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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 computationSat, 16 Dec 2017 17:11:38 +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/16/t1513442251ppac7xqygdko3at.htm/, Retrieved Wed, 07 May 2025 09:33:53 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=309919, Retrieved Wed, 07 May 2025 09:33:53 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact108

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
10 0 1
8 1 1
8 1 1
9 1 1
5 0 1
10 1 1
8 1 1
9 1 1
8 0 1
7 0 1
10 0 1
10 0 1
9 1 1
4 0 1
4 1 1
8 1 1
9 1 1
10 1 1
8 0 1
5 0 1
10 1 1
8 0 1
7 1 1
8 1 1
8 1 1
9 0 1
8 0 1
6 1 1
8 1 1
8 0 1
5 1 0
9 1 1
8 0 1
8 0 1
8 0 1
6 0 1
6 0 1
9 1 1
8 1 1
9 1 1
10 1 1
8 0 0
8 0 1
7 0 1
7 1 1
10 1 1
8 1 1
7 1 1
10 1 1
7 1 1
7 0 1
9 0 1
9 0 1
8 0 1
6 0 1
8 0 1
9 1 1
2 0 0
6 0 1
8 1 1
8 1 0
7 0 0
8 0 1
6 0 1
10 0 1
10 0 1
10 0 1
8 0 1
8 1 1
7 1 1
10 1 1
5 0 0
3 1 0
2 1 0
3 1 0
4 1 0
2 0 0
6 0 0
8 0 1
8 0 1
5 0 0
10 1 1
9 1 1
8 1 1
9 1 1
8 1 1
5 0 1
7 1 1
9 1 1
8 0 1
4 1 1
7 1 1
8 1 1
7 0 1
7 1 1
9 0 1
6 1 1
7 0 1
4 0 1
6 1 1
10 0 1
9 1 1
10 1 1
8 0 1
4 0 0
8 1 1
5 0 1
8 1 0
9 1 0
8 0 1
4 1 1
8 0 1
10 1 1
6 0 1
7 0 1
10 1 1
9 1 1
8 1 1
3 0 0
8 0 1
7 0 1
7 0 1
8 0 1
8 1 1
7 0 1
7 1 0
9 0 1
9 1 0
9 0 1
4 1 0
6 0 1
6 1 1
6 0 0
8 0 1
3 0 0
8 0 0
8 1 0
6 1 0
10 0 1
2 0 0
9 1 0
6 1 0
6 0 0
5 0 0
4 0 0
7 0 1
5 1 0
8 1 0
6 0 0
9 1 0
6 0 1
4 1 0
7 0 0
2 1 0
8 1 1
9 1 1
6 0 1
5 1 0
7 1 0
8 1 1
4 0 1
9 1 0
9 0 1
9 1 0
7 0 0
5 1 1
7 0 0
9 1 1
8 1 1
6 1 0
9 1 0
8 1 1
7 1 1
7 0 1
7 0 0
8 0 1
10 1 1
6 0 0
6 0 0

Tables (Output of Computation)





Summary of computational transaction
Raw Input view raw input (R code)
Raw Outputview raw output of R engine
Computing time10 seconds
R ServerBig Analytics Cloud Computing Center

\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 time10 seconds \tabularnewline
R ServerBig Analytics Cloud Computing Center \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=309919&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]10 seconds[/C][/ROW] [ROW]R Server[/C]Big Analytics Cloud Computing Center[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=309919&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=309919&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 time10 seconds
R ServerBig Analytics Cloud Computing Center






Multiple Linear Regression - Estimated Regression Equation
(1-B)Intention_to_Use[t] = -0.0112177 + 0.860692`(1-B)genderB`[t] + 2.00325`(1-B)groupB`[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
(1-B)Intention_to_Use[t] =  -0.0112177 +  0.860692`(1-B)genderB`[t] +  2.00325`(1-B)groupB`[t]  + e[t] \tabularnewline
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=309919&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C](1-B)Intention_to_Use[t] =  -0.0112177 +  0.860692`(1-B)genderB`[t] +  2.00325`(1-B)groupB`[t]  + e[t][/C][/ROW]
[ROW][C][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=309919&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=309919&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
(1-B)Intention_to_Use[t] = -0.0112177 + 0.860692`(1-B)genderB`[t] + 2.00325`(1-B)groupB`[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)-0.01122 0.1739-6.4490e-02 0.9487 0.4743
`(1-B)genderB`+0.8607 0.2513+3.4240e+00 0.0007677 0.0003838
`(1-B)groupB`+2.003 0.34+5.8920e+00 1.917e-08 9.584e-09

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Ordinary Least Squares \tabularnewline
Variable & Parameter & S.D. & T-STATH0: parameter = 0 & 2-tail p-value & 1-tail p-value \tabularnewline
(Intercept) & -0.01122 &  0.1739 & -6.4490e-02 &  0.9487 &  0.4743 \tabularnewline
`(1-B)genderB` & +0.8607 &  0.2513 & +3.4240e+00 &  0.0007677 &  0.0003838 \tabularnewline
`(1-B)groupB` & +2.003 &  0.34 & +5.8920e+00 &  1.917e-08 &  9.584e-09 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=309919&T=2

[TABLE]
[ROW][C]Multiple Linear Regression - Ordinary Least Squares[/C][/ROW]
[ROW][C]Variable[/C][C]Parameter[/C][C]S.D.[/C][C]T-STATH0: parameter = 0[/C][C]2-tail p-value[/C][C]1-tail p-value[/C][/ROW]
[ROW][C](Intercept)[/C][C]-0.01122[/C][C] 0.1739[/C][C]-6.4490e-02[/C][C] 0.9487[/C][C] 0.4743[/C][/ROW]
[ROW][C]`(1-B)genderB`[/C][C]+0.8607[/C][C] 0.2513[/C][C]+3.4240e+00[/C][C] 0.0007677[/C][C] 0.0003838[/C][/ROW]
[ROW][C]`(1-B)groupB`[/C][C]+2.003[/C][C] 0.34[/C][C]+5.8920e+00[/C][C] 1.917e-08[/C][C] 9.584e-09[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=309919&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=309919&T=2

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


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

Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)-0.01122 0.1739-6.4490e-02 0.9487 0.4743
`(1-B)genderB`+0.8607 0.2513+3.4240e+00 0.0007677 0.0003838
`(1-B)groupB`+2.003 0.34+5.8920e+00 1.917e-08 9.584e-09






Multiple Linear Regression - Regression Statistics
Multiple R 0.4442
R-squared 0.1973
Adjusted R-squared 0.1881
F-TEST (value) 21.51
F-TEST (DF numerator)2
F-TEST (DF denominator)175
p-value 4.453e-09
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation 2.32
Sum Squared Residuals 942.3

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R &  0.4442 \tabularnewline
R-squared &  0.1973 \tabularnewline
Adjusted R-squared &  0.1881 \tabularnewline
F-TEST (value) &  21.51 \tabularnewline
F-TEST (DF numerator) & 2 \tabularnewline
F-TEST (DF denominator) & 175 \tabularnewline
p-value &  4.453e-09 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation &  2.32 \tabularnewline
Sum Squared Residuals &  942.3 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=309919&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C] 0.4442[/C][/ROW]
[ROW][C]R-squared[/C][C] 0.1973[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C] 0.1881[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C] 21.51[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]2[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]175[/C][/ROW]
[ROW][C]p-value[/C][C] 4.453e-09[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C] 2.32[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C] 942.3[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=309919&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=309919&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 0.4442
R-squared 0.1973
Adjusted R-squared 0.1881
F-TEST (value) 21.51
F-TEST (DF numerator)2
F-TEST (DF denominator)175
p-value 4.453e-09
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation 2.32
Sum Squared Residuals 942.3






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=309919&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=309919&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=309919&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
1-2 0.8495-2.849
2 0-0.01122 0.01122
3 1-0.01122 1.011
4-4-0.8719-3.128
5 5 0.8495 4.151
6-2-0.01122-1.989
7 1-0.01122 1.011
8-1-0.8719-0.1281
9-1-0.01122-0.9888
10 3-0.01122 3.011
11 0-0.01122 0.01122
12-1 0.8495-1.849
13-5-0.8719-4.128
14 0 0.8495-0.8495
15 4-0.01122 4.011
16 1-0.01122 1.011
17 1-0.01122 1.011
18-2-0.8719-1.128
19-3-0.01122-2.989
20 5 0.8495 4.151
21-2-0.8719-1.128
22-1 0.8495-1.849
23 1-0.01122 1.011
24 0-0.01122 0.01122
25 1-0.8719 1.872
26-1-0.01122-0.9888
27-2 0.8495-2.849
28 2-0.01122 2.011
29 0-0.8719 0.8719
30-3-1.154-1.846
31 4 1.992 2.008
32-1-0.8719-0.1281
33 0-0.01122 0.01122
34 0-0.01122 0.01122
35-2-0.01122-1.989
36 0-0.01122 0.01122
37 3 0.8495 2.151
38-1-0.01122-0.9888
39 1-0.01122 1.011
40 1-0.01122 1.011
41-2-2.875 0.8752
42 0 1.992-1.992
43-1-0.01122-0.9888
44 0 0.8495-0.8495
45 3-0.01122 3.011
46-2-0.01122-1.989
47-1-0.01122-0.9888
48 3-0.01122 3.011
49-3-0.01122-2.989
50 0-0.8719 0.8719
51 2-0.01122 2.011
52 0-0.01122 0.01122
53-1-0.01122-0.9888
54-2-0.01122-1.989
55 2-0.01122 2.011
56 1 0.8495 0.1505
57-7-2.875-4.125
58 4 1.992 2.008
59 2 0.8495 1.151
60 0-2.014 2.014
61-1-0.8719-0.1281
62 1 1.992-0.992
63-2-0.01122-1.989
64 4-0.01122 4.011
65 0-0.01122 0.01122
66 0-0.01122 0.01122
67-2-0.01122-1.989
68 0 0.8495-0.8495
69-1-0.01122-0.9888
70 3-0.01122 3.011
71-5-2.875-2.125
72-2 0.8495-2.849
73-1-0.01122-0.9888
74 1-0.01122 1.011
75 1-0.01122 1.011
76-2-0.8719-1.128
77 4-0.01122 4.011
78 2 1.992 0.007964
79 0-0.01122 0.01122
80-3-2.014-0.9855
81 5 2.853 2.147
82-1-0.01122-0.9888
83-1-0.01122-0.9888
84 1-0.01122 1.011
85-1-0.01122-0.9888
86-3-0.8719-2.128
87 2 0.8495 1.151
88 2-0.01122 2.011
89-1-0.8719-0.1281
90-4 0.8495-4.849
91 3-0.01122 3.011
92 1-0.01122 1.011
93-1-0.8719-0.1281
94 0 0.8495-0.8495
95 2-0.8719 2.872
96-3 0.8495-3.849
97 1-0.8719 1.872
98-3-0.01122-2.989
99 2 0.8495 1.151
100 4-0.8719 4.872
101-1 0.8495-1.849
102 1-0.01122 1.011
103-2-0.8719-1.128
104-4-2.014-1.986
105 4 2.853 1.147
106-3-0.8719-2.128
107 3-1.154 4.154
108 1-0.01122 1.011
109-1 1.131-2.131
110-4 0.8495-4.849
111 4-0.8719 4.872
112 2 0.8495 1.151
113-4-0.8719-3.128
114 1-0.01122 1.011
115 3 0.8495 2.151
116-1-0.01122-0.9888
117-1-0.01122-0.9888
118-5-2.875-2.125
119 5 1.992 3.008
120-1-0.01122-0.9888
121 0-0.01122 0.01122
122 1-0.01122 1.011
123 0 0.8495-0.8495
124-1-0.8719-0.1281
125 0-1.154 1.154
126 2 1.131 0.8687
127 0-1.154 1.154
128 0 1.131-1.131
129-5-1.154-3.846
130 2 1.131 0.8687
131 0 0.8495-0.8495
132 0-2.875 2.875
133 2 1.992 0.007964
134-5-2.014-2.986
135 5-0.01122 5.011
136 0 0.8495-0.8495
137-2-0.01122-1.989
138 4 1.131 2.869
139-8-2.014-5.986
140 7 0.8495 6.151
141-3-0.01122-2.989
142 0-0.8719 0.8719
143-1-0.01122-0.9888
144-1-0.01122-0.9888
145 3 1.992 1.008
146-2-1.154-0.8462
147 3-0.01122 3.011
148-2-0.8719-1.128
149 3 0.8495 2.151
150-3 1.131-4.131
151-2-1.154-0.8462
152 3-0.8719 3.872
153-5 0.8495-5.849
154 6 1.992 4.008
155 1-0.01122 1.011
156-3-0.8719-2.128
157-1-1.154 0.1538
158 2-0.01122 2.011
159 1 1.992-0.992
160-4-0.8719-3.128
161 5-1.154 6.154
162 0 1.131-1.131
163 0-1.154 1.154
164-2-0.8719-1.128
165-2 2.853-4.853
166 2-2.875 4.875
167 2 2.853-0.8527
168-1-0.01122-0.9888
169-2-2.014 0.01447
170 3-0.01122 3.011
171-1 1.992-2.992
172-1-0.01122-0.9888
173 0-0.8719 0.8719
174 0-2.014 2.014
175 1 1.992-0.992
176 2 0.8495 1.151
177-4-2.875-1.125
178 0-0.01122 0.01122

