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Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationThu, 17 Nov 2011 07:56:55 -0500
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2011/Nov/17/t1321534659uhe9msghp8j2txu.htm/, Retrieved Tue, 30 Apr 2024 04:42:26 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=144461, Retrieved Tue, 30 Apr 2024 04:42:26 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact84

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Multiple Regression] [] [2011-11-17 12:56:55] [87b6e955a128bfb8d1e350b3ce0d281e] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
7	41	38	13	12	14	12	53	32
5	39	32	16	11	18	11	86	51
5	30	35	19	15	11	14	66	42
5	31	33	15	6	12	12	67	41
8	34	37	14	13	16	21	76	46
6	35	29	13	10	18	12	78	47
5	39	31	19	12	14	22	53	37
6	34	36	15	14	14	11	80	49
5	36	35	14	12	15	10	74	45
4	37	38	15	6	15	13	76	47
6	38	31	16	10	17	10	79	49
5	36	34	16	12	19	8	54	33
5	38	35	16	12	10	15	67	42
6	39	38	16	11	16	14	54	33
7	33	37	17	15	18	10	87	53
6	32	33	15	12	14	14	58	36
7	36	32	15	10	14	14	75	45
6	38	38	20	12	17	11	88	54
8	39	38	18	11	14	10	64	41
7	32	32	16	12	16	13	57	36
5	32	33	16	11	18	7	66	41
5	31	31	16	12	11	14	68	44
7	39	38	19	13	14	12	54	33
7	37	39	16	11	12	14	56	37
5	39	32	17	9	17	11	86	52
4	41	32	17	13	9	9	80	47
10	36	35	16	10	16	11	76	43
6	33	37	15	14	14	15	69	44
5	33	33	16	12	15	14	78	45
5	34	33	14	10	11	13	67	44
5	31	28	15	12	16	9	80	49
5	27	32	12	8	13	15	54	33
6	37	31	14	10	17	10	71	43
5	34	37	16	12	15	11	84	54
5	34	30	14	12	14	13	74	42
5	32	33	7	7	16	8	71	44
5	29	31	10	6	9	20	63	37
5	36	33	14	12	15	12	71	43
5	29	31	16	10	17	10	76	46
5	35	33	16	10	13	10	69	42
5	37	32	16	10	15	9	74	45
7	34	33	14	12	16	14	75	44
5	38	32	20	15	16	8	54	33
6	35	33	14	10	12	14	52	31
7	38	28	14	10	12	11	69	42
7	37	35	11	12	11	13	68	40
5	38	39	14	13	15	9	65	43
5	33	34	15	11	15	11	75	46
4	36	38	16	11	17	15	74	42
5	38	32	14	12	13	11	75	45
4	32	38	16	14	16	10	72	44
5	32	30	14	10	14	14	67	40
5	32	33	12	12	11	18	63	37
7	34	38	16	13	12	14	62	46
5	32	32	9	5	12	11	63	36
5	37	32	14	6	15	12	76	47
6	39	34	16	12	16	13	74	45
4	29	34	16	12	15	9	67	42
6	37	36	15	11	12	10	73	43
6	35	34	16	10	12	15	70	43
5	30	28	12	7	8	20	53	32
7	38	34	16	12	13	12	77	45
6	34	35	16	14	11	12	77	45
8	31	35	14	11	14	14	52	31
7	34	31	16	12	15	13	54	33
5	35	37	17	13	10	11	80	49
6	36	35	18	14	11	17	66	42
6	30	27	18	11	12	12	73	41
5	39	40	12	12	15	13	63	38
5	35	37	16	12	15	14	69	42
5	38	36	10	8	14	13	67	44
5	31	38	14	11	16	15	54	33
4	34	39	18	14	15	13	81	48
6	38	41	18	14	15	10	69	40
6	34	27	16	12	13	11	84	50
6	39	30	17	9	12	19	80	49
6	37	37	16	13	17	13	70	43
7	34	31	16	11	13	17	69	44
5	28	31	13	12	15	13	77	47
7	37	27	16	12	13	9	54	33
6	33	36	16	12	15	11	79	46
5	37	38	20	12	16	10	30	0
5	35	37	16	12	15	9	71	45
4	37	33	15	12	16	12	73	43
8	32	34	15	11	15	12	72	44
8	33	31	16	10	14	13	77	47
5	38	39	14	9	15	13	75	45
5	33	34	16	12	14	12	69	42
6	29	32	16	12	13	15	54	33
4	33	33	15	12	7	22	70	43
5	31	36	12	9	17	13	73	46
5	36	32	17	15	13	15	54	33
5	35	41	16	12	15	13	77	46
5	32	28	15	12	14	15	82	48
6	29	30	13	12	13	10	80	47
6	39	36	16	10	16	11	80	47
5	37	35	16	13	12	16	69	43
6	35	31	16	9	14	11	78	46
5	37	34	16	12	17	11	81	48
7	32	36	14	10	15	10	76	46
5	38	36	16	14	17	10	76	45
6	37	35	16	11	12	16	73	45
6	36	37	20	15	16	12	85	52
6	32	28	15	11	11	11	66	42
4	33	39	16	11	15	16	79	47
5	40	32	13	12	9	19	68	41
5	38	35	17	12	16	11	76	47
7	41	39	16	12	15	16	71	43
6	36	35	16	11	10	15	54	33
9	43	42	12	7	10	24	46	30
6	30	34	16	12	15	14	82	49
6	31	33	16	14	11	15	74	44
5	32	41	17	11	13	11	88	55
6	32	33	13	11	14	15	38	11
5	37	34	12	10	18	12	76	47
8	37	32	18	13	16	10	86	53
7	33	40	14	13	14	14	54	33
5	34	40	14	8	14	13	70	44
7	33	35	13	11	14	9	69	42
6	38	36	16	12	14	15	90	55
6	33	37	13	11	12	15	54	33
9	31	27	16	13	14	14	76	46
7	38	39	13	12	15	11	89	54
6	37	38	16	14	15	8	76	47
5	33	31	15	13	15	11	73	45
5	31	33	16	15	13	11	79	47
6	39	32	15	10	17	8	90	55
6	44	39	17	11	17	10	74	44
7	33	36	15	9	19	11	81	53
5	35	33	12	11	15	13	72	44
5	32	33	16	10	13	11	71	42
5	28	32	10	11	9	20	66	40
6	40	37	16	8	15	10	77	46
4	27	30	12	11	15	15	65	40
5	37	38	14	12	15	12	74	46
7	32	29	15	12	16	14	82	53
5	28	22	13	9	11	23	54	33
7	34	35	15	11	14	14	63	42
7	30	35	11	10	11	16	54	35
6	35	34	12	8	15	11	64	40
5	31	35	8	9	13	12	69	41
8	32	34	16	8	15	10	54	33
5	30	34	15	9	16	14	84	51
5	30	35	17	15	14	12	86	53
5	31	23	16	11	15	12	77	46
6	40	31	10	8	16	11	89	55
4	32	27	18	13	16	12	76	47
5	36	36	13	12	11	13	60	38
5	32	31	16	12	12	11	75	46
7	35	32	13	9	9	19	73	46
6	38	39	10	7	16	12	85	53
7	42	37	15	13	13	17	79	47
10	34	38	16	9	16	9	71	41
6	35	39	16	6	12	12	72	44
8	35	34	14	8	9	19	69	43
4	33	31	10	8	13	18	78	51
5	36	32	17	15	13	15	54	33
6	32	37	13	6	14	14	69	43
7	33	36	15	9	19	11	81	53
7	34	32	16	11	13	9	84	51
6	32	35	12	8	12	18	84	50
6	34	36	13	8	13	16	69	46

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time6 seconds
R Server'Gertrude Mary Cox' @ cox.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 & 6 seconds \tabularnewline
R Server & 'Gertrude Mary Cox' @ cox.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=144461&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]6 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gertrude Mary Cox' @ cox.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=144461&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=144461&T=0

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


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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time6 seconds
R Server'Gertrude Mary Cox' @ cox.wessa.net






Multiple Linear Regression - Estimated Regression Equation
belonging_final[t] = -4.62372287334383 + 0.126387024273453Age[t] -0.0191230046787836connected[t] + 0.0539328749695019separated[t] -0.0888898072249384learning[t] -0.0021394794507081software[t] -0.0580270838085641hapiness[t] + 0.0997065018245458depression[t] + 0.659910304893659belonging[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
belonging_final[t] =  -4.62372287334383 +  0.126387024273453Age[t] -0.0191230046787836connected[t] +  0.0539328749695019separated[t] -0.0888898072249384learning[t] -0.0021394794507081software[t] -0.0580270838085641hapiness[t] +  0.0997065018245458depression[t] +  0.659910304893659belonging[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=144461&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]belonging_final[t] =  -4.62372287334383 +  0.126387024273453Age[t] -0.0191230046787836connected[t] +  0.0539328749695019separated[t] -0.0888898072249384learning[t] -0.0021394794507081software[t] -0.0580270838085641hapiness[t] +  0.0997065018245458depression[t] +  0.659910304893659belonging[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=144461&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=144461&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] = -4.62372287334383 + 0.126387024273453Age[t] -0.0191230046787836connected[t] + 0.0539328749695019separated[t] -0.0888898072249384learning[t] -0.0021394794507081software[t] -0.0580270838085641hapiness[t] + 0.0997065018245458depression[t] + 0.659910304893659belonging[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)-4.623722873343833.354589-1.37830.1701140.085057
Age0.1263870242734530.1617020.78160.4356550.217828
connected-0.01912300467878360.060509-0.3160.7524060.376203
separated0.05393287496950190.056350.95710.3400260.170013
learning-0.08888980722493840.101541-0.87540.3827260.191363
software-0.00213947945070810.103282-0.02070.98350.49175
hapiness-0.05802708380856410.096267-0.60280.5475510.273776
depression0.09970650182454580.070881.40670.1615480.080774
belonging0.6599103048936590.01818936.280500

\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) & -4.62372287334383 & 3.354589 & -1.3783 & 0.170114 & 0.085057 \tabularnewline
Age & 0.126387024273453 & 0.161702 & 0.7816 & 0.435655 & 0.217828 \tabularnewline
connected & -0.0191230046787836 & 0.060509 & -0.316 & 0.752406 & 0.376203 \tabularnewline
separated & 0.0539328749695019 & 0.05635 & 0.9571 & 0.340026 & 0.170013 \tabularnewline
learning & -0.0888898072249384 & 0.101541 & -0.8754 & 0.382726 & 0.191363 \tabularnewline
software & -0.0021394794507081 & 0.103282 & -0.0207 & 0.9835 & 0.49175 \tabularnewline
hapiness & -0.0580270838085641 & 0.096267 & -0.6028 & 0.547551 & 0.273776 \tabularnewline
depression & 0.0997065018245458 & 0.07088 & 1.4067 & 0.161548 & 0.080774 \tabularnewline
belonging & 0.659910304893659 & 0.018189 & 36.2805 & 0 & 0 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=144461&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]-4.62372287334383[/C][C]3.354589[/C][C]-1.3783[/C][C]0.170114[/C][C]0.085057[/C][/ROW]
[ROW][C]Age[/C][C]0.126387024273453[/C][C]0.161702[/C][C]0.7816[/C][C]0.435655[/C][C]0.217828[/C][/ROW]
[ROW][C]connected[/C][C]-0.0191230046787836[/C][C]0.060509[/C][C]-0.316[/C][C]0.752406[/C][C]0.376203[/C][/ROW]
[ROW][C]separated[/C][C]0.0539328749695019[/C][C]0.05635[/C][C]0.9571[/C][C]0.340026[/C][C]0.170013[/C][/ROW]
[ROW][C]learning[/C][C]-0.0888898072249384[/C][C]0.101541[/C][C]-0.8754[/C][C]0.382726[/C][C]0.191363[/C][/ROW]
[ROW][C]software[/C][C]-0.0021394794507081[/C][C]0.103282[/C][C]-0.0207[/C][C]0.9835[/C][C]0.49175[/C][/ROW]
[ROW][C]hapiness[/C][C]-0.0580270838085641[/C][C]0.096267[/C][C]-0.6028[/C][C]0.547551[/C][C]0.273776[/C][/ROW]
[ROW][C]depression[/C][C]0.0997065018245458[/C][C]0.07088[/C][C]1.4067[/C][C]0.161548[/C][C]0.080774[/C][/ROW]
[ROW][C]belonging[/C][C]0.659910304893659[/C][C]0.018189[/C][C]36.2805[/C][C]0[/C][C]0[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=144461&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=144461&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)-4.623722873343833.354589-1.37830.1701140.085057
Age0.1263870242734530.1617020.78160.4356550.217828
connected-0.01912300467878360.060509-0.3160.7524060.376203
separated0.05393287496950190.056350.95710.3400260.170013
learning-0.08888980722493840.101541-0.87540.3827260.191363
software-0.00213947945070810.103282-0.02070.98350.49175
hapiness-0.05802708380856410.096267-0.60280.5475510.273776
depression0.09970650182454580.070881.40670.1615480.080774
belonging0.6599103048936590.01818936.280500






Multiple Linear Regression - Regression Statistics
Multiple R0.950330071335872
R-squared0.903127244485243
Adjusted R-squared0.898062002366825
F-TEST (value)178.298928930015
F-TEST (DF numerator)8
F-TEST (DF denominator)153
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.3291703569781
Sum Squared Residuals830.030286429299

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.950330071335872 \tabularnewline
R-squared & 0.903127244485243 \tabularnewline
Adjusted R-squared & 0.898062002366825 \tabularnewline
F-TEST (value) & 178.298928930015 \tabularnewline
F-TEST (DF numerator) & 8 \tabularnewline
F-TEST (DF denominator) & 153 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 2.3291703569781 \tabularnewline
Sum Squared Residuals & 830.030286429299 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=144461&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.950330071335872[/C][/ROW]
[ROW][C]R-squared[/C][C]0.903127244485243[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.898062002366825[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]178.298928930015[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]8[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]153[/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]2.3291703569781[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]830.030286429299[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=144461&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=144461&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 R0.950330071335872
R-squared0.903127244485243
Adjusted R-squared0.898062002366825
F-TEST (value)178.298928930015
F-TEST (DF numerator)8
F-TEST (DF denominator)153
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.3291703569781
Sum Squared Residuals830.030286429299






