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Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationSun, 04 Nov 2012 13:02:21 -0500
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2012/Nov/04/t13520521760wo18zhf1o0rwd7.htm/, Retrieved Fri, 03 May 2024 01:53:03 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=185886, Retrieved Fri, 03 May 2024 01:53:03 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact88

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Multiple Regression] [Workshop 7] [2012-11-04 18:02:21] [38c0fff34b8aa23b45468de8b641bfee] [Current]
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Dataset

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

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time10 seconds
R Server'Sir Maurice George Kendall' @ kendall.wessa.net

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 10 seconds \tabularnewline
R Server & 'Sir Maurice George Kendall' @ kendall.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185886&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]10 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Sir Maurice George Kendall' @ kendall.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185886&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185886&T=0

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


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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time10 seconds
R Server'Sir Maurice George Kendall' @ kendall.wessa.net






Multiple Linear Regression - Estimated Regression Equation
Learning[t] = + 0.768513215373454 + 0.668412598710118Month[t] + 0.105994800719641Connected[t] -0.0218800289097705Separate[t] + 0.512464755908523Software[t] + 0.0454143170346729Happiness[t] -0.0764190177004767Depression[t] + 0.00493162188751464Belonging[t] -0.0100384015979111t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Learning[t] =  +  0.768513215373454 +  0.668412598710118Month[t] +  0.105994800719641Connected[t] -0.0218800289097705Separate[t] +  0.512464755908523Software[t] +  0.0454143170346729Happiness[t] -0.0764190177004767Depression[t] +  0.00493162188751464Belonging[t] -0.0100384015979111t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185886&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Learning[t] =  +  0.768513215373454 +  0.668412598710118Month[t] +  0.105994800719641Connected[t] -0.0218800289097705Separate[t] +  0.512464755908523Software[t] +  0.0454143170346729Happiness[t] -0.0764190177004767Depression[t] +  0.00493162188751464Belonging[t] -0.0100384015979111t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185886&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185886&T=1

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


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

Multiple Linear Regression - Estimated Regression Equation
Learning[t] = + 0.768513215373454 + 0.668412598710118Month[t] + 0.105994800719641Connected[t] -0.0218800289097705Separate[t] + 0.512464755908523Software[t] + 0.0454143170346729Happiness[t] -0.0764190177004767Depression[t] + 0.00493162188751464Belonging[t] -0.0100384015979111t + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)0.7685132153734545.0274740.15290.8787080.439354
Month0.6684125987101180.5475361.22080.2240530.112026
Connected0.1059948007196410.0470922.25080.0258220.012911
Separate-0.02188002890977050.044797-0.48840.6259510.312975
Software0.5124647559085230.0713357.18400
Happiness0.04541431703467290.076620.59270.5542430.277121
Depression-0.07641901770047670.056182-1.36020.1757680.087884
Belonging0.004931621887514640.0146560.33650.7369620.368481
t-0.01003840159791110.005852-1.71540.0882980.044149

\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) & 0.768513215373454 & 5.027474 & 0.1529 & 0.878708 & 0.439354 \tabularnewline
Month & 0.668412598710118 & 0.547536 & 1.2208 & 0.224053 & 0.112026 \tabularnewline
Connected & 0.105994800719641 & 0.047092 & 2.2508 & 0.025822 & 0.012911 \tabularnewline
Separate & -0.0218800289097705 & 0.044797 & -0.4884 & 0.625951 & 0.312975 \tabularnewline
Software & 0.512464755908523 & 0.071335 & 7.184 & 0 & 0 \tabularnewline
Happiness & 0.0454143170346729 & 0.07662 & 0.5927 & 0.554243 & 0.277121 \tabularnewline
Depression & -0.0764190177004767 & 0.056182 & -1.3602 & 0.175768 & 0.087884 \tabularnewline
Belonging & 0.00493162188751464 & 0.014656 & 0.3365 & 0.736962 & 0.368481 \tabularnewline
t & -0.0100384015979111 & 0.005852 & -1.7154 & 0.088298 & 0.044149 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185886&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]0.768513215373454[/C][C]5.027474[/C][C]0.1529[/C][C]0.878708[/C][C]0.439354[/C][/ROW]
[ROW][C]Month[/C][C]0.668412598710118[/C][C]0.547536[/C][C]1.2208[/C][C]0.224053[/C][C]0.112026[/C][/ROW]
[ROW][C]Connected[/C][C]0.105994800719641[/C][C]0.047092[/C][C]2.2508[/C][C]0.025822[/C][C]0.012911[/C][/ROW]
[ROW][C]Separate[/C][C]-0.0218800289097705[/C][C]0.044797[/C][C]-0.4884[/C][C]0.625951[/C][C]0.312975[/C][/ROW]
[ROW][C]Software[/C][C]0.512464755908523[/C][C]0.071335[/C][C]7.184[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Happiness[/C][C]0.0454143170346729[/C][C]0.07662[/C][C]0.5927[/C][C]0.554243[/C][C]0.277121[/C][/ROW]
[ROW][C]Depression[/C][C]-0.0764190177004767[/C][C]0.056182[/C][C]-1.3602[/C][C]0.175768[/C][C]0.087884[/C][/ROW]
[ROW][C]Belonging[/C][C]0.00493162188751464[/C][C]0.014656[/C][C]0.3365[/C][C]0.736962[/C][C]0.368481[/C][/ROW]
[ROW][C]t[/C][C]-0.0100384015979111[/C][C]0.005852[/C][C]-1.7154[/C][C]0.088298[/C][C]0.044149[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185886&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185886&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)0.7685132153734545.0274740.15290.8787080.439354
Month0.6684125987101180.5475361.22080.2240530.112026
Connected0.1059948007196410.0470922.25080.0258220.012911
Separate-0.02188002890977050.044797-0.48840.6259510.312975
Software0.5124647559085230.0713357.18400
Happiness0.04541431703467290.076620.59270.5542430.277121
Depression-0.07641901770047670.056182-1.36020.1757680.087884
Belonging0.004931621887514640.0146560.33650.7369620.368481
t-0.01003840159791110.005852-1.71540.0882980.044149






Multiple Linear Regression - Regression Statistics
Multiple R0.605542414116741
R-squared0.366681615294331
Adjusted R-squared0.333566928512335
F-TEST (value)11.0730811892714
F-TEST (DF numerator)8
F-TEST (DF denominator)153
p-value2.83573164949757e-12
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1.84190664855202
Sum Squared Residuals519.070875602963

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.605542414116741 \tabularnewline
R-squared & 0.366681615294331 \tabularnewline
Adjusted R-squared & 0.333566928512335 \tabularnewline
F-TEST (value) & 11.0730811892714 \tabularnewline
F-TEST (DF numerator) & 8 \tabularnewline
F-TEST (DF denominator) & 153 \tabularnewline
p-value & 2.83573164949757e-12 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 1.84190664855202 \tabularnewline
Sum Squared Residuals & 519.070875602963 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185886&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.605542414116741[/C][/ROW]
[ROW][C]R-squared[/C][C]0.366681615294331[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.333566928512335[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]11.0730811892714[/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]2.83573164949757e-12[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]1.84190664855202[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]519.070875602963[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185886&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185886&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 R0.605542414116741
R-squared0.366681615294331
Adjusted R-squared0.333566928512335
F-TEST (value)11.0730811892714
F-TEST (DF numerator)8
F-TEST (DF denominator)153
p-value2.83573164949757e-12
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1.84190664855202
Sum Squared Residuals519.070875602963






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11316.4182591901209-3.41825919012087
21616.2358664127609-0.235866412760915
31916.61030403149662.38969596850341
41512.34102165958432.65897834041571
51415.6869715416879-1.68697154168792
61315.2090369415109-2.2090369415109
71915.53500920445773.46499079554226
81516.884289152198-1.88428915219796
91416.1754344725421-2.17543447254211
101512.9115684401572.08843155984301
111615.54542461811450.454575381885477
121616.4030621624475-0.4030621624475
131615.70358244070140.296417559298603
141615.50622783255610.493772167443885
151717.4912079063435-0.491207906343461
161515.2949501782609-0.294950178260897
171514.7896790687220.210320931277979
182016.3148906956653.68510930433504
191815.72019948017432.27980051982573
201615.43899263066130.561007369338669
211615.4883367815050.511663218495037
221615.08555829354450.914441706455542
231916.50282113132362.49717886867643
241615.00018016186430.99981983813571
251714.93463934715722.06536065284276
261716.94638333843120.0536166615688124
271614.94867227507191.05132772492811
281516.1957223790458-1.19572237904575
291615.41449251299270.585507487007341
301414.3260333090965-0.326033309096465
311515.7291989022185-0.729198902218493
321212.4348229320866-0.434822932086584
331415.1791320071407-1.17913200714072
341615.64162197451020.35837802548982
351415.5371752039699-1.53717520396989
36713.14531219157-6.14531219156995
371011.0742032829756-1.07420328297559
381415.7604479829587-1.76044798295873
391614.29560130123371.7043986987663
401614.66159302478281.3384069752172
411615.07733001474130.922669985258663
421415.4206075443116-1.42060754431158
432017.74877268879262.25122731120737
441414.1865114584668-0.186511458466828
451414.9169522287659-0.91695222876587
461115.4695043615738-4.46950436157383
471416.4629438742431-2.46294387424306
481514.90388028525290.0961197147470597
491614.90452711155481.09547288844521
501415.879173665314-1.87917366531404
511616.3246829008987-0.324682900898661
521414.018662892636-0.0186628926359934
531214.5062684066698-2.50626840666983
541615.45744298381990.542557016180059
55911.5011657819621-2.50116578196213
561412.65750115781221.34249884218782
571615.84961289084430.150387109155687
581615.00536688260460.994633117395374
591515.1039898355564-0.103989835556369
601614.01636718026531.98363281973473
611211.42265075207340.57734924792662
621615.64839701439410.351602985605881
631616.1266002587556-0.126600258755576
641414.1212975557884-0.121297555788371
651615.16092500640720.839074993592833
661716.36046720599190.639532794008115
671816.53050592324831.46949407675171
681814.98417543963483.01582456036522
691216.1666223391235-4.16662233912354
701615.7514155350010.24858446499901
711014.0525239977285-4.05252399772845
721414.6680357151298-0.668035715129788
731816.73207346383581.26792653616422
741817.27233179774810.72766820225186
751616.0264317627343-0.0264317627343026
761714.26684006409122.73315993590883
771616.5777803548208-0.577780354820755
781614.86384325187741.13615674812262
791315.1662584818415-2.16625848184152
801616.2991135356792-0.299113535679184
811615.7294568168710.270543183128966
822015.97982142257914.02017857742092
831616.0128746465056-0.0128746465055591
841516.1283664696943-1.12836646969428
851515.0036633407577-0.00366334075768709
861614.55561984540261.44438015459737
871414.4236015334759-0.423601533475877
881615.53179850989490.468201490105099
891614.79289526478911.20710473521085
901514.45644296124890.543557038751113
911213.7880897990704-1.78808979907038
921717.0421379327585-0.0421379327584678
931615.54888417541050.451115824589453
941515.3317075044827-0.331707504482677
951315.286742170599-2.28674217059898
961615.24026602432540.759733975674648
971615.95951212051980.0404878794802353
981614.29245352904691.70754647095308
991616.1171967266511-0.117196726651113
1001414.469427026012-0.469427026011968
1011617.2360450864353-1.23604508643534
1021614.90411708826321.09588291173678
1032017.3406956533512.659304346649
1041514.80938590209270.190614097907303
1051614.52833524738091.47166475261905
1061315.3698946130253-2.36989461302531
1071716.05093185920540.949068140794551
1081615.81919022915270.180809770847252
1091614.61974304412661.38025695587344
1101212.4214248871592-0.421424887159235
1111614.93961823715541.06038176284457
1121615.78485491606470.215145083935304
1131714.63392422747912.36607577252085
1141314.2920832090164-1.29208320901644
1151214.8759899791641-2.87598997916411
1161816.55843152331811.44156847668193
1171415.3950570822917-1.39505708229171
1181413.08401466977150.91598533022847
1191314.9155203286428-1.91552032864279
1201615.57109061107680.428909388923214
1211314.2283663990425-1.2283663990425
1221615.52581153021520.474188469784791
1231315.8214940855028-2.82149408550281
1241616.9174163924758-0.917416392475812
1251515.8800423156952-0.880042315695234
1261616.5779448639113-0.577944863911289
1271515.3405832794404-0.340583279440439
1281715.98807944937971.01192055062033
1291513.90173978435951.09826021564055
1301214.8153806822199-2.8153806822199
1311614.03197090199861.96802909800136
1321013.2382115454599-3.23821154545991
1331613.94424026019872.05575973980127
1341213.8055493681869-1.80554936818689
1351415.4665251485048-1.46652514850481
1361515.0554622602305-0.0554622602304631
1371312.18428243306870.815717566931252
1381514.41910067917470.580899320825348
1391113.1391527352971-2.13915273529705
1401213.2691074299063-1.26910742990629
141813.1830850100963-5.18308501009632
1421612.95814902337693.04185097662313
1431513.13627267910641.86372732089359
1441716.25101542915650.748984570843493
1451614.56070287160841.43929712839157
1461013.9731959748689-3.97319597486888
1471815.62451296045742.37548703954263
1481314.9466721925675-1.94667219256749
1491614.89428141338821.10571858661179
1501312.8854947808310.114505219168977
1511012.9273638730028-2.92736387300282
1521515.9119254966227-0.91192549662266
1531613.69033175433152.30966824566853
1541611.82103115746534.17896884253475
1551411.59093887285322.40906112714677
1561012.4054244380823-2.40542443808226
1571716.38964182889420.610358171105753
1581311.42984893974011.57015106025992
1591513.60058773642211.39941226357788
1601614.70414076185541.29585923814456
1611212.1458929280244-0.1458929280244
1621312.45024212307890.549757876921094