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & -2 &  0.8495 & -2.849 \tabularnewline
2 &  0 & -0.01122 &  0.01122 \tabularnewline
3 &  1 & -0.01122 &  1.011 \tabularnewline
4 & -4 & -0.8719 & -3.128 \tabularnewline
5 &  5 &  0.8495 &  4.151 \tabularnewline
6 & -2 & -0.01122 & -1.989 \tabularnewline
7 &  1 & -0.01122 &  1.011 \tabularnewline
8 & -1 & -0.8719 & -0.1281 \tabularnewline
9 & -1 & -0.01122 & -0.9888 \tabularnewline
10 &  3 & -0.01122 &  3.011 \tabularnewline
11 &  0 & -0.01122 &  0.01122 \tabularnewline
12 & -1 &  0.8495 & -1.849 \tabularnewline
13 & -5 & -0.8719 & -4.128 \tabularnewline
14 &  0 &  0.8495 & -0.8495 \tabularnewline
15 &  4 & -0.01122 &  4.011 \tabularnewline
16 &  1 & -0.01122 &  1.011 \tabularnewline
17 &  1 & -0.01122 &  1.011 \tabularnewline
18 & -2 & -0.8719 & -1.128 \tabularnewline
19 & -3 & -0.01122 & -2.989 \tabularnewline
20 &  5 &  0.8495 &  4.151 \tabularnewline
21 & -2 & -0.8719 & -1.128 \tabularnewline
22 & -1 &  0.8495 & -1.849 \tabularnewline
23 &  1 & -0.01122 &  1.011 \tabularnewline
24 &  0 & -0.01122 &  0.01122 \tabularnewline
25 &  1 & -0.8719 &  1.872 \tabularnewline
26 & -1 & -0.01122 & -0.9888 \tabularnewline
27 & -2 &  0.8495 & -2.849 \tabularnewline
28 &  2 & -0.01122 &  2.011 \tabularnewline
29 &  0 & -0.8719 &  0.8719 \tabularnewline
30 & -3 & -1.154 & -1.846 \tabularnewline
31 &  4 &  1.992 &  2.008 \tabularnewline
32 & -1 & -0.8719 & -0.1281 \tabularnewline
33 &  0 & -0.01122 &  0.01122 \tabularnewline
34 &  0 & -0.01122 &  0.01122 \tabularnewline
35 & -2 & -0.01122 & -1.989 \tabularnewline
36 &  0 & -0.01122 &  0.01122 \tabularnewline
37 &  3 &  0.8495 &  2.151 \tabularnewline
38 & -1 & -0.01122 & -0.9888 \tabularnewline
39 &  1 & -0.01122 &  1.011 \tabularnewline
40 &  1 & -0.01122 &  1.011 \tabularnewline
41 & -2 & -2.875 &  0.8752 \tabularnewline
42 &  0 &  1.992 & -1.992 \tabularnewline
43 & -1 & -0.01122 & -0.9888 \tabularnewline
44 &  0 &  0.8495 & -0.8495 \tabularnewline
45 &  3 & -0.01122 &  3.011 \tabularnewline
46 & -2 & -0.01122 & -1.989 \tabularnewline
47 & -1 & -0.01122 & -0.9888 \tabularnewline
48 &  3 & -0.01122 &  3.011 \tabularnewline
49 & -3 & -0.01122 & -2.989 \tabularnewline
50 &  0 & -0.8719 &  0.8719 \tabularnewline
51 &  2 & -0.01122 &  2.011 \tabularnewline
52 &  0 & -0.01122 &  0.01122 \tabularnewline
53 & -1 & -0.01122 & -0.9888 \tabularnewline
54 & -2 & -0.01122 & -1.989 \tabularnewline
55 &  2 & -0.01122 &  2.011 \tabularnewline
56 &  1 &  0.8495 &  0.1505 \tabularnewline
57 & -7 & -2.875 & -4.125 \tabularnewline
58 &  4 &  1.992 &  2.008 \tabularnewline
59 &  2 &  0.8495 &  1.151 \tabularnewline
60 &  0 & -2.014 &  2.014 \tabularnewline
61 & -1 & -0.8719 & -0.1281 \tabularnewline
62 &  1 &  1.992 & -0.992 \tabularnewline
63 & -2 & -0.01122 & -1.989 \tabularnewline
64 &  4 & -0.01122 &  4.011 \tabularnewline
65 &  0 & -0.01122 &  0.01122 \tabularnewline
66 &  0 & -0.01122 &  0.01122 \tabularnewline
67 & -2 & -0.01122 & -1.989 \tabularnewline
68 &  0 &  0.8495 & -0.8495 \tabularnewline
69 & -1 & -0.01122 & -0.9888 \tabularnewline
70 &  3 & -0.01122 &  3.011 \tabularnewline
71 & -5 & -2.875 & -2.125 \tabularnewline
72 & -2 &  0.8495 & -2.849 \tabularnewline
73 & -1 & -0.01122 & -0.9888 \tabularnewline
74 &  1 & -0.01122 &  1.011 \tabularnewline
75 &  1 & -0.01122 &  1.011 \tabularnewline
76 & -2 & -0.8719 & -1.128 \tabularnewline
77 &  4 & -0.01122 &  4.011 \tabularnewline
78 &  2 &  1.992 &  0.007964 \tabularnewline
79 &  0 & -0.01122 &  0.01122 \tabularnewline
80 & -3 & -2.014 & -0.9855 \tabularnewline
81 &  5 &  2.853 &  2.147 \tabularnewline
82 & -1 & -0.01122 & -0.9888 \tabularnewline
83 & -1 & -0.01122 & -0.9888 \tabularnewline
84 &  1 & -0.01122 &  1.011 \tabularnewline
85 & -1 & -0.01122 & -0.9888 \tabularnewline
86 & -3 & -0.8719 & -2.128 \tabularnewline
87 &  2 &  0.8495 &  1.151 \tabularnewline
88 &  2 & -0.01122 &  2.011 \tabularnewline
89 & -1 & -0.8719 & -0.1281 \tabularnewline
90 & -4 &  0.8495 & -4.849 \tabularnewline
91 &  3 & -0.01122 &  3.011 \tabularnewline
92 &  1 & -0.01122 &  1.011 \tabularnewline
93 & -1 & -0.8719 & -0.1281 \tabularnewline
94 &  0 &  0.8495 & -0.8495 \tabularnewline
95 &  2 & -0.8719 &  2.872 \tabularnewline
96 & -3 &  0.8495 & -3.849 \tabularnewline
97 &  1 & -0.8719 &  1.872 \tabularnewline
98 & -3 & -0.01122 & -2.989 \tabularnewline
99 &  2 &  0.8495 &  1.151 \tabularnewline
100 &  4 & -0.8719 &  4.872 \tabularnewline
101 & -1 &  0.8495 & -1.849 \tabularnewline
102 &  1 & -0.01122 &  1.011 \tabularnewline
103 & -2 & -0.8719 & -1.128 \tabularnewline
104 & -4 & -2.014 & -1.986 \tabularnewline
105 &  4 &  2.853 &  1.147 \tabularnewline
106 & -3 & -0.8719 & -2.128 \tabularnewline
107 &  3 & -1.154 &  4.154 \tabularnewline
108 &  1 & -0.01122 &  1.011 \tabularnewline
109 & -1 &  1.131 & -2.131 \tabularnewline
110 & -4 &  0.8495 & -4.849 \tabularnewline
111 &  4 & -0.8719 &  4.872 \tabularnewline
112 &  2 &  0.8495 &  1.151 \tabularnewline
113 & -4 & -0.8719 & -3.128 \tabularnewline
114 &  1 & -0.01122 &  1.011 \tabularnewline
115 &  3 &  0.8495 &  2.151 \tabularnewline
116 & -1 & -0.01122 & -0.9888 \tabularnewline
117 & -1 & -0.01122 & -0.9888 \tabularnewline
118 & -5 & -2.875 & -2.125 \tabularnewline
119 &  5 &  1.992 &  3.008 \tabularnewline
120 & -1 & -0.01122 & -0.9888 \tabularnewline
121 &  0 & -0.01122 &  0.01122 \tabularnewline
122 &  1 & -0.01122 &  1.011 \tabularnewline
123 &  0 &  0.8495 & -0.8495 \tabularnewline
124 & -1 & -0.8719 & -0.1281 \tabularnewline
125 &  0 & -1.154 &  1.154 \tabularnewline
126 &  2 &  1.131 &  0.8687 \tabularnewline
127 &  0 & -1.154 &  1.154 \tabularnewline
128 &  0 &  1.131 & -1.131 \tabularnewline
129 & -5 & -1.154 & -3.846 \tabularnewline
130 &  2 &  1.131 &  0.8687 \tabularnewline
131 &  0 &  0.8495 & -0.8495 \tabularnewline
132 &  0 & -2.875 &  2.875 \tabularnewline
133 &  2 &  1.992 &  0.007964 \tabularnewline
134 & -5 & -2.014 & -2.986 \tabularnewline
135 &  5 & -0.01122 &  5.011 \tabularnewline
136 &  0 &  0.8495 & -0.8495 \tabularnewline
137 & -2 & -0.01122 & -1.989 \tabularnewline
138 &  4 &  1.131 &  2.869 \tabularnewline
139 & -8 & -2.014 & -5.986 \tabularnewline
140 &  7 &  0.8495 &  6.151 \tabularnewline
141 & -3 & -0.01122 & -2.989 \tabularnewline
142 &  0 & -0.8719 &  0.8719 \tabularnewline
143 & -1 & -0.01122 & -0.9888 \tabularnewline
144 & -1 & -0.01122 & -0.9888 \tabularnewline
145 &  3 &  1.992 &  1.008 \tabularnewline
146 & -2 & -1.154 & -0.8462 \tabularnewline
147 &  3 & -0.01122 &  3.011 \tabularnewline
148 & -2 & -0.8719 & -1.128 \tabularnewline
149 &  3 &  0.8495 &  2.151 \tabularnewline
150 & -3 &  1.131 & -4.131 \tabularnewline
151 & -2 & -1.154 & -0.8462 \tabularnewline
152 &  3 & -0.8719 &  3.872 \tabularnewline
153 & -5 &  0.8495 & -5.849 \tabularnewline
154 &  6 &  1.992 &  4.008 \tabularnewline
155 &  1 & -0.01122 &  1.011 \tabularnewline
156 & -3 & -0.8719 & -2.128 \tabularnewline
157 & -1 & -1.154 &  0.1538 \tabularnewline
158 &  2 & -0.01122 &  2.011 \tabularnewline
159 &  1 &  1.992 & -0.992 \tabularnewline
160 & -4 & -0.8719 & -3.128 \tabularnewline
161 &  5 & -1.154 &  6.154 \tabularnewline
162 &  0 &  1.131 & -1.131 \tabularnewline
163 &  0 & -1.154 &  1.154 \tabularnewline
164 & -2 & -0.8719 & -1.128 \tabularnewline
