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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 computationSun, 04 Nov 2012 07:30:53 -0500
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2012/Nov/04/t1352032282u6l3n0muqn91sgf.htm/, Retrieved Thu, 02 May 2024 18:00:15 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=185800, Retrieved Thu, 02 May 2024 18:00:15 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact95

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [Competence to learn] [2010-11-17 07:43:53] [b98453cac15ba1066b407e146608df68]
-   PD    [Multiple Regression] [] [2012-11-04 12:30:53] [24c042819fd2b1ab385eb96782c689cf] [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 time10 seconds
R Server'Sir Ronald Aylmer Fisher' @ fisher.wessa.net

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 10 seconds \tabularnewline
R Server & 'Sir Ronald Aylmer Fisher' @ fisher.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185800&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]10 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Sir Ronald Aylmer Fisher' @ fisher.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185800&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185800&T=0

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


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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time10 seconds
R Server'Sir Ronald Aylmer Fisher' @ fisher.wessa.net






Multiple Linear Regression - Estimated Regression Equation
Belonging_Final[t] = -4.15016520024616 -0.0138900563993642Connected[t] + 0.0563768650883617Separate[t] -0.0825593641025312Learning[t] -0.0101629601067501Software[t] -0.0560342490511005Happiness[t] + 0.102192401128386Depression[t] + 0.658887864782968Belonging[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Belonging_Final[t] =  -4.15016520024616 -0.0138900563993642Connected[t] +  0.0563768650883617Separate[t] -0.0825593641025312Learning[t] -0.0101629601067501Software[t] -0.0560342490511005Happiness[t] +  0.102192401128386Depression[t] +  0.658887864782968Belonging[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185800&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Belonging_Final[t] =  -4.15016520024616 -0.0138900563993642Connected[t] +  0.0563768650883617Separate[t] -0.0825593641025312Learning[t] -0.0101629601067501Software[t] -0.0560342490511005Happiness[t] +  0.102192401128386Depression[t] +  0.658887864782968Belonging[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185800&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185800&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
Belonging_Final[t] = -4.15016520024616 -0.0138900563993642Connected[t] + 0.0563768650883617Separate[t] -0.0825593641025312Learning[t] -0.0101629601067501Software[t] -0.0560342490511005Happiness[t] + 0.102192401128386Depression[t] + 0.658887864782968Belonging[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)-4.150165200246163.29525-1.25940.2097770.104889
Connected-0.01389005639936420.060061-0.23130.8174170.408708
Separate0.05637686508836170.0561921.00330.3172980.158649
Learning-0.08255936410253120.10109-0.81670.4153660.207683
Software-0.01016296010675010.102641-0.0990.9212550.460628
Happiness-0.05603424905110050.096111-0.5830.5607360.280368
Depression0.1021924011283860.0707191.4450.1504790.075239
Belonging0.6588878647829680.01811936.364300

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Ordinary Least Squares \tabularnewline
Variable & Parameter & S.D. & T-STATH0: parameter = 0 & 2-tail p-value & 1-tail p-value \tabularnewline
(Intercept) & -4.15016520024616 & 3.29525 & -1.2594 & 0.209777 & 0.104889 \tabularnewline
Connected & -0.0138900563993642 & 0.060061 & -0.2313 & 0.817417 & 0.408708 \tabularnewline
Separate & 0.0563768650883617 & 0.056192 & 1.0033 & 0.317298 & 0.158649 \tabularnewline
Learning & -0.0825593641025312 & 0.10109 & -0.8167 & 0.415366 & 0.207683 \tabularnewline
Software & -0.0101629601067501 & 0.102641 & -0.099 & 0.921255 & 0.460628 \tabularnewline
Happiness & -0.0560342490511005 & 0.096111 & -0.583 & 0.560736 & 0.280368 \tabularnewline
Depression & 0.102192401128386 & 0.070719 & 1.445 & 0.150479 & 0.075239 \tabularnewline
Belonging & 0.658887864782968 & 0.018119 & 36.3643 & 0 & 0 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185800&T=2

[TABLE]
[ROW][C]Multiple Linear Regression - Ordinary Least Squares[/C][/ROW]
[ROW][C]Variable[/C][C]Parameter[/C][C]S.D.[/C][C]T-STATH0: parameter = 0[/C][C]2-tail p-value[/C][C]1-tail p-value[/C][/ROW]
[ROW][C](Intercept)[/C][C]-4.15016520024616[/C][C]3.29525[/C][C]-1.2594[/C][C]0.209777[/C][C]0.104889[/C][/ROW]
[ROW][C]Connected[/C][C]-0.0138900563993642[/C][C]0.060061[/C][C]-0.2313[/C][C]0.817417[/C][C]0.408708[/C][/ROW]
[ROW][C]Separate[/C][C]0.0563768650883617[/C][C]0.056192[/C][C]1.0033[/C][C]0.317298[/C][C]0.158649[/C][/ROW]
[ROW][C]Learning[/C][C]-0.0825593641025312[/C][C]0.10109[/C][C]-0.8167[/C][C]0.415366[/C][C]0.207683[/C][/ROW]
[ROW][C]Software[/C][C]-0.0101629601067501[/C][C]0.102641[/C][C]-0.099[/C][C]0.921255[/C][C]0.460628[/C][/ROW]
[ROW][C]Happiness[/C][C]-0.0560342490511005[/C][C]0.096111[/C][C]-0.583[/C][C]0.560736[/C][C]0.280368[/C][/ROW]
[ROW][C]Depression[/C][C]0.102192401128386[/C][C]0.070719[/C][C]1.445[/C][C]0.150479[/C][C]0.075239[/C][/ROW]
[ROW][C]Belonging[/C][C]0.658887864782968[/C][C]0.018119[/C][C]36.3643[/C][C]0[/C][C]0[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185800&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185800&T=2

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


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

Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)-4.150165200246163.29525-1.25940.2097770.104889
Connected-0.01389005639936420.060061-0.23130.8174170.408708
Separate0.05637686508836170.0561921.00330.3172980.158649
Learning-0.08255936410253120.10109-0.81670.4153660.207683
Software-0.01016296010675010.102641-0.0990.9212550.460628
Happiness-0.05603424905110050.096111-0.5830.5607360.280368
Depression0.1021924011283860.0707191.4450.1504790.075239
Belonging0.6588878647829680.01811936.364300






Multiple Linear Regression - Regression Statistics
Multiple R0.950126542673743
R-squared0.902740447093159
Adjusted R-squared0.898319558324666
F-TEST (value)204.198860085986
F-TEST (DF numerator)7
F-TEST (DF denominator)154
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.32622606183604
Sum Squared Residuals833.344464377844

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.950126542673743 \tabularnewline
R-squared & 0.902740447093159 \tabularnewline
Adjusted R-squared & 0.898319558324666 \tabularnewline
F-TEST (value) & 204.198860085986 \tabularnewline
F-TEST (DF numerator) & 7 \tabularnewline
F-TEST (DF denominator) & 154 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 2.32622606183604 \tabularnewline
Sum Squared Residuals & 833.344464377844 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185800&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.950126542673743[/C][/ROW]
[ROW][C]R-squared[/C][C]0.902740447093159[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.898319558324666[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]204.198860085986[/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]0[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]2.32622606183604[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]833.344464377844[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185800&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185800&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 R0.950126542673743
R-squared0.902740447093159
Adjusted R-squared0.898319558324666
F-TEST (value)204.198860085986
F-TEST (DF numerator)7
F-TEST (DF denominator)154
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.32622606183604
Sum Squared Residuals833.344464377844






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
13231.59032226644630.4096777335537
25152.4592961970192-1.45929619701917
34239.98616701822742.01383298177257
44140.67969614249730.320303857502687
54647.5005374740057-1.50053747400573
64747.4346563626305-0.434656362630515
73731.75003215029515.24996784970485
84949.0771342306583-0.077134230658302
94544.984308698210.0156913017900486
104746.74232056656470.257679433435267
114947.76859914287891.23140085712114
123331.1565340107961.84346598920401
134240.97032805462281.02967194537719
143332.13179141598170.868208584018294
155353.2580051199822-0.258005119982212
163634.76715384656531.2328461534347
174545.8766363774034-0.876636377403445
185453.84485700604890.155142993951137
194138.2588502291952.74114977080501
203633.75506885336092.24493114663915
214139.02637655673021.97362344326983
224441.34271220366842.6572877963316
233331.7714710993061.22852890069396
243733.75786111963913.24213888036087
255252.4530970021812-0.453097002181235
264748.6752270504097-1.67522705040973
274346.2034497718616-3.20344977186161
284442.30840424404691.69159575595308
294547.7924274726717-2.79242747267168
304440.83816014715423.16183985284576
314948.3716620990040.628337900995968
323332.5912323872560.408767612744029
334342.67650500921950.323494990780465
345451.65079486193842.34920513806159
354245.0928159380031-3.09281593800313
364443.31876289722540.681237102774623
373739.3576098426805-2.35760984268049
384343.0992761759411-0.099276175941094
394645.91694605612420.0830539438757718
404241.55828139062840.441718609371611
414544.55430283742560.445697162574446
424445.9109583010774-1.91095830107737
433330.82337657824352.17662342175654
443130.98711027108760.0128897289123697
454241.5580722743730.441927725626963
464041.7954837450099-1.79548374500993
474339.13968990148283.86031009851718
484645.65828586423530.341714135764745
494245.3973770329165-3.39737703291645
504545.6605467541597-0.660546754159693
514443.64974489203690.350255107963107
524040.630899318663-0.630899318662964
533738.8861436144546-1.88614361445458
544637.67615569223328.32384430776682
553638.3772045054713-2.37720450547126
564746.38442633900870.615573660991289
574544.97168589005250.0283141099475099
584240.14563604510291.85436395489709
594344.4636139852735-1.4636139852735
604342.84054237519270.159457624807253
613232.4664631039256-0.466463103925641
624547.0281498868257-2.02814988682568
634547.2318295554002-2.2318295554002
643131.0331927686529-0.0331927686528642
653331.7502825301761.24971746982404
664949.1888022674534-0.188802267453393
674240.30212620742571.69787379257432
684144.010159304223-3.01015930422297
693838.4484522734312-0.448452273431234
704242.0601640371797-0.0601640371796765
714441.13419114629222.86580885370781
723332.51022299651010.489777003489934
734849.8058251516044-1.80582515160443
744041.6497870754029-1.64978707540293
755051.199094709157-1.19909470915699
764949.4847265375893-0.484726537589288
774342.46684792982660.53315207017342
784442.1646015646431.83539843535702
794747.235721850888-0.235721850888011
803331.18640379421311.81359620578692
814648.3138687293346-2.31386872933457
82015.5970927529588-15.5970927529588
834542.86697776110372.13302223889631
844344.264568235954-1.26456823595403
854443.79770472741410.202295272585901
864746.99495364584820.00504635415180158
874546.177989994253-1.17798999425304
884241.77046300150770.229536998492351
893332.19256297762010.807437022379885
904343.8696971199463-0.869697119946347
914644.84136429432041.15863570567957
923331.98228433840181.01771566159822
934647.4545820146685-1.45458201466848
944850.4007706770431-2.40077067704312
954748.9476098184662-1.94760981846623
964748.8537079268838-1.85370792688375
974342.28195478350750.718045216492453
984647.4318395556824-1.43183955568241
994849.3512620050241-1.35126200502413
1004646.4343474386752-0.434347438675208
1014546.0331680335448-1.03316803354476
1024544.93783216285290.0621678371470785
1035251.96733442926960.0326655707304391
1044239.62806094326212.37193905673786
1054749.0041242903483-2.00412429034833
1064142.1447871572142-1.1447871572142
1074746.07278437474690.927215625253149
1084343.6117379607829-0.611737960782922
1093332.44272888534970.557271114650296
1103028.75965453490151.24034546509847
1114950.52602596609-1.52602596609
1124445.4906596034578-1.49065960345782
1135554.57930598832890.42069401167112
1141121.8668706403461-10.8668706403461
1154746.45354420981020.546455790189806
1165352.31150775837310.688492241626854
1173332.58474679064840.415253209351624
1184443.06168497018190.938315029818128
1194241.77810371562520.221896284374802
1205555.956988813515-0.956988813515041
1213332.73276237892990.267237621070089
1224646.2100419543186-0.210041954318644
1235455.2501057827401-1.25010578274011
1244746.06749551596630.932504484033725
1254544.15105361919070.848946380809268
1264748.2540978646502-1.25409786465016
1275554.93702702602630.0629729739737349
1284444.7491120770654-0.749112077065439
1295349.52055570711893.47944429288111
1304444.0495281865636-0.0495281865636252
1314243.0199196905208-1.0199196905208
1324041.3537255579834-1.35372555798337
1334646.8936989093601-0.893698909360059
1344039.58368779126940.416312208730554
1354645.34393403933240.656065960667557
1365350.24288664290092.75711335709911
1373332.8504590628930.149540937107007
1384238.15672974796493.84327025203511
1393532.99518715644262.00481284355742
1404038.66090621145171.33909378854828
1414142.6016180215863-1.60161802158634
1423331.68126787528161.31873212471838
1435151.9008156910276-0.900815691027617
1445352.95655549268180.0434445073182159
1454646.4033292276538-0.403329227653794
1465555.003606432918-0.00360643291798442
1474745.71457986935521.28542013064475
1483836.42952902002881.57047097997116
1494645.57842574831360.421574251686445
1504645.53916564344610.460834356553884
1515352.95920536852690.0407946314731348
1524749.0428543956969-2.04285439569695
1534142.9116993138606-1.91169931386056
1544444.1742770672423-0.174277067242337
1554342.94397151049520.056028489504815
1565148.73651987015292.26348012984712
1573331.98228433840181.01771566159822
1584342.4665243083770.533475691623035
1595349.52055570711893.47944429288111
1605151.2867571924488-0.286757192448781
1615052.8201600964495-2.82016009644954
1624642.62246046158423.37753953841576

