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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 computationMon, 10 Dec 2012 12:13:26 -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/Dec/10/t135515964726w872ops5h2488.htm/, Retrieved Thu, 18 Apr 2024 21:31:31 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=198248, Retrieved Thu, 18 Apr 2024 21:31:31 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact110

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [] [2010-12-05 18:56:24] [b98453cac15ba1066b407e146608df68]
- R PD    [Multiple Regression] [WS 10, Multiple R...] [2012-12-10 17:13:26] [e4c351aee2a0bb2c047702ea90f356fa] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
2	7	41	38	13	12	14	12	53
2	5	39	32	16	11	18	11	86
2	5	30	35	19	15	11	14	66
1	5	31	33	15	6	12	12	67
2	8	34	37	14	13	16	21	76
2	6	35	29	13	10	18	12	78
2	5	39	31	19	12	14	22	53
2	6	34	36	15	14	14	11	80
2	5	36	35	14	12	15	10	74
2	4	37	38	15	6	15	13	76
1	6	38	31	16	10	17	10	79
2	5	36	34	16	12	19	8	54
1	5	38	35	16	12	10	15	67
2	6	39	38	16	11	16	14	54
2	7	33	37	17	15	18	10	87
1	6	32	33	15	12	14	14	58
1	7	36	32	15	10	14	14	75
2	6	38	38	20	12	17	11	88
1	8	39	38	18	11	14	10	64
2	7	32	32	16	12	16	13	57
1	5	32	33	16	11	18	7	66
2	5	31	31	16	12	11	14	68
2	7	39	38	19	13	14	12	54
2	7	37	39	16	11	12	14	56
1	5	39	32	17	9	17	11	86
2	4	41	32	17	13	9	9	80
1	10	36	35	16	10	16	11	76
2	6	33	37	15	14	14	15	69
2	5	33	33	16	12	15	14	78
1	5	34	33	14	10	11	13	67
2	5	31	28	15	12	16	9	80
1	5	27	32	12	8	13	15	54
2	6	37	31	14	10	17	10	71
2	5	34	37	16	12	15	11	84
1	5	34	30	14	12	14	13	74
1	5	32	33	7	7	16	8	71
1	5	29	31	10	6	9	20	63
1	5	36	33	14	12	15	12	71
2	5	29	31	16	10	17	10	76
1	5	35	33	16	10	13	10	69
1	5	37	32	16	10	15	9	74
2	7	34	33	14	12	16	14	75
1	5	38	32	20	15	16	8	54
1	6	35	33	14	10	12	14	52
2	7	38	28	14	10	12	11	69
2	7	37	35	11	12	11	13	68
2	5	38	39	14	13	15	9	65
2	5	33	34	15	11	15	11	75
2	4	36	38	16	11	17	15	74
1	5	38	32	14	12	13	11	75
2	4	32	38	16	14	16	10	72
1	5	32	30	14	10	14	14	67
1	5	32	33	12	12	11	18	63
2	7	34	38	16	13	12	14	62
1	5	32	32	9	5	12	11	63
2	5	37	32	14	6	15	12	76
2	6	39	34	16	12	16	13	74
2	4	29	34	16	12	15	9	67
1	6	37	36	15	11	12	10	73
2	6	35	34	16	10	12	15	70
1	5	30	28	12	7	8	20	53
1	7	38	34	16	12	13	12	77
2	6	34	35	16	14	11	12	77
2	8	31	35	14	11	14	14	52
2	7	34	31	16	12	15	13	54
1	5	35	37	17	13	10	11	80
2	6	36	35	18	14	11	17	66
1	6	30	27	18	11	12	12	73
2	5	39	40	12	12	15	13	63
1	5	35	37	16	12	15	14	69
1	5	38	36	10	8	14	13	67
2	5	31	38	14	11	16	15	54
2	4	34	39	18	14	15	13	81
1	6	38	41	18	14	15	10	69
1	6	34	27	16	12	13	11	84
2	6	39	30	17	9	12	19	80
2	6	37	37	16	13	17	13	70
2	7	34	31	16	11	13	17	69
1	5	28	31	13	12	15	13	77
1	7	37	27	16	12	13	9	54
1	6	33	36	16	12	15	11	79
1	5	37	38	20	12	16	10	30
2	5	35	37	16	12	15	9	71
1	4	37	33	15	12	16	12	73
2	8	32	34	15	11	15	12	72
2	8	33	31	16	10	14	13	77
1	5	38	39	14	9	15	13	75
2	5	33	34	16	12	14	12	69
2	6	29	32	16	12	13	15	54
2	4	33	33	15	12	7	22	70
2	5	31	36	12	9	17	13	73
2	5	36	32	17	15	13	15	54
2	5	35	41	16	12	15	13	77
2	5	32	28	15	12	14	15	82
2	6	29	30	13	12	13	10	80
2	6	39	36	16	10	16	11	80
2	5	37	35	16	13	12	16	69
2	6	35	31	16	9	14	11	78
1	5	37	34	16	12	17	11	81
1	7	32	36	14	10	15	10	76
2	5	38	36	16	14	17	10	76
1	6	37	35	16	11	12	16	73
2	6	36	37	20	15	16	12	85
1	6	32	28	15	11	11	11	66
2	4	33	39	16	11	15	16	79
1	5	40	32	13	12	9	19	68
2	5	38	35	17	12	16	11	76
1	7	41	39	16	12	15	16	71
1	6	36	35	16	11	10	15	54
2	9	43	42	12	7	10	24	46
2	6	30	34	16	12	15	14	82
2	6	31	33	16	14	11	15	74
2	5	32	41	17	11	13	11	88
1	6	32	33	13	11	14	15	38
2	5	37	34	12	10	18	12	76
1	8	37	32	18	13	16	10	86
2	7	33	40	14	13	14	14	54
2	5	34	40	14	8	14	13	70
2	7	33	35	13	11	14	9	69
2	6	38	36	16	12	14	15	90
2	6	33	37	13	11	12	15	54
2	9	31	27	16	13	14	14	76
2	7	38	39	13	12	15	11	89
2	6	37	38	16	14	15	8	76
2	5	33	31	15	13	15	11	73
2	5	31	33	16	15	13	11	79
1	6	39	32	15	10	17	8	90
2	6	44	39	17	11	17	10	74
2	7	33	36	15	9	19	11	81
2	5	35	33	12	11	15	13	72
1	5	32	33	16	10	13	11	71
1	5	28	32	10	11	9	20	66
2	6	40	37	16	8	15	10	77
1	4	27	30	12	11	15	15	65
1	5	37	38	14	12	15	12	74
2	7	32	29	15	12	16	14	82
1	5	28	22	13	9	11	23	54
1	7	34	35	15	11	14	14	63
2	7	30	35	11	10	11	16	54
2	6	35	34	12	8	15	11	64
1	5	31	35	8	9	13	12	69
2	8	32	34	16	8	15	10	54
1	5	30	34	15	9	16	14	84
2	5	30	35	17	15	14	12	86
1	5	31	23	16	11	15	12	77
2	6	40	31	10	8	16	11	89
2	4	32	27	18	13	16	12	76
1	5	36	36	13	12	11	13	60
1	5	32	31	16	12	12	11	75
1	7	35	32	13	9	9	19	73
2	6	38	39	10	7	16	12	85
2	7	42	37	15	13	13	17	79
1	10	34	38	16	9	16	9	71
2	6	35	39	16	6	12	12	72
2	8	35	34	14	8	9	19	69
2	4	33	31	10	8	13	18	78
2	5	36	32	17	15	13	15	54
2	6	32	37	13	6	14	14	69
2	7	33	36	15	9	19	11	81
2	7	34	32	16	11	13	9	84
2	6	32	35	12	8	12	18	84
2	6	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=198248&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=198248&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=198248&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] = + 5.38994150998993 + 0.108112979773634Gender[t] + 0.123741363584274Age[t] + 0.110167055205388Connected[t] -0.0294036180161816Separate[t] + 0.545974579340655Software[t] + 0.0553358684756296Happiness[t] -0.082873772739656Depression[t] + 0.00162165885175675Belonging[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Learning[t] =  +  5.38994150998993 +  0.108112979773634Gender[t] +  0.123741363584274Age[t] +  0.110167055205388Connected[t] -0.0294036180161816Separate[t] +  0.545974579340655Software[t] +  0.0553358684756296Happiness[t] -0.082873772739656Depression[t] +  0.00162165885175675Belonging[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=198248&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Learning[t] =  +  5.38994150998993 +  0.108112979773634Gender[t] +  0.123741363584274Age[t] +  0.110167055205388Connected[t] -0.0294036180161816Separate[t] +  0.545974579340655Software[t] +  0.0553358684756296Happiness[t] -0.082873772739656Depression[t] +  0.00162165885175675Belonging[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=198248&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=198248&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] = + 5.38994150998993 + 0.108112979773634Gender[t] + 0.123741363584274Age[t] + 0.110167055205388Connected[t] -0.0294036180161816Separate[t] + 0.545974579340655Software[t] + 0.0553358684756296Happiness[t] -0.082873772739656Depression[t] + 0.00162165885175675Belonging[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)5.389941509989932.6754792.01460.0457020.022851
Gender0.1081129797736340.3277160.32990.7419280.370964
Age0.1237413635842740.1286730.96170.3377310.168865
Connected0.1101670552053880.0474182.32330.0214780.010739
Separate-0.02940361801618160.04596-0.63980.5232820.261641
Software0.5459745793406550.0702367.773400
Happiness0.05533586847562960.0780510.7090.4794190.23971
Depression-0.0828737727396560.05737-1.44450.150630.075315
Belonging0.001621658851756750.0146520.11070.9120160.456008

\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) & 5.38994150998993 & 2.675479 & 2.0146 & 0.045702 & 0.022851 \tabularnewline
Gender & 0.108112979773634 & 0.327716 & 0.3299 & 0.741928 & 0.370964 \tabularnewline
Age & 0.123741363584274 & 0.128673 & 0.9617 & 0.337731 & 0.168865 \tabularnewline
Connected & 0.110167055205388 & 0.047418 & 2.3233 & 0.021478 & 0.010739 \tabularnewline
Separate & -0.0294036180161816 & 0.04596 & -0.6398 & 0.523282 & 0.261641 \tabularnewline
Software & 0.545974579340655 & 0.070236 & 7.7734 & 0 & 0 \tabularnewline
Happiness & 0.0553358684756296 & 0.078051 & 0.709 & 0.479419 & 0.23971 \tabularnewline
Depression & -0.082873772739656 & 0.05737 & -1.4445 & 0.15063 & 0.075315 \tabularnewline
Belonging & 0.00162165885175675 & 0.014652 & 0.1107 & 0.912016 & 0.456008 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=198248&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]5.38994150998993[/C][C]2.675479[/C][C]2.0146[/C][C]0.045702[/C][C]0.022851[/C][/ROW]
[ROW][C]Gender[/C][C]0.108112979773634[/C][C]0.327716[/C][C]0.3299[/C][C]0.741928[/C][C]0.370964[/C][/ROW]
[ROW][C]Age[/C][C]0.123741363584274[/C][C]0.128673[/C][C]0.9617[/C][C]0.337731[/C][C]0.168865[/C][/ROW]
[ROW][C]Connected[/C][C]0.110167055205388[/C][C]0.047418[/C][C]2.3233[/C][C]0.021478[/C][C]0.010739[/C][/ROW]
[ROW][C]Separate[/C][C]-0.0294036180161816[/C][C]0.04596[/C][C]-0.6398[/C][C]0.523282[/C][C]0.261641[/C][/ROW]
[ROW][C]Software[/C][C]0.545974579340655[/C][C]0.070236[/C][C]7.7734[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Happiness[/C][C]0.0553358684756296[/C][C]0.078051[/C][C]0.709[/C][C]0.479419[/C][C]0.23971[/C][/ROW]
[ROW][C]Depression[/C][C]-0.082873772739656[/C][C]0.05737[/C][C]-1.4445[/C][C]0.15063[/C][C]0.075315[/C][/ROW]
[ROW][C]Belonging[/C][C]0.00162165885175675[/C][C]0.014652[/C][C]0.1107[/C][C]0.912016[/C][C]0.456008[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=198248&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=198248&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)5.389941509989932.6754792.01460.0457020.022851
Gender0.1081129797736340.3277160.32990.7419280.370964
Age0.1237413635842740.1286730.96170.3377310.168865
Connected0.1101670552053880.0474182.32330.0214780.010739
Separate-0.02940361801618160.04596-0.63980.5232820.261641
Software0.5459745793406550.0702367.773400
Happiness0.05533586847562960.0780510.7090.4794190.23971
Depression-0.0828737727396560.05737-1.44450.150630.075315
Belonging0.001621658851756750.0146520.11070.9120160.456008






Multiple Linear Regression - Regression Statistics
Multiple R0.598740994700229
R-squared0.35849077873462
Adjusted R-squared0.324947812916822
F-TEST (value)10.6875098845436
F-TEST (DF numerator)8
F-TEST (DF denominator)153
p-value7.09965419787295e-12
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1.85377926236319
Sum Squared Residuals525.784125695877

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.598740994700229 \tabularnewline
R-squared & 0.35849077873462 \tabularnewline
Adjusted R-squared & 0.324947812916822 \tabularnewline
F-TEST (value) & 10.6875098845436 \tabularnewline
F-TEST (DF numerator) & 8 \tabularnewline
F-TEST (DF denominator) & 153 \tabularnewline
p-value & 7.09965419787295e-12 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 1.85377926236319 \tabularnewline
Sum Squared Residuals & 525.784125695877 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=198248&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.598740994700229[/C][/ROW]
[ROW][C]R-squared[/C][C]0.35849077873462[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.324947812916822[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]10.6875098845436[/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]7.09965419787295e-12[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]1.85377926236319[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]525.784125695877[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=198248&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=198248&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 R0.598740994700229