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 13 & 16.4182591901209 & -3.41825919012087 \tabularnewline
2 & 16 & 16.2358664127609 & -0.235866412760915 \tabularnewline
3 & 19 & 16.6103040314966 & 2.38969596850341 \tabularnewline
4 & 15 & 12.3410216595843 & 2.65897834041571 \tabularnewline
5 & 14 & 15.6869715416879 & -1.68697154168792 \tabularnewline
6 & 13 & 15.2090369415109 & -2.2090369415109 \tabularnewline
7 & 19 & 15.5350092044577 & 3.46499079554226 \tabularnewline
8 & 15 & 16.884289152198 & -1.88428915219796 \tabularnewline
9 & 14 & 16.1754344725421 & -2.17543447254211 \tabularnewline
10 & 15 & 12.911568440157 & 2.08843155984301 \tabularnewline
11 & 16 & 15.5454246181145 & 0.454575381885477 \tabularnewline
12 & 16 & 16.4030621624475 & -0.4030621624475 \tabularnewline
13 & 16 & 15.7035824407014 & 0.296417559298603 \tabularnewline
14 & 16 & 15.5062278325561 & 0.493772167443885 \tabularnewline
15 & 17 & 17.4912079063435 & -0.491207906343461 \tabularnewline
16 & 15 & 15.2949501782609 & -0.294950178260897 \tabularnewline
17 & 15 & 14.789679068722 & 0.210320931277979 \tabularnewline
18 & 20 & 16.314890695665 & 3.68510930433504 \tabularnewline
19 & 18 & 15.7201994801743 & 2.27980051982573 \tabularnewline
20 & 16 & 15.4389926306613 & 0.561007369338669 \tabularnewline
21 & 16 & 15.488336781505 & 0.511663218495037 \tabularnewline
22 & 16 & 15.0855582935445 & 0.914441706455542 \tabularnewline
23 & 19 & 16.5028211313236 & 2.49717886867643 \tabularnewline
24 & 16 & 15.0001801618643 & 0.99981983813571 \tabularnewline
25 & 17 & 14.9346393471572 & 2.06536065284276 \tabularnewline
26 & 17 & 16.9463833384312 & 0.0536166615688124 \tabularnewline
27 & 16 & 14.9486722750719 & 1.05132772492811 \tabularnewline
28 & 15 & 16.1957223790458 & -1.19572237904575 \tabularnewline
29 & 16 & 15.4144925129927 & 0.585507487007341 \tabularnewline
30 & 14 & 14.3260333090965 & -0.326033309096465 \tabularnewline
31 & 15 & 15.7291989022185 & -0.729198902218493 \tabularnewline
32 & 12 & 12.4348229320866 & -0.434822932086584 \tabularnewline
33 & 14 & 15.1791320071407 & -1.17913200714072 \tabularnewline
34 & 16 & 15.6416219745102 & 0.35837802548982 \tabularnewline
35 & 14 & 15.5371752039699 & -1.53717520396989 \tabularnewline
36 & 7 & 13.14531219157 & -6.14531219156995 \tabularnewline
37 & 10 & 11.0742032829756 & -1.07420328297559 \tabularnewline
38 & 14 & 15.7604479829587 & -1.76044798295873 \tabularnewline
39 & 16 & 14.2956013012337 & 1.7043986987663 \tabularnewline
40 & 16 & 14.6615930247828 & 1.3384069752172 \tabularnewline
41 & 16 & 15.0773300147413 & 0.922669985258663 \tabularnewline
42 & 14 & 15.4206075443116 & -1.42060754431158 \tabularnewline
43 & 20 & 17.7487726887926 & 2.25122731120737 \tabularnewline
44 & 14 & 14.1865114584668 & -0.186511458466828 \tabularnewline
45 & 14 & 14.9169522287659 & -0.91695222876587 \tabularnewline
46 & 11 & 15.4695043615738 & -4.46950436157383 \tabularnewline
47 & 14 & 16.4629438742431 & -2.46294387424306 \tabularnewline
48 & 15 & 14.9038802852529 & 0.0961197147470597 \tabularnewline
49 & 16 & 14.9045271115548 & 1.09547288844521 \tabularnewline
50 & 14 & 15.879173665314 & -1.87917366531404 \tabularnewline
51 & 16 & 16.3246829008987 & -0.324682900898661 \tabularnewline
52 & 14 & 14.018662892636 & -0.0186628926359934 \tabularnewline
53 & 12 & 14.5062684066698 & -2.50626840666983 \tabularnewline
54 & 16 & 15.4574429838199 & 0.542557016180059 \tabularnewline
55 & 9 & 11.5011657819621 & -2.50116578196213 \tabularnewline
56 & 14 & 12.6575011578122 & 1.34249884218782 \tabularnewline
57 & 16 & 15.8496128908443 & 0.150387109155687 \tabularnewline
58 & 16 & 15.0053668826046 & 0.994633117395374 \tabularnewline
59 & 15 & 15.1039898355564 & -0.103989835556369 \tabularnewline
60 & 16 & 14.0163671802653 & 1.98363281973473 \tabularnewline
61 & 12 & 11.4226507520734 & 0.57734924792662 \tabularnewline
62 & 16 & 15.6483970143941 & 0.351602985605881 \tabularnewline
63 & 16 & 16.1266002587556 & -0.126600258755576 \tabularnewline
64 & 14 & 14.1212975557884 & -0.121297555788371 \tabularnewline
65 & 16 & 15.1609250064072 & 0.839074993592833 \tabularnewline
66 & 17 & 16.3604672059919 & 0.639532794008115 \tabularnewline
67 & 18 & 16.5305059232483 & 1.46949407675171 \tabularnewline
68 & 18 & 14.9841754396348 & 3.01582456036522 \tabularnewline
69 & 12 & 16.1666223391235 & -4.16662233912354 \tabularnewline
70 & 16 & 15.751415535001 & 0.24858446499901 \tabularnewline
71 & 10 & 14.0525239977285 & -4.05252399772845 \tabularnewline
72 & 14 & 14.6680357151298 & -0.668035715129788 \tabularnewline
73 & 18 & 16.7320734638358 & 1.26792653616422 \tabularnewline
74 & 18 & 17.2723317977481 & 0.72766820225186 \tabularnewline
75 & 16 & 16.0264317627343 & -0.0264317627343026 \tabularnewline
76 & 17 & 14.2668400640912 & 2.73315993590883 \tabularnewline
77 & 16 & 16.5777803548208 & -0.577780354820755 \tabularnewline
78 & 16 & 14.8638432518774 & 1.13615674812262 \tabularnewline
79 & 13 & 15.1662584818415 & -2.16625848184152 \tabularnewline
80 & 16 & 16.2991135356792 & -0.299113535679184 \tabularnewline
81 & 16 & 15.729456816871 & 0.270543183128966 \tabularnewline
82 & 20 & 15.9798214225791 & 4.02017857742092 \tabularnewline
83 & 16 & 16.0128746465056 & -0.0128746465055591 \tabularnewline
84 & 15 & 16.1283664696943 & -1.12836646969428 \tabularnewline
85 & 15 & 15.0036633407577 & -0.00366334075768709 \tabularnewline
86 & 16 & 14.5556198454026 & 1.44438015459737 \tabularnewline
87 & 14 & 14.4236015334759 & -0.423601533475877 \tabularnewline
88 & 16 & 15.5317985098949 & 0.468201490105099 \tabularnewline
89 & 16 & 14.7928952647891 & 1.20710473521085 \tabularnewline
90 & 15 & 14.4564429612489 & 0.543557038751113 \tabularnewline
91 & 12 & 13.7880897990704 & -1.78808979907038 \tabularnewline
92 & 17 & 17.0421379327585 & -0.0421379327584678 \tabularnewline
93 & 16 & 15.5488841754105 & 0.451115824589453 \tabularnewline
94 & 15 & 15.3317075044827 & -0.331707504482677 \tabularnewline
95 & 13 & 15.286742170599 & -2.28674217059898 \tabularnewline
96 & 16 & 15.2402660243254 & 0.759733975674648 \tabularnewline
97 & 16 & 15.9595121205198 & 0.0404878794802353 \tabularnewline
98 & 16 & 14.2924535290469 & 1.70754647095308 \tabularnewline
99 & 16 & 16.1171967266511 & -0.117196726651113 \tabularnewline
100 & 14 & 14.469427026012 & -0.469427026011968 \tabularnewline
101 & 16 & 17.2360450864353 & -1.23604508643534 \tabularnewline
102 & 16 & 14.9041170882632 & 1.09588291173678 \tabularnewline
103 & 20 & 17.340695653351 & 2.659304346649 \tabularnewline
104 & 15 & 14.8093859020927 & 0.190614097907303 \tabularnewline
105 & 16 & 14.5283352473809 & 1.47166475261905 \tabularnewline
106 & 13 & 15.3698946130253 & -2.36989461302531 \tabularnewline
107 & 17 & 16.0509318592054 & 0.949068140794551 \tabularnewline
108 & 16 & 15.8191902291527 & 0.180809770847252 \tabularnewline
109 & 16 & 14.6197430441266 & 1.38025695587344 \tabularnewline
110 & 12 & 12.4214248871592 & -0.421424887159235 \tabularnewline
111 & 16 & 14.9396182371554 & 1.06038176284457 \tabularnewline
112 & 16 & 15.7848549160647 & 0.215145083935304 \tabularnewline
113 & 17 & 14.6339242274791 & 2.36607577252085 \tabularnewline
114 & 13 & 14.2920832090164 & -1.29208320901644 \tabularnewline
115 & 12 & 14.8759899791641 & -2.87598997916411 \tabularnewline
116 & 18 & 16.5584315233181 & 1.44156847668193 \tabularnewline
117 & 14 & 15.3950570822917 & -1.39505708229171 \tabularnewline
118 & 14 & 13.0840146697715 & 0.91598533022847 \tabularnewline
119 & 13 & 14.9155203286428 & -1.91552032864279 \tabularnewline
120 & 16 & 15.5710906110768 & 0.428909388923214 \tabularnewline
121 & 13 & 14.2283663990425 & -1.2283663990425 \tabularnewline
122 & 16 & 15.5258115302152 & 0.474188469784791 \tabularnewline
123 & 13 & 15.8214940855028 & -2.82149408550281 \tabularnewline
124 & 16 & 16.9174163924758 & -0.917416392475812 \tabularnewline
125 & 15 & 15.8800423156952 & -0.880042315695234 \tabularnewline
126 & 16 & 16.5779448639113 & -0.577944863911289 \tabularnewline
127 & 15 & 15.3405832794404 & -0.340583279440439 \tabularnewline
128 & 17 & 15.9880794493797 & 1.01192055062033 \tabularnewline
129 & 15 & 13.9017397843595 & 1.09826021564055 \tabularnewline
130 & 12 & 14.8153806822199 & -2.8153806822199 \tabularnewline
131 & 16 & 14.0319709019986 & 1.96802909800136 \tabularnewline
132 & 10 & 13.2382115454599 & -3.23821154545991 \tabularnewline
133 & 16 & 13.9442402601987 & 2.05575973980127 \tabularnewline
134 & 12 & 13.8055493681869 & -1.80554936818689 \tabularnewline
135 & 14 & 15.4665251485048 & -1.46652514850481 \tabularnewline
136 & 15 & 15.0554622602305 & -0.0554622602304631 \tabularnewline
137 & 13 & 12.1842824330687 & 0.815717566931252 \tabularnewline
138 & 15 & 14.4191006791747 & 0.580899320825348 \tabularnewline
139 & 11 & 13.1391527352971 & -2.13915273529705 \tabularnewline
140 & 12 & 13.2691074299063 & -1.26910742990629 \tabularnewline
141 & 8 & 13.1830850100963 & -5.18308501009632 \tabularnewline
142 & 16 & 12.9581490233769 & 3.04185097662313 \tabularnewline
143 & 15 & 13.1362726791064 & 1.86372732089359 \tabularnewline
144 & 17 & 16.2510154291565 & 0.748984570843493 \tabularnewline
145 & 16 & 14.5607028716084 & 1.43929712839157 \tabularnewline
146 & 10 & 13.9731959748689 & -3.97319597486888 \tabularnewline
147 & 18 & 15.6245129604574 & 2.37548703954263 \tabularnewline
148 & 13 & 14.9466721925675 & -1.94667219256749 \tabularnewline
149 & 16 & 14.8942814133882 & 1.10571858661179 \tabularnewline
150 & 13 & 12.885494780831 & 0.114505219168977 \tabularnewline
151 & 10 & 12.9273638730028 & -2.92736387300282 \tabularnewline
152 & 15 & 15.9119254966227 & -0.91192549662266 \tabularnewline
153 & 16 & 13.6903317543315 & 2.30966824566853 \tabularnewline
154 & 16 & 11.8210311574653 & 4.17896884253475 \tabularnewline
155 & 14 & 11.5909388728532 & 2.40906112714677 \tabularnewline
156 & 10 & 12.4054244380823 & -2.40542443808226 \tabularnewline
157 & 17 & 16.3896418288942 & 0.610358171105753 \tabularnewline
158 & 13 & 11.4298489397401 & 1.57015106025992 \tabularnewline
159 & 15 & 13.6005877364221 & 1.39941226357788 \tabularnewline
160 & 16 & 14.7041407618554 & 1.29585923814456 \tabularnewline
161 & 12 & 12.1458929280244 & -0.1458929280244 \tabularnewline
162 & 13 & 12.4502421230789 & 0.549757876921094 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185886&T=4