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 32 & 31.5903222664463 & 0.4096777335537 \tabularnewline
2 & 51 & 52.4592961970192 & -1.45929619701917 \tabularnewline
3 & 42 & 39.9861670182274 & 2.01383298177257 \tabularnewline
4 & 41 & 40.6796961424973 & 0.320303857502687 \tabularnewline
5 & 46 & 47.5005374740057 & -1.50053747400573 \tabularnewline
6 & 47 & 47.4346563626305 & -0.434656362630515 \tabularnewline
7 & 37 & 31.7500321502951 & 5.24996784970485 \tabularnewline
8 & 49 & 49.0771342306583 & -0.077134230658302 \tabularnewline
9 & 45 & 44.98430869821 & 0.0156913017900486 \tabularnewline
10 & 47 & 46.7423205665647 & 0.257679433435267 \tabularnewline
11 & 49 & 47.7685991428789 & 1.23140085712114 \tabularnewline
12 & 33 & 31.156534010796 & 1.84346598920401 \tabularnewline
13 & 42 & 40.9703280546228 & 1.02967194537719 \tabularnewline
14 & 33 & 32.1317914159817 & 0.868208584018294 \tabularnewline
15 & 53 & 53.2580051199822 & -0.258005119982212 \tabularnewline
16 & 36 & 34.7671538465653 & 1.2328461534347 \tabularnewline
17 & 45 & 45.8766363774034 & -0.876636377403445 \tabularnewline
18 & 54 & 53.8448570060489 & 0.155142993951137 \tabularnewline
19 & 41 & 38.258850229195 & 2.74114977080501 \tabularnewline
20 & 36 & 33.7550688533609 & 2.24493114663915 \tabularnewline
21 & 41 & 39.0263765567302 & 1.97362344326983 \tabularnewline
22 & 44 & 41.3427122036684 & 2.6572877963316 \tabularnewline
23 & 33 & 31.771471099306 & 1.22852890069396 \tabularnewline
24 & 37 & 33.7578611196391 & 3.24213888036087 \tabularnewline
25 & 52 & 52.4530970021812 & -0.453097002181235 \tabularnewline
26 & 47 & 48.6752270504097 & -1.67522705040973 \tabularnewline
27 & 43 & 46.2034497718616 & -3.20344977186161 \tabularnewline
28 & 44 & 42.3084042440469 & 1.69159575595308 \tabularnewline
29 & 45 & 47.7924274726717 & -2.79242747267168 \tabularnewline
30 & 44 & 40.8381601471542 & 3.16183985284576 \tabularnewline
31 & 49 & 48.371662099004 & 0.628337900995968 \tabularnewline
32 & 33 & 32.591232387256 & 0.408767612744029 \tabularnewline
33 & 43 & 42.6765050092195 & 0.323494990780465 \tabularnewline
34 & 54 & 51.6507948619384 & 2.34920513806159 \tabularnewline
35 & 42 & 45.0928159380031 & -3.09281593800313 \tabularnewline
36 & 44 & 43.3187628972254 & 0.681237102774623 \tabularnewline
37 & 37 & 39.3576098426805 & -2.35760984268049 \tabularnewline
38 & 43 & 43.0992761759411 & -0.099276175941094 \tabularnewline
39 & 46 & 45.9169460561242 & 0.0830539438757718 \tabularnewline
40 & 42 & 41.5582813906284 & 0.441718609371611 \tabularnewline
41 & 45 & 44.5543028374256 & 0.445697162574446 \tabularnewline
42 & 44 & 45.9109583010774 & -1.91095830107737 \tabularnewline
43 & 33 & 30.8233765782435 & 2.17662342175654 \tabularnewline
44 & 31 & 30.9871102710876 & 0.0128897289123697 \tabularnewline
45 & 42 & 41.558072274373 & 0.441927725626963 \tabularnewline
46 & 40 & 41.7954837450099 & -1.79548374500993 \tabularnewline
47 & 43 & 39.1396899014828 & 3.86031009851718 \tabularnewline
48 & 46 & 45.6582858642353 & 0.341714135764745 \tabularnewline
49 & 42 & 45.3973770329165 & -3.39737703291645 \tabularnewline
50 & 45 & 45.6605467541597 & -0.660546754159693 \tabularnewline
51 & 44 & 43.6497448920369 & 0.350255107963107 \tabularnewline
52 & 40 & 40.630899318663 & -0.630899318662964 \tabularnewline
53 & 37 & 38.8861436144546 & -1.88614361445458 \tabularnewline
54 & 46 & 37.6761556922332 & 8.32384430776682 \tabularnewline
55 & 36 & 38.3772045054713 & -2.37720450547126 \tabularnewline
56 & 47 & 46.3844263390087 & 0.615573660991289 \tabularnewline
57 & 45 & 44.9716858900525 & 0.0283141099475099 \tabularnewline
58 & 42 & 40.1456360451029 & 1.85436395489709 \tabularnewline
59 & 43 & 44.4636139852735 & -1.4636139852735 \tabularnewline
60 & 43 & 42.8405423751927 & 0.159457624807253 \tabularnewline
61 & 32 & 32.4664631039256 & -0.466463103925641 \tabularnewline
62 & 45 & 47.0281498868257 & -2.02814988682568 \tabularnewline
63 & 45 & 47.2318295554002 & -2.2318295554002 \tabularnewline
64 & 31 & 31.0331927686529 & -0.0331927686528642 \tabularnewline
65 & 33 & 31.750282530176 & 1.24971746982404 \tabularnewline
66 & 49 & 49.1888022674534 & -0.188802267453393 \tabularnewline
67 & 42 & 40.3021262074257 & 1.69787379257432 \tabularnewline
68 & 41 & 44.010159304223 & -3.01015930422297 \tabularnewline
69 & 38 & 38.4484522734312 & -0.448452273431234 \tabularnewline
70 & 42 & 42.0601640371797 & -0.0601640371796765 \tabularnewline
71 & 44 & 41.1341911462922 & 2.86580885370781 \tabularnewline
72 & 33 & 32.5102229965101 & 0.489777003489934 \tabularnewline
73 & 48 & 49.8058251516044 & -1.80582515160443 \tabularnewline
74 & 40 & 41.6497870754029 & -1.64978707540293 \tabularnewline
75 & 50 & 51.199094709157 & -1.19909470915699 \tabularnewline
76 & 49 & 49.4847265375893 & -0.484726537589288 \tabularnewline
77 & 43 & 42.4668479298266 & 0.53315207017342 \tabularnewline
78 & 44 & 42.164601564643 & 1.83539843535702 \tabularnewline
79 & 47 & 47.235721850888 & -0.235721850888011 \tabularnewline
80 & 33 & 31.1864037942131 & 1.81359620578692 \tabularnewline
81 & 46 & 48.3138687293346 & -2.31386872933457 \tabularnewline
82 & 0 & 15.5970927529588 & -15.5970927529588 \tabularnewline
83 & 45 & 42.8669777611037 & 2.13302223889631 \tabularnewline
84 & 43 & 44.264568235954 & -1.26456823595403 \tabularnewline
85 & 44 & 43.7977047274141 & 0.202295272585901 \tabularnewline
86 & 47 & 46.9949536458482 & 0.00504635415180158 \tabularnewline
87 & 45 & 46.177989994253 & -1.17798999425304 \tabularnewline
88 & 42 & 41.7704630015077 & 0.229536998492351 \tabularnewline
89 & 33 & 32.1925629776201 & 0.807437022379885 \tabularnewline
90 & 43 & 43.8696971199463 & -0.869697119946347 \tabularnewline
91 & 46 & 44.8413642943204 & 1.15863570567957 \tabularnewline
92 & 33 & 31.9822843384018 & 1.01771566159822 \tabularnewline
93 & 46 & 47.4545820146685 & -1.45458201466848 \tabularnewline
94 & 48 & 50.4007706770431 & -2.40077067704312 \tabularnewline
95 & 47 & 48.9476098184662 & -1.94760981846623 \tabularnewline
96 & 47 & 48.8537079268838 & -1.85370792688375 \tabularnewline
97 & 43 & 42.2819547835075 & 0.718045216492453 \tabularnewline
98 & 46 & 47.4318395556824 & -1.43183955568241 \tabularnewline
99 & 48 & 49.3512620050241 & -1.35126200502413 \tabularnewline
100 & 46 & 46.4343474386752 & -0.434347438675208 \tabularnewline
101 & 45 & 46.0331680335448 & -1.03316803354476 \tabularnewline
102 & 45 & 44.9378321628529 & 0.0621678371470785 \tabularnewline
103 & 52 & 51.9673344292696 & 0.0326655707304391 \tabularnewline
104 & 42 & 39.6280609432621 & 2.37193905673786 \tabularnewline
105 & 47 & 49.0041242903483 & -2.00412429034833 \tabularnewline
106 & 41 & 42.1447871572142 & -1.1447871572142 \tabularnewline
107 & 47 & 46.0727843747469 & 0.927215625253149 \tabularnewline
108 & 43 & 43.6117379607829 & -0.611737960782922 \tabularnewline
109 & 33 & 32.4427288853497 & 0.557271114650296 \tabularnewline
110 & 30 & 28.7596545349015 & 1.24034546509847 \tabularnewline
111 & 49 & 50.52602596609 & -1.52602596609 \tabularnewline
112 & 44 & 45.4906596034578 & -1.49065960345782 \tabularnewline
113 & 55 & 54.5793059883289 & 0.42069401167112 \tabularnewline
114 & 11 & 21.8668706403461 & -10.8668706403461 \tabularnewline
115 & 47 & 46.4535442098102 & 0.546455790189806 \tabularnewline
116 & 53 & 52.3115077583731 & 0.688492241626854 \tabularnewline
117 & 33 & 32.5847467906484 & 0.415253209351624 \tabularnewline
118 & 44 & 43.0616849701819 & 0.938315029818128 \tabularnewline
119 & 42 & 41.7781037156252 & 0.221896284374802 \tabularnewline
120 & 55 & 55.956988813515 & -0.956988813515041 \tabularnewline
121 & 33 & 32.7327623789299 & 0.267237621070089 \tabularnewline
122 & 46 & 46.2100419543186 & -0.210041954318644 \tabularnewline
123 & 54 & 55.2501057827401 & -1.25010578274011 \tabularnewline
124 & 47 & 46.0674955159663 & 0.932504484033725 \tabularnewline
125 & 45 & 44.1510536191907 & 0.848946380809268 \tabularnewline
126 & 47 & 48.2540978646502 & -1.25409786465016 \tabularnewline
127 & 55 & 54.9370270260263 & 0.0629729739737349 \tabularnewline
128 & 44 & 44.7491120770654 & -0.749112077065439 \tabularnewline
129 & 53 & 49.5205557071189 & 3.47944429288111 \tabularnewline
130 & 44 & 44.0495281865636 & -0.0495281865636252 \tabularnewline
131 & 42 & 43.0199196905208 & -1.0199196905208 \tabularnewline
132 & 40 & 41.3537255579834 & -1.35372555798337 \tabularnewline
133 & 46 & 46.8936989093601 & -0.893698909360059 \tabularnewline
134 & 40 & 39.5836877912694 & 0.416312208730554 \tabularnewline
135 & 46 & 45.3439340393324 & 0.656065960667557 \tabularnewline
136 & 53 & 50.2428866429009 & 2.75711335709911 \tabularnewline
137 & 33 & 32.850459062893 & 0.149540937107007 \tabularnewline
138 & 42 & 38.1567297479649 & 3.84327025203511 \tabularnewline
139 & 35 & 32.9951871564426 & 2.00481284355742 \tabularnewline
140 & 40 & 38.6609062114517 & 1.33909378854828 \tabularnewline
141 & 41 & 42.6016180215863 & -1.60161802158634 \tabularnewline
142 & 33 & 31.6812678752816 & 1.31873212471838 \tabularnewline
143 & 51 & 51.9008156910276 & -0.900815691027617 \tabularnewline
144 & 53 & 52.9565554926818 & 0.0434445073182159 \tabularnewline
145 & 46 & 46.4033292276538 & -0.403329227653794 \tabularnewline
146 & 55 & 55.003606432918 & -0.00360643291798442 \tabularnewline
147 & 47 & 45.7145798693552 & 1.28542013064475 \tabularnewline
148 & 38 & 36.4295290200288 & 1.57047097997116 \tabularnewline
149 & 46 & 45.5784257483136 & 0.421574251686445 \tabularnewline
150 & 46 & 45.5391656434461 & 0.460834356553884 \tabularnewline
151 & 53 & 52.9592053685269 & 0.0407946314731348 \tabularnewline
152 & 47 & 49.0428543956969 & -2.04285439569695 \tabularnewline
153 & 41 & 42.9116993138606 & -1.91169931386056 \tabularnewline
