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Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationSun, 17 Dec 2017 12:25:43 +0100
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2017/Dec/17/t1513514127vp3yjffyh2oksyd.htm/, Retrieved Thu, 16 May 2024 01:58:27 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=309961, Retrieved Thu, 16 May 2024 01:58:27 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact112

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Multiple Regression] [] [2017-12-17 11:25:43] [834c75312b1a933b06457deba9c9b5e8] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
0,00	0,00	0,00	0,00	180,00	21,00
125,00	121,00	11,00	0,00	326,00	21,00
0,00	0,00	1,00	0,00	332,00	21,00
9,00	28,00	0,00	0,00	274,00	21,00
24,00	26,00	1,00	0,00	459,00	21,00
4,00	1,00	3,00	2,00	263,00	21,00
26,00	12,00	5,00	2,00	243,00	21,00
71,00	66,00	11,00	12,00	365,00	21,00
37,00	30,00	7,00	16,00	300,00	21,00
151,00	155,00	8,00	9,00	262,00	21,00
0,00	0,00	0,00	0,00	100,00	21,00
60,00	27,00	3,00	0,00	132,00	21,00
2,00	2,00	25,00	2,00	206,00	21,00
3,00	0,00	12,00	0,00	130,00	21,00
52,00	99,00	4,00	17,00	864,00	21,00
29,00	25,00	2,00	0,00	526,00	21,00
43,00	0,00	2,00	0,00	852,00	21,00
79,00	180,00	10,00	5,00	827,00	21,00
14,00	13,00	0,00	0,00	431,00	21,00
7,00	12,00	2,00	3,00	350,00	21,00
22,00	40,00	0,00	4,00	294,00	21,00
4,00	2,00	7,00	34,00	187,00	21,00
0,00	0,00	0,00	0,00	314,00	21,00
13,00	15,00	2,00	7,00	82,00	21,00
1,00	0,00	3,00	2,00	440,00	21,00
99,00	105,00	6,00	0,00	516,00	21,00
1,00	3,00	0,00	0,00	90,00	21,00
6,00	2,00	2,00	0,00	194,00	21,00
0,00	0,00	0,00	0,00	183,00	20,00
25,00	24,00	11,00	12,00	500,00	20,00
14,00	17,00	2,00	6,00	263,00	20,00
26,00	12,00	8,00	8,00	261,00	20,00
0,00	0,00	0,00	0,00	228,00	20,00
0,00	0,00	0,00	0,00	187,00	20,00
11,00	13,00	0,00	3,00	310,00	20,00
0,00	0,00	0,00	0,00	167,00	20,00
5,00	22,00	1,00	6,00	282,00	20,00
6,00	8,00	0,00	1,00	305,00	20,00
9,00	4,00	13,00	7,00	490,00	20,00
358,00	342,00	14,00	28,00	448,00	20,00
144,00	164,00	8,00	7,00	747,00	20,00
17,00	19,00	0,00	1,00	441,00	20,00
43,00	39,00	1,00	27,00	387,00	20,00
21,00	12,00	4,00	5,00	366,00	20,00
80,00	125,00	24,00	47,00	660,00	20,00
2,00	0,00	4,00	2,00	510,00	20,00
16,00	8,00	2,00	2,00	255,00	20,00
33,00	41,00	7,00	11,00	286,00	20,00
26,00	33,00	0,00	2,00	425,00	20,00
264,00	279,00	19,00	15,00	296,00	20,00
78,00	77,00	15,00	2,00	470,00	20,00
73,00	84,00	4,00	6,00	134,00	20,00
23,00	22,00	0,00	2,00	195,00	20,00
0,00	22,00	0,00	0,00	123,00	20,00
0,00	0,00	0,00	0,00	115,00	20,00
15,00	30,00	17,00	21,00	507,00	20,00
0,00	0,00	0,00	0,00	109,00	20,00
104,00	129,00	24,00	12,00	618,00	20,00
1,00	0,00	6,00	0,00	274,00	20,00
0,00	0,00	0,00	0,00	168,00	20,00
0,00	0,00	3,00	0,00	190,00	20,00
82,00	54,00	15,00	2,00	126,00	20,00
28,00	38,00	1,00	1,00	294,00	20,00
8,00	9,00	1,00	2,00	399,00	20,00
7,00	7,00	3,00	2,00	317,00	20,00
67,00	131,00	7,00	75,00	331,00	20,00
50,00	95,00	3,00	3,00	308,00	20,00
0,00	0,00	3,00	0,00	1144,00	20,00
2,00	63,00	2,00	1,00	452,00	20,00
0,00	0,00	0,00	0,00	322,00	20,00
0,00	0,00	0,00	0,00	990,00	20,00
0,00	0,00	0,00	0,00	547,00	20,00
31,00	43,00	3,00	2,00	464,00	20,00
0,00	0,00	0,00	0,00	143,00	20,00
0,00	0,00	0,00	0,00	138,00	20,00
3,00	0,00	12,00	0,00	560,00	20,00
37,00	211,00	10,00	7,00	400,00	20,00
0,00	0,00	0,00	0,00	221,00	20,00
0,00	0,00	0,00	0,00	310,00	20,00
0,00	0,00	3,00	0,00	74,00	20,00
0,00	0,00	0,00	0,00	162,00	20,00
0,00	0,00	0,00	0,00	67,00	20,00
3,00	11,00	0,00	1,00	198,00	20,00
36,00	46,00	1,00	7,00	259,00	20,00
7,00	3,00	1,00	2,00	140,00	20,00
0,00	0,00	0,00	0,00	94,00	20,00
11,00	10,00	1,00	1,00	285,00	19,00
0,00	0,00	0,00	0,00	270,00	19,00
0,00	0,00	0,00	0,00	755,00	19,00
5,00	0,00	1,00	3,00	148,00	19,00
6,00	2,00	0,00	3,00	244,00	19,00
31,00	31,00	7,00	0,00	697,00	18,00
2,00	0,00	0,00	0,00	226,00	18,00
2,00	3,00	0,00	0,00	400,00	18,00
2,00	2,00	2,00	2,00	105,00	17,00
0,00	0,00	6,00	5,00	376,00	17,00
0,00	0,00	0,00	0,00	79,00	17,00
1,00	4,00	1,00	0,00	160,00	17,00
0,00	0,00	0,00	0,00	70,00	17,00
0,00	0,00	5,00	0,00	87,00	17,00
0,00	0,00	0,00	0,00	234,00	17,00
0,00	0,00	0,00	0,00	223,00	16,00
0,00	0,00	7,00	0,00	682,00	16,00
4,00	6,00	0,00	1,00	149,00	16,00
0,00	0,00	3,00	0,00	195,00	16,00
0,00	0,00	0,00	0,00	236,00	16,00
0,00	0,00	0,00	0,00	112,00	16,00
0,00	0,00	0,00	0,00	184,00	16,00
3,00	0,00	0,00	0,00	65,00	16,00
0,00	0,00	0,00	0,00	62,00	15,00
0,00	0,00	0,00	0,00	424,00	15,00
0,00	0,00	0,00	0,00	906,00	15,00
0,00	0,00	0,00	0,00	271,00	15,00
4,00	1,00	0,00	34,00	712,00	14,00
0,00	0,00	0,00	0,00	200,00	13,00
0,00	0,00	0,00	0,00	520,00	13,00
0,00	0,00	0,00	0,00	113,00	13,00
0,00	2,00	0,00	0,00	89,00	13,00
0,00	0,00	0,00	0,00	600,00	12,00
0,00	3,00	0,00	0,00	148,00	12,00
6,00	0,00	5,00	13,00	912,00	12,00

Tables (Output of Computation)





Summary of computational transaction
Raw Input view raw input (R code)
Raw Outputview raw output of R engine
Computing time8 seconds
R ServerBig Analytics Cloud Computing Center
R Framework error message
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input view raw input (R code)  \tabularnewline
Raw Outputview raw output of R engine  \tabularnewline
Computing time8 seconds \tabularnewline
R ServerBig Analytics Cloud Computing Center \tabularnewline
R Framework error message & 
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=309961&T=0

[TABLE]
[ROW]
Summary of computational transaction[/C][/ROW] [ROW]Raw Input[/C] view raw input (R code) [/C][/ROW] [ROW]Raw Output[/C]view raw output of R engine [/C][/ROW] [ROW]Computing time[/C]8 seconds[/C][/ROW] [ROW]R Server[/C]Big Analytics Cloud Computing Center[/C][/ROW] [ROW]R Framework error message[/C][C]
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.
[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=309961&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=309961&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 Input view raw input (R code)
Raw Outputview raw output of R engine
Computing time8 seconds
R ServerBig Analytics Cloud Computing Center
R Framework error message
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.






Multiple Linear Regression - Estimated Regression Equation
llnabo17[t] = + 2.44853 + 0.788574llnabo16[t] + 0.637917lkrabo17[t] -0.206061leesabo17[t] -0.00713219llnaantal[t] -0.0141912`duurklant\r\r`[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
llnabo17[t] =  +  2.44853 +  0.788574llnabo16[t] +  0.637917lkrabo17[t] -0.206061leesabo17[t] -0.00713219llnaantal[t] -0.0141912`duurklant\r\r`[t]  + e[t] \tabularnewline
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=309961&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]llnabo17[t] =  +  2.44853 +  0.788574llnabo16[t] +  0.637917lkrabo17[t] -0.206061leesabo17[t] -0.00713219llnaantal[t] -0.0141912`duurklant\r\r`[t]  + e[t][/C][/ROW]
[ROW][C][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=309961&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=309961&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
llnabo17[t] = + 2.44853 + 0.788574llnabo16[t] + 0.637917lkrabo17[t] -0.206061leesabo17[t] -0.00713219llnaantal[t] -0.0141912`duurklant\r\r`[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)+2.449 15.57+1.5720e-01 0.8754 0.4377
llnabo16+0.7886 0.04115+1.9160e+01 1.346e-37 6.729e-38
lkrabo17+0.6379 0.4204+1.5170e+00 0.1319 0.06597
leesabo17-0.2061 0.206-1.0000e+00 0.3193 0.1596
llnaantal-0.007132 0.00842-8.4700e-01 0.3987 0.1994
`duurklant\r\r`-0.01419 0.8057-1.7610e-02 0.986 0.493