165 & -2 &  2.853 & -4.853 \tabularnewline
166 &  2 & -2.875 &  4.875 \tabularnewline
167 &  2 &  2.853 & -0.8527 \tabularnewline
168 & -1 & -0.01122 & -0.9888 \tabularnewline
169 & -2 & -2.014 &  0.01447 \tabularnewline
170 &  3 & -0.01122 &  3.011 \tabularnewline
171 & -1 &  1.992 & -2.992 \tabularnewline
172 & -1 & -0.01122 & -0.9888 \tabularnewline
173 &  0 & -0.8719 &  0.8719 \tabularnewline
174 &  0 & -2.014 &  2.014 \tabularnewline
175 &  1 &  1.992 & -0.992 \tabularnewline
176 &  2 &  0.8495 &  1.151 \tabularnewline
177 & -4 & -2.875 & -1.125 \tabularnewline
178 &  0 & -0.01122 &  0.01122 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=309919&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]-2[/C][C] 0.8495[/C][C]-2.849[/C][/ROW]
[ROW][C]2[/C][C] 0[/C][C]-0.01122[/C][C] 0.01122[/C][/ROW]
[ROW][C]3[/C][C] 1[/C][C]-0.01122[/C][C] 1.011[/C][/ROW]
[ROW][C]4[/C][C]-4[/C][C]-0.8719[/C][C]-3.128[/C][/ROW]
[ROW][C]5[/C][C] 5[/C][C] 0.8495[/C][C] 4.151[/C][/ROW]
[ROW][C]6[/C][C]-2[/C][C]-0.01122[/C][C]-1.989[/C][/ROW]
[ROW][C]7[/C][C] 1[/C][C]-0.01122[/C][C] 1.011[/C][/ROW]
[ROW][C]8[/C][C]-1[/C][C]-0.8719[/C][C]-0.1281[/C][/ROW]
[ROW][C]9[/C][C]-1[/C][C]-0.01122[/C][C]-0.9888[/C][/ROW]
[ROW][C]10[/C][C] 3[/C][C]-0.01122[/C][C] 3.011[/C][/ROW]
[ROW][C]11[/C][C] 0[/C][C]-0.01122[/C][C] 0.01122[/C][/ROW]
[ROW][C]12[/C][C]-1[/C][C] 0.8495[/C][C]-1.849[/C][/ROW]
[ROW][C]13[/C][C]-5[/C][C]-0.8719[/C][C]-4.128[/C][/ROW]
[ROW][C]14[/C][C] 0[/C][C] 0.8495[/C][C]-0.8495[/C][/ROW]
[ROW][C]15[/C][C] 4[/C][C]-0.01122[/C][C] 4.011[/C][/ROW]
[ROW][C]16[/C][C] 1[/C][C]-0.01122[/C][C] 1.011[/C][/ROW]
[ROW][C]17[/C][C] 1[/C][C]-0.01122[/C][C] 1.011[/C][/ROW]
[ROW][C]18[/C][C]-2[/C][C]-0.8719[/C][C]-1.128[/C][/ROW]
[ROW][C]19[/C][C]-3[/C][C]-0.01122[/C][C]-2.989[/C][/ROW]
[ROW][C]20[/C][C] 5[/C][C] 0.8495[/C][C] 4.151[/C][/ROW]
[ROW][C]21[/C][C]-2[/C][C]-0.8719[/C][C]-1.128[/C][/ROW]
[ROW][C]22[/C][C]-1[/C][C] 0.8495[/C][C]-1.849[/C][/ROW]
[ROW][C]23[/C][C] 1[/C][C]-0.01122[/C][C] 1.011[/C][/ROW]
[ROW][C]24[/C][C] 0[/C][C]-0.01122[/C][C] 0.01122[/C][/ROW]
[ROW][C]25[/C][C] 1[/C][C]-0.8719[/C][C] 1.872[/C][/ROW]
[ROW][C]26[/C][C]-1[/C][C]-0.01122[/C][C]-0.9888[/C][/ROW]
[ROW][C]27[/C][C]-2[/C][C] 0.8495[/C][C]-2.849[/C][/ROW]
[ROW][C]28[/C][C] 2[/C][C]-0.01122[/C][C] 2.011[/C][/ROW]
[ROW][C]29[/C][C] 0[/C][C]-0.8719[/C][C] 0.8719[/C][/ROW]
[ROW][C]30[/C][C]-3[/C][C]-1.154[/C][C]-1.846[/C][/ROW]
[ROW][C]31[/C][C] 4[/C][C] 1.992[/C][C] 2.008[/C][/ROW]
[ROW][C]32[/C][C]-1[/C][C]-0.8719[/C][C]-0.1281[/C][/ROW]
[ROW][C]33[/C][C] 0[/C][C]-0.01122[/C][C] 0.01122[/C][/ROW]
[ROW][C]34[/C][C] 0[/C][C]-0.01122[/C][C] 0.01122[/C][/ROW]
[ROW][C]35[/C][C]-2[/C][C]-0.01122[/C][C]-1.989[/C][/ROW]
[ROW][C]36[/C][C] 0[/C][C]-0.01122[/C][C] 0.01122[/C][/ROW]
[ROW][C]37[/C][C] 3[/C][C] 0.8495[/C][C] 2.151[/C][/ROW]
[ROW][C]38[/C][C]-1[/C][C]-0.01122[/C][C]-0.9888[/C][/ROW]
[ROW][C]39[/C][C] 1[/C][C]-0.01122[/C][C] 1.011[/C][/ROW]
[ROW][C]40[/C][C] 1[/C][C]-0.01122[/C][C] 1.011[/C][/ROW]
[ROW][C]41[/C][C]-2[/C][C]-2.875[/C][C] 0.8752[/C][/ROW]
[ROW][C]42[/C][C] 0[/C][C] 1.992[/C][C]-1.992[/C][/ROW]
[ROW][C]43[/C][C]-1[/C][C]-0.01122[/C][C]-0.9888[/C][/ROW]
[ROW][C]44[/C][C] 0[/C][C] 0.8495[/C][C]-0.8495[/C][/ROW]
[ROW][C]45[/C][C] 3[/C][C]-0.01122[/C][C] 3.011[/C][/ROW]
[ROW][C]46[/C][C]-2[/C][C]-0.01122[/C][C]-1.989[/C][/ROW]
[ROW][C]47[/C][C]-1[/C][C]-0.01122[/C][C]-0.9888[/C][/ROW]
[ROW][C]48[/C][C] 3[/C][C]-0.01122[/C][C] 3.011[/C][/ROW]
[ROW][C]49[/C][C]-3[/C][C]-0.01122[/C][C]-2.989[/C][/ROW]
[ROW][C]50[/C][C] 0[/C][C]-0.8719[/C][C] 0.8719[/C][/ROW]
[ROW][C]51[/C][C] 2[/C][C]-0.01122[/C][C] 2.011[/C][/ROW]
[ROW][C]52[/C][C] 0[/C][C]-0.01122[/C][C] 0.01122[/C][/ROW]
[ROW][C]53[/C][C]-1[/C][C]-0.01122[/C][C]-0.9888[/C][/ROW]
[ROW][C]54[/C][C]-2[/C][C]-0.01122[/C][C]-1.989[/C][/ROW]
[ROW][C]55[/C][C] 2[/C][C]-0.01122[/C][C] 2.011[/C][/ROW]
[ROW][C]56[/C][C] 1[/C][C] 0.8495[/C][C] 0.1505[/C][/ROW]
[ROW][C]57[/C][C]-7[/C][C]-2.875[/C][C]-4.125[/C][/ROW]
[ROW][C]58[/C][C] 4[/C][C] 1.992[/C][C] 2.008[/C][/ROW]
[ROW][C]59[/C][C] 2[/C][C] 0.8495[/C][C] 1.151[/C][/ROW]
[ROW][C]60[/C][C] 0[/C][C]-2.014[/C][C] 2.014[/C][/ROW]
[ROW][C]61[/C][C]-1[/C][C]-0.8719[/C][C]-0.1281[/C][/ROW]
[ROW][C]62[/C][C] 1[/C][C] 1.992[/C][C]-0.992[/C][/ROW]
[ROW][C]63[/C][C]-2[/C][C]-0.01122[/C][C]-1.989[/C][/ROW]
[ROW][C]64[/C][C] 4[/C][C]-0.01122[/C][C] 4.011[/C][/ROW]
[ROW][C]65[/C][C] 0[/C][C]-0.01122[/C][C] 0.01122[/C][/ROW]
[ROW][C]66[/C][C] 0[/C][C]-0.01122[/C][C] 0.01122[/C][/ROW]
[ROW][C]67[/C][C]-2[/C][C]-0.01122[/C][C]-1.989[/C][/ROW]
[ROW][C]68[/C][C] 0[/C][C] 0.8495[/C][C]-0.8495[/C][/ROW]
[ROW][C]69[/C][C]-1[/C][C]-0.01122[/C][C]-0.9888[/C][/ROW]
[ROW][C]70[/C][C] 3[/C][C]-0.01122[/C][C] 3.011[/C][/ROW]
[ROW][C]71[/C][C]-5[/C][C]-2.875[/C][C]-2.125[/C][/ROW]
[ROW][C]72[/C][C]-2[/C][C] 0.8495[/C][C]-2.849[/C][/ROW]
[ROW][C]73[/C][C]-1[/C][C]-0.01122[/C][C]-0.9888[/C][/ROW]
[ROW][C]74[/C][C] 1[/C][C]-0.01122[/C][C] 1.011[/C][/ROW]
[ROW][C]75[/C][C] 1[/C][C]-0.01122[/C][C] 1.011[/C][/ROW]
[ROW][C]76[/C][C]-2[/C][C]-0.8719[/C][C]-1.128[/C][/ROW]
[ROW][C]77[/C][C] 4[/C][C]-0.01122[/C][C] 4.011[/C][/ROW]
[ROW][C]78[/C][C] 2[/C][C] 1.992[/C][C] 0.007964[/C][/ROW]
[ROW][C]79[/C][C] 0[/C][C]-0.01122[/C][C] 0.01122[/C][/ROW]
[ROW][C]80[/C][C]-3[/C][C]-2.014[/C][C]-0.9855[/C][/ROW]
[ROW][C]81[/C][C] 5[/C][C] 2.853[/C][C] 2.147[/C][/ROW]
[ROW][C]82[/C][C]-1[/C][C]-0.01122[/C][C]-0.9888[/C][/ROW]
[ROW][C]83[/C][C]-1[/C][C]-0.01122[/C][C]-0.9888[/C][/ROW]
[ROW][C]84[/C][C] 1[/C][C]-0.01122[/C][C] 1.011[/C][/ROW]
[ROW][C]85[/C][C]-1[/C][C]-0.01122[/C][C]-0.9888[/C][/ROW]
[ROW][C]86[/C][C]-3[/C][C]-0.8719[/C][C]-2.128[/C][/ROW]
[ROW][C]87[/C][C] 2[/C][C] 0.8495[/C][C] 1.151[/C][/ROW]
[ROW][C]88[/C][C] 2[/C][C]-0.01122[/C][C] 2.011[/C][/ROW]
[ROW][C]89[/C][C]-1[/C][C]-0.8719[/C][C]-0.1281[/C][/ROW]
[ROW][C]90[/C][C]-4[/C][C] 0.8495[/C][C]-4.849[/C][/ROW]
[ROW][C]91[/C][C] 3[/C][C]-0.01122[/C][C] 3.011[/C][/ROW]
[ROW][C]92[/C][C] 1[/C][C]-0.01122[/C][C] 1.011[/C][/ROW]
[ROW][C]93[/C][C]-1[/C][C]-0.8719[/C][C]-0.1281[/C][/ROW]
[ROW][C]94[/C][C] 0[/C][C] 0.8495[/C][C]-0.8495[/C][/ROW]
[ROW][C]95[/C][C] 2[/C][C]-0.8719[/C][C] 2.872[/C][/ROW]
[ROW][C]96[/C][C]-3[/C][C] 0.8495[/C][C]-3.849[/C][/ROW]
[ROW][C]97[/C][C] 1[/C][C]-0.8719[/C][C] 1.872[/C][/ROW]
[ROW][C]98[/C][C]-3[/C][C]-0.01122[/C][C]-2.989[/C][/ROW]
[ROW][C]99[/C][C] 2[/C][C] 0.8495[/C][C] 1.151[/C][/ROW]
[ROW][C]100[/C][C] 4[/C][C]-0.8719[/C][C] 4.872[/C][/ROW]
[ROW][C]101[/C][C]-1[/C][C] 0.8495[/C][C]-1.849[/C][/ROW]
[ROW][C]102[/C][C] 1[/C][C]-0.01122[/C][C] 1.011[/C][/ROW]
[ROW][C]103[/C][C]-2[/C][C]-0.8719[/C][C]-1.128[/C][/ROW]
[ROW][C]104[/C][C]-4[/C][C]-2.014[/C][C]-1.986[/C][/ROW]
[ROW][C]105[/C][C] 4[/C][C] 2.853[/C][C] 1.147[/C][/ROW]
[ROW][C]106[/C][C]-3[/C][C]-0.8719[/C][C]-2.128[/C][/ROW]
[ROW][C]107[/C][C] 3[/C][C]-1.154[/C][C] 4.154[/C][/ROW]
[ROW][C]108[/C][C] 1[/C][C]-0.01122[/C][C] 1.011[/C][/ROW]
[ROW][C]109[/C][C]-1[/C][C] 1.131[/C][C]-2.131[/C][/ROW]
[ROW][C]110[/C][C]-4[/C][C] 0.8495[/C][C]-4.849[/C][/ROW]
[ROW][C]111[/C][C] 4[/C][C]-0.8719[/C][C] 4.872[/C][/ROW]
[ROW][C]112[/C][C] 2[/C][C] 0.8495[/C][C] 1.151[/C][/ROW]
[ROW][C]113[/C][C]-4[/C][C]-0.8719[/C][C]-3.128[/C][/ROW]
[ROW][C]114[/C][C] 1[/C][C]-0.01122[/C][C] 1.011[/C][/ROW]