154 & 44 & 44.1742770672423 & -0.174277067242337 \tabularnewline
155 & 43 & 42.9439715104952 & 0.056028489504815 \tabularnewline
156 & 51 & 48.7365198701529 & 2.26348012984712 \tabularnewline
157 & 33 & 31.9822843384018 & 1.01771566159822 \tabularnewline
158 & 43 & 42.466524308377 & 0.533475691623035 \tabularnewline
159 & 53 & 49.5205557071189 & 3.47944429288111 \tabularnewline
160 & 51 & 51.2867571924488 & -0.286757192448781 \tabularnewline
161 & 50 & 52.8201600964495 & -2.82016009644954 \tabularnewline
162 & 46 & 42.6224604615842 & 3.37753953841576 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185800&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]32[/C][C]31.5903222664463[/C][C]0.4096777335537[/C][/ROW]
[ROW][C]2[/C][C]51[/C][C]52.4592961970192[/C][C]-1.45929619701917[/C][/ROW]
[ROW][C]3[/C][C]42[/C][C]39.9861670182274[/C][C]2.01383298177257[/C][/ROW]
[ROW][C]4[/C][C]41[/C][C]40.6796961424973[/C][C]0.320303857502687[/C][/ROW]
[ROW][C]5[/C][C]46[/C][C]47.5005374740057[/C][C]-1.50053747400573[/C][/ROW]
[ROW][C]6[/C][C]47[/C][C]47.4346563626305[/C][C]-0.434656362630515[/C][/ROW]
[ROW][C]7[/C][C]37[/C][C]31.7500321502951[/C][C]5.24996784970485[/C][/ROW]
[ROW][C]8[/C][C]49[/C][C]49.0771342306583[/C][C]-0.077134230658302[/C][/ROW]
[ROW][C]9[/C][C]45[/C][C]44.98430869821[/C][C]0.0156913017900486[/C][/ROW]
[ROW][C]10[/C][C]47[/C][C]46.7423205665647[/C][C]0.257679433435267[/C][/ROW]
[ROW][C]11[/C][C]49[/C][C]47.7685991428789[/C][C]1.23140085712114[/C][/ROW]
[ROW][C]12[/C][C]33[/C][C]31.156534010796[/C][C]1.84346598920401[/C][/ROW]
[ROW][C]13[/C][C]42[/C][C]40.9703280546228[/C][C]1.02967194537719[/C][/ROW]
[ROW][C]14[/C][C]33[/C][C]32.1317914159817[/C][C]0.868208584018294[/C][/ROW]
[ROW][C]15[/C][C]53[/C][C]53.2580051199822[/C][C]-0.258005119982212[/C][/ROW]
[ROW][C]16[/C][C]36[/C][C]34.7671538465653[/C][C]1.2328461534347[/C][/ROW]
[ROW][C]17[/C][C]45[/C][C]45.8766363774034[/C][C]-0.876636377403445[/C][/ROW]
[ROW][C]18[/C][C]54[/C][C]53.8448570060489[/C][C]0.155142993951137[/C][/ROW]
[ROW][C]19[/C][C]41[/C][C]38.258850229195[/C][C]2.74114977080501[/C][/ROW]
[ROW][C]20[/C][C]36[/C][C]33.7550688533609[/C][C]2.24493114663915[/C][/ROW]
[ROW][C]21[/C][C]41[/C][C]39.0263765567302[/C][C]1.97362344326983[/C][/ROW]
[ROW][C]22[/C][C]44[/C][C]41.3427122036684[/C][C]2.6572877963316[/C][/ROW]
[ROW][C]23[/C][C]33[/C][C]31.771471099306[/C][C]1.22852890069396[/C][/ROW]
[ROW][C]24[/C][C]37[/C][C]33.7578611196391[/C][C]3.24213888036087[/C][/ROW]
[ROW][C]25[/C][C]52[/C][C]52.4530970021812[/C][C]-0.453097002181235[/C][/ROW]
[ROW][C]26[/C][C]47[/C][C]48.6752270504097[/C][C]-1.67522705040973[/C][/ROW]
[ROW][C]27[/C][C]43[/C][C]46.2034497718616[/C][C]-3.20344977186161[/C][/ROW]
[ROW][C]28[/C][C]44[/C][C]42.3084042440469[/C][C]1.69159575595308[/C][/ROW]
[ROW][C]29[/C][C]45[/C][C]47.7924274726717[/C][C]-2.79242747267168[/C][/ROW]
[ROW][C]30[/C][C]44[/C][C]40.8381601471542[/C][C]3.16183985284576[/C][/ROW]
[ROW][C]31[/C][C]49[/C][C]48.371662099004[/C][C]0.628337900995968[/C][/ROW]
[ROW][C]32[/C][C]33[/C][C]32.591232387256[/C][C]0.408767612744029[/C][/ROW]
[ROW][C]33[/C][C]43[/C][C]42.6765050092195[/C][C]0.323494990780465[/C][/ROW]
[ROW][C]34[/C][C]54[/C][C]51.6507948619384[/C][C]2.34920513806159[/C][/ROW]
[ROW][C]35[/C][C]42[/C][C]45.0928159380031[/C][C]-3.09281593800313[/C][/ROW]
[ROW][C]36[/C][C]44[/C][C]43.3187628972254[/C][C]0.681237102774623[/C][/ROW]
[ROW][C]37[/C][C]37[/C][C]39.3576098426805[/C][C]-2.35760984268049[/C][/ROW]
[ROW][C]38[/C][C]43[/C][C]43.0992761759411[/C][C]-0.099276175941094[/C][/ROW]
[ROW][C]39[/C][C]46[/C][C]45.9169460561242[/C][C]0.0830539438757718[/C][/ROW]
[ROW][C]40[/C][C]42[/C][C]41.5582813906284[/C][C]0.441718609371611[/C][/ROW]
[ROW][C]41[/C][C]45[/C][C]44.5543028374256[/C][C]0.445697162574446[/C][/ROW]
[ROW][C]42[/C][C]44[/C][C]45.9109583010774[/C][C]-1.91095830107737[/C][/ROW]
[ROW][C]43[/C][C]33[/C][C]30.8233765782435[/C][C]2.17662342175654[/C][/ROW]
[ROW][C]44[/C][C]31[/C][C]30.9871102710876[/C][C]0.0128897289123697[/C][/ROW]
[ROW][C]45[/C][C]42[/C][C]41.558072274373[/C][C]0.441927725626963[/C][/ROW]
[ROW][C]46[/C][C]40[/C][C]41.7954837450099[/C][C]-1.79548374500993[/C][/ROW]
[ROW][C]47[/C][C]43[/C][C]39.1396899014828[/C][C]3.86031009851718[/C][/ROW]
[ROW][C]48[/C][C]46[/C][C]45.6582858642353[/C][C]0.341714135764745[/C][/ROW]
[ROW][C]49[/C][C]42[/C][C]45.3973770329165[/C][C]-3.39737703291645[/C][/ROW]
[ROW][C]50[/C][C]45[/C][C]45.6605467541597[/C][C]-0.660546754159693[/C][/ROW]
[ROW][C]51[/C][C]44[/C][C]43.6497448920369[/C][C]0.350255107963107[/C][/ROW]
[ROW][C]52[/C][C]40[/C][C]40.630899318663[/C][C]-0.630899318662964[/C][/ROW]
[ROW][C]53[/C][C]37[/C][C]38.8861436144546[/C][C]-1.88614361445458[/C][/ROW]
[ROW][C]54[/C][C]46[/C][C]37.6761556922332[/C][C]8.32384430776682[/C][/ROW]
[ROW][C]55[/C][C]36[/C][C]38.3772045054713[/C][C]-2.37720450547126[/C][/ROW]
[ROW][C]56[/C][C]47[/C][C]46.3844263390087[/C][C]0.615573660991289[/C][/ROW]
[ROW][C]57[/C][C]45[/C][C]44.9716858900525[/C][C]0.0283141099475099[/C][/ROW]
[ROW][C]58[/C][C]42[/C][C]40.1456360451029[/C][C]1.85436395489709[/C][/ROW]
[ROW][C]59[/C][C]43[/C][C]44.4636139852735[/C][C]-1.4636139852735[/C][/ROW]
[ROW][C]60[/C][C]43[/C][C]42.8405423751927[/C][C]0.159457624807253[/C][/ROW]
[ROW][C]61[/C][C]32[/C][C]32.4664631039256[/C][C]-0.466463103925641[/C][/ROW]
[ROW][C]62[/C][C]45[/C][C]47.0281498868257[/C][C]-2.02814988682568[/C][/ROW]
[ROW][C]63[/C][C]45[/C][C]47.2318295554002[/C][C]-2.2318295554002[/C][/ROW]
[ROW][C]64[/C][C]31[/C][C]31.0331927686529[/C][C]-0.0331927686528642[/C][/ROW]
[ROW][C]65[/C][C]33[/C][C]31.750282530176[/C][C]1.24971746982404[/C][/ROW]
[ROW][C]66[/C][C]49[/C][C]49.1888022674534[/C][C]-0.188802267453393[/C][/ROW]
[ROW][C]67[/C][C]42[/C][C]40.3021262074257[/C][C]1.69787379257432[/C][/ROW]
[ROW][C]68[/C][C]41[/C][C]44.010159304223[/C][C]-3.01015930422297[/C][/ROW]
[ROW][C]69[/C][C]38[/C][C]38.4484522734312[/C][C]-0.448452273431234[/C][/ROW]
[ROW][C]70[/C][C]42[/C][C]42.0601640371797[/C][C]-0.0601640371796765[/C][/ROW]
[ROW][C]71[/C][C]44[/C][C]41.1341911462922[/C][C]2.86580885370781[/C][/ROW]
[ROW][C]72[/C][C]33[/C][C]32.5102229965101[/C][C]0.489777003489934[/C][/ROW]
[ROW][C]73[/C][C]48[/C][C]49.8058251516044[/C][C]-1.80582515160443[/C][/ROW]
[ROW][C]74[/C][C]40[/C][C]41.6497870754029[/C][C]-1.64978707540293[/C][/ROW]
[ROW][C]75[/C][C]50[/C][C]51.199094709157[/C][C]-1.19909470915699[/C][/ROW]
[ROW][C]76[/C][C]49[/C][C]49.4847265375893[/C][C]-0.484726537589288[/C][/ROW]
[ROW][C]77[/C][C]43[/C][C]42.4668479298266[/C][C]0.53315207017342[/C][/ROW]
[ROW][C]78[/C][C]44[/C][C]42.164601564643[/C][C]1.83539843535702[/C][/ROW]
[ROW][C]79[/C][C]47[/C][C]47.235721850888[/C][C]-0.235721850888011[/C][/ROW]
[ROW][C]80[/C][C]33[/C][C]31.1864037942131[/C][C]1.81359620578692[/C][/ROW]
[ROW][C]81[/C][C]46[/C][C]48.3138687293346[/C][C]-2.31386872933457[/C][/ROW]
[ROW][C]82[/C][C]0[/C][C]15.5970927529588[/C][C]-15.5970927529588[/C][/ROW]
[ROW][C]83[/C][C]45[/C][C]42.8669777611037[/C][C]2.13302223889631[/C][/ROW]
[ROW][C]84[/C][C]43[/C][C]44.264568235954[/C][C]-1.26456823595403[/C][/ROW]
[ROW][C]85[/C][C]44[/C][C]43.7977047274141[/C][C]0.202295272585901[/C][/ROW]
[ROW][C]86[/C][C]47[/C][C]46.9949536458482[/C][C]0.00504635415180158[/C][/ROW]
[ROW][C]87[/C][C]45[/C][C]46.177989994253[/C][C]-1.17798999425304[/C][/ROW]
[ROW][C]88[/C][C]42[/C][C]41.7704630015077[/C][C]0.229536998492351[/C][/ROW]
[ROW][C]89[/C][C]33[/C][C]32.1925629776201[/C][C]0.807437022379885[/C][/ROW]
[ROW][C]90[/C][C]43[/C][C]43.8696971199463[/C][C]-0.869697119946347[/C][/ROW]
[ROW][C]91[/C][C]46[/C][C]44.8413642943204[/C][C]1.15863570567957[/C][/ROW]
[ROW][C]92[/C][C]33[/C][C]31.9822843384018[/C][C]1.01771566159822[/C][/ROW]
[ROW][C]93[/C][C]46[/C][C]47.4545820146685[/C][C]-1.45458201466848[/C][/ROW]
[ROW][C]94[/C][C]48[/C][C]50.4007706770431[/C][C]-2.40077067704312[/C][/ROW]
[ROW][C]95[/C][C]47[/C][C]48.9476098184662[/C][C]-1.94760981846623[/C][/ROW]
[ROW][C]96[/C][C]47[/C][C]48.8537079268838[/C][C]-1.85370792688375[/C][/ROW]
[ROW][C]97[/C][C]43[/C][C]42.2819547835075[/C][C]0.718045216492453[/C][/ROW]
[ROW][C]98[/C][C]46[/C][C]47.4318395556824[/C][C]-1.43183955568241[/C][/ROW]
[ROW][C]99[/C][C]48[/C][C]49.3512620050241[/C][C]-1.35126200502413[/C][/ROW]
[ROW][C]100[/C][C]46[/C][C]46.4343474386752[/C][C]-0.434347438675208[/C][/ROW]
[ROW][C]101[/C][C]45[/C][C]46.0331680335448[/C][C]-1.03316803354476[/C][/ROW]
[ROW][C]102[/C][C]45[/C][C]44.9378321628529[/C][C]0.0621678371470785[/C][/ROW]
[ROW][C]103[/C][C]52[/C][C]51.9673344292696[/C][C]0.0326655707304391[/C][/ROW]
[ROW][C]104[/C][C]42[/C][C]39.6280609432621[/C][C]2.37193905673786[/C][/ROW]
[ROW][C]105[/C][C]47[/C][C]49.0041242903483[/C][C]-2.00412429034833[/C][/ROW]
[ROW][C]106[/C][C]41[/C][C]42.1447871572142[/C][C]-1.1447871572142[/C][/ROW]
[ROW][C]107[/C][C]47[/C][C]46.0727843747469[/C][C]0.927215625253149[/C][/ROW]
[ROW][C]108[/C][C]43[/C][C]43.6117379607829[/C][C]-0.611737960782922[/C][/ROW]
[ROW][C]109[/C][C]33[/C][C]32.4427288853497[/C][C]0.557271114650296[/C][/ROW]
[ROW][C]110[/C][C]30[/C][C]28.7596545349015[/C][C]1.24034546509847[/C][/ROW]
[ROW][C]111[/C][C]49[/C][C]50.52602596609[/C][C]-1.52602596609[/C][/ROW]
[ROW][C]112[/C][C]44[/C][C]45.4906596034578[/C][C]-1.49065960345782[/C][/ROW]
[ROW][C]113[/C][C]55[/C][C]54.5793059883289[/C][C]0.42069401167112[/C][/ROW]
[ROW][C]114[/C][C]11[/C][C]21.8668706403461[/C][C]-10.8668706403461[/C][/ROW]
[ROW][C]115[/C][C]47[/C][C]46.4535442098102[/C][C]0.546455790189806[/C][/ROW]
[ROW][C]116[/C][C]53[/C][C]52.3115077583731[/C][C]0.688492241626854[/C][/ROW]
[ROW][C]117[/C][C]33[/C][C]32.5847467906484[/C][C]0.415253209351624[/C][/ROW]
[ROW][C]118[/C][C]44[/C][C]43.0616849701819[/C][C]0.938315029818128[/C][/ROW]
[ROW][C]119[/C][C]42[/C][C]41.7781037156252[/C][C]0.221896284374802[/C][/ROW]