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
13231.70449611418720.29550388581284
25152.3470661073886-1.34706610738862
34239.91284742918892.08715257081105
44140.56314343596330.436856564036711
54647.7790233709249-1.77902337092487
64747.4773794892695-0.477379489269502
73731.57616412510715.42383587489288
84949.1399175296791-0.139917529679119
94544.89732497220990.102675027790124
104746.45650075290640.543499247093577
114947.77973118855051.22026881144953
123331.0358850460341.96411495396601
134240.85059514231241.14940485768762
143332.09509429797280.90490570202723
155353.4469986368971-0.446998636897082
163634.80173667084271.19826332915726
174546.0204529435252-1.02045294352517
185453.83632237140330.16367762859671
194138.48641994132532.51358005867466
203633.90963003858642.09036996141358
214138.93782790993822.06217209006175
224441.27090139444642.72909860555359
233331.8671741056381.13282589436197
243733.86558915159493.13441084840513
255252.3204823428737-0.320482342873662
264748.4526332288973-1.45263322889728
274346.717259465832-3.717259465832
284442.35278606279531.64721393720466
294547.7075158487302-2.70751584873018
304440.74383989698223.25616010301785
314948.32824830732120.671751692678802
323332.51035150052980.48964849947017
334342.69735136852990.302648631470137
345451.56446666781772.43553333218228
354245.0230531960022-3.02305319600215
364443.26170628667550.738293713324503
373739.270064777954-2.27006477795397
384343.009141311239-0.00914131123900135
394645.84572029170510.154279708294904
404241.451584214550.548415785449956
414544.44319618524960.556803814750406
424446.0811885085586-2.08118850855864
433330.70187687091122.29812312908876
443130.99412876118790.00587123881207831
454241.71283807429610.287161925703924
464041.9694114490988-1.96941144909877
474339.03377173741213.96622826258785
484645.57362759022070.426372409779287
494245.1395747793514-3.13957477935137
504545.5729513122792-0.572951312279149
514443.44932232461290.550677675387098
524040.5459125318301-0.545912531830068
533738.8144778514363-1.81447785143627
544637.82420816112238.17579183887774
553638.2862181577162-2.28621815771616
564746.24847383276330.751526167236743
574544.97572291469270.0242770853073146
584239.95400785518842.04599214481161
594344.4859424855319-1.48594248553187
604342.84837401161790.151625988382049
613232.3681480893381-0.368148089338074
624547.175338607927-2.17533860792704
634547.2911516860539-2.29115168605394
643131.3130669313211-0.313066931321131
653331.89574732338691.10425267661306
664949.1048085759315-0.10480857593147
674240.3146452175391.68535478246102
684144.0671512255321-3.06715122553211
693838.3275060991143-0.327506099114316
704241.94580859520770.0541914047923105
714441.0149034395512.985096560449
723332.3991774274040.600822572595985
734849.5835689091001-1.58356890910009
744041.6496735246733-1.64967352467332
755051.2675791100133-1.26757911001328
764949.4673292214853-0.467329221485346
774342.47595976612490.524040233875148
784442.31142155115781.68857844884216
794747.2023177371417-0.202317737141702
803331.33987496979151.66012503020847
814648.3564922973322-2.35649229733216
82015.4125812499604-15.4125812499604
834542.76709669587232.23290330412772
844344.0365350010406-1.03653500104058
854444.0918872548634-0.0918872548634258
864747.2815004076033-0.281500407603308
874546.0402587114498-1.04025871144975
884241.68087005981620.319129940183776
893332.23437536874310.76562463125687
904343.6526048775973-0.652604877597275
914644.75422595633821.24577404366184
923331.87881906614111.12118093385887
934647.3411160324104-1.34111603241042
944850.3432390909941-2.34323909099413
954749.0523144585913-2.05231445859134
964748.847916449246-1.84791644924597
974342.17105161153520.828948388464805
984647.4531171303941-1.45311713039411
994849.2495139465748-1.24951394657475
1004646.6046234831302-0.604623483130165
1014545.9347197066407-0.93471970664072
1024544.94135881428470.0586411857152999
1035251.99221973839140.00778026160863292
1044239.68845596054732.31154403945273
1054748.7661888622672-1.76618886226719
1064142.0339833257215-1.03398332572153
1074745.95390956898081.04609043101919
1084343.7109439790573-0.710943979057307
1093332.43853369177650.561466308223449
1103029.04355708060620.95644291939383
1114950.5848459815841-1.58484598158412
1124445.560043540944-1.56004354094395
1135554.48738923647080.512610763529176
1141121.8831561686947-10.8831561686947
1154746.35148002792360.648519972076388
1165352.59876228200780.401237717992161
1173332.73363992342350.266360076576477
1184442.93129864392541.06870135607463
1194241.95726637898450.0427336210154821
1205555.9767437188752-0.976743718875233
1213332.75438370980960.245616290190408
1224646.6637796999351-0.663779699935086
1235455.4148353937316-1.4148353937316
1244746.10473664950.895263350500001
1254544.08772939662350.912270603376527
1264748.2161883867728-1.21618838677277
1275554.96303121624730.03696878375272
1284444.7058753490898-0.705875349089845
1295349.66589984173573.33410015826427
1304443.96780021653660.0321997834634258
1314242.9284803401983-0.928480340198258
1324041.3121541750297-1.31215417502969
1334646.8655949457411-0.865594945741143
1344039.41263947417840.587360525821635
1354645.23941359608870.760586403911297
1365350.43418534506892.56581465493113
1373332.77457664169390.225423358306125
1384238.29943443961663.70056556038337
1393533.16792667771411.83207332228591
1404038.67584311133331.32415688866671
1414142.5486129241035-1.54861292410349
1423331.93161739425571.06838260574434
1435151.8155607288665-0.815560728866481
1445352.91533888643720.0846611135627889
1454646.3492492793007-0.349249279300684
1465555.0359396160136-0.0359396160136493
1474745.51947478817291.48052521182706
1483836.3326312266011.667368773399
1494645.5140039347410.485996065258963
1504645.68834236048280.311657639517224
1515352.96785338682780.0321466131721517
1524749.1657486608047-2.16574866080469
1534143.4204790514314-2.42047905143139
1544444.147297408582-0.147297408581974
1554343.1962035873464-0.196203587346366
1565148.53004001058552.46995998941451
1573331.87881906614111.12118093385887
1584342.46709801570510.532901984294878
1595349.66589984173573.33410015826427
1605151.4663569849359-0.46635698493586
1615052.8573778624098-2.85737786240983
1624642.62808025993433.37191974006571

