15	9	71	45
37	33	15	12	16	12	73	43
32	34	15	11	15	12	72	44
33	31	16	10	14	13	77	47
38	39	14	9	15	13	75	45
33	34	16	12	14	12	69	42
29	32	16	12	13	15	54	33
33	33	15	12	7	22	70	43
31	36	12	9	17	13	73	46
36	32	17	15	13	15	54	33
35	41	16	12	15	13	77	46
32	28	15	12	14	15	82	48
29	30	13	12	13	10	80	47
39	36	16	10	16	11	80	47
37	35	16	13	12	16	69	43
35	31	16	9	14	11	78	46
37	34	16	12	17	11	81	48
32	36	14	10	15	10	76	46
38	36	16	14	17	10	76	45
37	35	16	11	12	16	73	45
36	37	20	15	16	12	85	52
32	28	15	11	11	11	66	42
33	39	16	11	15	16	79	47
40	32	13	12	9	19	68	41
38	35	17	12	16	11	76	47
41	39	16	12	15	16	71	43
36	35	16	11	10	15	54	33
43	42	12	7	10	24	46	30
30	34	16	12	15	14	82	49
31	33	16	14	11	15	74	44
32	41	17	11	13	11	88	55
32	33	13	11	14	15	38	11
37	34	12	10	18	12	76	47
37	32	18	13	16	10	86	53
33	40	14	13	14	14	54	33
34	40	14	8	14	13	70	44
33	35	13	11	14	9	69	42
38	36	16	12	14	15	90	55
33	37	13	11	12	15	54	33
31	27	16	13	14	14	76	46
38	39	13	12	15	11	89	54
37	38	16	14	15	8	76	47
33	31	15	13	15	11	73	45
31	33	16	15	13	11	79	47
39	32	15	10	17	8	90	55
44	39	17	11	17	10	74	44
33	36	15	9	19	11	81	53
35	33	12	11	15	13	72	44
32	33	16	10	13	11	71	42
28	32	10	11	9	20	66	40
40	37	16	8	15	10	77	46
27	30	12	11	15	15	65	40
37	38	14	12	15	12	74	46
32	29	15	12	16	14	82	53
28	22	13	9	11	23	54	33
34	35	15	11	14	14	63	42
30	35	11	10	11	16	54	35
35	34	12	8	15	11	64	40
31	35	8	9	13	12	69	41
32	34	16	8	15	10	54	33
30	34	15	9	16	14	84	51
30	35	17	15	14	12	86	53
31	23	16	11	15	12	77	46
40	31	10	8	16	11	89	55
32	27	18	13	16	12	76	47
36	36	13	12	11	13	60	38
32	31	16	12	12	11	75	46
35	32	13	9	9	19	73	46
38	39	10	7	16	12	85	53
42	37	15	13	13	17	79	47
34	38	16	9	16	9	71	41
35	39	16	6	12	12	72	44
35	34	14	8	9	19	69	43
33	31	10	8	13	18	78	51
36	32	17	15	13	15	54	33
32	37	13	6	14	14	69	43
33	36	15	9	19	11	81	53
34	32	16	11	13	9	84	51
32	35	12	8	12	18	84	50
34	36	13	8	13	16	69	46

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'Herman Ole Andreas Wold' @ wold.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 & 'Herman Ole Andreas Wold' @ wold.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=147198&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]'Herman Ole Andreas Wold' @ wold.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=147198&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147198&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'Herman Ole Andreas Wold' @ wold.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
Connected[t] = + 18.2629853555223 + 0.336031993936646Separate[t] + 0.324387357179274Learning[t] -0.139120947054739Software[t] + 0.0353030642697486Happiness[t] -0.0228636599266878Depression[t] + 0.0349149616469267Belonging[t] -0.0249943108224535`Belonging_Final `[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Connected[t] =  +  18.2629853555223 +  0.336031993936646Separate[t] +  0.324387357179274Learning[t] -0.139120947054739Software[t] +  0.0353030642697486Happiness[t] -0.0228636599266878Depression[t] +  0.0349149616469267Belonging[t] -0.0249943108224535`Belonging_Final
`[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147198&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Connected[t] =  +  18.2629853555223 +  0.336031993936646Separate[t] +  0.324387357179274Learning[t] -0.139120947054739Software[t] +  0.0353030642697486Happiness[t] -0.0228636599266878Depression[t] +  0.0349149616469267Belonging[t] -0.0249943108224535`Belonging_Final
`[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147198&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147198&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
Connected[t] = + 18.2629853555223 + 0.336031993936646Separate[t] + 0.324387357179274Learning[t] -0.139120947054739Software[t] + 0.0353030642697486Happiness[t] -0.0228636599266878Depression[t] + 0.0349149616469267Belonging[t] -0.0249943108224535`Belonging_Final `[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)18.26298535552234.1922484.35642.4e-051.2e-05
Separate0.3360319939366460.070614.7594e-062e-06
Learning0.3243873571792740.1333612.43240.0161450.008073
Software-0.1391209470547390.137233-1.01380.3122890.156145
Happiness0.03530306426974860.1290380.27360.7847680.392384
Depression-0.02286365992668780.095489-0.23940.8110840.405542
Belonging0.03491496164692670.0752030.46430.6431070.321554
`Belonging_Final `-0.02499431082245350.108077-0.23130.8174170.408708

\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) & 18.2629853555223 & 4.192248 & 4.3564 & 2.4e-05 & 1.2e-05 \tabularnewline
Separate & 0.336031993936646 & 0.07061 & 4.759 & 4e-06 & 2e-06 \tabularnewline
Learning & 0.324387357179274 & 0.133361 & 2.4324 & 0.016145 & 0.008073 \tabularnewline
Software & -0.139120947054739 & 0.137233 & -1.0138 & 0.312289 & 0.156145 \tabularnewline
Happiness & 0.0353030642697486 & 0.129038 & 0.2736 & 0.784768 & 0.392384 \tabularnewline
Depression & -0.0228636599266878 & 0.095489 & -0.2394 & 0.811084 & 0.405542 \tabularnewline
Belonging & 0.0349149616469267 & 0.075203 & 0.4643 & 0.643107 & 0.321554 \tabularnewline
`Belonging_Final
` & -0.0249943108224535 & 0.108077 & -0.2313 & 0.817417 & 0.408708 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147198&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]18.2629853555223[/C][C]4.192248[/C][C]4.3564[/C][C]2.4e-05[/C][C]1.2e-05[/C][/ROW]
[ROW][C]Separate[/C][C]0.336031993936646[/C][C]0.07061[/C][C]4.759[/C][C]4e-06[/C][C]2e-06[/C][/ROW]
[ROW][C]Learning[/C][C]0.324387357179274[/C][C]0.133361[/C][C]2.4324[/C][C]0.016145[/C][C]0.008073[/C][/ROW]
[ROW][C]Software[/C][C]-0.139120947054739[/C][C]0.137233[/C][C]-1.0138[/C][C]0.312289[/C][C]0.156145[/C][/ROW]
[ROW][C]Happiness[/C][C]0.0353030642697486[/C][C]0.129038[/C][C]0.2736[/C][C]0.784768[/C][C]0.392384[/C][/ROW]
[ROW][C]Depression[/C][C]-0.0228636599266878[/C][C]0.095489[/C][C]-0.2394[/C][C]0.811084[/C][C]0.405542[/C][/ROW]
[ROW][C]Belonging[/C][C]0.0349149616469267[/C][C]0.075203[/C][C]0.4643[/C][C]0.643107[/C][C]0.321554[/C][/ROW]
[ROW][C]`Belonging_Final
`[/C][C]-0.0249943108224535[/C][C]0.108077[/C][C]-0.2313[/C][C]0.817417[/C][C]0.408708[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147198&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147198&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)18.26298535552234.1922484.35642.4e-051.2e-05
Separate0.3360319939366460.070614.7594e-062e-06
Learning0.3243873571792740.1333612.43240.0161450.008073
Software-0.1391209470547390.137233-1.01380.3122890.156145
Happiness0.03530306426974860.1290380.27360.7847680.392384
Depression-0.02286365992668780.095489-0.23940.8110840.405542
Belonging0.03491496164692670.0752030.46430.6431070.321554
`Belonging_Final `-0.02499431082245350.108077-0.23130.8174170.408708






Multiple Linear Regression - Regression Statistics
Multiple R0.427051855095818
R-squared0.182373286940779
Adjusted R-squared0.145208436347178
F-TEST (value)4.90714435892768
F-TEST (DF numerator)7
F-TEST (DF denominator)154
p-value5.02586788058146e-05
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation3.1204734771678
Sum Squared Residuals1499.55262714299

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.427051855095818 \tabularnewline
R-squared & 0.182373286940779 \tabularnewline
Adjusted R-squared & 0.145208436347178 \tabularnewline
F-TEST (value) & 4.90714435892768 \tabularnewline
F-TEST (DF numerator) & 7 \tabularnewline
F-TEST (DF denominator) & 154 \tabularnewline
p-value & 5.02586788058146e-05 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 3.1204734771678 \tabularnewline
Sum Squared Residuals & 1499.55262714299 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147198&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.427051855095818[/C][/ROW]
[ROW][C]R-squared[/C][C]0.182373286940779[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.145208436347178[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]4.90714435892768[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]7[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]154[/C][/ROW]
[ROW][C]p-value[/C][C]5.02586788058146e-05[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]3.1204734771678[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]1499.55262714299[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147198&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147198&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 R0.427051855095818
R-squared0.182373286940779
Adjusted R-squared0.145208436347178
F-TEST (value)4.90714435892768
F-TEST (DF numerator)7
F-TEST (DF denominator)154
p-value5.02586788058146e-05
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation3.1204734771678
Sum Squared Residuals1499.55262714299






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
14134.85033940541346.14966059458662
23934.78780820611364.21219179388636
33035.4235196060377-5.42351960603768
43134.8469343695324-3.84693436953245
53435.0175307771655-1.01753077716547
63532.74346499000832.25653500999167
73934.09083143755334.90916856244672
83435.0894725782011-1.08947257820108
93634.65594931879931.34405068120067
103736.77440866198590.225591338014142
113834.38404164500513.61595835499491
123634.75726411308451.24273588691549
133834.84446861311463.15553138688543
143935.99742188351653.00257811648354
153336.2436617446859-3.24366174468589
163233.8478243951801-1.84782439518007
173634.15863984594861.84136015405143
183837.92197257795260.0780274220473866
193936.81624023893192.1837597610681
203233.894734585242-1.89473458524196
213234.766938715043-2.76693871504304
223133.5434337015664-2.54343370156644
233936.66746325225872.33253674774132
243736.16209430037820.837905699621849
253935.33014008231023.66985991768981
264134.45244088401736.54755911598267
273635.71522387654920.284776123450821
283335.0749569084271-2.07495690842705
293334.6808652521655-1.68086525216554
303433.83291356747040.167086432529573
313132.7957919692103-1.79579196921027
322732.9722704796078-5.97227047960777
333733.60591310240593.39408689759415
343436.0781251801717-2.07812518017167
353432.94687823753371.05312176246635
363232.4100583759496-0.410058375949615
372932.2244325202422-3.22443252024215
383633.88340174777682.11659825222321
392934.3542796925317-5.35427969253167
403534.74070393508730.259296064912704
413734.59773360538412.40226639461589
423433.98764302795840.0123569720415818
433834.85947751995493.14052248004515
443533.64655458780151.35344541219852
453832.35360252684915.64639747315092
463733.38846579463323.61153420536684
473835.61957397424042.38042602575957
483334.7704826199945-1.77048261999448
493636.483211723396-0.483211723395974
503833.58929851017014.41070148982986
513236.0250455726567-4.02504557265669
523233.0078403618329-1.00784036183286
533232.8268789885384-0.826878988538431
543435.5323613848115-1.53236138481151
553232.7118745470261-0.711874547026151
563734.45669300111342.5433069988866
573934.93540412371094.06459587628908
582934.8221339000868-5.8221339000868
593735.36465408415871.63534591584128
603534.93703521594520.0629647840547729
613031.4665091791093-1.46650917910928
623834.95710347576913.04289652423086
633434.9442874470568-0.944287447056812
643134.2501037571595-3.25010375715953
653433.49363757456210.506362425437858
663536.072187976472-1.07218797647202
673635.16966221613310.830337783866912
683033.3177895120583-3.31778951205827
693935.40963919198493.59036080801507
703535.7857415055571-0.785741505557147
713833.9276112074824.07238879251801
723135.3257835092312-4.32578350923122
733437.1202156484346-3.1202156484346
743837.64184556290450.358154437095542
753432.74717635555551.25282364444447
763934.16414465626054.83585534373955
773735.75001099779311.24998900220693
783433.71948475902750.280515240972456
792832.9735992693893-4.97359926938928
803732.17035810998284.82964189001718
813335.76747286458-2.76747286458002
823737.2341581825003-0.234158182500295
833535.8949067960171-0.894906796017078
843734.31292209251972.68707790748033
853234.6928626967719-2.69286269677192
863334.1897001707668-1.18970017076684
873836.3837641175771.61623588242301
883334.7880697793308-1.78806977933084
892933.7133361201059-4.71333612010591
903333.6618130298843-0.661813029884273
913134.7026753158317-3.7026753158317
923633.6203606361212.37963936387903
933537.332075591116-2.33207559111602
943232.682828115227-0.682828115226952
952932.740297011634-3.74029701163399
963936.09093847378372.90906152621628
973734.7979257471442.20207425285596
983534.43445771014430.56554228985567
993735.18585630689521.81414369310483
1003235.3150588193169-3.31505881931686
1013835.50295018481842.4970498151816
1023735.16583886619631.83416113380368
1033637.0556547553594-1.05565475535944
1043232.3988209877631-0.39882098776309
1053336.7753771829888-3.7753771829888
1064032.79636212825977.20363787174031
1073835.66139209632922.33860790367084
1084136.45691378604854.54308621395154
1093634.75464385616131.24535614383866
1104335.95569247317157.04430752682852
1113035.0565798494001-5.05657984940008
1123134.1238819052851-3.12388190528513
1133237.929820867378-5.92982086737802
1143233.2418655055724-1.24186550557238
1153734.02940767921842.97059232078157
1163735.0606099361051.93939006389501
1173335.6718631343822-2.67186313438223
1183436.6740334968865-2.67403349688645
1193334.3586516285645-1.35865162856446
1203835.79983094131762.20016905868237
1213334.5241519010363-1.52415190103631
1223132.3954250431049-1.39542504310488
1233835.95160190474182.04839809525823
1243736.10014674236070.89985325763934
1253333.4393091316037-0.43930913160367
1263134.2464136022439-3.24641360224391
1273934.67551231479734.32448768520269
1284437.20796075250046.79203924749957
1293335.8965303531806-2.89653035318063
1303533.36080497137081.63919502862923
1313234.7876701984545-2.78767019845446
1322831.8946217313785-3.89462173137852
1334036.61302238336853.38697761663155
1342732.1625541814688-5.16255418146878
1353735.59332366993341.40667633006656
1363232.9873583435175-0.987358343517515
1372829.5437015424129-1.54370154241285
1383434.683618273408-0.683618273408011
1393033.2342788000179-3.23427880001787
1403533.98058467643941.01941532356059
1413132.9360570035502-1.93605700355021
1423235.1268083243711-3.1268083243711
1433035.2046996993037-5.20469969930372
1443035.3497432182333-5.3497432182333
1453131.4454843072378-0.445484307237821
1464032.8569764233777.14302357662297
1473233.1355388950338-1.13553889503384
1483634.14394143139781.85605856860215
1493233.8417438554998-1.84174385549984
1503533.26332822354631.73667177645372
1513835.57181843705542.42818156294455
1524235.40721415536066.59278584463943
1533436.7835819386775-2.7835819386775
1543537.2871055660989-2.28710556609887
1553534.33422360153320.665776398466767
1563332.30693427625460.693065723745405
1573633.6203606361212.37963936387903
1583235.5870077412556-3.58700774125556
1593335.8965303531806-2.89653035318063
1603434.5871902813244-0.587190281324418
1613234.499017982794-2.49901798279399
1623434.8167205366189-0.816720536618947