R-squared0.35849077873462
Adjusted R-squared0.324947812916822
F-TEST (value)10.6875098845436
F-TEST (DF numerator)8
F-TEST (DF denominator)153
p-value7.09965419787295e-12
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1.85377926236319
Sum Squared Residuals525.784125695877






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11316.289728550447-3.28972855044704
21615.81009083037430.189909169625724
31916.24586922225642.75413077774365
41511.61566439246133.38433560753873
51415.6197846608352-1.61978466083517
61314.9395532042912-1.93955320429118
71915.19899931158443.80100068841559
81516.6722327568758-1.67223275687579
91415.8347596511419-1.83475965114191
101512.21174901215512.78825098784491
111615.21516748995680.784832510043216
121616.2188211115048-0.218821111504787
131615.24458096374030.755419036259674
141615.3462243474350.653775652564983
151717.5179468851836-0.517946885183623
161515.055749549101-0.0557495491010007
171514.58518179332160.414818206678439
182016.14112545922463.85887454077539
191815.72263403735492.27736596264514
201615.50893136131420.491068638685751
211615.20046676007020.799533239929763
221614.8389703292081.16102967079198
231916.61699067822872.38300932177135
241615.00212782639330.997872173606671
251714.55469282344372.4453071765563
261716.65662751197020.343372488029807
271615.22910974404780.770890255952179
281516.1833287453263-1.18332874532627
291615.23805726600650.761942733993486
301413.99185423422480.00814576577522982
311515.6376892955541-0.637689295554069
321212.081981933521-0.0819819335210383
331415.200140143711-1.20014014371098
341615.48896112047670.511038879523317
351415.3493734643438-1.34937346434381
36712.8311312272755-5.83113122727548
371010.6186530953317-0.618653095331697
381415.6148413853661-1.61484138536606
391614.20317063274241.79682936725761
401614.46455766230391.5354423376961
411614.91594919468061.08405080531945
421415.6461779403008-1.64617794030081
432017.86176561076942.13823438923063
441414.1738998659741-0.173899865974054
451415.1594629837279-1.15946298372786
461115.7127146882838-4.71271468828382
471416.5517327119025-2.55173271190246
481514.90643541031340.0935645896866252
491614.77313572742141.22686427257858
501415.8432677849884-1.84326778498844
511616.326180922141-0.326180922140968
521413.94286481054980.0571351894502274
531214.44261378339-2.44261378339
541615.80270939058520.197290609414802
55911.2856476236748-2.28564762367482
561412.59478585657611.40521414342389
571616.1251203486152-0.12512034861519
581615.04077467991350.959225320086536
591515.2175476285225-0.217547628522455
601614.19892531432351.80107468567653
611211.29145312693310.708546873066912
621615.95231282108850.0476871789115212
631616.4478900201702-0.447890020170158
641414.6866664323542-0.686666432354236
651615.69846824471030.301531755289695
661715.75382379746391.24617620253608
671816.23601701951331.76398298048667
681814.54526325875113.45473674124894
691215.7517831610889-3.75178316108887
701615.21806899491310.781931005086882
711013.4183700477434-3.41837004774335
721414.258272769468-0.258272769467987
731816.22774915750681.77225084249323
741816.97814130168881.02185869831124
751615.68795394749790.312046052502121
761713.8959549254283.10404507457196
771616.4113991965651-0.411399196565127
781614.73465172023611.26534827976388
791314.7191683601262-1.7191683601262
801616.2592942566249-0.259294256624925
811615.41571777283930.584282227160667
822015.73258575152344.26741424847655
831615.74379415608850.256205843911455
841515.6598462631663-0.659846263166314
851515.0797536965659-0.0797536965658817
861614.60205567952271.39794432047734
871413.94444291232510.0555570876748513
881615.30447039532820.695529604671796
891614.71806870465231.28193129534766
901513.99566550188591.0043344981141
911213.4770257789574-1.47702577895736
921717.0034204655277-0.00342046552774608
931615.30441454617570.695585453824265
941515.1431852950738-0.143185295073776
951315.2334079345287-2.23340793452866
961615.14984145249140.850158547508631
971615.81954274956430.180457250435656
981614.19630168775531.8036983122447
991615.90536692076370.0946330792362544
1001414.4153517187213-0.415351718721295
1011617.2315543568726-1.23155435687259
1021614.74970861010071.2502913898993
1032017.44504408708142.55495591291857
1041514.75238004344740.247619956552613
1051614.22779372835691.77220627164306
1061315.1679166276171-2.16791662761715
1071716.03079917499220.969200825007827
1081615.90524258950580.0947574104941714
1091614.58093207250031.41906792749967
1101212.6828776605177-0.682877660517651
1111615.00838048136550.99161951863453
1121615.92270979581210.0772902041878789
1131714.50085285711632.49914714288371
1141314.3944680199856-1.39446801998555
1151214.8858845433333-2.88588454333326
1161816.91701902541241.08298097458761
1171415.7314335654847-1.73143356548465
1181412.9730653111861.02693468881402
1191315.2251962433589-2.22519624335888
1201615.7056733165750.294326683425024
1211314.4104083875767-1.4104083875767
1221616.1765057111914-0.176505711191434
1231316.1264131266932-3.1264131266932
1241617.2413972377472-1.24139723774716
1251516.0833521053397-1.08335210533972
1261616.7952181337372-0.795218133737169
1271515.4795167199946-0.479516719994626
1281716.28692014191520.713079858084837
1291514.23423516978130.765764830218692
1301214.9855606167057-2.98556061670575
1311614.05442604165161.94557395834841
1321013.2138202953687-3.21382029536867
1331614.1613286587081.83867134129198
1341213.7834815283117-1.78348152831168
1351415.5828553270458-1.58285532704581
1361515.5548099139171-0.554809913917056
1371312.25849782936040.74150217063961
1381514.80315150198180.196848498018185
1391113.5782716010212-2.57827160102123
1401213.5947488989171-1.59474889891713
141813.25336008063-5.25336008063002
1421613.57838764469162.42161235530839
1431513.19718158616471.80281841383526
1441716.61005755019770.38994244980231
1451614.82179766327081.17820233672918
1461014.3296723687622-4.32967236876216
1471815.9443852309062.05561476909398
1481315.204575037306-2.20457503730601
1491615.15633320329670.843666796703336
1501313.2347486349956-0.234748634995578
1511013.2387743267346-3.2387743267346
1521516.547732200981-1.54773220098101
1531614.53222945146841.46777054853163
1541612.11987354280243.88012645719757
1551412.85533452757341.14466547242656
1561012.5470579931821-2.5470579931821
1571717.0034204655277-0.00342046552774608
1581311.78823882813531.2117611718647
1591514.23423516978130.765764830218692
1601615.39256316691350.607436833086464
1611212.5211532777154-0.521153277715445
1621312.90884230128860.0911576987113719

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 13 & 16.289728550447 & -3.28972855044704 \tabularnewline
2 & 16 & 15.8100908303743 & 0.189909169625724 \tabularnewline
3 & 19 & 16.2458692222564 & 2.75413077774365 \tabularnewline
4 & 15 & 11.6156643924613 & 3.38433560753873 \tabularnewline
5 & 14 & 15.6197846608352 & -1.61978466083517 \tabularnewline
6 & 13 & 14.9395532042912 & -1.93955320429118 \tabularnewline
7 & 19 & 15.1989993115844 & 3.80100068841559 \tabularnewline
8 & 15 & 16.6722327568758 & -1.67223275687579 \tabularnewline
9 & 14 & 15.8347596511419 & -1.83475965114191 \tabularnewline
10 & 15 & 12.2117490121551 & 2.78825098784491 \tabularnewline
11 & 16 & 15.2151674899568 & 0.784832510043216 \tabularnewline
12 & 16 & 16.2188211115048 & -0.218821111504787 \tabularnewline
13 & 16 & 15.2445809637403 & 0.755419036259674 \tabularnewline
14 & 16 & 15.346224347435 & 0.653775652564983 \tabularnewline
15 & 17 & 17.5179468851836 & -0.517946885183623 \tabularnewline
16 & 15 & 15.055749549101 & -0.0557495491010007 \tabularnewline
17 & 15 & 14.5851817933216 & 0.414818206678439 \tabularnewline
18 & 20 & 16.1411254592246 & 3.85887454077539 \tabularnewline
19 & 18 & 15.7226340373549 & 2.27736596264514 \tabularnewline
20 & 16 & 15.5089313613142 & 0.491068638685751 \tabularnewline
21 & 16 & 15.2004667600702 & 0.799533239929763 \tabularnewline
22 & 16 & 14.838970329208 & 1.16102967079198 \tabularnewline
23 & 19 & 16.6169906782287 & 2.38300932177135 \tabularnewline
24 & 16 & 15.0021278263933 & 0.997872173606671 \tabularnewline
25 & 17 & 14.5546928234437 & 2.4453071765563 \tabularnewline
26 & 17 & 16.6566275119702 & 0.343372488029807 \tabularnewline
27 & 16 & 15.2291097440478 & 0.770890255952179 \tabularnewline
28 & 15 & 16.1833287453263 & -1.18332874532627 \tabularnewline
29 & 16 & 15.2380572660065 & 0.761942733993486 \tabularnewline
30 & 14 & 13.9918542342248 & 0.00814576577522982 \tabularnewline
31 & 15 & 15.6376892955541 & -0.637689295554069 \tabularnewline
32 & 12 & 12.081981933521 & -0.0819819335210383 \tabularnewline
33 & 14 & 15.200140143711 & -1.20014014371098 \tabularnewline
34 & 16 & 15.4889611204767 & 0.511038879523317 \tabularnewline
35 & 14 & 15.3493734643438 & -1.34937346434381 \tabularnewline
36 & 7 & 12.8311312272755 & -5.83113122727548 \tabularnewline
37 & 10 & 10.6186530953317 & -0.618653095331697 \tabularnewline
38 & 14 & 15.6148413853661 & -1.61484138536606 \tabularnewline
39 & 16 & 14.2031706327424 & 1.79682936725761 \tabularnewline
40 & 16 & 14.4645576623039 & 1.5354423376961 \tabularnewline
41 & 16 & 14.9159491946806 & 1.08405080531945 \tabularnewline
42 & 14 & 15.6461779403008 & -1.64617794030081 \tabularnewline
43 & 20 & 17.8617656107694 & 2.13823438923063 \tabularnewline
44 & 14 & 14.1738998659741 & -0.173899865974054 \tabularnewline
45 & 14 & 15.1594629837279 & -1.15946298372786 \tabularnewline
46 & 11 & 15.7127146882838 & -4.71271468828382 \tabularnewline
47 & 14 & 16.5517327119025 & -2.55173271190246 \tabularnewline
48 & 15 & 14.9064354103134 & 0.0935645896866252 \tabularnewline
49 & 16 & 14.7731357274214 & 1.22686427257858 \tabularnewline
50 & 14 & 15.8432677849884 & -1.84326778498844 \tabularnewline
51 & 16 & 16.326180922141 & -0.326180922140968 \tabularnewline
52 & 14 & 13.9428648105498 & 0.0571351894502274 \tabularnewline
53 & 12 & 14.44261378339 & -2.44261378339 \tabularnewline
54 & 16 & 15.8027093905852 & 0.197290609414802 \tabularnewline
55 & 9 & 11.2856476236748 & -2.28564762367482 \tabularnewline
56 & 14 & 12.5947858565761 & 1.40521414342389 \tabularnewline
57 & 16 & 16.1251203486152 & -0.12512034861519 \tabularnewline
58 & 16 & 15.0407746799135 & 0.959225320086536 \tabularnewline
59 & 15 & 15.2175476285225 & -0.217547628522455 \tabularnewline
60 & 16 & 14.1989253143235 & 1.80107468567653 \tabularnewline
61 & 12 & 11.2914531269331 & 0.708546873066912 \tabularnewline
62 & 16 & 15.9523128210885 & 0.0476871789115212 \tabularnewline
63 & 16 & 16.4478900201702 & -0.447890020170158 \tabularnewline
64 & 14 & 14.6866664323542 & -0.686666432354236 \tabularnewline
65 & 16 & 15.6984682447103 & 0.301531755289695 \tabularnewline