[ROW][C]115[/C][C] 3[/C][C] 0.8495[/C][C] 2.151[/C][/ROW]
[ROW][C]116[/C][C]-1[/C][C]-0.01122[/C][C]-0.9888[/C][/ROW]
[ROW][C]117[/C][C]-1[/C][C]-0.01122[/C][C]-0.9888[/C][/ROW]
[ROW][C]118[/C][C]-5[/C][C]-2.875[/C][C]-2.125[/C][/ROW]
[ROW][C]119[/C][C] 5[/C][C] 1.992[/C][C] 3.008[/C][/ROW]
[ROW][C]120[/C][C]-1[/C][C]-0.01122[/C][C]-0.9888[/C][/ROW]
[ROW][C]121[/C][C] 0[/C][C]-0.01122[/C][C] 0.01122[/C][/ROW]
[ROW][C]122[/C][C] 1[/C][C]-0.01122[/C][C] 1.011[/C][/ROW]
[ROW][C]123[/C][C] 0[/C][C] 0.8495[/C][C]-0.8495[/C][/ROW]
[ROW][C]124[/C][C]-1[/C][C]-0.8719[/C][C]-0.1281[/C][/ROW]
[ROW][C]125[/C][C] 0[/C][C]-1.154[/C][C] 1.154[/C][/ROW]
[ROW][C]126[/C][C] 2[/C][C] 1.131[/C][C] 0.8687[/C][/ROW]
[ROW][C]127[/C][C] 0[/C][C]-1.154[/C][C] 1.154[/C][/ROW]
[ROW][C]128[/C][C] 0[/C][C] 1.131[/C][C]-1.131[/C][/ROW]
[ROW][C]129[/C][C]-5[/C][C]-1.154[/C][C]-3.846[/C][/ROW]
[ROW][C]130[/C][C] 2[/C][C] 1.131[/C][C] 0.8687[/C][/ROW]
[ROW][C]131[/C][C] 0[/C][C] 0.8495[/C][C]-0.8495[/C][/ROW]
[ROW][C]132[/C][C] 0[/C][C]-2.875[/C][C] 2.875[/C][/ROW]
[ROW][C]133[/C][C] 2[/C][C] 1.992[/C][C] 0.007964[/C][/ROW]
[ROW][C]134[/C][C]-5[/C][C]-2.014[/C][C]-2.986[/C][/ROW]
[ROW][C]135[/C][C] 5[/C][C]-0.01122[/C][C] 5.011[/C][/ROW]
[ROW][C]136[/C][C] 0[/C][C] 0.8495[/C][C]-0.8495[/C][/ROW]
[ROW][C]137[/C][C]-2[/C][C]-0.01122[/C][C]-1.989[/C][/ROW]
[ROW][C]138[/C][C] 4[/C][C] 1.131[/C][C] 2.869[/C][/ROW]
[ROW][C]139[/C][C]-8[/C][C]-2.014[/C][C]-5.986[/C][/ROW]
[ROW][C]140[/C][C] 7[/C][C] 0.8495[/C][C] 6.151[/C][/ROW]
[ROW][C]141[/C][C]-3[/C][C]-0.01122[/C][C]-2.989[/C][/ROW]
[ROW][C]142[/C][C] 0[/C][C]-0.8719[/C][C] 0.8719[/C][/ROW]
[ROW][C]143[/C][C]-1[/C][C]-0.01122[/C][C]-0.9888[/C][/ROW]
[ROW][C]144[/C][C]-1[/C][C]-0.01122[/C][C]-0.9888[/C][/ROW]
[ROW][C]145[/C][C] 3[/C][C] 1.992[/C][C] 1.008[/C][/ROW]
[ROW][C]146[/C][C]-2[/C][C]-1.154[/C][C]-0.8462[/C][/ROW]
[ROW][C]147[/C][C] 3[/C][C]-0.01122[/C][C] 3.011[/C][/ROW]
[ROW][C]148[/C][C]-2[/C][C]-0.8719[/C][C]-1.128[/C][/ROW]
[ROW][C]149[/C][C] 3[/C][C] 0.8495[/C][C] 2.151[/C][/ROW]
[ROW][C]150[/C][C]-3[/C][C] 1.131[/C][C]-4.131[/C][/ROW]
[ROW][C]151[/C][C]-2[/C][C]-1.154[/C][C]-0.8462[/C][/ROW]
[ROW][C]152[/C][C] 3[/C][C]-0.8719[/C][C] 3.872[/C][/ROW]
[ROW][C]153[/C][C]-5[/C][C] 0.8495[/C][C]-5.849[/C][/ROW]
[ROW][C]154[/C][C] 6[/C][C] 1.992[/C][C] 4.008[/C][/ROW]
[ROW][C]155[/C][C] 1[/C][C]-0.01122[/C][C] 1.011[/C][/ROW]
[ROW][C]156[/C][C]-3[/C][C]-0.8719[/C][C]-2.128[/C][/ROW]
[ROW][C]157[/C][C]-1[/C][C]-1.154[/C][C] 0.1538[/C][/ROW]
[ROW][C]158[/C][C] 2[/C][C]-0.01122[/C][C] 2.011[/C][/ROW]
[ROW][C]159[/C][C] 1[/C][C] 1.992[/C][C]-0.992[/C][/ROW]
[ROW][C]160[/C][C]-4[/C][C]-0.8719[/C][C]-3.128[/C][/ROW]
[ROW][C]161[/C][C] 5[/C][C]-1.154[/C][C] 6.154[/C][/ROW]
[ROW][C]162[/C][C] 0[/C][C] 1.131[/C][C]-1.131[/C][/ROW]
[ROW][C]163[/C][C] 0[/C][C]-1.154[/C][C] 1.154[/C][/ROW]
[ROW][C]164[/C][C]-2[/C][C]-0.8719[/C][C]-1.128[/C][/ROW]
[ROW][C]165[/C][C]-2[/C][C] 2.853[/C][C]-4.853[/C][/ROW]
[ROW][C]166[/C][C] 2[/C][C]-2.875[/C][C] 4.875[/C][/ROW]
[ROW][C]167[/C][C] 2[/C][C] 2.853[/C][C]-0.8527[/C][/ROW]
[ROW][C]168[/C][C]-1[/C][C]-0.01122[/C][C]-0.9888[/C][/ROW]
[ROW][C]169[/C][C]-2[/C][C]-2.014[/C][C] 0.01447[/C][/ROW]
[ROW][C]170[/C][C] 3[/C][C]-0.01122[/C][C] 3.011[/C][/ROW]
[ROW][C]171[/C][C]-1[/C][C] 1.992[/C][C]-2.992[/C][/ROW]
[ROW][C]172[/C][C]-1[/C][C]-0.01122[/C][C]-0.9888[/C][/ROW]
[ROW][C]173[/C][C] 0[/C][C]-0.8719[/C][C] 0.8719[/C][/ROW]
[ROW][C]174[/C][C] 0[/C][C]-2.014[/C][C] 2.014[/C][/ROW]
[ROW][C]175[/C][C] 1[/C][C] 1.992[/C][C]-0.992[/C][/ROW]
[ROW][C]176[/C][C] 2[/C][C] 0.8495[/C][C] 1.151[/C][/ROW]
[ROW][C]177[/C][C]-4[/C][C]-2.875[/C][C]-1.125[/C][/ROW]
[ROW][C]178[/C][C] 0[/C][C]-0.01122[/C][C] 0.01122[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=309919&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=309919&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
1-2 0.8495-2.849
2 0-0.01122 0.01122
3 1-0.01122 1.011
4-4-0.8719-3.128
5 5 0.8495 4.151
6-2-0.01122-1.989
7 1-0.01122 1.011
8-1-0.8719-0.1281
9-1-0.01122-0.9888
10 3-0.01122 3.011
11 0-0.01122 0.01122
12-1 0.8495-1.849
13-5-0.8719-4.128
14 0 0.8495-0.8495
15 4-0.01122 4.011
16 1-0.01122 1.011
17 1-0.01122 1.011
18-2-0.8719-1.128
19-3-0.01122-2.989
20 5 0.8495 4.151
21-2-0.8719-1.128
22-1 0.8495-1.849
23 1-0.01122 1.011
24 0-0.01122 0.01122
25 1-0.8719 1.872
26-1-0.01122-0.9888
27-2 0.8495-2.849
28 2-0.01122 2.011
29 0-0.8719 0.8719
30-3-1.154-1.846
31 4 1.992 2.008
32-1-0.8719-0.1281
33 0-0.01122 0.01122
34 0-0.01122 0.01122
35-2-0.01122-1.989
36 0-0.01122 0.01122
37 3 0.8495 2.151
38-1-0.01122-0.9888
39 1-0.01122 1.011
40 1-0.01122 1.011
41-2-2.875 0.8752
42 0 1.992-1.992
43-1-0.01122-0.9888
44 0 0.8495-0.8495
45 3-0.01122 3.011
46-2-0.01122-1.989
47-1-0.01122-0.9888
48 3-0.01122 3.011
49-3-0.01122-2.989
50 0-0.8719 0.8719
51 2-0.01122 2.011
52 0-0.01122 0.01122
53-1-0.01122-0.9888
54-2-0.01122-1.989
55 2-0.01122 2.011
56 1 0.8495 0.1505
57-7-2.875-4.125
58 4 1.992 2.008
59 2 0.8495 1.151
60 0-2.014 2.014
61-1-0.8719-0.1281
62 1 1.992-0.992
63-2-0.01122-1.989
64 4-0.01122 4.011
65 0-0.01122 0.01122
66 0-0.01122 0.01122
67-2-0.01122-1.989
68 0 0.8495-0.8495
69-1-0.01122-0.9888
70 3-0.01122 3.011
71-5-2.875-2.125
72-2 0.8495-2.849
73-1-0.01122-0.9888
74 1-0.01122 1.011
75 1-0.01122 1.011
76-2-0.8719-1.128
77 4-0.01122 4.011
78 2 1.992 0.007964
79 0-0.01122 0.01122
80-3-2.014-0.9855
81 5 2.853 2.147
82-1-0.01122-0.9888
83-1-0.01122-0.9888
84 1-0.01122 1.011
85-1-0.01122-0.9888
86-3-0.8719-2.128
87 2 0.8495 1.151
88 2-0.01122 2.011
89-1-0.8719-0.1281
90-4 0.8495-4.849
91 3-0.01122 3.011
92 1-0.01122 1.011
93-1-0.8719-0.1281
94 0 0.8495-0.8495
95 2-0.8719 2.872
96-3 0.8495-3.849
97 1-0.8719 1.872
98-3-0.01122-2.989
99 2 0.8495 1.151
100 4-0.8719 4.872
101-1 0.8495-1.849
102 1-0.01122 1.011
103-2-0.8719-1.128
104-4-2.014-1.986
105 4 2.853 1.147
106-3-0.8719-2.128
107 3-1.154 4.154
108 1-0.01122 1.011
109-1 1.131-2.131
110-4 0.8495-4.849
111 4-0.8719 4.872
112 2 0.8495 1.151
113-4-0.8719-3.128
114 1-0.01122 1.011
115 3 0.8495 2.151
116-1-0.01122-0.9888
117-1-0.01122-0.9888
118-5-2.875-2.125
119 5 1.992 3.008
120-1-0.01122-0.9888
121 0-0.01122 0.01122
122 1-0.01122 1.011
123 0 0.8495-0.8495
124-1-0.8719-0.1281
125 0-1.154 1.154
126 2 1.131 0.8687
127 0-1.154 1.154
128 0 1.131-1.131
129-5-1.154-3.846
130 2 1.131 0.8687
131 0 0.8495-0.8495
132 0-2.875 2.875
133 2 1.992 0.007964
134-5-2.014-2.986
135 5-0.01122 5.011
136 0 0.8495-0.8495
137-2-0.01122-1.989
138 4 1.131 2.869
139-8-2.014-5.986
140 7 0.8495 6.151
141-3-0.01122-2.989
142 0-0.8719 0.8719
143-1-0.01122-0.9888
144-1-0.01122-0.9888
145 3 1.992 1.008
146-2-1.154-0.8462
147 3-0.01122 3.011
148-2-0.8719-1.128
149 3 0.8495 2.151
150-3 1.131-4.131
151-2-1.154-0.8462
152 3-0.8719 3.872
153-5 0.8495-5.849
154 6 1.992 4.008
155 1-0.01122 1.011
156-3-0.8719-2.128
157-1-1.154 0.1538
158 2-0.01122 2.011
159 1 1.992-0.992
160-4-0.8719-3.128
161 5-1.154 6.154
162 0 1.131-1.131
163 0-1.154 1.154
164-2-0.8719-1.128
165-2 2.853-4.853
166 2-2.875 4.875
167 2 2.853-0.8527
168-1-0.01122-0.9888
169-2-2.014 0.01447
170 3-0.01122 3.011
171-1 1.992-2.992
172-1-0.01122-0.9888
173 0-0.8719 0.8719
174 0-2.014 2.014
175 1 1.992-0.992
176 2 0.8495 1.151
177-4-2.875-1.125
178 0-0.01122 0.01122