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 32 & 31.7044961141872 & 0.29550388581284 \tabularnewline
2 & 51 & 52.3470661073886 & -1.34706610738862 \tabularnewline
3 & 42 & 39.9128474291889 & 2.08715257081105 \tabularnewline
4 & 41 & 40.5631434359633 & 0.436856564036711 \tabularnewline
5 & 46 & 47.7790233709249 & -1.77902337092487 \tabularnewline
6 & 47 & 47.4773794892695 & -0.477379489269502 \tabularnewline
7 & 37 & 31.5761641251071 & 5.42383587489288 \tabularnewline
8 & 49 & 49.1399175296791 & -0.139917529679119 \tabularnewline
9 & 45 & 44.8973249722099 & 0.102675027790124 \tabularnewline
10 & 47 & 46.4565007529064 & 0.543499247093577 \tabularnewline
11 & 49 & 47.7797311885505 & 1.22026881144953 \tabularnewline
12 & 33 & 31.035885046034 & 1.96411495396601 \tabularnewline
13 & 42 & 40.8505951423124 & 1.14940485768762 \tabularnewline
14 & 33 & 32.0950942979728 & 0.90490570202723 \tabularnewline
15 & 53 & 53.4469986368971 & -0.446998636897082 \tabularnewline
16 & 36 & 34.8017366708427 & 1.19826332915726 \tabularnewline
17 & 45 & 46.0204529435252 & -1.02045294352517 \tabularnewline
18 & 54 & 53.8363223714033 & 0.16367762859671 \tabularnewline
19 & 41 & 38.4864199413253 & 2.51358005867466 \tabularnewline
20 & 36 & 33.9096300385864 & 2.09036996141358 \tabularnewline
21 & 41 & 38.9378279099382 & 2.06217209006175 \tabularnewline
22 & 44 & 41.2709013944464 & 2.72909860555359 \tabularnewline
23 & 33 & 31.867174105638 & 1.13282589436197 \tabularnewline
24 & 37 & 33.8655891515949 & 3.13441084840513 \tabularnewline
25 & 52 & 52.3204823428737 & -0.320482342873662 \tabularnewline
26 & 47 & 48.4526332288973 & -1.45263322889728 \tabularnewline
27 & 43 & 46.717259465832 & -3.717259465832 \tabularnewline
28 & 44 & 42.3527860627953 & 1.64721393720466 \tabularnewline
29 & 45 & 47.7075158487302 & -2.70751584873018 \tabularnewline
30 & 44 & 40.7438398969822 & 3.25616010301785 \tabularnewline
31 & 49 & 48.3282483073212 & 0.671751692678802 \tabularnewline
32 & 33 & 32.5103515005298 & 0.48964849947017 \tabularnewline
33 & 43 & 42.6973513685299 & 0.302648631470137 \tabularnewline
34 & 54 & 51.5644666678177 & 2.43553333218228 \tabularnewline
35 & 42 & 45.0230531960022 & -3.02305319600215 \tabularnewline
36 & 44 & 43.2617062866755 & 0.738293713324503 \tabularnewline
37 & 37 & 39.270064777954 & -2.27006477795397 \tabularnewline
38 & 43 & 43.009141311239 & -0.00914131123900135 \tabularnewline
39 & 46 & 45.8457202917051 & 0.154279708294904 \tabularnewline
40 & 42 & 41.45158421455 & 0.548415785449956 \tabularnewline
41 & 45 & 44.4431961852496 & 0.556803814750406 \tabularnewline
42 & 44 & 46.0811885085586 & -2.08118850855864 \tabularnewline
43 & 33 & 30.7018768709112 & 2.29812312908876 \tabularnewline
44 & 31 & 30.9941287611879 & 0.00587123881207831 \tabularnewline
45 & 42 & 41.7128380742961 & 0.287161925703924 \tabularnewline
46 & 40 & 41.9694114490988 & -1.96941144909877 \tabularnewline
47 & 43 & 39.0337717374121 & 3.96622826258785 \tabularnewline
48 & 46 & 45.5736275902207 & 0.426372409779287 \tabularnewline
49 & 42 & 45.1395747793514 & -3.13957477935137 \tabularnewline
50 & 45 & 45.5729513122792 & -0.572951312279149 \tabularnewline
51 & 44 & 43.4493223246129 & 0.550677675387098 \tabularnewline
52 & 40 & 40.5459125318301 & -0.545912531830068 \tabularnewline
53 & 37 & 38.8144778514363 & -1.81447785143627 \tabularnewline
54 & 46 & 37.8242081611223 & 8.17579183887774 \tabularnewline
55 & 36 & 38.2862181577162 & -2.28621815771616 \tabularnewline
56 & 47 & 46.2484738327633 & 0.751526167236743 \tabularnewline
57 & 45 & 44.9757229146927 & 0.0242770853073146 \tabularnewline
58 & 42 & 39.9540078551884 & 2.04599214481161 \tabularnewline
59 & 43 & 44.4859424855319 & -1.48594248553187 \tabularnewline
60 & 43 & 42.8483740116179 & 0.151625988382049 \tabularnewline
61 & 32 & 32.3681480893381 & -0.368148089338074 \tabularnewline
62 & 45 & 47.175338607927 & -2.17533860792704 \tabularnewline
63 & 45 & 47.2911516860539 & -2.29115168605394 \tabularnewline
64 & 31 & 31.3130669313211 & -0.313066931321131 \tabularnewline
65 & 33 & 31.8957473233869 & 1.10425267661306 \tabularnewline
66 & 49 & 49.1048085759315 & -0.10480857593147 \tabularnewline
67 & 42 & 40.314645217539 & 1.68535478246102 \tabularnewline
68 & 41 & 44.0671512255321 & -3.06715122553211 \tabularnewline
69 & 38 & 38.3275060991143 & -0.327506099114316 \tabularnewline
70 & 42 & 41.9458085952077 & 0.0541914047923105 \tabularnewline
71 & 44 & 41.014903439551 & 2.985096560449 \tabularnewline
72 & 33 & 32.399177427404 & 0.600822572595985 \tabularnewline
73 & 48 & 49.5835689091001 & -1.58356890910009 \tabularnewline
74 & 40 & 41.6496735246733 & -1.64967352467332 \tabularnewline
75 & 50 & 51.2675791100133 & -1.26757911001328 \tabularnewline
76 & 49 & 49.4673292214853 & -0.467329221485346 \tabularnewline
77 & 43 & 42.4759597661249 & 0.524040233875148 \tabularnewline
78 & 44 & 42.3114215511578 & 1.68857844884216 \tabularnewline
79 & 47 & 47.2023177371417 & -0.202317737141702 \tabularnewline
80 & 33 & 31.3398749697915 & 1.66012503020847 \tabularnewline
81 & 46 & 48.3564922973322 & -2.35649229733216 \tabularnewline
82 & 0 & 15.4125812499604 & -15.4125812499604 \tabularnewline
83 & 45 & 42.7670966958723 & 2.23290330412772 \tabularnewline
84 & 43 & 44.0365350010406 & -1.03653500104058 \tabularnewline
85 & 44 & 44.0918872548634 & -0.0918872548634258 \tabularnewline
86 & 47 & 47.2815004076033 & -0.281500407603308 \tabularnewline
87 & 45 & 46.0402587114498 & -1.04025871144975 \tabularnewline
88 & 42 & 41.6808700598162 & 0.319129940183776 \tabularnewline
89 & 33 & 32.2343753687431 & 0.76562463125687 \tabularnewline
90 & 43 & 43.6526048775973 & -0.652604877597275 \tabularnewline
91 & 46 & 44.7542259563382 & 1.24577404366184 \tabularnewline
92 & 33 & 31.8788190661411 & 1.12118093385887 \tabularnewline
93 & 46 & 47.3411160324104 & -1.34111603241042 \tabularnewline
94 & 48 & 50.3432390909941 & -2.34323909099413 \tabularnewline
95 & 47 & 49.0523144585913 & -2.05231445859134 \tabularnewline
96 & 47 & 48.847916449246 & -1.84791644924597 \tabularnewline
97 & 43 & 42.1710516115352 & 0.828948388464805 \tabularnewline
98 & 46 & 47.4531171303941 & -1.45311713039411 \tabularnewline
99 & 48 & 49.2495139465748 & -1.24951394657475 \tabularnewline
100 & 46 & 46.6046234831302 & -0.604623483130165 \tabularnewline
101 & 45 & 45.9347197066407 & -0.93471970664072 \tabularnewline
102 & 45 & 44.9413588142847 & 0.0586411857152999 \tabularnewline
103 & 52 & 51.9922197383914 & 0.00778026160863292 \tabularnewline
104 & 42 & 39.6884559605473 & 2.31154403945273 \tabularnewline
105 & 47 & 48.7661888622672 & -1.76618886226719 \tabularnewline
106 & 41 & 42.0339833257215 & -1.03398332572153 \tabularnewline
107 & 47 & 45.9539095689808 & 1.04609043101919 \tabularnewline
108 & 43 & 43.7109439790573 & -0.710943979057307 \tabularnewline
109 & 33 & 32.4385336917765 & 0.561466308223449 \tabularnewline
110 & 30 & 29.0435570806062 & 0.95644291939383 \tabularnewline
111 & 49 & 50.5848459815841 & -1.58484598158412 \tabularnewline
112 & 44 & 45.560043540944 & -1.56004354094395 \tabularnewline
113 & 55 & 54.4873892364708 & 0.512610763529176 \tabularnewline
114 & 11 & 21.8831561686947 & -10.8831561686947 \tabularnewline
115 & 47 & 46.3514800279236 & 0.648519972076388 \tabularnewline
116 & 53 & 52.5987622820078 & 0.401237717992161 \tabularnewline
117 & 33 & 32.7336399234235 & 0.266360076576477 \tabularnewline
118 & 44 & 42.9312986439254 & 1.06870135607463 \tabularnewline
119 & 42 & 41.9572663789845 & 0.0427336210154821 \tabularnewline
120 & 55 & 55.9767437188752 & -0.976743718875233 \tabularnewline
121 & 33 & 32.7543837098096 & 0.245616290190408 \tabularnewline
122 & 46 & 46.6637796999351 & -0.663779699935086 \tabularnewline
123 & 54 & 55.4148353937316 & -1.4148353937316 \tabularnewline
124 & 47 & 46.1047366495 & 0.895263350500001 \tabularnewline
125 & 45 & 44.0877293966235 & 0.912270603376527 \tabularnewline
126 & 47 & 48.2161883867728 & -1.21618838677277 \tabularnewline
127 & 55 & 54.9630312162473 & 0.03696878375272 \tabularnewline
128 & 44 & 44.7058753490898 & -0.705875349089845 \tabularnewline
129 & 53 & 49.6658998417357 & 3.33410015826427 \tabularnewline
130 & 44 & 43.9678002165366 & 0.0321997834634258 \tabularnewline
131 & 42 & 42.9284803401983 & -0.928480340198258 \tabularnewline
132 & 40 & 41.3121541750297 & -1.31215417502969 \tabularnewline
133 & 46 & 46.8655949457411 & -0.865594945741143 \tabularnewline
134 & 40 & 39.4126394741784 & 0.587360525821635 \tabularnewline
135 & 46 & 45.2394135960887 & 0.760586403911297 \tabularnewline
136 & 53 & 50.4341853450689 & 2.56581465493113 \tabularnewline
137 & 33 & 32.7745766416939 & 0.225423358306125 \tabularnewline
138 & 42 & 38.2994344396166 & 3.70056556038337 \tabularnewline
139 & 35 & 33.1679266777141 & 1.83207332228591 \tabularnewline
140 & 40 & 38.6758431113333 & 1.32415688866671 \tabularnewline
141 & 41 & 42.5486129241035 & -1.54861292410349 \tabularnewline
142 & 33 & 31.9316173942557 & 1.06838260574434 \tabularnewline
143 & 51 & 51.8155607288665 & -0.815560728866481 \tabularnewline
144 & 53 & 52.9153388864372 & 0.0846611135627889 \tabularnewline
145 & 46 & 46.3492492793007 & -0.349249279300684 \tabularnewline
146 & 55 & 55.0359396160136 & -0.0359396160136493 \tabularnewline
147 & 47 & 45.5194747881729 & 1.48052521182706 \tabularnewline
148 & 38 & 36.332631226601 & 1.667368773399 \tabularnewline
149 & 46 & 45.514003934741 & 0.485996065258963 \tabularnewline
150 & 46 & 45.6883423604828 & 0.311657639517224 \tabularnewline
151 & 53 & 52.9678533868278 & 0.0321466131721517 \tabularnewline
152 & 47 & 49.1657486608047 & -2.16574866080469 \tabularnewline
153 & 41 & 43.4204790514314 & -2.42047905143139 \tabularnewline
154 & 44 & 44.147297408582 & -0.147297408581974 \tabularnewline
155 & 43 & 43.1962035873464 & -0.196203587346366 \tabularnewline
156 & 51 & 48.5300400105855 & 2.46995998941451 \tabularnewline
157 & 33 & 31.8788190661411 & 1.12118093385887 \tabularnewline
158 & 43 & 42.4670980157051 & 0.532901984294878 \tabularnewline
159 & 53 & 49.6658998417357 & 3.33410015826427 \tabularnewline
160 & 51 & 51.4663569849359 & -0.46635698493586 \tabularnewline
161 & 50 & 52.8573778624098 & -2.85737786240983 \tabularnewline
162 & 46 & 42.6280802599343 & 3.37191974006571 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=144461&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]31.7044961141872[/C][C]0.29550388581284[/C][/ROW]
[ROW][C]2[/C][C]51[/C][C]52.3470661073886[/C][C]-1.34706610738862[/C][/ROW]
[ROW][C]3[/C][C]42[/C][C]39.9128474291889[/C][C]2.08715257081105[/C][/ROW]
[ROW][C]4[/C][C]41[/C][C]40.5631434359633[/C][C]0.436856564036711[/C][/ROW]
[ROW][C]5[/C][C]46[/C][C]47.7790233709249[/C][C]-1.77902337092487[/C][/ROW]
[ROW][C]6[/C][C]47[/C][C]47.4773794892695[/C][C]-0.477379489269502[/C][/ROW]
[ROW][C]7[/C][C]37[/C][C]31.5761641251071[/C][C]5.42383587489288[/C][/ROW]
[ROW][C]8[/C][C]49[/C][C]49.1399175296791[/C][C]-0.139917529679119[/C][/ROW]
[ROW][C]9[/C][C]45[/C][C]44.8973249722099[/C][C]0.102675027790124[/C][/ROW]
[ROW][C]10[/C][C]47[/C][C]46.4565007529064[/C][C]0.543499247093577[/C][/ROW]
[ROW][C]11[/C][C]49[/C][C]47.7797311885505[/C][C]1.22026881144953[/C][/ROW]
[ROW][C]12[/C][C]33[/C][C]31.035885046034[/C][C]1.96411495396601[/C][/ROW]
[ROW][C]13[/C][C]42[/C][C]40.8505951423124[/C][C]1.14940485768762[/C][/ROW]
[ROW][C]14[/C][C]33[/C][C]32.0950942979728[/C][C]0.90490570202723[/C][/ROW]
[ROW][C]15[/C][C]53[/C][C]53.4469986368971[/C][C]-0.446998636897082[/C][/ROW]
[ROW][C]16[/C][C]36[/C][C]34.8017366708427[/C][C]1.19826332915726[/C][/ROW]
[ROW][C]17[/C][C]45[/C][C]46.0204529435252[/C][C]-1.02045294352517[/C][/ROW]
[ROW][C]18[/C][C]54[/C][C]53.8363223714033[/C][C]0.16367762859671[/C][/ROW]
[ROW][C]19[/C][C]41[/C][C]38.4864199413253[/C][C]2.51358005867466[/C][/ROW]
[ROW][C]20[/C][C]36[/C][C]33.9096300385864[/C][C]2.09036996141358[/C][/ROW]
[ROW][C]21[/C][C]41[/C][C]38.9378279099382[/C][C]2.06217209006175[/C][/ROW]
[ROW][C]22[/C][C]44[/C][C]41.2709013944464[/C][C]2.72909860555359[/C][/ROW]
[ROW][C]23[/C][C]33[/C][C]31.867174105638[/C][C]1.13282589436197[/C][/ROW]
[ROW][C]24[/C][C]37[/C][C]33.8655891515949[/C][C]3.13441084840513[/C][/ROW]
[ROW][C]25[/C][C]52[/C][C]52.3204823428737[/C][C]-0.320482342873662[/C][/ROW]
[ROW][C]26[/C][C]47[/C][C]48.4526332288973[/C][C]-1.45263322889728[/C][/ROW]
[ROW][C]27[/C][C]43[/C][C]46.717259465832[/C][C]-3.717259465832[/C][/ROW]
[ROW][C]28[/C][C]44[/C][C]42.3527860627953[/C][C]1.64721393720466[/C][/ROW]
[ROW][C]29[/C][C]45[/C][C]47.7075158487302[/C][C]-2.70751584873018[/C][/ROW]
[ROW][C]30[/C][C]44[/C][C]40.7438398969822[/C][C]3.25616010301785[/C][/ROW]
[ROW][C]31[/C][C]49[/C][C]48.3282483073212[/C][C]0.671751692678802[/C][/ROW]
[ROW][C]32[/C][C]33[/C][C]32.5103515005298[/C][C]0.48964849947017[/C][/ROW]
[ROW][C]33[/C][C]43[/C][C]42.6973513685299[/C][C]0.302648631470137[/C][/ROW]
[ROW][C]34[/C][C]54[/C][C]51.5644666678177[/C][C]2.43553333218228[/C][/ROW]
[ROW][C]35[/C][C]42[/C][C]45.0230531960022[/C][C]-3.02305319600215[/C][/ROW]
[ROW][C]36[/C][C]44[/C][C]43.2617062866755[/C][C]0.738293713324503[/C][/ROW]
[ROW][C]37[/C][C]37[/C][C]39.270064777954[/C][C]-2.27006477795397[/C][/ROW]
[ROW][C]38[/C][C]43[/C][C]43.009141311239[/C][C]-0.00914131123900135[/C][/ROW]
[ROW][C]39[/C][C]46[/C][C]45.8457202917051[/C][C]0.154279708294904[/C][/ROW]
[ROW][C]40[/C][C]42[/C][C]41.45158421455[/C][C]0.548415785449956[/C][/ROW]
[ROW][C]41[/C][C]45[/C][C]44.4431961852496[/C][C]0.556803814750406[/C][/ROW]
[ROW][C]42[/C][C]44[/C][C]46.0811885085586[/C][C]-2.08118850855864[/C][/ROW]
[ROW][C]43[/C][C]33[/C][C]30.7018768709112[/C][C]2.29812312908876[/C][/ROW]
[ROW][C]44[/C][C]31[/C][C]30.9941287611879[/C][C]0.00587123881207831[/C][/ROW]
[ROW][C]45[/C][C]42[/C][C]41.7128380742961[/C][C]0.287161925703924[/C][/ROW]
[ROW][C]46[/C][C]40[/C][C]41.9694114490988[/C][C]-1.96941144909877[/C][/ROW]
[ROW][C]47[/C][C]43[/C][C]39.0337717374121[/C][C]3.96622826258785[/C][/ROW]
[ROW][C]48[/C][C]46[/C][C]45.5736275902207[/C][C]0.426372409779287[/C][/ROW]
[ROW][C]49[/C][C]42[/C][C]45.1395747793514[/C][C]-3.13957477935137[/C][/ROW]
[ROW][C]50[/C][C]45[/C][C]45.5729513122792[/C][C]-0.572951312279149[/C][/ROW]
[ROW][C]51[/C][C]44[/C][C]43.4493223246129[/C][C]0.550677675387098[/C][/ROW]
[ROW][C]52[/C][C]40[/C][C]40.5459125318301[/C][C]-0.545912531830068[/C][/ROW]
[ROW][C]53[/C][C]37[/C][C]38.8144778514363[/C][C]-1.81447785143627[/C][/ROW]
[ROW][C]54[/C][C]46[/C][C]37.8242081611223[/C][C]8.17579183887774[/C][/ROW]
[ROW][C]55[/C][C]36[/C][C]38.2862181577162[/C][C]-2.28621815771616[/C][/ROW]
[ROW][C]56[/C][C]47[/C][C]46.2484738327633[/C][C]0.751526167236743[/C][/ROW]