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 41 & 34.8503394054134 & 6.14966059458662 \tabularnewline
2 & 39 & 34.7878082061136 & 4.21219179388636 \tabularnewline
3 & 30 & 35.4235196060377 & -5.42351960603768 \tabularnewline
4 & 31 & 34.8469343695324 & -3.84693436953245 \tabularnewline
5 & 34 & 35.0175307771655 & -1.01753077716547 \tabularnewline
6 & 35 & 32.7434649900083 & 2.25653500999167 \tabularnewline
7 & 39 & 34.0908314375533 & 4.90916856244672 \tabularnewline
8 & 34 & 35.0894725782011 & -1.08947257820108 \tabularnewline
9 & 36 & 34.6559493187993 & 1.34405068120067 \tabularnewline
10 & 37 & 36.7744086619859 & 0.225591338014142 \tabularnewline
11 & 38 & 34.3840416450051 & 3.61595835499491 \tabularnewline
12 & 36 & 34.7572641130845 & 1.24273588691549 \tabularnewline
13 & 38 & 34.8444686131146 & 3.15553138688543 \tabularnewline
14 & 39 & 35.9974218835165 & 3.00257811648354 \tabularnewline
15 & 33 & 36.2436617446859 & -3.24366174468589 \tabularnewline
16 & 32 & 33.8478243951801 & -1.84782439518007 \tabularnewline
17 & 36 & 34.1586398459486 & 1.84136015405143 \tabularnewline
18 & 38 & 37.9219725779526 & 0.0780274220473866 \tabularnewline
19 & 39 & 36.8162402389319 & 2.1837597610681 \tabularnewline
20 & 32 & 33.894734585242 & -1.89473458524196 \tabularnewline
21 & 32 & 34.766938715043 & -2.76693871504304 \tabularnewline
22 & 31 & 33.5434337015664 & -2.54343370156644 \tabularnewline
23 & 39 & 36.6674632522587 & 2.33253674774132 \tabularnewline
24 & 37 & 36.1620943003782 & 0.837905699621849 \tabularnewline
25 & 39 & 35.3301400823102 & 3.66985991768981 \tabularnewline
26 & 41 & 34.4524408840173 & 6.54755911598267 \tabularnewline
27 & 36 & 35.7152238765492 & 0.284776123450821 \tabularnewline
28 & 33 & 35.0749569084271 & -2.07495690842705 \tabularnewline
29 & 33 & 34.6808652521655 & -1.68086525216554 \tabularnewline
30 & 34 & 33.8329135674704 & 0.167086432529573 \tabularnewline
31 & 31 & 32.7957919692103 & -1.79579196921027 \tabularnewline
32 & 27 & 32.9722704796078 & -5.97227047960777 \tabularnewline
33 & 37 & 33.6059131024059 & 3.39408689759415 \tabularnewline
34 & 34 & 36.0781251801717 & -2.07812518017167 \tabularnewline
35 & 34 & 32.9468782375337 & 1.05312176246635 \tabularnewline
36 & 32 & 32.4100583759496 & -0.410058375949615 \tabularnewline
37 & 29 & 32.2244325202422 & -3.22443252024215 \tabularnewline
38 & 36 & 33.8834017477768 & 2.11659825222321 \tabularnewline
39 & 29 & 34.3542796925317 & -5.35427969253167 \tabularnewline
40 & 35 & 34.7407039350873 & 0.259296064912704 \tabularnewline
41 & 37 & 34.5977336053841 & 2.40226639461589 \tabularnewline
42 & 34 & 33.9876430279584 & 0.0123569720415818 \tabularnewline
43 & 38 & 34.8594775199549 & 3.14052248004515 \tabularnewline
44 & 35 & 33.6465545878015 & 1.35344541219852 \tabularnewline
45 & 38 & 32.3536025268491 & 5.64639747315092 \tabularnewline
46 & 37 & 33.3884657946332 & 3.61153420536684 \tabularnewline
47 & 38 & 35.6195739742404 & 2.38042602575957 \tabularnewline
48 & 33 & 34.7704826199945 & -1.77048261999448 \tabularnewline
49 & 36 & 36.483211723396 & -0.483211723395974 \tabularnewline
50 & 38 & 33.5892985101701 & 4.41070148982986 \tabularnewline
51 & 32 & 36.0250455726567 & -4.02504557265669 \tabularnewline
52 & 32 & 33.0078403618329 & -1.00784036183286 \tabularnewline
53 & 32 & 32.8268789885384 & -0.826878988538431 \tabularnewline
54 & 34 & 35.5323613848115 & -1.53236138481151 \tabularnewline
55 & 32 & 32.7118745470261 & -0.711874547026151 \tabularnewline
56 & 37 & 34.4566930011134 & 2.5433069988866 \tabularnewline
57 & 39 & 34.9354041237109 & 4.06459587628908 \tabularnewline
58 & 29 & 34.8221339000868 & -5.8221339000868 \tabularnewline
59 & 37 & 35.3646540841587 & 1.63534591584128 \tabularnewline
60 & 35 & 34.9370352159452 & 0.0629647840547729 \tabularnewline
61 & 30 & 31.4665091791093 & -1.46650917910928 \tabularnewline
62 & 38 & 34.9571034757691 & 3.04289652423086 \tabularnewline
63 & 34 & 34.9442874470568 & -0.944287447056812 \tabularnewline
64 & 31 & 34.2501037571595 & -3.25010375715953 \tabularnewline
65 & 34 & 33.4936375745621 & 0.506362425437858 \tabularnewline
66 & 35 & 36.072187976472 & -1.07218797647202 \tabularnewline
67 & 36 & 35.1696622161331 & 0.830337783866912 \tabularnewline
68 & 30 & 33.3177895120583 & -3.31778951205827 \tabularnewline
69 & 39 & 35.4096391919849 & 3.59036080801507 \tabularnewline
70 & 35 & 35.7857415055571 & -0.785741505557147 \tabularnewline
71 & 38 & 33.927611207482 & 4.07238879251801 \tabularnewline
72 & 31 & 35.3257835092312 & -4.32578350923122 \tabularnewline
73 & 34 & 37.1202156484346 & -3.1202156484346 \tabularnewline
74 & 38 & 37.6418455629045 & 0.358154437095542 \tabularnewline
75 & 34 & 32.7471763555555 & 1.25282364444447 \tabularnewline
76 & 39 & 34.1641446562605 & 4.83585534373955 \tabularnewline
77 & 37 & 35.7500109977931 & 1.24998900220693 \tabularnewline
78 & 34 & 33.7194847590275 & 0.280515240972456 \tabularnewline
79 & 28 & 32.9735992693893 & -4.97359926938928 \tabularnewline
80 & 37 & 32.1703581099828 & 4.82964189001718 \tabularnewline
81 & 33 & 35.76747286458 & -2.76747286458002 \tabularnewline
82 & 37 & 37.2341581825003 & -0.234158182500295 \tabularnewline
83 & 35 & 35.8949067960171 & -0.894906796017078 \tabularnewline
84 & 37 & 34.3129220925197 & 2.68707790748033 \tabularnewline
85 & 32 & 34.6928626967719 & -2.69286269677192 \tabularnewline
86 & 33 & 34.1897001707668 & -1.18970017076684 \tabularnewline
87 & 38 & 36.383764117577 & 1.61623588242301 \tabularnewline
88 & 33 & 34.7880697793308 & -1.78806977933084 \tabularnewline
89 & 29 & 33.7133361201059 & -4.71333612010591 \tabularnewline
90 & 33 & 33.6618130298843 & -0.661813029884273 \tabularnewline
91 & 31 & 34.7026753158317 & -3.7026753158317 \tabularnewline
92 & 36 & 33.620360636121 & 2.37963936387903 \tabularnewline
93 & 35 & 37.332075591116 & -2.33207559111602 \tabularnewline
94 & 32 & 32.682828115227 & -0.682828115226952 \tabularnewline
95 & 29 & 32.740297011634 & -3.74029701163399 \tabularnewline
96 & 39 & 36.0909384737837 & 2.90906152621628 \tabularnewline
97 & 37 & 34.797925747144 & 2.20207425285596 \tabularnewline
98 & 35 & 34.4344577101443 & 0.56554228985567 \tabularnewline
99 & 37 & 35.1858563068952 & 1.81414369310483 \tabularnewline
100 & 32 & 35.3150588193169 & -3.31505881931686 \tabularnewline
101 & 38 & 35.5029501848184 & 2.4970498151816 \tabularnewline
102 & 37 & 35.1658388661963 & 1.83416113380368 \tabularnewline
103 & 36 & 37.0556547553594 & -1.05565475535944 \tabularnewline
104 & 32 & 32.3988209877631 & -0.39882098776309 \tabularnewline
105 & 33 & 36.7753771829888 & -3.7753771829888 \tabularnewline
106 & 40 & 32.7963621282597 & 7.20363787174031 \tabularnewline
107 & 38 & 35.6613920963292 & 2.33860790367084 \tabularnewline
108 & 41 & 36.4569137860485 & 4.54308621395154 \tabularnewline
109 & 36 & 34.7546438561613 & 1.24535614383866 \tabularnewline
110 & 43 & 35.9556924731715 & 7.04430752682852 \tabularnewline
111 & 30 & 35.0565798494001 & -5.05657984940008 \tabularnewline
112 & 31 & 34.1238819052851 & -3.12388190528513 \tabularnewline
113 & 32 & 37.929820867378 & -5.92982086737802 \tabularnewline
114 & 32 & 33.2418655055724 & -1.24186550557238 \tabularnewline
115 & 37 & 34.0294076792184 & 2.97059232078157 \tabularnewline
116 & 37 & 35.060609936105 & 1.93939006389501 \tabularnewline
117 & 33 & 35.6718631343822 & -2.67186313438223 \tabularnewline
118 & 34 & 36.6740334968865 & -2.67403349688645 \tabularnewline
119 & 33 & 34.3586516285645 & -1.35865162856446 \tabularnewline
120 & 38 & 35.7998309413176 & 2.20016905868237 \tabularnewline
121 & 33 & 34.5241519010363 & -1.52415190103631 \tabularnewline
122 & 31 & 32.3954250431049 & -1.39542504310488 \tabularnewline
123 & 38 & 35.9516019047418 & 2.04839809525823 \tabularnewline
124 & 37 & 36.1001467423607 & 0.89985325763934 \tabularnewline
125 & 33 & 33.4393091316037 & -0.43930913160367 \tabularnewline
126 & 31 & 34.2464136022439 & -3.24641360224391 \tabularnewline
127 & 39 & 34.6755123147973 & 4.32448768520269 \tabularnewline
128 & 44 & 37.2079607525004 & 6.79203924749957 \tabularnewline
129 & 33 & 35.8965303531806 & -2.89653035318063 \tabularnewline
130 & 35 & 33.3608049713708 & 1.63919502862923 \tabularnewline
131 & 32 & 34.7876701984545 & -2.78767019845446 \tabularnewline
132 & 28 & 31.8946217313785 & -3.89462173137852 \tabularnewline
133 & 40 & 36.6130223833685 & 3.38697761663155 \tabularnewline
134 & 27 & 32.1625541814688 & -5.16255418146878 \tabularnewline
135 & 37 & 35.5933236699334 & 1.40667633006656 \tabularnewline
136 & 32 & 32.9873583435175 & -0.987358343517515 \tabularnewline
137 & 28 & 29.5437015424129 & -1.54370154241285 \tabularnewline
138 & 34 & 34.683618273408 & -0.683618273408011 \tabularnewline
139 & 30 & 33.2342788000179 & -3.23427880001787 \tabularnewline
140 & 35 & 33.9805846764394 & 1.01941532356059 \tabularnewline
141 & 31 & 32.9360570035502 & -1.93605700355021 \tabularnewline
142 & 32 & 35.1268083243711 & -3.1268083243711 \tabularnewline
143 & 30 & 35.2046996993037 & -5.20469969930372 \tabularnewline
144 & 30 & 35.3497432182333 & -5.3497432182333 \tabularnewline
145 & 31 & 31.4454843072378 & -0.445484307237821 \tabularnewline
146 & 40 & 32.856976423377 & 7.14302357662297 \tabularnewline
147 & 32 & 33.1355388950338 & -1.13553889503384 \tabularnewline
148 & 36 & 34.1439414313978 & 1.85605856860215 \tabularnewline
149 & 32 & 33.8417438554998 & -1.84174385549984 \tabularnewline
150 & 35 & 33.2633282235463 & 1.73667177645372 \tabularnewline
151 & 38 & 35.5718184370554 & 2.42818156294455 \tabularnewline
152 & 42 & 35.4072141553606 & 6.59278584463943 \tabularnewline
153 & 34 & 36.7835819386775 & -2.7835819386775 \tabularnewline