[TABLE]
[ROW][C]Multiple Linear Regression - Actuals, Interpolation, and Residuals[/C][/ROW]
[ROW][C]Time or Index[/C][C]Actuals[/C][C]InterpolationForecast[/C][C]ResidualsPrediction Error[/C][/ROW]
[ROW][C]1[/C][C]13[/C][C]16.4182591901209[/C][C]-3.41825919012087[/C][/ROW]
[ROW][C]2[/C][C]16[/C][C]16.2358664127609[/C][C]-0.235866412760915[/C][/ROW]
[ROW][C]3[/C][C]19[/C][C]16.6103040314966[/C][C]2.38969596850341[/C][/ROW]
[ROW][C]4[/C][C]15[/C][C]12.3410216595843[/C][C]2.65897834041571[/C][/ROW]
[ROW][C]5[/C][C]14[/C][C]15.6869715416879[/C][C]-1.68697154168792[/C][/ROW]
[ROW][C]6[/C][C]13[/C][C]15.2090369415109[/C][C]-2.2090369415109[/C][/ROW]
[ROW][C]7[/C][C]19[/C][C]15.5350092044577[/C][C]3.46499079554226[/C][/ROW]
[ROW][C]8[/C][C]15[/C][C]16.884289152198[/C][C]-1.88428915219796[/C][/ROW]
[ROW][C]9[/C][C]14[/C][C]16.1754344725421[/C][C]-2.17543447254211[/C][/ROW]
[ROW][C]10[/C][C]15[/C][C]12.911568440157[/C][C]2.08843155984301[/C][/ROW]
[ROW][C]11[/C][C]16[/C][C]15.5454246181145[/C][C]0.454575381885477[/C][/ROW]
[ROW][C]12[/C][C]16[/C][C]16.4030621624475[/C][C]-0.4030621624475[/C][/ROW]
[ROW][C]13[/C][C]16[/C][C]15.7035824407014[/C][C]0.296417559298603[/C][/ROW]
[ROW][C]14[/C][C]16[/C][C]15.5062278325561[/C][C]0.493772167443885[/C][/ROW]
[ROW][C]15[/C][C]17[/C][C]17.4912079063435[/C][C]-0.491207906343461[/C][/ROW]
[ROW][C]16[/C][C]15[/C][C]15.2949501782609[/C][C]-0.294950178260897[/C][/ROW]
[ROW][C]17[/C][C]15[/C][C]14.789679068722[/C][C]0.210320931277979[/C][/ROW]
[ROW][C]18[/C][C]20[/C][C]16.314890695665[/C][C]3.68510930433504[/C][/ROW]
[ROW][C]19[/C][C]18[/C][C]15.7201994801743[/C][C]2.27980051982573[/C][/ROW]
[ROW][C]20[/C][C]16[/C][C]15.4389926306613[/C][C]0.561007369338669[/C][/ROW]
[ROW][C]21[/C][C]16[/C][C]15.488336781505[/C][C]0.511663218495037[/C][/ROW]
[ROW][C]22[/C][C]16[/C][C]15.0855582935445[/C][C]0.914441706455542[/C][/ROW]
[ROW][C]23[/C][C]19[/C][C]16.5028211313236[/C][C]2.49717886867643[/C][/ROW]
[ROW][C]24[/C][C]16[/C][C]15.0001801618643[/C][C]0.99981983813571[/C][/ROW]
[ROW][C]25[/C][C]17[/C][C]14.9346393471572[/C][C]2.06536065284276[/C][/ROW]
[ROW][C]26[/C][C]17[/C][C]16.9463833384312[/C][C]0.0536166615688124[/C][/ROW]
[ROW][C]27[/C][C]16[/C][C]14.9486722750719[/C][C]1.05132772492811[/C][/ROW]
[ROW][C]28[/C][C]15[/C][C]16.1957223790458[/C][C]-1.19572237904575[/C][/ROW]
[ROW][C]29[/C][C]16[/C][C]15.4144925129927[/C][C]0.585507487007341[/C][/ROW]
[ROW][C]30[/C][C]14[/C][C]14.3260333090965[/C][C]-0.326033309096465[/C][/ROW]
[ROW][C]31[/C][C]15[/C][C]15.7291989022185[/C][C]-0.729198902218493[/C][/ROW]
[ROW][C]32[/C][C]12[/C][C]12.4348229320866[/C][C]-0.434822932086584[/C][/ROW]
[ROW][C]33[/C][C]14[/C][C]15.1791320071407[/C][C]-1.17913200714072[/C][/ROW]
[ROW][C]34[/C][C]16[/C][C]15.6416219745102[/C][C]0.35837802548982[/C][/ROW]
[ROW][C]35[/C][C]14[/C][C]15.5371752039699[/C][C]-1.53717520396989[/C][/ROW]
[ROW][C]36[/C][C]7[/C][C]13.14531219157[/C][C]-6.14531219156995[/C][/ROW]
[ROW][C]37[/C][C]10[/C][C]11.0742032829756[/C][C]-1.07420328297559[/C][/ROW]
[ROW][C]38[/C][C]14[/C][C]15.7604479829587[/C][C]-1.76044798295873[/C][/ROW]
[ROW][C]39[/C][C]16[/C][C]14.2956013012337[/C][C]1.7043986987663[/C][/ROW]
[ROW][C]40[/C][C]16[/C][C]14.6615930247828[/C][C]1.3384069752172[/C][/ROW]
[ROW][C]41[/C][C]16[/C][C]15.0773300147413[/C][C]0.922669985258663[/C][/ROW]
[ROW][C]42[/C][C]14[/C][C]15.4206075443116[/C][C]-1.42060754431158[/C][/ROW]
[ROW][C]43[/C][C]20[/C][C]17.7487726887926[/C][C]2.25122731120737[/C][/ROW]
[ROW][C]44[/C][C]14[/C][C]14.1865114584668[/C][C]-0.186511458466828[/C][/ROW]
[ROW][C]45[/C][C]14[/C][C]14.9169522287659[/C][C]-0.91695222876587[/C][/ROW]
[ROW][C]46[/C][C]11[/C][C]15.4695043615738[/C][C]-4.46950436157383[/C][/ROW]
[ROW][C]47[/C][C]14[/C][C]16.4629438742431[/C][C]-2.46294387424306[/C][/ROW]
[ROW][C]48[/C][C]15[/C][C]14.9038802852529[/C][C]0.0961197147470597[/C][/ROW]
[ROW][C]49[/C][C]16[/C][C]14.9045271115548[/C][C]1.09547288844521[/C][/ROW]
[ROW][C]50[/C][C]14[/C][C]15.879173665314[/C][C]-1.87917366531404[/C][/ROW]
[ROW][C]51[/C][C]16[/C][C]16.3246829008987[/C][C]-0.324682900898661[/C][/ROW]
[ROW][C]52[/C][C]14[/C][C]14.018662892636[/C][C]-0.0186628926359934[/C][/ROW]
[ROW][C]53[/C][C]12[/C][C]14.5062684066698[/C][C]-2.50626840666983[/C][/ROW]
[ROW][C]54[/C][C]16[/C][C]15.4574429838199[/C][C]0.542557016180059[/C][/ROW]
[ROW][C]55[/C][C]9[/C][C]11.5011657819621[/C][C]-2.50116578196213[/C][/ROW]
[ROW][C]56[/C][C]14[/C][C]12.6575011578122[/C][C]1.34249884218782[/C][/ROW]
[ROW][C]57[/C][C]16[/C][C]15.8496128908443[/C][C]0.150387109155687[/C][/ROW]
[ROW][C]58[/C][C]16[/C][C]15.0053668826046[/C][C]0.994633117395374[/C][/ROW]
[ROW][C]59[/C][C]15[/C][C]15.1039898355564[/C][C]-0.103989835556369[/C][/ROW]
[ROW][C]60[/C][C]16[/C][C]14.0163671802653[/C][C]1.98363281973473[/C][/ROW]
[ROW][C]61[/C][C]12[/C][C]11.4226507520734[/C][C]0.57734924792662[/C][/ROW]
[ROW][C]62[/C][C]16[/C][C]15.6483970143941[/C][C]0.351602985605881[/C][/ROW]
[ROW][C]63[/C][C]16[/C][C]16.1266002587556[/C][C]-0.126600258755576[/C][/ROW]
[ROW][C]64[/C][C]14[/C][C]14.1212975557884[/C][C]-0.121297555788371[/C][/ROW]
[ROW][C]65[/C][C]16[/C][C]15.1609250064072[/C][C]0.839074993592833[/C][/ROW]
[ROW][C]66[/C][C]17[/C][C]16.3604672059919[/C][C]0.639532794008115[/C][/ROW]
[ROW][C]67[/C][C]18[/C][C]16.5305059232483[/C][C]1.46949407675171[/C][/ROW]
[ROW][C]68[/C][C]18[/C][C]14.9841754396348[/C][C]3.01582456036522[/C][/ROW]
[ROW][C]69[/C][C]12[/C][C]16.1666223391235[/C][C]-4.16662233912354[/C][/ROW]
[ROW][C]70[/C][C]16[/C][C]15.751415535001[/C][C]0.24858446499901[/C][/ROW]
[ROW][C]71[/C][C]10[/C][C]14.0525239977285[/C][C]-4.05252399772845[/C][/ROW]
[ROW][C]72[/C][C]14[/C][C]14.6680357151298[/C][C]-0.668035715129788[/C][/ROW]
[ROW][C]73[/C][C]18[/C][C]16.7320734638358[/C][C]1.26792653616422[/C][/ROW]
[ROW][C]74[/C][C]18[/C][C]17.2723317977481[/C][C]0.72766820225186[/C][/ROW]
[ROW][C]75[/C][C]16[/C][C]16.0264317627343[/C][C]-0.0264317627343026[/C][/ROW]
[ROW][C]76[/C][C]17[/C][C]14.2668400640912[/C][C]2.73315993590883[/C][/ROW]
[ROW][C]77[/C][C]16[/C][C]16.5777803548208[/C][C]-0.577780354820755[/C][/ROW]
[ROW][C]78[/C][C]16[/C][C]14.8638432518774[/C][C]1.13615674812262[/C][/ROW]
[ROW][C]79[/C][C]13[/C][C]15.1662584818415[/C][C]-2.16625848184152[/C][/ROW]
[ROW][C]80[/C][C]16[/C][C]16.2991135356792[/C][C]-0.299113535679184[/C][/ROW]
[ROW][C]81[/C][C]16[/C][C]15.729456816871[/C][C]0.270543183128966[/C][/ROW]
[ROW][C]82[/C][C]20[/C][C]15.9798214225791[/C][C]4.02017857742092[/C][/ROW]
[ROW][C]83[/C][C]16[/C][C]16.0128746465056[/C][C]-0.0128746465055591[/C][/ROW]
[ROW][C]84[/C][C]15[/C][C]16.1283664696943[/C][C]-1.12836646969428[/C][/ROW]
[ROW][C]85[/C][C]15[/C][C]15.0036633407577[/C][C]-0.00366334075768709[/C][/ROW]
[ROW][C]86[/C][C]16[/C][C]14.5556198454026[/C][C]1.44438015459737[/C][/ROW]
[ROW][C]87[/C][C]14[/C][C]14.4236015334759[/C][C]-0.423601533475877[/C][/ROW]
[ROW][C]88[/C][C]16[/C][C]15.5317985098949[/C][C]0.468201490105099[/C][/ROW]
[ROW][C]89[/C][C]16[/C][C]14.7928952647891[/C][C]1.20710473521085[/C][/ROW]
[ROW][C]90[/C][C]15[/C][C]14.4564429612489[/C][C]0.543557038751113[/C][/ROW]
[ROW][C]91[/C][C]12[/C][C]13.7880897990704[/C][C]-1.78808979907038[/C][/ROW]
[ROW][C]92[/C][C]17[/C][C]17.0421379327585[/C][C]-0.0421379327584678[/C][/ROW]
[ROW][C]93[/C][C]16[/C][C]15.5488841754105[/C][C]0.451115824589453[/C][/ROW]
[ROW][C]94[/C][C]15[/C][C]15.3317075044827[/C][C]-0.331707504482677[/C][/ROW]
[ROW][C]95[/C][C]13[/C][C]15.286742170599[/C][C]-2.28674217059898[/C][/ROW]
[ROW][C]96[/C][C]16[/C][C]15.2402660243254[/C][C]0.759733975674648[/C][/ROW]
[ROW][C]97[/C][C]16[/C][C]15.9595121205198[/C][C]0.0404878794802353[/C][/ROW]
[ROW][C]98[/C][C]16[/C][C]14.2924535290469[/C][C]1.70754647095308[/C][/ROW]
[ROW][C]99[/C][C]16[/C][C]16.1171967266511[/C][C]-0.117196726651113[/C][/ROW]
[ROW][C]100[/C][C]14[/C][C]14.469427026012[/C][C]-0.469427026011968[/C][/ROW]
[ROW][C]101[/C][C]16[/C][C]17.2360450864353[/C][C]-1.23604508643534[/C][/ROW]
[ROW][C]102[/C][C]16[/C][C]14.9041170882632[/C][C]1.09588291173678[/C][/ROW]
[ROW][C]103[/C][C]20[/C][C]17.340695653351[/C][C]2.659304346649[/C][/ROW]
[ROW][C]104[/C][C]15[/C][C]14.8093859020927[/C][C]0.190614097907303[/C][/ROW]
[ROW][C]105[/C][C]16[/C][C]14.5283352473809[/C][C]1.47166475261905[/C][/ROW]
[ROW][C]106[/C][C]13[/C][C]15.3698946130253[/C][C]-2.36989461302531[/C][/ROW]
[ROW][C]107[/C][C]17[/C][C]16.0509318592054[/C][C]0.949068140794551[/C][/ROW]
[ROW][C]108[/C][C]16[/C][C]15.8191902291527[/C][C]0.180809770847252[/C][/ROW]
[ROW][C]109[/C][C]16[/C][C]14.6197430441266[/C][C]1.38025695587344[/C][/ROW]
[ROW][C]110[/C][C]12[/C][C]12.4214248871592[/C][C]-0.421424887159235[/C][/ROW]
[ROW][C]111[/C][C]16[/C][C]14.9396182371554[/C][C]1.06038176284457[/C][/ROW]
[ROW][C]112[/C][C]16[/C][C]15.7848549160647[/C][C]0.215145083935304[/C][/ROW]
[ROW][C]113[/C][C]17[/C][C]14.6339242274791[/C][C]2.36607577252085[/C][/ROW]
[ROW][C]114[/C][C]13[/C][C]14.2920832090164[/C][C]-1.29208320901644[/C][/ROW]
[ROW][C]115[/C][C]12[/C][C]14.8759899791641[/C][C]-2.87598997916411[/C][/ROW]
[ROW][C]116[/C][C]18[/C][C]16.5584315233181[/C][C]1.44156847668193[/C][/ROW]
[ROW][C]117[/C][C]14[/C][C]15.3950570822917[/C][C]-1.39505708229171[/C][/ROW]
[ROW][C]118[/C][C]14[/C][C]13.0840146697715[/C][C]0.91598533022847[/C][/ROW]
[ROW][C]119[/C][C]13[/C][C]14.9155203286428[/C][C]-1.91552032864279[/C][/ROW]
[ROW][C]120[/C][C]16[/C][C]15.5710906110768[/C][C]0.428909388923214[/C][/ROW]
[ROW][C]121[/C][C]13[/C][C]14.2283663990425[/C][C]-1.2283663990425[/C][/ROW]