[ROW][C]57[/C][C]45[/C][C]44.9757229146927[/C][C]0.0242770853073146[/C][/ROW]
[ROW][C]58[/C][C]42[/C][C]39.9540078551884[/C][C]2.04599214481161[/C][/ROW]
[ROW][C]59[/C][C]43[/C][C]44.4859424855319[/C][C]-1.48594248553187[/C][/ROW]
[ROW][C]60[/C][C]43[/C][C]42.8483740116179[/C][C]0.151625988382049[/C][/ROW]
[ROW][C]61[/C][C]32[/C][C]32.3681480893381[/C][C]-0.368148089338074[/C][/ROW]
[ROW][C]62[/C][C]45[/C][C]47.175338607927[/C][C]-2.17533860792704[/C][/ROW]
[ROW][C]63[/C][C]45[/C][C]47.2911516860539[/C][C]-2.29115168605394[/C][/ROW]
[ROW][C]64[/C][C]31[/C][C]31.3130669313211[/C][C]-0.313066931321131[/C][/ROW]
[ROW][C]65[/C][C]33[/C][C]31.8957473233869[/C][C]1.10425267661306[/C][/ROW]
[ROW][C]66[/C][C]49[/C][C]49.1048085759315[/C][C]-0.10480857593147[/C][/ROW]
[ROW][C]67[/C][C]42[/C][C]40.314645217539[/C][C]1.68535478246102[/C][/ROW]
[ROW][C]68[/C][C]41[/C][C]44.0671512255321[/C][C]-3.06715122553211[/C][/ROW]
[ROW][C]69[/C][C]38[/C][C]38.3275060991143[/C][C]-0.327506099114316[/C][/ROW]
[ROW][C]70[/C][C]42[/C][C]41.9458085952077[/C][C]0.0541914047923105[/C][/ROW]
[ROW][C]71[/C][C]44[/C][C]41.014903439551[/C][C]2.985096560449[/C][/ROW]
[ROW][C]72[/C][C]33[/C][C]32.399177427404[/C][C]0.600822572595985[/C][/ROW]
[ROW][C]73[/C][C]48[/C][C]49.5835689091001[/C][C]-1.58356890910009[/C][/ROW]
[ROW][C]74[/C][C]40[/C][C]41.6496735246733[/C][C]-1.64967352467332[/C][/ROW]
[ROW][C]75[/C][C]50[/C][C]51.2675791100133[/C][C]-1.26757911001328[/C][/ROW]
[ROW][C]76[/C][C]49[/C][C]49.4673292214853[/C][C]-0.467329221485346[/C][/ROW]
[ROW][C]77[/C][C]43[/C][C]42.4759597661249[/C][C]0.524040233875148[/C][/ROW]
[ROW][C]78[/C][C]44[/C][C]42.3114215511578[/C][C]1.68857844884216[/C][/ROW]
[ROW][C]79[/C][C]47[/C][C]47.2023177371417[/C][C]-0.202317737141702[/C][/ROW]
[ROW][C]80[/C][C]33[/C][C]31.3398749697915[/C][C]1.66012503020847[/C][/ROW]
[ROW][C]81[/C][C]46[/C][C]48.3564922973322[/C][C]-2.35649229733216[/C][/ROW]
[ROW][C]82[/C][C]0[/C][C]15.4125812499604[/C][C]-15.4125812499604[/C][/ROW]
[ROW][C]83[/C][C]45[/C][C]42.7670966958723[/C][C]2.23290330412772[/C][/ROW]
[ROW][C]84[/C][C]43[/C][C]44.0365350010406[/C][C]-1.03653500104058[/C][/ROW]
[ROW][C]85[/C][C]44[/C][C]44.0918872548634[/C][C]-0.0918872548634258[/C][/ROW]
[ROW][C]86[/C][C]47[/C][C]47.2815004076033[/C][C]-0.281500407603308[/C][/ROW]
[ROW][C]87[/C][C]45[/C][C]46.0402587114498[/C][C]-1.04025871144975[/C][/ROW]
[ROW][C]88[/C][C]42[/C][C]41.6808700598162[/C][C]0.319129940183776[/C][/ROW]
[ROW][C]89[/C][C]33[/C][C]32.2343753687431[/C][C]0.76562463125687[/C][/ROW]
[ROW][C]90[/C][C]43[/C][C]43.6526048775973[/C][C]-0.652604877597275[/C][/ROW]
[ROW][C]91[/C][C]46[/C][C]44.7542259563382[/C][C]1.24577404366184[/C][/ROW]
[ROW][C]92[/C][C]33[/C][C]31.8788190661411[/C][C]1.12118093385887[/C][/ROW]
[ROW][C]93[/C][C]46[/C][C]47.3411160324104[/C][C]-1.34111603241042[/C][/ROW]
[ROW][C]94[/C][C]48[/C][C]50.3432390909941[/C][C]-2.34323909099413[/C][/ROW]
[ROW][C]95[/C][C]47[/C][C]49.0523144585913[/C][C]-2.05231445859134[/C][/ROW]
[ROW][C]96[/C][C]47[/C][C]48.847916449246[/C][C]-1.84791644924597[/C][/ROW]
[ROW][C]97[/C][C]43[/C][C]42.1710516115352[/C][C]0.828948388464805[/C][/ROW]
[ROW][C]98[/C][C]46[/C][C]47.4531171303941[/C][C]-1.45311713039411[/C][/ROW]
[ROW][C]99[/C][C]48[/C][C]49.2495139465748[/C][C]-1.24951394657475[/C][/ROW]
[ROW][C]100[/C][C]46[/C][C]46.6046234831302[/C][C]-0.604623483130165[/C][/ROW]
[ROW][C]101[/C][C]45[/C][C]45.9347197066407[/C][C]-0.93471970664072[/C][/ROW]
[ROW][C]102[/C][C]45[/C][C]44.9413588142847[/C][C]0.0586411857152999[/C][/ROW]
[ROW][C]103[/C][C]52[/C][C]51.9922197383914[/C][C]0.00778026160863292[/C][/ROW]
[ROW][C]104[/C][C]42[/C][C]39.6884559605473[/C][C]2.31154403945273[/C][/ROW]
[ROW][C]105[/C][C]47[/C][C]48.7661888622672[/C][C]-1.76618886226719[/C][/ROW]
[ROW][C]106[/C][C]41[/C][C]42.0339833257215[/C][C]-1.03398332572153[/C][/ROW]
[ROW][C]107[/C][C]47[/C][C]45.9539095689808[/C][C]1.04609043101919[/C][/ROW]
[ROW][C]108[/C][C]43[/C][C]43.7109439790573[/C][C]-0.710943979057307[/C][/ROW]
[ROW][C]109[/C][C]33[/C][C]32.4385336917765[/C][C]0.561466308223449[/C][/ROW]
[ROW][C]110[/C][C]30[/C][C]29.0435570806062[/C][C]0.95644291939383[/C][/ROW]
[ROW][C]111[/C][C]49[/C][C]50.5848459815841[/C][C]-1.58484598158412[/C][/ROW]
[ROW][C]112[/C][C]44[/C][C]45.560043540944[/C][C]-1.56004354094395[/C][/ROW]
[ROW][C]113[/C][C]55[/C][C]54.4873892364708[/C][C]0.512610763529176[/C][/ROW]
[ROW][C]114[/C][C]11[/C][C]21.8831561686947[/C][C]-10.8831561686947[/C][/ROW]
[ROW][C]115[/C][C]47[/C][C]46.3514800279236[/C][C]0.648519972076388[/C][/ROW]
[ROW][C]116[/C][C]53[/C][C]52.5987622820078[/C][C]0.401237717992161[/C][/ROW]
[ROW][C]117[/C][C]33[/C][C]32.7336399234235[/C][C]0.266360076576477[/C][/ROW]
[ROW][C]118[/C][C]44[/C][C]42.9312986439254[/C][C]1.06870135607463[/C][/ROW]
[ROW][C]119[/C][C]42[/C][C]41.9572663789845[/C][C]0.0427336210154821[/C][/ROW]
[ROW][C]120[/C][C]55[/C][C]55.9767437188752[/C][C]-0.976743718875233[/C][/ROW]
[ROW][C]121[/C][C]33[/C][C]32.7543837098096[/C][C]0.245616290190408[/C][/ROW]
[ROW][C]122[/C][C]46[/C][C]46.6637796999351[/C][C]-0.663779699935086[/C][/ROW]
[ROW][C]123[/C][C]54[/C][C]55.4148353937316[/C][C]-1.4148353937316[/C][/ROW]
[ROW][C]124[/C][C]47[/C][C]46.1047366495[/C][C]0.895263350500001[/C][/ROW]
[ROW][C]125[/C][C]45[/C][C]44.0877293966235[/C][C]0.912270603376527[/C][/ROW]
[ROW][C]126[/C][C]47[/C][C]48.2161883867728[/C][C]-1.21618838677277[/C][/ROW]
[ROW][C]127[/C][C]55[/C][C]54.9630312162473[/C][C]0.03696878375272[/C][/ROW]
[ROW][C]128[/C][C]44[/C][C]44.7058753490898[/C][C]-0.705875349089845[/C][/ROW]
[ROW][C]129[/C][C]53[/C][C]49.6658998417357[/C][C]3.33410015826427[/C][/ROW]
[ROW][C]130[/C][C]44[/C][C]43.9678002165366[/C][C]0.0321997834634258[/C][/ROW]
[ROW][C]131[/C][C]42[/C][C]42.9284803401983[/C][C]-0.928480340198258[/C][/ROW]
[ROW][C]132[/C][C]40[/C][C]41.3121541750297[/C][C]-1.31215417502969[/C][/ROW]
[ROW][C]133[/C][C]46[/C][C]46.8655949457411[/C][C]-0.865594945741143[/C][/ROW]
[ROW][C]134[/C][C]40[/C][C]39.4126394741784[/C][C]0.587360525821635[/C][/ROW]
[ROW][C]135[/C][C]46[/C][C]45.2394135960887[/C][C]0.760586403911297[/C][/ROW]
[ROW][C]136[/C][C]53[/C][C]50.4341853450689[/C][C]2.56581465493113[/C][/ROW]
[ROW][C]137[/C][C]33[/C][C]32.7745766416939[/C][C]0.225423358306125[/C][/ROW]
[ROW][C]138[/C][C]42[/C][C]38.2994344396166[/C][C]3.70056556038337[/C][/ROW]
[ROW][C]139[/C][C]35[/C][C]33.1679266777141[/C][C]1.83207332228591[/C][/ROW]
[ROW][C]140[/C][C]40[/C][C]38.6758431113333[/C][C]1.32415688866671[/C][/ROW]
[ROW][C]141[/C][C]41[/C][C]42.5486129241035[/C][C]-1.54861292410349[/C][/ROW]
[ROW][C]142[/C][C]33[/C][C]31.9316173942557[/C][C]1.06838260574434[/C][/ROW]
[ROW][C]143[/C][C]51[/C][C]51.8155607288665[/C][C]-0.815560728866481[/C][/ROW]
[ROW][C]144[/C][C]53[/C][C]52.9153388864372[/C][C]0.0846611135627889[/C][/ROW]
[ROW][C]145[/C][C]46[/C][C]46.3492492793007[/C][C]-0.349249279300684[/C][/ROW]
[ROW][C]146[/C][C]55[/C][C]55.0359396160136[/C][C]-0.0359396160136493[/C][/ROW]
[ROW][C]147[/C][C]47[/C][C]45.5194747881729[/C][C]1.48052521182706[/C][/ROW]
[ROW][C]148[/C][C]38[/C][C]36.332631226601[/C][C]1.667368773399[/C][/ROW]
[ROW][C]149[/C][C]46[/C][C]45.514003934741[/C][C]0.485996065258963[/C][/ROW]
[ROW][C]150[/C][C]46[/C][C]45.6883423604828[/C][C]0.311657639517224[/C][/ROW]
[ROW][C]151[/C][C]53[/C][C]52.9678533868278[/C][C]0.0321466131721517[/C][/ROW]
[ROW][C]152[/C][C]47[/C][C]49.1657486608047[/C][C]-2.16574866080469[/C][/ROW]
[ROW][C]153[/C][C]41[/C][C]43.4204790514314[/C][C]-2.42047905143139[/C][/ROW]
[ROW][C]154[/C][C]44[/C][C]44.147297408582[/C][C]-0.147297408581974[/C][/ROW]
[ROW][C]155[/C][C]43[/C][C]43.1962035873464[/C][C]-0.196203587346366[/C][/ROW]
[ROW][C]156[/C][C]51[/C][C]48.5300400105855[/C][C]2.46995998941451[/C][/ROW]
[ROW][C]157[/C][C]33[/C][C]31.8788190661411[/C][C]1.12118093385887[/C][/ROW]
[ROW][C]158[/C][C]43[/C][C]42.4670980157051[/C][C]0.532901984294878[/C][/ROW]
[ROW][C]159[/C][C]53[/C][C]49.6658998417357[/C][C]3.33410015826427[/C][/ROW]
[ROW][C]160[/C][C]51[/C][C]51.4663569849359[/C][C]-0.46635698493586[/C][/ROW]
[ROW][C]161[/C][C]50[/C][C]52.8573778624098[/C][C]-2.85737786240983[/C][/ROW]
[ROW][C]162[/C][C]46[/C][C]42.6280802599343[/C][C]3.37191974006571[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=144461&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=144461&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
13231.70449611418720.29550388581284
25152.3470661073886-1.34706610738862
34239.91284742918892.08715257081105
44140.56314343596330.436856564036711
54647.7790233709249-1.77902337092487
64747.4773794892695-0.477379489269502
73731.57616412510715.42383587489288
84949.1399175296791-0.139917529679119
94544.89732497220990.102675027790124
104746.45650075290640.543499247093577
114947.77973118855051.22026881144953
123331.0358850460341.96411495396601
134240.85059514231241.14940485768762
143332.09509429797280.90490570202723
155353.4469986368971-0.446998636897082
163634.80173667084271.19826332915726
174546.0204529435252-1.02045294352517
185453.83632237140330.16367762859671
194138.48641994132532.51358005867466
203633.90963003858642.09036996141358
214138.93782790993822.06217209006175
224441.27090139444642.72909860555359
233331.8671741056381.13282589436197
243733.86558915159493.13441084840513
255252.3204823428737-0.320482342873662
264748.4526332288973-1.45263322889728
274346.717259465832-3.717259465832
284442.35278606279531.64721393720466
294547.7075158487302-2.70751584873018
304440.74383989698223.25616010301785
314948.32824830732120.671751692678802
323332.51035150052980.48964849947017
334342.69735136852990.302648631470137
345451.56446666781772.43553333218228
354245.0230531960022-3.02305319600215
364443.26170628667550.738293713324503
373739.270064777954-2.27006477795397
384343.009141311239-0.00914131123900135
394645.84572029170510.154279708294904
404241.451584214550.548415785449956
414544.44319618524960.556803814750406
424446.0811885085586-2.08118850855864
433330.70187687091122.29812312908876
443130.99412876118790.00587123881207831
454241.71283807429610.287161925703924
464041.9694114490988-1.96941144909877
474339.03377173741213.96622826258785
484645.57362759022070.426372409779287
494245.1395747793514-3.13957477935137
504545.5729513122792-0.572951312279149
514443.44932232461290.550677675387098
524040.5459125318301-0.545912531830068
533738.8144778514363-1.81447785143627
544637.82420816112238.17579183887774
553638.2862181577162-2.28621815771616
564746.24847383276330.751526167236743
574544.97572291469270.0242770853073146
584239.95400785518842.04599214481161
594344.4859424855319-1.48594248553187
604342.84837401161790.151625988382049
613232.3681480893381-0.368148089338074
624547.175338607927-2.17533860792704
634547.2911516860539-2.29115168605394
643131.3130669313211-0.313066931321131
653331.89574732338691.10425267661306
664949.1048085759315-0.10480857593147
674240.3146452175391.68535478246102
684144.0671512255321-3.06715122553211
693838.3275060991143-0.327506099114316
704241.94580859520770.0541914047923105
714441.0149034395512.985096560449
723332.3991774274040.600822572595985
734849.5835689091001-1.58356890910009
744041.6496735246733-1.64967352467332
755051.2675791100133-1.26757911001328
764949.4673292214853-0.467329221485346
774342.47595976612490.524040233875148
784442.31142155115781.68857844884216
794747.2023177371417-0.202317737141702
803331.33987496979151.66012503020847
814648.3564922973322-2.35649229733216
82015.4125812499604-15.4125812499604
834542.76709669587232.23290330412772
844344.0365350010406-1.03653500104058
854444.0918872548634-0.0918872548634258
864747.2815004076033-0.281500407603308
874546.0402587114498-1.04025871144975
884241.68087005981620.319129940183776
893332.23437536874310.76562463125687
904343.6526048775973-0.652604877597275
914644.75422595633821.24577404366184
923331.87881906614111.12118093385887
934647.3411160324104-1.34111603241042
944850.3432390909941-2.34323909099413
954749.0523144585913-2.05231445859134
964748.847916449246-1.84791644924597
974342.17105161153520.828948388464805
984647.4531171303941-1.45311713039411
994849.2495139465748-1.24951394657475
1004646.6046234831302-0.604623483130165
1014545.9347197066407-0.93471970664072
1024544.94135881428470.0586411857152999
1035251.99221973839140.00778026160863292
1044239.68845596054732.31154403945273
1054748.7661888622672-1.76618886226719
1064142.0339833257215-1.03398332572153
1074745.95390956898081.04609043101919
1084343.7109439790573-0.710943979057307
1093332.43853369177650.561466308223449
1103029.04355708060620.95644291939383
1114950.5848459815841-1.58484598158412
1124445.560043540944-1.56004354094395
1135554.48738923647080.512610763529176
1141121.8831561686947-10.8831561686947
1154746.35148002792360.648519972076388
1165352.59876228200780.401237717992161
1173332.73363992342350.266360076576477
1184442.93129864392541.06870135607463
1194241.95726637898450.0427336210154821
1205555.9767437188752-0.976743718875233
1213332.75438370980960.245616290190408
1224646.6637796999351-0.663779699935086
1235455.4148353937316-1.4148353937316
1244746.10473664950.895263350500001
1254544.08772939662350.912270603376527
1264748.2161883867728-1.21618838677277
1275554.96303121624730.03696878375272
1284444.7058753490898-0.705875349089845
1295349.66589984173573.33410015826427
1304443.96780021653660.0321997834634258
1314242.9284803401983-0.928480340198258
1324041.3121541750297-1.31215417502969
1334646.8655949457411-0.865594945741143
1344039.41263947417840.587360525821635
1354645.23941359608870.760586403911297
1365350.43418534506892.56581465493113
1373332.77457664169390.225423358306125
1384238.29943443961663.70056556038337
1393533.16792667771411.83207332228591
1404038.67584311133331.32415688866671
1414142.5486129241035-1.54861292410349
1423331.93161739425571.06838260574434
1435151.8155607288665-0.815560728866481
1445352.91533888643720.0846611135627889
1454646.3492492793007-0.349249279300684
1465555.0359396160136-0.0359396160136493
1474745.51947478817291.48052521182706
1483836.3326312266011.667368773399
1494645.5140039347410.485996065258963
1504645.68834236048280.311657639517224
1515352.96785338682780.0321466131721517
1524749.1657486608047-2.16574866080469
1534143.4204790514314-2.42047905143139
1544444.147297408582-0.147297408581974
1554343.1962035873464-0.196203587346366
1565148.53004001058552.46995998941451
1573331.87881906614111.12118093385887
1584342.46709801570510.532901984294878
1595349.66589984173573.33410015826427
1605151.4663569849359-0.46635698493586
1615052.8573778624098-2.85737786240983
1624642.62808025993433.37191974006571