154 & 35 & 37.2871055660989 & -2.28710556609887 \tabularnewline
155 & 35 & 34.3342236015332 & 0.665776398466767 \tabularnewline
156 & 33 & 32.3069342762546 & 0.693065723745405 \tabularnewline
157 & 36 & 33.620360636121 & 2.37963936387903 \tabularnewline
158 & 32 & 35.5870077412556 & -3.58700774125556 \tabularnewline
159 & 33 & 35.8965303531806 & -2.89653035318063 \tabularnewline
160 & 34 & 34.5871902813244 & -0.587190281324418 \tabularnewline
161 & 32 & 34.499017982794 & -2.49901798279399 \tabularnewline
162 & 34 & 34.8167205366189 & -0.816720536618947 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147198&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]41[/C][C]34.8503394054134[/C][C]6.14966059458662[/C][/ROW]
[ROW][C]2[/C][C]39[/C][C]34.7878082061136[/C][C]4.21219179388636[/C][/ROW]
[ROW][C]3[/C][C]30[/C][C]35.4235196060377[/C][C]-5.42351960603768[/C][/ROW]
[ROW][C]4[/C][C]31[/C][C]34.8469343695324[/C][C]-3.84693436953245[/C][/ROW]
[ROW][C]5[/C][C]34[/C][C]35.0175307771655[/C][C]-1.01753077716547[/C][/ROW]
[ROW][C]6[/C][C]35[/C][C]32.7434649900083[/C][C]2.25653500999167[/C][/ROW]
[ROW][C]7[/C][C]39[/C][C]34.0908314375533[/C][C]4.90916856244672[/C][/ROW]
[ROW][C]8[/C][C]34[/C][C]35.0894725782011[/C][C]-1.08947257820108[/C][/ROW]
[ROW][C]9[/C][C]36[/C][C]34.6559493187993[/C][C]1.34405068120067[/C][/ROW]
[ROW][C]10[/C][C]37[/C][C]36.7744086619859[/C][C]0.225591338014142[/C][/ROW]
[ROW][C]11[/C][C]38[/C][C]34.3840416450051[/C][C]3.61595835499491[/C][/ROW]
[ROW][C]12[/C][C]36[/C][C]34.7572641130845[/C][C]1.24273588691549[/C][/ROW]
[ROW][C]13[/C][C]38[/C][C]34.8444686131146[/C][C]3.15553138688543[/C][/ROW]
[ROW][C]14[/C][C]39[/C][C]35.9974218835165[/C][C]3.00257811648354[/C][/ROW]
[ROW][C]15[/C][C]33[/C][C]36.2436617446859[/C][C]-3.24366174468589[/C][/ROW]
[ROW][C]16[/C][C]32[/C][C]33.8478243951801[/C][C]-1.84782439518007[/C][/ROW]
[ROW][C]17[/C][C]36[/C][C]34.1586398459486[/C][C]1.84136015405143[/C][/ROW]
[ROW][C]18[/C][C]38[/C][C]37.9219725779526[/C][C]0.0780274220473866[/C][/ROW]
[ROW][C]19[/C][C]39[/C][C]36.8162402389319[/C][C]2.1837597610681[/C][/ROW]
[ROW][C]20[/C][C]32[/C][C]33.894734585242[/C][C]-1.89473458524196[/C][/ROW]
[ROW][C]21[/C][C]32[/C][C]34.766938715043[/C][C]-2.76693871504304[/C][/ROW]
[ROW][C]22[/C][C]31[/C][C]33.5434337015664[/C][C]-2.54343370156644[/C][/ROW]
[ROW][C]23[/C][C]39[/C][C]36.6674632522587[/C][C]2.33253674774132[/C][/ROW]
[ROW][C]24[/C][C]37[/C][C]36.1620943003782[/C][C]0.837905699621849[/C][/ROW]
[ROW][C]25[/C][C]39[/C][C]35.3301400823102[/C][C]3.66985991768981[/C][/ROW]
[ROW][C]26[/C][C]41[/C][C]34.4524408840173[/C][C]6.54755911598267[/C][/ROW]
[ROW][C]27[/C][C]36[/C][C]35.7152238765492[/C][C]0.284776123450821[/C][/ROW]
[ROW][C]28[/C][C]33[/C][C]35.0749569084271[/C][C]-2.07495690842705[/C][/ROW]
[ROW][C]29[/C][C]33[/C][C]34.6808652521655[/C][C]-1.68086525216554[/C][/ROW]
[ROW][C]30[/C][C]34[/C][C]33.8329135674704[/C][C]0.167086432529573[/C][/ROW]
[ROW][C]31[/C][C]31[/C][C]32.7957919692103[/C][C]-1.79579196921027[/C][/ROW]
[ROW][C]32[/C][C]27[/C][C]32.9722704796078[/C][C]-5.97227047960777[/C][/ROW]
[ROW][C]33[/C][C]37[/C][C]33.6059131024059[/C][C]3.39408689759415[/C][/ROW]
[ROW][C]34[/C][C]34[/C][C]36.0781251801717[/C][C]-2.07812518017167[/C][/ROW]
[ROW][C]35[/C][C]34[/C][C]32.9468782375337[/C][C]1.05312176246635[/C][/ROW]
[ROW][C]36[/C][C]32[/C][C]32.4100583759496[/C][C]-0.410058375949615[/C][/ROW]
[ROW][C]37[/C][C]29[/C][C]32.2244325202422[/C][C]-3.22443252024215[/C][/ROW]
[ROW][C]38[/C][C]36[/C][C]33.8834017477768[/C][C]2.11659825222321[/C][/ROW]
[ROW][C]39[/C][C]29[/C][C]34.3542796925317[/C][C]-5.35427969253167[/C][/ROW]
[ROW][C]40[/C][C]35[/C][C]34.7407039350873[/C][C]0.259296064912704[/C][/ROW]
[ROW][C]41[/C][C]37[/C][C]34.5977336053841[/C][C]2.40226639461589[/C][/ROW]
[ROW][C]42[/C][C]34[/C][C]33.9876430279584[/C][C]0.0123569720415818[/C][/ROW]
[ROW][C]43[/C][C]38[/C][C]34.8594775199549[/C][C]3.14052248004515[/C][/ROW]
[ROW][C]44[/C][C]35[/C][C]33.6465545878015[/C][C]1.35344541219852[/C][/ROW]
[ROW][C]45[/C][C]38[/C][C]32.3536025268491[/C][C]5.64639747315092[/C][/ROW]
[ROW][C]46[/C][C]37[/C][C]33.3884657946332[/C][C]3.61153420536684[/C][/ROW]
[ROW][C]47[/C][C]38[/C][C]35.6195739742404[/C][C]2.38042602575957[/C][/ROW]
[ROW][C]48[/C][C]33[/C][C]34.7704826199945[/C][C]-1.77048261999448[/C][/ROW]
[ROW][C]49[/C][C]36[/C][C]36.483211723396[/C][C]-0.483211723395974[/C][/ROW]
[ROW][C]50[/C][C]38[/C][C]33.5892985101701[/C][C]4.41070148982986[/C][/ROW]
[ROW][C]51[/C][C]32[/C][C]36.0250455726567[/C][C]-4.02504557265669[/C][/ROW]
[ROW][C]52[/C][C]32[/C][C]33.0078403618329[/C][C]-1.00784036183286[/C][/ROW]
[ROW][C]53[/C][C]32[/C][C]32.8268789885384[/C][C]-0.826878988538431[/C][/ROW]
[ROW][C]54[/C][C]34[/C][C]35.5323613848115[/C][C]-1.53236138481151[/C][/ROW]
[ROW][C]55[/C][C]32[/C][C]32.7118745470261[/C][C]-0.711874547026151[/C][/ROW]
[ROW][C]56[/C][C]37[/C][C]34.4566930011134[/C][C]2.5433069988866[/C][/ROW]
[ROW][C]57[/C][C]39[/C][C]34.9354041237109[/C][C]4.06459587628908[/C][/ROW]
[ROW][C]58[/C][C]29[/C][C]34.8221339000868[/C][C]-5.8221339000868[/C][/ROW]
[ROW][C]59[/C][C]37[/C][C]35.3646540841587[/C][C]1.63534591584128[/C][/ROW]
[ROW][C]60[/C][C]35[/C][C]34.9370352159452[/C][C]0.0629647840547729[/C][/ROW]
[ROW][C]61[/C][C]30[/C][C]31.4665091791093[/C][C]-1.46650917910928[/C][/ROW]
[ROW][C]62[/C][C]38[/C][C]34.9571034757691[/C][C]3.04289652423086[/C][/ROW]
[ROW][C]63[/C][C]34[/C][C]34.9442874470568[/C][C]-0.944287447056812[/C][/ROW]
[ROW][C]64[/C][C]31[/C][C]34.2501037571595[/C][C]-3.25010375715953[/C][/ROW]
[ROW][C]65[/C][C]34[/C][C]33.4936375745621[/C][C]0.506362425437858[/C][/ROW]
[ROW][C]66[/C][C]35[/C][C]36.072187976472[/C][C]-1.07218797647202[/C][/ROW]
[ROW][C]67[/C][C]36[/C][C]35.1696622161331[/C][C]0.830337783866912[/C][/ROW]
[ROW][C]68[/C][C]30[/C][C]33.3177895120583[/C][C]-3.31778951205827[/C][/ROW]
[ROW][C]69[/C][C]39[/C][C]35.4096391919849[/C][C]3.59036080801507[/C][/ROW]
[ROW][C]70[/C][C]35[/C][C]35.7857415055571[/C][C]-0.785741505557147[/C][/ROW]
[ROW][C]71[/C][C]38[/C][C]33.927611207482[/C][C]4.07238879251801[/C][/ROW]
[ROW][C]72[/C][C]31[/C][C]35.3257835092312[/C][C]-4.32578350923122[/C][/ROW]
[ROW][C]73[/C][C]34[/C][C]37.1202156484346[/C][C]-3.1202156484346[/C][/ROW]
[ROW][C]74[/C][C]38[/C][C]37.6418455629045[/C][C]0.358154437095542[/C][/ROW]
[ROW][C]75[/C][C]34[/C][C]32.7471763555555[/C][C]1.25282364444447[/C][/ROW]
[ROW][C]76[/C][C]39[/C][C]34.1641446562605[/C][C]4.83585534373955[/C][/ROW]
[ROW][C]77[/C][C]37[/C][C]35.7500109977931[/C][C]1.24998900220693[/C][/ROW]
[ROW][C]78[/C][C]34[/C][C]33.7194847590275[/C][C]0.280515240972456[/C][/ROW]
[ROW][C]79[/C][C]28[/C][C]32.9735992693893[/C][C]-4.97359926938928[/C][/ROW]
[ROW][C]80[/C][C]37[/C][C]32.1703581099828[/C][C]4.82964189001718[/C][/ROW]
[ROW][C]81[/C][C]33[/C][C]35.76747286458[/C][C]-2.76747286458002[/C][/ROW]
[ROW][C]82[/C][C]37[/C][C]37.2341581825003[/C][C]-0.234158182500295[/C][/ROW]
[ROW][C]83[/C][C]35[/C][C]35.8949067960171[/C][C]-0.894906796017078[/C][/ROW]
[ROW][C]84[/C][C]37[/C][C]34.3129220925197[/C][C]2.68707790748033[/C][/ROW]
[ROW][C]85[/C][C]32[/C][C]34.6928626967719[/C][C]-2.69286269677192[/C][/ROW]
[ROW][C]86[/C][C]33[/C][C]34.1897001707668[/C][C]-1.18970017076684[/C][/ROW]
[ROW][C]87[/C][C]38[/C][C]36.383764117577[/C][C]1.61623588242301[/C][/ROW]
[ROW][C]88[/C][C]33[/C][C]34.7880697793308[/C][C]-1.78806977933084[/C][/ROW]
[ROW][C]89[/C][C]29[/C][C]33.7133361201059[/C][C]-4.71333612010591[/C][/ROW]
[ROW][C]90[/C][C]33[/C][C]33.6618130298843[/C][C]-0.661813029884273[/C][/ROW]
[ROW][C]91[/C][C]31[/C][C]34.7026753158317[/C][C]-3.7026753158317[/C][/ROW]
[ROW][C]92[/C][C]36[/C][C]33.620360636121[/C][C]2.37963936387903[/C][/ROW]
[ROW][C]93[/C][C]35[/C][C]37.332075591116[/C][C]-2.33207559111602[/C][/ROW]
[ROW][C]94[/C][C]32[/C][C]32.682828115227[/C][C]-0.682828115226952[/C][/ROW]
[ROW][C]95[/C][C]29[/C][C]32.740297011634[/C][C]-3.74029701163399[/C][/ROW]
[ROW][C]96[/C][C]39[/C][C]36.0909384737837[/C][C]2.90906152621628[/C][/ROW]
[ROW][C]97[/C][C]37[/C][C]34.797925747144[/C][C]2.20207425285596[/C][/ROW]
[ROW][C]98[/C][C]35[/C][C]34.4344577101443[/C][C]0.56554228985567[/C][/ROW]
[ROW][C]99[/C][C]37[/C][C]35.1858563068952[/C][C]1.81414369310483[/C][/ROW]
[ROW][C]100[/C][C]32[/C][C]35.3150588193169[/C][C]-3.31505881931686[/C][/ROW]
[ROW][C]101[/C][C]38[/C][C]35.5029501848184[/C][C]2.4970498151816[/C][/ROW]
[ROW][C]102[/C][C]37[/C][C]35.1658388661963[/C][C]1.83416113380368[/C][/ROW]
[ROW][C]103[/C][C]36[/C][C]37.0556547553594[/C][C]-1.05565475535944[/C][/ROW]
[ROW][C]104[/C][C]32[/C][C]32.3988209877631[/C][C]-0.39882098776309[/C][/ROW]
[ROW][C]105[/C][C]33[/C][C]36.7753771829888[/C][C]-3.7753771829888[/C][/ROW]
[ROW][C]106[/C][C]40[/C][C]32.7963621282597[/C][C]7.20363787174031[/C][/ROW]
[ROW][C]107[/C][C]38[/C][C]35.6613920963292[/C][C]2.33860790367084[/C][/ROW]
[ROW][C]108[/C][C]41[/C][C]36.4569137860485[/C][C]4.54308621395154[/C][/ROW]
[ROW][C]109[/C][C]36[/C][C]34.7546438561613[/C][C]1.24535614383866[/C][/ROW]
[ROW][C]110[/C][C]43[/C][C]35.9556924731715[/C][C]7.04430752682852[/C][/ROW]
[ROW][C]111[/C][C]30[/C][C]35.0565798494001[/C][C]-5.05657984940008[/C][/ROW]
[ROW][C]112[/C][C]31[/C][C]34.1238819052851[/C][C]-3.12388190528513[/C][/ROW]
[ROW][C]113[/C][C]32[/C][C]37.929820867378[/C][C]-5.92982086737802[/C][/ROW]
[ROW][C]114[/C][C]32[/C][C]33.2418655055724[/C][C]-1.24186550557238[/C][/ROW]
[ROW][C]115[/C][C]37[/C][C]34.0294076792184[/C][C]2.97059232078157[/C][/ROW]
[ROW][C]116[/C][C]37[/C][C]35.060609936105[/C][C]1.93939006389501[/C][/ROW]
[ROW][C]117[/C][C]33[/C][C]35.6718631343822[/C][C]-2.67186313438223[/C][/ROW]
[ROW][C]118[/C][C]34[/C][C]36.6740334968865[/C][C]-2.67403349688645[/C][/ROW]