66 & 17 & 15.7538237974639 & 1.24617620253608 \tabularnewline
67 & 18 & 16.2360170195133 & 1.76398298048667 \tabularnewline
68 & 18 & 14.5452632587511 & 3.45473674124894 \tabularnewline
69 & 12 & 15.7517831610889 & -3.75178316108887 \tabularnewline
70 & 16 & 15.2180689949131 & 0.781931005086882 \tabularnewline
71 & 10 & 13.4183700477434 & -3.41837004774335 \tabularnewline
72 & 14 & 14.258272769468 & -0.258272769467987 \tabularnewline
73 & 18 & 16.2277491575068 & 1.77225084249323 \tabularnewline
74 & 18 & 16.9781413016888 & 1.02185869831124 \tabularnewline
75 & 16 & 15.6879539474979 & 0.312046052502121 \tabularnewline
76 & 17 & 13.895954925428 & 3.10404507457196 \tabularnewline
77 & 16 & 16.4113991965651 & -0.411399196565127 \tabularnewline
78 & 16 & 14.7346517202361 & 1.26534827976388 \tabularnewline
79 & 13 & 14.7191683601262 & -1.7191683601262 \tabularnewline
80 & 16 & 16.2592942566249 & -0.259294256624925 \tabularnewline
81 & 16 & 15.4157177728393 & 0.584282227160667 \tabularnewline
82 & 20 & 15.7325857515234 & 4.26741424847655 \tabularnewline
83 & 16 & 15.7437941560885 & 0.256205843911455 \tabularnewline
84 & 15 & 15.6598462631663 & -0.659846263166314 \tabularnewline
85 & 15 & 15.0797536965659 & -0.0797536965658817 \tabularnewline
86 & 16 & 14.6020556795227 & 1.39794432047734 \tabularnewline
87 & 14 & 13.9444429123251 & 0.0555570876748513 \tabularnewline
88 & 16 & 15.3044703953282 & 0.695529604671796 \tabularnewline
89 & 16 & 14.7180687046523 & 1.28193129534766 \tabularnewline
90 & 15 & 13.9956655018859 & 1.0043344981141 \tabularnewline
91 & 12 & 13.4770257789574 & -1.47702577895736 \tabularnewline
92 & 17 & 17.0034204655277 & -0.00342046552774608 \tabularnewline
93 & 16 & 15.3044145461757 & 0.695585453824265 \tabularnewline
94 & 15 & 15.1431852950738 & -0.143185295073776 \tabularnewline
95 & 13 & 15.2334079345287 & -2.23340793452866 \tabularnewline
96 & 16 & 15.1498414524914 & 0.850158547508631 \tabularnewline
97 & 16 & 15.8195427495643 & 0.180457250435656 \tabularnewline
98 & 16 & 14.1963016877553 & 1.8036983122447 \tabularnewline
99 & 16 & 15.9053669207637 & 0.0946330792362544 \tabularnewline
100 & 14 & 14.4153517187213 & -0.415351718721295 \tabularnewline
101 & 16 & 17.2315543568726 & -1.23155435687259 \tabularnewline
102 & 16 & 14.7497086101007 & 1.2502913898993 \tabularnewline
103 & 20 & 17.4450440870814 & 2.55495591291857 \tabularnewline
104 & 15 & 14.7523800434474 & 0.247619956552613 \tabularnewline
105 & 16 & 14.2277937283569 & 1.77220627164306 \tabularnewline
106 & 13 & 15.1679166276171 & -2.16791662761715 \tabularnewline
107 & 17 & 16.0307991749922 & 0.969200825007827 \tabularnewline
108 & 16 & 15.9052425895058 & 0.0947574104941714 \tabularnewline
109 & 16 & 14.5809320725003 & 1.41906792749967 \tabularnewline
110 & 12 & 12.6828776605177 & -0.682877660517651 \tabularnewline
111 & 16 & 15.0083804813655 & 0.99161951863453 \tabularnewline
112 & 16 & 15.9227097958121 & 0.0772902041878789 \tabularnewline
113 & 17 & 14.5008528571163 & 2.49914714288371 \tabularnewline
114 & 13 & 14.3944680199856 & -1.39446801998555 \tabularnewline
115 & 12 & 14.8858845433333 & -2.88588454333326 \tabularnewline
116 & 18 & 16.9170190254124 & 1.08298097458761 \tabularnewline
117 & 14 & 15.7314335654847 & -1.73143356548465 \tabularnewline
118 & 14 & 12.973065311186 & 1.02693468881402 \tabularnewline
119 & 13 & 15.2251962433589 & -2.22519624335888 \tabularnewline
120 & 16 & 15.705673316575 & 0.294326683425024 \tabularnewline
121 & 13 & 14.4104083875767 & -1.4104083875767 \tabularnewline
122 & 16 & 16.1765057111914 & -0.176505711191434 \tabularnewline
123 & 13 & 16.1264131266932 & -3.1264131266932 \tabularnewline
124 & 16 & 17.2413972377472 & -1.24139723774716 \tabularnewline
125 & 15 & 16.0833521053397 & -1.08335210533972 \tabularnewline
126 & 16 & 16.7952181337372 & -0.795218133737169 \tabularnewline
127 & 15 & 15.4795167199946 & -0.479516719994626 \tabularnewline
128 & 17 & 16.2869201419152 & 0.713079858084837 \tabularnewline
129 & 15 & 14.2342351697813 & 0.765764830218692 \tabularnewline
130 & 12 & 14.9855606167057 & -2.98556061670575 \tabularnewline
131 & 16 & 14.0544260416516 & 1.94557395834841 \tabularnewline
132 & 10 & 13.2138202953687 & -3.21382029536867 \tabularnewline
133 & 16 & 14.161328658708 & 1.83867134129198 \tabularnewline
134 & 12 & 13.7834815283117 & -1.78348152831168 \tabularnewline
135 & 14 & 15.5828553270458 & -1.58285532704581 \tabularnewline
136 & 15 & 15.5548099139171 & -0.554809913917056 \tabularnewline
137 & 13 & 12.2584978293604 & 0.74150217063961 \tabularnewline
138 & 15 & 14.8031515019818 & 0.196848498018185 \tabularnewline
139 & 11 & 13.5782716010212 & -2.57827160102123 \tabularnewline
140 & 12 & 13.5947488989171 & -1.59474889891713 \tabularnewline
141 & 8 & 13.25336008063 & -5.25336008063002 \tabularnewline
142 & 16 & 13.5783876446916 & 2.42161235530839 \tabularnewline
143 & 15 & 13.1971815861647 & 1.80281841383526 \tabularnewline
144 & 17 & 16.6100575501977 & 0.38994244980231 \tabularnewline
145 & 16 & 14.8217976632708 & 1.17820233672918 \tabularnewline
146 & 10 & 14.3296723687622 & -4.32967236876216 \tabularnewline
147 & 18 & 15.944385230906 & 2.05561476909398 \tabularnewline
148 & 13 & 15.204575037306 & -2.20457503730601 \tabularnewline
149 & 16 & 15.1563332032967 & 0.843666796703336 \tabularnewline
150 & 13 & 13.2347486349956 & -0.234748634995578 \tabularnewline
151 & 10 & 13.2387743267346 & -3.2387743267346 \tabularnewline
152 & 15 & 16.547732200981 & -1.54773220098101 \tabularnewline
153 & 16 & 14.5322294514684 & 1.46777054853163 \tabularnewline
154 & 16 & 12.1198735428024 & 3.88012645719757 \tabularnewline
155 & 14 & 12.8553345275734 & 1.14466547242656 \tabularnewline
156 & 10 & 12.5470579931821 & -2.5470579931821 \tabularnewline
157 & 17 & 17.0034204655277 & -0.00342046552774608 \tabularnewline
158 & 13 & 11.7882388281353 & 1.2117611718647 \tabularnewline
159 & 15 & 14.2342351697813 & 0.765764830218692 \tabularnewline
160 & 16 & 15.3925631669135 & 0.607436833086464 \tabularnewline
161 & 12 & 12.5211532777154 & -0.521153277715445 \tabularnewline
162 & 13 & 12.9088423012886 & 0.0911576987113719 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=198248&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]16.289728550447[/C][C]-3.28972855044704[/C][/ROW]
[ROW][C]2[/C][C]16[/C][C]15.8100908303743[/C][C]0.189909169625724[/C][/ROW]
[ROW][C]3[/C][C]19[/C][C]16.2458692222564[/C][C]2.75413077774365[/C][/ROW]
[ROW][C]4[/C][C]15[/C][C]11.6156643924613[/C][C]3.38433560753873[/C][/ROW]
[ROW][C]5[/C][C]14[/C][C]15.6197846608352[/C][C]-1.61978466083517[/C][/ROW]
[ROW][C]6[/C][C]13[/C][C]14.9395532042912[/C][C]-1.93955320429118[/C][/ROW]
[ROW][C]7[/C][C]19[/C][C]15.1989993115844[/C][C]3.80100068841559[/C][/ROW]
[ROW][C]8[/C][C]15[/C][C]16.6722327568758[/C][C]-1.67223275687579[/C][/ROW]
[ROW][C]9[/C][C]14[/C][C]15.8347596511419[/C][C]-1.83475965114191[/C][/ROW]
[ROW][C]10[/C][C]15[/C][C]12.2117490121551[/C][C]2.78825098784491[/C][/ROW]
[ROW][C]11[/C][C]16[/C][C]15.2151674899568[/C][C]0.784832510043216[/C][/ROW]
[ROW][C]12[/C][C]16[/C][C]16.2188211115048[/C][C]-0.218821111504787[/C][/ROW]
[ROW][C]13[/C][C]16[/C][C]15.2445809637403[/C][C]0.755419036259674[/C][/ROW]
[ROW][C]14[/C][C]16[/C][C]15.346224347435[/C][C]0.653775652564983[/C][/ROW]
[ROW][C]15[/C][C]17[/C][C]17.5179468851836[/C][C]-0.517946885183623[/C][/ROW]
[ROW][C]16[/C][C]15[/C][C]15.055749549101[/C][C]-0.0557495491010007[/C][/ROW]
[ROW][C]17[/C][C]15[/C][C]14.5851817933216[/C][C]0.414818206678439[/C][/ROW]
[ROW][C]18[/C][C]20[/C][C]16.1411254592246[/C][C]3.85887454077539[/C][/ROW]
[ROW][C]19[/C][C]18[/C][C]15.7226340373549[/C][C]2.27736596264514[/C][/ROW]
[ROW][C]20[/C][C]16[/C][C]15.5089313613142[/C][C]0.491068638685751[/C][/ROW]
[ROW][C]21[/C][C]16[/C][C]15.2004667600702[/C][C]0.799533239929763[/C][/ROW]
[ROW][C]22[/C][C]16[/C][C]14.838970329208[/C][C]1.16102967079198[/C][/ROW]
[ROW][C]23[/C][C]19[/C][C]16.6169906782287[/C][C]2.38300932177135[/C][/ROW]
[ROW][C]24[/C][C]16[/C][C]15.0021278263933[/C][C]0.997872173606671[/C][/ROW]
[ROW][C]25[/C][C]17[/C][C]14.5546928234437[/C][C]2.4453071765563[/C][/ROW]
[ROW][C]26[/C][C]17[/C][C]16.6566275119702[/C][C]0.343372488029807[/C][/ROW]
[ROW][C]27[/C][C]16[/C][C]15.2291097440478[/C][C]0.770890255952179[/C][/ROW]
[ROW][C]28[/C][C]15[/C][C]16.1833287453263[/C][C]-1.18332874532627[/C][/ROW]
[ROW][C]29[/C][C]16[/C][C]15.2380572660065[/C][C]0.761942733993486[/C][/ROW]
[ROW][C]30[/C][C]14[/C][C]13.9918542342248[/C][C]0.00814576577522982[/C][/ROW]
[ROW][C]31[/C][C]15[/C][C]15.6376892955541[/C][C]-0.637689295554069[/C][/ROW]
[ROW][C]32[/C][C]12[/C][C]12.081981933521[/C][C]-0.0819819335210383[/C][/ROW]
[ROW][C]33[/C][C]14[/C][C]15.200140143711[/C][C]-1.20014014371098[/C][/ROW]
[ROW][C]34[/C][C]16[/C][C]15.4889611204767[/C][C]0.511038879523317[/C][/ROW]
[ROW][C]35[/C][C]14[/C][C]15.3493734643438[/C][C]-1.34937346434381[/C][/ROW]
[ROW][C]36[/C][C]7[/C][C]12.8311312272755[/C][C]-5.83113122727548[/C][/ROW]
[ROW][C]37[/C][C]10[/C][C]10.6186530953317[/C][C]-0.618653095331697[/C][/ROW]
[ROW][C]38[/C][C]14[/C][C]15.6148413853661[/C][C]-1.61484138536606[/C][/ROW]
[ROW][C]39[/C][C]16[/C][C]14.2031706327424[/C][C]1.79682936725761[/C][/ROW]
[ROW][C]40[/C][C]16[/C][C]14.4645576623039[/C][C]1.5354423376961[/C][/ROW]
[ROW][C]41[/C][C]16[/C][C]14.9159491946806[/C][C]1.08405080531945[/C][/ROW]
[ROW][C]42[/C][C]14[/C][C]15.6461779403008[/C][C]-1.64617794030081[/C][/ROW]
[ROW][C]43[/C][C]20[/C][C]17.8617656107694[/C][C]2.13823438923063[/C][/ROW]
[ROW][C]44[/C][C]14[/C][C]14.1738998659741[/C][C]-0.173899865974054[/C][/ROW]
[ROW][C]45[/C][C]14[/C][C]15.1594629837279[/C][C]-1.15946298372786[/C][/ROW]
[ROW][C]46[/C][C]11[/C][C]15.7127146882838[/C][C]-4.71271468828382[/C][/ROW]
[ROW][C]47[/C][C]14[/C][C]16.5517327119025[/C][C]-2.55173271190246[/C][/ROW]
[ROW][C]48[/C][C]15[/C][C]14.9064354103134[/C][C]0.0935645896866252[/C][/ROW]
[ROW][C]49[/C][C]16[/C][C]14.7731357274214[/C][C]1.22686427257858[/C][/ROW]
[ROW][C]50[/C][C]14[/C][C]15.8432677849884[/C][C]-1.84326778498844[/C][/ROW]
[ROW][C]51[/C][C]16[/C][C]16.326180922141[/C][C]-0.326180922140968[/C][/ROW]
[ROW][C]52[/C][C]14[/C][C]13.9428648105498[/C][C]0.0571351894502274[/C][/ROW]
[ROW][C]53[/C][C]12[/C][C]14.44261378339[/C][C]-2.44261378339[/C][/ROW]
[ROW][C]54[/C][C]16[/C][C]15.8027093905852[/C][C]0.197290609414802[/C][/ROW]
[ROW][C]55[/C][C]9[/C][C]11.2856476236748[/C][C]-2.28564762367482[/C][/ROW]
[ROW][C]56[/C][C]14[/C][C]12.5947858565761[/C][C]1.40521414342389[/C][/ROW]
[ROW][C]57[/C][C]16[/C][C]16.1251203486152[/C][C]-0.12512034861519[/C][/ROW]
[ROW][C]58[/C][C]16[/C][C]15.0407746799135[/C][C]0.959225320086536[/C][/ROW]
[ROW][C]59[/C][C]15[/C][C]15.2175476285225[/C][C]-0.217547628522455[/C][/ROW]
[ROW][C]60[/C][C]16[/C][C]14.1989253143235[/C][C]1.80107468567653[/C][/ROW]