[ROW][C]120[/C][C]55[/C][C]55.956988813515[/C][C]-0.956988813515041[/C][/ROW]
[ROW][C]121[/C][C]33[/C][C]32.7327623789299[/C][C]0.267237621070089[/C][/ROW]
[ROW][C]122[/C][C]46[/C][C]46.2100419543186[/C][C]-0.210041954318644[/C][/ROW]
[ROW][C]123[/C][C]54[/C][C]55.2501057827401[/C][C]-1.25010578274011[/C][/ROW]
[ROW][C]124[/C][C]47[/C][C]46.0674955159663[/C][C]0.932504484033725[/C][/ROW]
[ROW][C]125[/C][C]45[/C][C]44.1510536191907[/C][C]0.848946380809268[/C][/ROW]
[ROW][C]126[/C][C]47[/C][C]48.2540978646502[/C][C]-1.25409786465016[/C][/ROW]
[ROW][C]127[/C][C]55[/C][C]54.9370270260263[/C][C]0.0629729739737349[/C][/ROW]
[ROW][C]128[/C][C]44[/C][C]44.7491120770654[/C][C]-0.749112077065439[/C][/ROW]
[ROW][C]129[/C][C]53[/C][C]49.5205557071189[/C][C]3.47944429288111[/C][/ROW]
[ROW][C]130[/C][C]44[/C][C]44.0495281865636[/C][C]-0.0495281865636252[/C][/ROW]
[ROW][C]131[/C][C]42[/C][C]43.0199196905208[/C][C]-1.0199196905208[/C][/ROW]
[ROW][C]132[/C][C]40[/C][C]41.3537255579834[/C][C]-1.35372555798337[/C][/ROW]
[ROW][C]133[/C][C]46[/C][C]46.8936989093601[/C][C]-0.893698909360059[/C][/ROW]
[ROW][C]134[/C][C]40[/C][C]39.5836877912694[/C][C]0.416312208730554[/C][/ROW]
[ROW][C]135[/C][C]46[/C][C]45.3439340393324[/C][C]0.656065960667557[/C][/ROW]
[ROW][C]136[/C][C]53[/C][C]50.2428866429009[/C][C]2.75711335709911[/C][/ROW]
[ROW][C]137[/C][C]33[/C][C]32.850459062893[/C][C]0.149540937107007[/C][/ROW]
[ROW][C]138[/C][C]42[/C][C]38.1567297479649[/C][C]3.84327025203511[/C][/ROW]
[ROW][C]139[/C][C]35[/C][C]32.9951871564426[/C][C]2.00481284355742[/C][/ROW]
[ROW][C]140[/C][C]40[/C][C]38.6609062114517[/C][C]1.33909378854828[/C][/ROW]
[ROW][C]141[/C][C]41[/C][C]42.6016180215863[/C][C]-1.60161802158634[/C][/ROW]
[ROW][C]142[/C][C]33[/C][C]31.6812678752816[/C][C]1.31873212471838[/C][/ROW]
[ROW][C]143[/C][C]51[/C][C]51.9008156910276[/C][C]-0.900815691027617[/C][/ROW]
[ROW][C]144[/C][C]53[/C][C]52.9565554926818[/C][C]0.0434445073182159[/C][/ROW]
[ROW][C]145[/C][C]46[/C][C]46.4033292276538[/C][C]-0.403329227653794[/C][/ROW]
[ROW][C]146[/C][C]55[/C][C]55.003606432918[/C][C]-0.00360643291798442[/C][/ROW]
[ROW][C]147[/C][C]47[/C][C]45.7145798693552[/C][C]1.28542013064475[/C][/ROW]
[ROW][C]148[/C][C]38[/C][C]36.4295290200288[/C][C]1.57047097997116[/C][/ROW]
[ROW][C]149[/C][C]46[/C][C]45.5784257483136[/C][C]0.421574251686445[/C][/ROW]
[ROW][C]150[/C][C]46[/C][C]45.5391656434461[/C][C]0.460834356553884[/C][/ROW]
[ROW][C]151[/C][C]53[/C][C]52.9592053685269[/C][C]0.0407946314731348[/C][/ROW]
[ROW][C]152[/C][C]47[/C][C]49.0428543956969[/C][C]-2.04285439569695[/C][/ROW]
[ROW][C]153[/C][C]41[/C][C]42.9116993138606[/C][C]-1.91169931386056[/C][/ROW]
[ROW][C]154[/C][C]44[/C][C]44.1742770672423[/C][C]-0.174277067242337[/C][/ROW]
[ROW][C]155[/C][C]43[/C][C]42.9439715104952[/C][C]0.056028489504815[/C][/ROW]
[ROW][C]156[/C][C]51[/C][C]48.7365198701529[/C][C]2.26348012984712[/C][/ROW]
[ROW][C]157[/C][C]33[/C][C]31.9822843384018[/C][C]1.01771566159822[/C][/ROW]
[ROW][C]158[/C][C]43[/C][C]42.466524308377[/C][C]0.533475691623035[/C][/ROW]
[ROW][C]159[/C][C]53[/C][C]49.5205557071189[/C][C]3.47944429288111[/C][/ROW]
[ROW][C]160[/C][C]51[/C][C]51.2867571924488[/C][C]-0.286757192448781[/C][/ROW]
[ROW][C]161[/C][C]50[/C][C]52.8201600964495[/C][C]-2.82016009644954[/C][/ROW]
[ROW][C]162[/C][C]46[/C][C]42.6224604615842[/C][C]3.37753953841576[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185800&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185800&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
13231.59032226644630.4096777335537
25152.4592961970192-1.45929619701917
34239.98616701822742.01383298177257
44140.67969614249730.320303857502687
54647.5005374740057-1.50053747400573
64747.4346563626305-0.434656362630515
73731.75003215029515.24996784970485
84949.0771342306583-0.077134230658302
94544.984308698210.0156913017900486
104746.74232056656470.257679433435267
114947.76859914287891.23140085712114
123331.1565340107961.84346598920401
134240.97032805462281.02967194537719
143332.13179141598170.868208584018294
155353.2580051199822-0.258005119982212
163634.76715384656531.2328461534347
174545.8766363774034-0.876636377403445
185453.84485700604890.155142993951137
194138.2588502291952.74114977080501
203633.75506885336092.24493114663915
214139.02637655673021.97362344326983
224441.34271220366842.6572877963316
233331.7714710993061.22852890069396
243733.75786111963913.24213888036087
255252.4530970021812-0.453097002181235
264748.6752270504097-1.67522705040973
274346.2034497718616-3.20344977186161
284442.30840424404691.69159575595308
294547.7924274726717-2.79242747267168
304440.83816014715423.16183985284576
314948.3716620990040.628337900995968
323332.5912323872560.408767612744029
334342.67650500921950.323494990780465
345451.65079486193842.34920513806159
354245.0928159380031-3.09281593800313
364443.31876289722540.681237102774623
373739.3576098426805-2.35760984268049
384343.0992761759411-0.099276175941094
394645.91694605612420.0830539438757718
404241.55828139062840.441718609371611
414544.55430283742560.445697162574446
424445.9109583010774-1.91095830107737
433330.82337657824352.17662342175654
443130.98711027108760.0128897289123697
454241.5580722743730.441927725626963
464041.7954837450099-1.79548374500993
474339.13968990148283.86031009851718
484645.65828586423530.341714135764745
494245.3973770329165-3.39737703291645
504545.6605467541597-0.660546754159693
514443.64974489203690.350255107963107
524040.630899318663-0.630899318662964
533738.8861436144546-1.88614361445458
544637.67615569223328.32384430776682
553638.3772045054713-2.37720450547126
564746.38442633900870.615573660991289
574544.97168589005250.0283141099475099
584240.14563604510291.85436395489709
594344.4636139852735-1.4636139852735
604342.84054237519270.159457624807253
613232.4664631039256-0.466463103925641
624547.0281498868257-2.02814988682568
634547.2318295554002-2.2318295554002
643131.0331927686529-0.0331927686528642
653331.7502825301761.24971746982404
664949.1888022674534-0.188802267453393
674240.30212620742571.69787379257432
684144.010159304223-3.01015930422297
693838.4484522734312-0.448452273431234
704242.0601640371797-0.0601640371796765
714441.13419114629222.86580885370781
723332.51022299651010.489777003489934
734849.8058251516044-1.80582515160443
744041.6497870754029-1.64978707540293
755051.199094709157-1.19909470915699
764949.4847265375893-0.484726537589288
774342.46684792982660.53315207017342
784442.1646015646431.83539843535702
794747.235721850888-0.235721850888011
803331.18640379421311.81359620578692
814648.3138687293346-2.31386872933457
82015.5970927529588-15.5970927529588
834542.86697776110372.13302223889631
844344.264568235954-1.26456823595403
854443.79770472741410.202295272585901
864746.99495364584820.00504635415180158
874546.177989994253-1.17798999425304
884241.77046300150770.229536998492351
893332.19256297762010.807437022379885
904343.8696971199463-0.869697119946347
914644.84136429432041.15863570567957
923331.98228433840181.01771566159822
934647.4545820146685-1.45458201466848
944850.4007706770431-2.40077067704312
954748.9476098184662-1.94760981846623
964748.8537079268838-1.85370792688375
974342.28195478350750.718045216492453
984647.4318395556824-1.43183955568241
994849.3512620050241-1.35126200502413
1004646.4343474386752-0.434347438675208
1014546.0331680335448-1.03316803354476
1024544.93783216285290.0621678371470785
1035251.96733442926960.0326655707304391
1044239.62806094326212.37193905673786
1054749.0041242903483-2.00412429034833
1064142.1447871572142-1.1447871572142
1074746.07278437474690.927215625253149
1084343.6117379607829-0.611737960782922
1093332.44272888534970.557271114650296
1103028.75965453490151.24034546509847
1114950.52602596609-1.52602596609
1124445.4906596034578-1.49065960345782
1135554.57930598832890.42069401167112
1141121.8668706403461-10.8668706403461
1154746.45354420981020.546455790189806
1165352.31150775837310.688492241626854
1173332.58474679064840.415253209351624
1184443.06168497018190.938315029818128
1194241.77810371562520.221896284374802
1205555.956988813515-0.956988813515041
1213332.73276237892990.267237621070089
1224646.2100419543186-0.210041954318644
1235455.2501057827401-1.25010578274011
1244746.06749551596630.932504484033725
1254544.15105361919070.848946380809268
1264748.2540978646502-1.25409786465016
1275554.93702702602630.0629729739737349
1284444.7491120770654-0.749112077065439
1295349.52055570711893.47944429288111
1304444.0495281865636-0.0495281865636252
1314243.0199196905208-1.0199196905208
1324041.3537255579834-1.35372555798337
1334646.8936989093601-0.893698909360059
1344039.58368779126940.416312208730554
1354645.34393403933240.656065960667557
1365350.24288664290092.75711335709911
1373332.8504590628930.149540937107007
1384238.15672974796493.84327025203511
1393532.99518715644262.00481284355742
1404038.66090621145171.33909378854828
1414142.6016180215863-1.60161802158634
1423331.68126787528161.31873212471838
1435151.9008156910276-0.900815691027617
1445352.95655549268180.0434445073182159
1454646.4033292276538-0.403329227653794
1465555.003606432918-0.00360643291798442
1474745.71457986935521.28542013064475
1483836.42952902002881.57047097997116
1494645.57842574831360.421574251686445
1504645.53916564344610.460834356553884
1515352.95920536852690.0407946314731348
1524749.0428543956969-2.04285439569695
1534142.9116993138606-1.91169931386056
1544444.1742770672423-0.174277067242337
1554342.94397151049520.056028489504815
1565148.73651987015292.26348012984712
1573331.98228433840181.01771566159822
1584342.4665243083770.533475691623035
1595349.52055570711893.47944429288111
1605151.2867571924488-0.286757192448781
1615052.8201600964495-2.82016009644954
1624642.62246046158423.37753953841576