[ROW][C]119[/C][C]33[/C][C]34.3586516285645[/C][C]-1.35865162856446[/C][/ROW]
[ROW][C]120[/C][C]38[/C][C]35.7998309413176[/C][C]2.20016905868237[/C][/ROW]
[ROW][C]121[/C][C]33[/C][C]34.5241519010363[/C][C]-1.52415190103631[/C][/ROW]
[ROW][C]122[/C][C]31[/C][C]32.3954250431049[/C][C]-1.39542504310488[/C][/ROW]
[ROW][C]123[/C][C]38[/C][C]35.9516019047418[/C][C]2.04839809525823[/C][/ROW]
[ROW][C]124[/C][C]37[/C][C]36.1001467423607[/C][C]0.89985325763934[/C][/ROW]
[ROW][C]125[/C][C]33[/C][C]33.4393091316037[/C][C]-0.43930913160367[/C][/ROW]
[ROW][C]126[/C][C]31[/C][C]34.2464136022439[/C][C]-3.24641360224391[/C][/ROW]
[ROW][C]127[/C][C]39[/C][C]34.6755123147973[/C][C]4.32448768520269[/C][/ROW]
[ROW][C]128[/C][C]44[/C][C]37.2079607525004[/C][C]6.79203924749957[/C][/ROW]
[ROW][C]129[/C][C]33[/C][C]35.8965303531806[/C][C]-2.89653035318063[/C][/ROW]
[ROW][C]130[/C][C]35[/C][C]33.3608049713708[/C][C]1.63919502862923[/C][/ROW]
[ROW][C]131[/C][C]32[/C][C]34.7876701984545[/C][C]-2.78767019845446[/C][/ROW]
[ROW][C]132[/C][C]28[/C][C]31.8946217313785[/C][C]-3.89462173137852[/C][/ROW]
[ROW][C]133[/C][C]40[/C][C]36.6130223833685[/C][C]3.38697761663155[/C][/ROW]
[ROW][C]134[/C][C]27[/C][C]32.1625541814688[/C][C]-5.16255418146878[/C][/ROW]
[ROW][C]135[/C][C]37[/C][C]35.5933236699334[/C][C]1.40667633006656[/C][/ROW]
[ROW][C]136[/C][C]32[/C][C]32.9873583435175[/C][C]-0.987358343517515[/C][/ROW]
[ROW][C]137[/C][C]28[/C][C]29.5437015424129[/C][C]-1.54370154241285[/C][/ROW]
[ROW][C]138[/C][C]34[/C][C]34.683618273408[/C][C]-0.683618273408011[/C][/ROW]
[ROW][C]139[/C][C]30[/C][C]33.2342788000179[/C][C]-3.23427880001787[/C][/ROW]
[ROW][C]140[/C][C]35[/C][C]33.9805846764394[/C][C]1.01941532356059[/C][/ROW]
[ROW][C]141[/C][C]31[/C][C]32.9360570035502[/C][C]-1.93605700355021[/C][/ROW]
[ROW][C]142[/C][C]32[/C][C]35.1268083243711[/C][C]-3.1268083243711[/C][/ROW]
[ROW][C]143[/C][C]30[/C][C]35.2046996993037[/C][C]-5.20469969930372[/C][/ROW]
[ROW][C]144[/C][C]30[/C][C]35.3497432182333[/C][C]-5.3497432182333[/C][/ROW]
[ROW][C]145[/C][C]31[/C][C]31.4454843072378[/C][C]-0.445484307237821[/C][/ROW]
[ROW][C]146[/C][C]40[/C][C]32.856976423377[/C][C]7.14302357662297[/C][/ROW]
[ROW][C]147[/C][C]32[/C][C]33.1355388950338[/C][C]-1.13553889503384[/C][/ROW]
[ROW][C]148[/C][C]36[/C][C]34.1439414313978[/C][C]1.85605856860215[/C][/ROW]
[ROW][C]149[/C][C]32[/C][C]33.8417438554998[/C][C]-1.84174385549984[/C][/ROW]
[ROW][C]150[/C][C]35[/C][C]33.2633282235463[/C][C]1.73667177645372[/C][/ROW]
[ROW][C]151[/C][C]38[/C][C]35.5718184370554[/C][C]2.42818156294455[/C][/ROW]
[ROW][C]152[/C][C]42[/C][C]35.4072141553606[/C][C]6.59278584463943[/C][/ROW]
[ROW][C]153[/C][C]34[/C][C]36.7835819386775[/C][C]-2.7835819386775[/C][/ROW]
[ROW][C]154[/C][C]35[/C][C]37.2871055660989[/C][C]-2.28710556609887[/C][/ROW]
[ROW][C]155[/C][C]35[/C][C]34.3342236015332[/C][C]0.665776398466767[/C][/ROW]
[ROW][C]156[/C][C]33[/C][C]32.3069342762546[/C][C]0.693065723745405[/C][/ROW]
[ROW][C]157[/C][C]36[/C][C]33.620360636121[/C][C]2.37963936387903[/C][/ROW]
[ROW][C]158[/C][C]32[/C][C]35.5870077412556[/C][C]-3.58700774125556[/C][/ROW]
[ROW][C]159[/C][C]33[/C][C]35.8965303531806[/C][C]-2.89653035318063[/C][/ROW]
[ROW][C]160[/C][C]34[/C][C]34.5871902813244[/C][C]-0.587190281324418[/C][/ROW]
[ROW][C]161[/C][C]32[/C][C]34.499017982794[/C][C]-2.49901798279399[/C][/ROW]
[ROW][C]162[/C][C]34[/C][C]34.8167205366189[/C][C]-0.816720536618947[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147198&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147198&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
14134.85033940541346.14966059458662
23934.78780820611364.21219179388636
33035.4235196060377-5.42351960603768
43134.8469343695324-3.84693436953245
53435.0175307771655-1.01753077716547
63532.74346499000832.25653500999167
73934.09083143755334.90916856244672
83435.0894725782011-1.08947257820108
93634.65594931879931.34405068120067
103736.77440866198590.225591338014142
113834.38404164500513.61595835499491
123634.75726411308451.24273588691549
133834.84446861311463.15553138688543
143935.99742188351653.00257811648354
153336.2436617446859-3.24366174468589
163233.8478243951801-1.84782439518007
173634.15863984594861.84136015405143
183837.92197257795260.0780274220473866
193936.81624023893192.1837597610681
203233.894734585242-1.89473458524196
213234.766938715043-2.76693871504304
223133.5434337015664-2.54343370156644
233936.66746325225872.33253674774132
243736.16209430037820.837905699621849
253935.33014008231023.66985991768981
264134.45244088401736.54755911598267
273635.71522387654920.284776123450821
283335.0749569084271-2.07495690842705
293334.6808652521655-1.68086525216554
303433.83291356747040.167086432529573
313132.7957919692103-1.79579196921027
322732.9722704796078-5.97227047960777
333733.60591310240593.39408689759415
343436.0781251801717-2.07812518017167
353432.94687823753371.05312176246635
363232.4100583759496-0.410058375949615
372932.2244325202422-3.22443252024215
383633.88340174777682.11659825222321
392934.3542796925317-5.35427969253167
403534.74070393508730.259296064912704
413734.59773360538412.40226639461589
423433.98764302795840.0123569720415818
433834.85947751995493.14052248004515
443533.64655458780151.35344541219852
453832.35360252684915.64639747315092
463733.38846579463323.61153420536684
473835.61957397424042.38042602575957
483334.7704826199945-1.77048261999448
493636.483211723396-0.483211723395974
503833.58929851017014.41070148982986
513236.0250455726567-4.02504557265669
523233.0078403618329-1.00784036183286
533232.8268789885384-0.826878988538431
543435.5323613848115-1.53236138481151
553232.7118745470261-0.711874547026151
563734.45669300111342.5433069988866
573934.93540412371094.06459587628908
582934.8221339000868-5.8221339000868
593735.36465408415871.63534591584128
603534.93703521594520.0629647840547729
613031.4665091791093-1.46650917910928
623834.95710347576913.04289652423086
633434.9442874470568-0.944287447056812
643134.2501037571595-3.25010375715953
653433.49363757456210.506362425437858
663536.072187976472-1.07218797647202
673635.16966221613310.830337783866912
683033.3177895120583-3.31778951205827
693935.40963919198493.59036080801507
703535.7857415055571-0.785741505557147
713833.9276112074824.07238879251801
723135.3257835092312-4.32578350923122
733437.1202156484346-3.1202156484346
743837.64184556290450.358154437095542
753432.74717635555551.25282364444447
763934.16414465626054.83585534373955
773735.75001099779311.24998900220693
783433.71948475902750.280515240972456
792832.9735992693893-4.97359926938928
803732.17035810998284.82964189001718
813335.76747286458-2.76747286458002
823737.2341581825003-0.234158182500295
833535.8949067960171-0.894906796017078
843734.31292209251972.68707790748033
853234.6928626967719-2.69286269677192
863334.1897001707668-1.18970017076684
873836.3837641175771.61623588242301
883334.7880697793308-1.78806977933084
892933.7133361201059-4.71333612010591
903333.6618130298843-0.661813029884273
913134.7026753158317-3.7026753158317
923633.6203606361212.37963936387903
933537.332075591116-2.33207559111602
943232.682828115227-0.682828115226952
952932.740297011634-3.74029701163399
963936.09093847378372.90906152621628
973734.7979257471442.20207425285596
983534.43445771014430.56554228985567
993735.18585630689521.81414369310483
1003235.3150588193169-3.31505881931686
1013835.50295018481842.4970498151816
1023735.16583886619631.83416113380368
1033637.0556547553594-1.05565475535944
1043232.3988209877631-0.39882098776309
1053336.7753771829888-3.7753771829888
1064032.79636212825977.20363787174031
1073835.66139209632922.33860790367084
1084136.45691378604854.54308621395154
1093634.75464385616131.24535614383866
1104335.95569247317157.04430752682852
1113035.0565798494001-5.05657984940008
1123134.1238819052851-3.12388190528513
1133237.929820867378-5.92982086737802
1143233.2418655055724-1.24186550557238
1153734.02940767921842.97059232078157
1163735.0606099361051.93939006389501
1173335.6718631343822-2.67186313438223
1183436.6740334968865-2.67403349688645
1193334.3586516285645-1.35865162856446
1203835.79983094131762.20016905868237
1213334.5241519010363-1.52415190103631
1223132.3954250431049-1.39542504310488
1233835.95160190474182.04839809525823
1243736.10014674236070.89985325763934
1253333.4393091316037-0.43930913160367
1263134.2464136022439-3.24641360224391
1273934.67551231479734.32448768520269
1284437.20796075250046.79203924749957
1293335.8965303531806-2.89653035318063
1303533.36080497137081.63919502862923
1313234.7876701984545-2.78767019845446
1322831.8946217313785-3.89462173137852
1334036.61302238336853.38697761663155
1342732.1625541814688-5.16255418146878
1353735.59332366993341.40667633006656
1363232.9873583435175-0.987358343517515
1372829.5437015424129-1.54370154241285
1383434.683618273408-0.683618273408011
1393033.2342788000179-3.23427880001787
1403533.98058467643941.01941532356059
1413132.9360570035502-1.93605700355021
1423235.1268083243711-3.1268083243711
1433035.2046996993037-5.20469969930372
1443035.3497432182333-5.3497432182333
1453131.4454843072378-0.445484307237821
1464032.8569764233777.14302357662297
1473233.1355388950338-1.13553889503384
1483634.14394143139781.85605856860215
1493233.8417438554998-1.84174385549984
1503533.26332822354631.73667177645372
1513835.57181843705542.42818156294455
1524235.40721415536066.59278584463943
1533436.7835819386775-2.7835819386775
1543537.2871055660989-2.28710556609887
1553534.33422360153320.665776398466767
1563332.30693427625460.693065723745405
1573633.6203606361212.37963936387903
1583235.5870077412556-3.58700774125556
1593335.8965303531806-2.89653035318063
1603434.5871902813244-0.587190281324418
1613234.499017982794-2.49901798279399
1623434.8167205366189-0.816720536618947