[ROW][C]122[/C][C]16[/C][C]15.5258115302152[/C][C]0.474188469784791[/C][/ROW]
[ROW][C]123[/C][C]13[/C][C]15.8214940855028[/C][C]-2.82149408550281[/C][/ROW]
[ROW][C]124[/C][C]16[/C][C]16.9174163924758[/C][C]-0.917416392475812[/C][/ROW]
[ROW][C]125[/C][C]15[/C][C]15.8800423156952[/C][C]-0.880042315695234[/C][/ROW]
[ROW][C]126[/C][C]16[/C][C]16.5779448639113[/C][C]-0.577944863911289[/C][/ROW]
[ROW][C]127[/C][C]15[/C][C]15.3405832794404[/C][C]-0.340583279440439[/C][/ROW]
[ROW][C]128[/C][C]17[/C][C]15.9880794493797[/C][C]1.01192055062033[/C][/ROW]
[ROW][C]129[/C][C]15[/C][C]13.9017397843595[/C][C]1.09826021564055[/C][/ROW]
[ROW][C]130[/C][C]12[/C][C]14.8153806822199[/C][C]-2.8153806822199[/C][/ROW]
[ROW][C]131[/C][C]16[/C][C]14.0319709019986[/C][C]1.96802909800136[/C][/ROW]
[ROW][C]132[/C][C]10[/C][C]13.2382115454599[/C][C]-3.23821154545991[/C][/ROW]
[ROW][C]133[/C][C]16[/C][C]13.9442402601987[/C][C]2.05575973980127[/C][/ROW]
[ROW][C]134[/C][C]12[/C][C]13.8055493681869[/C][C]-1.80554936818689[/C][/ROW]
[ROW][C]135[/C][C]14[/C][C]15.4665251485048[/C][C]-1.46652514850481[/C][/ROW]
[ROW][C]136[/C][C]15[/C][C]15.0554622602305[/C][C]-0.0554622602304631[/C][/ROW]
[ROW][C]137[/C][C]13[/C][C]12.1842824330687[/C][C]0.815717566931252[/C][/ROW]
[ROW][C]138[/C][C]15[/C][C]14.4191006791747[/C][C]0.580899320825348[/C][/ROW]
[ROW][C]139[/C][C]11[/C][C]13.1391527352971[/C][C]-2.13915273529705[/C][/ROW]
[ROW][C]140[/C][C]12[/C][C]13.2691074299063[/C][C]-1.26910742990629[/C][/ROW]
[ROW][C]141[/C][C]8[/C][C]13.1830850100963[/C][C]-5.18308501009632[/C][/ROW]
[ROW][C]142[/C][C]16[/C][C]12.9581490233769[/C][C]3.04185097662313[/C][/ROW]
[ROW][C]143[/C][C]15[/C][C]13.1362726791064[/C][C]1.86372732089359[/C][/ROW]
[ROW][C]144[/C][C]17[/C][C]16.2510154291565[/C][C]0.748984570843493[/C][/ROW]
[ROW][C]145[/C][C]16[/C][C]14.5607028716084[/C][C]1.43929712839157[/C][/ROW]
[ROW][C]146[/C][C]10[/C][C]13.9731959748689[/C][C]-3.97319597486888[/C][/ROW]
[ROW][C]147[/C][C]18[/C][C]15.6245129604574[/C][C]2.37548703954263[/C][/ROW]
[ROW][C]148[/C][C]13[/C][C]14.9466721925675[/C][C]-1.94667219256749[/C][/ROW]
[ROW][C]149[/C][C]16[/C][C]14.8942814133882[/C][C]1.10571858661179[/C][/ROW]
[ROW][C]150[/C][C]13[/C][C]12.885494780831[/C][C]0.114505219168977[/C][/ROW]
[ROW][C]151[/C][C]10[/C][C]12.9273638730028[/C][C]-2.92736387300282[/C][/ROW]
[ROW][C]152[/C][C]15[/C][C]15.9119254966227[/C][C]-0.91192549662266[/C][/ROW]
[ROW][C]153[/C][C]16[/C][C]13.6903317543315[/C][C]2.30966824566853[/C][/ROW]
[ROW][C]154[/C][C]16[/C][C]11.8210311574653[/C][C]4.17896884253475[/C][/ROW]
[ROW][C]155[/C][C]14[/C][C]11.5909388728532[/C][C]2.40906112714677[/C][/ROW]
[ROW][C]156[/C][C]10[/C][C]12.4054244380823[/C][C]-2.40542443808226[/C][/ROW]
[ROW][C]157[/C][C]17[/C][C]16.3896418288942[/C][C]0.610358171105753[/C][/ROW]
[ROW][C]158[/C][C]13[/C][C]11.4298489397401[/C][C]1.57015106025992[/C][/ROW]
[ROW][C]159[/C][C]15[/C][C]13.6005877364221[/C][C]1.39941226357788[/C][/ROW]
[ROW][C]160[/C][C]16[/C][C]14.7041407618554[/C][C]1.29585923814456[/C][/ROW]
[ROW][C]161[/C][C]12[/C][C]12.1458929280244[/C][C]-0.1458929280244[/C][/ROW]
[ROW][C]162[/C][C]13[/C][C]12.4502421230789[/C][C]0.549757876921094[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185886&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185886&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
11316.4182591901209-3.41825919012087
21616.2358664127609-0.235866412760915
31916.61030403149662.38969596850341
41512.34102165958432.65897834041571
51415.6869715416879-1.68697154168792
61315.2090369415109-2.2090369415109
71915.53500920445773.46499079554226
81516.884289152198-1.88428915219796
91416.1754344725421-2.17543447254211
101512.9115684401572.08843155984301
111615.54542461811450.454575381885477
121616.4030621624475-0.4030621624475
131615.70358244070140.296417559298603
141615.50622783255610.493772167443885
151717.4912079063435-0.491207906343461
161515.2949501782609-0.294950178260897
171514.7896790687220.210320931277979
182016.3148906956653.68510930433504
191815.72019948017432.27980051982573
201615.43899263066130.561007369338669
211615.4883367815050.511663218495037
221615.08555829354450.914441706455542
231916.50282113132362.49717886867643
241615.00018016186430.99981983813571
251714.93463934715722.06536065284276
261716.94638333843120.0536166615688124
271614.94867227507191.05132772492811
281516.1957223790458-1.19572237904575
291615.41449251299270.585507487007341
301414.3260333090965-0.326033309096465
311515.7291989022185-0.729198902218493
321212.4348229320866-0.434822932086584
331415.1791320071407-1.17913200714072
341615.64162197451020.35837802548982
351415.5371752039699-1.53717520396989
36713.14531219157-6.14531219156995
371011.0742032829756-1.07420328297559
381415.7604479829587-1.76044798295873
391614.29560130123371.7043986987663
401614.66159302478281.3384069752172
411615.07733001474130.922669985258663
421415.4206075443116-1.42060754431158
432017.74877268879262.25122731120737
441414.1865114584668-0.186511458466828
451414.9169522287659-0.91695222876587
461115.4695043615738-4.46950436157383
471416.4629438742431-2.46294387424306
481514.90388028525290.0961197147470597
491614.90452711155481.09547288844521
501415.879173665314-1.87917366531404
511616.3246829008987-0.324682900898661
521414.018662892636-0.0186628926359934
531214.5062684066698-2.50626840666983
541615.45744298381990.542557016180059
55911.5011657819621-2.50116578196213
561412.65750115781221.34249884218782
571615.84961289084430.150387109155687
581615.00536688260460.994633117395374
591515.1039898355564-0.103989835556369
601614.01636718026531.98363281973473
611211.42265075207340.57734924792662
621615.64839701439410.351602985605881
631616.1266002587556-0.126600258755576
641414.1212975557884-0.121297555788371
651615.16092500640720.839074993592833
661716.36046720599190.639532794008115
671816.53050592324831.46949407675171
681814.98417543963483.01582456036522
691216.1666223391235-4.16662233912354
701615.7514155350010.24858446499901
711014.0525239977285-4.05252399772845
721414.6680357151298-0.668035715129788
731816.73207346383581.26792653616422
741817.27233179774810.72766820225186
751616.0264317627343-0.0264317627343026
761714.26684006409122.73315993590883
771616.5777803548208-0.577780354820755
781614.86384325187741.13615674812262
791315.1662584818415-2.16625848184152
801616.2991135356792-0.299113535679184
811615.7294568168710.270543183128966
822015.97982142257914.02017857742092
831616.0128746465056-0.0128746465055591
841516.1283664696943-1.12836646969428
851515.0036633407577-0.00366334075768709
861614.55561984540261.44438015459737
871414.4236015334759-0.423601533475877
881615.53179850989490.468201490105099
891614.79289526478911.20710473521085
901514.45644296124890.543557038751113
911213.7880897990704-1.78808979907038
921717.0421379327585-0.0421379327584678
931615.54888417541050.451115824589453
941515.3317075044827-0.331707504482677
951315.286742170599-2.28674217059898
961615.24026602432540.759733975674648
971615.95951212051980.0404878794802353
981614.29245352904691.70754647095308
991616.1171967266511-0.117196726651113
1001414.469427026012-0.469427026011968
1011617.2360450864353-1.23604508643534
1021614.90411708826321.09588291173678
1032017.3406956533512.659304346649
1041514.80938590209270.190614097907303
1051614.52833524738091.47166475261905
1061315.3698946130253-2.36989461302531
1071716.05093185920540.949068140794551
1081615.81919022915270.180809770847252
1091614.61974304412661.38025695587344
1101212.4214248871592-0.421424887159235
1111614.93961823715541.06038176284457
1121615.78485491606470.215145083935304
1131714.63392422747912.36607577252085
1141314.2920832090164-1.29208320901644
1151214.8759899791641-2.87598997916411
1161816.55843152331811.44156847668193
1171415.3950570822917-1.39505708229171
1181413.08401466977150.91598533022847
1191314.9155203286428-1.91552032864279
1201615.57109061107680.428909388923214
1211314.2283663990425-1.2283663990425
1221615.52581153021520.474188469784791
1231315.8214940855028-2.82149408550281
1241616.9174163924758-0.917416392475812
1251515.8800423156952-0.880042315695234
1261616.5779448639113-0.577944863911289
1271515.3405832794404-0.340583279440439
1281715.98807944937971.01192055062033
1291513.90173978435951.09826021564055
1301214.8153806822199-2.8153806822199
1311614.03197090199861.96802909800136
1321013.2382115454599-3.23821154545991
1331613.94424026019872.05575973980127
1341213.8055493681869-1.80554936818689
1351415.4665251485048-1.46652514850481
1361515.0554622602305-0.0554622602304631
1371312.18428243306870.815717566931252
1381514.41910067917470.580899320825348
1391113.1391527352971-2.13915273529705
1401213.2691074299063-1.26910742990629
141813.1830850100963-5.18308501009632
1421612.95814902337693.04185097662313
1431513.13627267910641.86372732089359
1441716.25101542915650.748984570843493
1451614.56070287160841.43929712839157
1461013.9731959748689-3.97319597486888
1471815.62451296045742.37548703954263
1481314.9466721925675-1.94667219256749
1491614.89428141338821.10571858661179
1501312.8854947808310.114505219168977
1511012.9273638730028-2.92736387300282
1521515.9119254966227-0.91192549662266
1531613.69033175433152.30966824566853
1541611.82103115746534.17896884253475
1551411.59093887285322.40906112714677
1561012.4054244380823-2.40542443808226
1571716.38964182889420.610358171105753
1581311.42984893974011.57015106025992
1591513.60058773642211.39941226357788
1601614.70414076185541.29585923814456
1611212.1458929280244-0.1458929280244
1621312.45024212307890.549757876921094