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
6 0.8678 0.2644 0.1322
7 0.8067 0.3866 0.1933
8 0.7405 0.5191 0.2595
9 0.6395 0.721 0.3605
10 0.704 0.5921 0.296
11 0.6052 0.7896 0.3948
12 0.6165 0.767 0.3835
13 0.6645 0.6709 0.3355
14 0.6056 0.7888 0.3944
15 0.7704 0.4592 0.2296
16 0.7198 0.5605 0.2802
17 0.6643 0.6713 0.3357
18 0.5925 0.8149 0.4075
19 0.6221 0.7558 0.3779
20 0.6866 0.6268 0.3134
21 0.6235 0.753 0.3765
22 0.6421 0.7158 0.3579
23 0.5927 0.8146 0.4073
24 0.5267 0.9466 0.4733
25 0.5471 0.9059 0.4529
26 0.4931 0.9861 0.5069
27 0.5503 0.8994 0.4497
28 0.539 0.9219 0.461
29 0.4962 0.9925 0.5038
30 0.4414 0.8829 0.5586
31 0.3899 0.7797 0.6101
32 0.3349 0.6699 0.6651
33 0.2832 0.5664 0.7168
34 0.2361 0.4722 0.7639
35 0.2243 0.4487 0.7757
36 0.1841 0.3681 0.8159
37 0.1737 0.3474 0.8263
38 0.1458 0.2915 0.8542
39 0.1227 0.2453 0.8773
40 0.1022 0.2045 0.8978
41 0.1088 0.2176 0.8912
42 0.1152 0.2303 0.8848
43 0.09534 0.1907 0.9047
44 0.07844 0.1569 0.9216
45 0.09672 0.1934 0.9033
46 0.0918 0.1836 0.9082
47 0.07554 0.1511 0.9245
48 0.09247 0.1849 0.9075
49 0.1089 0.2178 0.8911
50 0.09164 0.1833 0.9084
51 0.08799 0.176 0.912
52 0.06981 0.1396 0.9302
53 0.05739 0.1148 0.9426
54 0.05422 0.1084 0.9458
55 0.05206 0.1041 0.9479
56 0.04038 0.08076 0.9596
57 0.05852 0.117 0.9415
58 0.05035 0.1007 0.9496
59 0.04128 0.08256 0.9587
60 0.04647 0.09294 0.9535
61 0.03617 0.07235 0.9638
62 0.03095 0.0619 0.9691
63 0.02917 0.05834 0.9708
64 0.05101 0.102 0.949
65 0.04 0.08 0.96
66 0.03103 0.06205 0.969
67 0.02933 0.05866 0.9707
68 0.02379 0.04758 0.9762
69 0.01904 0.03807 0.981
70 0.0238 0.0476 0.9762
71 0.0212 0.04241 0.9788
72 0.02585 0.05169 0.9742
73 0.02078 0.04156 0.9792
74 0.01676 0.03351 0.9832
75 0.01341 0.02682 0.9866
76 0.01065 0.02129 0.9894
77 0.01973 0.03945 0.9803
78 0.01519 0.03038 0.9848
79 0.01143 0.02285 0.9886
80 0.008799 0.0176 0.9912
81 0.007905 0.01581 0.9921
82 0.006165 0.01233 0.9938
83 0.004773 0.009547 0.9952
84 0.003691 0.007383 0.9963
85 0.002819 0.005639 0.9972
86 0.002639 0.005278 0.9974
87 0.002023 0.004047 0.998
88 0.001896 0.003791 0.9981
89 0.00134 0.002681 0.9987
90 0.004753 0.009506 0.9952
91 0.006034 0.01207 0.994
92 0.004699 0.009399 0.9953
93 0.003417 0.006834 0.9966
94 0.002577 0.005155 0.9974
95 0.003136 0.006272 0.9969
96 0.005496 0.01099 0.9945
97 0.004884 0.009769 0.9951
98 0.006016 0.01203 0.994
99 0.004801 0.009602 0.9952
100 0.01279 0.02559 0.9872
101 0.01137 0.02274 0.9886
102 0.008989 0.01798 0.991
103 0.00723 0.01446 0.9928
104 0.006322 0.01264 0.9937
105 0.004913 0.009826 0.9951
106 0.004774 0.009548 0.9952
107 0.01068 0.02137 0.9893
108 0.008406 0.01681 0.9916
109 0.008282 0.01656 0.9917
110 0.0202 0.04039 0.9798
111 0.0446 0.0892 0.9554
112 0.03721 0.07442 0.9628
113 0.0445 0.08901 0.9555
114 0.03654 0.07309 0.9635
115 0.03512 0.07024 0.9649
116 0.0285 0.05701 0.9715
117 0.02296 0.04591 0.977
118 0.02209 0.04417 0.9779
119 0.02583 0.05167 0.9742
120 0.02069 0.04137 0.9793
121 0.01564 0.03128 0.9844
122 0.01231 0.02461 0.9877
123 0.009367 0.01873 0.9906
124 0.006868 0.01374 0.9931
125 0.005458 0.01092 0.9945
126 0.004074 0.008147 0.9959
127 0.003161 0.006323 0.9968
128 0.00241 0.004819 0.9976
129 0.004133 0.008266 0.9959
130 0.003087 0.006175 0.9969
131 0.002237 0.004473 0.9978
132 0.002482 0.004964 0.9975
133 0.001722 0.003445 0.9983
134 0.002263 0.004527 0.9977
135 0.006942 0.01388 0.9931
136 0.005108 0.01022 0.9949
137 0.004504 0.009008 0.9955
138 0.006021 0.01204 0.994
139 0.04204 0.08407 0.958
140 0.1571 0.3143 0.8429
141 0.1764 0.3528 0.8236
142 0.1471 0.2942 0.8529
143 0.123 0.246 0.877
144 0.1018 0.2037 0.8982
145 0.09485 0.1897 0.9052
146 0.08771 0.1754 0.9123
147 0.1007 0.2013 0.8993
148 0.08266 0.1653 0.9173
149 0.08028 0.1606 0.9197
150 0.1032 0.2064 0.8968
151 0.0919 0.1838 0.9081
152 0.1312 0.2624 0.8688
153 0.4091 0.8182 0.5909
154 0.7131 0.5739 0.2869
155 0.666 0.668 0.334
156 0.6431 0.7137 0.3569
157 0.6459 0.7082 0.3541
158 0.6306 0.7388 0.3694
159 0.5801 0.8397 0.4199
160 0.6252 0.7495 0.3748
161 0.8332 0.3336 0.1668
162 0.7791 0.4419 0.2209
163 0.7117 0.5766 0.2883
164 0.6558 0.6884 0.3442
165 0.7576 0.4848 0.2424
166 0.9169 0.1663 0.08314
167 0.8665 0.267 0.1335
168 0.8114 0.3773 0.1886
169 0.7612 0.4776 0.2388
170 0.8806 0.2389 0.1194
171 0.8813 0.2374 0.1187
172 0.8212 0.3576 0.1788