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
120.1416618774865830.2833237549731660.858338122513417
130.0677061007698010.1354122015396020.932293899230199
140.03305345700836670.06610691401673340.966946542991633
150.01663765472600190.03327530945200370.983362345273998
160.006141626749631870.01228325349926370.993858373250368
170.003249611872686650.00649922374537330.996750388127313
180.00115697911510270.00231395823020540.998843020884897
190.001127869728833440.002255739457666880.998872130271167
200.0004322835588594620.0008645671177189230.999567716441141
210.0002062777785107030.0004125555570214060.999793722221489
220.0001440618677447310.0002881237354894630.999855938132255
230.0002376425524443110.0004752851048886230.999762357447556
240.0004568841177944460.0009137682355888930.999543115882206
250.000213240809948860.0004264816198977190.999786759190051
260.0003014118526932070.0006028237053864150.999698588147307
270.0008643325023267290.001728665004653460.999135667497673
280.0005426171312875460.001085234262575090.999457382868712
290.003120363015006980.006240726030013960.996879636984993
300.00570408007690030.01140816015380060.9942959199231
310.003516860025785380.007033720051570770.996483139974215
320.003080815467077630.006161630934155270.996919184532922
330.001805179097662780.003610358195325560.998194820902337
340.003346612276200890.006693224552401770.996653387723799
350.007572608155549490.0151452163110990.992427391844451
360.008023366481503530.01604673296300710.991976633518497
370.008794177735897720.01758835547179540.991205822264102
380.005708373141426620.01141674628285320.994291626858573
390.003743403955375450.00748680791075090.996256596044625
400.00241136022001210.00482272044002420.997588639779988
410.001479437430877760.002958874861755510.998520562569122
420.001122415418221030.002244830836442050.998877584581779
430.001069990627308010.002139981254616020.998930009372692
440.000801872186660270.001603744373320540.99919812781334
450.0006436822107782070.001287364421556410.999356317789222
460.000451706485102820.000903412970205640.999548293514897
470.0008322496734508540.001664499346901710.999167750326549
480.0005067556431088070.001013511286217610.999493244356891
490.00277728631097620.00555457262195240.997222713689024
500.001814886582911750.003629773165823490.998185113417088
510.001305194504578730.002610389009157460.998694805495421
520.0008823609400599090.001764721880119820.99911763905994
530.0007843971741718370.001568794348343670.999215602825828
540.06080968779552780.1216193755910560.939190312204472
550.05904174918377740.1180834983675550.940958250816223
560.0516228283584150.103245656716830.948377171641585
570.03938235847694090.07876471695388190.960617641523059
580.03505606119994870.07011212239989740.964943938800051
590.03245082216329920.06490164432659830.967549177836701
600.02445840629748770.04891681259497550.975541593702512
610.0183233415501590.03664668310031810.981676658449841
620.01727355089070830.03454710178141660.982726449109292
630.0182892834051410.0365785668102820.981710716594859
640.01539021047345540.03078042094691090.984609789526545
650.01239688572956710.02479377145913420.987603114270433
660.009069726848804230.01813945369760850.990930273151196
670.007809858118879880.01561971623775980.99219014188112
680.01070052335363420.02140104670726830.989299476646366
690.008577582327569580.01715516465513920.99142241767243
700.00661373723459770.01322747446919540.993386262765402
710.01055066514285170.02110133028570340.989449334857148
720.009533672340231820.01906734468046360.990466327659768
730.009156087477994070.01831217495598810.990843912522006
740.01133936513664890.02267873027329780.988660634863351
750.009022833680027130.01804566736005430.990977166319973
760.006781158805344710.01356231761068940.993218841194655
770.005092321510821630.01018464302164330.994907678489178
780.004896358418841620.009792716837683230.995103641581158
790.003610920082861260.007221840165722510.996389079917139
800.003214038446974150.006428076893948310.996785961553026
810.003133033874736050.006266067749472110.996866966125264
820.9976733482229970.004653303554006410.0023266517770032
830.9976606395373910.004678720925216990.0023393604626085
840.9968840116179310.00623197676413850.00311598838206925
850.9955390845312780.008921830937444630.00446091546872232
860.9937400660328830.01251986793423320.0062599339671166
870.9918808281285140.01623834374297210.00811917187148607
880.9888483527405810.02230329451883880.0111516472594194
890.9853039496060110.02939210078797770.0146960503939889
900.9807762610792610.03844747784147850.0192237389207393
910.9761985437385560.04760291252288860.0238014562614443
920.9714664260498990.05706714790020130.0285335739501006
930.9652515068431120.06949698631377590.034748493156888
940.9659492462326430.06810150753471340.0340507537673567
950.963396205653470.07320758869305980.0366037943465299
960.9605452678507910.07890946429841840.0394547321492092
970.9513599720029270.09728005599414570.0486400279970729
980.9462554820704130.1074890358591750.0537445179295875
990.9369543449923180.1260913100153630.0630456550076816
1000.9214566408641490.1570867182717020.0785433591358512
1010.9043805792766280.1912388414467430.0956194207233717
1020.8812357339792580.2375285320414850.118764266020742
1030.8543365378106440.2913269243787130.145663462189356
1040.8543970911459990.2912058177080020.145602908854001
1050.8502281986003670.2995436027992650.149771801399633
1060.8232743261358910.3534513477282190.176725673864109
1070.7930181359461640.4139637281076720.206981864053836
1080.7590517415104830.4818965169790340.240948258489517
1090.7248450383543580.5503099232912840.275154961645642
1100.6993315843781490.6013368312437020.300668415621851
1110.6880384062536030.6239231874927930.311961593746397
1120.6573709894353760.6852580211292490.342629010564624
1130.6111765840303710.7776468319392590.388823415969629
1140.9999751992430014.96015139978988e-052.48007569989494e-05
1150.9999590111233358.19777533297823e-054.09888766648912e-05
1160.9999291998288690.0001416003422612947.08001711306472e-05
1170.9998915611792450.0002168776415094350.000108438820754718
1180.9998117025776450.0003765948447100520.000188297422355026
1190.9996650205177060.0006699589645879420.000334979482293971
1200.9994370020746850.001125995850630010.000562997925315006
1210.99910257583310.00179484833380020.0008974241669001
1220.9985781312299740.002843737540052570.00142186877002628
1230.9978820396424440.004235920715112240.00211796035755612
1240.9968390068429290.006321986314141490.00316099315707075
1250.9949867972337840.01002640553243210.00501320276621603
1260.9926400892040530.01471982159189370.00735991079594687
1270.9885005676678910.02299886466421850.0114994323321093
1280.987336188706370.02532762258725990.0126638112936299
1290.9877406755006290.02451864899874180.0122593244993709
1300.9824344433000820.03513111339983620.0175655566999181
1310.9761886815291820.04762263694163590.023811318470818
1320.9692890181280180.0614219637439630.0307109818719815
1330.9605649158088030.07887016838239450.0394350841911973
1340.9488984317269230.1022031365461530.0511015682730765
1350.9263963560532510.1472072878934980.0736036439467488
1360.9395948044954620.1208103910090760.0604051955045381
1370.9359810895475050.128037820904990.064018910452495
1380.9558580783184650.08828384336306990.044141921681535
1390.944529734115740.110940531768520.05547026588426
1400.9150447638136860.1699104723726280.0849552361863142
1410.9330143026533520.1339713946932960.0669856973466479
1420.8975738768159580.2048522463680840.102426123184042
1430.8795693971309060.2408612057381880.120430602869094
1440.8224143120564780.3551713758870440.177585687943522
1450.8162409709870620.3675180580258760.183759029012938
1460.7810160636313960.4379678727372080.218983936368604
1470.7458158459864690.5083683080270620.254184154013531
1480.8045494414842090.3909011170315820.195450558515791
1490.7168310665385190.5663378669229620.283168933461481
1500.5564759440939810.8870481118120380.443524055906019