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
120.9223380094256590.1553239811486820.0776619905743411
130.896424656416990.2071506871660210.10357534358301
140.8314757425502880.3370485148994250.168524257449712
150.7852910236378890.4294179527242220.214708976362111
160.8087975054811820.3824049890376360.191202494518818
170.7526688679827160.4946622640345680.247331132017284
180.9099730258000380.1800539483999230.0900269741999617
190.8877369362209010.2245261275581990.112263063779099
200.8450082641627080.3099834716745850.154991735837292
210.7894841005341730.4210317989316540.210515899465827
220.7526108500235170.4947782999529660.247389149976483
230.7218982704902640.5562034590194720.278101729509736
240.6985210822726760.6029578354546470.301478917727324
250.6437864491283480.7124271017433040.356213550871652
260.5953139041046340.8093721917907310.404686095895365
270.5446787498956340.9106425002087310.455321250104366
280.5908114620488370.8183770759023260.409188537951163
290.53254540689130.9349091862174010.4674545931087
300.5442881317694340.9114237364611320.455711868230566
310.4888629646520290.9777259293040580.511137035347971
320.4853180417673250.970636083534650.514681958232675
330.4697198940282610.9394397880565220.530280105971739
340.4097601429305450.8195202858610910.590239857069455
350.3920682769396150.7841365538792290.607931723060385
360.8678635228409210.2642729543181570.132136477159079
370.8524615165500390.2950769668999230.147538483449961
380.8361611361374460.3276777277251080.163838863862554
390.8649152908298590.2701694183402830.135084709170142
400.8534135754289550.293172849142090.146586424571045
410.8303369723122610.3393260553754770.169663027687739
420.8084627665566950.383074466886610.191537233443305
430.8291566664910660.3416866670178690.170843333508934
440.7945564468979040.4108871062041930.205443553102096
450.7634958214816040.4730083570367920.236504178518396
460.9001516339238940.1996967321522120.0998483660761058
470.9048662806413610.1902674387172770.0951337193586387
480.8851600326046160.2296799347907670.114839967395384
490.8728750060388250.254249987922350.127124993961175
500.8637684524206830.2724630951586350.136231547579317
510.8359826164550740.3280347670898520.164017383544926
520.8052458278161070.3895083443677870.194754172183893
530.8168756088249820.3662487823500360.183124391175018
540.7909750000755110.4180499998489780.209024999924489
550.8096654301223190.3806691397553620.190334569877681
560.7998844038752050.400231192249590.200115596124795
570.7650809086241440.4698381827517130.234919091375856
580.7532317594206990.4935364811586030.246768240579301
590.7192169415324680.5615661169350650.280783058467532
600.725241434397940.5495171312041190.27475856560206
610.6908101556311450.6183796887377090.309189844368855
620.6501285543768350.699742891246330.349871445623165
630.6128550594457250.774289881108550.387144940554275
640.5771113659610110.8457772680779770.422888634038989
650.5472061159164840.9055877681670320.452793884083516
660.4993706831491250.9987413662982510.500629316850875
670.4667526811822020.9335053623644050.533247318817798
680.4935058970208340.9870117940416680.506494102979166
690.7319452930248970.5361094139502060.268054706975103
700.6915332320361810.6169335359276380.308466767963819
710.8164137952554260.3671724094891480.183586204744574
720.7875666477955390.4248667044089210.212433352204461
730.7730242608724390.4539514782551220.226975739127561
740.7453899616361430.5092200767277150.254610038363857
750.7063118678692240.5873762642615530.293688132130776
760.7493401052775170.5013197894449670.250659894722483
770.713888988632250.57222202273550.28611101136775
780.6886266998607260.6227466002785480.311373300139274
790.7061475202263820.5877049595472350.293852479773618
800.6668177447284240.6663645105431520.333182255271576
810.6250845388456960.7498309223086070.374915461154304
820.7692216876844750.4615566246310490.230778312315525
830.7319672763598080.5360654472803850.268032723640192
840.7069145655561630.5861708688876740.293085434443837
850.6651772664105280.6696454671789440.334822733589472
860.6462698028102280.7074603943795430.353730197189772
870.6027894382756180.7944211234487650.397210561724382
880.5587313248912980.8825373502174050.441268675108702
890.5313660698998440.9372678602003110.468633930100156
900.496276237964550.99255247592910.50372376203545
910.4913586468999420.9827172937998850.508641353100058
920.4503199447602250.9006398895204510.549680055239774
930.4076808764252410.8153617528504820.592319123574759
940.3631921182834050.726384236566810.636807881716595
950.3943041513241350.788608302648270.605695848675865
960.3567423311862860.7134846623725720.643257668813714
970.3151795674952250.630359134990450.684820432504775
980.3069189289690240.6138378579380490.693081071030976
990.2656315484338320.5312630968676640.734368451566168
1000.2331023603083720.4662047206167430.766897639691628
1010.211720573342070.423441146684140.78827942665793
1020.1942902887852990.3885805775705970.805709711214701
1030.2368599101278830.4737198202557660.763140089872117
1040.2008578677976970.4017157355953950.799142132202303
1050.1949829846953910.3899659693907820.805017015304609
1060.2004459756069510.4008919512139020.799554024393049
1070.1793857976431490.3587715952862970.820614202356851
1080.1582262342188170.3164524684376340.841773765781183
1090.1569457929091780.3138915858183560.843054207090822
1100.161483316613120.3229666332262390.83851668338688
1110.147812846831550.2956256936631010.85218715316845
1120.128182402744670.256364805489340.87181759725533
1130.1734614927278450.346922985455690.826538507272155
1140.1481874689335270.2963749378670530.851812531066473
1150.1681999633246510.3363999266493010.831800036675349
1160.1720855860405610.3441711720811230.827914413959439
1170.1459547815987450.2919095631974910.854045218401255
1180.1460882303458410.2921764606916830.853911769654159
1190.1358746948044570.2717493896089140.864125305195543
1200.1587437087469790.3174874174939570.841256291253021
1210.1305050397136350.261010079427270.869494960286365
1220.1165814951783440.2331629903566890.883418504821656
1230.1085355244664650.2170710489329290.891464475533535
1240.08529621550525760.1705924310105150.914703784494742
1250.06640456940010150.1328091388002030.933595430599898
1260.05001252324415520.100025046488310.949987476755845
1270.03665950310106630.07331900620213260.963340496898934
1280.03777829988408430.07555659976816850.962221700115916
1290.0348284688643020.0696569377286040.965171531135698
1300.03415874023968790.06831748047937580.965841259760312
1310.04018119834368630.08036239668737260.959818801656314
1320.03785574239289640.07571148478579280.962144257607104
1330.09160378727229470.1832075745445890.908396212727705
1340.09608445533104050.1921689106620810.90391554466896
1350.07350868343409610.1470173668681920.926491316565904
1360.05485387520575470.1097077504115090.945146124794245
1370.04617779680777190.09235559361554380.953822203192228
1380.04273418817059370.08546837634118740.957265811829406
1390.0374377374868110.0748754749736220.962562262513189
1400.02538965823994340.05077931647988680.974610341760057
1410.4147114964437590.8294229928875170.585288503556241
1420.3633173432823830.7266346865647660.636682656717617
1430.3214126345506780.6428252691013560.678587365449322
1440.2601995042648540.5203990085297090.739800495735146
1450.2150452449452650.4300904898905310.784954755054735
1460.194724920711760.389449841423520.80527507928824
1470.2775479119443170.5550958238886340.722452088055683
1480.622346405025730.755307189948540.37765359497427
1490.5159822936326990.9680354127346010.484017706367301
1500.3953806248623030.7907612497246050.604619375137697