[ROW][C]61[/C][C]12[/C][C]11.2914531269331[/C][C]0.708546873066912[/C][/ROW]
[ROW][C]62[/C][C]16[/C][C]15.9523128210885[/C][C]0.0476871789115212[/C][/ROW]
[ROW][C]63[/C][C]16[/C][C]16.4478900201702[/C][C]-0.447890020170158[/C][/ROW]
[ROW][C]64[/C][C]14[/C][C]14.6866664323542[/C][C]-0.686666432354236[/C][/ROW]
[ROW][C]65[/C][C]16[/C][C]15.6984682447103[/C][C]0.301531755289695[/C][/ROW]
[ROW][C]66[/C][C]17[/C][C]15.7538237974639[/C][C]1.24617620253608[/C][/ROW]
[ROW][C]67[/C][C]18[/C][C]16.2360170195133[/C][C]1.76398298048667[/C][/ROW]
[ROW][C]68[/C][C]18[/C][C]14.5452632587511[/C][C]3.45473674124894[/C][/ROW]
[ROW][C]69[/C][C]12[/C][C]15.7517831610889[/C][C]-3.75178316108887[/C][/ROW]
[ROW][C]70[/C][C]16[/C][C]15.2180689949131[/C][C]0.781931005086882[/C][/ROW]
[ROW][C]71[/C][C]10[/C][C]13.4183700477434[/C][C]-3.41837004774335[/C][/ROW]
[ROW][C]72[/C][C]14[/C][C]14.258272769468[/C][C]-0.258272769467987[/C][/ROW]
[ROW][C]73[/C][C]18[/C][C]16.2277491575068[/C][C]1.77225084249323[/C][/ROW]
[ROW][C]74[/C][C]18[/C][C]16.9781413016888[/C][C]1.02185869831124[/C][/ROW]
[ROW][C]75[/C][C]16[/C][C]15.6879539474979[/C][C]0.312046052502121[/C][/ROW]
[ROW][C]76[/C][C]17[/C][C]13.895954925428[/C][C]3.10404507457196[/C][/ROW]
[ROW][C]77[/C][C]16[/C][C]16.4113991965651[/C][C]-0.411399196565127[/C][/ROW]
[ROW][C]78[/C][C]16[/C][C]14.7346517202361[/C][C]1.26534827976388[/C][/ROW]
[ROW][C]79[/C][C]13[/C][C]14.7191683601262[/C][C]-1.7191683601262[/C][/ROW]
[ROW][C]80[/C][C]16[/C][C]16.2592942566249[/C][C]-0.259294256624925[/C][/ROW]
[ROW][C]81[/C][C]16[/C][C]15.4157177728393[/C][C]0.584282227160667[/C][/ROW]
[ROW][C]82[/C][C]20[/C][C]15.7325857515234[/C][C]4.26741424847655[/C][/ROW]
[ROW][C]83[/C][C]16[/C][C]15.7437941560885[/C][C]0.256205843911455[/C][/ROW]
[ROW][C]84[/C][C]15[/C][C]15.6598462631663[/C][C]-0.659846263166314[/C][/ROW]
[ROW][C]85[/C][C]15[/C][C]15.0797536965659[/C][C]-0.0797536965658817[/C][/ROW]
[ROW][C]86[/C][C]16[/C][C]14.6020556795227[/C][C]1.39794432047734[/C][/ROW]
[ROW][C]87[/C][C]14[/C][C]13.9444429123251[/C][C]0.0555570876748513[/C][/ROW]
[ROW][C]88[/C][C]16[/C][C]15.3044703953282[/C][C]0.695529604671796[/C][/ROW]
[ROW][C]89[/C][C]16[/C][C]14.7180687046523[/C][C]1.28193129534766[/C][/ROW]
[ROW][C]90[/C][C]15[/C][C]13.9956655018859[/C][C]1.0043344981141[/C][/ROW]
[ROW][C]91[/C][C]12[/C][C]13.4770257789574[/C][C]-1.47702577895736[/C][/ROW]
[ROW][C]92[/C][C]17[/C][C]17.0034204655277[/C][C]-0.00342046552774608[/C][/ROW]
[ROW][C]93[/C][C]16[/C][C]15.3044145461757[/C][C]0.695585453824265[/C][/ROW]
[ROW][C]94[/C][C]15[/C][C]15.1431852950738[/C][C]-0.143185295073776[/C][/ROW]
[ROW][C]95[/C][C]13[/C][C]15.2334079345287[/C][C]-2.23340793452866[/C][/ROW]
[ROW][C]96[/C][C]16[/C][C]15.1498414524914[/C][C]0.850158547508631[/C][/ROW]
[ROW][C]97[/C][C]16[/C][C]15.8195427495643[/C][C]0.180457250435656[/C][/ROW]
[ROW][C]98[/C][C]16[/C][C]14.1963016877553[/C][C]1.8036983122447[/C][/ROW]
[ROW][C]99[/C][C]16[/C][C]15.9053669207637[/C][C]0.0946330792362544[/C][/ROW]
[ROW][C]100[/C][C]14[/C][C]14.4153517187213[/C][C]-0.415351718721295[/C][/ROW]
[ROW][C]101[/C][C]16[/C][C]17.2315543568726[/C][C]-1.23155435687259[/C][/ROW]
[ROW][C]102[/C][C]16[/C][C]14.7497086101007[/C][C]1.2502913898993[/C][/ROW]
[ROW][C]103[/C][C]20[/C][C]17.4450440870814[/C][C]2.55495591291857[/C][/ROW]
[ROW][C]104[/C][C]15[/C][C]14.7523800434474[/C][C]0.247619956552613[/C][/ROW]
[ROW][C]105[/C][C]16[/C][C]14.2277937283569[/C][C]1.77220627164306[/C][/ROW]
[ROW][C]106[/C][C]13[/C][C]15.1679166276171[/C][C]-2.16791662761715[/C][/ROW]
[ROW][C]107[/C][C]17[/C][C]16.0307991749922[/C][C]0.969200825007827[/C][/ROW]
[ROW][C]108[/C][C]16[/C][C]15.9052425895058[/C][C]0.0947574104941714[/C][/ROW]
[ROW][C]109[/C][C]16[/C][C]14.5809320725003[/C][C]1.41906792749967[/C][/ROW]
[ROW][C]110[/C][C]12[/C][C]12.6828776605177[/C][C]-0.682877660517651[/C][/ROW]
[ROW][C]111[/C][C]16[/C][C]15.0083804813655[/C][C]0.99161951863453[/C][/ROW]
[ROW][C]112[/C][C]16[/C][C]15.9227097958121[/C][C]0.0772902041878789[/C][/ROW]
[ROW][C]113[/C][C]17[/C][C]14.5008528571163[/C][C]2.49914714288371[/C][/ROW]
[ROW][C]114[/C][C]13[/C][C]14.3944680199856[/C][C]-1.39446801998555[/C][/ROW]
[ROW][C]115[/C][C]12[/C][C]14.8858845433333[/C][C]-2.88588454333326[/C][/ROW]
[ROW][C]116[/C][C]18[/C][C]16.9170190254124[/C][C]1.08298097458761[/C][/ROW]
[ROW][C]117[/C][C]14[/C][C]15.7314335654847[/C][C]-1.73143356548465[/C][/ROW]
[ROW][C]118[/C][C]14[/C][C]12.973065311186[/C][C]1.02693468881402[/C][/ROW]
[ROW][C]119[/C][C]13[/C][C]15.2251962433589[/C][C]-2.22519624335888[/C][/ROW]
[ROW][C]120[/C][C]16[/C][C]15.705673316575[/C][C]0.294326683425024[/C][/ROW]
[ROW][C]121[/C][C]13[/C][C]14.4104083875767[/C][C]-1.4104083875767[/C][/ROW]
[ROW][C]122[/C][C]16[/C][C]16.1765057111914[/C][C]-0.176505711191434[/C][/ROW]
[ROW][C]123[/C][C]13[/C][C]16.1264131266932[/C][C]-3.1264131266932[/C][/ROW]
[ROW][C]124[/C][C]16[/C][C]17.2413972377472[/C][C]-1.24139723774716[/C][/ROW]
[ROW][C]125[/C][C]15[/C][C]16.0833521053397[/C][C]-1.08335210533972[/C][/ROW]
[ROW][C]126[/C][C]16[/C][C]16.7952181337372[/C][C]-0.795218133737169[/C][/ROW]
[ROW][C]127[/C][C]15[/C][C]15.4795167199946[/C][C]-0.479516719994626[/C][/ROW]
[ROW][C]128[/C][C]17[/C][C]16.2869201419152[/C][C]0.713079858084837[/C][/ROW]
[ROW][C]129[/C][C]15[/C][C]14.2342351697813[/C][C]0.765764830218692[/C][/ROW]
[ROW][C]130[/C][C]12[/C][C]14.9855606167057[/C][C]-2.98556061670575[/C][/ROW]
[ROW][C]131[/C][C]16[/C][C]14.0544260416516[/C][C]1.94557395834841[/C][/ROW]
[ROW][C]132[/C][C]10[/C][C]13.2138202953687[/C][C]-3.21382029536867[/C][/ROW]
[ROW][C]133[/C][C]16[/C][C]14.161328658708[/C][C]1.83867134129198[/C][/ROW]
[ROW][C]134[/C][C]12[/C][C]13.7834815283117[/C][C]-1.78348152831168[/C][/ROW]
[ROW][C]135[/C][C]14[/C][C]15.5828553270458[/C][C]-1.58285532704581[/C][/ROW]
[ROW][C]136[/C][C]15[/C][C]15.5548099139171[/C][C]-0.554809913917056[/C][/ROW]
[ROW][C]137[/C][C]13[/C][C]12.2584978293604[/C][C]0.74150217063961[/C][/ROW]
[ROW][C]138[/C][C]15[/C][C]14.8031515019818[/C][C]0.196848498018185[/C][/ROW]
[ROW][C]139[/C][C]11[/C][C]13.5782716010212[/C][C]-2.57827160102123[/C][/ROW]
[ROW][C]140[/C][C]12[/C][C]13.5947488989171[/C][C]-1.59474889891713[/C][/ROW]
[ROW][C]141[/C][C]8[/C][C]13.25336008063[/C][C]-5.25336008063002[/C][/ROW]
[ROW][C]142[/C][C]16[/C][C]13.5783876446916[/C][C]2.42161235530839[/C][/ROW]
[ROW][C]143[/C][C]15[/C][C]13.1971815861647[/C][C]1.80281841383526[/C][/ROW]
[ROW][C]144[/C][C]17[/C][C]16.6100575501977[/C][C]0.38994244980231[/C][/ROW]
[ROW][C]145[/C][C]16[/C][C]14.8217976632708[/C][C]1.17820233672918[/C][/ROW]
[ROW][C]146[/C][C]10[/C][C]14.3296723687622[/C][C]-4.32967236876216[/C][/ROW]
[ROW][C]147[/C][C]18[/C][C]15.944385230906[/C][C]2.05561476909398[/C][/ROW]
[ROW][C]148[/C][C]13[/C][C]15.204575037306[/C][C]-2.20457503730601[/C][/ROW]
[ROW][C]149[/C][C]16[/C][C]15.1563332032967[/C][C]0.843666796703336[/C][/ROW]
[ROW][C]150[/C][C]13[/C][C]13.2347486349956[/C][C]-0.234748634995578[/C][/ROW]
[ROW][C]151[/C][C]10[/C][C]13.2387743267346[/C][C]-3.2387743267346[/C][/ROW]
[ROW][C]152[/C][C]15[/C][C]16.547732200981[/C][C]-1.54773220098101[/C][/ROW]
[ROW][C]153[/C][C]16[/C][C]14.5322294514684[/C][C]1.46777054853163[/C][/ROW]
[ROW][C]154[/C][C]16[/C][C]12.1198735428024[/C][C]3.88012645719757[/C][/ROW]
[ROW][C]155[/C][C]14[/C][C]12.8553345275734[/C][C]1.14466547242656[/C][/ROW]
[ROW][C]156[/C][C]10[/C][C]12.5470579931821[/C][C]-2.5470579931821[/C][/ROW]
[ROW][C]157[/C][C]17[/C][C]17.0034204655277[/C][C]-0.00342046552774608[/C][/ROW]
[ROW][C]158[/C][C]13[/C][C]11.7882388281353[/C][C]1.2117611718647[/C][/ROW]
[ROW][C]159[/C][C]15[/C][C]14.2342351697813[/C][C]0.765764830218692[/C][/ROW]
[ROW][C]160[/C][C]16[/C][C]15.3925631669135[/C][C]0.607436833086464[/C][/ROW]
[ROW][C]161[/C][C]12[/C][C]12.5211532777154[/C][C]-0.521153277715445[/C][/ROW]
[ROW][C]162[/C][C]13[/C][C]12.9088423012886[/C][C]0.0911576987113719[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=198248&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=198248&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
11316.289728550447-3.28972855044704
21615.81009083037430.189909169625724
31916.24586922225642.75413077774365
41511.61566439246133.38433560753873
51415.6197846608352-1.61978466083517
61314.9395532042912-1.93955320429118
71915.19899931158443.80100068841559
81516.6722327568758-1.67223275687579
91415.8347596511419-1.83475965114191
101512.21174901215512.78825098784491
111615.21516748995680.784832510043216
121616.2188211115048-0.218821111504787
131615.24458096374030.755419036259674
141615.3462243474350.653775652564983
151717.5179468851836-0.517946885183623
161515.055749549101-0.0557495491010007
171514.58518179332160.414818206678439
182016.14112545922463.85887454077539
191815.72263403735492.27736596264514
201615.50893136131420.491068638685751
211615.20046676007020.799533239929763
221614.8389703292081.16102967079198
231916.61699067822872.38300932177135
241615.00212782639330.997872173606671
251714.55469282344372.4453071765563
261716.65662751197020.343372488029807
271615.22910974404780.770890255952179
281516.1833287453263-1.18332874532627
291615.23805726600650.761942733993486
301413.99185423422480.00814576577522982
311515.6376892955541-0.637689295554069
321212.081981933521-0.0819819335210383
331415.200140143711-1.20014014371098
341615.48896112047670.511038879523317
351415.3493734643438-1.34937346434381
36712.8311312272755-5.83113122727548
371010.6186530953317-0.618653095331697
381415.6148413853661-1.61484138536606
391614.20317063274241.79682936725761
401614.46455766230391.5354423376961
411614.91594919468061.08405080531945
421415.6461779403008-1.64617794030081
432017.86176561076942.13823438923063
441414.1738998659741-0.173899865974054
451415.1594629837279-1.15946298372786
461115.7127146882838-4.71271468828382
471416.5517327119025-2.55173271190246
481514.90643541031340.0935645896866252
491614.77313572742141.22686427257858
501415.8432677849884-1.84326778498844
511616.326180922141-0.326180922140968
521413.94286481054980.0571351894502274
531214.44261378339-2.44261378339
541615.80270939058520.197290609414802
55911.2856476236748-2.28564762367482
561412.59478585657611.40521414342389
571616.1251203486152-0.12512034861519
581615.04077467991350.959225320086536
591515.2175476285225-0.217547628522455
601614.19892531432351.80107468567653
611211.29145312693310.708546873066912
621615.95231282108850.0476871789115212
631616.4478900201702-0.447890020170158
641414.6866664323542-0.686666432354236
651615.69846824471030.301531755289695
661715.75382379746391.24617620253608
671816.23601701951331.76398298048667
681814.54526325875113.45473674124894
691215.7517831610889-3.75178316108887
701615.21806899491310.781931005086882
711013.4183700477434-3.41837004774335
721414.258272769468-0.258272769467987
731816.22774915750681.77225084249323
741816.97814130168881.02185869831124