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
110.1858689979561010.3717379959122020.814131002043899
120.8711265198208840.2577469603582330.128873480179116
130.9177856073633610.1644287852732790.0822143926366395
140.8782549710655490.2434900578689030.121745028934452
150.8409748745624570.3180502508750850.159025125437543
160.8650212906900450.269957418619910.134978709309955
170.8216938897658040.3566122204683920.178306110234196
180.7890062562805150.4219874874389710.210993743719485
190.7427074467205680.5145851065588640.257292553279432
200.7705474341228570.4589051317542860.229452565877143
210.7902548525762610.4194902948474770.209745147423739
220.7492177578348370.5015644843303270.250782242165163
230.7059472602460190.5881054795079620.294052739753981
240.6388008599893830.7223982800212340.361199140010617
250.6332683712805110.7334632574389790.366731628719489
260.7795034963227430.4409930073545140.220496503677257
270.7731025924187280.4537948151625450.226897407581272
280.7293224658350680.5413550683298630.270677534164932
290.7383900771481530.5232198457036940.261609922851847
300.6826102923862960.6347794152274080.317389707613704
310.6451341949604360.7097316100791280.354865805039564
320.8036532056193830.3926935887612340.196346794380617
330.8012536826432240.3974926347135520.198746317356776
340.7650746236652690.4698507526694620.234925376334731
350.717559050892450.56488189821510.28244094910755
360.6711103240942140.6577793518115730.328889675905786
370.6590730000160950.681853999967810.340926999983905
380.6262948773022420.7474102453955150.373705122697758
390.7442330541417220.5115338917165560.255766945858278
400.6971112879335630.6057774241328730.302888712066437
410.6672128522335550.6655742955328910.332787147766445
420.6154486431136750.769102713772650.384551356886325
430.5861251239017570.8277497521964860.413874876098243
440.5375554111245320.9248891777509360.462444588875468
450.6347300884154850.7305398231690310.365269911584515
460.6410405470007990.7179189059984010.358959452999201
470.6237605004674870.7524789990650270.376239499532513
480.5930500522892990.8138998954214020.406949947710701
490.5453454367630860.9093091264738280.454654563236914
500.5723870471296280.8552259057407450.427612952870372
510.6188628680546390.7622742638907220.381137131945361
520.5798674180973140.8402651638053710.420132581902686
530.5349780662111850.9300438675776310.465021933788815
540.4862341915947640.9724683831895280.513765808405236
550.4413071656985930.8826143313971860.558692834301407
560.4304866465178270.8609732930356540.569513353482173
570.4579753075115840.9159506150231680.542024692488416
580.5930836817318480.8138326365363050.406916318268152
590.5539529181863690.8920941636272620.446047081813631
600.505559403401580.9888811931968410.49444059659842
610.4690263593896760.9380527187793520.530973640610324
620.4535232639334230.9070465278668460.546476736066577
630.4198101516587640.8396203033175290.580189848341236
640.4262472069042050.852494413808410.573752793095795
650.3807441904738630.7614883809477270.619255809526137
660.3447150556384750.6894301112769510.655284944361525
670.3042239283740670.6084478567481350.695776071625933
680.3348925450790330.6697850901580660.665107454920967
690.3454863652890560.6909727305781130.654513634710944
700.3060211139377050.6120422278754110.693978886062295
710.339072452269970.678144904539940.66092754773003
720.3779145721855590.7558291443711180.622085427814441
730.3789251460676610.7578502921353220.621074853932339
740.3354068212201780.6708136424403560.664593178779822
750.30077185882630.6015437176525990.6992281411737
760.3563087178066860.7126174356133730.643691282193314
770.3187674452748230.6375348905496450.681232554725178
780.2782536788275070.5565073576550140.721746321172493
790.3421658677495780.6843317354991570.657834132250422
800.4178073496552030.8356146993104050.582192650344797
810.4075681614624820.8151363229249630.592431838537518
820.3629451928759740.7258903857519490.637054807124026
830.32340506477260.64681012954520.6765949352274
840.31179469515420.6235893903084010.6882053048458
850.2988765327212880.5977530654425760.701123467278712
860.2650828659056180.5301657318112360.734917134094382
870.2382934810190270.4765869620380550.761706518980973
880.2130079935830820.4260159871661640.786992006416918
890.2503840176144410.5007680352288830.749615982385559
900.2164345260985060.4328690521970120.783565473901494
910.2306292760226840.4612585520453680.769370723977316
920.2160680742553650.432136148510730.783931925744635
930.2024424479209670.4048848958419350.797557552079033
940.1722363295019220.3444726590038440.827763670498078
950.1795821426805190.3591642853610380.820417857319481
960.1754924614480190.3509849228960390.824507538551981
970.1602890452649620.3205780905299230.839710954735038
980.1370486479003810.2740972958007620.862951352099619
990.1202701079778680.2405402159557360.879729892022132
1000.1191637999744470.2383275999488940.880836200025553
1010.109987033288770.219974066577540.89001296671123
1020.09652946431977560.1930589286395510.903470535680224
1030.07908564216874750.1581712843374950.920914357831253
1040.06490920954573540.1298184190914710.935090790454265
1050.07627202492009060.1525440498401810.923727975079909
1060.1827067294171980.3654134588343970.817293270582802
1070.1698591350137220.3397182700274440.830140864986278
1080.1937948316745130.3875896633490260.806205168325487
1090.1800406058931180.3600812117862360.819959394106882
1100.3807243372514540.7614486745029090.619275662748546
1110.4567150875552450.9134301751104910.543284912444755
1120.4402094206972810.8804188413945620.559790579302719
1130.5690955350178820.8618089299642360.430904464982118
1140.5222926214597440.9554147570805120.477707378540256
1150.5077196772307020.9845606455385960.492280322769298
1160.4720816983318360.9441633966636730.527918301668164
1170.4423322543281330.8846645086562660.557667745671867
1180.4183084114809510.8366168229619010.581691588519049
1190.3725985470299160.7451970940598320.627401452970084
1200.3364030956608270.6728061913216540.663596904339173
1210.2919728559341240.5839457118682480.708027144065876
1220.2509440689365070.5018881378730140.749055931063493
1230.2156456226379230.4312912452758450.784354377362077
1240.1784445227088750.3568890454177510.821555477291125
1250.143758678157620.287517356315240.85624132184238
1260.1534414046925950.306882809385190.846558595307405
1270.1707955489856040.3415910979712070.829204451014396
1280.3496285532832850.699257106566570.650371446716715
1290.3207509728858020.6415019457716040.679249027114198
1300.2840011166064660.5680022332129330.715998883393534
1310.2523594790835020.5047189581670040.747640520916498
1320.2947143677287810.5894287354575620.705285632271219
1330.3658416073725570.7316832147451130.634158392627443
1340.4761863918852570.9523727837705140.523813608114743
1350.4202245722774820.8404491445549630.579775427722518
1360.358131421700580.7162628434011590.64186857829942
1370.3143919327905470.6287838655810940.685608067209453
1380.2543868428273890.5087736856547780.745613157172611
1390.3035720261943950.607144052388790.696427973805605
1400.248588645879610.497177291759220.75141135412039
1410.4765229413846080.9530458827692170.523477058615392
1420.4149872337799880.8299744675599760.585012766220012
1430.4412122143355560.8824244286711130.558787785664444
1440.7027614498989560.5944771002020880.297238550101044
1450.6132553504205320.7734892991589360.386744649579468
1460.9442888645712040.1114222708575910.0557111354287957
1470.924296562059530.151406875880940.0757034379404701
1480.9464807511732260.1070384976535480.0535192488267738
1490.9447529667845710.1104940664308580.0552470332154288
1500.8824242881943190.2351514236113630.117575711805681
1510.7693679172047750.4612641655904490.230632082795225