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
110.1519395829244810.3038791658489610.848060417075519
120.06373789494488710.1274757898897740.936262105055113
130.02781171253128810.05562342506257620.972188287468712
140.01464750373195040.02929500746390080.98535249626805
150.006342346464181080.01268469292836220.993657653535819
160.002218382397865510.004436764795731020.997781617602134
170.001759818806264740.003519637612529480.998240181193735
180.0007496296578095030.001499259315619010.99925037034219
190.0004133585636743280.0008267171273486560.999586641436326
200.0001527390301114910.0003054780602229810.999847260969889
217.34448925026419e-050.0001468897850052840.999926555107497
225.34034362223847e-050.0001068068724447690.999946596563778
230.0001134221952977190.0002268443905954380.999886577804702
240.0002071700747610820.0004143401495221630.999792829925239
259.45852010826995e-050.0001891704021653990.999905414798917
260.0001371222540332440.0002742445080664870.999862877745967
270.001468746411830770.002937492823661540.998531253588169
280.001103513870965530.002207027741931070.998896486129034
290.003449072095471840.006898144190943670.996550927904528
300.006297758863081810.01259551772616360.993702241136918
310.003920022595723290.007840045191446580.996079977404277
320.003160906262779910.006321812525559820.99683909373722
330.001850024303832250.00370004860766450.998149975696168
340.004455580125411970.008911160250823940.995544419874588
350.008703450561786760.01740690112357350.991296549438213
360.0100188385297090.02003767705941790.989981161470291
370.01143199788683880.02286399577367750.988568002113161
380.007454070263903220.01490814052780640.992545929736097
390.004909199834481810.009818399668963620.995090800165518
400.003184782210204030.006369564420408070.996815217789796
410.001983937789950370.003967875579900740.99801606221005
420.001665145250689730.003330290501379450.99833485474931
430.001418445120348850.00283689024069770.998581554879651
440.001146324528979620.002292649057959240.99885367547102
450.0007322033624775510.00146440672495510.999267796637522
460.0005287170341060850.001057434068212170.999471282965894
470.001306880024286240.002613760048572480.998693119975714
480.0008133064569424640.001626612913884930.999186693543058
490.002198152074326970.004396304148653930.997801847925673
500.001419311156718010.002838622313436020.998580688843282
510.0009339044694556530.001867808938911310.999066095530544
520.0006200074657085150.001240014931417030.999379992534291
530.0005262864049543870.001052572809908770.999473713595046
540.04644464139830740.09288928279661480.953555358601693
550.04622656035684310.09245312071368630.953773439643157
560.04009985399302170.08019970798604340.959900146006978
570.0302511146883420.0605022293766840.969748885311658
580.02543131281239830.05086262562479660.974568687187602
590.02369571865822550.0473914373164510.976304281341774
600.01762018590005670.03524037180011330.982379814099943
610.01310287002365560.02620574004731120.986897129976344
620.01232595923441230.02465191846882460.987674040765588
630.01310563170933370.02621126341866750.986894368290666
640.01193058917231860.02386117834463730.988069410827681
650.009991255881658160.01998251176331630.990008744118342
660.007210170285466140.01442034057093230.992789829714534
670.006249339469452640.01249867893890530.993750660530547
680.009560433504158620.01912086700831720.990439566495841
690.007300255414514420.01460051082902880.992699744585486
700.005443419124839040.01088683824967810.994556580875161
710.008835460339949420.01767092067989880.991164539660051
720.007551218926263320.01510243785252660.992448781073737
730.006887477573471950.01377495514694390.993112522426528
740.008643650541503340.01728730108300670.991356349458497
750.006766659921170470.01353331984234090.99323334007883
760.00507643058903250.0101528611780650.994923569410967
770.003817228323397970.007634456646795950.996182771676602
780.003667016258366420.007334032516732840.996332983741634
790.002700696023910930.005401392047821860.997299303976089
800.002499937389878920.004999874779757840.997500062610121
810.002465705713903640.004931411427807290.997534294286096
820.9978012488300970.004397502339806560.00219875116990328
830.9977419655053190.004516068989361320.00225803449468066
840.9970828905748110.005834218850377210.00291710942518861
850.9958352868358390.008329426328321530.00416471316416076
860.9941295952101780.01174080957964480.0058704047898224
870.9924990114784010.01500197704319870.00750098852159934
880.9896645170681530.02067096586369460.0103354829318473
890.9863950366254930.02720992674901460.0136049633745073
900.982440257475920.03511948504815980.0175597425240799
910.9780987862164810.04380242756703790.021901213783519
920.9734453120231040.05310937595379170.0265546879768959
930.9680120622174480.06397587556510440.0319879377825522
940.9691359794543730.06172804109125450.0308640205456273
950.9663553058947050.06728938821059040.0336446941052952
960.963925748669070.07214850266186060.0360742513309303
970.9550784580249030.08984308395019390.044921541975097
980.9505444914687270.09891101706254530.0494555085312727
990.9425671911437980.1148656177124040.0574328088562018
1000.927904881897240.144190236205520.0720951181027602
1010.9125737698347560.1748524603304890.0874262301652443
1020.8910252640853430.2179494718293150.108974735914657
1030.8658784350961230.2682431298077540.134121564903877
1040.8668263440420290.2663473119159420.133173655957971
1050.86549661467470.26900677065060.1345033853253
1060.841219740069610.3175605198607790.15878025993039
1070.8126974867445690.3746050265108620.187302513255431
1080.7805672661549780.4388654676900430.219432733845022
1090.7483282604914870.5033434790170260.251671739508513
1100.7259399491288080.5481201017423830.274060050871192
1110.7152328302257790.5695343395484420.284767169774221
1120.6856098668424830.6287802663150350.314390133157517
1130.6409927568372150.7180144863255710.359007243162785
1140.9999845383671583.09232656839859e-051.54616328419929e-05
1150.9999742265870655.15468258689698e-052.57734129344849e-05
1160.9999552408847568.95182304875276e-054.47591152437638e-05
1170.9999317076752290.0001365846495423766.82923247711882e-05
1180.9998799255316090.0002401489367813330.000120074468390667
1190.9997843387383530.000431322523293270.000215661261646635
1200.9996335086055110.0007329827889770190.000366491394488509
1210.9994111771772010.001177645645598280.000588822822799138
1220.9990374045330680.001925190933864040.000962595466932019
1230.9985215003122220.00295699937555560.0014784996877778
1240.9977880168866170.00442396622676510.00221198311338255
1250.9964450835633530.007109832873293460.00355491643664673
1260.9947550798563470.01048984028730630.00524492014365317
1270.9917171014401330.01656579711973480.0082828985598674
1280.9911497152289730.01770056954205490.00885028477102745
1290.9917029567025690.01659408659486290.00829704329743143
1300.9879681017049190.02406379659016140.0120318982950807
1310.9834269512530270.03314609749394540.0165730487469727
1320.9785919141435170.04281617171296560.0214080858564828
1330.9715833396641450.05683332067170970.0284166603358549
1340.9621877443843720.07562451123125580.0378122556156279
1350.9441746256605810.1116507486788380.0558253743394191
1360.9464747173818280.1070505652363440.053525282618172
1370.946362333273390.107275333453220.0536376667266102
1380.9596561406995510.08068771860089760.0403438593004488
1390.9420705399769230.1158589200461530.0579294600230767
1400.9134205660528990.1731588678942010.0865794339471007
1410.9397052825179270.1205894349641460.0602947174820731
1420.9143849764728670.1712300470542660.0856150235271331
1430.8901651898610680.2196696202778640.109834810138932
1440.8422726682057880.3154546635884230.157727331794212
1450.8503188116133070.2993623767733860.149681188386693
1460.8370808341570590.3258383316858810.162919165842941
1470.7840765046262480.4318469907475040.215923495373752
1480.8327256742171710.3345486515656590.167274325782829
1490.7951079851311990.4097840297376020.204892014868801
1500.6656357568509090.6687284862981820.334364243149091
1510.5034322107764150.9931355784471710.496567789223585