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
12 & 0.141661877486583 & 0.283323754973166 & 0.858338122513417 \tabularnewline
13 & 0.067706100769801 & 0.135412201539602 & 0.932293899230199 \tabularnewline
14 & 0.0330534570083667 & 0.0661069140167334 & 0.966946542991633 \tabularnewline
15 & 0.0166376547260019 & 0.0332753094520037 & 0.983362345273998 \tabularnewline
16 & 0.00614162674963187 & 0.0122832534992637 & 0.993858373250368 \tabularnewline
17 & 0.00324961187268665 & 0.0064992237453733 & 0.996750388127313 \tabularnewline
18 & 0.0011569791151027 & 0.0023139582302054 & 0.998843020884897 \tabularnewline
19 & 0.00112786972883344 & 0.00225573945766688 & 0.998872130271167 \tabularnewline
20 & 0.000432283558859462 & 0.000864567117718923 & 0.999567716441141 \tabularnewline
21 & 0.000206277778510703 & 0.000412555557021406 & 0.999793722221489 \tabularnewline
22 & 0.000144061867744731 & 0.000288123735489463 & 0.999855938132255 \tabularnewline
23 & 0.000237642552444311 & 0.000475285104888623 & 0.999762357447556 \tabularnewline
24 & 0.000456884117794446 & 0.000913768235588893 & 0.999543115882206 \tabularnewline
25 & 0.00021324080994886 & 0.000426481619897719 & 0.999786759190051 \tabularnewline
26 & 0.000301411852693207 & 0.000602823705386415 & 0.999698588147307 \tabularnewline
27 & 0.000864332502326729 & 0.00172866500465346 & 0.999135667497673 \tabularnewline
28 & 0.000542617131287546 & 0.00108523426257509 & 0.999457382868712 \tabularnewline
29 & 0.00312036301500698 & 0.00624072603001396 & 0.996879636984993 \tabularnewline
30 & 0.0057040800769003 & 0.0114081601538006 & 0.9942959199231 \tabularnewline
31 & 0.00351686002578538 & 0.00703372005157077 & 0.996483139974215 \tabularnewline
32 & 0.00308081546707763 & 0.00616163093415527 & 0.996919184532922 \tabularnewline
33 & 0.00180517909766278 & 0.00361035819532556 & 0.998194820902337 \tabularnewline
34 & 0.00334661227620089 & 0.00669322455240177 & 0.996653387723799 \tabularnewline
35 & 0.00757260815554949 & 0.015145216311099 & 0.992427391844451 \tabularnewline
36 & 0.00802336648150353 & 0.0160467329630071 & 0.991976633518497 \tabularnewline
37 & 0.00879417773589772 & 0.0175883554717954 & 0.991205822264102 \tabularnewline
38 & 0.00570837314142662 & 0.0114167462828532 & 0.994291626858573 \tabularnewline
39 & 0.00374340395537545 & 0.0074868079107509 & 0.996256596044625 \tabularnewline
40 & 0.0024113602200121 & 0.0048227204400242 & 0.997588639779988 \tabularnewline
41 & 0.00147943743087776 & 0.00295887486175551 & 0.998520562569122 \tabularnewline
42 & 0.00112241541822103 & 0.00224483083644205 & 0.998877584581779 \tabularnewline
43 & 0.00106999062730801 & 0.00213998125461602 & 0.998930009372692 \tabularnewline
44 & 0.00080187218666027 & 0.00160374437332054 & 0.99919812781334 \tabularnewline
45 & 0.000643682210778207 & 0.00128736442155641 & 0.999356317789222 \tabularnewline
46 & 0.00045170648510282 & 0.00090341297020564 & 0.999548293514897 \tabularnewline
47 & 0.000832249673450854 & 0.00166449934690171 & 0.999167750326549 \tabularnewline
48 & 0.000506755643108807 & 0.00101351128621761 & 0.999493244356891 \tabularnewline
49 & 0.0027772863109762 & 0.0055545726219524 & 0.997222713689024 \tabularnewline
50 & 0.00181488658291175 & 0.00362977316582349 & 0.998185113417088 \tabularnewline
51 & 0.00130519450457873 & 0.00261038900915746 & 0.998694805495421 \tabularnewline
52 & 0.000882360940059909 & 0.00176472188011982 & 0.99911763905994 \tabularnewline
53 & 0.000784397174171837 & 0.00156879434834367 & 0.999215602825828 \tabularnewline
54 & 0.0608096877955278 & 0.121619375591056 & 0.939190312204472 \tabularnewline
55 & 0.0590417491837774 & 0.118083498367555 & 0.940958250816223 \tabularnewline
56 & 0.051622828358415 & 0.10324565671683 & 0.948377171641585 \tabularnewline
57 & 0.0393823584769409 & 0.0787647169538819 & 0.960617641523059 \tabularnewline
58 & 0.0350560611999487 & 0.0701121223998974 & 0.964943938800051 \tabularnewline
59 & 0.0324508221632992 & 0.0649016443265983 & 0.967549177836701 \tabularnewline
60 & 0.0244584062974877 & 0.0489168125949755 & 0.975541593702512 \tabularnewline
61 & 0.018323341550159 & 0.0366466831003181 & 0.981676658449841 \tabularnewline
62 & 0.0172735508907083 & 0.0345471017814166 & 0.982726449109292 \tabularnewline
63 & 0.018289283405141 & 0.036578566810282 & 0.981710716594859 \tabularnewline
64 & 0.0153902104734554 & 0.0307804209469109 & 0.984609789526545 \tabularnewline
65 & 0.0123968857295671 & 0.0247937714591342 & 0.987603114270433 \tabularnewline
66 & 0.00906972684880423 & 0.0181394536976085 & 0.990930273151196 \tabularnewline
67 & 0.00780985811887988 & 0.0156197162377598 & 0.99219014188112 \tabularnewline
68 & 0.0107005233536342 & 0.0214010467072683 & 0.989299476646366 \tabularnewline
69 & 0.00857758232756958 & 0.0171551646551392 & 0.99142241767243 \tabularnewline
70 & 0.0066137372345977 & 0.0132274744691954 & 0.993386262765402 \tabularnewline
71 & 0.0105506651428517 & 0.0211013302857034 & 0.989449334857148 \tabularnewline
72 & 0.00953367234023182 & 0.0190673446804636 & 0.990466327659768 \tabularnewline
73 & 0.00915608747799407 & 0.0183121749559881 & 0.990843912522006 \tabularnewline
74 & 0.0113393651366489 & 0.0226787302732978 & 0.988660634863351 \tabularnewline
75 & 0.00902283368002713 & 0.0180456673600543 & 0.990977166319973 \tabularnewline
76 & 0.00678115880534471 & 0.0135623176106894 & 0.993218841194655 \tabularnewline
77 & 0.00509232151082163 & 0.0101846430216433 & 0.994907678489178 \tabularnewline
78 & 0.00489635841884162 & 0.00979271683768323 & 0.995103641581158 \tabularnewline
79 & 0.00361092008286126 & 0.00722184016572251 & 0.996389079917139 \tabularnewline
80 & 0.00321403844697415 & 0.00642807689394831 & 0.996785961553026 \tabularnewline
81 & 0.00313303387473605 & 0.00626606774947211 & 0.996866966125264 \tabularnewline
82 & 0.997673348222997 & 0.00465330355400641 & 0.0023266517770032 \tabularnewline
83 & 0.997660639537391 & 0.00467872092521699 & 0.0023393604626085 \tabularnewline
84 & 0.996884011617931 & 0.0062319767641385 & 0.00311598838206925 \tabularnewline
85 & 0.995539084531278 & 0.00892183093744463 & 0.00446091546872232 \tabularnewline
86 & 0.993740066032883 & 0.0125198679342332 & 0.0062599339671166 \tabularnewline
87 & 0.991880828128514 & 0.0162383437429721 & 0.00811917187148607 \tabularnewline
88 & 0.988848352740581 & 0.0223032945188388 & 0.0111516472594194 \tabularnewline
89 & 0.985303949606011 & 0.0293921007879777 & 0.0146960503939889 \tabularnewline
90 & 0.980776261079261 & 0.0384474778414785 & 0.0192237389207393 \tabularnewline
91 & 0.976198543738556 & 0.0476029125228886 & 0.0238014562614443 \tabularnewline
92 & 0.971466426049899 & 0.0570671479002013 & 0.0285335739501006 \tabularnewline
93 & 0.965251506843112 & 0.0694969863137759 & 0.034748493156888 \tabularnewline
94 & 0.965949246232643 & 0.0681015075347134 & 0.0340507537673567 \tabularnewline
95 & 0.96339620565347 & 0.0732075886930598 & 0.0366037943465299 \tabularnewline
96 & 0.960545267850791 & 0.0789094642984184 & 0.0394547321492092 \tabularnewline
97 & 0.951359972002927 & 0.0972800559941457 & 0.0486400279970729 \tabularnewline
98 & 0.946255482070413 & 0.107489035859175 & 0.0537445179295875 \tabularnewline
99 & 0.936954344992318 & 0.126091310015363 & 0.0630456550076816 \tabularnewline
100 & 0.921456640864149 & 0.157086718271702 & 0.0785433591358512 \tabularnewline
101 & 0.904380579276628 & 0.191238841446743 & 0.0956194207233717 \tabularnewline
102 & 0.881235733979258 & 0.237528532041485 & 0.118764266020742 \tabularnewline
103 & 0.854336537810644 & 0.291326924378713 & 0.145663462189356 \tabularnewline
104 & 0.854397091145999 & 0.291205817708002 & 0.145602908854001 \tabularnewline
105 & 0.850228198600367 & 0.299543602799265 & 0.149771801399633 \tabularnewline
106 & 0.823274326135891 & 0.353451347728219 & 0.176725673864109 \tabularnewline
107 & 0.793018135946164 & 0.413963728107672 & 0.206981864053836 \tabularnewline
108 & 0.759051741510483 & 0.481896516979034 & 0.240948258489517 \tabularnewline
109 & 0.724845038354358 & 0.550309923291284 & 0.275154961645642 \tabularnewline
110 & 0.699331584378149 & 0.601336831243702 & 0.300668415621851 \tabularnewline
111 & 0.688038406253603 & 0.623923187492793 & 0.311961593746397 \tabularnewline
112 & 0.657370989435376 & 0.685258021129249 & 0.342629010564624 \tabularnewline
113 & 0.611176584030371 & 0.777646831939259 & 0.388823415969629 \tabularnewline
114 & 0.999975199243001 & 4.96015139978988e-05 & 2.48007569989494e-05 \tabularnewline
115 & 0.999959011123335 & 8.19777533297823e-05 & 4.09888766648912e-05 \tabularnewline
116 & 0.999929199828869 & 0.000141600342261294 & 7.08001711306472e-05 \tabularnewline
117 & 0.999891561179245 & 0.000216877641509435 & 0.000108438820754718 \tabularnewline
118 & 0.999811702577645 & 0.000376594844710052 & 0.000188297422355026 \tabularnewline
119 & 0.999665020517706 & 0.000669958964587942 & 0.000334979482293971 \tabularnewline
120 & 0.999437002074685 & 0.00112599585063001 & 0.000562997925315006 \tabularnewline
121 & 0.9991025758331 & 0.0017948483338002 & 0.0008974241669001 \tabularnewline
122 & 0.998578131229974 & 0.00284373754005257 & 0.00142186877002628 \tabularnewline
123 & 0.997882039642444 & 0.00423592071511224 & 0.00211796035755612 \tabularnewline
124 & 0.996839006842929 & 0.00632198631414149 & 0.00316099315707075 \tabularnewline
125 & 0.994986797233784 & 0.0100264055324321 & 0.00501320276621603 \tabularnewline
126 & 0.992640089204053 & 0.0147198215918937 & 0.00735991079594687 \tabularnewline
127 & 0.988500567667891 & 0.0229988646642185 & 0.0114994323321093 \tabularnewline
128 & 0.98733618870637 & 0.0253276225872599 & 0.0126638112936299 \tabularnewline
129 & 0.987740675500629 & 0.0245186489987418 & 0.0122593244993709 \tabularnewline
130 & 0.982434443300082 & 0.0351311133998362 & 0.0175655566999181 \tabularnewline
131 & 0.976188681529182 & 0.0476226369416359 & 0.023811318470818 \tabularnewline
132 & 0.969289018128018 & 0.061421963743963 & 0.0307109818719815 \tabularnewline
133 & 0.960564915808803 & 0.0788701683823945 & 0.0394350841911973 \tabularnewline
134 & 0.948898431726923 & 0.102203136546153 & 0.0511015682730765 \tabularnewline
135 & 0.926396356053251 & 0.147207287893498 & 0.0736036439467488 \tabularnewline
136 & 0.939594804495462 & 0.120810391009076 & 0.0604051955045381 \tabularnewline
137 & 0.935981089547505 & 0.12803782090499 & 0.064018910452495 \tabularnewline
138 & 0.955858078318465 & 0.0882838433630699 & 0.044141921681535 \tabularnewline
139 & 0.94452973411574 & 0.11094053176852 & 0.05547026588426 \tabularnewline
140 & 0.915044763813686 & 0.169910472372628 & 0.0849552361863142 \tabularnewline
141 & 0.933014302653352 & 0.133971394693296 & 0.0669856973466479 \tabularnewline
142 & 0.897573876815958 & 0.204852246368084 & 0.102426123184042 \tabularnewline
143 & 0.879569397130906 & 0.240861205738188 & 0.120430602869094 \tabularnewline
144 & 0.822414312056478 & 0.355171375887044 & 0.177585687943522 \tabularnewline
145 & 0.816240970987062 & 0.367518058025876 & 0.183759029012938 \tabularnewline
146 & 0.781016063631396 & 0.437967872737208 & 0.218983936368604 \tabularnewline
147 & 0.745815845986469 & 0.508368308027062 & 0.254184154013531 \tabularnewline
148 & 0.804549441484209 & 0.390901117031582 & 0.195450558515791 \tabularnewline
149 & 0.716831066538519 & 0.566337866922962 & 0.283168933461481 \tabularnewline