751615.68795394749790.312046052502121
761713.8959549254283.10404507457196
771616.4113991965651-0.411399196565127
781614.73465172023611.26534827976388
791314.7191683601262-1.7191683601262
801616.2592942566249-0.259294256624925
811615.41571777283930.584282227160667
822015.73258575152344.26741424847655
831615.74379415608850.256205843911455
841515.6598462631663-0.659846263166314
851515.0797536965659-0.0797536965658817
861614.60205567952271.39794432047734
871413.94444291232510.0555570876748513
881615.30447039532820.695529604671796
891614.71806870465231.28193129534766
901513.99566550188591.0043344981141
911213.4770257789574-1.47702577895736
921717.0034204655277-0.00342046552774608
931615.30441454617570.695585453824265
941515.1431852950738-0.143185295073776
951315.2334079345287-2.23340793452866
961615.14984145249140.850158547508631
971615.81954274956430.180457250435656
981614.19630168775531.8036983122447
991615.90536692076370.0946330792362544
1001414.4153517187213-0.415351718721295
1011617.2315543568726-1.23155435687259
1021614.74970861010071.2502913898993
1032017.44504408708142.55495591291857
1041514.75238004344740.247619956552613
1051614.22779372835691.77220627164306
1061315.1679166276171-2.16791662761715
1071716.03079917499220.969200825007827
1081615.90524258950580.0947574104941714
1091614.58093207250031.41906792749967
1101212.6828776605177-0.682877660517651
1111615.00838048136550.99161951863453
1121615.92270979581210.0772902041878789
1131714.50085285711632.49914714288371
1141314.3944680199856-1.39446801998555
1151214.8858845433333-2.88588454333326
1161816.91701902541241.08298097458761
1171415.7314335654847-1.73143356548465
1181412.9730653111861.02693468881402
1191315.2251962433589-2.22519624335888
1201615.7056733165750.294326683425024
1211314.4104083875767-1.4104083875767
1221616.1765057111914-0.176505711191434
1231316.1264131266932-3.1264131266932
1241617.2413972377472-1.24139723774716
1251516.0833521053397-1.08335210533972
1261616.7952181337372-0.795218133737169
1271515.4795167199946-0.479516719994626
1281716.28692014191520.713079858084837
1291514.23423516978130.765764830218692
1301214.9855606167057-2.98556061670575
1311614.05442604165161.94557395834841
1321013.2138202953687-3.21382029536867
1331614.1613286587081.83867134129198
1341213.7834815283117-1.78348152831168
1351415.5828553270458-1.58285532704581
1361515.5548099139171-0.554809913917056
1371312.25849782936040.74150217063961
1381514.80315150198180.196848498018185
1391113.5782716010212-2.57827160102123
1401213.5947488989171-1.59474889891713
141813.25336008063-5.25336008063002
1421613.57838764469162.42161235530839
1431513.19718158616471.80281841383526
1441716.61005755019770.38994244980231
1451614.82179766327081.17820233672918
1461014.3296723687622-4.32967236876216
1471815.9443852309062.05561476909398
1481315.204575037306-2.20457503730601
1491615.15633320329670.843666796703336
1501313.2347486349956-0.234748634995578
1511013.2387743267346-3.2387743267346
1521516.547732200981-1.54773220098101
1531614.53222945146841.46777054853163
1541612.11987354280243.88012645719757
1551412.85533452757341.14466547242656
1561012.5470579931821-2.5470579931821
1571717.0034204655277-0.00342046552774608
1581311.78823882813531.2117611718647
1591514.23423516978130.765764830218692
1601615.39256316691350.607436833086464
1611212.5211532777154-0.521153277715445
1621312.90884230128860.0911576987113719






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
120.2430607688952870.4861215377905740.756939231104713
130.4076903906615880.8153807813231760.592309609338412
140.309274457366390.6185489147327790.69072554263361
150.3292209712350370.6584419424700730.670779028764963
160.384108395133640.7682167902672810.61589160486636
170.3327772576598190.6655545153196390.667222742340181
180.7022321743331650.595535651333670.297767825666835
190.8595587430693630.2808825138612730.140441256930637
200.8612422909262150.277515418147570.138757709073785
210.8166395856452110.3667208287095790.183360414354789
220.7657883743692360.4684232512615280.234211625630764
230.8197194869858420.3605610260283150.180280513014158
240.7677162350421480.4645675299157040.232283764957852
250.7363172446783450.527365510643310.263682755321655
260.6744950106375770.6510099787248460.325504989362423
270.6418790172118720.7162419655762550.358120982788127
280.6271766959144880.7456466081710240.372823304085512
290.5621595845112070.8756808309775870.437840415488793
300.5578705733530940.8842588532938120.442129426646906
310.4924885828655360.9849771657310710.507511417134464
320.4805072580154080.9610145160308160.519492741984592
330.4374659618937460.8749319237874930.562534038106253
340.3773361007234070.7546722014468150.622663899276593
350.3787301275966450.757460255193290.621269872403355
360.8746775522164270.2506448955671450.125322447783573
370.8630801338221770.2738397323556460.136919866177823
380.8638037807330080.2723924385339850.136196219266992
390.875282663407830.2494346731843410.12471733659217
400.8609058146964880.2781883706070240.139094185303512
410.8362873175694880.3274253648610230.163712682430512
420.8244960503619860.3510078992760290.175503949638014
430.8292700031269190.3414599937461620.170729996873081
440.7963050070814240.4073899858371510.203694992918576
450.7605674992680220.4788650014639570.239432500731978
460.9056608882745480.1886782234509040.094339111725452
470.9311326702448730.1377346595102540.0688673297551271
480.9122197740812520.1755604518374960.0877802259187481
490.8959442580137230.2081114839725550.104055741986277
500.901183977727950.1976320445441010.0988160222720504
510.8793932563643830.2412134872712350.120606743635617
520.8525232694519260.2949534610961480.147476730548074
530.8798026209728710.2403947580542570.120197379027129
540.8546020067972730.2907959864054550.145397993202727
550.8636289813781260.2727420372437490.136371018621874
560.8486040336260750.302791932747850.151395966373925
570.8189240800410790.3621518399178420.181075919958921
580.7967132986536270.4065734026927460.203286701346373
590.7606243455681980.4787513088636040.239375654431802
600.7576903401682310.4846193196635380.242309659831769
610.7237915246163360.5524169507673290.276208475383664
620.68219214639250.6356157072150.3178078536075
630.6404225136253480.7191549727493030.359577486374652
640.5989045408610270.8021909182779470.401095459138973
650.5563363824822050.8873272350355890.443663617517795
660.5271258272718910.9457483454562180.472874172728109
670.514246315508850.9715073689823010.48575368449115
680.6484809859616960.7030380280766090.351519014038304
690.7754934805838310.4490130388323390.224506519416169
700.7437089872777610.5125820254444780.256291012722239
710.8196433838215540.3607132323568920.180356616178446
720.7871189584348550.425762083130290.212881041565145
730.7799162230678150.4401675538643690.220083776932185
740.7553037186583290.4893925626833420.244696281341671
750.7178716739429120.5642566521141770.282128326057088
760.7758537734044790.4482924531910410.224146226595521
770.7410252814739750.517949437052050.258974718526025
780.7190459724012520.5619080551974960.280954027598748
790.7171760833993940.5656478332012120.282823916600606
800.6767788202267670.6464423595464670.323221179773233
810.6372621376083870.7254757247832270.362737862391613
820.8101311737772590.3797376524454830.189868826222741
830.7778910643632580.4442178712734850.222108935636742
840.7496557748271390.5006884503457220.250344225172861
850.7120401127888820.5759197744222360.287959887211118
860.6942012508794820.6115974982410360.305798749120518
870.6527688880798680.6944622238402640.347231111920132
880.6160082752786550.767983449442690.383991724721345
890.5948216829854010.8103566340291990.405178317014599
900.5697416225409720.8605167549180560.430258377459028
910.5481103642797150.903779271440570.451889635720285
920.5125056854726790.9749886290546420.487494314527321
930.4737257969452730.9474515938905460.526274203054727
940.4283693498210.8567386996419990.571630650179
950.4493109846485270.8986219692970540.550689015351473
960.4150920554024320.8301841108048650.584907944597568
970.3780162664165660.7560325328331330.621983733583434
980.378867599284410.7577351985688210.62113240071559
990.3356756765625130.6713513531250260.664324323437487
1000.2987478410005750.5974956820011490.701252158999425
1010.2683370501399360.5366741002798720.731662949860064
1020.2527697561703760.5055395123407510.747230243829625
1030.3001520472328550.600304094465710.699847952767145
1040.2595408203713050.519081640742610.740459179628695
1050.2789116113755450.557823222751090.721088388624455
1060.2809684450418710.5619368900837410.719031554958129
1070.2695406629755460.5390813259510910.730459337024454
1080.2424582810608230.4849165621216450.757541718939177
1090.2439619706776610.4879239413553210.756038029322339
1100.2186891226315410.4373782452630820.781310877368459
1110.1934622448138430.3869244896276860.806537755186157
1120.1622027653446890.3244055306893770.837797234655311
1130.2021420400827670.4042840801655350.797857959917233
1140.1765808615837560.3531617231675120.823419138416244
1150.2018368655334660.4036737310669330.798163134466534
1160.1787157437098070.3574314874196140.821284256290193
1170.1590979241280780.3181958482561550.840902075871922
1180.1482370422835060.2964740845670110.851762957716494
1190.1672293371741430.3344586743482860.832770662825857
1200.1580297344420570.3160594688841130.841970265557943
1210.1358837947417930.2717675894835870.864116205258207
1220.1153927289910710.2307854579821410.884607271008929
1230.1387116709632130.2774233419264260.861288329036787
1240.1161038492938390.2322076985876770.883896150706161
1250.09675449503101310.1935089900620260.903245504968987
1260.07678143599324340.1535628719864870.923218564006757
1270.05836726526839160.1167345305367830.941632734731608
1280.05516174759302430.1103234951860490.944838252406976
1290.04168182127088860.08336364254177730.958318178729111
1300.05033162817273120.1006632563454620.949668371827269
1310.05352070923650010.1070414184730.9464792907635
1320.06784596244927230.1356919248985450.932154037550728
1330.092079217238580.184158434477160.90792078276142
1340.08711823571508780.1742364714301760.912881764284912
1350.06722650477733850.1344530095546770.932773495222662
1360.05995811167935780.1199162233587160.940041888320642
1370.04214500943314720.08429001886629430.957854990566853
1380.02912354406090320.05824708812180640.970876455939097
1390.1121013860171730.2242027720343460.887898613982827
1400.096931011519160.193862023038320.90306898848084
1410.5971922948139810.8056154103720380.402807705186019
1420.5573891226226890.8852217547546230.442610877377311
1430.6114286309394980.7771427381210050.388571369060502
1440.5350939559648820.9298120880702360.464906044035118
1450.5088466220791660.9823067558416690.491153377920834
1460.4858089528506180.9716179057012360.514191047149382
1470.5730859102404310.8538281795191390.426914089759569
1480.7171615544605770.5656768910788460.282838445539423