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
12 & 0.922338009425659 & 0.155323981148682 & 0.0776619905743411 \tabularnewline
13 & 0.89642465641699 & 0.207150687166021 & 0.10357534358301 \tabularnewline
14 & 0.831475742550288 & 0.337048514899425 & 0.168524257449712 \tabularnewline
15 & 0.785291023637889 & 0.429417952724222 & 0.214708976362111 \tabularnewline
16 & 0.808797505481182 & 0.382404989037636 & 0.191202494518818 \tabularnewline
17 & 0.752668867982716 & 0.494662264034568 & 0.247331132017284 \tabularnewline
18 & 0.909973025800038 & 0.180053948399923 & 0.0900269741999617 \tabularnewline
19 & 0.887736936220901 & 0.224526127558199 & 0.112263063779099 \tabularnewline
20 & 0.845008264162708 & 0.309983471674585 & 0.154991735837292 \tabularnewline
21 & 0.789484100534173 & 0.421031798931654 & 0.210515899465827 \tabularnewline
22 & 0.752610850023517 & 0.494778299952966 & 0.247389149976483 \tabularnewline
23 & 0.721898270490264 & 0.556203459019472 & 0.278101729509736 \tabularnewline
24 & 0.698521082272676 & 0.602957835454647 & 0.301478917727324 \tabularnewline
25 & 0.643786449128348 & 0.712427101743304 & 0.356213550871652 \tabularnewline
26 & 0.595313904104634 & 0.809372191790731 & 0.404686095895365 \tabularnewline
27 & 0.544678749895634 & 0.910642500208731 & 0.455321250104366 \tabularnewline
28 & 0.590811462048837 & 0.818377075902326 & 0.409188537951163 \tabularnewline
29 & 0.5325454068913 & 0.934909186217401 & 0.4674545931087 \tabularnewline
30 & 0.544288131769434 & 0.911423736461132 & 0.455711868230566 \tabularnewline
31 & 0.488862964652029 & 0.977725929304058 & 0.511137035347971 \tabularnewline
32 & 0.485318041767325 & 0.97063608353465 & 0.514681958232675 \tabularnewline
33 & 0.469719894028261 & 0.939439788056522 & 0.530280105971739 \tabularnewline
34 & 0.409760142930545 & 0.819520285861091 & 0.590239857069455 \tabularnewline
35 & 0.392068276939615 & 0.784136553879229 & 0.607931723060385 \tabularnewline
36 & 0.867863522840921 & 0.264272954318157 & 0.132136477159079 \tabularnewline
37 & 0.852461516550039 & 0.295076966899923 & 0.147538483449961 \tabularnewline
38 & 0.836161136137446 & 0.327677727725108 & 0.163838863862554 \tabularnewline
39 & 0.864915290829859 & 0.270169418340283 & 0.135084709170142 \tabularnewline
40 & 0.853413575428955 & 0.29317284914209 & 0.146586424571045 \tabularnewline
41 & 0.830336972312261 & 0.339326055375477 & 0.169663027687739 \tabularnewline
42 & 0.808462766556695 & 0.38307446688661 & 0.191537233443305 \tabularnewline
43 & 0.829156666491066 & 0.341686667017869 & 0.170843333508934 \tabularnewline
44 & 0.794556446897904 & 0.410887106204193 & 0.205443553102096 \tabularnewline
45 & 0.763495821481604 & 0.473008357036792 & 0.236504178518396 \tabularnewline
46 & 0.900151633923894 & 0.199696732152212 & 0.0998483660761058 \tabularnewline
47 & 0.904866280641361 & 0.190267438717277 & 0.0951337193586387 \tabularnewline
48 & 0.885160032604616 & 0.229679934790767 & 0.114839967395384 \tabularnewline
49 & 0.872875006038825 & 0.25424998792235 & 0.127124993961175 \tabularnewline
50 & 0.863768452420683 & 0.272463095158635 & 0.136231547579317 \tabularnewline
51 & 0.835982616455074 & 0.328034767089852 & 0.164017383544926 \tabularnewline
52 & 0.805245827816107 & 0.389508344367787 & 0.194754172183893 \tabularnewline
53 & 0.816875608824982 & 0.366248782350036 & 0.183124391175018 \tabularnewline
54 & 0.790975000075511 & 0.418049999848978 & 0.209024999924489 \tabularnewline
55 & 0.809665430122319 & 0.380669139755362 & 0.190334569877681 \tabularnewline
56 & 0.799884403875205 & 0.40023119224959 & 0.200115596124795 \tabularnewline
57 & 0.765080908624144 & 0.469838182751713 & 0.234919091375856 \tabularnewline
58 & 0.753231759420699 & 0.493536481158603 & 0.246768240579301 \tabularnewline
59 & 0.719216941532468 & 0.561566116935065 & 0.280783058467532 \tabularnewline
60 & 0.72524143439794 & 0.549517131204119 & 0.27475856560206 \tabularnewline
61 & 0.690810155631145 & 0.618379688737709 & 0.309189844368855 \tabularnewline
62 & 0.650128554376835 & 0.69974289124633 & 0.349871445623165 \tabularnewline
63 & 0.612855059445725 & 0.77428988110855 & 0.387144940554275 \tabularnewline
64 & 0.577111365961011 & 0.845777268077977 & 0.422888634038989 \tabularnewline
65 & 0.547206115916484 & 0.905587768167032 & 0.452793884083516 \tabularnewline
66 & 0.499370683149125 & 0.998741366298251 & 0.500629316850875 \tabularnewline
67 & 0.466752681182202 & 0.933505362364405 & 0.533247318817798 \tabularnewline
68 & 0.493505897020834 & 0.987011794041668 & 0.506494102979166 \tabularnewline
69 & 0.731945293024897 & 0.536109413950206 & 0.268054706975103 \tabularnewline
70 & 0.691533232036181 & 0.616933535927638 & 0.308466767963819 \tabularnewline
71 & 0.816413795255426 & 0.367172409489148 & 0.183586204744574 \tabularnewline
72 & 0.787566647795539 & 0.424866704408921 & 0.212433352204461 \tabularnewline
73 & 0.773024260872439 & 0.453951478255122 & 0.226975739127561 \tabularnewline
74 & 0.745389961636143 & 0.509220076727715 & 0.254610038363857 \tabularnewline
75 & 0.706311867869224 & 0.587376264261553 & 0.293688132130776 \tabularnewline
76 & 0.749340105277517 & 0.501319789444967 & 0.250659894722483 \tabularnewline
77 & 0.71388898863225 & 0.5722220227355 & 0.28611101136775 \tabularnewline
78 & 0.688626699860726 & 0.622746600278548 & 0.311373300139274 \tabularnewline
79 & 0.706147520226382 & 0.587704959547235 & 0.293852479773618 \tabularnewline
80 & 0.666817744728424 & 0.666364510543152 & 0.333182255271576 \tabularnewline
81 & 0.625084538845696 & 0.749830922308607 & 0.374915461154304 \tabularnewline
82 & 0.769221687684475 & 0.461556624631049 & 0.230778312315525 \tabularnewline
83 & 0.731967276359808 & 0.536065447280385 & 0.268032723640192 \tabularnewline
84 & 0.706914565556163 & 0.586170868887674 & 0.293085434443837 \tabularnewline
85 & 0.665177266410528 & 0.669645467178944 & 0.334822733589472 \tabularnewline
86 & 0.646269802810228 & 0.707460394379543 & 0.353730197189772 \tabularnewline
87 & 0.602789438275618 & 0.794421123448765 & 0.397210561724382 \tabularnewline
88 & 0.558731324891298 & 0.882537350217405 & 0.441268675108702 \tabularnewline
89 & 0.531366069899844 & 0.937267860200311 & 0.468633930100156 \tabularnewline
90 & 0.49627623796455 & 0.9925524759291 & 0.50372376203545 \tabularnewline
91 & 0.491358646899942 & 0.982717293799885 & 0.508641353100058 \tabularnewline
92 & 0.450319944760225 & 0.900639889520451 & 0.549680055239774 \tabularnewline
93 & 0.407680876425241 & 0.815361752850482 & 0.592319123574759 \tabularnewline
94 & 0.363192118283405 & 0.72638423656681 & 0.636807881716595 \tabularnewline
95 & 0.394304151324135 & 0.78860830264827 & 0.605695848675865 \tabularnewline
96 & 0.356742331186286 & 0.713484662372572 & 0.643257668813714 \tabularnewline
97 & 0.315179567495225 & 0.63035913499045 & 0.684820432504775 \tabularnewline
98 & 0.306918928969024 & 0.613837857938049 & 0.693081071030976 \tabularnewline
99 & 0.265631548433832 & 0.531263096867664 & 0.734368451566168 \tabularnewline
100 & 0.233102360308372 & 0.466204720616743 & 0.766897639691628 \tabularnewline
101 & 0.21172057334207 & 0.42344114668414 & 0.78827942665793 \tabularnewline
102 & 0.194290288785299 & 0.388580577570597 & 0.805709711214701 \tabularnewline
103 & 0.236859910127883 & 0.473719820255766 & 0.763140089872117 \tabularnewline
104 & 0.200857867797697 & 0.401715735595395 & 0.799142132202303 \tabularnewline
105 & 0.194982984695391 & 0.389965969390782 & 0.805017015304609 \tabularnewline
106 & 0.200445975606951 & 0.400891951213902 & 0.799554024393049 \tabularnewline
107 & 0.179385797643149 & 0.358771595286297 & 0.820614202356851 \tabularnewline
108 & 0.158226234218817 & 0.316452468437634 & 0.841773765781183 \tabularnewline
109 & 0.156945792909178 & 0.313891585818356 & 0.843054207090822 \tabularnewline
110 & 0.16148331661312 & 0.322966633226239 & 0.83851668338688 \tabularnewline
111 & 0.14781284683155 & 0.295625693663101 & 0.85218715316845 \tabularnewline
112 & 0.12818240274467 & 0.25636480548934 & 0.87181759725533 \tabularnewline
113 & 0.173461492727845 & 0.34692298545569 & 0.826538507272155 \tabularnewline
114 & 0.148187468933527 & 0.296374937867053 & 0.851812531066473 \tabularnewline
115 & 0.168199963324651 & 0.336399926649301 & 0.831800036675349 \tabularnewline
116 & 0.172085586040561 & 0.344171172081123 & 0.827914413959439 \tabularnewline
117 & 0.145954781598745 & 0.291909563197491 & 0.854045218401255 \tabularnewline
118 & 0.146088230345841 & 0.292176460691683 & 0.853911769654159 \tabularnewline
119 & 0.135874694804457 & 0.271749389608914 & 0.864125305195543 \tabularnewline
120 & 0.158743708746979 & 0.317487417493957 & 0.841256291253021 \tabularnewline
121 & 0.130505039713635 & 0.26101007942727 & 0.869494960286365 \tabularnewline
122 & 0.116581495178344 & 0.233162990356689 & 0.883418504821656 \tabularnewline
123 & 0.108535524466465 & 0.217071048932929 & 0.891464475533535 \tabularnewline
124 & 0.0852962155052576 & 0.170592431010515 & 0.914703784494742 \tabularnewline
125 & 0.0664045694001015 & 0.132809138800203 & 0.933595430599898 \tabularnewline
126 & 0.0500125232441552 & 0.10002504648831 & 0.949987476755845 \tabularnewline
127 & 0.0366595031010663 & 0.0733190062021326 & 0.963340496898934 \tabularnewline
128 & 0.0377782998840843 & 0.0755565997681685 & 0.962221700115916 \tabularnewline
129 & 0.034828468864302 & 0.069656937728604 & 0.965171531135698 \tabularnewline
130 & 0.0341587402396879 & 0.0683174804793758 & 0.965841259760312 \tabularnewline
131 & 0.0401811983436863 & 0.0803623966873726 & 0.959818801656314 \tabularnewline
132 & 0.0378557423928964 & 0.0757114847857928 & 0.962144257607104 \tabularnewline
133 & 0.0916037872722947 & 0.183207574544589 & 0.908396212727705 \tabularnewline
134 & 0.0960844553310405 & 0.192168910662081 & 0.90391554466896 \tabularnewline
135 & 0.0735086834340961 & 0.147017366868192 & 0.926491316565904 \tabularnewline
136 & 0.0548538752057547 & 0.109707750411509 & 0.945146124794245 \tabularnewline
137 & 0.0461777968077719 & 0.0923555936155438 & 0.953822203192228 \tabularnewline
138 & 0.0427341881705937 & 0.0854683763411874 & 0.957265811829406 \tabularnewline
139 & 0.037437737486811 & 0.074875474973622 & 0.962562262513189 \tabularnewline
140 & 0.0253896582399434 & 0.0507793164798868 & 0.974610341760057 \tabularnewline
141 & 0.414711496443759 & 0.829422992887517 & 0.585288503556241 \tabularnewline
142 & 0.363317343282383 & 0.726634686564766 & 0.636682656717617 \tabularnewline
143 & 0.321412634550678 & 0.642825269101356 & 0.678587365449322 \tabularnewline
144 & 0.260199504264854 & 0.520399008529709 & 0.739800495735146 \tabularnewline
145 & 0.215045244945265 & 0.430090489890531 & 0.784954755054735 \tabularnewline
146 & 0.19472492071176 & 0.38944984142352 & 0.80527507928824 \tabularnewline
147 & 0.277547911944317 & 0.555095823888634 & 0.722452088055683 \tabularnewline
148 & 0.62234640502573 & 0.75530718994854 & 0.37765359497427 \tabularnewline
149 & 0.515982293632699 & 0.968035412734601 & 0.484017706367301 \tabularnewline
150 & 0.395380624862303 & 0.790761249724605 & 0.604619375137697 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185886&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]0.922338009425659[/C][C]0.155323981148682[/C][C]0.0776619905743411[/C][/ROW]