\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) & +2.449 &  15.57 & +1.5720e-01 &  0.8754 &  0.4377 \tabularnewline
llnabo16 & +0.7886 &  0.04115 & +1.9160e+01 &  1.346e-37 &  6.729e-38 \tabularnewline
lkrabo17 & +0.6379 &  0.4204 & +1.5170e+00 &  0.1319 &  0.06597 \tabularnewline
leesabo17 & -0.2061 &  0.206 & -1.0000e+00 &  0.3193 &  0.1596 \tabularnewline
llnaantal & -0.007132 &  0.00842 & -8.4700e-01 &  0.3987 &  0.1994 \tabularnewline
`duurklant\r\r` & -0.01419 &  0.8057 & -1.7610e-02 &  0.986 &  0.493 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=309961&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]+2.449[/C][C] 15.57[/C][C]+1.5720e-01[/C][C] 0.8754[/C][C] 0.4377[/C][/ROW]
[ROW][C]llnabo16[/C][C]+0.7886[/C][C] 0.04115[/C][C]+1.9160e+01[/C][C] 1.346e-37[/C][C] 6.729e-38[/C][/ROW]
[ROW][C]lkrabo17[/C][C]+0.6379[/C][C] 0.4204[/C][C]+1.5170e+00[/C][C] 0.1319[/C][C] 0.06597[/C][/ROW]
[ROW][C]leesabo17[/C][C]-0.2061[/C][C] 0.206[/C][C]-1.0000e+00[/C][C] 0.3193[/C][C] 0.1596[/C][/ROW]
[ROW][C]llnaantal[/C][C]-0.007132[/C][C] 0.00842[/C][C]-8.4700e-01[/C][C] 0.3987[/C][C] 0.1994[/C][/ROW]
[ROW][C]`duurklant\r\r`[/C][C]-0.01419[/C][C] 0.8057[/C][C]-1.7610e-02[/C][C] 0.986[/C][C] 0.493[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=309961&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=309961&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)+2.449 15.57+1.5720e-01 0.8754 0.4377
llnabo16+0.7886 0.04115+1.9160e+01 1.346e-37 6.729e-38
lkrabo17+0.6379 0.4204+1.5170e+00 0.1319 0.06597
leesabo17-0.2061 0.206-1.0000e+00 0.3193 0.1596
llnaantal-0.007132 0.00842-8.4700e-01 0.3987 0.1994
`duurklant\r\r`-0.01419 0.8057-1.7610e-02 0.986 0.493






Multiple Linear Regression - Regression Statistics
Multiple R 0.9189
R-squared 0.8444
Adjusted R-squared 0.8376
F-TEST (value) 124.8
F-TEST (DF numerator)5
F-TEST (DF denominator)115
p-value 0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation 19.64
Sum Squared Residuals 4.437e+04

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R &  0.9189 \tabularnewline
R-squared &  0.8444 \tabularnewline
Adjusted R-squared &  0.8376 \tabularnewline
F-TEST (value) &  124.8 \tabularnewline
F-TEST (DF numerator) & 5 \tabularnewline
F-TEST (DF denominator) & 115 \tabularnewline
p-value &  0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation &  19.64 \tabularnewline
Sum Squared Residuals &  4.437e+04 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=309961&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C] 0.9189[/C][/ROW]
[ROW][C]R-squared[/C][C] 0.8444[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C] 0.8376[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C] 124.8[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]5[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]115[/C][/ROW]
[ROW][C]p-value[/C][C] 0[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C] 19.64[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C] 4.437e+04[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=309961&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=309961&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 R 0.9189
R-squared 0.8444
Adjusted R-squared 0.8376
F-TEST (value) 124.8
F-TEST (DF numerator)5
F-TEST (DF denominator)115
p-value 0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation 19.64
Sum Squared Residuals 4.437e+04






Menu of Residual Diagnostics
DescriptionLink
HistogramCompute
Central TendencyCompute
QQ PlotCompute
Kernel Density PlotCompute
Skewness/Kurtosis TestCompute
Skewness-Kurtosis PlotCompute
Harrell-Davis PlotCompute
Bootstrap Plot -- Central TendencyCompute
Blocked Bootstrap Plot -- Central TendencyCompute
(Partial) Autocorrelation PlotCompute
Spectral AnalysisCompute
Tukey lambda PPCC PlotCompute
Box-Cox Normality PlotCompute
Summary StatisticsCompute

\begin{tabular}{lllllllll}
\hline
Menu of Residual Diagnostics \tabularnewline
Description & Link \tabularnewline
Histogram & Compute \tabularnewline
Central Tendency & Compute \tabularnewline
QQ Plot & Compute \tabularnewline
Kernel Density Plot & Compute \tabularnewline
Skewness/Kurtosis Test & Compute \tabularnewline
Skewness-Kurtosis Plot & Compute \tabularnewline
Harrell-Davis Plot & Compute \tabularnewline
Bootstrap Plot -- Central Tendency & Compute \tabularnewline
Blocked Bootstrap Plot -- Central Tendency & Compute \tabularnewline
(Partial) Autocorrelation Plot & Compute \tabularnewline
Spectral Analysis & Compute \tabularnewline
Tukey lambda PPCC Plot & Compute \tabularnewline
Box-Cox Normality Plot & Compute \tabularnewline
Summary Statistics & Compute \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=309961&T=4

[TABLE]
[ROW][C]Menu of Residual Diagnostics[/C][/ROW]
[ROW][C]Description[/C][C]Link[/C][/ROW]
[ROW][C]Histogram[/C][C]Compute[/C][/ROW]
[ROW][C]Central Tendency[/C][C]Compute[/C][/ROW]
[ROW][C]QQ Plot[/C][C]Compute[/C][/ROW]
[ROW][C]Kernel Density Plot[/C][C]Compute[/C][/ROW]
[ROW][C]Skewness/Kurtosis Test[/C][C]Compute[/C][/ROW]
[ROW][C]Skewness-Kurtosis Plot[/C][C]Compute[/C][/ROW]
[ROW][C]Harrell-Davis Plot[/C][C]Compute[/C][/ROW]
[ROW][C]Bootstrap Plot -- Central Tendency[/C][C]Compute[/C][/ROW]
[ROW][C]Blocked Bootstrap Plot -- Central Tendency[/C][C]Compute[/C][/ROW]
[ROW][C](Partial) Autocorrelation Plot[/C][C]Compute[/C][/ROW]
[ROW][C]Spectral Analysis[/C][C]Compute[/C][/ROW]
[ROW][C]Tukey lambda PPCC Plot[/C][C]Compute[/C][/ROW]
[ROW][C]Box-Cox Normality Plot[/C][C]Compute[/C][/ROW]
[ROW][C]Summary Statistics[/C][C]Compute[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=309961&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=309961&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:

Menu of Residual Diagnostics
DescriptionLink
HistogramCompute
Central TendencyCompute
QQ PlotCompute
Kernel Density PlotCompute
Skewness/Kurtosis TestCompute
Skewness-Kurtosis PlotCompute
Harrell-Davis PlotCompute
Bootstrap Plot -- Central TendencyCompute
Blocked Bootstrap Plot -- Central TendencyCompute
(Partial) Autocorrelation PlotCompute
Spectral AnalysisCompute
Tukey lambda PPCC PlotCompute
Box-Cox Normality PlotCompute
Summary StatisticsCompute






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
1 0 0.8667-0.8667
2 125 102.3 22.74
3 0 0.4205-0.4205
4 9 22.28-13.28
5 24 20.02 3.982
6 4 2.565 1.435
7 26 12.66 13.34
8 71 56.14 14.86
9 37 24.84 12.16
10 151 125.8 25.24
11 0 1.437-1.437
12 60 24.41 35.59
13 2 17.79-15.79
14 3 8.878-5.878
15 52 73.11-21.11
16 29 19.39 9.611
17 43-2.65 45.65
18 79 143.5-64.54
19 14 9.328 4.672
20 7 9.775-2.775
21 22 30.77-8.772
22 4-0.1467 4.147
23 0-0.089 0.089
24 13 13.23-0.2277
25 1 0.514 0.486
26 99 85.1 13.9
27 1 3.874-2.874
28 6 3.62 2.38
29 0 0.8595-0.8595
30 25 22.07 2.931
31 14 13.73 0.2658
32 26 13.22 12.78
33 0 0.5386-0.5386
34 0 0.831-0.831
35 11 9.587 1.413
36 0 0.9736-0.9736
37 5 16.9-11.9
38 6 6.092-0.09192
39 9 8.675 0.3253
40 358 271.8 86.18
41 144 129.8 14.18
42 17 13.8 3.204
43 43 25.23 17.77
44 21 10.54 10.46
45 80 101.7-21.65
46 2 0.6668 1.333
47 16 7.518 8.482
48 33 34.66-1.655
49 26 24.74 1.256
50 264 229.1 34.9
51 78 68.69 9.311
52 73 68.76 4.235
53 23 17.71 5.29
54 0 18.64-18.64
55 0 1.345-1.345
56 15 28.72-13.72
57 0 1.387-1.387
58 104 112.3-8.32
59 1 4.038-3.038
60 0 0.9665-0.9665
61 0 2.723-2.723
62 82 53.01 28.99
63 28 30.47-2.466
64 8 6.642 1.358
65 7 6.925 0.07455
66 67 92.12-25.12
67 50 76.18-26.18
68 0-4.081 4.081
69 2 49.69-47.69
70 0-0.1319 0.1319
71 0-4.896 4.896
72 0-1.737 1.737
73 31 34.27-3.266
74 0 1.145-1.145
75 0 1.18-1.18
76 3 5.826-2.826
77 37 170.6-133.6
78 0 0.5885-0.5885
79 0-0.04628 0.04628
80 0 3.551-3.551
81 0 1.009-1.009
82 0 1.687-1.687
83 3 9.221-6.221
84 36 35.79 0.2126
85 7 3.758 3.242
86 0 1.494-1.494
87 11 8.464 2.536
88 0 0.2532-0.2532
89 0-3.206 3.206
90 5 1.143 3.857
91 6 1.398 4.602
92 31 26.13 4.867
93 2 0.5812 1.419
94 2 1.706 0.2941
95 2 3.899-1.899
96 0 2.323-2.323
97 0 1.644-1.644
98 1 4.858-3.858
99 0 1.708-1.708
100 0 4.776-4.776
101 0 0.5383-0.5383
102 0 0.631-0.631
103 0 1.823-1.823
104 4 5.684-1.684
105 0 2.744-2.744
106 0 0.5383-0.5383
107 0 1.423-1.423
108 0 0.9091-0.9091
109 3 1.758 1.242
110 0 1.793-1.793
111 0-0.7884 0.7884
112 0-4.226 4.226
113 0 0.3028-0.3028
114 4-9.046 13.05
115 0 0.8376-0.8376
116 0-1.445 1.445
117 0 1.458-1.458
118 0 3.206-3.206
119 0-2.001 2.001
120 0 3.588-3.588
121 6-3.716 9.716