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
6 &  0.8678 &  0.2644 &  0.1322 \tabularnewline
7 &  0.8067 &  0.3866 &  0.1933 \tabularnewline
8 &  0.7405 &  0.5191 &  0.2595 \tabularnewline
9 &  0.6395 &  0.721 &  0.3605 \tabularnewline
10 &  0.704 &  0.5921 &  0.296 \tabularnewline
11 &  0.6052 &  0.7896 &  0.3948 \tabularnewline
12 &  0.6165 &  0.767 &  0.3835 \tabularnewline
13 &  0.6645 &  0.6709 &  0.3355 \tabularnewline
14 &  0.6056 &  0.7888 &  0.3944 \tabularnewline
15 &  0.7704 &  0.4592 &  0.2296 \tabularnewline
16 &  0.7198 &  0.5605 &  0.2802 \tabularnewline
17 &  0.6643 &  0.6713 &  0.3357 \tabularnewline
18 &  0.5925 &  0.8149 &  0.4075 \tabularnewline
19 &  0.6221 &  0.7558 &  0.3779 \tabularnewline
20 &  0.6866 &  0.6268 &  0.3134 \tabularnewline
21 &  0.6235 &  0.753 &  0.3765 \tabularnewline
22 &  0.6421 &  0.7158 &  0.3579 \tabularnewline
23 &  0.5927 &  0.8146 &  0.4073 \tabularnewline
24 &  0.5267 &  0.9466 &  0.4733 \tabularnewline
25 &  0.5471 &  0.9059 &  0.4529 \tabularnewline
26 &  0.4931 &  0.9861 &  0.5069 \tabularnewline
27 &  0.5503 &  0.8994 &  0.4497 \tabularnewline
28 &  0.539 &  0.9219 &  0.461 \tabularnewline
29 &  0.4962 &  0.9925 &  0.5038 \tabularnewline
30 &  0.4414 &  0.8829 &  0.5586 \tabularnewline
31 &  0.3899 &  0.7797 &  0.6101 \tabularnewline
32 &  0.3349 &  0.6699 &  0.6651 \tabularnewline
33 &  0.2832 &  0.5664 &  0.7168 \tabularnewline
34 &  0.2361 &  0.4722 &  0.7639 \tabularnewline
35 &  0.2243 &  0.4487 &  0.7757 \tabularnewline
36 &  0.1841 &  0.3681 &  0.8159 \tabularnewline
37 &  0.1737 &  0.3474 &  0.8263 \tabularnewline
38 &  0.1458 &  0.2915 &  0.8542 \tabularnewline
39 &  0.1227 &  0.2453 &  0.8773 \tabularnewline
40 &  0.1022 &  0.2045 &  0.8978 \tabularnewline
41 &  0.1088 &  0.2176 &  0.8912 \tabularnewline
42 &  0.1152 &  0.2303 &  0.8848 \tabularnewline
43 &  0.09534 &  0.1907 &  0.9047 \tabularnewline
44 &  0.07844 &  0.1569 &  0.9216 \tabularnewline
45 &  0.09672 &  0.1934 &  0.9033 \tabularnewline
46 &  0.0918 &  0.1836 &  0.9082 \tabularnewline
47 &  0.07554 &  0.1511 &  0.9245 \tabularnewline
48 &  0.09247 &  0.1849 &  0.9075 \tabularnewline
49 &  0.1089 &  0.2178 &  0.8911 \tabularnewline
50 &  0.09164 &  0.1833 &  0.9084 \tabularnewline
51 &  0.08799 &  0.176 &  0.912 \tabularnewline
52 &  0.06981 &  0.1396 &  0.9302 \tabularnewline
53 &  0.05739 &  0.1148 &  0.9426 \tabularnewline
54 &  0.05422 &  0.1084 &  0.9458 \tabularnewline
55 &  0.05206 &  0.1041 &  0.9479 \tabularnewline
56 &  0.04038 &  0.08076 &  0.9596 \tabularnewline
57 &  0.05852 &  0.117 &  0.9415 \tabularnewline
58 &  0.05035 &  0.1007 &  0.9496 \tabularnewline
59 &  0.04128 &  0.08256 &  0.9587 \tabularnewline
60 &  0.04647 &  0.09294 &  0.9535 \tabularnewline
61 &  0.03617 &  0.07235 &  0.9638 \tabularnewline
62 &  0.03095 &  0.0619 &  0.9691 \tabularnewline
63 &  0.02917 &  0.05834 &  0.9708 \tabularnewline
64 &  0.05101 &  0.102 &  0.949 \tabularnewline
65 &  0.04 &  0.08 &  0.96 \tabularnewline
66 &  0.03103 &  0.06205 &  0.969 \tabularnewline
67 &  0.02933 &  0.05866 &  0.9707 \tabularnewline
68 &  0.02379 &  0.04758 &  0.9762 \tabularnewline
69 &  0.01904 &  0.03807 &  0.981 \tabularnewline
70 &  0.0238 &  0.0476 &  0.9762 \tabularnewline
71 &  0.0212 &  0.04241 &  0.9788 \tabularnewline
72 &  0.02585 &  0.05169 &  0.9742 \tabularnewline
73 &  0.02078 &  0.04156 &  0.9792 \tabularnewline
74 &  0.01676 &  0.03351 &  0.9832 \tabularnewline
75 &  0.01341 &  0.02682 &  0.9866 \tabularnewline
76 &  0.01065 &  0.02129 &  0.9894 \tabularnewline
77 &  0.01973 &  0.03945 &  0.9803 \tabularnewline
78 &  0.01519 &  0.03038 &  0.9848 \tabularnewline
79 &  0.01143 &  0.02285 &  0.9886 \tabularnewline
80 &  0.008799 &  0.0176 &  0.9912 \tabularnewline
81 &  0.007905 &  0.01581 &  0.9921 \tabularnewline
82 &  0.006165 &  0.01233 &  0.9938 \tabularnewline
83 &  0.004773 &  0.009547 &  0.9952 \tabularnewline
84 &  0.003691 &  0.007383 &  0.9963 \tabularnewline
85 &  0.002819 &  0.005639 &  0.9972 \tabularnewline
86 &  0.002639 &  0.005278 &  0.9974 \tabularnewline
87 &  0.002023 &  0.004047 &  0.998 \tabularnewline
88 &  0.001896 &  0.003791 &  0.9981 \tabularnewline
89 &  0.00134 &  0.002681 &  0.9987 \tabularnewline
90 &  0.004753 &  0.009506 &  0.9952 \tabularnewline
91 &  0.006034 &  0.01207 &  0.994 \tabularnewline
92 &  0.004699 &  0.009399 &  0.9953 \tabularnewline
93 &  0.003417 &  0.006834 &  0.9966 \tabularnewline
94 &  0.002577 &  0.005155 &  0.9974 \tabularnewline
95 &  0.003136 &  0.006272 &  0.9969 \tabularnewline
96 &  0.005496 &  0.01099 &  0.9945 \tabularnewline
97 &  0.004884 &  0.009769 &  0.9951 \tabularnewline
98 &  0.006016 &  0.01203 &  0.994 \tabularnewline
99 &  0.004801 &  0.009602 &  0.9952 \tabularnewline
100 &  0.01279 &  0.02559 &  0.9872 \tabularnewline
101 &  0.01137 &  0.02274 &  0.9886 \tabularnewline
102 &  0.008989 &  0.01798 &  0.991 \tabularnewline
103 &  0.00723 &  0.01446 &  0.9928 \tabularnewline
104 &  0.006322 &  0.01264 &  0.9937 \tabularnewline
105 &  0.004913 &  0.009826 &  0.9951 \tabularnewline
106 &  0.004774 &  0.009548 &  0.9952 \tabularnewline
107 &  0.01068 &  0.02137 &  0.9893 \tabularnewline
108 &  0.008406 &  0.01681 &  0.9916 \tabularnewline
109 &  0.008282 &  0.01656 &  0.9917 \tabularnewline
110 &  0.0202 &  0.04039 &  0.9798 \tabularnewline
111 &  0.0446 &  0.0892 &  0.9554 \tabularnewline
112 &  0.03721 &  0.07442 &  0.9628 \tabularnewline
113 &  0.0445 &  0.08901 &  0.9555 \tabularnewline
114 &  0.03654 &  0.07309 &  0.9635 \tabularnewline
115 &  0.03512 &  0.07024 &  0.9649 \tabularnewline
116 &  0.0285 &  0.05701 &  0.9715 \tabularnewline
117 &  0.02296 &  0.04591 &  0.977 \tabularnewline
118 &  0.02209 &  0.04417 &  0.9779 \tabularnewline
119 &  0.02583 &  0.05167 &  0.9742 \tabularnewline
120 &  0.02069 &  0.04137 &  0.9793 \tabularnewline
121 &  0.01564 &  0.03128 &  0.9844 \tabularnewline
122 &  0.01231 &  0.02461 &  0.9877 \tabularnewline
123 &  0.009367 &  0.01873 &  0.9906 \tabularnewline
124 &  0.006868 &  0.01374 &  0.9931 \tabularnewline
125 &  0.005458 &  0.01092 &  0.9945 \tabularnewline
126 &  0.004074 &  0.008147 &  0.9959 \tabularnewline
127 &  0.003161 &  0.006323 &  0.9968 \tabularnewline
128 &  0.00241 &  0.004819 &  0.9976 \tabularnewline
129 &  0.004133 &  0.008266 &  0.9959 \tabularnewline
130 &  0.003087 &  0.006175 &  0.9969 \tabularnewline
131 &  0.002237 &  0.004473 &  0.9978 \tabularnewline
132 &  0.002482 &  0.004964 &  0.9975 \tabularnewline
133 &  0.001722 &  0.003445 &  0.9983 \tabularnewline
134 &  0.002263 &  0.004527 &  0.9977 \tabularnewline
135 &  0.006942 &  0.01388 &  0.9931 \tabularnewline
136 &  0.005108 &  0.01022 &  0.9949 \tabularnewline
137 &  0.004504 &  0.009008 &  0.9955 \tabularnewline
138 &  0.006021 &  0.01204 &  0.994 \tabularnewline
139 &  0.04204 &  0.08407 &  0.958 \tabularnewline
140 &  0.1571 &  0.3143 &  0.8429 \tabularnewline
141 &  0.1764 &  0.3528 &  0.8236 \tabularnewline
142 &  0.1471 &  0.2942 &  0.8529 \tabularnewline
143 &  0.123 &  0.246 &  0.877 \tabularnewline
144 &  0.1018 &  0.2037 &  0.8982 \tabularnewline
145 &  0.09485 &  0.1897 &  0.9052 \tabularnewline
146 &  0.08771 &  0.1754 &  0.9123 \tabularnewline
147 &  0.1007 &  0.2013 &  0.8993 \tabularnewline
148 &  0.08266 &  0.1653 &  0.9173 \tabularnewline
149 &  0.08028 &  0.1606 &  0.9197 \tabularnewline
150 &  0.1032 &  0.2064 &  0.8968 \tabularnewline
151 &  0.0919 &  0.1838 &  0.9081 \tabularnewline
152 &  0.1312 &  0.2624 &  0.8688 \tabularnewline
153 &  0.4091 &  0.8182 &  0.5909 \tabularnewline
154 &  0.7131 &  0.5739 &  0.2869 \tabularnewline
155 &  0.666 &  0.668 &  0.334 \tabularnewline