1490.5786800222932830.8426399554134350.421319977706717
1500.5019512861501070.9960974276997870.498048713849893

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
12 & 0.243060768895287 & 0.486121537790574 & 0.756939231104713 \tabularnewline
13 & 0.407690390661588 & 0.815380781323176 & 0.592309609338412 \tabularnewline
14 & 0.30927445736639 & 0.618548914732779 & 0.69072554263361 \tabularnewline
15 & 0.329220971235037 & 0.658441942470073 & 0.670779028764963 \tabularnewline
16 & 0.38410839513364 & 0.768216790267281 & 0.61589160486636 \tabularnewline
17 & 0.332777257659819 & 0.665554515319639 & 0.667222742340181 \tabularnewline
18 & 0.702232174333165 & 0.59553565133367 & 0.297767825666835 \tabularnewline
19 & 0.859558743069363 & 0.280882513861273 & 0.140441256930637 \tabularnewline
20 & 0.861242290926215 & 0.27751541814757 & 0.138757709073785 \tabularnewline
21 & 0.816639585645211 & 0.366720828709579 & 0.183360414354789 \tabularnewline
22 & 0.765788374369236 & 0.468423251261528 & 0.234211625630764 \tabularnewline
23 & 0.819719486985842 & 0.360561026028315 & 0.180280513014158 \tabularnewline
24 & 0.767716235042148 & 0.464567529915704 & 0.232283764957852 \tabularnewline
25 & 0.736317244678345 & 0.52736551064331 & 0.263682755321655 \tabularnewline
26 & 0.674495010637577 & 0.651009978724846 & 0.325504989362423 \tabularnewline
27 & 0.641879017211872 & 0.716241965576255 & 0.358120982788127 \tabularnewline
28 & 0.627176695914488 & 0.745646608171024 & 0.372823304085512 \tabularnewline
29 & 0.562159584511207 & 0.875680830977587 & 0.437840415488793 \tabularnewline
30 & 0.557870573353094 & 0.884258853293812 & 0.442129426646906 \tabularnewline
31 & 0.492488582865536 & 0.984977165731071 & 0.507511417134464 \tabularnewline
32 & 0.480507258015408 & 0.961014516030816 & 0.519492741984592 \tabularnewline
33 & 0.437465961893746 & 0.874931923787493 & 0.562534038106253 \tabularnewline
34 & 0.377336100723407 & 0.754672201446815 & 0.622663899276593 \tabularnewline
35 & 0.378730127596645 & 0.75746025519329 & 0.621269872403355 \tabularnewline
36 & 0.874677552216427 & 0.250644895567145 & 0.125322447783573 \tabularnewline
37 & 0.863080133822177 & 0.273839732355646 & 0.136919866177823 \tabularnewline
38 & 0.863803780733008 & 0.272392438533985 & 0.136196219266992 \tabularnewline
39 & 0.87528266340783 & 0.249434673184341 & 0.12471733659217 \tabularnewline
40 & 0.860905814696488 & 0.278188370607024 & 0.139094185303512 \tabularnewline
41 & 0.836287317569488 & 0.327425364861023 & 0.163712682430512 \tabularnewline
42 & 0.824496050361986 & 0.351007899276029 & 0.175503949638014 \tabularnewline
43 & 0.829270003126919 & 0.341459993746162 & 0.170729996873081 \tabularnewline
44 & 0.796305007081424 & 0.407389985837151 & 0.203694992918576 \tabularnewline
45 & 0.760567499268022 & 0.478865001463957 & 0.239432500731978 \tabularnewline
46 & 0.905660888274548 & 0.188678223450904 & 0.094339111725452 \tabularnewline
47 & 0.931132670244873 & 0.137734659510254 & 0.0688673297551271 \tabularnewline
48 & 0.912219774081252 & 0.175560451837496 & 0.0877802259187481 \tabularnewline
49 & 0.895944258013723 & 0.208111483972555 & 0.104055741986277 \tabularnewline
50 & 0.90118397772795 & 0.197632044544101 & 0.0988160222720504 \tabularnewline
51 & 0.879393256364383 & 0.241213487271235 & 0.120606743635617 \tabularnewline
52 & 0.852523269451926 & 0.294953461096148 & 0.147476730548074 \tabularnewline
53 & 0.879802620972871 & 0.240394758054257 & 0.120197379027129 \tabularnewline
54 & 0.854602006797273 & 0.290795986405455 & 0.145397993202727 \tabularnewline
55 & 0.863628981378126 & 0.272742037243749 & 0.136371018621874 \tabularnewline
56 & 0.848604033626075 & 0.30279193274785 & 0.151395966373925 \tabularnewline
57 & 0.818924080041079 & 0.362151839917842 & 0.181075919958921 \tabularnewline
58 & 0.796713298653627 & 0.406573402692746 & 0.203286701346373 \tabularnewline
59 & 0.760624345568198 & 0.478751308863604 & 0.239375654431802 \tabularnewline
60 & 0.757690340168231 & 0.484619319663538 & 0.242309659831769 \tabularnewline
61 & 0.723791524616336 & 0.552416950767329 & 0.276208475383664 \tabularnewline
62 & 0.6821921463925 & 0.635615707215 & 0.3178078536075 \tabularnewline
63 & 0.640422513625348 & 0.719154972749303 & 0.359577486374652 \tabularnewline
64 & 0.598904540861027 & 0.802190918277947 & 0.401095459138973 \tabularnewline
65 & 0.556336382482205 & 0.887327235035589 & 0.443663617517795 \tabularnewline
66 & 0.527125827271891 & 0.945748345456218 & 0.472874172728109 \tabularnewline
67 & 0.51424631550885 & 0.971507368982301 & 0.48575368449115 \tabularnewline
68 & 0.648480985961696 & 0.703038028076609 & 0.351519014038304 \tabularnewline
69 & 0.775493480583831 & 0.449013038832339 & 0.224506519416169 \tabularnewline
70 & 0.743708987277761 & 0.512582025444478 & 0.256291012722239 \tabularnewline
71 & 0.819643383821554 & 0.360713232356892 & 0.180356616178446 \tabularnewline
72 & 0.787118958434855 & 0.42576208313029 & 0.212881041565145 \tabularnewline
73 & 0.779916223067815 & 0.440167553864369 & 0.220083776932185 \tabularnewline
74 & 0.755303718658329 & 0.489392562683342 & 0.244696281341671 \tabularnewline
75 & 0.717871673942912 & 0.564256652114177 & 0.282128326057088 \tabularnewline
76 & 0.775853773404479 & 0.448292453191041 & 0.224146226595521 \tabularnewline
77 & 0.741025281473975 & 0.51794943705205 & 0.258974718526025 \tabularnewline
78 & 0.719045972401252 & 0.561908055197496 & 0.280954027598748 \tabularnewline
79 & 0.717176083399394 & 0.565647833201212 & 0.282823916600606 \tabularnewline
80 & 0.676778820226767 & 0.646442359546467 & 0.323221179773233 \tabularnewline
81 & 0.637262137608387 & 0.725475724783227 & 0.362737862391613 \tabularnewline
82 & 0.810131173777259 & 0.379737652445483 & 0.189868826222741 \tabularnewline
83 & 0.777891064363258 & 0.444217871273485 & 0.222108935636742 \tabularnewline
84 & 0.749655774827139 & 0.500688450345722 & 0.250344225172861 \tabularnewline
85 & 0.712040112788882 & 0.575919774422236 & 0.287959887211118 \tabularnewline
86 & 0.694201250879482 & 0.611597498241036 & 0.305798749120518 \tabularnewline
87 & 0.652768888079868 & 0.694462223840264 & 0.347231111920132 \tabularnewline
88 & 0.616008275278655 & 0.76798344944269 & 0.383991724721345 \tabularnewline
89 & 0.594821682985401 & 0.810356634029199 & 0.405178317014599 \tabularnewline
90 & 0.569741622540972 & 0.860516754918056 & 0.430258377459028 \tabularnewline
91 & 0.548110364279715 & 0.90377927144057 & 0.451889635720285 \tabularnewline
92 & 0.512505685472679 & 0.974988629054642 & 0.487494314527321 \tabularnewline
93 & 0.473725796945273 & 0.947451593890546 & 0.526274203054727 \tabularnewline
94 & 0.428369349821 & 0.856738699641999 & 0.571630650179 \tabularnewline
95 & 0.449310984648527 & 0.898621969297054 & 0.550689015351473 \tabularnewline
96 & 0.415092055402432 & 0.830184110804865 & 0.584907944597568 \tabularnewline
97 & 0.378016266416566 & 0.756032532833133 & 0.621983733583434 \tabularnewline
98 & 0.37886759928441 & 0.757735198568821 & 0.62113240071559 \tabularnewline
99 & 0.335675676562513 & 0.671351353125026 & 0.664324323437487 \tabularnewline
100 & 0.298747841000575 & 0.597495682001149 & 0.701252158999425 \tabularnewline
101 & 0.268337050139936 & 0.536674100279872 & 0.731662949860064 \tabularnewline
102 & 0.252769756170376 & 0.505539512340751 & 0.747230243829625 \tabularnewline
103 & 0.300152047232855 & 0.60030409446571 & 0.699847952767145 \tabularnewline
104 & 0.259540820371305 & 0.51908164074261 & 0.740459179628695 \tabularnewline
105 & 0.278911611375545 & 0.55782322275109 & 0.721088388624455 \tabularnewline
106 & 0.280968445041871 & 0.561936890083741 & 0.719031554958129 \tabularnewline
107 & 0.269540662975546 & 0.539081325951091 & 0.730459337024454 \tabularnewline
108 & 0.242458281060823 & 0.484916562121645 & 0.757541718939177 \tabularnewline
109 & 0.243961970677661 & 0.487923941355321 & 0.756038029322339 \tabularnewline
110 & 0.218689122631541 & 0.437378245263082 & 0.781310877368459 \tabularnewline
111 & 0.193462244813843 & 0.386924489627686 & 0.806537755186157 \tabularnewline
112 & 0.162202765344689 & 0.324405530689377 & 0.837797234655311 \tabularnewline
113 & 0.202142040082767 & 0.404284080165535 & 0.797857959917233 \tabularnewline
114 & 0.176580861583756 & 0.353161723167512 & 0.823419138416244 \tabularnewline
115 & 0.201836865533466 & 0.403673731066933 & 0.798163134466534 \tabularnewline
116 & 0.178715743709807 & 0.357431487419614 & 0.821284256290193 \tabularnewline
117 & 0.159097924128078 & 0.318195848256155 & 0.840902075871922 \tabularnewline
118 & 0.148237042283506 & 0.296474084567011 & 0.851762957716494 \tabularnewline
119 & 0.167229337174143 & 0.334458674348286 & 0.832770662825857 \tabularnewline
120 & 0.158029734442057 & 0.316059468884113 & 0.841970265557943 \tabularnewline
121 & 0.135883794741793 & 0.271767589483587 & 0.864116205258207 \tabularnewline
122 & 0.115392728991071 & 0.230785457982141 & 0.884607271008929 \tabularnewline
123 & 0.138711670963213 & 0.277423341926426 & 0.861288329036787 \tabularnewline
124 & 0.116103849293839 & 0.232207698587677 & 0.883896150706161 \tabularnewline
125 & 0.0967544950310131 & 0.193508990062026 & 0.903245504968987 \tabularnewline
126 & 0.0767814359932434 & 0.153562871986487 & 0.923218564006757 \tabularnewline
127 & 0.0583672652683916 & 0.116734530536783 & 0.941632734731608 \tabularnewline
128 & 0.0551617475930243 & 0.110323495186049 & 0.944838252406976 \tabularnewline
129 & 0.0416818212708886 & 0.0833636425417773 & 0.958318178729111 \tabularnewline
130 & 0.0503316281727312 & 0.100663256345462 & 0.949668371827269 \tabularnewline
131 & 0.0535207092365001 & 0.107041418473 & 0.9464792907635 \tabularnewline
132 & 0.0678459624492723 & 0.135691924898545 & 0.932154037550728 \tabularnewline
133 & 0.09207921723858 & 0.18415843447716 & 0.90792078276142 \tabularnewline
134 & 0.0871182357150878 & 0.174236471430176 & 0.912881764284912 \tabularnewline
135 & 0.0672265047773385 & 0.134453009554677 & 0.932773495222662 \tabularnewline
136 & 0.0599581116793578 & 0.119916223358716 & 0.940041888320642 \tabularnewline
137 & 0.0421450094331472 & 0.0842900188662943 & 0.957854990566853 \tabularnewline
138 & 0.0291235440609032 & 0.0582470881218064 & 0.970876455939097 \tabularnewline
139 & 0.112101386017173 & 0.224202772034346 & 0.887898613982827 \tabularnewline
140 & 0.09693101151916 & 0.19386202303832 & 0.90306898848084 \tabularnewline
141 & 0.597192294813981 & 0.805615410372038 & 0.402807705186019 \tabularnewline
142 & 0.557389122622689 & 0.885221754754623 & 0.442610877377311 \tabularnewline
143 & 0.611428630939498 & 0.777142738121005 & 0.388571369060502 \tabularnewline
144 & 0.535093955964882 & 0.929812088070236 & 0.464906044035118 \tabularnewline
145 & 0.508846622079166 & 0.982306755841669 & 0.491153377920834 \tabularnewline