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
11 & 0.185868997956101 & 0.371737995912202 & 0.814131002043899 \tabularnewline
12 & 0.871126519820884 & 0.257746960358233 & 0.128873480179116 \tabularnewline
13 & 0.917785607363361 & 0.164428785273279 & 0.0822143926366395 \tabularnewline
14 & 0.878254971065549 & 0.243490057868903 & 0.121745028934452 \tabularnewline
15 & 0.840974874562457 & 0.318050250875085 & 0.159025125437543 \tabularnewline
16 & 0.865021290690045 & 0.26995741861991 & 0.134978709309955 \tabularnewline
17 & 0.821693889765804 & 0.356612220468392 & 0.178306110234196 \tabularnewline
18 & 0.789006256280515 & 0.421987487438971 & 0.210993743719485 \tabularnewline
19 & 0.742707446720568 & 0.514585106558864 & 0.257292553279432 \tabularnewline
20 & 0.770547434122857 & 0.458905131754286 & 0.229452565877143 \tabularnewline
21 & 0.790254852576261 & 0.419490294847477 & 0.209745147423739 \tabularnewline
22 & 0.749217757834837 & 0.501564484330327 & 0.250782242165163 \tabularnewline
23 & 0.705947260246019 & 0.588105479507962 & 0.294052739753981 \tabularnewline
24 & 0.638800859989383 & 0.722398280021234 & 0.361199140010617 \tabularnewline
25 & 0.633268371280511 & 0.733463257438979 & 0.366731628719489 \tabularnewline
26 & 0.779503496322743 & 0.440993007354514 & 0.220496503677257 \tabularnewline
27 & 0.773102592418728 & 0.453794815162545 & 0.226897407581272 \tabularnewline
28 & 0.729322465835068 & 0.541355068329863 & 0.270677534164932 \tabularnewline
29 & 0.738390077148153 & 0.523219845703694 & 0.261609922851847 \tabularnewline
30 & 0.682610292386296 & 0.634779415227408 & 0.317389707613704 \tabularnewline
31 & 0.645134194960436 & 0.709731610079128 & 0.354865805039564 \tabularnewline
32 & 0.803653205619383 & 0.392693588761234 & 0.196346794380617 \tabularnewline
33 & 0.801253682643224 & 0.397492634713552 & 0.198746317356776 \tabularnewline
34 & 0.765074623665269 & 0.469850752669462 & 0.234925376334731 \tabularnewline
35 & 0.71755905089245 & 0.5648818982151 & 0.28244094910755 \tabularnewline
36 & 0.671110324094214 & 0.657779351811573 & 0.328889675905786 \tabularnewline
37 & 0.659073000016095 & 0.68185399996781 & 0.340926999983905 \tabularnewline
38 & 0.626294877302242 & 0.747410245395515 & 0.373705122697758 \tabularnewline
39 & 0.744233054141722 & 0.511533891716556 & 0.255766945858278 \tabularnewline
40 & 0.697111287933563 & 0.605777424132873 & 0.302888712066437 \tabularnewline
41 & 0.667212852233555 & 0.665574295532891 & 0.332787147766445 \tabularnewline
42 & 0.615448643113675 & 0.76910271377265 & 0.384551356886325 \tabularnewline
43 & 0.586125123901757 & 0.827749752196486 & 0.413874876098243 \tabularnewline
44 & 0.537555411124532 & 0.924889177750936 & 0.462444588875468 \tabularnewline
45 & 0.634730088415485 & 0.730539823169031 & 0.365269911584515 \tabularnewline
46 & 0.641040547000799 & 0.717918905998401 & 0.358959452999201 \tabularnewline
47 & 0.623760500467487 & 0.752478999065027 & 0.376239499532513 \tabularnewline
48 & 0.593050052289299 & 0.813899895421402 & 0.406949947710701 \tabularnewline
49 & 0.545345436763086 & 0.909309126473828 & 0.454654563236914 \tabularnewline
50 & 0.572387047129628 & 0.855225905740745 & 0.427612952870372 \tabularnewline
51 & 0.618862868054639 & 0.762274263890722 & 0.381137131945361 \tabularnewline
52 & 0.579867418097314 & 0.840265163805371 & 0.420132581902686 \tabularnewline
53 & 0.534978066211185 & 0.930043867577631 & 0.465021933788815 \tabularnewline
54 & 0.486234191594764 & 0.972468383189528 & 0.513765808405236 \tabularnewline
55 & 0.441307165698593 & 0.882614331397186 & 0.558692834301407 \tabularnewline
56 & 0.430486646517827 & 0.860973293035654 & 0.569513353482173 \tabularnewline
57 & 0.457975307511584 & 0.915950615023168 & 0.542024692488416 \tabularnewline
58 & 0.593083681731848 & 0.813832636536305 & 0.406916318268152 \tabularnewline
59 & 0.553952918186369 & 0.892094163627262 & 0.446047081813631 \tabularnewline
60 & 0.50555940340158 & 0.988881193196841 & 0.49444059659842 \tabularnewline
61 & 0.469026359389676 & 0.938052718779352 & 0.530973640610324 \tabularnewline
62 & 0.453523263933423 & 0.907046527866846 & 0.546476736066577 \tabularnewline
63 & 0.419810151658764 & 0.839620303317529 & 0.580189848341236 \tabularnewline
64 & 0.426247206904205 & 0.85249441380841 & 0.573752793095795 \tabularnewline
65 & 0.380744190473863 & 0.761488380947727 & 0.619255809526137 \tabularnewline
66 & 0.344715055638475 & 0.689430111276951 & 0.655284944361525 \tabularnewline
67 & 0.304223928374067 & 0.608447856748135 & 0.695776071625933 \tabularnewline
68 & 0.334892545079033 & 0.669785090158066 & 0.665107454920967 \tabularnewline
69 & 0.345486365289056 & 0.690972730578113 & 0.654513634710944 \tabularnewline
70 & 0.306021113937705 & 0.612042227875411 & 0.693978886062295 \tabularnewline
71 & 0.33907245226997 & 0.67814490453994 & 0.66092754773003 \tabularnewline
72 & 0.377914572185559 & 0.755829144371118 & 0.622085427814441 \tabularnewline
73 & 0.378925146067661 & 0.757850292135322 & 0.621074853932339 \tabularnewline
74 & 0.335406821220178 & 0.670813642440356 & 0.664593178779822 \tabularnewline
75 & 0.3007718588263 & 0.601543717652599 & 0.6992281411737 \tabularnewline
76 & 0.356308717806686 & 0.712617435613373 & 0.643691282193314 \tabularnewline
77 & 0.318767445274823 & 0.637534890549645 & 0.681232554725178 \tabularnewline
78 & 0.278253678827507 & 0.556507357655014 & 0.721746321172493 \tabularnewline
79 & 0.342165867749578 & 0.684331735499157 & 0.657834132250422 \tabularnewline
80 & 0.417807349655203 & 0.835614699310405 & 0.582192650344797 \tabularnewline
81 & 0.407568161462482 & 0.815136322924963 & 0.592431838537518 \tabularnewline
82 & 0.362945192875974 & 0.725890385751949 & 0.637054807124026 \tabularnewline
83 & 0.3234050647726 & 0.6468101295452 & 0.6765949352274 \tabularnewline
84 & 0.3117946951542 & 0.623589390308401 & 0.6882053048458 \tabularnewline
85 & 0.298876532721288 & 0.597753065442576 & 0.701123467278712 \tabularnewline
86 & 0.265082865905618 & 0.530165731811236 & 0.734917134094382 \tabularnewline
87 & 0.238293481019027 & 0.476586962038055 & 0.761706518980973 \tabularnewline
88 & 0.213007993583082 & 0.426015987166164 & 0.786992006416918 \tabularnewline
89 & 0.250384017614441 & 0.500768035228883 & 0.749615982385559 \tabularnewline
90 & 0.216434526098506 & 0.432869052197012 & 0.783565473901494 \tabularnewline
91 & 0.230629276022684 & 0.461258552045368 & 0.769370723977316 \tabularnewline
92 & 0.216068074255365 & 0.43213614851073 & 0.783931925744635 \tabularnewline
93 & 0.202442447920967 & 0.404884895841935 & 0.797557552079033 \tabularnewline
94 & 0.172236329501922 & 0.344472659003844 & 0.827763670498078 \tabularnewline
95 & 0.179582142680519 & 0.359164285361038 & 0.820417857319481 \tabularnewline
96 & 0.175492461448019 & 0.350984922896039 & 0.824507538551981 \tabularnewline
97 & 0.160289045264962 & 0.320578090529923 & 0.839710954735038 \tabularnewline
98 & 0.137048647900381 & 0.274097295800762 & 0.862951352099619 \tabularnewline
99 & 0.120270107977868 & 0.240540215955736 & 0.879729892022132 \tabularnewline
100 & 0.119163799974447 & 0.238327599948894 & 0.880836200025553 \tabularnewline
101 & 0.10998703328877 & 0.21997406657754 & 0.89001296671123 \tabularnewline
102 & 0.0965294643197756 & 0.193058928639551 & 0.903470535680224 \tabularnewline
103 & 0.0790856421687475 & 0.158171284337495 & 0.920914357831253 \tabularnewline
104 & 0.0649092095457354 & 0.129818419091471 & 0.935090790454265 \tabularnewline
105 & 0.0762720249200906 & 0.152544049840181 & 0.923727975079909 \tabularnewline
106 & 0.182706729417198 & 0.365413458834397 & 0.817293270582802 \tabularnewline
107 & 0.169859135013722 & 0.339718270027444 & 0.830140864986278 \tabularnewline
108 & 0.193794831674513 & 0.387589663349026 & 0.806205168325487 \tabularnewline
109 & 0.180040605893118 & 0.360081211786236 & 0.819959394106882 \tabularnewline
110 & 0.380724337251454 & 0.761448674502909 & 0.619275662748546 \tabularnewline
111 & 0.456715087555245 & 0.913430175110491 & 0.543284912444755 \tabularnewline
112 & 0.440209420697281 & 0.880418841394562 & 0.559790579302719 \tabularnewline
113 & 0.569095535017882 & 0.861808929964236 & 0.430904464982118 \tabularnewline
114 & 0.522292621459744 & 0.955414757080512 & 0.477707378540256 \tabularnewline
115 & 0.507719677230702 & 0.984560645538596 & 0.492280322769298 \tabularnewline
116 & 0.472081698331836 & 0.944163396663673 & 0.527918301668164 \tabularnewline
117 & 0.442332254328133 & 0.884664508656266 & 0.557667745671867 \tabularnewline
118 & 0.418308411480951 & 0.836616822961901 & 0.581691588519049 \tabularnewline
119 & 0.372598547029916 & 0.745197094059832 & 0.627401452970084 \tabularnewline
120 & 0.336403095660827 & 0.672806191321654 & 0.663596904339173 \tabularnewline
121 & 0.291972855934124 & 0.583945711868248 & 0.708027144065876 \tabularnewline
122 & 0.250944068936507 & 0.501888137873014 & 0.749055931063493 \tabularnewline
123 & 0.215645622637923 & 0.431291245275845 & 0.784354377362077 \tabularnewline
124 & 0.178444522708875 & 0.356889045417751 & 0.821555477291125 \tabularnewline
125 & 0.14375867815762 & 0.28751735631524 & 0.85624132184238 \tabularnewline
126 & 0.153441404692595 & 0.30688280938519 & 0.846558595307405 \tabularnewline
127 & 0.170795548985604 & 0.341591097971207 & 0.829204451014396 \tabularnewline
128 & 0.349628553283285 & 0.69925710656657 & 0.650371446716715 \tabularnewline
129 & 0.320750972885802 & 0.641501945771604 & 0.679249027114198 \tabularnewline
130 & 0.284001116606466 & 0.568002233212933 & 0.715998883393534 \tabularnewline
131 & 0.252359479083502 & 0.504718958167004 & 0.747640520916498 \tabularnewline
132 & 0.294714367728781 & 0.589428735457562 & 0.705285632271219 \tabularnewline
133 & 0.365841607372557 & 0.731683214745113 & 0.634158392627443 \tabularnewline
134 & 0.476186391885257 & 0.952372783770514 & 0.523813608114743 \tabularnewline
135 & 0.420224572277482 & 0.840449144554963 & 0.579775427722518 \tabularnewline
136 & 0.35813142170058 & 0.716262843401159 & 0.64186857829942 \tabularnewline
137 & 0.314391932790547 & 0.628783865581094 & 0.685608067209453 \tabularnewline
138 & 0.254386842827389 & 0.508773685654778 & 0.745613157172611 \tabularnewline
139 & 0.303572026194395 & 0.60714405238879 & 0.696427973805605 \tabularnewline
140 & 0.24858864587961 & 0.49717729175922 & 0.75141135412039 \tabularnewline
141 & 0.476522941384608 & 0.953045882769217 & 0.523477058615392 \tabularnewline
142 & 0.414987233779988 & 0.829974467559976 & 0.585012766220012 \tabularnewline
143 & 0.441212214335556 & 0.882424428671113 & 0.558787785664444 \tabularnewline
144 & 0.702761449898956 & 0.594477100202088 & 0.297238550101044 \tabularnewline
145 & 0.613255350420532 & 0.773489299158936 & 0.386744649579468 \tabularnewline
146 & 0.944288864571204 & 0.111422270857591 & 0.0557111354287957 \tabularnewline
147 & 0.92429656205953 & 0.15140687588094 & 0.0757034379404701 \tabularnewline
148 & 0.946480751173226 & 0.107038497653548 & 0.0535192488267738 \tabularnewline
149 & 0.944752966784571 & 0.110494066430858 & 0.0552470332154288 \tabularnewline
150 & 0.882424288194319 & 0.235151423611363 & 0.117575711805681 \tabularnewline
151 & 0.769367917204775 & 0.461264165590449 & 0.230632082795225 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147198&T=5