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 &  0 &  0.8667 & -0.8667 \tabularnewline
2 &  125 &  102.3 &  22.74 \tabularnewline
3 &  0 &  0.4205 & -0.4205 \tabularnewline
4 &  9 &  22.28 & -13.28 \tabularnewline
5 &  24 &  20.02 &  3.982 \tabularnewline
6 &  4 &  2.565 &  1.435 \tabularnewline
7 &  26 &  12.66 &  13.34 \tabularnewline
8 &  71 &  56.14 &  14.86 \tabularnewline
9 &  37 &  24.84 &  12.16 \tabularnewline
10 &  151 &  125.8 &  25.24 \tabularnewline
11 &  0 &  1.437 & -1.437 \tabularnewline
12 &  60 &  24.41 &  35.59 \tabularnewline
13 &  2 &  17.79 & -15.79 \tabularnewline
14 &  3 &  8.878 & -5.878 \tabularnewline
15 &  52 &  73.11 & -21.11 \tabularnewline
16 &  29 &  19.39 &  9.611 \tabularnewline
17 &  43 & -2.65 &  45.65 \tabularnewline
18 &  79 &  143.5 & -64.54 \tabularnewline
19 &  14 &  9.328 &  4.672 \tabularnewline
20 &  7 &  9.775 & -2.775 \tabularnewline
21 &  22 &  30.77 & -8.772 \tabularnewline
22 &  4 & -0.1467 &  4.147 \tabularnewline
23 &  0 & -0.089 &  0.089 \tabularnewline
24 &  13 &  13.23 & -0.2277 \tabularnewline
25 &  1 &  0.514 &  0.486 \tabularnewline
26 &  99 &  85.1 &  13.9 \tabularnewline
27 &  1 &  3.874 & -2.874 \tabularnewline
28 &  6 &  3.62 &  2.38 \tabularnewline
29 &  0 &  0.8595 & -0.8595 \tabularnewline
30 &  25 &  22.07 &  2.931 \tabularnewline
31 &  14 &  13.73 &  0.2658 \tabularnewline
32 &  26 &  13.22 &  12.78 \tabularnewline
33 &  0 &  0.5386 & -0.5386 \tabularnewline
34 &  0 &  0.831 & -0.831 \tabularnewline
35 &  11 &  9.587 &  1.413 \tabularnewline
36 &  0 &  0.9736 & -0.9736 \tabularnewline
37 &  5 &  16.9 & -11.9 \tabularnewline
38 &  6 &  6.092 & -0.09192 \tabularnewline
39 &  9 &  8.675 &  0.3253 \tabularnewline
40 &  358 &  271.8 &  86.18 \tabularnewline
41 &  144 &  129.8 &  14.18 \tabularnewline
42 &  17 &  13.8 &  3.204 \tabularnewline
43 &  43 &  25.23 &  17.77 \tabularnewline
44 &  21 &  10.54 &  10.46 \tabularnewline
45 &  80 &  101.7 & -21.65 \tabularnewline
46 &  2 &  0.6668 &  1.333 \tabularnewline
47 &  16 &  7.518 &  8.482 \tabularnewline
48 &  33 &  34.66 & -1.655 \tabularnewline
49 &  26 &  24.74 &  1.256 \tabularnewline
50 &  264 &  229.1 &  34.9 \tabularnewline
51 &  78 &  68.69 &  9.311 \tabularnewline
52 &  73 &  68.76 &  4.235 \tabularnewline
53 &  23 &  17.71 &  5.29 \tabularnewline
54 &  0 &  18.64 & -18.64 \tabularnewline
55 &  0 &  1.345 & -1.345 \tabularnewline
56 &  15 &  28.72 & -13.72 \tabularnewline
57 &  0 &  1.387 & -1.387 \tabularnewline
58 &  104 &  112.3 & -8.32 \tabularnewline
59 &  1 &  4.038 & -3.038 \tabularnewline
60 &  0 &  0.9665 & -0.9665 \tabularnewline
61 &  0 &  2.723 & -2.723 \tabularnewline
62 &  82 &  53.01 &  28.99 \tabularnewline
63 &  28 &  30.47 & -2.466 \tabularnewline
64 &  8 &  6.642 &  1.358 \tabularnewline
65 &  7 &  6.925 &  0.07455 \tabularnewline
66 &  67 &  92.12 & -25.12 \tabularnewline
67 &  50 &  76.18 & -26.18 \tabularnewline
68 &  0 & -4.081 &  4.081 \tabularnewline
69 &  2 &  49.69 & -47.69 \tabularnewline
70 &  0 & -0.1319 &  0.1319 \tabularnewline
71 &  0 & -4.896 &  4.896 \tabularnewline
72 &  0 & -1.737 &  1.737 \tabularnewline
73 &  31 &  34.27 & -3.266 \tabularnewline
74 &  0 &  1.145 & -1.145 \tabularnewline
75 &  0 &  1.18 & -1.18 \tabularnewline
76 &  3 &  5.826 & -2.826 \tabularnewline
77 &  37 &  170.6 & -133.6 \tabularnewline
78 &  0 &  0.5885 & -0.5885 \tabularnewline
79 &  0 & -0.04628 &  0.04628 \tabularnewline
80 &  0 &  3.551 & -3.551 \tabularnewline
81 &  0 &  1.009 & -1.009 \tabularnewline
82 &  0 &  1.687 & -1.687 \tabularnewline
83 &  3 &  9.221 & -6.221 \tabularnewline
84 &  36 &  35.79 &  0.2126 \tabularnewline
85 &  7 &  3.758 &  3.242 \tabularnewline
86 &  0 &  1.494 & -1.494 \tabularnewline
87 &  11 &  8.464 &  2.536 \tabularnewline
88 &  0 &  0.2532 & -0.2532 \tabularnewline
89 &  0 & -3.206 &  3.206 \tabularnewline
90 &  5 &  1.143 &  3.857 \tabularnewline
91 &  6 &  1.398 &  4.602 \tabularnewline
92 &  31 &  26.13 &  4.867 \tabularnewline
93 &  2 &  0.5812 &  1.419 \tabularnewline
94 &  2 &  1.706 &  0.2941 \tabularnewline
95 &  2 &  3.899 & -1.899 \tabularnewline
96 &  0 &  2.323 & -2.323 \tabularnewline
97 &  0 &  1.644 & -1.644 \tabularnewline
98 &  1 &  4.858 & -3.858 \tabularnewline
99 &  0 &  1.708 & -1.708 \tabularnewline
100 &  0 &  4.776 & -4.776 \tabularnewline
101 &  0 &  0.5383 & -0.5383 \tabularnewline
102 &  0 &  0.631 & -0.631 \tabularnewline
103 &  0 &  1.823 & -1.823 \tabularnewline
104 &  4 &  5.684 & -1.684 \tabularnewline
105 &  0 &  2.744 & -2.744 \tabularnewline
106 &  0 &  0.5383 & -0.5383 \tabularnewline
107 &  0 &  1.423 & -1.423 \tabularnewline
108 &  0 &  0.9091 & -0.9091 \tabularnewline
109 &  3 &  1.758 &  1.242 \tabularnewline
110 &  0 &  1.793 & -1.793 \tabularnewline
111 &  0 & -0.7884 &  0.7884 \tabularnewline
112 &  0 & -4.226 &  4.226 \tabularnewline
113 &  0 &  0.3028 & -0.3028 \tabularnewline
114 &  4 & -9.046 &  13.05 \tabularnewline
115 &  0 &  0.8376 & -0.8376 \tabularnewline
116 &  0 & -1.445 &  1.445 \tabularnewline
117 &  0 &  1.458 & -1.458 \tabularnewline
118 &  0 &  3.206 & -3.206 \tabularnewline
119 &  0 & -2.001 &  2.001 \tabularnewline
120 &  0 &  3.588 & -3.588 \tabularnewline
121 &  6 & -3.716 &  9.716 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=309961&T=5