150 & 0.556475944093981 & 0.887048111812038 & 0.443524055906019 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=144461&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]12[/C][C]0.141661877486583[/C][C]0.283323754973166[/C][C]0.858338122513417[/C][/ROW]
[ROW][C]13[/C][C]0.067706100769801[/C][C]0.135412201539602[/C][C]0.932293899230199[/C][/ROW]
[ROW][C]14[/C][C]0.0330534570083667[/C][C]0.0661069140167334[/C][C]0.966946542991633[/C][/ROW]
[ROW][C]15[/C][C]0.0166376547260019[/C][C]0.0332753094520037[/C][C]0.983362345273998[/C][/ROW]
[ROW][C]16[/C][C]0.00614162674963187[/C][C]0.0122832534992637[/C][C]0.993858373250368[/C][/ROW]
[ROW][C]17[/C][C]0.00324961187268665[/C][C]0.0064992237453733[/C][C]0.996750388127313[/C][/ROW]
[ROW][C]18[/C][C]0.0011569791151027[/C][C]0.0023139582302054[/C][C]0.998843020884897[/C][/ROW]
[ROW][C]19[/C][C]0.00112786972883344[/C][C]0.00225573945766688[/C][C]0.998872130271167[/C][/ROW]
[ROW][C]20[/C][C]0.000432283558859462[/C][C]0.000864567117718923[/C][C]0.999567716441141[/C][/ROW]
[ROW][C]21[/C][C]0.000206277778510703[/C][C]0.000412555557021406[/C][C]0.999793722221489[/C][/ROW]
[ROW][C]22[/C][C]0.000144061867744731[/C][C]0.000288123735489463[/C][C]0.999855938132255[/C][/ROW]
[ROW][C]23[/C][C]0.000237642552444311[/C][C]0.000475285104888623[/C][C]0.999762357447556[/C][/ROW]
[ROW][C]24[/C][C]0.000456884117794446[/C][C]0.000913768235588893[/C][C]0.999543115882206[/C][/ROW]
[ROW][C]25[/C][C]0.00021324080994886[/C][C]0.000426481619897719[/C][C]0.999786759190051[/C][/ROW]
[ROW][C]26[/C][C]0.000301411852693207[/C][C]0.000602823705386415[/C][C]0.999698588147307[/C][/ROW]
[ROW][C]27[/C][C]0.000864332502326729[/C][C]0.00172866500465346[/C][C]0.999135667497673[/C][/ROW]
[ROW][C]28[/C][C]0.000542617131287546[/C][C]0.00108523426257509[/C][C]0.999457382868712[/C][/ROW]
[ROW][C]29[/C][C]0.00312036301500698[/C][C]0.00624072603001396[/C][C]0.996879636984993[/C][/ROW]
[ROW][C]30[/C][C]0.0057040800769003[/C][C]0.0114081601538006[/C][C]0.9942959199231[/C][/ROW]
[ROW][C]31[/C][C]0.00351686002578538[/C][C]0.00703372005157077[/C][C]0.996483139974215[/C][/ROW]
[ROW][C]32[/C][C]0.00308081546707763[/C][C]0.00616163093415527[/C][C]0.996919184532922[/C][/ROW]
[ROW][C]33[/C][C]0.00180517909766278[/C][C]0.00361035819532556[/C][C]0.998194820902337[/C][/ROW]
[ROW][C]34[/C][C]0.00334661227620089[/C][C]0.00669322455240177[/C][C]0.996653387723799[/C][/ROW]
[ROW][C]35[/C][C]0.00757260815554949[/C][C]0.015145216311099[/C][C]0.992427391844451[/C][/ROW]
[ROW][C]36[/C][C]0.00802336648150353[/C][C]0.0160467329630071[/C][C]0.991976633518497[/C][/ROW]
[ROW][C]37[/C][C]0.00879417773589772[/C][C]0.0175883554717954[/C][C]0.991205822264102[/C][/ROW]
[ROW][C]38[/C][C]0.00570837314142662[/C][C]0.0114167462828532[/C][C]0.994291626858573[/C][/ROW]
[ROW][C]39[/C][C]0.00374340395537545[/C][C]0.0074868079107509[/C][C]0.996256596044625[/C][/ROW]
[ROW][C]40[/C][C]0.0024113602200121[/C][C]0.0048227204400242[/C][C]0.997588639779988[/C][/ROW]
[ROW][C]41[/C][C]0.00147943743087776[/C][C]0.00295887486175551[/C][C]0.998520562569122[/C][/ROW]
[ROW][C]42[/C][C]0.00112241541822103[/C][C]0.00224483083644205[/C][C]0.998877584581779[/C][/ROW]
[ROW][C]43[/C][C]0.00106999062730801[/C][C]0.00213998125461602[/C][C]0.998930009372692[/C][/ROW]
[ROW][C]44[/C][C]0.00080187218666027[/C][C]0.00160374437332054[/C][C]0.99919812781334[/C][/ROW]
[ROW][C]45[/C][C]0.000643682210778207[/C][C]0.00128736442155641[/C][C]0.999356317789222[/C][/ROW]
[ROW][C]46[/C][C]0.00045170648510282[/C][C]0.00090341297020564[/C][C]0.999548293514897[/C][/ROW]
[ROW][C]47[/C][C]0.000832249673450854[/C][C]0.00166449934690171[/C][C]0.999167750326549[/C][/ROW]
[ROW][C]48[/C][C]0.000506755643108807[/C][C]0.00101351128621761[/C][C]0.999493244356891[/C][/ROW]
[ROW][C]49[/C][C]0.0027772863109762[/C][C]0.0055545726219524[/C][C]0.997222713689024[/C][/ROW]
[ROW][C]50[/C][C]0.00181488658291175[/C][C]0.00362977316582349[/C][C]0.998185113417088[/C][/ROW]
[ROW][C]51[/C][C]0.00130519450457873[/C][C]0.00261038900915746[/C][C]0.998694805495421[/C][/ROW]
[ROW][C]52[/C][C]0.000882360940059909[/C][C]0.00176472188011982[/C][C]0.99911763905994[/C][/ROW]
[ROW][C]53[/C][C]0.000784397174171837[/C][C]0.00156879434834367[/C][C]0.999215602825828[/C][/ROW]
[ROW][C]54[/C][C]0.0608096877955278[/C][C]0.121619375591056[/C][C]0.939190312204472[/C][/ROW]
[ROW][C]55[/C][C]0.0590417491837774[/C][C]0.118083498367555[/C][C]0.940958250816223[/C][/ROW]
[ROW][C]56[/C][C]0.051622828358415[/C][C]0.10324565671683[/C][C]0.948377171641585[/C][/ROW]
[ROW][C]57[/C][C]0.0393823584769409[/C][C]0.0787647169538819[/C][C]0.960617641523059[/C][/ROW]
[ROW][C]58[/C][C]0.0350560611999487[/C][C]0.0701121223998974[/C][C]0.964943938800051[/C][/ROW]
[ROW][C]59[/C][C]0.0324508221632992[/C][C]0.0649016443265983[/C][C]0.967549177836701[/C][/ROW]
[ROW][C]60[/C][C]0.0244584062974877[/C][C]0.0489168125949755[/C][C]0.975541593702512[/C][/ROW]
[ROW][C]61[/C][C]0.018323341550159[/C][C]0.0366466831003181[/C][C]0.981676658449841[/C][/ROW]
[ROW][C]62[/C][C]0.0172735508907083[/C][C]0.0345471017814166[/C][C]0.982726449109292[/C][/ROW]
[ROW][C]63[/C][C]0.018289283405141[/C][C]0.036578566810282[/C][C]0.981710716594859[/C][/ROW]
[ROW][C]64[/C][C]0.0153902104734554[/C][C]0.0307804209469109[/C][C]0.984609789526545[/C][/ROW]
[ROW][C]65[/C][C]0.0123968857295671[/C][C]0.0247937714591342[/C][C]0.987603114270433[/C][/ROW]
[ROW][C]66[/C][C]0.00906972684880423[/C][C]0.0181394536976085[/C][C]0.990930273151196[/C][/ROW]
[ROW][C]67[/C][C]0.00780985811887988[/C][C]0.0156197162377598[/C][C]0.99219014188112[/C][/ROW]
[ROW][C]68[/C][C]0.0107005233536342[/C][C]0.0214010467072683[/C][C]0.989299476646366[/C][/ROW]
[ROW][C]69[/C][C]0.00857758232756958[/C][C]0.0171551646551392[/C][C]0.99142241767243[/C][/ROW]
[ROW][C]70[/C][C]0.0066137372345977[/C][C]0.0132274744691954[/C][C]0.993386262765402[/C][/ROW]
[ROW][C]71[/C][C]0.0105506651428517[/C][C]0.0211013302857034[/C][C]0.989449334857148[/C][/ROW]
[ROW][C]72[/C][C]0.00953367234023182[/C][C]0.0190673446804636[/C][C]0.990466327659768[/C][/ROW]
[ROW][C]73[/C][C]0.00915608747799407[/C][C]0.0183121749559881[/C][C]0.990843912522006[/C][/ROW]
[ROW][C]74[/C][C]0.0113393651366489[/C][C]0.0226787302732978[/C][C]0.988660634863351[/C][/ROW]
[ROW][C]75[/C][C]0.00902283368002713[/C][C]0.0180456673600543[/C][C]0.990977166319973[/C][/ROW]
[ROW][C]76[/C][C]0.00678115880534471[/C][C]0.0135623176106894[/C][C]0.993218841194655[/C][/ROW]
[ROW][C]77[/C][C]0.00509232151082163[/C][C]0.0101846430216433[/C][C]0.994907678489178[/C][/ROW]
[ROW][C]78[/C][C]0.00489635841884162[/C][C]0.00979271683768323[/C][C]0.995103641581158[/C][/ROW]
[ROW][C]79[/C][C]0.00361092008286126[/C][C]0.00722184016572251[/C][C]0.996389079917139[/C][/ROW]
[ROW][C]80[/C][C]0.00321403844697415[/C][C]0.00642807689394831[/C][C]0.996785961553026[/C][/ROW]
[ROW][C]81[/C][C]0.00313303387473605[/C][C]0.00626606774947211[/C][C]0.996866966125264[/C][/ROW]
[ROW][C]82[/C][C]0.997673348222997[/C][C]0.00465330355400641[/C][C]0.0023266517770032[/C][/ROW]
[ROW][C]83[/C][C]0.997660639537391[/C][C]0.00467872092521699[/C][C]0.0023393604626085[/C][/ROW]
[ROW][C]84[/C][C]0.996884011617931[/C][C]0.0062319767641385[/C][C]0.00311598838206925[/C][/ROW]
[ROW][C]85[/C][C]0.995539084531278[/C][C]0.00892183093744463[/C][C]0.00446091546872232[/C][/ROW]
[ROW][C]86[/C][C]0.993740066032883[/C][C]0.0125198679342332[/C][C]0.0062599339671166[/C][/ROW]
[ROW][C]87[/C][C]0.991880828128514[/C][C]0.0162383437429721[/C][C]0.00811917187148607[/C][/ROW]
[ROW][C]88[/C][C]0.988848352740581[/C][C]0.0223032945188388[/C][C]0.0111516472594194[/C][/ROW]
[ROW][C]89[/C][C]0.985303949606011[/C][C]0.0293921007879777[/C][C]0.0146960503939889[/C][/ROW]
[ROW][C]90[/C][C]0.980776261079261[/C][C]0.0384474778414785[/C][C]0.0192237389207393[/C][/ROW]
[ROW][C]91[/C][C]0.976198543738556[/C][C]0.0476029125228886[/C][C]0.0238014562614443[/C][/ROW]
[ROW][C]92[/C][C]0.971466426049899[/C][C]0.0570671479002013[/C][C]0.0285335739501006[/C][/ROW]
[ROW][C]93[/C][C]0.965251506843112[/C][C]0.0694969863137759[/C][C]0.034748493156888[/C][/ROW]
[ROW][C]94[/C][C]0.965949246232643[/C][C]0.0681015075347134[/C][C]0.0340507537673567[/C][/ROW]
[ROW][C]95[/C][C]0.96339620565347[/C][C]0.0732075886930598[/C][C]0.0366037943465299[/C][/ROW]
[ROW][C]96[/C][C]0.960545267850791[/C][C]0.0789094642984184[/C][C]0.0394547321492092[/C][/ROW]
[ROW][C]97[/C][C]0.951359972002927[/C][C]0.0972800559941457[/C][C]0.0486400279970729[/C][/ROW]
[ROW][C]98[/C][C]0.946255482070413[/C][C]0.107489035859175[/C][C]0.0537445179295875[/C][/ROW]
[ROW][C]99[/C][C]0.936954344992318[/C][C]0.126091310015363[/C][C]0.0630456550076816[/C][/ROW]
[ROW][C]100[/C][C]0.921456640864149[/C][C]0.157086718271702[/C][C]0.0785433591358512[/C][/ROW]
[ROW][C]101[/C][C]0.904380579276628[/C][C]0.191238841446743[/C][C]0.0956194207233717[/C][/ROW]
[ROW][C]102[/C][C]0.881235733979258[/C][C]0.237528532041485[/C][C]0.118764266020742[/C][/ROW]
[ROW][C]103[/C][C]0.854336537810644[/C][C]0.291326924378713[/C][C]0.145663462189356[/C][/ROW]
[ROW][C]104[/C][C]0.854397091145999[/C][C]0.291205817708002[/C][C]0.145602908854001[/C][/ROW]
[ROW][C]105[/C][C]0.850228198600367[/C][C]0.299543602799265[/C][C]0.149771801399633[/C][/ROW]
[ROW][C]106[/C][C]0.823274326135891[/C][C]0.353451347728219[/C][C]0.176725673864109[/C][/ROW]
[ROW][C]107[/C][C]0.793018135946164[/C][C]0.413963728107672[/C][C]0.206981864053836[/C][/ROW]
[ROW][C]108[/C][C]0.759051741510483[/C][C]0.481896516979034[/C][C]0.240948258489517[/C][/ROW]
[ROW][C]109[/C][C]0.724845038354358[/C][C]0.550309923291284[/C][C]0.275154961645642[/C][/ROW]
[ROW][C]110[/C][C]0.699331584378149[/C][C]0.601336831243702[/C][C]0.300668415621851[/C][/ROW]
[ROW][C]111[/C][C]0.688038406253603[/C][C]0.623923187492793[/C][C]0.311961593746397[/C][/ROW]
[ROW][C]112[/C][C]0.657370989435376[/C][C]0.685258021129249[/C][C]0.342629010564624[/C][/ROW]
[ROW][C]113[/C][C]0.611176584030371[/C][C]0.777646831939259[/C][C]0.388823415969629[/C][/ROW]
[ROW][C]114[/C][C]0.999975199243001[/C][C]4.96015139978988e-05[/C][C]2.48007569989494e-05[/C][/ROW]
[ROW][C]115[/C][C]0.999959011123335[/C][C]8.19777533297823e-05[/C][C]4.09888766648912e-05[/C][/ROW]
[ROW][C]116[/C][C]0.999929199828869[/C][C]0.000141600342261294[/C][C]7.08001711306472e-05[/C][/ROW]
[ROW][C]117[/C][C]0.999891561179245[/C][C]0.000216877641509435[/C][C]0.000108438820754718[/C][/ROW]
[ROW][C]118[/C][C]0.999811702577645[/C][C]0.000376594844710052[/C][C]0.000188297422355026[/C][/ROW]
[ROW][C]119[/C][C]0.999665020517706[/C][C]0.000669958964587942[/C][C]0.000334979482293971[/C][/ROW]
[ROW][C]120[/C][C]0.999437002074685[/C][C]0.00112599585063001[/C][C]0.000562997925315006[/C][/ROW]
[ROW][C]121[/C][C]0.9991025758331[/C][C]0.0017948483338002[/C][C]0.0008974241669001[/C][/ROW]
[ROW][C]122[/C][C]0.998578131229974[/C][C]0.00284373754005257[/C][C]0.00142186877002628[/C][/ROW]
[ROW][C]123[/C][C]0.997882039642444[/C][C]0.00423592071511224[/C][C]0.00211796035755612[/C][/ROW]
[ROW][C]124[/C][C]0.996839006842929[/C][C]0.00632198631414149[/C][C]0.00316099315707075[/C][/ROW]
[ROW][C]125[/C][C]0.994986797233784[/C][C]0.0100264055324321[/C][C]0.00501320276621603[/C][/ROW]
[ROW][C]126[/C][C]0.992640089204053[/C][C]0.0147198215918937[/C][C]0.00735991079594687[/C][/ROW]
[ROW][C]127[/C][C]0.988500567667891[/C][C]0.0229988646642185[/C][C]0.0114994323321093[/C][/ROW]
[ROW][C]128[/C][C]0.98733618870637[/C][C]0.0253276225872599[/C][C]0.0126638112936299[/C][/ROW]