[ROW][C]13[/C][C]0.89642465641699[/C][C]0.207150687166021[/C][C]0.10357534358301[/C][/ROW]
[ROW][C]14[/C][C]0.831475742550288[/C][C]0.337048514899425[/C][C]0.168524257449712[/C][/ROW]
[ROW][C]15[/C][C]0.785291023637889[/C][C]0.429417952724222[/C][C]0.214708976362111[/C][/ROW]
[ROW][C]16[/C][C]0.808797505481182[/C][C]0.382404989037636[/C][C]0.191202494518818[/C][/ROW]
[ROW][C]17[/C][C]0.752668867982716[/C][C]0.494662264034568[/C][C]0.247331132017284[/C][/ROW]
[ROW][C]18[/C][C]0.909973025800038[/C][C]0.180053948399923[/C][C]0.0900269741999617[/C][/ROW]
[ROW][C]19[/C][C]0.887736936220901[/C][C]0.224526127558199[/C][C]0.112263063779099[/C][/ROW]
[ROW][C]20[/C][C]0.845008264162708[/C][C]0.309983471674585[/C][C]0.154991735837292[/C][/ROW]
[ROW][C]21[/C][C]0.789484100534173[/C][C]0.421031798931654[/C][C]0.210515899465827[/C][/ROW]
[ROW][C]22[/C][C]0.752610850023517[/C][C]0.494778299952966[/C][C]0.247389149976483[/C][/ROW]
[ROW][C]23[/C][C]0.721898270490264[/C][C]0.556203459019472[/C][C]0.278101729509736[/C][/ROW]
[ROW][C]24[/C][C]0.698521082272676[/C][C]0.602957835454647[/C][C]0.301478917727324[/C][/ROW]
[ROW][C]25[/C][C]0.643786449128348[/C][C]0.712427101743304[/C][C]0.356213550871652[/C][/ROW]
[ROW][C]26[/C][C]0.595313904104634[/C][C]0.809372191790731[/C][C]0.404686095895365[/C][/ROW]
[ROW][C]27[/C][C]0.544678749895634[/C][C]0.910642500208731[/C][C]0.455321250104366[/C][/ROW]
[ROW][C]28[/C][C]0.590811462048837[/C][C]0.818377075902326[/C][C]0.409188537951163[/C][/ROW]
[ROW][C]29[/C][C]0.5325454068913[/C][C]0.934909186217401[/C][C]0.4674545931087[/C][/ROW]
[ROW][C]30[/C][C]0.544288131769434[/C][C]0.911423736461132[/C][C]0.455711868230566[/C][/ROW]
[ROW][C]31[/C][C]0.488862964652029[/C][C]0.977725929304058[/C][C]0.511137035347971[/C][/ROW]
[ROW][C]32[/C][C]0.485318041767325[/C][C]0.97063608353465[/C][C]0.514681958232675[/C][/ROW]
[ROW][C]33[/C][C]0.469719894028261[/C][C]0.939439788056522[/C][C]0.530280105971739[/C][/ROW]
[ROW][C]34[/C][C]0.409760142930545[/C][C]0.819520285861091[/C][C]0.590239857069455[/C][/ROW]
[ROW][C]35[/C][C]0.392068276939615[/C][C]0.784136553879229[/C][C]0.607931723060385[/C][/ROW]
[ROW][C]36[/C][C]0.867863522840921[/C][C]0.264272954318157[/C][C]0.132136477159079[/C][/ROW]
[ROW][C]37[/C][C]0.852461516550039[/C][C]0.295076966899923[/C][C]0.147538483449961[/C][/ROW]
[ROW][C]38[/C][C]0.836161136137446[/C][C]0.327677727725108[/C][C]0.163838863862554[/C][/ROW]
[ROW][C]39[/C][C]0.864915290829859[/C][C]0.270169418340283[/C][C]0.135084709170142[/C][/ROW]
[ROW][C]40[/C][C]0.853413575428955[/C][C]0.29317284914209[/C][C]0.146586424571045[/C][/ROW]
[ROW][C]41[/C][C]0.830336972312261[/C][C]0.339326055375477[/C][C]0.169663027687739[/C][/ROW]
[ROW][C]42[/C][C]0.808462766556695[/C][C]0.38307446688661[/C][C]0.191537233443305[/C][/ROW]
[ROW][C]43[/C][C]0.829156666491066[/C][C]0.341686667017869[/C][C]0.170843333508934[/C][/ROW]
[ROW][C]44[/C][C]0.794556446897904[/C][C]0.410887106204193[/C][C]0.205443553102096[/C][/ROW]
[ROW][C]45[/C][C]0.763495821481604[/C][C]0.473008357036792[/C][C]0.236504178518396[/C][/ROW]
[ROW][C]46[/C][C]0.900151633923894[/C][C]0.199696732152212[/C][C]0.0998483660761058[/C][/ROW]
[ROW][C]47[/C][C]0.904866280641361[/C][C]0.190267438717277[/C][C]0.0951337193586387[/C][/ROW]
[ROW][C]48[/C][C]0.885160032604616[/C][C]0.229679934790767[/C][C]0.114839967395384[/C][/ROW]
[ROW][C]49[/C][C]0.872875006038825[/C][C]0.25424998792235[/C][C]0.127124993961175[/C][/ROW]
[ROW][C]50[/C][C]0.863768452420683[/C][C]0.272463095158635[/C][C]0.136231547579317[/C][/ROW]
[ROW][C]51[/C][C]0.835982616455074[/C][C]0.328034767089852[/C][C]0.164017383544926[/C][/ROW]
[ROW][C]52[/C][C]0.805245827816107[/C][C]0.389508344367787[/C][C]0.194754172183893[/C][/ROW]
[ROW][C]53[/C][C]0.816875608824982[/C][C]0.366248782350036[/C][C]0.183124391175018[/C][/ROW]
[ROW][C]54[/C][C]0.790975000075511[/C][C]0.418049999848978[/C][C]0.209024999924489[/C][/ROW]
[ROW][C]55[/C][C]0.809665430122319[/C][C]0.380669139755362[/C][C]0.190334569877681[/C][/ROW]
[ROW][C]56[/C][C]0.799884403875205[/C][C]0.40023119224959[/C][C]0.200115596124795[/C][/ROW]
[ROW][C]57[/C][C]0.765080908624144[/C][C]0.469838182751713[/C][C]0.234919091375856[/C][/ROW]
[ROW][C]58[/C][C]0.753231759420699[/C][C]0.493536481158603[/C][C]0.246768240579301[/C][/ROW]
[ROW][C]59[/C][C]0.719216941532468[/C][C]0.561566116935065[/C][C]0.280783058467532[/C][/ROW]
[ROW][C]60[/C][C]0.72524143439794[/C][C]0.549517131204119[/C][C]0.27475856560206[/C][/ROW]
[ROW][C]61[/C][C]0.690810155631145[/C][C]0.618379688737709[/C][C]0.309189844368855[/C][/ROW]
[ROW][C]62[/C][C]0.650128554376835[/C][C]0.69974289124633[/C][C]0.349871445623165[/C][/ROW]
[ROW][C]63[/C][C]0.612855059445725[/C][C]0.77428988110855[/C][C]0.387144940554275[/C][/ROW]
[ROW][C]64[/C][C]0.577111365961011[/C][C]0.845777268077977[/C][C]0.422888634038989[/C][/ROW]
[ROW][C]65[/C][C]0.547206115916484[/C][C]0.905587768167032[/C][C]0.452793884083516[/C][/ROW]
[ROW][C]66[/C][C]0.499370683149125[/C][C]0.998741366298251[/C][C]0.500629316850875[/C][/ROW]
[ROW][C]67[/C][C]0.466752681182202[/C][C]0.933505362364405[/C][C]0.533247318817798[/C][/ROW]
[ROW][C]68[/C][C]0.493505897020834[/C][C]0.987011794041668[/C][C]0.506494102979166[/C][/ROW]
[ROW][C]69[/C][C]0.731945293024897[/C][C]0.536109413950206[/C][C]0.268054706975103[/C][/ROW]
[ROW][C]70[/C][C]0.691533232036181[/C][C]0.616933535927638[/C][C]0.308466767963819[/C][/ROW]
[ROW][C]71[/C][C]0.816413795255426[/C][C]0.367172409489148[/C][C]0.183586204744574[/C][/ROW]
[ROW][C]72[/C][C]0.787566647795539[/C][C]0.424866704408921[/C][C]0.212433352204461[/C][/ROW]
[ROW][C]73[/C][C]0.773024260872439[/C][C]0.453951478255122[/C][C]0.226975739127561[/C][/ROW]
[ROW][C]74[/C][C]0.745389961636143[/C][C]0.509220076727715[/C][C]0.254610038363857[/C][/ROW]
[ROW][C]75[/C][C]0.706311867869224[/C][C]0.587376264261553[/C][C]0.293688132130776[/C][/ROW]
[ROW][C]76[/C][C]0.749340105277517[/C][C]0.501319789444967[/C][C]0.250659894722483[/C][/ROW]
[ROW][C]77[/C][C]0.71388898863225[/C][C]0.5722220227355[/C][C]0.28611101136775[/C][/ROW]
[ROW][C]78[/C][C]0.688626699860726[/C][C]0.622746600278548[/C][C]0.311373300139274[/C][/ROW]
[ROW][C]79[/C][C]0.706147520226382[/C][C]0.587704959547235[/C][C]0.293852479773618[/C][/ROW]
[ROW][C]80[/C][C]0.666817744728424[/C][C]0.666364510543152[/C][C]0.333182255271576[/C][/ROW]
[ROW][C]81[/C][C]0.625084538845696[/C][C]0.749830922308607[/C][C]0.374915461154304[/C][/ROW]
[ROW][C]82[/C][C]0.769221687684475[/C][C]0.461556624631049[/C][C]0.230778312315525[/C][/ROW]
[ROW][C]83[/C][C]0.731967276359808[/C][C]0.536065447280385[/C][C]0.268032723640192[/C][/ROW]
[ROW][C]84[/C][C]0.706914565556163[/C][C]0.586170868887674[/C][C]0.293085434443837[/C][/ROW]
[ROW][C]85[/C][C]0.665177266410528[/C][C]0.669645467178944[/C][C]0.334822733589472[/C][/ROW]
[ROW][C]86[/C][C]0.646269802810228[/C][C]0.707460394379543[/C][C]0.353730197189772[/C][/ROW]
[ROW][C]87[/C][C]0.602789438275618[/C][C]0.794421123448765[/C][C]0.397210561724382[/C][/ROW]
[ROW][C]88[/C][C]0.558731324891298[/C][C]0.882537350217405[/C][C]0.441268675108702[/C][/ROW]
[ROW][C]89[/C][C]0.531366069899844[/C][C]0.937267860200311[/C][C]0.468633930100156[/C][/ROW]
[ROW][C]90[/C][C]0.49627623796455[/C][C]0.9925524759291[/C][C]0.50372376203545[/C][/ROW]
[ROW][C]91[/C][C]0.491358646899942[/C][C]0.982717293799885[/C][C]0.508641353100058[/C][/ROW]
[ROW][C]92[/C][C]0.450319944760225[/C][C]0.900639889520451[/C][C]0.549680055239774[/C][/ROW]
[ROW][C]93[/C][C]0.407680876425241[/C][C]0.815361752850482[/C][C]0.592319123574759[/C][/ROW]
[ROW][C]94[/C][C]0.363192118283405[/C][C]0.72638423656681[/C][C]0.636807881716595[/C][/ROW]
[ROW][C]95[/C][C]0.394304151324135[/C][C]0.78860830264827[/C][C]0.605695848675865[/C][/ROW]
[ROW][C]96[/C][C]0.356742331186286[/C][C]0.713484662372572[/C][C]0.643257668813714[/C][/ROW]
[ROW][C]97[/C][C]0.315179567495225[/C][C]0.63035913499045[/C][C]0.684820432504775[/C][/ROW]
[ROW][C]98[/C][C]0.306918928969024[/C][C]0.613837857938049[/C][C]0.693081071030976[/C][/ROW]
[ROW][C]99[/C][C]0.265631548433832[/C][C]0.531263096867664[/C][C]0.734368451566168[/C][/ROW]
[ROW][C]100[/C][C]0.233102360308372[/C][C]0.466204720616743[/C][C]0.766897639691628[/C][/ROW]
[ROW][C]101[/C][C]0.21172057334207[/C][C]0.42344114668414[/C][C]0.78827942665793[/C][/ROW]
[ROW][C]102[/C][C]0.194290288785299[/C][C]0.388580577570597[/C][C]0.805709711214701[/C][/ROW]
[ROW][C]103[/C][C]0.236859910127883[/C][C]0.473719820255766[/C][C]0.763140089872117[/C][/ROW]
[ROW][C]104[/C][C]0.200857867797697[/C][C]0.401715735595395[/C][C]0.799142132202303[/C][/ROW]
[ROW][C]105[/C][C]0.194982984695391[/C][C]0.389965969390782[/C][C]0.805017015304609[/C][/ROW]
[ROW][C]106[/C][C]0.200445975606951[/C][C]0.400891951213902[/C][C]0.799554024393049[/C][/ROW]
[ROW][C]107[/C][C]0.179385797643149[/C][C]0.358771595286297[/C][C]0.820614202356851[/C][/ROW]
[ROW][C]108[/C][C]0.158226234218817[/C][C]0.316452468437634[/C][C]0.841773765781183[/C][/ROW]
[ROW][C]109[/C][C]0.156945792909178[/C][C]0.313891585818356[/C][C]0.843054207090822[/C][/ROW]
[ROW][C]110[/C][C]0.16148331661312[/C][C]0.322966633226239[/C][C]0.83851668338688[/C][/ROW]
[ROW][C]111[/C][C]0.14781284683155[/C][C]0.295625693663101[/C][C]0.85218715316845[/C][/ROW]
[ROW][C]112[/C][C]0.12818240274467[/C][C]0.25636480548934[/C][C]0.87181759725533[/C][/ROW]
[ROW][C]113[/C][C]0.173461492727845[/C][C]0.34692298545569[/C][C]0.826538507272155[/C][/ROW]
[ROW][C]114[/C][C]0.148187468933527[/C][C]0.296374937867053[/C][C]0.851812531066473[/C][/ROW]
[ROW][C]115[/C][C]0.168199963324651[/C][C]0.336399926649301[/C][C]0.831800036675349[/C][/ROW]
[ROW][C]116[/C][C]0.172085586040561[/C][C]0.344171172081123[/C][C]0.827914413959439[/C][/ROW]
[ROW][C]117[/C][C]0.145954781598745[/C][C]0.291909563197491[/C][C]0.854045218401255[/C][/ROW]
[ROW][C]118[/C][C]0.146088230345841[/C][C]0.292176460691683[/C][C]0.853911769654159[/C][/ROW]
[ROW][C]119[/C][C]0.135874694804457[/C][C]0.271749389608914[/C][C]0.864125305195543[/C][/ROW]
[ROW][C]120[/C][C]0.158743708746979[/C][C]0.317487417493957[/C][C]0.841256291253021[/C][/ROW]
[ROW][C]121[/C][C]0.130505039713635[/C][C]0.26101007942727[/C][C]0.869494960286365[/C][/ROW]
[ROW][C]122[/C][C]0.116581495178344[/C][C]0.233162990356689[/C][C]0.883418504821656[/C][/ROW]
[ROW][C]123[/C][C]0.108535524466465[/C][C]0.217071048932929[/C][C]0.891464475533535[/C][/ROW]
[ROW][C]124[/C][C]0.0852962155052576[/C][C]0.170592431010515[/C][C]0.914703784494742[/C][/ROW]
[ROW][C]125[/C][C]0.0664045694001015[/C][C]0.132809138800203[/C][C]0.933595430599898[/C][/ROW]
[ROW][C]126[/C][C]0.0500125232441552[/C][C]0.10002504648831[/C][C]0.949987476755845[/C][/ROW]
[ROW][C]127[/C][C]0.0366595031010663[/C][C]0.0733190062021326[/C][C]0.963340496898934[/C][/ROW]
[ROW][C]128[/C][C]0.0377782998840843[/C][C]0.0755565997681685[/C][C]0.962221700115916[/C][/ROW]
[ROW][C]129[/C][C]0.034828468864302[/C][C]0.069656937728604[/C][C]0.965171531135698[/C][/ROW]
[ROW][C]130[/C][C]0.0341587402396879[/C][C]0.0683174804793758[/C][C]0.965841259760312[/C][/ROW]