[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] 0[/C][C] 0.8667[/C][C]-0.8667[/C][/ROW]
[ROW][C]2[/C][C] 125[/C][C] 102.3[/C][C] 22.74[/C][/ROW]
[ROW][C]3[/C][C] 0[/C][C] 0.4205[/C][C]-0.4205[/C][/ROW]
[ROW][C]4[/C][C] 9[/C][C] 22.28[/C][C]-13.28[/C][/ROW]
[ROW][C]5[/C][C] 24[/C][C] 20.02[/C][C] 3.982[/C][/ROW]
[ROW][C]6[/C][C] 4[/C][C] 2.565[/C][C] 1.435[/C][/ROW]
[ROW][C]7[/C][C] 26[/C][C] 12.66[/C][C] 13.34[/C][/ROW]
[ROW][C]8[/C][C] 71[/C][C] 56.14[/C][C] 14.86[/C][/ROW]
[ROW][C]9[/C][C] 37[/C][C] 24.84[/C][C] 12.16[/C][/ROW]
[ROW][C]10[/C][C] 151[/C][C] 125.8[/C][C] 25.24[/C][/ROW]
[ROW][C]11[/C][C] 0[/C][C] 1.437[/C][C]-1.437[/C][/ROW]
[ROW][C]12[/C][C] 60[/C][C] 24.41[/C][C] 35.59[/C][/ROW]
[ROW][C]13[/C][C] 2[/C][C] 17.79[/C][C]-15.79[/C][/ROW]
[ROW][C]14[/C][C] 3[/C][C] 8.878[/C][C]-5.878[/C][/ROW]
[ROW][C]15[/C][C] 52[/C][C] 73.11[/C][C]-21.11[/C][/ROW]
[ROW][C]16[/C][C] 29[/C][C] 19.39[/C][C] 9.611[/C][/ROW]
[ROW][C]17[/C][C] 43[/C][C]-2.65[/C][C] 45.65[/C][/ROW]
[ROW][C]18[/C][C] 79[/C][C] 143.5[/C][C]-64.54[/C][/ROW]
[ROW][C]19[/C][C] 14[/C][C] 9.328[/C][C] 4.672[/C][/ROW]
[ROW][C]20[/C][C] 7[/C][C] 9.775[/C][C]-2.775[/C][/ROW]
[ROW][C]21[/C][C] 22[/C][C] 30.77[/C][C]-8.772[/C][/ROW]
[ROW][C]22[/C][C] 4[/C][C]-0.1467[/C][C] 4.147[/C][/ROW]
[ROW][C]23[/C][C] 0[/C][C]-0.089[/C][C] 0.089[/C][/ROW]
[ROW][C]24[/C][C] 13[/C][C] 13.23[/C][C]-0.2277[/C][/ROW]
[ROW][C]25[/C][C] 1[/C][C] 0.514[/C][C] 0.486[/C][/ROW]
[ROW][C]26[/C][C] 99[/C][C] 85.1[/C][C] 13.9[/C][/ROW]
[ROW][C]27[/C][C] 1[/C][C] 3.874[/C][C]-2.874[/C][/ROW]
[ROW][C]28[/C][C] 6[/C][C] 3.62[/C][C] 2.38[/C][/ROW]
[ROW][C]29[/C][C] 0[/C][C] 0.8595[/C][C]-0.8595[/C][/ROW]
[ROW][C]30[/C][C] 25[/C][C] 22.07[/C][C] 2.931[/C][/ROW]
[ROW][C]31[/C][C] 14[/C][C] 13.73[/C][C] 0.2658[/C][/ROW]
[ROW][C]32[/C][C] 26[/C][C] 13.22[/C][C] 12.78[/C][/ROW]
[ROW][C]33[/C][C] 0[/C][C] 0.5386[/C][C]-0.5386[/C][/ROW]
[ROW][C]34[/C][C] 0[/C][C] 0.831[/C][C]-0.831[/C][/ROW]
[ROW][C]35[/C][C] 11[/C][C] 9.587[/C][C] 1.413[/C][/ROW]
[ROW][C]36[/C][C] 0[/C][C] 0.9736[/C][C]-0.9736[/C][/ROW]
[ROW][C]37[/C][C] 5[/C][C] 16.9[/C][C]-11.9[/C][/ROW]
[ROW][C]38[/C][C] 6[/C][C] 6.092[/C][C]-0.09192[/C][/ROW]
[ROW][C]39[/C][C] 9[/C][C] 8.675[/C][C] 0.3253[/C][/ROW]
[ROW][C]40[/C][C] 358[/C][C] 271.8[/C][C] 86.18[/C][/ROW]
[ROW][C]41[/C][C] 144[/C][C] 129.8[/C][C] 14.18[/C][/ROW]
[ROW][C]42[/C][C] 17[/C][C] 13.8[/C][C] 3.204[/C][/ROW]
[ROW][C]43[/C][C] 43[/C][C] 25.23[/C][C] 17.77[/C][/ROW]
[ROW][C]44[/C][C] 21[/C][C] 10.54[/C][C] 10.46[/C][/ROW]
[ROW][C]45[/C][C] 80[/C][C] 101.7[/C][C]-21.65[/C][/ROW]
[ROW][C]46[/C][C] 2[/C][C] 0.6668[/C][C] 1.333[/C][/ROW]
[ROW][C]47[/C][C] 16[/C][C] 7.518[/C][C] 8.482[/C][/ROW]
[ROW][C]48[/C][C] 33[/C][C] 34.66[/C][C]-1.655[/C][/ROW]
[ROW][C]49[/C][C] 26[/C][C] 24.74[/C][C] 1.256[/C][/ROW]
[ROW][C]50[/C][C] 264[/C][C] 229.1[/C][C] 34.9[/C][/ROW]
[ROW][C]51[/C][C] 78[/C][C] 68.69[/C][C] 9.311[/C][/ROW]
[ROW][C]52[/C][C] 73[/C][C] 68.76[/C][C] 4.235[/C][/ROW]
[ROW][C]53[/C][C] 23[/C][C] 17.71[/C][C] 5.29[/C][/ROW]
[ROW][C]54[/C][C] 0[/C][C] 18.64[/C][C]-18.64[/C][/ROW]
[ROW][C]55[/C][C] 0[/C][C] 1.345[/C][C]-1.345[/C][/ROW]
[ROW][C]56[/C][C] 15[/C][C] 28.72[/C][C]-13.72[/C][/ROW]
[ROW][C]57[/C][C] 0[/C][C] 1.387[/C][C]-1.387[/C][/ROW]
[ROW][C]58[/C][C] 104[/C][C] 112.3[/C][C]-8.32[/C][/ROW]
[ROW][C]59[/C][C] 1[/C][C] 4.038[/C][C]-3.038[/C][/ROW]
[ROW][C]60[/C][C] 0[/C][C] 0.9665[/C][C]-0.9665[/C][/ROW]
[ROW][C]61[/C][C] 0[/C][C] 2.723[/C][C]-2.723[/C][/ROW]
[ROW][C]62[/C][C] 82[/C][C] 53.01[/C][C] 28.99[/C][/ROW]
[ROW][C]63[/C][C] 28[/C][C] 30.47[/C][C]-2.466[/C][/ROW]
[ROW][C]64[/C][C] 8[/C][C] 6.642[/C][C] 1.358[/C][/ROW]
[ROW][C]65[/C][C] 7[/C][C] 6.925[/C][C] 0.07455[/C][/ROW]
[ROW][C]66[/C][C] 67[/C][C] 92.12[/C][C]-25.12[/C][/ROW]
[ROW][C]67[/C][C] 50[/C][C] 76.18[/C][C]-26.18[/C][/ROW]
[ROW][C]68[/C][C] 0[/C][C]-4.081[/C][C] 4.081[/C][/ROW]
[ROW][C]69[/C][C] 2[/C][C] 49.69[/C][C]-47.69[/C][/ROW]
[ROW][C]70[/C][C] 0[/C][C]-0.1319[/C][C] 0.1319[/C][/ROW]
[ROW][C]71[/C][C] 0[/C][C]-4.896[/C][C] 4.896[/C][/ROW]
[ROW][C]72[/C][C] 0[/C][C]-1.737[/C][C] 1.737[/C][/ROW]
[ROW][C]73[/C][C] 31[/C][C] 34.27[/C][C]-3.266[/C][/ROW]
[ROW][C]74[/C][C] 0[/C][C] 1.145[/C][C]-1.145[/C][/ROW]
[ROW][C]75[/C][C] 0[/C][C] 1.18[/C][C]-1.18[/C][/ROW]
[ROW][C]76[/C][C] 3[/C][C] 5.826[/C][C]-2.826[/C][/ROW]
[ROW][C]77[/C][C] 37[/C][C] 170.6[/C][C]-133.6[/C][/ROW]
[ROW][C]78[/C][C] 0[/C][C] 0.5885[/C][C]-0.5885[/C][/ROW]
[ROW][C]79[/C][C] 0[/C][C]-0.04628[/C][C] 0.04628[/C][/ROW]
[ROW][C]80[/C][C] 0[/C][C] 3.551[/C][C]-3.551[/C][/ROW]
[ROW][C]81[/C][C] 0[/C][C] 1.009[/C][C]-1.009[/C][/ROW]
[ROW][C]82[/C][C] 0[/C][C] 1.687[/C][C]-1.687[/C][/ROW]
[ROW][C]83[/C][C] 3[/C][C] 9.221[/C][C]-6.221[/C][/ROW]
[ROW][C]84[/C][C] 36[/C][C] 35.79[/C][C] 0.2126[/C][/ROW]
[ROW][C]85[/C][C] 7[/C][C] 3.758[/C][C] 3.242[/C][/ROW]
[ROW][C]86[/C][C] 0[/C][C] 1.494[/C][C]-1.494[/C][/ROW]
[ROW][C]87[/C][C] 11[/C][C] 8.464[/C][C] 2.536[/C][/ROW]
[ROW][C]88[/C][C] 0[/C][C] 0.2532[/C][C]-0.2532[/C][/ROW]
[ROW][C]89[/C][C] 0[/C][C]-3.206[/C][C] 3.206[/C][/ROW]
[ROW][C]90[/C][C] 5[/C][C] 1.143[/C][C] 3.857[/C][/ROW]
[ROW][C]91[/C][C] 6[/C][C] 1.398[/C][C] 4.602[/C][/ROW]
[ROW][C]92[/C][C] 31[/C][C] 26.13[/C][C] 4.867[/C][/ROW]
[ROW][C]93[/C][C] 2[/C][C] 0.5812[/C][C] 1.419[/C][/ROW]
[ROW][C]94[/C][C] 2[/C][C] 1.706[/C][C] 0.2941[/C][/ROW]
[ROW][C]95[/C][C] 2[/C][C] 3.899[/C][C]-1.899[/C][/ROW]
[ROW][C]96[/C][C] 0[/C][C] 2.323[/C][C]-2.323[/C][/ROW]
[ROW][C]97[/C][C] 0[/C][C] 1.644[/C][C]-1.644[/C][/ROW]
[ROW][C]98[/C][C] 1[/C][C] 4.858[/C][C]-3.858[/C][/ROW]
[ROW][C]99[/C][C] 0[/C][C] 1.708[/C][C]-1.708[/C][/ROW]
[ROW][C]100[/C][C] 0[/C][C] 4.776[/C][C]-4.776[/C][/ROW]
[ROW][C]101[/C][C] 0[/C][C] 0.5383[/C][C]-0.5383[/C][/ROW]
[ROW][C]102[/C][C] 0[/C][C] 0.631[/C][C]-0.631[/C][/ROW]
[ROW][C]103[/C][C] 0[/C][C] 1.823[/C][C]-1.823[/C][/ROW]
[ROW][C]104[/C][C] 4[/C][C] 5.684[/C][C]-1.684[/C][/ROW]
[ROW][C]105[/C][C] 0[/C][C] 2.744[/C][C]-2.744[/C][/ROW]
[ROW][C]106[/C][C] 0[/C][C] 0.5383[/C][C]-0.5383[/C][/ROW]
[ROW][C]107[/C][C] 0[/C][C] 1.423[/C][C]-1.423[/C][/ROW]
[ROW][C]108[/C][C] 0[/C][C] 0.9091[/C][C]-0.9091[/C][/ROW]
[ROW][C]109[/C][C] 3[/C][C] 1.758[/C][C] 1.242[/C][/ROW]
[ROW][C]110[/C][C] 0[/C][C] 1.793[/C][C]-1.793[/C][/ROW]
[ROW][C]111[/C][C] 0[/C][C]-0.7884[/C][C] 0.7884[/C][/ROW]
[ROW][C]112[/C][C] 0[/C][C]-4.226[/C][C] 4.226[/C][/ROW]
[ROW][C]113[/C][C] 0[/C][C] 0.3028[/C][C]-0.3028[/C][/ROW]
[ROW][C]114[/C][C] 4[/C][C]-9.046[/C][C] 13.05[/C][/ROW]
[ROW][C]115[/C][C] 0[/C][C] 0.8376[/C][C]-0.8376[/C][/ROW]
[ROW][C]116[/C][C] 0[/C][C]-1.445[/C][C] 1.445[/C][/ROW]
[ROW][C]117[/C][C] 0[/C][C] 1.458[/C][C]-1.458[/C][/ROW]
[ROW][C]118[/C][C] 0[/C][C] 3.206[/C][C]-3.206[/C][/ROW]
[ROW][C]119[/C][C] 0[/C][C]-2.001[/C][C] 2.001[/C][/ROW]
[ROW][C]120[/C][C] 0[/C][C] 3.588[/C][C]-3.588[/C][/ROW]
[ROW][C]121[/C][C] 6[/C][C]-3.716[/C][C] 9.716[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=309961&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=309961&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:

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
1 0 0.8667-0.8667
2 125 102.3 22.74
3 0 0.4205-0.4205
4 9 22.28-13.28
5 24 20.02 3.982
6 4 2.565 1.435
7 26 12.66 13.34
8 71 56.14 14.86
9 37 24.84 12.16
10 151 125.8 25.24
11 0 1.437-1.437
12 60 24.41 35.59
13 2 17.79-15.79
14 3 8.878-5.878
15 52 73.11-21.11
16 29 19.39 9.611
17 43-2.65 45.65
18 79 143.5-64.54
19 14 9.328 4.672
20 7 9.775-2.775
21 22 30.77-8.772
22 4-0.1467 4.147
23 0-0.089 0.089
24 13 13.23-0.2277
25 1 0.514 0.486
26 99 85.1 13.9
27 1 3.874-2.874
28 6 3.62 2.38
29 0 0.8595-0.8595
30 25 22.07 2.931
31 14 13.73 0.2658
32 26 13.22 12.78
33 0 0.5386-0.5386
34 0 0.831-0.831
35 11 9.587 1.413
36 0 0.9736-0.9736
37 5 16.9-11.9
38 6 6.092-0.09192
39 9 8.675 0.3253
40 358 271.8 86.18
41 144 129.8 14.18
42 17 13.8 3.204
43 43 25.23 17.77
44 21 10.54 10.46
45 80 101.7-21.65
46 2 0.6668 1.333
47 16 7.518 8.482
48 33 34.66-1.655
49 26 24.74 1.256
50 264 229.1 34.9
51 78 68.69 9.311
52 73 68.76 4.235
53 23 17.71 5.29
54 0 18.64-18.64
55 0 1.345-1.345
56 15 28.72-13.72
57 0 1.387-1.387
58 104 112.3-8.32
59 1 4.038-3.038
60 0 0.9665-0.9665
61 0 2.723-2.723
62 82 53.01 28.99
63 28 30.47-2.466
64 8 6.642 1.358
65 7 6.925 0.07455
66 67 92.12-25.12
67 50 76.18-26.18
68 0-4.081 4.081
69 2 49.69-47.69
70 0-0.1319 0.1319
71 0-4.896 4.896
72 0-1.737 1.737
73 31 34.27-3.266
74 0 1.145-1.145
75 0 1.18-1.18
76 3 5.826-2.826
77 37 170.6-133.6
78 0 0.5885-0.5885
79 0-0.04628 0.04628
80 0 3.551-3.551
81 0 1.009-1.009
82 0 1.687-1.687
83 3 9.221-6.221
84 36 35.79 0.2126
85 7 3.758 3.242
86 0 1.494-1.494
87 11 8.464 2.536
88 0 0.2532-0.2532
89 0-3.206 3.206
90 5 1.143 3.857
91 6 1.398 4.602
92 31 26.13 4.867
93 2 0.5812 1.419
94 2 1.706 0.2941
95 2 3.899-1.899
96 0 2.323-2.323
97 0 1.644-1.644
98 1 4.858-3.858
99 0 1.708-1.708
100 0 4.776-4.776
101 0 0.5383-0.5383
102 0 0.631-0.631
103 0 1.823-1.823
104 4 5.684-1.684
105 0 2.744-2.744
106 0 0.5383-0.5383
107 0 1.423-1.423
108 0 0.9091-0.9091
109 3 1.758 1.242
110 0 1.793-1.793
111 0-0.7884 0.7884
112 0-4.226 4.226
113 0 0.3028-0.3028
114 4-9.046 13.05
115 0 0.8376-0.8376
116 0-1.445 1.445
117 0 1.458-1.458
118 0 3.206-3.206
119 0-2.001 2.001
120 0 3.588-3.588
121 6-3.716 9.716