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
11 & 0.151939582924481 & 0.303879165848961 & 0.848060417075519 \tabularnewline
12 & 0.0637378949448871 & 0.127475789889774 & 0.936262105055113 \tabularnewline
13 & 0.0278117125312881 & 0.0556234250625762 & 0.972188287468712 \tabularnewline
14 & 0.0146475037319504 & 0.0292950074639008 & 0.98535249626805 \tabularnewline
15 & 0.00634234646418108 & 0.0126846929283622 & 0.993657653535819 \tabularnewline
16 & 0.00221838239786551 & 0.00443676479573102 & 0.997781617602134 \tabularnewline
17 & 0.00175981880626474 & 0.00351963761252948 & 0.998240181193735 \tabularnewline
18 & 0.000749629657809503 & 0.00149925931561901 & 0.99925037034219 \tabularnewline
19 & 0.000413358563674328 & 0.000826717127348656 & 0.999586641436326 \tabularnewline
20 & 0.000152739030111491 & 0.000305478060222981 & 0.999847260969889 \tabularnewline
21 & 7.34448925026419e-05 & 0.000146889785005284 & 0.999926555107497 \tabularnewline
22 & 5.34034362223847e-05 & 0.000106806872444769 & 0.999946596563778 \tabularnewline
23 & 0.000113422195297719 & 0.000226844390595438 & 0.999886577804702 \tabularnewline
24 & 0.000207170074761082 & 0.000414340149522163 & 0.999792829925239 \tabularnewline
25 & 9.45852010826995e-05 & 0.000189170402165399 & 0.999905414798917 \tabularnewline
26 & 0.000137122254033244 & 0.000274244508066487 & 0.999862877745967 \tabularnewline
27 & 0.00146874641183077 & 0.00293749282366154 & 0.998531253588169 \tabularnewline
28 & 0.00110351387096553 & 0.00220702774193107 & 0.998896486129034 \tabularnewline
29 & 0.00344907209547184 & 0.00689814419094367 & 0.996550927904528 \tabularnewline
30 & 0.00629775886308181 & 0.0125955177261636 & 0.993702241136918 \tabularnewline
31 & 0.00392002259572329 & 0.00784004519144658 & 0.996079977404277 \tabularnewline
32 & 0.00316090626277991 & 0.00632181252555982 & 0.99683909373722 \tabularnewline
33 & 0.00185002430383225 & 0.0037000486076645 & 0.998149975696168 \tabularnewline
34 & 0.00445558012541197 & 0.00891116025082394 & 0.995544419874588 \tabularnewline
35 & 0.00870345056178676 & 0.0174069011235735 & 0.991296549438213 \tabularnewline
36 & 0.010018838529709 & 0.0200376770594179 & 0.989981161470291 \tabularnewline
37 & 0.0114319978868388 & 0.0228639957736775 & 0.988568002113161 \tabularnewline
38 & 0.00745407026390322 & 0.0149081405278064 & 0.992545929736097 \tabularnewline
39 & 0.00490919983448181 & 0.00981839966896362 & 0.995090800165518 \tabularnewline
40 & 0.00318478221020403 & 0.00636956442040807 & 0.996815217789796 \tabularnewline
41 & 0.00198393778995037 & 0.00396787557990074 & 0.99801606221005 \tabularnewline
42 & 0.00166514525068973 & 0.00333029050137945 & 0.99833485474931 \tabularnewline
43 & 0.00141844512034885 & 0.0028368902406977 & 0.998581554879651 \tabularnewline
44 & 0.00114632452897962 & 0.00229264905795924 & 0.99885367547102 \tabularnewline
45 & 0.000732203362477551 & 0.0014644067249551 & 0.999267796637522 \tabularnewline
46 & 0.000528717034106085 & 0.00105743406821217 & 0.999471282965894 \tabularnewline
47 & 0.00130688002428624 & 0.00261376004857248 & 0.998693119975714 \tabularnewline
48 & 0.000813306456942464 & 0.00162661291388493 & 0.999186693543058 \tabularnewline
49 & 0.00219815207432697 & 0.00439630414865393 & 0.997801847925673 \tabularnewline
50 & 0.00141931115671801 & 0.00283862231343602 & 0.998580688843282 \tabularnewline
51 & 0.000933904469455653 & 0.00186780893891131 & 0.999066095530544 \tabularnewline
52 & 0.000620007465708515 & 0.00124001493141703 & 0.999379992534291 \tabularnewline
53 & 0.000526286404954387 & 0.00105257280990877 & 0.999473713595046 \tabularnewline
54 & 0.0464446413983074 & 0.0928892827966148 & 0.953555358601693 \tabularnewline
55 & 0.0462265603568431 & 0.0924531207136863 & 0.953773439643157 \tabularnewline
56 & 0.0400998539930217 & 0.0801997079860434 & 0.959900146006978 \tabularnewline
57 & 0.030251114688342 & 0.060502229376684 & 0.969748885311658 \tabularnewline
58 & 0.0254313128123983 & 0.0508626256247966 & 0.974568687187602 \tabularnewline
59 & 0.0236957186582255 & 0.047391437316451 & 0.976304281341774 \tabularnewline
60 & 0.0176201859000567 & 0.0352403718001133 & 0.982379814099943 \tabularnewline
61 & 0.0131028700236556 & 0.0262057400473112 & 0.986897129976344 \tabularnewline
62 & 0.0123259592344123 & 0.0246519184688246 & 0.987674040765588 \tabularnewline
63 & 0.0131056317093337 & 0.0262112634186675 & 0.986894368290666 \tabularnewline
64 & 0.0119305891723186 & 0.0238611783446373 & 0.988069410827681 \tabularnewline
65 & 0.00999125588165816 & 0.0199825117633163 & 0.990008744118342 \tabularnewline
66 & 0.00721017028546614 & 0.0144203405709323 & 0.992789829714534 \tabularnewline
67 & 0.00624933946945264 & 0.0124986789389053 & 0.993750660530547 \tabularnewline
68 & 0.00956043350415862 & 0.0191208670083172 & 0.990439566495841 \tabularnewline
69 & 0.00730025541451442 & 0.0146005108290288 & 0.992699744585486 \tabularnewline
70 & 0.00544341912483904 & 0.0108868382496781 & 0.994556580875161 \tabularnewline
71 & 0.00883546033994942 & 0.0176709206798988 & 0.991164539660051 \tabularnewline
72 & 0.00755121892626332 & 0.0151024378525266 & 0.992448781073737 \tabularnewline
73 & 0.00688747757347195 & 0.0137749551469439 & 0.993112522426528 \tabularnewline
74 & 0.00864365054150334 & 0.0172873010830067 & 0.991356349458497 \tabularnewline
75 & 0.00676665992117047 & 0.0135333198423409 & 0.99323334007883 \tabularnewline
76 & 0.0050764305890325 & 0.010152861178065 & 0.994923569410967 \tabularnewline
77 & 0.00381722832339797 & 0.00763445664679595 & 0.996182771676602 \tabularnewline
78 & 0.00366701625836642 & 0.00733403251673284 & 0.996332983741634 \tabularnewline
79 & 0.00270069602391093 & 0.00540139204782186 & 0.997299303976089 \tabularnewline
80 & 0.00249993738987892 & 0.00499987477975784 & 0.997500062610121 \tabularnewline
81 & 0.00246570571390364 & 0.00493141142780729 & 0.997534294286096 \tabularnewline
82 & 0.997801248830097 & 0.00439750233980656 & 0.00219875116990328 \tabularnewline
83 & 0.997741965505319 & 0.00451606898936132 & 0.00225803449468066 \tabularnewline
84 & 0.997082890574811 & 0.00583421885037721 & 0.00291710942518861 \tabularnewline
85 & 0.995835286835839 & 0.00832942632832153 & 0.00416471316416076 \tabularnewline
86 & 0.994129595210178 & 0.0117408095796448 & 0.0058704047898224 \tabularnewline
87 & 0.992499011478401 & 0.0150019770431987 & 0.00750098852159934 \tabularnewline
88 & 0.989664517068153 & 0.0206709658636946 & 0.0103354829318473 \tabularnewline
89 & 0.986395036625493 & 0.0272099267490146 & 0.0136049633745073 \tabularnewline
90 & 0.98244025747592 & 0.0351194850481598 & 0.0175597425240799 \tabularnewline
91 & 0.978098786216481 & 0.0438024275670379 & 0.021901213783519 \tabularnewline
92 & 0.973445312023104 & 0.0531093759537917 & 0.0265546879768959 \tabularnewline
93 & 0.968012062217448 & 0.0639758755651044 & 0.0319879377825522 \tabularnewline
94 & 0.969135979454373 & 0.0617280410912545 & 0.0308640205456273 \tabularnewline
95 & 0.966355305894705 & 0.0672893882105904 & 0.0336446941052952 \tabularnewline
96 & 0.96392574866907 & 0.0721485026618606 & 0.0360742513309303 \tabularnewline
97 & 0.955078458024903 & 0.0898430839501939 & 0.044921541975097 \tabularnewline
98 & 0.950544491468727 & 0.0989110170625453 & 0.0494555085312727 \tabularnewline
99 & 0.942567191143798 & 0.114865617712404 & 0.0574328088562018 \tabularnewline
100 & 0.92790488189724 & 0.14419023620552 & 0.0720951181027602 \tabularnewline
101 & 0.912573769834756 & 0.174852460330489 & 0.0874262301652443 \tabularnewline
102 & 0.891025264085343 & 0.217949471829315 & 0.108974735914657 \tabularnewline
103 & 0.865878435096123 & 0.268243129807754 & 0.134121564903877 \tabularnewline
104 & 0.866826344042029 & 0.266347311915942 & 0.133173655957971 \tabularnewline
105 & 0.8654966146747 & 0.2690067706506 & 0.1345033853253 \tabularnewline
106 & 0.84121974006961 & 0.317560519860779 & 0.15878025993039 \tabularnewline
107 & 0.812697486744569 & 0.374605026510862 & 0.187302513255431 \tabularnewline
108 & 0.780567266154978 & 0.438865467690043 & 0.219432733845022 \tabularnewline
109 & 0.748328260491487 & 0.503343479017026 & 0.251671739508513 \tabularnewline
110 & 0.725939949128808 & 0.548120101742383 & 0.274060050871192 \tabularnewline
111 & 0.715232830225779 & 0.569534339548442 & 0.284767169774221 \tabularnewline
112 & 0.685609866842483 & 0.628780266315035 & 0.314390133157517 \tabularnewline
113 & 0.640992756837215 & 0.718014486325571 & 0.359007243162785 \tabularnewline
114 & 0.999984538367158 & 3.09232656839859e-05 & 1.54616328419929e-05 \tabularnewline
115 & 0.999974226587065 & 5.15468258689698e-05 & 2.57734129344849e-05 \tabularnewline
116 & 0.999955240884756 & 8.95182304875276e-05 & 4.47591152437638e-05 \tabularnewline
117 & 0.999931707675229 & 0.000136584649542376 & 6.82923247711882e-05 \tabularnewline
118 & 0.999879925531609 & 0.000240148936781333 & 0.000120074468390667 \tabularnewline
119 & 0.999784338738353 & 0.00043132252329327 & 0.000215661261646635 \tabularnewline
120 & 0.999633508605511 & 0.000732982788977019 & 0.000366491394488509 \tabularnewline
121 & 0.999411177177201 & 0.00117764564559828 & 0.000588822822799138 \tabularnewline
122 & 0.999037404533068 & 0.00192519093386404 & 0.000962595466932019 \tabularnewline
123 & 0.998521500312222 & 0.0029569993755556 & 0.0014784996877778 \tabularnewline
124 & 0.997788016886617 & 0.0044239662267651 & 0.00221198311338255 \tabularnewline
125 & 0.996445083563353 & 0.00710983287329346 & 0.00355491643664673 \tabularnewline
126 & 0.994755079856347 & 0.0104898402873063 & 0.00524492014365317 \tabularnewline
127 & 0.991717101440133 & 0.0165657971197348 & 0.0082828985598674 \tabularnewline
128 & 0.991149715228973 & 0.0177005695420549 & 0.00885028477102745 \tabularnewline
129 & 0.991702956702569 & 0.0165940865948629 & 0.00829704329743143 \tabularnewline
130 & 0.987968101704919 & 0.0240637965901614 & 0.0120318982950807 \tabularnewline
131 & 0.983426951253027 & 0.0331460974939454 & 0.0165730487469727 \tabularnewline
132 & 0.978591914143517 & 0.0428161717129656 & 0.0214080858564828 \tabularnewline
133 & 0.971583339664145 & 0.0568333206717097 & 0.0284166603358549 \tabularnewline
134 & 0.962187744384372 & 0.0756245112312558 & 0.0378122556156279 \tabularnewline
135 & 0.944174625660581 & 0.111650748678838 & 0.0558253743394191 \tabularnewline
136 & 0.946474717381828 & 0.107050565236344 & 0.053525282618172 \tabularnewline
137 & 0.94636233327339 & 0.10727533345322 & 0.0536376667266102 \tabularnewline
138 & 0.959656140699551 & 0.0806877186008976 & 0.0403438593004488 \tabularnewline
139 & 0.942070539976923 & 0.115858920046153 & 0.0579294600230767 \tabularnewline
140 & 0.913420566052899 & 0.173158867894201 & 0.0865794339471007 \tabularnewline
141 & 0.939705282517927 & 0.120589434964146 & 0.0602947174820731 \tabularnewline
142 & 0.914384976472867 & 0.171230047054266 & 0.0856150235271331 \tabularnewline
143 & 0.890165189861068 & 0.219669620277864 & 0.109834810138932 \tabularnewline
144 & 0.842272668205788 & 0.315454663588423 & 0.157727331794212 \tabularnewline
145 & 0.850318811613307 & 0.299362376773386 & 0.149681188386693 \tabularnewline
146 & 0.837080834157059 & 0.325838331685881 & 0.162919165842941 \tabularnewline
147 & 0.784076504626248 & 0.431846990747504 & 0.215923495373752 \tabularnewline
148 & 0.832725674217171 & 0.334548651565659 & 0.167274325782829 \tabularnewline
149 & 0.795107985131199 & 0.409784029737602 & 0.204892014868801 \tabularnewline
150 & 0.665635756850909 & 0.668728486298182 & 0.334364243149091 \tabularnewline