156 &  0.6431 &  0.7137 &  0.3569 \tabularnewline
157 &  0.6459 &  0.7082 &  0.3541 \tabularnewline
158 &  0.6306 &  0.7388 &  0.3694 \tabularnewline
159 &  0.5801 &  0.8397 &  0.4199 \tabularnewline
160 &  0.6252 &  0.7495 &  0.3748 \tabularnewline
161 &  0.8332 &  0.3336 &  0.1668 \tabularnewline
162 &  0.7791 &  0.4419 &  0.2209 \tabularnewline
163 &  0.7117 &  0.5766 &  0.2883 \tabularnewline
164 &  0.6558 &  0.6884 &  0.3442 \tabularnewline
165 &  0.7576 &  0.4848 &  0.2424 \tabularnewline
166 &  0.9169 &  0.1663 &  0.08314 \tabularnewline
167 &  0.8665 &  0.267 &  0.1335 \tabularnewline
168 &  0.8114 &  0.3773 &  0.1886 \tabularnewline
169 &  0.7612 &  0.4776 &  0.2388 \tabularnewline
170 &  0.8806 &  0.2389 &  0.1194 \tabularnewline
171 &  0.8813 &  0.2374 &  0.1187 \tabularnewline
172 &  0.8212 &  0.3576 &  0.1788 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=309919&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]6[/C][C] 0.8678[/C][C] 0.2644[/C][C] 0.1322[/C][/ROW]
[ROW][C]7[/C][C] 0.8067[/C][C] 0.3866[/C][C] 0.1933[/C][/ROW]
[ROW][C]8[/C][C] 0.7405[/C][C] 0.5191[/C][C] 0.2595[/C][/ROW]
[ROW][C]9[/C][C] 0.6395[/C][C] 0.721[/C][C] 0.3605[/C][/ROW]
[ROW][C]10[/C][C] 0.704[/C][C] 0.5921[/C][C] 0.296[/C][/ROW]
[ROW][C]11[/C][C] 0.6052[/C][C] 0.7896[/C][C] 0.3948[/C][/ROW]
[ROW][C]12[/C][C] 0.6165[/C][C] 0.767[/C][C] 0.3835[/C][/ROW]
[ROW][C]13[/C][C] 0.6645[/C][C] 0.6709[/C][C] 0.3355[/C][/ROW]
[ROW][C]14[/C][C] 0.6056[/C][C] 0.7888[/C][C] 0.3944[/C][/ROW]
[ROW][C]15[/C][C] 0.7704[/C][C] 0.4592[/C][C] 0.2296[/C][/ROW]
[ROW][C]16[/C][C] 0.7198[/C][C] 0.5605[/C][C] 0.2802[/C][/ROW]
[ROW][C]17[/C][C] 0.6643[/C][C] 0.6713[/C][C] 0.3357[/C][/ROW]
[ROW][C]18[/C][C] 0.5925[/C][C] 0.8149[/C][C] 0.4075[/C][/ROW]
[ROW][C]19[/C][C] 0.6221[/C][C] 0.7558[/C][C] 0.3779[/C][/ROW]
[ROW][C]20[/C][C] 0.6866[/C][C] 0.6268[/C][C] 0.3134[/C][/ROW]
[ROW][C]21[/C][C] 0.6235[/C][C] 0.753[/C][C] 0.3765[/C][/ROW]
[ROW][C]22[/C][C] 0.6421[/C][C] 0.7158[/C][C] 0.3579[/C][/ROW]
[ROW][C]23[/C][C] 0.5927[/C][C] 0.8146[/C][C] 0.4073[/C][/ROW]
[ROW][C]24[/C][C] 0.5267[/C][C] 0.9466[/C][C] 0.4733[/C][/ROW]
[ROW][C]25[/C][C] 0.5471[/C][C] 0.9059[/C][C] 0.4529[/C][/ROW]
[ROW][C]26[/C][C] 0.4931[/C][C] 0.9861[/C][C] 0.5069[/C][/ROW]
[ROW][C]27[/C][C] 0.5503[/C][C] 0.8994[/C][C] 0.4497[/C][/ROW]
[ROW][C]28[/C][C] 0.539[/C][C] 0.9219[/C][C] 0.461[/C][/ROW]
[ROW][C]29[/C][C] 0.4962[/C][C] 0.9925[/C][C] 0.5038[/C][/ROW]
[ROW][C]30[/C][C] 0.4414[/C][C] 0.8829[/C][C] 0.5586[/C][/ROW]
[ROW][C]31[/C][C] 0.3899[/C][C] 0.7797[/C][C] 0.6101[/C][/ROW]
[ROW][C]32[/C][C] 0.3349[/C][C] 0.6699[/C][C] 0.6651[/C][/ROW]
[ROW][C]33[/C][C] 0.2832[/C][C] 0.5664[/C][C] 0.7168[/C][/ROW]
[ROW][C]34[/C][C] 0.2361[/C][C] 0.4722[/C][C] 0.7639[/C][/ROW]
[ROW][C]35[/C][C] 0.2243[/C][C] 0.4487[/C][C] 0.7757[/C][/ROW]
[ROW][C]36[/C][C] 0.1841[/C][C] 0.3681[/C][C] 0.8159[/C][/ROW]
[ROW][C]37[/C][C] 0.1737[/C][C] 0.3474[/C][C] 0.8263[/C][/ROW]
[ROW][C]38[/C][C] 0.1458[/C][C] 0.2915[/C][C] 0.8542[/C][/ROW]
[ROW][C]39[/C][C] 0.1227[/C][C] 0.2453[/C][C] 0.8773[/C][/ROW]
[ROW][C]40[/C][C] 0.1022[/C][C] 0.2045[/C][C] 0.8978[/C][/ROW]
[ROW][C]41[/C][C] 0.1088[/C][C] 0.2176[/C][C] 0.8912[/C][/ROW]
[ROW][C]42[/C][C] 0.1152[/C][C] 0.2303[/C][C] 0.8848[/C][/ROW]
[ROW][C]43[/C][C] 0.09534[/C][C] 0.1907[/C][C] 0.9047[/C][/ROW]
[ROW][C]44[/C][C] 0.07844[/C][C] 0.1569[/C][C] 0.9216[/C][/ROW]
[ROW][C]45[/C][C] 0.09672[/C][C] 0.1934[/C][C] 0.9033[/C][/ROW]
[ROW][C]46[/C][C] 0.0918[/C][C] 0.1836[/C][C] 0.9082[/C][/ROW]
[ROW][C]47[/C][C] 0.07554[/C][C] 0.1511[/C][C] 0.9245[/C][/ROW]
[ROW][C]48[/C][C] 0.09247[/C][C] 0.1849[/C][C] 0.9075[/C][/ROW]
[ROW][C]49[/C][C] 0.1089[/C][C] 0.2178[/C][C] 0.8911[/C][/ROW]
[ROW][C]50[/C][C] 0.09164[/C][C] 0.1833[/C][C] 0.9084[/C][/ROW]
[ROW][C]51[/C][C] 0.08799[/C][C] 0.176[/C][C] 0.912[/C][/ROW]
[ROW][C]52[/C][C] 0.06981[/C][C] 0.1396[/C][C] 0.9302[/C][/ROW]
[ROW][C]53[/C][C] 0.05739[/C][C] 0.1148[/C][C] 0.9426[/C][/ROW]
[ROW][C]54[/C][C] 0.05422[/C][C] 0.1084[/C][C] 0.9458[/C][/ROW]
[ROW][C]55[/C][C] 0.05206[/C][C] 0.1041[/C][C] 0.9479[/C][/ROW]
[ROW][C]56[/C][C] 0.04038[/C][C] 0.08076[/C][C] 0.9596[/C][/ROW]
[ROW][C]57[/C][C] 0.05852[/C][C] 0.117[/C][C] 0.9415[/C][/ROW]
[ROW][C]58[/C][C] 0.05035[/C][C] 0.1007[/C][C] 0.9496[/C][/ROW]
[ROW][C]59[/C][C] 0.04128[/C][C] 0.08256[/C][C] 0.9587[/C][/ROW]
[ROW][C]60[/C][C] 0.04647[/C][C] 0.09294[/C][C] 0.9535[/C][/ROW]
[ROW][C]61[/C][C] 0.03617[/C][C] 0.07235[/C][C] 0.9638[/C][/ROW]
[ROW][C]62[/C][C] 0.03095[/C][C] 0.0619[/C][C] 0.9691[/C][/ROW]
[ROW][C]63[/C][C] 0.02917[/C][C] 0.05834[/C][C] 0.9708[/C][/ROW]
[ROW][C]64[/C][C] 0.05101[/C][C] 0.102[/C][C] 0.949[/C][/ROW]
[ROW][C]65[/C][C] 0.04[/C][C] 0.08[/C][C] 0.96[/C][/ROW]
[ROW][C]66[/C][C] 0.03103[/C][C] 0.06205[/C][C] 0.969[/C][/ROW]
[ROW][C]67[/C][C] 0.02933[/C][C] 0.05866[/C][C] 0.9707[/C][/ROW]
[ROW][C]68[/C][C] 0.02379[/C][C] 0.04758[/C][C] 0.9762[/C][/ROW]
[ROW][C]69[/C][C] 0.01904[/C][C] 0.03807[/C][C] 0.981[/C][/ROW]
[ROW][C]70[/C][C] 0.0238[/C][C] 0.0476[/C][C] 0.9762[/C][/ROW]
[ROW][C]71[/C][C] 0.0212[/C][C] 0.04241[/C][C] 0.9788[/C][/ROW]
[ROW][C]72[/C][C] 0.02585[/C][C] 0.05169[/C][C] 0.9742[/C][/ROW]
[ROW][C]73[/C][C] 0.02078[/C][C] 0.04156[/C][C] 0.9792[/C][/ROW]
[ROW][C]74[/C][C] 0.01676[/C][C] 0.03351[/C][C] 0.9832[/C][/ROW]
[ROW][C]75[/C][C] 0.01341[/C][C] 0.02682[/C][C] 0.9866[/C][/ROW]
[ROW][C]76[/C][C] 0.01065[/C][C] 0.02129[/C][C] 0.9894[/C][/ROW]
[ROW][C]77[/C][C] 0.01973[/C][C] 0.03945[/C][C] 0.9803[/C][/ROW]
[ROW][C]78[/C][C] 0.01519[/C][C] 0.03038[/C][C] 0.9848[/C][/ROW]
[ROW][C]79[/C][C] 0.01143[/C][C] 0.02285[/C][C] 0.9886[/C][/ROW]
[ROW][C]80[/C][C] 0.008799[/C][C] 0.0176[/C][C] 0.9912[/C][/ROW]
[ROW][C]81[/C][C] 0.007905[/C][C] 0.01581[/C][C] 0.9921[/C][/ROW]
[ROW][C]82[/C][C] 0.006165[/C][C] 0.01233[/C][C] 0.9938[/C][/ROW]
[ROW][C]83[/C][C] 0.004773[/C][C] 0.009547[/C][C] 0.9952[/C][/ROW]
[ROW][C]84[/C][C] 0.003691[/C][C] 0.007383[/C][C] 0.9963[/C][/ROW]
[ROW][C]85[/C][C] 0.002819[/C][C] 0.005639[/C][C] 0.9972[/C][/ROW]
[ROW][C]86[/C][C] 0.002639[/C][C] 0.005278[/C][C] 0.9974[/C][/ROW]
[ROW][C]87[/C][C] 0.002023[/C][C] 0.004047[/C][C] 0.998[/C][/ROW]
[ROW][C]88[/C][C] 0.001896[/C][C] 0.003791[/C][C] 0.9981[/C][/ROW]
[ROW][C]89[/C][C] 0.00134[/C][C] 0.002681[/C][C] 0.9987[/C][/ROW]
[ROW][C]90[/C][C] 0.004753[/C][C] 0.009506[/C][C] 0.9952[/C][/ROW]
[ROW][C]91[/C][C] 0.006034[/C][C] 0.01207[/C][C] 0.994[/C][/ROW]
[ROW][C]92[/C][C] 0.004699[/C][C] 0.009399[/C][C] 0.9953[/C][/ROW]
[ROW][C]93[/C][C] 0.003417[/C][C] 0.006834[/C][C] 0.9966[/C][/ROW]
[ROW][C]94[/C][C] 0.002577[/C][C] 0.005155[/C][C] 0.9974[/C][/ROW]
[ROW][C]95[/C][C] 0.003136[/C][C] 0.006272[/C][C] 0.9969[/C][/ROW]
[ROW][C]96[/C][C] 0.005496[/C][C] 0.01099[/C][C] 0.9945[/C][/ROW]
[ROW][C]97[/C][C] 0.004884[/C][C] 0.009769[/C][C] 0.9951[/C][/ROW]
[ROW][C]98[/C][C] 0.006016[/C][C] 0.01203[/C][C] 0.994[/C][/ROW]
[ROW][C]99[/C][C] 0.004801[/C][C] 0.009602[/C][C] 0.9952[/C][/ROW]
[ROW][C]100[/C][C] 0.01279[/C][C] 0.02559[/C][C] 0.9872[/C][/ROW]
[ROW][C]101[/C][C] 0.01137[/C][C] 0.02274[/C][C] 0.9886[/C][/ROW]
[ROW][C]102[/C][C] 0.008989[/C][C] 0.01798[/C][C] 0.991[/C][/ROW]
[ROW][C]103[/C][C] 0.00723[/C][C] 0.01446[/C][C] 0.9928[/C][/ROW]
[ROW][C]104[/C][C] 0.006322[/C][C] 0.01264[/C][C] 0.9937[/C][/ROW]
[ROW][C]105[/C][C] 0.004913[/C][C] 0.009826[/C][C] 0.9951[/C][/ROW]
[ROW][C]106[/C][C] 0.004774[/C][C] 0.009548[/C][C] 0.9952[/C][/ROW]
[ROW][C]107[/C][C] 0.01068[/C][C] 0.02137[/C][C] 0.9893[/C][/ROW]
[ROW][C]108[/C][C] 0.008406[/C][C] 0.01681[/C][C] 0.9916[/C][/ROW]
[ROW][C]109[/C][C] 0.008282[/C][C] 0.01656[/C][C] 0.9917[/C][/ROW]