[TABLE]
[ROW][C]Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]p-values[/C][C]Alternative Hypothesis[/C][/ROW]
[ROW][C]breakpoint index[/C][C]greater[/C][C]2-sided[/C][C]less[/C][/ROW]
[ROW][C]11[/C][C]0.185868997956101[/C][C]0.371737995912202[/C][C]0.814131002043899[/C][/ROW]
[ROW][C]12[/C][C]0.871126519820884[/C][C]0.257746960358233[/C][C]0.128873480179116[/C][/ROW]
[ROW][C]13[/C][C]0.917785607363361[/C][C]0.164428785273279[/C][C]0.0822143926366395[/C][/ROW]
[ROW][C]14[/C][C]0.878254971065549[/C][C]0.243490057868903[/C][C]0.121745028934452[/C][/ROW]
[ROW][C]15[/C][C]0.840974874562457[/C][C]0.318050250875085[/C][C]0.159025125437543[/C][/ROW]
[ROW][C]16[/C][C]0.865021290690045[/C][C]0.26995741861991[/C][C]0.134978709309955[/C][/ROW]
[ROW][C]17[/C][C]0.821693889765804[/C][C]0.356612220468392[/C][C]0.178306110234196[/C][/ROW]
[ROW][C]18[/C][C]0.789006256280515[/C][C]0.421987487438971[/C][C]0.210993743719485[/C][/ROW]
[ROW][C]19[/C][C]0.742707446720568[/C][C]0.514585106558864[/C][C]0.257292553279432[/C][/ROW]
[ROW][C]20[/C][C]0.770547434122857[/C][C]0.458905131754286[/C][C]0.229452565877143[/C][/ROW]
[ROW][C]21[/C][C]0.790254852576261[/C][C]0.419490294847477[/C][C]0.209745147423739[/C][/ROW]
[ROW][C]22[/C][C]0.749217757834837[/C][C]0.501564484330327[/C][C]0.250782242165163[/C][/ROW]
[ROW][C]23[/C][C]0.705947260246019[/C][C]0.588105479507962[/C][C]0.294052739753981[/C][/ROW]
[ROW][C]24[/C][C]0.638800859989383[/C][C]0.722398280021234[/C][C]0.361199140010617[/C][/ROW]
[ROW][C]25[/C][C]0.633268371280511[/C][C]0.733463257438979[/C][C]0.366731628719489[/C][/ROW]
[ROW][C]26[/C][C]0.779503496322743[/C][C]0.440993007354514[/C][C]0.220496503677257[/C][/ROW]
[ROW][C]27[/C][C]0.773102592418728[/C][C]0.453794815162545[/C][C]0.226897407581272[/C][/ROW]
[ROW][C]28[/C][C]0.729322465835068[/C][C]0.541355068329863[/C][C]0.270677534164932[/C][/ROW]
[ROW][C]29[/C][C]0.738390077148153[/C][C]0.523219845703694[/C][C]0.261609922851847[/C][/ROW]
[ROW][C]30[/C][C]0.682610292386296[/C][C]0.634779415227408[/C][C]0.317389707613704[/C][/ROW]
[ROW][C]31[/C][C]0.645134194960436[/C][C]0.709731610079128[/C][C]0.354865805039564[/C][/ROW]
[ROW][C]32[/C][C]0.803653205619383[/C][C]0.392693588761234[/C][C]0.196346794380617[/C][/ROW]
[ROW][C]33[/C][C]0.801253682643224[/C][C]0.397492634713552[/C][C]0.198746317356776[/C][/ROW]
[ROW][C]34[/C][C]0.765074623665269[/C][C]0.469850752669462[/C][C]0.234925376334731[/C][/ROW]
[ROW][C]35[/C][C]0.71755905089245[/C][C]0.5648818982151[/C][C]0.28244094910755[/C][/ROW]
[ROW][C]36[/C][C]0.671110324094214[/C][C]0.657779351811573[/C][C]0.328889675905786[/C][/ROW]
[ROW][C]37[/C][C]0.659073000016095[/C][C]0.68185399996781[/C][C]0.340926999983905[/C][/ROW]
[ROW][C]38[/C][C]0.626294877302242[/C][C]0.747410245395515[/C][C]0.373705122697758[/C][/ROW]
[ROW][C]39[/C][C]0.744233054141722[/C][C]0.511533891716556[/C][C]0.255766945858278[/C][/ROW]
[ROW][C]40[/C][C]0.697111287933563[/C][C]0.605777424132873[/C][C]0.302888712066437[/C][/ROW]
[ROW][C]41[/C][C]0.667212852233555[/C][C]0.665574295532891[/C][C]0.332787147766445[/C][/ROW]
[ROW][C]42[/C][C]0.615448643113675[/C][C]0.76910271377265[/C][C]0.384551356886325[/C][/ROW]
[ROW][C]43[/C][C]0.586125123901757[/C][C]0.827749752196486[/C][C]0.413874876098243[/C][/ROW]
[ROW][C]44[/C][C]0.537555411124532[/C][C]0.924889177750936[/C][C]0.462444588875468[/C][/ROW]
[ROW][C]45[/C][C]0.634730088415485[/C][C]0.730539823169031[/C][C]0.365269911584515[/C][/ROW]
[ROW][C]46[/C][C]0.641040547000799[/C][C]0.717918905998401[/C][C]0.358959452999201[/C][/ROW]
[ROW][C]47[/C][C]0.623760500467487[/C][C]0.752478999065027[/C][C]0.376239499532513[/C][/ROW]
[ROW][C]48[/C][C]0.593050052289299[/C][C]0.813899895421402[/C][C]0.406949947710701[/C][/ROW]
[ROW][C]49[/C][C]0.545345436763086[/C][C]0.909309126473828[/C][C]0.454654563236914[/C][/ROW]
[ROW][C]50[/C][C]0.572387047129628[/C][C]0.855225905740745[/C][C]0.427612952870372[/C][/ROW]
[ROW][C]51[/C][C]0.618862868054639[/C][C]0.762274263890722[/C][C]0.381137131945361[/C][/ROW]
[ROW][C]52[/C][C]0.579867418097314[/C][C]0.840265163805371[/C][C]0.420132581902686[/C][/ROW]
[ROW][C]53[/C][C]0.534978066211185[/C][C]0.930043867577631[/C][C]0.465021933788815[/C][/ROW]
[ROW][C]54[/C][C]0.486234191594764[/C][C]0.972468383189528[/C][C]0.513765808405236[/C][/ROW]
[ROW][C]55[/C][C]0.441307165698593[/C][C]0.882614331397186[/C][C]0.558692834301407[/C][/ROW]
[ROW][C]56[/C][C]0.430486646517827[/C][C]0.860973293035654[/C][C]0.569513353482173[/C][/ROW]
[ROW][C]57[/C][C]0.457975307511584[/C][C]0.915950615023168[/C][C]0.542024692488416[/C][/ROW]
[ROW][C]58[/C][C]0.593083681731848[/C][C]0.813832636536305[/C][C]0.406916318268152[/C][/ROW]
[ROW][C]59[/C][C]0.553952918186369[/C][C]0.892094163627262[/C][C]0.446047081813631[/C][/ROW]
[ROW][C]60[/C][C]0.50555940340158[/C][C]0.988881193196841[/C][C]0.49444059659842[/C][/ROW]
[ROW][C]61[/C][C]0.469026359389676[/C][C]0.938052718779352[/C][C]0.530973640610324[/C][/ROW]
[ROW][C]62[/C][C]0.453523263933423[/C][C]0.907046527866846[/C][C]0.546476736066577[/C][/ROW]
[ROW][C]63[/C][C]0.419810151658764[/C][C]0.839620303317529[/C][C]0.580189848341236[/C][/ROW]
[ROW][C]64[/C][C]0.426247206904205[/C][C]0.85249441380841[/C][C]0.573752793095795[/C][/ROW]
[ROW][C]65[/C][C]0.380744190473863[/C][C]0.761488380947727[/C][C]0.619255809526137[/C][/ROW]
[ROW][C]66[/C][C]0.344715055638475[/C][C]0.689430111276951[/C][C]0.655284944361525[/C][/ROW]
[ROW][C]67[/C][C]0.304223928374067[/C][C]0.608447856748135[/C][C]0.695776071625933[/C][/ROW]
[ROW][C]68[/C][C]0.334892545079033[/C][C]0.669785090158066[/C][C]0.665107454920967[/C][/ROW]
[ROW][C]69[/C][C]0.345486365289056[/C][C]0.690972730578113[/C][C]0.654513634710944[/C][/ROW]
[ROW][C]70[/C][C]0.306021113937705[/C][C]0.612042227875411[/C][C]0.693978886062295[/C][/ROW]
[ROW][C]71[/C][C]0.33907245226997[/C][C]0.67814490453994[/C][C]0.66092754773003[/C][/ROW]
[ROW][C]72[/C][C]0.377914572185559[/C][C]0.755829144371118[/C][C]0.622085427814441[/C][/ROW]
[ROW][C]73[/C][C]0.378925146067661[/C][C]0.757850292135322[/C][C]0.621074853932339[/C][/ROW]
[ROW][C]74[/C][C]0.335406821220178[/C][C]0.670813642440356[/C][C]0.664593178779822[/C][/ROW]
[ROW][C]75[/C][C]0.3007718588263[/C][C]0.601543717652599[/C][C]0.6992281411737[/C][/ROW]
[ROW][C]76[/C][C]0.356308717806686[/C][C]0.712617435613373[/C][C]0.643691282193314[/C][/ROW]
[ROW][C]77[/C][C]0.318767445274823[/C][C]0.637534890549645[/C][C]0.681232554725178[/C][/ROW]
[ROW][C]78[/C][C]0.278253678827507[/C][C]0.556507357655014[/C][C]0.721746321172493[/C][/ROW]
[ROW][C]79[/C][C]0.342165867749578[/C][C]0.684331735499157[/C][C]0.657834132250422[/C][/ROW]
[ROW][C]80[/C][C]0.417807349655203[/C][C]0.835614699310405[/C][C]0.582192650344797[/C][/ROW]
[ROW][C]81[/C][C]0.407568161462482[/C][C]0.815136322924963[/C][C]0.592431838537518[/C][/ROW]
[ROW][C]82[/C][C]0.362945192875974[/C][C]0.725890385751949[/C][C]0.637054807124026[/C][/ROW]
[ROW][C]83[/C][C]0.3234050647726[/C][C]0.6468101295452[/C][C]0.6765949352274[/C][/ROW]
[ROW][C]84[/C][C]0.3117946951542[/C][C]0.623589390308401[/C][C]0.6882053048458[/C][/ROW]
[ROW][C]85[/C][C]0.298876532721288[/C][C]0.597753065442576[/C][C]0.701123467278712[/C][/ROW]
[ROW][C]86[/C][C]0.265082865905618[/C][C]0.530165731811236[/C][C]0.734917134094382[/C][/ROW]
[ROW][C]87[/C][C]0.238293481019027[/C][C]0.476586962038055[/C][C]0.761706518980973[/C][/ROW]
[ROW][C]88[/C][C]0.213007993583082[/C][C]0.426015987166164[/C][C]0.786992006416918[/C][/ROW]
[ROW][C]89[/C][C]0.250384017614441[/C][C]0.500768035228883[/C][C]0.749615982385559[/C][/ROW]
[ROW][C]90[/C][C]0.216434526098506[/C][C]0.432869052197012[/C][C]0.783565473901494[/C][/ROW]
[ROW][C]91[/C][C]0.230629276022684[/C][C]0.461258552045368[/C][C]0.769370723977316[/C][/ROW]
[ROW][C]92[/C][C]0.216068074255365[/C][C]0.43213614851073[/C][C]0.783931925744635[/C][/ROW]
[ROW][C]93[/C][C]0.202442447920967[/C][C]0.404884895841935[/C][C]0.797557552079033[/C][/ROW]
[ROW][C]94[/C][C]0.172236329501922[/C][C]0.344472659003844[/C][C]0.827763670498078[/C][/ROW]
[ROW][C]95[/C][C]0.179582142680519[/C][C]0.359164285361038[/C][C]0.820417857319481[/C][/ROW]
[ROW][C]96[/C][C]0.175492461448019[/C][C]0.350984922896039[/C][C]0.824507538551981[/C][/ROW]
[ROW][C]97[/C][C]0.160289045264962[/C][C]0.320578090529923[/C][C]0.839710954735038[/C][/ROW]
[ROW][C]98[/C][C]0.137048647900381[/C][C]0.274097295800762[/C][C]0.862951352099619[/C][/ROW]
[ROW][C]99[/C][C]0.120270107977868[/C][C]0.240540215955736[/C][C]0.879729892022132[/C][/ROW]
[ROW][C]100[/C][C]0.119163799974447[/C][C]0.238327599948894[/C][C]0.880836200025553[/C][/ROW]
[ROW][C]101[/C][C]0.10998703328877[/C][C]0.21997406657754[/C][C]0.89001296671123[/C][/ROW]
[ROW][C]102[/C][C]0.0965294643197756[/C][C]0.193058928639551[/C][C]0.903470535680224[/C][/ROW]
[ROW][C]103[/C][C]0.0790856421687475[/C][C]0.158171284337495[/C][C]0.920914357831253[/C][/ROW]
[ROW][C]104[/C][C]0.0649092095457354[/C][C]0.129818419091471[/C][C]0.935090790454265[/C][/ROW]
[ROW][C]105[/C][C]0.0762720249200906[/C][C]0.152544049840181[/C][C]0.923727975079909[/C][/ROW]
[ROW][C]106[/C][C]0.182706729417198[/C][C]0.365413458834397[/C][C]0.817293270582802[/C][/ROW]
[ROW][C]107[/C][C]0.169859135013722[/C][C]0.339718270027444[/C][C]0.830140864986278[/C][/ROW]
[ROW][C]108[/C][C]0.193794831674513[/C][C]0.387589663349026[/C][C]0.806205168325487[/C][/ROW]
[ROW][C]109[/C][C]0.180040605893118[/C][C]0.360081211786236[/C][C]0.819959394106882[/C][/ROW]
[ROW][C]110[/C][C]0.380724337251454[/C][C]0.761448674502909[/C][C]0.619275662748546[/C][/ROW]
[ROW][C]111[/C][C]0.456715087555245[/C][C]0.913430175110491[/C][C]0.543284912444755[/C][/ROW]
[ROW][C]112[/C][C]0.440209420697281[/C][C]0.880418841394562[/C][C]0.559790579302719[/C][/ROW]
[ROW][C]113[/C][C]0.569095535017882[/C][C]0.861808929964236[/C][C]0.430904464982118[/C][/ROW]
[ROW][C]114[/C][C]0.522292621459744[/C][C]0.955414757080512[/C][C]0.477707378540256[/C][/ROW]
[ROW][C]115[/C][C]0.507719677230702[/C][C]0.984560645538596[/C][C]0.492280322769298[/C][/ROW]
[ROW][C]116[/C][C]0.472081698331836[/C][C]0.944163396663673[/C][C]0.527918301668164[/C][/ROW]
[ROW][C]117[/C][C]0.442332254328133[/C][C]0.884664508656266[/C][C]0.557667745671867[/C][/ROW]
[ROW][C]118[/C][C]0.418308411480951[/C][C]0.836616822961901[/C][C]0.581691588519049[/C][/ROW]
[ROW][C]119[/C][C]0.372598547029916[/C][C]0.745197094059832[/C][C]0.627401452970084[/C][/ROW]
[ROW][C]120[/C][C]0.336403095660827[/C][C]0.672806191321654[/C][C]0.663596904339173[/C][/ROW]
[ROW][C]121[/C][C]0.291972855934124[/C][C]0.583945711868248[/C][C]0.708027144065876[/C][/ROW]
[ROW][C]122[/C][C]0.250944068936507[/C][C]0.501888137873014[/C][C]0.749055931063493[/C][/ROW]
[ROW][C]123[/C][C]0.215645622637923[/C][C]0.431291245275845[/C][C]0.784354377362077[/C][/ROW]
[ROW][C]124[/C][C]0.178444522708875[/C][C]0.356889045417751[/C][C]0.821555477291125[/C][/ROW]
[ROW][C]125[/C][C]0.14375867815762[/C][C]0.28751735631524[/C][C]0.85624132184238[/C][/ROW]
[ROW][C]126[/C][C]0.153441404692595[/C][C]0.30688280938519[/C][C]0.846558595307405[/C][/ROW]
[ROW][C]127[/C][C]0.170795548985604[/C][C]0.341591097971207[/C][C]0.829204451014396[/C][/ROW]
[ROW][C]128[/C][C]0.349628553283285[/C][C]0.69925710656657[/C][C]0.650371446716715[/C][/ROW]
[ROW][C]129[/C][C]0.320750972885802[/C][C]0.641501945771604[/C][C]0.679249027114198[/C][/ROW]
[ROW][C]130[/C][C]0.284001116606466[/C][C]0.568002233212933[/C][C]0.715998883393534[/C][/ROW]
[ROW][C]131[/C][C]0.252359479083502[/C][C]0.504718958167004[/C][C]0.747640520916498[/C][/ROW]
[ROW][C]132[/C][C]0.294714367728781[/C][C]0.589428735457562[/C][C]0.705285632271219[/C][/ROW]
[ROW][C]133[/C][C]0.365841607372557[/C][C]0.731683214745113[/C][C]0.634158392627443[/C][/ROW]
[ROW][C]134[/C][C]0.476186391885257[/C][C]0.952372783770514[/C][C]0.523813608114743[/C][/ROW]
[ROW][C]135[/C][C]0.420224572277482[/C][C]0.840449144554963[/C][C]0.579775427722518[/C][/ROW]
[ROW][C]136[/C][C]0.35813142170058[/C][C]0.716262843401159[/C][C]0.64186857829942[/C][/ROW]
[ROW][C]137[/C][C]0.314391932790547[/C][C]0.628783865581094[/C][C]0.685608067209453[/C][/ROW]
[ROW][C]138[/C][C]0.254386842827389[/C][C]0.508773685654778[/C][C]0.745613157172611[/C][/ROW]
[ROW][C]139[/C][C]0.303572026194395[/C][C]0.60714405238879[/C][C]0.696427973805605[/C][/ROW]
[ROW][C]140[/C][C]0.24858864587961[/C][C]0.49717729175922[/C][C]0.75141135412039[/C][/ROW]
[ROW][C]141[/C][C]0.476522941384608[/C][C]0.953045882769217[/C][C]0.523477058615392[/C][/ROW]
[ROW][C]142[/C][C]0.414987233779988[/C][C]0.829974467559976[/C][C]0.585012766220012[/C][/ROW]
[ROW][C]143[/C][C]0.441212214335556[/C][C]0.882424428671113[/C][C]0.558787785664444[/C][/ROW]
[ROW][C]144[/C][C]0.702761449898956[/C][C]0.594477100202088[/C][C]0.297238550101044[/C][/ROW]
[ROW][C]145[/C][C]0.613255350420532[/C][C]0.773489299158936[/C][C]0.386744649579468[/C][/ROW]
[ROW][C]146[/C][C]0.944288864571204[/C][C]0.111422270857591[/C][C]0.0557111354287957[/C][/ROW]
[ROW][C]147[/C][C]0.92429656205953[/C][C]0.15140687588094[/C][C]0.0757034379404701[/C][/ROW]
[ROW][C]148[/C][C]0.946480751173226[/C][C]0.107038497653548[/C][C]0.0535192488267738[/C][/ROW]
[ROW][C]149[/C][C]0.944752966784571[/C][C]0.110494066430858[/C][C]0.0552470332154288[/C][/ROW]
[ROW][C]150[/C][C]0.882424288194319[/C][C]0.235151423611363[/C][C]0.117575711805681[/C][/ROW]
[ROW][C]151[/C][C]0.769367917204775[/C][C]0.461264165590449[/C][C]0.230632082795225[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147198&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147198&T=5