[ROW][C]131[/C][C]0.0401811983436863[/C][C]0.0803623966873726[/C][C]0.959818801656314[/C][/ROW]
[ROW][C]132[/C][C]0.0378557423928964[/C][C]0.0757114847857928[/C][C]0.962144257607104[/C][/ROW]
[ROW][C]133[/C][C]0.0916037872722947[/C][C]0.183207574544589[/C][C]0.908396212727705[/C][/ROW]
[ROW][C]134[/C][C]0.0960844553310405[/C][C]0.192168910662081[/C][C]0.90391554466896[/C][/ROW]
[ROW][C]135[/C][C]0.0735086834340961[/C][C]0.147017366868192[/C][C]0.926491316565904[/C][/ROW]
[ROW][C]136[/C][C]0.0548538752057547[/C][C]0.109707750411509[/C][C]0.945146124794245[/C][/ROW]
[ROW][C]137[/C][C]0.0461777968077719[/C][C]0.0923555936155438[/C][C]0.953822203192228[/C][/ROW]
[ROW][C]138[/C][C]0.0427341881705937[/C][C]0.0854683763411874[/C][C]0.957265811829406[/C][/ROW]
[ROW][C]139[/C][C]0.037437737486811[/C][C]0.074875474973622[/C][C]0.962562262513189[/C][/ROW]
[ROW][C]140[/C][C]0.0253896582399434[/C][C]0.0507793164798868[/C][C]0.974610341760057[/C][/ROW]
[ROW][C]141[/C][C]0.414711496443759[/C][C]0.829422992887517[/C][C]0.585288503556241[/C][/ROW]
[ROW][C]142[/C][C]0.363317343282383[/C][C]0.726634686564766[/C][C]0.636682656717617[/C][/ROW]
[ROW][C]143[/C][C]0.321412634550678[/C][C]0.642825269101356[/C][C]0.678587365449322[/C][/ROW]
[ROW][C]144[/C][C]0.260199504264854[/C][C]0.520399008529709[/C][C]0.739800495735146[/C][/ROW]
[ROW][C]145[/C][C]0.215045244945265[/C][C]0.430090489890531[/C][C]0.784954755054735[/C][/ROW]
[ROW][C]146[/C][C]0.19472492071176[/C][C]0.38944984142352[/C][C]0.80527507928824[/C][/ROW]
[ROW][C]147[/C][C]0.277547911944317[/C][C]0.555095823888634[/C][C]0.722452088055683[/C][/ROW]
[ROW][C]148[/C][C]0.62234640502573[/C][C]0.75530718994854[/C][C]0.37765359497427[/C][/ROW]
[ROW][C]149[/C][C]0.515982293632699[/C][C]0.968035412734601[/C][C]0.484017706367301[/C][/ROW]
[ROW][C]150[/C][C]0.395380624862303[/C][C]0.790761249724605[/C][C]0.604619375137697[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185886&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185886&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
120.9223380094256590.1553239811486820.0776619905743411
130.896424656416990.2071506871660210.10357534358301
140.8314757425502880.3370485148994250.168524257449712
150.7852910236378890.4294179527242220.214708976362111
160.8087975054811820.3824049890376360.191202494518818
170.7526688679827160.4946622640345680.247331132017284
180.9099730258000380.1800539483999230.0900269741999617
190.8877369362209010.2245261275581990.112263063779099
200.8450082641627080.3099834716745850.154991735837292
210.7894841005341730.4210317989316540.210515899465827
220.7526108500235170.4947782999529660.247389149976483
230.7218982704902640.5562034590194720.278101729509736
240.6985210822726760.6029578354546470.301478917727324
250.6437864491283480.7124271017433040.356213550871652
260.5953139041046340.8093721917907310.404686095895365
270.5446787498956340.9106425002087310.455321250104366
280.5908114620488370.8183770759023260.409188537951163
290.53254540689130.9349091862174010.4674545931087
300.5442881317694340.9114237364611320.455711868230566
310.4888629646520290.9777259293040580.511137035347971
320.4853180417673250.970636083534650.514681958232675
330.4697198940282610.9394397880565220.530280105971739
340.4097601429305450.8195202858610910.590239857069455
350.3920682769396150.7841365538792290.607931723060385
360.8678635228409210.2642729543181570.132136477159079
370.8524615165500390.2950769668999230.147538483449961
380.8361611361374460.3276777277251080.163838863862554
390.8649152908298590.2701694183402830.135084709170142
400.8534135754289550.293172849142090.146586424571045
410.8303369723122610.3393260553754770.169663027687739
420.8084627665566950.383074466886610.191537233443305
430.8291566664910660.3416866670178690.170843333508934
440.7945564468979040.4108871062041930.205443553102096
450.7634958214816040.4730083570367920.236504178518396
460.9001516339238940.1996967321522120.0998483660761058
470.9048662806413610.1902674387172770.0951337193586387
480.8851600326046160.2296799347907670.114839967395384
490.8728750060388250.254249987922350.127124993961175
500.8637684524206830.2724630951586350.136231547579317
510.8359826164550740.3280347670898520.164017383544926
520.8052458278161070.3895083443677870.194754172183893
530.8168756088249820.3662487823500360.183124391175018
540.7909750000755110.4180499998489780.209024999924489
550.8096654301223190.3806691397553620.190334569877681
560.7998844038752050.400231192249590.200115596124795
570.7650809086241440.4698381827517130.234919091375856
580.7532317594206990.4935364811586030.246768240579301
590.7192169415324680.5615661169350650.280783058467532
600.725241434397940.5495171312041190.27475856560206
610.6908101556311450.6183796887377090.309189844368855
620.6501285543768350.699742891246330.349871445623165
630.6128550594457250.774289881108550.387144940554275
640.5771113659610110.8457772680779770.422888634038989
650.5472061159164840.9055877681670320.452793884083516
660.4993706831491250.9987413662982510.500629316850875
670.4667526811822020.9335053623644050.533247318817798
680.4935058970208340.9870117940416680.506494102979166
690.7319452930248970.5361094139502060.268054706975103
700.6915332320361810.6169335359276380.308466767963819
710.8164137952554260.3671724094891480.183586204744574
720.7875666477955390.4248667044089210.212433352204461
730.7730242608724390.4539514782551220.226975739127561
740.7453899616361430.5092200767277150.254610038363857
750.7063118678692240.5873762642615530.293688132130776
760.7493401052775170.5013197894449670.250659894722483
770.713888988632250.57222202273550.28611101136775
780.6886266998607260.6227466002785480.311373300139274
790.7061475202263820.5877049595472350.293852479773618
800.6668177447284240.6663645105431520.333182255271576
810.6250845388456960.7498309223086070.374915461154304
820.7692216876844750.4615566246310490.230778312315525
830.7319672763598080.5360654472803850.268032723640192
840.7069145655561630.5861708688876740.293085434443837
850.6651772664105280.6696454671789440.334822733589472
860.6462698028102280.7074603943795430.353730197189772
870.6027894382756180.7944211234487650.397210561724382
880.5587313248912980.8825373502174050.441268675108702
890.5313660698998440.9372678602003110.468633930100156
900.496276237964550.99255247592910.50372376203545
910.4913586468999420.9827172937998850.508641353100058
920.4503199447602250.9006398895204510.549680055239774
930.4076808764252410.8153617528504820.592319123574759
940.3631921182834050.726384236566810.636807881716595
950.3943041513241350.788608302648270.605695848675865
960.3567423311862860.7134846623725720.643257668813714
970.3151795674952250.630359134990450.684820432504775
980.3069189289690240.6138378579380490.693081071030976
990.2656315484338320.5312630968676640.734368451566168
1000.2331023603083720.4662047206167430.766897639691628
1010.211720573342070.423441146684140.78827942665793
1020.1942902887852990.3885805775705970.805709711214701
1030.2368599101278830.4737198202557660.763140089872117
1040.2008578677976970.4017157355953950.799142132202303
1050.1949829846953910.3899659693907820.805017015304609
1060.2004459756069510.4008919512139020.799554024393049
1070.1793857976431490.3587715952862970.820614202356851
1080.1582262342188170.3164524684376340.841773765781183
1090.1569457929091780.3138915858183560.843054207090822
1100.161483316613120.3229666332262390.83851668338688
1110.147812846831550.2956256936631010.85218715316845
1120.128182402744670.256364805489340.87181759725533
1130.1734614927278450.346922985455690.826538507272155
1140.1481874689335270.2963749378670530.851812531066473
1150.1681999633246510.3363999266493010.831800036675349
1160.1720855860405610.3441711720811230.827914413959439
1170.1459547815987450.2919095631974910.854045218401255
1180.1460882303458410.2921764606916830.853911769654159
1190.1358746948044570.2717493896089140.864125305195543
1200.1587437087469790.3174874174939570.841256291253021
1210.1305050397136350.261010079427270.869494960286365
1220.1165814951783440.2331629903566890.883418504821656
1230.1085355244664650.2170710489329290.891464475533535
1240.08529621550525760.1705924310105150.914703784494742
1250.06640456940010150.1328091388002030.933595430599898
1260.05001252324415520.100025046488310.949987476755845
1270.03665950310106630.07331900620213260.963340496898934
1280.03777829988408430.07555659976816850.962221700115916
1290.0348284688643020.0696569377286040.965171531135698
1300.03415874023968790.06831748047937580.965841259760312
1310.04018119834368630.08036239668737260.959818801656314
1320.03785574239289640.07571148478579280.962144257607104
1330.09160378727229470.1832075745445890.908396212727705
1340.09608445533104050.1921689106620810.90391554466896
1350.07350868343409610.1470173668681920.926491316565904
1360.05485387520575470.1097077504115090.945146124794245
1370.04617779680777190.09235559361554380.953822203192228
1380.04273418817059370.08546837634118740.957265811829406
1390.0374377374868110.0748754749736220.962562262513189
1400.02538965823994340.05077931647988680.974610341760057
1410.4147114964437590.8294229928875170.585288503556241
1420.3633173432823830.7266346865647660.636682656717617
1430.3214126345506780.6428252691013560.678587365449322
1440.2601995042648540.5203990085297090.739800495735146
1450.2150452449452650.4300904898905310.784954755054735
1460.194724920711760.389449841423520.80527507928824
1470.2775479119443170.5550958238886340.722452088055683
1480.622346405025730.755307189948540.37765359497427
1490.5159822936326990.9680354127346010.484017706367301
1500.3953806248623030.7907612497246050.604619375137697






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level00OK
10% type I error level100.0719424460431655OK

\begin{tabular}{lllllllll}
\hline
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
Description & # significant tests & % significant tests & OK/NOK \tabularnewline
1% type I error level & 0 & 0 & OK \tabularnewline
5% type I error level & 0 & 0 & OK \tabularnewline
10% type I error level & 10 & 0.0719424460431655 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185886&T=6

[TABLE]
[ROW][C]Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]Description[/C][C]# significant tests[/C][C]% significant tests[/C][C]OK/NOK[/C][/ROW]
[ROW][C]1% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]10[/C][C]0.0719424460431655[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185886&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185886&T=6

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


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

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level00OK
10% type I error level100.0719424460431655OK


Figures (Output of Computation)

Figure 1
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Figure 2
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Figure 3
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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 = 3 ; par2 = Include Quarterly Dummies ; par3 = Linear Trend ;
Parameters (R input):
par1 = 4 ; par2 = Do not include Seasonal Dummies ; par3 = 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')
}