151 & 0.503432210776415 & 0.993135578447171 & 0.496567789223585 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185800&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]0.151939582924481[/C][C]0.303879165848961[/C][C]0.848060417075519[/C][/ROW]
[ROW][C]12[/C][C]0.0637378949448871[/C][C]0.127475789889774[/C][C]0.936262105055113[/C][/ROW]
[ROW][C]13[/C][C]0.0278117125312881[/C][C]0.0556234250625762[/C][C]0.972188287468712[/C][/ROW]
[ROW][C]14[/C][C]0.0146475037319504[/C][C]0.0292950074639008[/C][C]0.98535249626805[/C][/ROW]
[ROW][C]15[/C][C]0.00634234646418108[/C][C]0.0126846929283622[/C][C]0.993657653535819[/C][/ROW]
[ROW][C]16[/C][C]0.00221838239786551[/C][C]0.00443676479573102[/C][C]0.997781617602134[/C][/ROW]
[ROW][C]17[/C][C]0.00175981880626474[/C][C]0.00351963761252948[/C][C]0.998240181193735[/C][/ROW]
[ROW][C]18[/C][C]0.000749629657809503[/C][C]0.00149925931561901[/C][C]0.99925037034219[/C][/ROW]
[ROW][C]19[/C][C]0.000413358563674328[/C][C]0.000826717127348656[/C][C]0.999586641436326[/C][/ROW]
[ROW][C]20[/C][C]0.000152739030111491[/C][C]0.000305478060222981[/C][C]0.999847260969889[/C][/ROW]
[ROW][C]21[/C][C]7.34448925026419e-05[/C][C]0.000146889785005284[/C][C]0.999926555107497[/C][/ROW]
[ROW][C]22[/C][C]5.34034362223847e-05[/C][C]0.000106806872444769[/C][C]0.999946596563778[/C][/ROW]
[ROW][C]23[/C][C]0.000113422195297719[/C][C]0.000226844390595438[/C][C]0.999886577804702[/C][/ROW]
[ROW][C]24[/C][C]0.000207170074761082[/C][C]0.000414340149522163[/C][C]0.999792829925239[/C][/ROW]
[ROW][C]25[/C][C]9.45852010826995e-05[/C][C]0.000189170402165399[/C][C]0.999905414798917[/C][/ROW]
[ROW][C]26[/C][C]0.000137122254033244[/C][C]0.000274244508066487[/C][C]0.999862877745967[/C][/ROW]
[ROW][C]27[/C][C]0.00146874641183077[/C][C]0.00293749282366154[/C][C]0.998531253588169[/C][/ROW]
[ROW][C]28[/C][C]0.00110351387096553[/C][C]0.00220702774193107[/C][C]0.998896486129034[/C][/ROW]
[ROW][C]29[/C][C]0.00344907209547184[/C][C]0.00689814419094367[/C][C]0.996550927904528[/C][/ROW]
[ROW][C]30[/C][C]0.00629775886308181[/C][C]0.0125955177261636[/C][C]0.993702241136918[/C][/ROW]
[ROW][C]31[/C][C]0.00392002259572329[/C][C]0.00784004519144658[/C][C]0.996079977404277[/C][/ROW]
[ROW][C]32[/C][C]0.00316090626277991[/C][C]0.00632181252555982[/C][C]0.99683909373722[/C][/ROW]
[ROW][C]33[/C][C]0.00185002430383225[/C][C]0.0037000486076645[/C][C]0.998149975696168[/C][/ROW]
[ROW][C]34[/C][C]0.00445558012541197[/C][C]0.00891116025082394[/C][C]0.995544419874588[/C][/ROW]
[ROW][C]35[/C][C]0.00870345056178676[/C][C]0.0174069011235735[/C][C]0.991296549438213[/C][/ROW]
[ROW][C]36[/C][C]0.010018838529709[/C][C]0.0200376770594179[/C][C]0.989981161470291[/C][/ROW]
[ROW][C]37[/C][C]0.0114319978868388[/C][C]0.0228639957736775[/C][C]0.988568002113161[/C][/ROW]
[ROW][C]38[/C][C]0.00745407026390322[/C][C]0.0149081405278064[/C][C]0.992545929736097[/C][/ROW]
[ROW][C]39[/C][C]0.00490919983448181[/C][C]0.00981839966896362[/C][C]0.995090800165518[/C][/ROW]
[ROW][C]40[/C][C]0.00318478221020403[/C][C]0.00636956442040807[/C][C]0.996815217789796[/C][/ROW]
[ROW][C]41[/C][C]0.00198393778995037[/C][C]0.00396787557990074[/C][C]0.99801606221005[/C][/ROW]
[ROW][C]42[/C][C]0.00166514525068973[/C][C]0.00333029050137945[/C][C]0.99833485474931[/C][/ROW]
[ROW][C]43[/C][C]0.00141844512034885[/C][C]0.0028368902406977[/C][C]0.998581554879651[/C][/ROW]
[ROW][C]44[/C][C]0.00114632452897962[/C][C]0.00229264905795924[/C][C]0.99885367547102[/C][/ROW]
[ROW][C]45[/C][C]0.000732203362477551[/C][C]0.0014644067249551[/C][C]0.999267796637522[/C][/ROW]
[ROW][C]46[/C][C]0.000528717034106085[/C][C]0.00105743406821217[/C][C]0.999471282965894[/C][/ROW]
[ROW][C]47[/C][C]0.00130688002428624[/C][C]0.00261376004857248[/C][C]0.998693119975714[/C][/ROW]
[ROW][C]48[/C][C]0.000813306456942464[/C][C]0.00162661291388493[/C][C]0.999186693543058[/C][/ROW]
[ROW][C]49[/C][C]0.00219815207432697[/C][C]0.00439630414865393[/C][C]0.997801847925673[/C][/ROW]
[ROW][C]50[/C][C]0.00141931115671801[/C][C]0.00283862231343602[/C][C]0.998580688843282[/C][/ROW]
[ROW][C]51[/C][C]0.000933904469455653[/C][C]0.00186780893891131[/C][C]0.999066095530544[/C][/ROW]
[ROW][C]52[/C][C]0.000620007465708515[/C][C]0.00124001493141703[/C][C]0.999379992534291[/C][/ROW]
[ROW][C]53[/C][C]0.000526286404954387[/C][C]0.00105257280990877[/C][C]0.999473713595046[/C][/ROW]
[ROW][C]54[/C][C]0.0464446413983074[/C][C]0.0928892827966148[/C][C]0.953555358601693[/C][/ROW]
[ROW][C]55[/C][C]0.0462265603568431[/C][C]0.0924531207136863[/C][C]0.953773439643157[/C][/ROW]
[ROW][C]56[/C][C]0.0400998539930217[/C][C]0.0801997079860434[/C][C]0.959900146006978[/C][/ROW]
[ROW][C]57[/C][C]0.030251114688342[/C][C]0.060502229376684[/C][C]0.969748885311658[/C][/ROW]
[ROW][C]58[/C][C]0.0254313128123983[/C][C]0.0508626256247966[/C][C]0.974568687187602[/C][/ROW]
[ROW][C]59[/C][C]0.0236957186582255[/C][C]0.047391437316451[/C][C]0.976304281341774[/C][/ROW]
[ROW][C]60[/C][C]0.0176201859000567[/C][C]0.0352403718001133[/C][C]0.982379814099943[/C][/ROW]
[ROW][C]61[/C][C]0.0131028700236556[/C][C]0.0262057400473112[/C][C]0.986897129976344[/C][/ROW]
[ROW][C]62[/C][C]0.0123259592344123[/C][C]0.0246519184688246[/C][C]0.987674040765588[/C][/ROW]
[ROW][C]63[/C][C]0.0131056317093337[/C][C]0.0262112634186675[/C][C]0.986894368290666[/C][/ROW]
[ROW][C]64[/C][C]0.0119305891723186[/C][C]0.0238611783446373[/C][C]0.988069410827681[/C][/ROW]
[ROW][C]65[/C][C]0.00999125588165816[/C][C]0.0199825117633163[/C][C]0.990008744118342[/C][/ROW]
[ROW][C]66[/C][C]0.00721017028546614[/C][C]0.0144203405709323[/C][C]0.992789829714534[/C][/ROW]
[ROW][C]67[/C][C]0.00624933946945264[/C][C]0.0124986789389053[/C][C]0.993750660530547[/C][/ROW]
[ROW][C]68[/C][C]0.00956043350415862[/C][C]0.0191208670083172[/C][C]0.990439566495841[/C][/ROW]
[ROW][C]69[/C][C]0.00730025541451442[/C][C]0.0146005108290288[/C][C]0.992699744585486[/C][/ROW]
[ROW][C]70[/C][C]0.00544341912483904[/C][C]0.0108868382496781[/C][C]0.994556580875161[/C][/ROW]
[ROW][C]71[/C][C]0.00883546033994942[/C][C]0.0176709206798988[/C][C]0.991164539660051[/C][/ROW]
[ROW][C]72[/C][C]0.00755121892626332[/C][C]0.0151024378525266[/C][C]0.992448781073737[/C][/ROW]
[ROW][C]73[/C][C]0.00688747757347195[/C][C]0.0137749551469439[/C][C]0.993112522426528[/C][/ROW]
[ROW][C]74[/C][C]0.00864365054150334[/C][C]0.0172873010830067[/C][C]0.991356349458497[/C][/ROW]
[ROW][C]75[/C][C]0.00676665992117047[/C][C]0.0135333198423409[/C][C]0.99323334007883[/C][/ROW]
[ROW][C]76[/C][C]0.0050764305890325[/C][C]0.010152861178065[/C][C]0.994923569410967[/C][/ROW]
[ROW][C]77[/C][C]0.00381722832339797[/C][C]0.00763445664679595[/C][C]0.996182771676602[/C][/ROW]
[ROW][C]78[/C][C]0.00366701625836642[/C][C]0.00733403251673284[/C][C]0.996332983741634[/C][/ROW]
[ROW][C]79[/C][C]0.00270069602391093[/C][C]0.00540139204782186[/C][C]0.997299303976089[/C][/ROW]
[ROW][C]80[/C][C]0.00249993738987892[/C][C]0.00499987477975784[/C][C]0.997500062610121[/C][/ROW]
[ROW][C]81[/C][C]0.00246570571390364[/C][C]0.00493141142780729[/C][C]0.997534294286096[/C][/ROW]
[ROW][C]82[/C][C]0.997801248830097[/C][C]0.00439750233980656[/C][C]0.00219875116990328[/C][/ROW]
[ROW][C]83[/C][C]0.997741965505319[/C][C]0.00451606898936132[/C][C]0.00225803449468066[/C][/ROW]
[ROW][C]84[/C][C]0.997082890574811[/C][C]0.00583421885037721[/C][C]0.00291710942518861[/C][/ROW]
[ROW][C]85[/C][C]0.995835286835839[/C][C]0.00832942632832153[/C][C]0.00416471316416076[/C][/ROW]
[ROW][C]86[/C][C]0.994129595210178[/C][C]0.0117408095796448[/C][C]0.0058704047898224[/C][/ROW]
[ROW][C]87[/C][C]0.992499011478401[/C][C]0.0150019770431987[/C][C]0.00750098852159934[/C][/ROW]
[ROW][C]88[/C][C]0.989664517068153[/C][C]0.0206709658636946[/C][C]0.0103354829318473[/C][/ROW]
[ROW][C]89[/C][C]0.986395036625493[/C][C]0.0272099267490146[/C][C]0.0136049633745073[/C][/ROW]
[ROW][C]90[/C][C]0.98244025747592[/C][C]0.0351194850481598[/C][C]0.0175597425240799[/C][/ROW]
[ROW][C]91[/C][C]0.978098786216481[/C][C]0.0438024275670379[/C][C]0.021901213783519[/C][/ROW]
[ROW][C]92[/C][C]0.973445312023104[/C][C]0.0531093759537917[/C][C]0.0265546879768959[/C][/ROW]
[ROW][C]93[/C][C]0.968012062217448[/C][C]0.0639758755651044[/C][C]0.0319879377825522[/C][/ROW]
[ROW][C]94[/C][C]0.969135979454373[/C][C]0.0617280410912545[/C][C]0.0308640205456273[/C][/ROW]
[ROW][C]95[/C][C]0.966355305894705[/C][C]0.0672893882105904[/C][C]0.0336446941052952[/C][/ROW]
[ROW][C]96[/C][C]0.96392574866907[/C][C]0.0721485026618606[/C][C]0.0360742513309303[/C][/ROW]
[ROW][C]97[/C][C]0.955078458024903[/C][C]0.0898430839501939[/C][C]0.044921541975097[/C][/ROW]
[ROW][C]98[/C][C]0.950544491468727[/C][C]0.0989110170625453[/C][C]0.0494555085312727[/C][/ROW]
[ROW][C]99[/C][C]0.942567191143798[/C][C]0.114865617712404[/C][C]0.0574328088562018[/C][/ROW]
[ROW][C]100[/C][C]0.92790488189724[/C][C]0.14419023620552[/C][C]0.0720951181027602[/C][/ROW]
[ROW][C]101[/C][C]0.912573769834756[/C][C]0.174852460330489[/C][C]0.0874262301652443[/C][/ROW]
[ROW][C]102[/C][C]0.891025264085343[/C][C]0.217949471829315[/C][C]0.108974735914657[/C][/ROW]
[ROW][C]103[/C][C]0.865878435096123[/C][C]0.268243129807754[/C][C]0.134121564903877[/C][/ROW]
[ROW][C]104[/C][C]0.866826344042029[/C][C]0.266347311915942[/C][C]0.133173655957971[/C][/ROW]
[ROW][C]105[/C][C]0.8654966146747[/C][C]0.2690067706506[/C][C]0.1345033853253[/C][/ROW]
[ROW][C]106[/C][C]0.84121974006961[/C][C]0.317560519860779[/C][C]0.15878025993039[/C][/ROW]
[ROW][C]107[/C][C]0.812697486744569[/C][C]0.374605026510862[/C][C]0.187302513255431[/C][/ROW]
[ROW][C]108[/C][C]0.780567266154978[/C][C]0.438865467690043[/C][C]0.219432733845022[/C][/ROW]
[ROW][C]109[/C][C]0.748328260491487[/C][C]0.503343479017026[/C][C]0.251671739508513[/C][/ROW]
[ROW][C]110[/C][C]0.725939949128808[/C][C]0.548120101742383[/C][C]0.274060050871192[/C][/ROW]
[ROW][C]111[/C][C]0.715232830225779[/C][C]0.569534339548442[/C][C]0.284767169774221[/C][/ROW]
[ROW][C]112[/C][C]0.685609866842483[/C][C]0.628780266315035[/C][C]0.314390133157517[/C][/ROW]
[ROW][C]113[/C][C]0.640992756837215[/C][C]0.718014486325571[/C][C]0.359007243162785[/C][/ROW]
[ROW][C]114[/C][C]0.999984538367158[/C][C]3.09232656839859e-05[/C][C]1.54616328419929e-05[/C][/ROW]
[ROW][C]115[/C][C]0.999974226587065[/C][C]5.15468258689698e-05[/C][C]2.57734129344849e-05[/C][/ROW]
[ROW][C]116[/C][C]0.999955240884756[/C][C]8.95182304875276e-05[/C][C]4.47591152437638e-05[/C][/ROW]
[ROW][C]117[/C][C]0.999931707675229[/C][C]0.000136584649542376[/C][C]6.82923247711882e-05[/C][/ROW]
[ROW][C]118[/C][C]0.999879925531609[/C][C]0.000240148936781333[/C][C]0.000120074468390667[/C][/ROW]
[ROW][C]119[/C][C]0.999784338738353[/C][C]0.00043132252329327[/C][C]0.000215661261646635[/C][/ROW]
[ROW][C]120[/C][C]0.999633508605511[/C][C]0.000732982788977019[/C][C]0.000366491394488509[/C][/ROW]
[ROW][C]121[/C][C]0.999411177177201[/C][C]0.00117764564559828[/C][C]0.000588822822799138[/C][/ROW]
[ROW][C]122[/C][C]0.999037404533068[/C][C]0.00192519093386404[/C][C]0.000962595466932019[/C][/ROW]
[ROW][C]123[/C][C]0.998521500312222[/C][C]0.0029569993755556[/C][C]0.0014784996877778[/C][/ROW]
[ROW][C]124[/C][C]0.997788016886617[/C][C]0.0044239662267651[/C][C]0.00221198311338255[/C][/ROW]
[ROW][C]125[/C][C]0.996445083563353[/C][C]0.00710983287329346[/C][C]0.00355491643664673[/C][/ROW]
[ROW][C]126[/C][C]0.994755079856347[/C][C]0.0104898402873063[/C][C]0.00524492014365317[/C][/ROW]
[ROW][C]127[/C][C]0.991717101440133[/C][C]0.0165657971197348[/C][C]0.0082828985598674[/C][/ROW]
[ROW][C]128[/C][C]0.991149715228973[/C][C]0.0177005695420549[/C][C]0.00885028477102745[/C][/ROW]