146 & 0.485808952850618 & 0.971617905701236 & 0.514191047149382 \tabularnewline
147 & 0.573085910240431 & 0.853828179519139 & 0.426914089759569 \tabularnewline
148 & 0.717161554460577 & 0.565676891078846 & 0.282838445539423 \tabularnewline
149 & 0.578680022293283 & 0.842639955413435 & 0.421319977706717 \tabularnewline
150 & 0.501951286150107 & 0.996097427699787 & 0.498048713849893 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=198248&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]0.243060768895287[/C][C]0.486121537790574[/C][C]0.756939231104713[/C][/ROW]
[ROW][C]13[/C][C]0.407690390661588[/C][C]0.815380781323176[/C][C]0.592309609338412[/C][/ROW]
[ROW][C]14[/C][C]0.30927445736639[/C][C]0.618548914732779[/C][C]0.69072554263361[/C][/ROW]
[ROW][C]15[/C][C]0.329220971235037[/C][C]0.658441942470073[/C][C]0.670779028764963[/C][/ROW]
[ROW][C]16[/C][C]0.38410839513364[/C][C]0.768216790267281[/C][C]0.61589160486636[/C][/ROW]
[ROW][C]17[/C][C]0.332777257659819[/C][C]0.665554515319639[/C][C]0.667222742340181[/C][/ROW]
[ROW][C]18[/C][C]0.702232174333165[/C][C]0.59553565133367[/C][C]0.297767825666835[/C][/ROW]
[ROW][C]19[/C][C]0.859558743069363[/C][C]0.280882513861273[/C][C]0.140441256930637[/C][/ROW]
[ROW][C]20[/C][C]0.861242290926215[/C][C]0.27751541814757[/C][C]0.138757709073785[/C][/ROW]
[ROW][C]21[/C][C]0.816639585645211[/C][C]0.366720828709579[/C][C]0.183360414354789[/C][/ROW]
[ROW][C]22[/C][C]0.765788374369236[/C][C]0.468423251261528[/C][C]0.234211625630764[/C][/ROW]
[ROW][C]23[/C][C]0.819719486985842[/C][C]0.360561026028315[/C][C]0.180280513014158[/C][/ROW]
[ROW][C]24[/C][C]0.767716235042148[/C][C]0.464567529915704[/C][C]0.232283764957852[/C][/ROW]
[ROW][C]25[/C][C]0.736317244678345[/C][C]0.52736551064331[/C][C]0.263682755321655[/C][/ROW]
[ROW][C]26[/C][C]0.674495010637577[/C][C]0.651009978724846[/C][C]0.325504989362423[/C][/ROW]
[ROW][C]27[/C][C]0.641879017211872[/C][C]0.716241965576255[/C][C]0.358120982788127[/C][/ROW]
[ROW][C]28[/C][C]0.627176695914488[/C][C]0.745646608171024[/C][C]0.372823304085512[/C][/ROW]
[ROW][C]29[/C][C]0.562159584511207[/C][C]0.875680830977587[/C][C]0.437840415488793[/C][/ROW]
[ROW][C]30[/C][C]0.557870573353094[/C][C]0.884258853293812[/C][C]0.442129426646906[/C][/ROW]
[ROW][C]31[/C][C]0.492488582865536[/C][C]0.984977165731071[/C][C]0.507511417134464[/C][/ROW]
[ROW][C]32[/C][C]0.480507258015408[/C][C]0.961014516030816[/C][C]0.519492741984592[/C][/ROW]
[ROW][C]33[/C][C]0.437465961893746[/C][C]0.874931923787493[/C][C]0.562534038106253[/C][/ROW]
[ROW][C]34[/C][C]0.377336100723407[/C][C]0.754672201446815[/C][C]0.622663899276593[/C][/ROW]
[ROW][C]35[/C][C]0.378730127596645[/C][C]0.75746025519329[/C][C]0.621269872403355[/C][/ROW]
[ROW][C]36[/C][C]0.874677552216427[/C][C]0.250644895567145[/C][C]0.125322447783573[/C][/ROW]
[ROW][C]37[/C][C]0.863080133822177[/C][C]0.273839732355646[/C][C]0.136919866177823[/C][/ROW]
[ROW][C]38[/C][C]0.863803780733008[/C][C]0.272392438533985[/C][C]0.136196219266992[/C][/ROW]
[ROW][C]39[/C][C]0.87528266340783[/C][C]0.249434673184341[/C][C]0.12471733659217[/C][/ROW]
[ROW][C]40[/C][C]0.860905814696488[/C][C]0.278188370607024[/C][C]0.139094185303512[/C][/ROW]
[ROW][C]41[/C][C]0.836287317569488[/C][C]0.327425364861023[/C][C]0.163712682430512[/C][/ROW]
[ROW][C]42[/C][C]0.824496050361986[/C][C]0.351007899276029[/C][C]0.175503949638014[/C][/ROW]
[ROW][C]43[/C][C]0.829270003126919[/C][C]0.341459993746162[/C][C]0.170729996873081[/C][/ROW]
[ROW][C]44[/C][C]0.796305007081424[/C][C]0.407389985837151[/C][C]0.203694992918576[/C][/ROW]
[ROW][C]45[/C][C]0.760567499268022[/C][C]0.478865001463957[/C][C]0.239432500731978[/C][/ROW]
[ROW][C]46[/C][C]0.905660888274548[/C][C]0.188678223450904[/C][C]0.094339111725452[/C][/ROW]
[ROW][C]47[/C][C]0.931132670244873[/C][C]0.137734659510254[/C][C]0.0688673297551271[/C][/ROW]
[ROW][C]48[/C][C]0.912219774081252[/C][C]0.175560451837496[/C][C]0.0877802259187481[/C][/ROW]
[ROW][C]49[/C][C]0.895944258013723[/C][C]0.208111483972555[/C][C]0.104055741986277[/C][/ROW]
[ROW][C]50[/C][C]0.90118397772795[/C][C]0.197632044544101[/C][C]0.0988160222720504[/C][/ROW]
[ROW][C]51[/C][C]0.879393256364383[/C][C]0.241213487271235[/C][C]0.120606743635617[/C][/ROW]
[ROW][C]52[/C][C]0.852523269451926[/C][C]0.294953461096148[/C][C]0.147476730548074[/C][/ROW]
[ROW][C]53[/C][C]0.879802620972871[/C][C]0.240394758054257[/C][C]0.120197379027129[/C][/ROW]
[ROW][C]54[/C][C]0.854602006797273[/C][C]0.290795986405455[/C][C]0.145397993202727[/C][/ROW]
[ROW][C]55[/C][C]0.863628981378126[/C][C]0.272742037243749[/C][C]0.136371018621874[/C][/ROW]
[ROW][C]56[/C][C]0.848604033626075[/C][C]0.30279193274785[/C][C]0.151395966373925[/C][/ROW]
[ROW][C]57[/C][C]0.818924080041079[/C][C]0.362151839917842[/C][C]0.181075919958921[/C][/ROW]
[ROW][C]58[/C][C]0.796713298653627[/C][C]0.406573402692746[/C][C]0.203286701346373[/C][/ROW]
[ROW][C]59[/C][C]0.760624345568198[/C][C]0.478751308863604[/C][C]0.239375654431802[/C][/ROW]
[ROW][C]60[/C][C]0.757690340168231[/C][C]0.484619319663538[/C][C]0.242309659831769[/C][/ROW]
[ROW][C]61[/C][C]0.723791524616336[/C][C]0.552416950767329[/C][C]0.276208475383664[/C][/ROW]
[ROW][C]62[/C][C]0.6821921463925[/C][C]0.635615707215[/C][C]0.3178078536075[/C][/ROW]
[ROW][C]63[/C][C]0.640422513625348[/C][C]0.719154972749303[/C][C]0.359577486374652[/C][/ROW]
[ROW][C]64[/C][C]0.598904540861027[/C][C]0.802190918277947[/C][C]0.401095459138973[/C][/ROW]
[ROW][C]65[/C][C]0.556336382482205[/C][C]0.887327235035589[/C][C]0.443663617517795[/C][/ROW]
[ROW][C]66[/C][C]0.527125827271891[/C][C]0.945748345456218[/C][C]0.472874172728109[/C][/ROW]
[ROW][C]67[/C][C]0.51424631550885[/C][C]0.971507368982301[/C][C]0.48575368449115[/C][/ROW]
[ROW][C]68[/C][C]0.648480985961696[/C][C]0.703038028076609[/C][C]0.351519014038304[/C][/ROW]
[ROW][C]69[/C][C]0.775493480583831[/C][C]0.449013038832339[/C][C]0.224506519416169[/C][/ROW]
[ROW][C]70[/C][C]0.743708987277761[/C][C]0.512582025444478[/C][C]0.256291012722239[/C][/ROW]
[ROW][C]71[/C][C]0.819643383821554[/C][C]0.360713232356892[/C][C]0.180356616178446[/C][/ROW]
[ROW][C]72[/C][C]0.787118958434855[/C][C]0.42576208313029[/C][C]0.212881041565145[/C][/ROW]
[ROW][C]73[/C][C]0.779916223067815[/C][C]0.440167553864369[/C][C]0.220083776932185[/C][/ROW]
[ROW][C]74[/C][C]0.755303718658329[/C][C]0.489392562683342[/C][C]0.244696281341671[/C][/ROW]
[ROW][C]75[/C][C]0.717871673942912[/C][C]0.564256652114177[/C][C]0.282128326057088[/C][/ROW]
[ROW][C]76[/C][C]0.775853773404479[/C][C]0.448292453191041[/C][C]0.224146226595521[/C][/ROW]
[ROW][C]77[/C][C]0.741025281473975[/C][C]0.51794943705205[/C][C]0.258974718526025[/C][/ROW]
[ROW][C]78[/C][C]0.719045972401252[/C][C]0.561908055197496[/C][C]0.280954027598748[/C][/ROW]
[ROW][C]79[/C][C]0.717176083399394[/C][C]0.565647833201212[/C][C]0.282823916600606[/C][/ROW]
[ROW][C]80[/C][C]0.676778820226767[/C][C]0.646442359546467[/C][C]0.323221179773233[/C][/ROW]
[ROW][C]81[/C][C]0.637262137608387[/C][C]0.725475724783227[/C][C]0.362737862391613[/C][/ROW]
[ROW][C]82[/C][C]0.810131173777259[/C][C]0.379737652445483[/C][C]0.189868826222741[/C][/ROW]
[ROW][C]83[/C][C]0.777891064363258[/C][C]0.444217871273485[/C][C]0.222108935636742[/C][/ROW]
[ROW][C]84[/C][C]0.749655774827139[/C][C]0.500688450345722[/C][C]0.250344225172861[/C][/ROW]
[ROW][C]85[/C][C]0.712040112788882[/C][C]0.575919774422236[/C][C]0.287959887211118[/C][/ROW]
[ROW][C]86[/C][C]0.694201250879482[/C][C]0.611597498241036[/C][C]0.305798749120518[/C][/ROW]
[ROW][C]87[/C][C]0.652768888079868[/C][C]0.694462223840264[/C][C]0.347231111920132[/C][/ROW]
[ROW][C]88[/C][C]0.616008275278655[/C][C]0.76798344944269[/C][C]0.383991724721345[/C][/ROW]
[ROW][C]89[/C][C]0.594821682985401[/C][C]0.810356634029199[/C][C]0.405178317014599[/C][/ROW]
[ROW][C]90[/C][C]0.569741622540972[/C][C]0.860516754918056[/C][C]0.430258377459028[/C][/ROW]
[ROW][C]91[/C][C]0.548110364279715[/C][C]0.90377927144057[/C][C]0.451889635720285[/C][/ROW]
[ROW][C]92[/C][C]0.512505685472679[/C][C]0.974988629054642[/C][C]0.487494314527321[/C][/ROW]
[ROW][C]93[/C][C]0.473725796945273[/C][C]0.947451593890546[/C][C]0.526274203054727[/C][/ROW]
[ROW][C]94[/C][C]0.428369349821[/C][C]0.856738699641999[/C][C]0.571630650179[/C][/ROW]
[ROW][C]95[/C][C]0.449310984648527[/C][C]0.898621969297054[/C][C]0.550689015351473[/C][/ROW]
[ROW][C]96[/C][C]0.415092055402432[/C][C]0.830184110804865[/C][C]0.584907944597568[/C][/ROW]
[ROW][C]97[/C][C]0.378016266416566[/C][C]0.756032532833133[/C][C]0.621983733583434[/C][/ROW]
[ROW][C]98[/C][C]0.37886759928441[/C][C]0.757735198568821[/C][C]0.62113240071559[/C][/ROW]
[ROW][C]99[/C][C]0.335675676562513[/C][C]0.671351353125026[/C][C]0.664324323437487[/C][/ROW]
[ROW][C]100[/C][C]0.298747841000575[/C][C]0.597495682001149[/C][C]0.701252158999425[/C][/ROW]
[ROW][C]101[/C][C]0.268337050139936[/C][C]0.536674100279872[/C][C]0.731662949860064[/C][/ROW]
[ROW][C]102[/C][C]0.252769756170376[/C][C]0.505539512340751[/C][C]0.747230243829625[/C][/ROW]
[ROW][C]103[/C][C]0.300152047232855[/C][C]0.60030409446571[/C][C]0.699847952767145[/C][/ROW]
[ROW][C]104[/C][C]0.259540820371305[/C][C]0.51908164074261[/C][C]0.740459179628695[/C][/ROW]
[ROW][C]105[/C][C]0.278911611375545[/C][C]0.55782322275109[/C][C]0.721088388624455[/C][/ROW]
[ROW][C]106[/C][C]0.280968445041871[/C][C]0.561936890083741[/C][C]0.719031554958129[/C][/ROW]
[ROW][C]107[/C][C]0.269540662975546[/C][C]0.539081325951091[/C][C]0.730459337024454[/C][/ROW]
[ROW][C]108[/C][C]0.242458281060823[/C][C]0.484916562121645[/C][C]0.757541718939177[/C][/ROW]
[ROW][C]109[/C][C]0.243961970677661[/C][C]0.487923941355321[/C][C]0.756038029322339[/C][/ROW]
[ROW][C]110[/C][C]0.218689122631541[/C][C]0.437378245263082[/C][C]0.781310877368459[/C][/ROW]
[ROW][C]111[/C][C]0.193462244813843[/C][C]0.386924489627686[/C][C]0.806537755186157[/C][/ROW]
[ROW][C]112[/C][C]0.162202765344689[/C][C]0.324405530689377[/C][C]0.837797234655311[/C][/ROW]
[ROW][C]113[/C][C]0.202142040082767[/C][C]0.404284080165535[/C][C]0.797857959917233[/C][/ROW]
[ROW][C]114[/C][C]0.176580861583756[/C][C]0.353161723167512[/C][C]0.823419138416244[/C][/ROW]
[ROW][C]115[/C][C]0.201836865533466[/C][C]0.403673731066933[/C][C]0.798163134466534[/C][/ROW]
[ROW][C]116[/C][C]0.178715743709807[/C][C]0.357431487419614[/C][C]0.821284256290193[/C][/ROW]
[ROW][C]117[/C][C]0.159097924128078[/C][C]0.318195848256155[/C][C]0.840902075871922[/C][/ROW]
[ROW][C]118[/C][C]0.148237042283506[/C][C]0.296474084567011[/C][C]0.851762957716494[/C][/ROW]
[ROW][C]119[/C][C]0.167229337174143[/C][C]0.334458674348286[/C][C]0.832770662825857[/C][/ROW]
[ROW][C]120[/C][C]0.158029734442057[/C][C]0.316059468884113[/C][C]0.841970265557943[/C][/ROW]
[ROW][C]121[/C][C]0.135883794741793[/C][C]0.271767589483587[/C][C]0.864116205258207[/C][/ROW]
[ROW][C]122[/C][C]0.115392728991071[/C][C]0.230785457982141[/C][C]0.884607271008929[/C][/ROW]
[ROW][C]123[/C][C]0.138711670963213[/C][C]0.277423341926426[/C][C]0.861288329036787[/C][/ROW]