[ROW][C]129[/C][C]0.987740675500629[/C][C]0.0245186489987418[/C][C]0.0122593244993709[/C][/ROW]
[ROW][C]130[/C][C]0.982434443300082[/C][C]0.0351311133998362[/C][C]0.0175655566999181[/C][/ROW]
[ROW][C]131[/C][C]0.976188681529182[/C][C]0.0476226369416359[/C][C]0.023811318470818[/C][/ROW]
[ROW][C]132[/C][C]0.969289018128018[/C][C]0.061421963743963[/C][C]0.0307109818719815[/C][/ROW]
[ROW][C]133[/C][C]0.960564915808803[/C][C]0.0788701683823945[/C][C]0.0394350841911973[/C][/ROW]
[ROW][C]134[/C][C]0.948898431726923[/C][C]0.102203136546153[/C][C]0.0511015682730765[/C][/ROW]
[ROW][C]135[/C][C]0.926396356053251[/C][C]0.147207287893498[/C][C]0.0736036439467488[/C][/ROW]
[ROW][C]136[/C][C]0.939594804495462[/C][C]0.120810391009076[/C][C]0.0604051955045381[/C][/ROW]
[ROW][C]137[/C][C]0.935981089547505[/C][C]0.12803782090499[/C][C]0.064018910452495[/C][/ROW]
[ROW][C]138[/C][C]0.955858078318465[/C][C]0.0882838433630699[/C][C]0.044141921681535[/C][/ROW]
[ROW][C]139[/C][C]0.94452973411574[/C][C]0.11094053176852[/C][C]0.05547026588426[/C][/ROW]
[ROW][C]140[/C][C]0.915044763813686[/C][C]0.169910472372628[/C][C]0.0849552361863142[/C][/ROW]
[ROW][C]141[/C][C]0.933014302653352[/C][C]0.133971394693296[/C][C]0.0669856973466479[/C][/ROW]
[ROW][C]142[/C][C]0.897573876815958[/C][C]0.204852246368084[/C][C]0.102426123184042[/C][/ROW]
[ROW][C]143[/C][C]0.879569397130906[/C][C]0.240861205738188[/C][C]0.120430602869094[/C][/ROW]
[ROW][C]144[/C][C]0.822414312056478[/C][C]0.355171375887044[/C][C]0.177585687943522[/C][/ROW]
[ROW][C]145[/C][C]0.816240970987062[/C][C]0.367518058025876[/C][C]0.183759029012938[/C][/ROW]
[ROW][C]146[/C][C]0.781016063631396[/C][C]0.437967872737208[/C][C]0.218983936368604[/C][/ROW]
[ROW][C]147[/C][C]0.745815845986469[/C][C]0.508368308027062[/C][C]0.254184154013531[/C][/ROW]
[ROW][C]148[/C][C]0.804549441484209[/C][C]0.390901117031582[/C][C]0.195450558515791[/C][/ROW]
[ROW][C]149[/C][C]0.716831066538519[/C][C]0.566337866922962[/C][C]0.283168933461481[/C][/ROW]
[ROW][C]150[/C][C]0.556475944093981[/C][C]0.887048111812038[/C][C]0.443524055906019[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=144461&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=144461&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
120.1416618774865830.2833237549731660.858338122513417
130.0677061007698010.1354122015396020.932293899230199
140.03305345700836670.06610691401673340.966946542991633
150.01663765472600190.03327530945200370.983362345273998
160.006141626749631870.01228325349926370.993858373250368
170.003249611872686650.00649922374537330.996750388127313
180.00115697911510270.00231395823020540.998843020884897
190.001127869728833440.002255739457666880.998872130271167
200.0004322835588594620.0008645671177189230.999567716441141
210.0002062777785107030.0004125555570214060.999793722221489
220.0001440618677447310.0002881237354894630.999855938132255
230.0002376425524443110.0004752851048886230.999762357447556
240.0004568841177944460.0009137682355888930.999543115882206
250.000213240809948860.0004264816198977190.999786759190051
260.0003014118526932070.0006028237053864150.999698588147307
270.0008643325023267290.001728665004653460.999135667497673
280.0005426171312875460.001085234262575090.999457382868712
290.003120363015006980.006240726030013960.996879636984993
300.00570408007690030.01140816015380060.9942959199231
310.003516860025785380.007033720051570770.996483139974215
320.003080815467077630.006161630934155270.996919184532922
330.001805179097662780.003610358195325560.998194820902337
340.003346612276200890.006693224552401770.996653387723799
350.007572608155549490.0151452163110990.992427391844451
360.008023366481503530.01604673296300710.991976633518497
370.008794177735897720.01758835547179540.991205822264102
380.005708373141426620.01141674628285320.994291626858573
390.003743403955375450.00748680791075090.996256596044625
400.00241136022001210.00482272044002420.997588639779988
410.001479437430877760.002958874861755510.998520562569122
420.001122415418221030.002244830836442050.998877584581779
430.001069990627308010.002139981254616020.998930009372692
440.000801872186660270.001603744373320540.99919812781334
450.0006436822107782070.001287364421556410.999356317789222
460.000451706485102820.000903412970205640.999548293514897
470.0008322496734508540.001664499346901710.999167750326549
480.0005067556431088070.001013511286217610.999493244356891
490.00277728631097620.00555457262195240.997222713689024
500.001814886582911750.003629773165823490.998185113417088
510.001305194504578730.002610389009157460.998694805495421
520.0008823609400599090.001764721880119820.99911763905994
530.0007843971741718370.001568794348343670.999215602825828
540.06080968779552780.1216193755910560.939190312204472
550.05904174918377740.1180834983675550.940958250816223
560.0516228283584150.103245656716830.948377171641585
570.03938235847694090.07876471695388190.960617641523059
580.03505606119994870.07011212239989740.964943938800051
590.03245082216329920.06490164432659830.967549177836701
600.02445840629748770.04891681259497550.975541593702512
610.0183233415501590.03664668310031810.981676658449841
620.01727355089070830.03454710178141660.982726449109292
630.0182892834051410.0365785668102820.981710716594859
640.01539021047345540.03078042094691090.984609789526545
650.01239688572956710.02479377145913420.987603114270433
660.009069726848804230.01813945369760850.990930273151196
670.007809858118879880.01561971623775980.99219014188112
680.01070052335363420.02140104670726830.989299476646366
690.008577582327569580.01715516465513920.99142241767243
700.00661373723459770.01322747446919540.993386262765402
710.01055066514285170.02110133028570340.989449334857148
720.009533672340231820.01906734468046360.990466327659768
730.009156087477994070.01831217495598810.990843912522006
740.01133936513664890.02267873027329780.988660634863351
750.009022833680027130.01804566736005430.990977166319973
760.006781158805344710.01356231761068940.993218841194655
770.005092321510821630.01018464302164330.994907678489178
780.004896358418841620.009792716837683230.995103641581158
790.003610920082861260.007221840165722510.996389079917139
800.003214038446974150.006428076893948310.996785961553026
810.003133033874736050.006266067749472110.996866966125264
820.9976733482229970.004653303554006410.0023266517770032
830.9976606395373910.004678720925216990.0023393604626085
840.9968840116179310.00623197676413850.00311598838206925
850.9955390845312780.008921830937444630.00446091546872232
860.9937400660328830.01251986793423320.0062599339671166
870.9918808281285140.01623834374297210.00811917187148607
880.9888483527405810.02230329451883880.0111516472594194
890.9853039496060110.02939210078797770.0146960503939889
900.9807762610792610.03844747784147850.0192237389207393
910.9761985437385560.04760291252288860.0238014562614443
920.9714664260498990.05706714790020130.0285335739501006
930.9652515068431120.06949698631377590.034748493156888
940.9659492462326430.06810150753471340.0340507537673567
950.963396205653470.07320758869305980.0366037943465299
960.9605452678507910.07890946429841840.0394547321492092
970.9513599720029270.09728005599414570.0486400279970729
980.9462554820704130.1074890358591750.0537445179295875
990.9369543449923180.1260913100153630.0630456550076816
1000.9214566408641490.1570867182717020.0785433591358512
1010.9043805792766280.1912388414467430.0956194207233717
1020.8812357339792580.2375285320414850.118764266020742
1030.8543365378106440.2913269243787130.145663462189356
1040.8543970911459990.2912058177080020.145602908854001
1050.8502281986003670.2995436027992650.149771801399633
1060.8232743261358910.3534513477282190.176725673864109
1070.7930181359461640.4139637281076720.206981864053836
1080.7590517415104830.4818965169790340.240948258489517
1090.7248450383543580.5503099232912840.275154961645642
1100.6993315843781490.6013368312437020.300668415621851
1110.6880384062536030.6239231874927930.311961593746397
1120.6573709894353760.6852580211292490.342629010564624
1130.6111765840303710.7776468319392590.388823415969629
1140.9999751992430014.96015139978988e-052.48007569989494e-05
1150.9999590111233358.19777533297823e-054.09888766648912e-05
1160.9999291998288690.0001416003422612947.08001711306472e-05
1170.9998915611792450.0002168776415094350.000108438820754718
1180.9998117025776450.0003765948447100520.000188297422355026
1190.9996650205177060.0006699589645879420.000334979482293971
1200.9994370020746850.001125995850630010.000562997925315006
1210.99910257583310.00179484833380020.0008974241669001
1220.9985781312299740.002843737540052570.00142186877002628
1230.9978820396424440.004235920715112240.00211796035755612
1240.9968390068429290.006321986314141490.00316099315707075
1250.9949867972337840.01002640553243210.00501320276621603
1260.9926400892040530.01471982159189370.00735991079594687
1270.9885005676678910.02299886466421850.0114994323321093
1280.987336188706370.02532762258725990.0126638112936299
1290.9877406755006290.02451864899874180.0122593244993709
1300.9824344433000820.03513111339983620.0175655566999181
1310.9761886815291820.04762263694163590.023811318470818
1320.9692890181280180.0614219637439630.0307109818719815
1330.9605649158088030.07887016838239450.0394350841911973
1340.9488984317269230.1022031365461530.0511015682730765
1350.9263963560532510.1472072878934980.0736036439467488
1360.9395948044954620.1208103910090760.0604051955045381
1370.9359810895475050.128037820904990.064018910452495
1380.9558580783184650.08828384336306990.044141921681535
1390.944529734115740.110940531768520.05547026588426
1400.9150447638136860.1699104723726280.0849552361863142
1410.9330143026533520.1339713946932960.0669856973466479
1420.8975738768159580.2048522463680840.102426123184042
1430.8795693971309060.2408612057381880.120430602869094
1440.8224143120564780.3551713758870440.177585687943522
1450.8162409709870620.3675180580258760.183759029012938
1460.7810160636313960.4379678727372080.218983936368604
1470.7458158459864690.5083683080270620.254184154013531
1480.8045494414842090.3909011170315820.195450558515791
1490.7168310665385190.5663378669229620.283168933461481
1500.5564759440939810.8870481118120380.443524055906019






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level510.366906474820144NOK
5% type I error level890.640287769784173NOK
10% type I error level1020.733812949640288NOK

\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 & 51 & 0.366906474820144 & NOK \tabularnewline
5% type I error level & 89 & 0.640287769784173 & NOK \tabularnewline
10% type I error level & 102 & 0.733812949640288 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=144461&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]51[/C][C]0.366906474820144[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]89[/C][C]0.640287769784173[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]102[/C][C]0.733812949640288[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=144461&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=144461&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 level510.366906474820144NOK
5% type I error level890.640287769784173NOK
10% type I error level1020.733812949640288NOK


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 = 9 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
Parameters (R input):
par1 = 9 ; 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')
}