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
9 0.05748 0.115 0.9425
10 0.04312 0.08623 0.9569
11 0.01449 0.02897 0.9855
12 0.1163 0.2327 0.8837
13 0.1805 0.361 0.8195
14 0.1156 0.2313 0.8844
15 0.1116 0.2232 0.8884
16 0.1183 0.2365 0.8817
17 0.5852 0.8296 0.4148
18 0.9721 0.0559 0.02795
19 0.9572 0.08569 0.04285
20 0.9411 0.1178 0.0589
21 0.9284 0.1431 0.07156
22 0.9018 0.1963 0.09816
23 0.8713 0.2574 0.1287
24 0.837 0.3261 0.163
25 0.791 0.4179 0.209
26 0.7747 0.4507 0.2253
27 0.7364 0.5272 0.2636
28 0.6815 0.6371 0.3185
29 0.6195 0.761 0.3805
30 0.5643 0.8714 0.4357
31 0.4996 0.9993 0.5004
32 0.4582 0.9164 0.5418
33 0.4 0.7999 0.6
34 0.3434 0.6868 0.6566
35 0.2879 0.5757 0.7121
36 0.2392 0.4783 0.7608
37 0.2093 0.4186 0.7907
38 0.1679 0.3358 0.8321
39 0.1329 0.2657 0.8671
40 0.8737 0.2526 0.1263
41 0.8909 0.2182 0.1091
42 0.8643 0.2715 0.1357
43 0.858 0.284 0.142
44 0.8325 0.3349 0.1675
45 0.8721 0.2558 0.1279
46 0.8421 0.3158 0.1579
47 0.8113 0.3774 0.1887
48 0.7767 0.4467 0.2233
49 0.7416 0.5169 0.2584
50 0.9992 0.001576 0.0007881
51 0.9995 0.0009201 0.00046
52 1 0.0001007 5.033e-05
53 0.9999 0.0001074 5.371e-05
54 1 8.322e-05 4.161e-05
55 0.9999 0.0001405 7.025e-05
56 1 2.246e-05 1.123e-05
57 1 3.954e-05 1.977e-05
58 1 1.65e-05 8.249e-06
59 1 2.08e-05 1.04e-05
60 1 3.699e-05 1.85e-05
61 1 5.448e-05 2.724e-05
62 1 1.762e-09 8.809e-10
63 1 7.587e-10 3.794e-10
64 1 1.827e-09 9.134e-10
65 1 4.447e-09 2.223e-09
66 1 3.566e-09 1.783e-09
67 1 5.617e-12 2.808e-12
68 1 9.825e-12 4.912e-12
69 1 2.978e-13 1.489e-13
70 1 8.433e-13 4.216e-13
71 1 1.402e-12 7.011e-13
72 1 3.056e-12 1.528e-12
73 1 1.916e-13 9.578e-14
74 1 5.866e-13 2.933e-13
75 1 1.75e-12 8.752e-13
76 1 4.602e-12 2.301e-12
77 1 1.407e-32 7.036e-33
78 1 1.67e-31 8.351e-32
79 1 1.898e-30 9.49e-31
80 1 1.868e-29 9.339e-30
81 1 2.078e-28 1.039e-28
82 1 2.288e-27 1.144e-27
83 1 1.249e-28 6.245e-29
84 1 5.884e-29 2.942e-29
85 1 2.088e-28 1.044e-28
86 1 2.603e-27 1.301e-27
87 1 3.259e-26 1.629e-26
88 1 3.908e-25 1.954e-25
89 1 2.828e-24 1.414e-24
90 1 2.964e-24 1.482e-24
91 1 3.474e-24 1.737e-24
92 1 5.801e-25 2.9e-25
93 1 3.659e-24 1.83e-24
94 1 6.292e-23 3.146e-23
95 1 9.224e-22 4.612e-22
96 1 8.935e-21 4.467e-21
97 1 1.646e-19 8.229e-20
98 1 2.416e-18 1.208e-18
99 1 4.227e-17 2.114e-17
100 1 5.684e-16 2.842e-16
101 1 9.385e-15 4.692e-15
102 1 1.492e-13 7.459e-14
103 1 1.898e-13 9.488e-14
104 1 8.899e-13 4.449e-13
105 1 6.81e-13 3.405e-13
106 1 1.436e-11 7.178e-12
107 1 2.442e-10 1.221e-10
108 1 3.389e-09 1.695e-09
109 1 2.321e-98 1.161e-98
110 1 1.575e-81 7.874e-82
111 1 2.156e-66 1.078e-66
112 1 1.696e-49 8.482e-50

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
9 &  0.05748 &  0.115 &  0.9425 \tabularnewline
10 &  0.04312 &  0.08623 &  0.9569 \tabularnewline
11 &  0.01449 &  0.02897 &  0.9855 \tabularnewline
12 &  0.1163 &  0.2327 &  0.8837 \tabularnewline
13 &  0.1805 &  0.361 &  0.8195 \tabularnewline
14 &  0.1156 &  0.2313 &  0.8844 \tabularnewline
15 &  0.1116 &  0.2232 &  0.8884 \tabularnewline
16 &  0.1183 &  0.2365 &  0.8817 \tabularnewline
17 &  0.5852 &  0.8296 &  0.4148 \tabularnewline
18 &  0.9721 &  0.0559 &  0.02795 \tabularnewline
19 &  0.9572 &  0.08569 &  0.04285 \tabularnewline
20 &  0.9411 &  0.1178 &  0.0589 \tabularnewline
21 &  0.9284 &  0.1431 &  0.07156 \tabularnewline
22 &  0.9018 &  0.1963 &  0.09816 \tabularnewline
23 &  0.8713 &  0.2574 &  0.1287 \tabularnewline
24 &  0.837 &  0.3261 &  0.163 \tabularnewline
25 &  0.791 &  0.4179 &  0.209 \tabularnewline
26 &  0.7747 &  0.4507 &  0.2253 \tabularnewline
27 &  0.7364 &  0.5272 &  0.2636 \tabularnewline
28 &  0.6815 &  0.6371 &  0.3185 \tabularnewline
29 &  0.6195 &  0.761 &  0.3805 \tabularnewline
30 &  0.5643 &  0.8714 &  0.4357 \tabularnewline
31 &  0.4996 &  0.9993 &  0.5004 \tabularnewline
32 &  0.4582 &  0.9164 &  0.5418 \tabularnewline
33 &  0.4 &  0.7999 &  0.6 \tabularnewline
34 &  0.3434 &  0.6868 &  0.6566 \tabularnewline
35 &  0.2879 &  0.5757 &  0.7121 \tabularnewline
36 &  0.2392 &  0.4783 &  0.7608 \tabularnewline
37 &  0.2093 &  0.4186 &  0.7907 \tabularnewline
38 &  0.1679 &  0.3358 &  0.8321 \tabularnewline
39 &  0.1329 &  0.2657 &  0.8671 \tabularnewline
40 &  0.8737 &  0.2526 &  0.1263 \tabularnewline
41 &  0.8909 &  0.2182 &  0.1091 \tabularnewline
42 &  0.8643 &  0.2715 &  0.1357 \tabularnewline
43 &  0.858 &  0.284 &  0.142 \tabularnewline
44 &  0.8325 &  0.3349 &  0.1675 \tabularnewline
45 &  0.8721 &  0.2558 &  0.1279 \tabularnewline
46 &  0.8421 &  0.3158 &  0.1579 \tabularnewline
47 &  0.8113 &  0.3774 &  0.1887 \tabularnewline
48 &  0.7767 &  0.4467 &  0.2233 \tabularnewline
49 &  0.7416 &  0.5169 &  0.2584 \tabularnewline
50 &  0.9992 &  0.001576 &  0.0007881 \tabularnewline
51 &  0.9995 &  0.0009201 &  0.00046 \tabularnewline
52 &  1 &  0.0001007 &  5.033e-05 \tabularnewline
53 &  0.9999 &  0.0001074 &  5.371e-05 \tabularnewline
54 &  1 &  8.322e-05 &  4.161e-05 \tabularnewline
55 &  0.9999 &  0.0001405 &  7.025e-05 \tabularnewline
56 &  1 &  2.246e-05 &  1.123e-05 \tabularnewline
57 &  1 &  3.954e-05 &  1.977e-05 \tabularnewline
58 &  1 &  1.65e-05 &  8.249e-06 \tabularnewline
59 &  1 &  2.08e-05 &  1.04e-05 \tabularnewline
60 &  1 &  3.699e-05 &  1.85e-05 \tabularnewline
61 &  1 &  5.448e-05 &  2.724e-05 \tabularnewline
62 &  1 &  1.762e-09 &  8.809e-10 \tabularnewline
63 &  1 &  7.587e-10 &  3.794e-10 \tabularnewline
64 &  1 &  1.827e-09 &  9.134e-10 \tabularnewline
65 &  1 &  4.447e-09 &  2.223e-09 \tabularnewline
66 &  1 &  3.566e-09 &  1.783e-09 \tabularnewline
67 &  1 &  5.617e-12 &  2.808e-12 \tabularnewline
68 &  1 &  9.825e-12 &  4.912e-12 \tabularnewline
69 &  1 &  2.978e-13 &  1.489e-13 \tabularnewline
70 &  1 &  8.433e-13 &  4.216e-13 \tabularnewline
71 &  1 &  1.402e-12 &  7.011e-13 \tabularnewline
72 &  1 &  3.056e-12 &  1.528e-12 \tabularnewline
73 &  1 &  1.916e-13 &  9.578e-14 \tabularnewline
74 &  1 &  5.866e-13 &  2.933e-13 \tabularnewline
75 &  1 &  1.75e-12 &  8.752e-13 \tabularnewline
76 &  1 &  4.602e-12 &  2.301e-12 \tabularnewline
77 &  1 &  1.407e-32 &  7.036e-33 \tabularnewline
78 &  1 &  1.67e-31 &  8.351e-32 \tabularnewline
79 &  1 &  1.898e-30 &  9.49e-31 \tabularnewline
80 &  1 &  1.868e-29 &  9.339e-30 \tabularnewline
81 &  1 &  2.078e-28 &  1.039e-28 \tabularnewline
82 &  1 &  2.288e-27 &  1.144e-27 \tabularnewline
83 &  1 &  1.249e-28 &  6.245e-29 \tabularnewline
84 &  1 &  5.884e-29 &  2.942e-29 \tabularnewline
85 &  1 &  2.088e-28 &  1.044e-28 \tabularnewline
86 &  1 &  2.603e-27 &  1.301e-27 \tabularnewline
87 &  1 &  3.259e-26 &  1.629e-26 \tabularnewline
88 &  1 &  3.908e-25 &  1.954e-25 \tabularnewline
89 &  1 &  2.828e-24 &  1.414e-24 \tabularnewline
90 &  1 &  2.964e-24 &  1.482e-24 \tabularnewline
91 &  1 &  3.474e-24 &  1.737e-24 \tabularnewline
92 &  1 &  5.801e-25 &  2.9e-25 \tabularnewline
93 &  1 &  3.659e-24 &  1.83e-24 \tabularnewline
94 &  1 &  6.292e-23 &  3.146e-23 \tabularnewline
95 &  1 &  9.224e-22 &  4.612e-22 \tabularnewline
96 &  1 &  8.935e-21 &  4.467e-21 \tabularnewline
97 &  1 &  1.646e-19 &  8.229e-20 \tabularnewline
98 &  1 &  2.416e-18 &  1.208e-18 \tabularnewline
99 &  1 &  4.227e-17 &  2.114e-17 \tabularnewline
100 &  1 &  5.684e-16 &  2.842e-16 \tabularnewline
101 &  1 &  9.385e-15 &  4.692e-15 \tabularnewline
102 &  1 &  1.492e-13 &  7.459e-14 \tabularnewline
103 &  1 &  1.898e-13 &  9.488e-14 \tabularnewline
104 &  1 &  8.899e-13 &  4.449e-13 \tabularnewline
105 &  1 &  6.81e-13 &  3.405e-13 \tabularnewline
106 &  1 &  1.436e-11 &  7.178e-12 \tabularnewline
107 &  1 &  2.442e-10 &  1.221e-10 \tabularnewline
108 &  1 &  3.389e-09 &  1.695e-09 \tabularnewline
109 &  1 &  2.321e-98 &  1.161e-98 \tabularnewline
110 &  1 &  1.575e-81 &  7.874e-82 \tabularnewline
111 &  1 &  2.156e-66 &  1.078e-66 \tabularnewline
112 &  1 &  1.696e-49 &  8.482e-50 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=309961&T=6