[ROW][C]124[/C][C]0.116103849293839[/C][C]0.232207698587677[/C][C]0.883896150706161[/C][/ROW]
[ROW][C]125[/C][C]0.0967544950310131[/C][C]0.193508990062026[/C][C]0.903245504968987[/C][/ROW]
[ROW][C]126[/C][C]0.0767814359932434[/C][C]0.153562871986487[/C][C]0.923218564006757[/C][/ROW]
[ROW][C]127[/C][C]0.0583672652683916[/C][C]0.116734530536783[/C][C]0.941632734731608[/C][/ROW]
[ROW][C]128[/C][C]0.0551617475930243[/C][C]0.110323495186049[/C][C]0.944838252406976[/C][/ROW]
[ROW][C]129[/C][C]0.0416818212708886[/C][C]0.0833636425417773[/C][C]0.958318178729111[/C][/ROW]
[ROW][C]130[/C][C]0.0503316281727312[/C][C]0.100663256345462[/C][C]0.949668371827269[/C][/ROW]
[ROW][C]131[/C][C]0.0535207092365001[/C][C]0.107041418473[/C][C]0.9464792907635[/C][/ROW]
[ROW][C]132[/C][C]0.0678459624492723[/C][C]0.135691924898545[/C][C]0.932154037550728[/C][/ROW]
[ROW][C]133[/C][C]0.09207921723858[/C][C]0.18415843447716[/C][C]0.90792078276142[/C][/ROW]
[ROW][C]134[/C][C]0.0871182357150878[/C][C]0.174236471430176[/C][C]0.912881764284912[/C][/ROW]
[ROW][C]135[/C][C]0.0672265047773385[/C][C]0.134453009554677[/C][C]0.932773495222662[/C][/ROW]
[ROW][C]136[/C][C]0.0599581116793578[/C][C]0.119916223358716[/C][C]0.940041888320642[/C][/ROW]
[ROW][C]137[/C][C]0.0421450094331472[/C][C]0.0842900188662943[/C][C]0.957854990566853[/C][/ROW]
[ROW][C]138[/C][C]0.0291235440609032[/C][C]0.0582470881218064[/C][C]0.970876455939097[/C][/ROW]
[ROW][C]139[/C][C]0.112101386017173[/C][C]0.224202772034346[/C][C]0.887898613982827[/C][/ROW]
[ROW][C]140[/C][C]0.09693101151916[/C][C]0.19386202303832[/C][C]0.90306898848084[/C][/ROW]
[ROW][C]141[/C][C]0.597192294813981[/C][C]0.805615410372038[/C][C]0.402807705186019[/C][/ROW]
[ROW][C]142[/C][C]0.557389122622689[/C][C]0.885221754754623[/C][C]0.442610877377311[/C][/ROW]
[ROW][C]143[/C][C]0.611428630939498[/C][C]0.777142738121005[/C][C]0.388571369060502[/C][/ROW]
[ROW][C]144[/C][C]0.535093955964882[/C][C]0.929812088070236[/C][C]0.464906044035118[/C][/ROW]
[ROW][C]145[/C][C]0.508846622079166[/C][C]0.982306755841669[/C][C]0.491153377920834[/C][/ROW]
[ROW][C]146[/C][C]0.485808952850618[/C][C]0.971617905701236[/C][C]0.514191047149382[/C][/ROW]
[ROW][C]147[/C][C]0.573085910240431[/C][C]0.853828179519139[/C][C]0.426914089759569[/C][/ROW]
[ROW][C]148[/C][C]0.717161554460577[/C][C]0.565676891078846[/C][C]0.282838445539423[/C][/ROW]
[ROW][C]149[/C][C]0.578680022293283[/C][C]0.842639955413435[/C][C]0.421319977706717[/C][/ROW]
[ROW][C]150[/C][C]0.501951286150107[/C][C]0.996097427699787[/C][C]0.498048713849893[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=198248&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=198248&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
120.2430607688952870.4861215377905740.756939231104713
130.4076903906615880.8153807813231760.592309609338412
140.309274457366390.6185489147327790.69072554263361
150.3292209712350370.6584419424700730.670779028764963
160.384108395133640.7682167902672810.61589160486636
170.3327772576598190.6655545153196390.667222742340181
180.7022321743331650.595535651333670.297767825666835
190.8595587430693630.2808825138612730.140441256930637
200.8612422909262150.277515418147570.138757709073785
210.8166395856452110.3667208287095790.183360414354789
220.7657883743692360.4684232512615280.234211625630764
230.8197194869858420.3605610260283150.180280513014158
240.7677162350421480.4645675299157040.232283764957852
250.7363172446783450.527365510643310.263682755321655
260.6744950106375770.6510099787248460.325504989362423
270.6418790172118720.7162419655762550.358120982788127
280.6271766959144880.7456466081710240.372823304085512
290.5621595845112070.8756808309775870.437840415488793
300.5578705733530940.8842588532938120.442129426646906
310.4924885828655360.9849771657310710.507511417134464
320.4805072580154080.9610145160308160.519492741984592
330.4374659618937460.8749319237874930.562534038106253
340.3773361007234070.7546722014468150.622663899276593
350.3787301275966450.757460255193290.621269872403355
360.8746775522164270.2506448955671450.125322447783573
370.8630801338221770.2738397323556460.136919866177823
380.8638037807330080.2723924385339850.136196219266992
390.875282663407830.2494346731843410.12471733659217
400.8609058146964880.2781883706070240.139094185303512
410.8362873175694880.3274253648610230.163712682430512
420.8244960503619860.3510078992760290.175503949638014
430.8292700031269190.3414599937461620.170729996873081
440.7963050070814240.4073899858371510.203694992918576
450.7605674992680220.4788650014639570.239432500731978
460.9056608882745480.1886782234509040.094339111725452
470.9311326702448730.1377346595102540.0688673297551271
480.9122197740812520.1755604518374960.0877802259187481
490.8959442580137230.2081114839725550.104055741986277
500.901183977727950.1976320445441010.0988160222720504
510.8793932563643830.2412134872712350.120606743635617
520.8525232694519260.2949534610961480.147476730548074
530.8798026209728710.2403947580542570.120197379027129
540.8546020067972730.2907959864054550.145397993202727
550.8636289813781260.2727420372437490.136371018621874
560.8486040336260750.302791932747850.151395966373925
570.8189240800410790.3621518399178420.181075919958921
580.7967132986536270.4065734026927460.203286701346373
590.7606243455681980.4787513088636040.239375654431802
600.7576903401682310.4846193196635380.242309659831769
610.7237915246163360.5524169507673290.276208475383664
620.68219214639250.6356157072150.3178078536075
630.6404225136253480.7191549727493030.359577486374652
640.5989045408610270.8021909182779470.401095459138973
650.5563363824822050.8873272350355890.443663617517795
660.5271258272718910.9457483454562180.472874172728109
670.514246315508850.9715073689823010.48575368449115
680.6484809859616960.7030380280766090.351519014038304
690.7754934805838310.4490130388323390.224506519416169
700.7437089872777610.5125820254444780.256291012722239
710.8196433838215540.3607132323568920.180356616178446
720.7871189584348550.425762083130290.212881041565145
730.7799162230678150.4401675538643690.220083776932185
740.7553037186583290.4893925626833420.244696281341671
750.7178716739429120.5642566521141770.282128326057088
760.7758537734044790.4482924531910410.224146226595521
770.7410252814739750.517949437052050.258974718526025
780.7190459724012520.5619080551974960.280954027598748
790.7171760833993940.5656478332012120.282823916600606
800.6767788202267670.6464423595464670.323221179773233
810.6372621376083870.7254757247832270.362737862391613
820.8101311737772590.3797376524454830.189868826222741
830.7778910643632580.4442178712734850.222108935636742
840.7496557748271390.5006884503457220.250344225172861
850.7120401127888820.5759197744222360.287959887211118
860.6942012508794820.6115974982410360.305798749120518
870.6527688880798680.6944622238402640.347231111920132
880.6160082752786550.767983449442690.383991724721345
890.5948216829854010.8103566340291990.405178317014599
900.5697416225409720.8605167549180560.430258377459028
910.5481103642797150.903779271440570.451889635720285
920.5125056854726790.9749886290546420.487494314527321
930.4737257969452730.9474515938905460.526274203054727
940.4283693498210.8567386996419990.571630650179
950.4493109846485270.8986219692970540.550689015351473
960.4150920554024320.8301841108048650.584907944597568
970.3780162664165660.7560325328331330.621983733583434
980.378867599284410.7577351985688210.62113240071559
990.3356756765625130.6713513531250260.664324323437487
1000.2987478410005750.5974956820011490.701252158999425
1010.2683370501399360.5366741002798720.731662949860064
1020.2527697561703760.5055395123407510.747230243829625
1030.3001520472328550.600304094465710.699847952767145
1040.2595408203713050.519081640742610.740459179628695
1050.2789116113755450.557823222751090.721088388624455
1060.2809684450418710.5619368900837410.719031554958129
1070.2695406629755460.5390813259510910.730459337024454
1080.2424582810608230.4849165621216450.757541718939177
1090.2439619706776610.4879239413553210.756038029322339
1100.2186891226315410.4373782452630820.781310877368459
1110.1934622448138430.3869244896276860.806537755186157
1120.1622027653446890.3244055306893770.837797234655311
1130.2021420400827670.4042840801655350.797857959917233
1140.1765808615837560.3531617231675120.823419138416244
1150.2018368655334660.4036737310669330.798163134466534
1160.1787157437098070.3574314874196140.821284256290193
1170.1590979241280780.3181958482561550.840902075871922
1180.1482370422835060.2964740845670110.851762957716494
1190.1672293371741430.3344586743482860.832770662825857
1200.1580297344420570.3160594688841130.841970265557943
1210.1358837947417930.2717675894835870.864116205258207
1220.1153927289910710.2307854579821410.884607271008929
1230.1387116709632130.2774233419264260.861288329036787
1240.1161038492938390.2322076985876770.883896150706161
1250.09675449503101310.1935089900620260.903245504968987
1260.07678143599324340.1535628719864870.923218564006757
1270.05836726526839160.1167345305367830.941632734731608
1280.05516174759302430.1103234951860490.944838252406976
1290.04168182127088860.08336364254177730.958318178729111
1300.05033162817273120.1006632563454620.949668371827269
1310.05352070923650010.1070414184730.9464792907635
1320.06784596244927230.1356919248985450.932154037550728
1330.092079217238580.184158434477160.90792078276142
1340.08711823571508780.1742364714301760.912881764284912
1350.06722650477733850.1344530095546770.932773495222662
1360.05995811167935780.1199162233587160.940041888320642
1370.04214500943314720.08429001886629430.957854990566853
1380.02912354406090320.05824708812180640.970876455939097
1390.1121013860171730.2242027720343460.887898613982827
1400.096931011519160.193862023038320.90306898848084
1410.5971922948139810.8056154103720380.402807705186019
1420.5573891226226890.8852217547546230.442610877377311
1430.6114286309394980.7771427381210050.388571369060502
1440.5350939559648820.9298120880702360.464906044035118
1450.5088466220791660.9823067558416690.491153377920834
1460.4858089528506180.9716179057012360.514191047149382
1470.5730859102404310.8538281795191390.426914089759569
1480.7171615544605770.5656768910788460.282838445539423
1490.5786800222932830.8426399554134350.421319977706717
1500.5019512861501070.9960974276997870.498048713849893






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 level30.0215827338129496OK

\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 & 3 & 0.0215827338129496 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=198248&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]3[/C][C]0.0215827338129496[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=198248&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=198248&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 level30.0215827338129496OK


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