[ROW][C]129[/C][C]0.991702956702569[/C][C]0.0165940865948629[/C][C]0.00829704329743143[/C][/ROW]
[ROW][C]130[/C][C]0.987968101704919[/C][C]0.0240637965901614[/C][C]0.0120318982950807[/C][/ROW]
[ROW][C]131[/C][C]0.983426951253027[/C][C]0.0331460974939454[/C][C]0.0165730487469727[/C][/ROW]
[ROW][C]132[/C][C]0.978591914143517[/C][C]0.0428161717129656[/C][C]0.0214080858564828[/C][/ROW]
[ROW][C]133[/C][C]0.971583339664145[/C][C]0.0568333206717097[/C][C]0.0284166603358549[/C][/ROW]
[ROW][C]134[/C][C]0.962187744384372[/C][C]0.0756245112312558[/C][C]0.0378122556156279[/C][/ROW]
[ROW][C]135[/C][C]0.944174625660581[/C][C]0.111650748678838[/C][C]0.0558253743394191[/C][/ROW]
[ROW][C]136[/C][C]0.946474717381828[/C][C]0.107050565236344[/C][C]0.053525282618172[/C][/ROW]
[ROW][C]137[/C][C]0.94636233327339[/C][C]0.10727533345322[/C][C]0.0536376667266102[/C][/ROW]
[ROW][C]138[/C][C]0.959656140699551[/C][C]0.0806877186008976[/C][C]0.0403438593004488[/C][/ROW]
[ROW][C]139[/C][C]0.942070539976923[/C][C]0.115858920046153[/C][C]0.0579294600230767[/C][/ROW]
[ROW][C]140[/C][C]0.913420566052899[/C][C]0.173158867894201[/C][C]0.0865794339471007[/C][/ROW]
[ROW][C]141[/C][C]0.939705282517927[/C][C]0.120589434964146[/C][C]0.0602947174820731[/C][/ROW]
[ROW][C]142[/C][C]0.914384976472867[/C][C]0.171230047054266[/C][C]0.0856150235271331[/C][/ROW]
[ROW][C]143[/C][C]0.890165189861068[/C][C]0.219669620277864[/C][C]0.109834810138932[/C][/ROW]
[ROW][C]144[/C][C]0.842272668205788[/C][C]0.315454663588423[/C][C]0.157727331794212[/C][/ROW]
[ROW][C]145[/C][C]0.850318811613307[/C][C]0.299362376773386[/C][C]0.149681188386693[/C][/ROW]
[ROW][C]146[/C][C]0.837080834157059[/C][C]0.325838331685881[/C][C]0.162919165842941[/C][/ROW]
[ROW][C]147[/C][C]0.784076504626248[/C][C]0.431846990747504[/C][C]0.215923495373752[/C][/ROW]
[ROW][C]148[/C][C]0.832725674217171[/C][C]0.334548651565659[/C][C]0.167274325782829[/C][/ROW]
[ROW][C]149[/C][C]0.795107985131199[/C][C]0.409784029737602[/C][C]0.204892014868801[/C][/ROW]
[ROW][C]150[/C][C]0.665635756850909[/C][C]0.668728486298182[/C][C]0.334364243149091[/C][/ROW]
[ROW][C]151[/C][C]0.503432210776415[/C][C]0.993135578447171[/C][C]0.496567789223585[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185800&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185800&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
110.1519395829244810.3038791658489610.848060417075519
120.06373789494488710.1274757898897740.936262105055113
130.02781171253128810.05562342506257620.972188287468712
140.01464750373195040.02929500746390080.98535249626805
150.006342346464181080.01268469292836220.993657653535819
160.002218382397865510.004436764795731020.997781617602134
170.001759818806264740.003519637612529480.998240181193735
180.0007496296578095030.001499259315619010.99925037034219
190.0004133585636743280.0008267171273486560.999586641436326
200.0001527390301114910.0003054780602229810.999847260969889
217.34448925026419e-050.0001468897850052840.999926555107497
225.34034362223847e-050.0001068068724447690.999946596563778
230.0001134221952977190.0002268443905954380.999886577804702
240.0002071700747610820.0004143401495221630.999792829925239
259.45852010826995e-050.0001891704021653990.999905414798917
260.0001371222540332440.0002742445080664870.999862877745967
270.001468746411830770.002937492823661540.998531253588169
280.001103513870965530.002207027741931070.998896486129034
290.003449072095471840.006898144190943670.996550927904528
300.006297758863081810.01259551772616360.993702241136918
310.003920022595723290.007840045191446580.996079977404277
320.003160906262779910.006321812525559820.99683909373722
330.001850024303832250.00370004860766450.998149975696168
340.004455580125411970.008911160250823940.995544419874588
350.008703450561786760.01740690112357350.991296549438213
360.0100188385297090.02003767705941790.989981161470291
370.01143199788683880.02286399577367750.988568002113161
380.007454070263903220.01490814052780640.992545929736097
390.004909199834481810.009818399668963620.995090800165518
400.003184782210204030.006369564420408070.996815217789796
410.001983937789950370.003967875579900740.99801606221005
420.001665145250689730.003330290501379450.99833485474931
430.001418445120348850.00283689024069770.998581554879651
440.001146324528979620.002292649057959240.99885367547102
450.0007322033624775510.00146440672495510.999267796637522
460.0005287170341060850.001057434068212170.999471282965894
470.001306880024286240.002613760048572480.998693119975714
480.0008133064569424640.001626612913884930.999186693543058
490.002198152074326970.004396304148653930.997801847925673
500.001419311156718010.002838622313436020.998580688843282
510.0009339044694556530.001867808938911310.999066095530544
520.0006200074657085150.001240014931417030.999379992534291
530.0005262864049543870.001052572809908770.999473713595046
540.04644464139830740.09288928279661480.953555358601693
550.04622656035684310.09245312071368630.953773439643157
560.04009985399302170.08019970798604340.959900146006978
570.0302511146883420.0605022293766840.969748885311658
580.02543131281239830.05086262562479660.974568687187602
590.02369571865822550.0473914373164510.976304281341774
600.01762018590005670.03524037180011330.982379814099943
610.01310287002365560.02620574004731120.986897129976344
620.01232595923441230.02465191846882460.987674040765588
630.01310563170933370.02621126341866750.986894368290666
640.01193058917231860.02386117834463730.988069410827681
650.009991255881658160.01998251176331630.990008744118342
660.007210170285466140.01442034057093230.992789829714534
670.006249339469452640.01249867893890530.993750660530547
680.009560433504158620.01912086700831720.990439566495841
690.007300255414514420.01460051082902880.992699744585486
700.005443419124839040.01088683824967810.994556580875161
710.008835460339949420.01767092067989880.991164539660051
720.007551218926263320.01510243785252660.992448781073737
730.006887477573471950.01377495514694390.993112522426528
740.008643650541503340.01728730108300670.991356349458497
750.006766659921170470.01353331984234090.99323334007883
760.00507643058903250.0101528611780650.994923569410967
770.003817228323397970.007634456646795950.996182771676602
780.003667016258366420.007334032516732840.996332983741634
790.002700696023910930.005401392047821860.997299303976089
800.002499937389878920.004999874779757840.997500062610121
810.002465705713903640.004931411427807290.997534294286096
820.9978012488300970.004397502339806560.00219875116990328
830.9977419655053190.004516068989361320.00225803449468066
840.9970828905748110.005834218850377210.00291710942518861
850.9958352868358390.008329426328321530.00416471316416076
860.9941295952101780.01174080957964480.0058704047898224
870.9924990114784010.01500197704319870.00750098852159934
880.9896645170681530.02067096586369460.0103354829318473
890.9863950366254930.02720992674901460.0136049633745073
900.982440257475920.03511948504815980.0175597425240799
910.9780987862164810.04380242756703790.021901213783519
920.9734453120231040.05310937595379170.0265546879768959
930.9680120622174480.06397587556510440.0319879377825522
940.9691359794543730.06172804109125450.0308640205456273
950.9663553058947050.06728938821059040.0336446941052952
960.963925748669070.07214850266186060.0360742513309303
970.9550784580249030.08984308395019390.044921541975097
980.9505444914687270.09891101706254530.0494555085312727
990.9425671911437980.1148656177124040.0574328088562018
1000.927904881897240.144190236205520.0720951181027602
1010.9125737698347560.1748524603304890.0874262301652443
1020.8910252640853430.2179494718293150.108974735914657
1030.8658784350961230.2682431298077540.134121564903877
1040.8668263440420290.2663473119159420.133173655957971
1050.86549661467470.26900677065060.1345033853253
1060.841219740069610.3175605198607790.15878025993039
1070.8126974867445690.3746050265108620.187302513255431
1080.7805672661549780.4388654676900430.219432733845022
1090.7483282604914870.5033434790170260.251671739508513
1100.7259399491288080.5481201017423830.274060050871192
1110.7152328302257790.5695343395484420.284767169774221
1120.6856098668424830.6287802663150350.314390133157517
1130.6409927568372150.7180144863255710.359007243162785
1140.9999845383671583.09232656839859e-051.54616328419929e-05
1150.9999742265870655.15468258689698e-052.57734129344849e-05
1160.9999552408847568.95182304875276e-054.47591152437638e-05
1170.9999317076752290.0001365846495423766.82923247711882e-05
1180.9998799255316090.0002401489367813330.000120074468390667
1190.9997843387383530.000431322523293270.000215661261646635
1200.9996335086055110.0007329827889770190.000366491394488509
1210.9994111771772010.001177645645598280.000588822822799138
1220.9990374045330680.001925190933864040.000962595466932019
1230.9985215003122220.00295699937555560.0014784996877778
1240.9977880168866170.00442396622676510.00221198311338255
1250.9964450835633530.007109832873293460.00355491643664673
1260.9947550798563470.01048984028730630.00524492014365317
1270.9917171014401330.01656579711973480.0082828985598674
1280.9911497152289730.01770056954205490.00885028477102745
1290.9917029567025690.01659408659486290.00829704329743143
1300.9879681017049190.02406379659016140.0120318982950807
1310.9834269512530270.03314609749394540.0165730487469727
1320.9785919141435170.04281617171296560.0214080858564828
1330.9715833396641450.05683332067170970.0284166603358549
1340.9621877443843720.07562451123125580.0378122556156279
1350.9441746256605810.1116507486788380.0558253743394191
1360.9464747173818280.1070505652363440.053525282618172
1370.946362333273390.107275333453220.0536376667266102
1380.9596561406995510.08068771860089760.0403438593004488
1390.9420705399769230.1158589200461530.0579294600230767
1400.9134205660528990.1731588678942010.0865794339471007
1410.9397052825179270.1205894349641460.0602947174820731
1420.9143849764728670.1712300470542660.0856150235271331
1430.8901651898610680.2196696202778640.109834810138932
1440.8422726682057880.3154546635884230.157727331794212
1450.8503188116133070.2993623767733860.149681188386693
1460.8370808341570590.3258383316858810.162919165842941
1470.7840765046262480.4318469907475040.215923495373752
1480.8327256742171710.3345486515656590.167274325782829
1490.7951079851311990.4097840297376020.204892014868801
1500.6656357568509090.6687284862981820.334364243149091
1510.5034322107764150.9931355784471710.496567789223585






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level540.382978723404255NOK
5% type I error level920.652482269503546NOK
10% type I error level1080.765957446808511NOK

\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 & 54 & 0.382978723404255 & NOK \tabularnewline
5% type I error level & 92 & 0.652482269503546 & NOK \tabularnewline
10% type I error level & 108 & 0.765957446808511 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185800&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]54[/C][C]0.382978723404255[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]92[/C][C]0.652482269503546[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]108[/C][C]0.765957446808511[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185800&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185800&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 level540.382978723404255NOK
5% type I error level920.652482269503546NOK
10% type I error level1080.765957446808511NOK


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 = 8 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
Parameters (R input):
par1 = 8 ; 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')
}