[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]9[/C][C] 0.05748[/C][C] 0.115[/C][C] 0.9425[/C][/ROW]
[ROW][C]10[/C][C] 0.04312[/C][C] 0.08623[/C][C] 0.9569[/C][/ROW]
[ROW][C]11[/C][C] 0.01449[/C][C] 0.02897[/C][C] 0.9855[/C][/ROW]
[ROW][C]12[/C][C] 0.1163[/C][C] 0.2327[/C][C] 0.8837[/C][/ROW]
[ROW][C]13[/C][C] 0.1805[/C][C] 0.361[/C][C] 0.8195[/C][/ROW]
[ROW][C]14[/C][C] 0.1156[/C][C] 0.2313[/C][C] 0.8844[/C][/ROW]
[ROW][C]15[/C][C] 0.1116[/C][C] 0.2232[/C][C] 0.8884[/C][/ROW]
[ROW][C]16[/C][C] 0.1183[/C][C] 0.2365[/C][C] 0.8817[/C][/ROW]
[ROW][C]17[/C][C] 0.5852[/C][C] 0.8296[/C][C] 0.4148[/C][/ROW]
[ROW][C]18[/C][C] 0.9721[/C][C] 0.0559[/C][C] 0.02795[/C][/ROW]
[ROW][C]19[/C][C] 0.9572[/C][C] 0.08569[/C][C] 0.04285[/C][/ROW]
[ROW][C]20[/C][C] 0.9411[/C][C] 0.1178[/C][C] 0.0589[/C][/ROW]
[ROW][C]21[/C][C] 0.9284[/C][C] 0.1431[/C][C] 0.07156[/C][/ROW]
[ROW][C]22[/C][C] 0.9018[/C][C] 0.1963[/C][C] 0.09816[/C][/ROW]
[ROW][C]23[/C][C] 0.8713[/C][C] 0.2574[/C][C] 0.1287[/C][/ROW]
[ROW][C]24[/C][C] 0.837[/C][C] 0.3261[/C][C] 0.163[/C][/ROW]
[ROW][C]25[/C][C] 0.791[/C][C] 0.4179[/C][C] 0.209[/C][/ROW]
[ROW][C]26[/C][C] 0.7747[/C][C] 0.4507[/C][C] 0.2253[/C][/ROW]
[ROW][C]27[/C][C] 0.7364[/C][C] 0.5272[/C][C] 0.2636[/C][/ROW]
[ROW][C]28[/C][C] 0.6815[/C][C] 0.6371[/C][C] 0.3185[/C][/ROW]
[ROW][C]29[/C][C] 0.6195[/C][C] 0.761[/C][C] 0.3805[/C][/ROW]
[ROW][C]30[/C][C] 0.5643[/C][C] 0.8714[/C][C] 0.4357[/C][/ROW]
[ROW][C]31[/C][C] 0.4996[/C][C] 0.9993[/C][C] 0.5004[/C][/ROW]
[ROW][C]32[/C][C] 0.4582[/C][C] 0.9164[/C][C] 0.5418[/C][/ROW]
[ROW][C]33[/C][C] 0.4[/C][C] 0.7999[/C][C] 0.6[/C][/ROW]
[ROW][C]34[/C][C] 0.3434[/C][C] 0.6868[/C][C] 0.6566[/C][/ROW]
[ROW][C]35[/C][C] 0.2879[/C][C] 0.5757[/C][C] 0.7121[/C][/ROW]
[ROW][C]36[/C][C] 0.2392[/C][C] 0.4783[/C][C] 0.7608[/C][/ROW]
[ROW][C]37[/C][C] 0.2093[/C][C] 0.4186[/C][C] 0.7907[/C][/ROW]
[ROW][C]38[/C][C] 0.1679[/C][C] 0.3358[/C][C] 0.8321[/C][/ROW]
[ROW][C]39[/C][C] 0.1329[/C][C] 0.2657[/C][C] 0.8671[/C][/ROW]
[ROW][C]40[/C][C] 0.8737[/C][C] 0.2526[/C][C] 0.1263[/C][/ROW]
[ROW][C]41[/C][C] 0.8909[/C][C] 0.2182[/C][C] 0.1091[/C][/ROW]
[ROW][C]42[/C][C] 0.8643[/C][C] 0.2715[/C][C] 0.1357[/C][/ROW]
[ROW][C]43[/C][C] 0.858[/C][C] 0.284[/C][C] 0.142[/C][/ROW]
[ROW][C]44[/C][C] 0.8325[/C][C] 0.3349[/C][C] 0.1675[/C][/ROW]
[ROW][C]45[/C][C] 0.8721[/C][C] 0.2558[/C][C] 0.1279[/C][/ROW]
[ROW][C]46[/C][C] 0.8421[/C][C] 0.3158[/C][C] 0.1579[/C][/ROW]
[ROW][C]47[/C][C] 0.8113[/C][C] 0.3774[/C][C] 0.1887[/C][/ROW]
[ROW][C]48[/C][C] 0.7767[/C][C] 0.4467[/C][C] 0.2233[/C][/ROW]
[ROW][C]49[/C][C] 0.7416[/C][C] 0.5169[/C][C] 0.2584[/C][/ROW]
[ROW][C]50[/C][C] 0.9992[/C][C] 0.001576[/C][C] 0.0007881[/C][/ROW]
[ROW][C]51[/C][C] 0.9995[/C][C] 0.0009201[/C][C] 0.00046[/C][/ROW]
[ROW][C]52[/C][C] 1[/C][C] 0.0001007[/C][C] 5.033e-05[/C][/ROW]
[ROW][C]53[/C][C] 0.9999[/C][C] 0.0001074[/C][C] 5.371e-05[/C][/ROW]
[ROW][C]54[/C][C] 1[/C][C] 8.322e-05[/C][C] 4.161e-05[/C][/ROW]
[ROW][C]55[/C][C] 0.9999[/C][C] 0.0001405[/C][C] 7.025e-05[/C][/ROW]
[ROW][C]56[/C][C] 1[/C][C] 2.246e-05[/C][C] 1.123e-05[/C][/ROW]
[ROW][C]57[/C][C] 1[/C][C] 3.954e-05[/C][C] 1.977e-05[/C][/ROW]
[ROW][C]58[/C][C] 1[/C][C] 1.65e-05[/C][C] 8.249e-06[/C][/ROW]
[ROW][C]59[/C][C] 1[/C][C] 2.08e-05[/C][C] 1.04e-05[/C][/ROW]
[ROW][C]60[/C][C] 1[/C][C] 3.699e-05[/C][C] 1.85e-05[/C][/ROW]
[ROW][C]61[/C][C] 1[/C][C] 5.448e-05[/C][C] 2.724e-05[/C][/ROW]
[ROW][C]62[/C][C] 1[/C][C] 1.762e-09[/C][C] 8.809e-10[/C][/ROW]
[ROW][C]63[/C][C] 1[/C][C] 7.587e-10[/C][C] 3.794e-10[/C][/ROW]
[ROW][C]64[/C][C] 1[/C][C] 1.827e-09[/C][C] 9.134e-10[/C][/ROW]
[ROW][C]65[/C][C] 1[/C][C] 4.447e-09[/C][C] 2.223e-09[/C][/ROW]
[ROW][C]66[/C][C] 1[/C][C] 3.566e-09[/C][C] 1.783e-09[/C][/ROW]
[ROW][C]67[/C][C] 1[/C][C] 5.617e-12[/C][C] 2.808e-12[/C][/ROW]
[ROW][C]68[/C][C] 1[/C][C] 9.825e-12[/C][C] 4.912e-12[/C][/ROW]
[ROW][C]69[/C][C] 1[/C][C] 2.978e-13[/C][C] 1.489e-13[/C][/ROW]
[ROW][C]70[/C][C] 1[/C][C] 8.433e-13[/C][C] 4.216e-13[/C][/ROW]
[ROW][C]71[/C][C] 1[/C][C] 1.402e-12[/C][C] 7.011e-13[/C][/ROW]
[ROW][C]72[/C][C] 1[/C][C] 3.056e-12[/C][C] 1.528e-12[/C][/ROW]
[ROW][C]73[/C][C] 1[/C][C] 1.916e-13[/C][C] 9.578e-14[/C][/ROW]
[ROW][C]74[/C][C] 1[/C][C] 5.866e-13[/C][C] 2.933e-13[/C][/ROW]
[ROW][C]75[/C][C] 1[/C][C] 1.75e-12[/C][C] 8.752e-13[/C][/ROW]
[ROW][C]76[/C][C] 1[/C][C] 4.602e-12[/C][C] 2.301e-12[/C][/ROW]
[ROW][C]77[/C][C] 1[/C][C] 1.407e-32[/C][C] 7.036e-33[/C][/ROW]
[ROW][C]78[/C][C] 1[/C][C] 1.67e-31[/C][C] 8.351e-32[/C][/ROW]
[ROW][C]79[/C][C] 1[/C][C] 1.898e-30[/C][C] 9.49e-31[/C][/ROW]
[ROW][C]80[/C][C] 1[/C][C] 1.868e-29[/C][C] 9.339e-30[/C][/ROW]
[ROW][C]81[/C][C] 1[/C][C] 2.078e-28[/C][C] 1.039e-28[/C][/ROW]
[ROW][C]82[/C][C] 1[/C][C] 2.288e-27[/C][C] 1.144e-27[/C][/ROW]
[ROW][C]83[/C][C] 1[/C][C] 1.249e-28[/C][C] 6.245e-29[/C][/ROW]
[ROW][C]84[/C][C] 1[/C][C] 5.884e-29[/C][C] 2.942e-29[/C][/ROW]
[ROW][C]85[/C][C] 1[/C][C] 2.088e-28[/C][C] 1.044e-28[/C][/ROW]
[ROW][C]86[/C][C] 1[/C][C] 2.603e-27[/C][C] 1.301e-27[/C][/ROW]
[ROW][C]87[/C][C] 1[/C][C] 3.259e-26[/C][C] 1.629e-26[/C][/ROW]
[ROW][C]88[/C][C] 1[/C][C] 3.908e-25[/C][C] 1.954e-25[/C][/ROW]
[ROW][C]89[/C][C] 1[/C][C] 2.828e-24[/C][C] 1.414e-24[/C][/ROW]
[ROW][C]90[/C][C] 1[/C][C] 2.964e-24[/C][C] 1.482e-24[/C][/ROW]
[ROW][C]91[/C][C] 1[/C][C] 3.474e-24[/C][C] 1.737e-24[/C][/ROW]
[ROW][C]92[/C][C] 1[/C][C] 5.801e-25[/C][C] 2.9e-25[/C][/ROW]
[ROW][C]93[/C][C] 1[/C][C] 3.659e-24[/C][C] 1.83e-24[/C][/ROW]
[ROW][C]94[/C][C] 1[/C][C] 6.292e-23[/C][C] 3.146e-23[/C][/ROW]
[ROW][C]95[/C][C] 1[/C][C] 9.224e-22[/C][C] 4.612e-22[/C][/ROW]
[ROW][C]96[/C][C] 1[/C][C] 8.935e-21[/C][C] 4.467e-21[/C][/ROW]
[ROW][C]97[/C][C] 1[/C][C] 1.646e-19[/C][C] 8.229e-20[/C][/ROW]
[ROW][C]98[/C][C] 1[/C][C] 2.416e-18[/C][C] 1.208e-18[/C][/ROW]
[ROW][C]99[/C][C] 1[/C][C] 4.227e-17[/C][C] 2.114e-17[/C][/ROW]
[ROW][C]100[/C][C] 1[/C][C] 5.684e-16[/C][C] 2.842e-16[/C][/ROW]
[ROW][C]101[/C][C] 1[/C][C] 9.385e-15[/C][C] 4.692e-15[/C][/ROW]
[ROW][C]102[/C][C] 1[/C][C] 1.492e-13[/C][C] 7.459e-14[/C][/ROW]
[ROW][C]103[/C][C] 1[/C][C] 1.898e-13[/C][C] 9.488e-14[/C][/ROW]
[ROW][C]104[/C][C] 1[/C][C] 8.899e-13[/C][C] 4.449e-13[/C][/ROW]
[ROW][C]105[/C][C] 1[/C][C] 6.81e-13[/C][C] 3.405e-13[/C][/ROW]
[ROW][C]106[/C][C] 1[/C][C] 1.436e-11[/C][C] 7.178e-12[/C][/ROW]
[ROW][C]107[/C][C] 1[/C][C] 2.442e-10[/C][C] 1.221e-10[/C][/ROW]
[ROW][C]108[/C][C] 1[/C][C] 3.389e-09[/C][C] 1.695e-09[/C][/ROW]
[ROW][C]109[/C][C] 1[/C][C] 2.321e-98[/C][C] 1.161e-98[/C][/ROW]
[ROW][C]110[/C][C] 1[/C][C] 1.575e-81[/C][C] 7.874e-82[/C][/ROW]
[ROW][C]111[/C][C] 1[/C][C] 2.156e-66[/C][C] 1.078e-66[/C][/ROW]
[ROW][C]112[/C][C] 1[/C][C] 1.696e-49[/C][C] 8.482e-50[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=309961&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=309961&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:

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
9 0.05748 0.115 0.9425
10 0.04312 0.08623 0.9569
11 0.01449 0.02897 0.9855
12 0.1163 0.2327 0.8837
13 0.1805 0.361 0.8195
14 0.1156 0.2313 0.8844
15 0.1116 0.2232 0.8884
16 0.1183 0.2365 0.8817
17 0.5852 0.8296 0.4148
18 0.9721 0.0559 0.02795
19 0.9572 0.08569 0.04285
20 0.9411 0.1178 0.0589
21 0.9284 0.1431 0.07156
22 0.9018 0.1963 0.09816
23 0.8713 0.2574 0.1287
24 0.837 0.3261 0.163
25 0.791 0.4179 0.209
26 0.7747 0.4507 0.2253
27 0.7364 0.5272 0.2636
28 0.6815 0.6371 0.3185
29 0.6195 0.761 0.3805
30 0.5643 0.8714 0.4357
31 0.4996 0.9993 0.5004
32 0.4582 0.9164 0.5418
33 0.4 0.7999 0.6
34 0.3434 0.6868 0.6566
35 0.2879 0.5757 0.7121
36 0.2392 0.4783 0.7608
37 0.2093 0.4186 0.7907
38 0.1679 0.3358 0.8321
39 0.1329 0.2657 0.8671
40 0.8737 0.2526 0.1263
41 0.8909 0.2182 0.1091
42 0.8643 0.2715 0.1357
43 0.858 0.284 0.142
44 0.8325 0.3349 0.1675
45 0.8721 0.2558 0.1279
46 0.8421 0.3158 0.1579
47 0.8113 0.3774 0.1887
48 0.7767 0.4467 0.2233
49 0.7416 0.5169 0.2584
50 0.9992 0.001576 0.0007881
51 0.9995 0.0009201 0.00046
52 1 0.0001007 5.033e-05
53 0.9999 0.0001074 5.371e-05
54 1 8.322e-05 4.161e-05
55 0.9999 0.0001405 7.025e-05
56 1 2.246e-05 1.123e-05
57 1 3.954e-05 1.977e-05
58 1 1.65e-05 8.249e-06
59 1 2.08e-05 1.04e-05
60 1 3.699e-05 1.85e-05
61 1 5.448e-05 2.724e-05
62 1 1.762e-09 8.809e-10
63 1 7.587e-10 3.794e-10
64 1 1.827e-09 9.134e-10
65 1 4.447e-09 2.223e-09
66 1 3.566e-09 1.783e-09
67 1 5.617e-12 2.808e-12
68 1 9.825e-12 4.912e-12
69 1 2.978e-13 1.489e-13
70 1 8.433e-13 4.216e-13
71 1 1.402e-12 7.011e-13
72 1 3.056e-12 1.528e-12
73 1 1.916e-13 9.578e-14
74 1 5.866e-13 2.933e-13
75 1 1.75e-12 8.752e-13
76 1 4.602e-12 2.301e-12
77 1 1.407e-32 7.036e-33
78 1 1.67e-31 8.351e-32
79 1 1.898e-30 9.49e-31
80 1 1.868e-29 9.339e-30
81 1 2.078e-28 1.039e-28
82 1 2.288e-27 1.144e-27
83 1 1.249e-28 6.245e-29
84 1 5.884e-29 2.942e-29
85 1 2.088e-28 1.044e-28
86 1 2.603e-27 1.301e-27
87 1 3.259e-26 1.629e-26
88 1 3.908e-25 1.954e-25
89 1 2.828e-24 1.414e-24
90 1 2.964e-24 1.482e-24
91 1 3.474e-24 1.737e-24
92 1 5.801e-25 2.9e-25
93 1 3.659e-24 1.83e-24
94 1 6.292e-23 3.146e-23
95 1 9.224e-22 4.612e-22
96 1 8.935e-21 4.467e-21
97 1 1.646e-19 8.229e-20
98 1 2.416e-18 1.208e-18
99 1 4.227e-17 2.114e-17
100 1 5.684e-16 2.842e-16
101 1 9.385e-15 4.692e-15
102 1 1.492e-13 7.459e-14
103 1 1.898e-13 9.488e-14
104 1 8.899e-13 4.449e-13
105 1 6.81e-13 3.405e-13
106 1 1.436e-11 7.178e-12
107 1 2.442e-10 1.221e-10
108 1 3.389e-09 1.695e-09
109 1 2.321e-98 1.161e-98
110 1 1.575e-81 7.874e-82
111 1 2.156e-66 1.078e-66
112 1 1.696e-49 8.482e-50






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level63 0.6058NOK
5% type I error level640.615385NOK
10% type I error level670.644231NOK

\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 & 63 &  0.6058 & NOK \tabularnewline
5% type I error level & 64 & 0.615385 & NOK \tabularnewline
10% type I error level & 67 & 0.644231 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=309961&T=7

[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]63[/C][C] 0.6058[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]64[/C][C]0.615385[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]67[/C][C]0.644231[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=309961&T=7

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=309961&T=7

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 level63 0.6058NOK
5% type I error level640.615385NOK
10% type I error level670.644231NOK






Ramsey RESET F-Test for powers (2 and 3) of fitted values
> reset_test_fitted
	RESET test
data:  mylm
RESET = 26.956, df1 = 2, df2 = 113, p-value = 2.682e-10
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors
	RESET test
data:  mylm
RESET = 6.0539, df1 = 10, df2 = 105, p-value = 3.247e-07
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components
	RESET test
data:  mylm
RESET = 0.06934, df1 = 2, df2 = 113, p-value = 0.933


\begin{tabular}{lllllllll}
\hline
Ramsey RESET F-Test for powers (2 and 3) of fitted values \tabularnewline

> reset_test_fitted
	RESET test
data:  mylm
RESET = 26.956, df1 = 2, df2 = 113, p-value = 2.682e-10

 \tabularnewline
Ramsey RESET F-Test for powers (2 and 3) of regressors \tabularnewline

> reset_test_regressors
	RESET test
data:  mylm
RESET = 6.0539, df1 = 10, df2 = 105, p-value = 3.247e-07

 \tabularnewline
Ramsey RESET F-Test for powers (2 and 3) of principal components \tabularnewline

> reset_test_principal_components
	RESET test
data:  mylm
RESET = 0.06934, df1 = 2, df2 = 113, p-value = 0.933