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


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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
110.1858689979561010.3717379959122020.814131002043899
120.8711265198208840.2577469603582330.128873480179116
130.9177856073633610.1644287852732790.0822143926366395
140.8782549710655490.2434900578689030.121745028934452
150.8409748745624570.3180502508750850.159025125437543
160.8650212906900450.269957418619910.134978709309955
170.8216938897658040.3566122204683920.178306110234196
180.7890062562805150.4219874874389710.210993743719485
190.7427074467205680.5145851065588640.257292553279432
200.7705474341228570.4589051317542860.229452565877143
210.7902548525762610.4194902948474770.209745147423739
220.7492177578348370.5015644843303270.250782242165163
230.7059472602460190.5881054795079620.294052739753981
240.6388008599893830.7223982800212340.361199140010617
250.6332683712805110.7334632574389790.366731628719489
260.7795034963227430.4409930073545140.220496503677257
270.7731025924187280.4537948151625450.226897407581272
280.7293224658350680.5413550683298630.270677534164932
290.7383900771481530.5232198457036940.261609922851847
300.6826102923862960.6347794152274080.317389707613704
310.6451341949604360.7097316100791280.354865805039564
320.8036532056193830.3926935887612340.196346794380617
330.8012536826432240.3974926347135520.198746317356776
340.7650746236652690.4698507526694620.234925376334731
350.717559050892450.56488189821510.28244094910755
360.6711103240942140.6577793518115730.328889675905786
370.6590730000160950.681853999967810.340926999983905
380.6262948773022420.7474102453955150.373705122697758
390.7442330541417220.5115338917165560.255766945858278
400.6971112879335630.6057774241328730.302888712066437
410.6672128522335550.6655742955328910.332787147766445
420.6154486431136750.769102713772650.384551356886325
430.5861251239017570.8277497521964860.413874876098243
440.5375554111245320.9248891777509360.462444588875468
450.6347300884154850.7305398231690310.365269911584515
460.6410405470007990.7179189059984010.358959452999201
470.6237605004674870.7524789990650270.376239499532513
480.5930500522892990.8138998954214020.406949947710701
490.5453454367630860.9093091264738280.454654563236914
500.5723870471296280.8552259057407450.427612952870372
510.6188628680546390.7622742638907220.381137131945361
520.5798674180973140.8402651638053710.420132581902686
530.5349780662111850.9300438675776310.465021933788815
540.4862341915947640.9724683831895280.513765808405236
550.4413071656985930.8826143313971860.558692834301407
560.4304866465178270.8609732930356540.569513353482173
570.4579753075115840.9159506150231680.542024692488416
580.5930836817318480.8138326365363050.406916318268152
590.5539529181863690.8920941636272620.446047081813631
600.505559403401580.9888811931968410.49444059659842
610.4690263593896760.9380527187793520.530973640610324
620.4535232639334230.9070465278668460.546476736066577
630.4198101516587640.8396203033175290.580189848341236
640.4262472069042050.852494413808410.573752793095795
650.3807441904738630.7614883809477270.619255809526137
660.3447150556384750.6894301112769510.655284944361525
670.3042239283740670.6084478567481350.695776071625933
680.3348925450790330.6697850901580660.665107454920967
690.3454863652890560.6909727305781130.654513634710944
700.3060211139377050.6120422278754110.693978886062295
710.339072452269970.678144904539940.66092754773003
720.3779145721855590.7558291443711180.622085427814441
730.3789251460676610.7578502921353220.621074853932339
740.3354068212201780.6708136424403560.664593178779822
750.30077185882630.6015437176525990.6992281411737
760.3563087178066860.7126174356133730.643691282193314
770.3187674452748230.6375348905496450.681232554725178
780.2782536788275070.5565073576550140.721746321172493
790.3421658677495780.6843317354991570.657834132250422
800.4178073496552030.8356146993104050.582192650344797
810.4075681614624820.8151363229249630.592431838537518
820.3629451928759740.7258903857519490.637054807124026
830.32340506477260.64681012954520.6765949352274
840.31179469515420.6235893903084010.6882053048458
850.2988765327212880.5977530654425760.701123467278712
860.2650828659056180.5301657318112360.734917134094382
870.2382934810190270.4765869620380550.761706518980973
880.2130079935830820.4260159871661640.786992006416918
890.2503840176144410.5007680352288830.749615982385559
900.2164345260985060.4328690521970120.783565473901494
910.2306292760226840.4612585520453680.769370723977316
920.2160680742553650.432136148510730.783931925744635
930.2024424479209670.4048848958419350.797557552079033
940.1722363295019220.3444726590038440.827763670498078
950.1795821426805190.3591642853610380.820417857319481
960.1754924614480190.3509849228960390.824507538551981
970.1602890452649620.3205780905299230.839710954735038
980.1370486479003810.2740972958007620.862951352099619
990.1202701079778680.2405402159557360.879729892022132
1000.1191637999744470.2383275999488940.880836200025553
1010.109987033288770.219974066577540.89001296671123
1020.09652946431977560.1930589286395510.903470535680224
1030.07908564216874750.1581712843374950.920914357831253
1040.06490920954573540.1298184190914710.935090790454265
1050.07627202492009060.1525440498401810.923727975079909
1060.1827067294171980.3654134588343970.817293270582802
1070.1698591350137220.3397182700274440.830140864986278
1080.1937948316745130.3875896633490260.806205168325487
1090.1800406058931180.3600812117862360.819959394106882
1100.3807243372514540.7614486745029090.619275662748546
1110.4567150875552450.9134301751104910.543284912444755
1120.4402094206972810.8804188413945620.559790579302719
1130.5690955350178820.8618089299642360.430904464982118
1140.5222926214597440.9554147570805120.477707378540256
1150.5077196772307020.9845606455385960.492280322769298
1160.4720816983318360.9441633966636730.527918301668164
1170.4423322543281330.8846645086562660.557667745671867
1180.4183084114809510.8366168229619010.581691588519049
1190.3725985470299160.7451970940598320.627401452970084
1200.3364030956608270.6728061913216540.663596904339173
1210.2919728559341240.5839457118682480.708027144065876
1220.2509440689365070.5018881378730140.749055931063493
1230.2156456226379230.4312912452758450.784354377362077
1240.1784445227088750.3568890454177510.821555477291125
1250.143758678157620.287517356315240.85624132184238
1260.1534414046925950.306882809385190.846558595307405
1270.1707955489856040.3415910979712070.829204451014396
1280.3496285532832850.699257106566570.650371446716715
1290.3207509728858020.6415019457716040.679249027114198
1300.2840011166064660.5680022332129330.715998883393534
1310.2523594790835020.5047189581670040.747640520916498
1320.2947143677287810.5894287354575620.705285632271219
1330.3658416073725570.7316832147451130.634158392627443
1340.4761863918852570.9523727837705140.523813608114743
1350.4202245722774820.8404491445549630.579775427722518
1360.358131421700580.7162628434011590.64186857829942
1370.3143919327905470.6287838655810940.685608067209453
1380.2543868428273890.5087736856547780.745613157172611
1390.3035720261943950.607144052388790.696427973805605
1400.248588645879610.497177291759220.75141135412039
1410.4765229413846080.9530458827692170.523477058615392
1420.4149872337799880.8299744675599760.585012766220012
1430.4412122143355560.8824244286711130.558787785664444
1440.7027614498989560.5944771002020880.297238550101044
1450.6132553504205320.7734892991589360.386744649579468
1460.9442888645712040.1114222708575910.0557111354287957
1470.924296562059530.151406875880940.0757034379404701
1480.9464807511732260.1070384976535480.0535192488267738
1490.9447529667845710.1104940664308580.0552470332154288
1500.8824242881943190.2351514236113630.117575711805681
1510.7693679172047750.4612641655904490.230632082795225






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

\begin{tabular}{lllllllll}
\hline
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
Description & # significant tests & % significant tests & OK/NOK \tabularnewline
1% type I error level & 0 & 0 & OK \tabularnewline
5% type I error level & 0 & 0 & OK \tabularnewline
10% type I error level & 0 & 0 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147198&T=6

[TABLE]
[ROW][C]Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]Description[/C][C]# significant tests[/C][C]% significant tests[/C][C]OK/NOK[/C][/ROW]
[ROW][C]1% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147198&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147198&T=6

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


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

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


Figures (Output of Computation)

Figure 1
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Figure 2
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Figure 4
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Figure 5
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Figure 6
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Figure 7
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Figure 8
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Figure 9
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Figure 10
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Input Parameters & R Code

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