 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=309961&T=8

[TABLE]
[ROW][C]Ramsey RESET F-Test for powers (2 and 3) of fitted values[/C][/ROW]
[ROW][C]
> reset_test_fitted
	RESET test
data:  mylm
RESET = 26.956, df1 = 2, df2 = 113, p-value = 2.682e-10

[/C][/ROW]
[ROW][C]Ramsey RESET F-Test for powers (2 and 3) of regressors[/C][/ROW]
[ROW][C]
> reset_test_regressors
	RESET test
data:  mylm
RESET = 6.0539, df1 = 10, df2 = 105, p-value = 3.247e-07

[/C][/ROW]
[ROW][C]Ramsey RESET F-Test for powers (2 and 3) of principal components[/C][/ROW]
[ROW][C]
> reset_test_principal_components
	RESET test
data:  mylm
RESET = 0.06934, df1 = 2, df2 = 113, p-value = 0.933

[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=309961&T=8

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=309961&T=8

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


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

Ramsey RESET F-Test for powers (2 and 3) of fitted values
> reset_test_fitted
	RESET test
data:  mylm
RESET = 26.956, df1 = 2, df2 = 113, p-value = 2.682e-10
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors
	RESET test
data:  mylm
RESET = 6.0539, df1 = 10, df2 = 105, p-value = 3.247e-07
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components
	RESET test
data:  mylm
RESET = 0.06934, df1 = 2, df2 = 113, p-value = 0.933






Variance Inflation Factors (Multicollinearity)
> vif
         llnabo16          lkrabo17         leesabo17         llnaantal 
         1.641076          1.615281          1.331040          1.094781 
`duurklant\\r\\r` 
         1.097865 


\begin{tabular}{lllllllll}
\hline
Variance Inflation Factors (Multicollinearity) \tabularnewline

> vif
         llnabo16          lkrabo17         leesabo17         llnaantal 
         1.641076          1.615281          1.331040          1.094781 
`duurklant\\r\\r` 
         1.097865 

 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=309961&T=9

[TABLE]
[ROW][C]Variance Inflation Factors (Multicollinearity)[/C][/ROW]
[ROW][C]
> vif
         llnabo16          lkrabo17         leesabo17         llnaantal 
         1.641076          1.615281          1.331040          1.094781 
`duurklant\\r\\r` 
         1.097865 

[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=309961&T=9

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=309961&T=9

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


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

Variance Inflation Factors (Multicollinearity)
> vif
         llnabo16          lkrabo17         leesabo17         llnaantal 
         1.641076          1.615281          1.331040          1.094781 
`duurklant\\r\\r` 
         1.097865


Figures (Output of Computation)

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

Parameters (Session):
par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ; par6 = 12 ;
Parameters (R input):
par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ; par4 = ; par5 = ; par6 = 12 ;
R code (references can be found in the software module):
par6 <- '12'
par5 <- ''
par4 <- ''
par3 <- 'No Linear Trend'
par2 <- 'Do not include Seasonal Dummies'
par1 <- '1'
library(lattice)
library(lmtest)
library(car)
library(MASS)
n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
mywarning <- ''
par6 <- as.numeric(par6)
if(is.na(par6)) {
par6 <- 12
mywarning = 'Warning: you did not specify the seasonality. The seasonal period was set to s = 12.'
}
par1 <- as.numeric(par1)
if(is.na(par1)) {
par1 <- 1
mywarning = 'Warning: you did not specify the column number of the endogenous series! The first column was selected by default.'
}
if (par4=='') par4 <- 0
par4 <- as.numeric(par4)
if (!is.numeric(par4)) par4 <- 0
if (par5=='') par5 <- 0
par5 <- as.numeric(par5)
if (!is.numeric(par5)) par5 <- 0
x <- na.omit(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'){
(n <- n -1)
x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-B)',colnames(x),sep='')))
for (i in 1:n) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
}
if (par3 == 'Seasonal Differences (s)'){
(n <- n - par6)
x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-Bs)',colnames(x),sep='')))
for (i in 1:n) {
for (j in 1:k) {
x2[i,j] <- x[i+par6,j] - x[i,j]
}
}
x <- x2
}
if (par3 == 'First and Seasonal Differences (s)'){
(n <- n -1)
x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-B)',colnames(x),sep='')))
for (i in 1:n) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
(n <- n - par6)
x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-Bs)',colnames(x),sep='')))
for (i in 1:n) {
for (j in 1:k) {
x2[i,j] <- x[i+par6,j] - x[i,j]
}
}
x <- x2
}
if(par4 > 0) {
x2 <- array(0, dim=c(n-par4,par4), dimnames=list(1:(n-par4), paste(colnames(x)[par1],'(t-',1:par4,')',sep='')))
for (i in 1:(n-par4)) {
for (j in 1:par4) {
x2[i,j] <- x[i+par4-j,par1]
}
}
x <- cbind(x[(par4+1):n,], x2)
n <- n - par4
}
if(par5 > 0) {
x2 <- array(0, dim=c(n-par5*par6,par5), dimnames=list(1:(n-par5*par6), paste(colnames(x)[par1],'(t-',1:par5,'s)',sep='')))
for (i in 1:(n-par5*par6)) {
for (j in 1:par5) {
x2[i,j] <- x[i+par5*par6-j*par6,par1]
}
}
x <- cbind(x[(par5*par6+1):n,], x2)
n <- n - par5*par6
}
if (par2 == 'Include Seasonal Dummies'){
x2 <- array(0, dim=c(n,par6-1), dimnames=list(1:n, paste('M', seq(1:(par6-1)), sep ='')))
for (i in 1:(par6-1)){
x2[seq(i,n,par6),i] <- 1
}
x <- cbind(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[n,]))
if (par3 == 'Linear Trend'){
x <- cbind(x, c(1:n))
colnames(x)[k+1] <- 't'
}
print(x)
(k <- length(x[n,]))
head(x)
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$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
grid()
dev.off()
bitmap(file='test2.png')
sresid <- studres(mylm)
hist(sresid, freq=FALSE, main='Distribution of Studentized Residuals')
xfit<-seq(min(sresid),max(sresid),length=40)
yfit<-dnorm(xfit)
lines(xfit, yfit)
grid()
dev.off()
bitmap(file='test3.png')
densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
dev.off()
bitmap(file='test4.png')
qqPlot(mylm, main='QQ Plot')
grid()
dev.off()
(myerror <- as.ts(mysum$resid))
bitmap(file='test5.png')
dum <- cbind(lag(myerror,k=1),myerror)
dum
dum1 <- dum[2:length(myerror),]
dum1
z <- as.data.frame(dum1)
print(z)
plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
lines(lowess(z))
abline(lm(z))
grid()
dev.off()
bitmap(file='test6.png')
acf(mysum$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, signif(mysum$coefficients[i,1],6), sep=' ')
if (rownames(mysum$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.row.start(a)
a<-table.element(a, mywarning)
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,'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$coefficients)[i],header=TRUE)
a<-table.element(a,formatC(signif(mysum$coefficients[i,1],5),format='g',flag='+'))
a<-table.element(a,formatC(signif(mysum$coefficients[i,2],5),format='g',flag=' '))
a<-table.element(a,formatC(signif(mysum$coefficients[i,3],4),format='e',flag='+'))
a<-table.element(a,formatC(signif(mysum$coefficients[i,4],4),format='g',flag=' '))
a<-table.element(a,formatC(signif(mysum$coefficients[i,4]/2,4),format='g',flag=' '))
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,formatC(signif(sqrt(mysum$r.squared),6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'R-squared',1,TRUE)
a<-table.element(a,formatC(signif(mysum$r.squared,6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Adjusted R-squared',1,TRUE)
a<-table.element(a,formatC(signif(mysum$adj.r.squared,6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (value)',1,TRUE)
a<-table.element(a,formatC(signif(mysum$fstatistic[1],6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
a<-table.element(a, signif(mysum$fstatistic[2],6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
a<-table.element(a, signif(mysum$fstatistic[3],6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'p-value',1,TRUE)
a<-table.element(a,formatC(signif(1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]),6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
a<-table.element(a,formatC(signif(mysum$sigma,6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
a<-table.element(a,formatC(signif(sum(myerror*myerror),6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
myr <- as.numeric(mysum$resid)
myr
a <-table.start()
a <- table.row.start(a)
a <- table.element(a,'Menu of Residual Diagnostics',2,TRUE)
a <- table.row.end(a)
a <- table.row.start(a)
a <- table.element(a,'Description',1,TRUE)
a <- table.element(a,'Link',1,TRUE)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Histogram',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_histogram.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Central Tendency',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_centraltendency.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'QQ Plot',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_fitdistrnorm.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Kernel Density Plot',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_density.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Skewness/Kurtosis Test',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_skewness_kurtosis.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Skewness-Kurtosis Plot',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_skewness_kurtosis_plot.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Harrell-Davis Plot',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_harrell_davis.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Bootstrap Plot -- Central Tendency',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_bootstrapplot1.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Blocked Bootstrap Plot -- Central Tendency',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_bootstrapplot.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'(Partial) Autocorrelation Plot',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_autocorrelation.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Spectral Analysis',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_spectrum.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Tukey lambda PPCC Plot',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_tukeylambda.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Box-Cox Normality Plot',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_boxcoxnorm.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <- table.element(a,'Summary Statistics',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_summary1.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable7.tab')
if(n < 200) {
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,formatC(signif(x[i],6),format='g',flag=' '))
a<-table.element(a,formatC(signif(x[i]-mysum$resid[i],6),format='g',flag=' '))
a<-table.element(a,formatC(signif(mysum$resid[i],6),format='g',flag=' '))
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,formatC(signif(gqarr[mypoint-kp3+1,1],6),format='g',flag=' '))
a<-table.element(a,formatC(signif(gqarr[mypoint-kp3+1,2],6),format='g',flag=' '))
a<-table.element(a,formatC(signif(gqarr[mypoint-kp3+1,3],6),format='g',flag=' '))
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,signif(numsignificant1,6))
a<-table.element(a,formatC(signif(numsignificant1/numgqtests,6),format='g',flag=' '))
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,signif(numsignificant5,6))
a<-table.element(a,signif(numsignificant5/numgqtests,6))
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,signif(numsignificant10,6))
a<-table.element(a,signif(numsignificant10/numgqtests,6))
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')
}
}
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Ramsey RESET F-Test for powers (2 and 3) of fitted values',1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
reset_test_fitted <- resettest(mylm,power=2:3,type='fitted')
a<-table.element(a,paste('
',RC.texteval('reset_test_fitted'),'
',sep=''))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Ramsey RESET F-Test for powers (2 and 3) of regressors',1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
reset_test_regressors <- resettest(mylm,power=2:3,type='regressor')
a<-table.element(a,paste('
',RC.texteval('reset_test_regressors'),'
',sep=''))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Ramsey RESET F-Test for powers (2 and 3) of principal components',1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
reset_test_principal_components <- resettest(mylm,power=2:3,type='princomp')
a<-table.element(a,paste('
',RC.texteval('reset_test_principal_components'),'
',sep=''))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable8.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Variance Inflation Factors (Multicollinearity)',1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
vif <- vif(mylm)
a<-table.element(a,paste('
',RC.texteval('vif'),'
',sep=''))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable9.tab')