[ROW][C]110[/C][C] 0.0202[/C][C] 0.04039[/C][C] 0.9798[/C][/ROW]
[ROW][C]111[/C][C] 0.0446[/C][C] 0.0892[/C][C] 0.9554[/C][/ROW]
[ROW][C]112[/C][C] 0.03721[/C][C] 0.07442[/C][C] 0.9628[/C][/ROW]
[ROW][C]113[/C][C] 0.0445[/C][C] 0.08901[/C][C] 0.9555[/C][/ROW]
[ROW][C]114[/C][C] 0.03654[/C][C] 0.07309[/C][C] 0.9635[/C][/ROW]
[ROW][C]115[/C][C] 0.03512[/C][C] 0.07024[/C][C] 0.9649[/C][/ROW]
[ROW][C]116[/C][C] 0.0285[/C][C] 0.05701[/C][C] 0.9715[/C][/ROW]
[ROW][C]117[/C][C] 0.02296[/C][C] 0.04591[/C][C] 0.977[/C][/ROW]
[ROW][C]118[/C][C] 0.02209[/C][C] 0.04417[/C][C] 0.9779[/C][/ROW]
[ROW][C]119[/C][C] 0.02583[/C][C] 0.05167[/C][C] 0.9742[/C][/ROW]
[ROW][C]120[/C][C] 0.02069[/C][C] 0.04137[/C][C] 0.9793[/C][/ROW]
[ROW][C]121[/C][C] 0.01564[/C][C] 0.03128[/C][C] 0.9844[/C][/ROW]
[ROW][C]122[/C][C] 0.01231[/C][C] 0.02461[/C][C] 0.9877[/C][/ROW]
[ROW][C]123[/C][C] 0.009367[/C][C] 0.01873[/C][C] 0.9906[/C][/ROW]
[ROW][C]124[/C][C] 0.006868[/C][C] 0.01374[/C][C] 0.9931[/C][/ROW]
[ROW][C]125[/C][C] 0.005458[/C][C] 0.01092[/C][C] 0.9945[/C][/ROW]
[ROW][C]126[/C][C] 0.004074[/C][C] 0.008147[/C][C] 0.9959[/C][/ROW]
[ROW][C]127[/C][C] 0.003161[/C][C] 0.006323[/C][C] 0.9968[/C][/ROW]
[ROW][C]128[/C][C] 0.00241[/C][C] 0.004819[/C][C] 0.9976[/C][/ROW]
[ROW][C]129[/C][C] 0.004133[/C][C] 0.008266[/C][C] 0.9959[/C][/ROW]
[ROW][C]130[/C][C] 0.003087[/C][C] 0.006175[/C][C] 0.9969[/C][/ROW]
[ROW][C]131[/C][C] 0.002237[/C][C] 0.004473[/C][C] 0.9978[/C][/ROW]
[ROW][C]132[/C][C] 0.002482[/C][C] 0.004964[/C][C] 0.9975[/C][/ROW]
[ROW][C]133[/C][C] 0.001722[/C][C] 0.003445[/C][C] 0.9983[/C][/ROW]
[ROW][C]134[/C][C] 0.002263[/C][C] 0.004527[/C][C] 0.9977[/C][/ROW]
[ROW][C]135[/C][C] 0.006942[/C][C] 0.01388[/C][C] 0.9931[/C][/ROW]
[ROW][C]136[/C][C] 0.005108[/C][C] 0.01022[/C][C] 0.9949[/C][/ROW]
[ROW][C]137[/C][C] 0.004504[/C][C] 0.009008[/C][C] 0.9955[/C][/ROW]
[ROW][C]138[/C][C] 0.006021[/C][C] 0.01204[/C][C] 0.994[/C][/ROW]
[ROW][C]139[/C][C] 0.04204[/C][C] 0.08407[/C][C] 0.958[/C][/ROW]
[ROW][C]140[/C][C] 0.1571[/C][C] 0.3143[/C][C] 0.8429[/C][/ROW]
[ROW][C]141[/C][C] 0.1764[/C][C] 0.3528[/C][C] 0.8236[/C][/ROW]
[ROW][C]142[/C][C] 0.1471[/C][C] 0.2942[/C][C] 0.8529[/C][/ROW]
[ROW][C]143[/C][C] 0.123[/C][C] 0.246[/C][C] 0.877[/C][/ROW]
[ROW][C]144[/C][C] 0.1018[/C][C] 0.2037[/C][C] 0.8982[/C][/ROW]
[ROW][C]145[/C][C] 0.09485[/C][C] 0.1897[/C][C] 0.9052[/C][/ROW]
[ROW][C]146[/C][C] 0.08771[/C][C] 0.1754[/C][C] 0.9123[/C][/ROW]
[ROW][C]147[/C][C] 0.1007[/C][C] 0.2013[/C][C] 0.8993[/C][/ROW]
[ROW][C]148[/C][C] 0.08266[/C][C] 0.1653[/C][C] 0.9173[/C][/ROW]
[ROW][C]149[/C][C] 0.08028[/C][C] 0.1606[/C][C] 0.9197[/C][/ROW]
[ROW][C]150[/C][C] 0.1032[/C][C] 0.2064[/C][C] 0.8968[/C][/ROW]
[ROW][C]151[/C][C] 0.0919[/C][C] 0.1838[/C][C] 0.9081[/C][/ROW]
[ROW][C]152[/C][C] 0.1312[/C][C] 0.2624[/C][C] 0.8688[/C][/ROW]
[ROW][C]153[/C][C] 0.4091[/C][C] 0.8182[/C][C] 0.5909[/C][/ROW]
[ROW][C]154[/C][C] 0.7131[/C][C] 0.5739[/C][C] 0.2869[/C][/ROW]
[ROW][C]155[/C][C] 0.666[/C][C] 0.668[/C][C] 0.334[/C][/ROW]
[ROW][C]156[/C][C] 0.6431[/C][C] 0.7137[/C][C] 0.3569[/C][/ROW]
[ROW][C]157[/C][C] 0.6459[/C][C] 0.7082[/C][C] 0.3541[/C][/ROW]
[ROW][C]158[/C][C] 0.6306[/C][C] 0.7388[/C][C] 0.3694[/C][/ROW]
[ROW][C]159[/C][C] 0.5801[/C][C] 0.8397[/C][C] 0.4199[/C][/ROW]
[ROW][C]160[/C][C] 0.6252[/C][C] 0.7495[/C][C] 0.3748[/C][/ROW]
[ROW][C]161[/C][C] 0.8332[/C][C] 0.3336[/C][C] 0.1668[/C][/ROW]
[ROW][C]162[/C][C] 0.7791[/C][C] 0.4419[/C][C] 0.2209[/C][/ROW]
[ROW][C]163[/C][C] 0.7117[/C][C] 0.5766[/C][C] 0.2883[/C][/ROW]
[ROW][C]164[/C][C] 0.6558[/C][C] 0.6884[/C][C] 0.3442[/C][/ROW]
[ROW][C]165[/C][C] 0.7576[/C][C] 0.4848[/C][C] 0.2424[/C][/ROW]
[ROW][C]166[/C][C] 0.9169[/C][C] 0.1663[/C][C] 0.08314[/C][/ROW]
[ROW][C]167[/C][C] 0.8665[/C][C] 0.267[/C][C] 0.1335[/C][/ROW]
[ROW][C]168[/C][C] 0.8114[/C][C] 0.3773[/C][C] 0.1886[/C][/ROW]
[ROW][C]169[/C][C] 0.7612[/C][C] 0.4776[/C][C] 0.2388[/C][/ROW]
[ROW][C]170[/C][C] 0.8806[/C][C] 0.2389[/C][C] 0.1194[/C][/ROW]
[ROW][C]171[/C][C] 0.8813[/C][C] 0.2374[/C][C] 0.1187[/C][/ROW]
[ROW][C]172[/C][C] 0.8212[/C][C] 0.3576[/C][C] 0.1788[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=309919&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=309919&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
6 0.8678 0.2644 0.1322
7 0.8067 0.3866 0.1933
8 0.7405 0.5191 0.2595
9 0.6395 0.721 0.3605
10 0.704 0.5921 0.296
11 0.6052 0.7896 0.3948
12 0.6165 0.767 0.3835
13 0.6645 0.6709 0.3355
14 0.6056 0.7888 0.3944
15 0.7704 0.4592 0.2296
16 0.7198 0.5605 0.2802
17 0.6643 0.6713 0.3357
18 0.5925 0.8149 0.4075
19 0.6221 0.7558 0.3779
20 0.6866 0.6268 0.3134
21 0.6235 0.753 0.3765
22 0.6421 0.7158 0.3579
23 0.5927 0.8146 0.4073
24 0.5267 0.9466 0.4733
25 0.5471 0.9059 0.4529
26 0.4931 0.9861 0.5069
27 0.5503 0.8994 0.4497
28 0.539 0.9219 0.461
29 0.4962 0.9925 0.5038
30 0.4414 0.8829 0.5586
31 0.3899 0.7797 0.6101
32 0.3349 0.6699 0.6651
33 0.2832 0.5664 0.7168
34 0.2361 0.4722 0.7639
35 0.2243 0.4487 0.7757
36 0.1841 0.3681 0.8159
37 0.1737 0.3474 0.8263
38 0.1458 0.2915 0.8542
39 0.1227 0.2453 0.8773
40 0.1022 0.2045 0.8978
41 0.1088 0.2176 0.8912
42 0.1152 0.2303 0.8848
43 0.09534 0.1907 0.9047
44 0.07844 0.1569 0.9216
45 0.09672 0.1934 0.9033
46 0.0918 0.1836 0.9082
47 0.07554 0.1511 0.9245
48 0.09247 0.1849 0.9075
49 0.1089 0.2178 0.8911
50 0.09164 0.1833 0.9084
51 0.08799 0.176 0.912
52 0.06981 0.1396 0.9302
53 0.05739 0.1148 0.9426
54 0.05422 0.1084 0.9458
55 0.05206 0.1041 0.9479
56 0.04038 0.08076 0.9596
57 0.05852 0.117 0.9415
58 0.05035 0.1007 0.9496
59 0.04128 0.08256 0.9587
60 0.04647 0.09294 0.9535
61 0.03617 0.07235 0.9638
62 0.03095 0.0619 0.9691
63 0.02917 0.05834 0.9708
64 0.05101 0.102 0.949
65 0.04 0.08 0.96
66 0.03103 0.06205 0.969
67 0.02933 0.05866 0.9707
68 0.02379 0.04758 0.9762
69 0.01904 0.03807 0.981
70 0.0238 0.0476 0.9762
71 0.0212 0.04241 0.9788
72 0.02585 0.05169 0.9742
73 0.02078 0.04156 0.9792
74 0.01676 0.03351 0.9832
75 0.01341 0.02682 0.9866
76 0.01065 0.02129 0.9894
77 0.01973 0.03945 0.9803
78 0.01519 0.03038 0.9848
79 0.01143 0.02285 0.9886
80 0.008799 0.0176 0.9912
81 0.007905 0.01581 0.9921
82 0.006165 0.01233 0.9938
83 0.004773 0.009547 0.9952
84 0.003691 0.007383 0.9963
85 0.002819 0.005639 0.9972
86 0.002639 0.005278 0.9974
87 0.002023 0.004047 0.998
88 0.001896 0.003791 0.9981
89 0.00134 0.002681 0.9987
90 0.004753 0.009506 0.9952
91 0.006034 0.01207 0.994
92 0.004699 0.009399 0.9953
93 0.003417 0.006834 0.9966
94 0.002577 0.005155 0.9974
95 0.003136 0.006272 0.9969
96 0.005496 0.01099 0.9945
97 0.004884 0.009769 0.9951
98 0.006016 0.01203 0.994
99 0.004801 0.009602 0.9952
100 0.01279 0.02559 0.9872
101 0.01137 0.02274 0.9886
102 0.008989 0.01798 0.991
103 0.00723 0.01446 0.9928
104 0.006322 0.01264 0.9937
105 0.004913 0.009826 0.9951
106 0.004774 0.009548 0.9952
107 0.01068 0.02137 0.9893
108 0.008406 0.01681 0.9916
109 0.008282 0.01656 0.9917
110 0.0202 0.04039 0.9798
111 0.0446 0.0892 0.9554
112 0.03721 0.07442 0.9628
113 0.0445 0.08901 0.9555
114 0.03654 0.07309 0.9635
115 0.03512 0.07024 0.9649
116 0.0285 0.05701 0.9715
117 0.02296 0.04591 0.977
118 0.02209 0.04417 0.9779
119 0.02583 0.05167 0.9742
120 0.02069 0.04137 0.9793
121 0.01564 0.03128 0.9844
122 0.01231 0.02461 0.9877
123 0.009367 0.01873 0.9906
124 0.006868 0.01374 0.9931
125 0.005458 0.01092 0.9945
126 0.004074 0.008147 0.9959
127 0.003161 0.006323 0.9968
128 0.00241 0.004819 0.9976
129 0.004133 0.008266 0.9959
130 0.003087 0.006175 0.9969
131 0.002237 0.004473 0.9978
132 0.002482 0.004964 0.9975
133 0.001722 0.003445 0.9983
134 0.002263 0.004527 0.9977
135 0.006942 0.01388 0.9931
136 0.005108 0.01022 0.9949
137 0.004504 0.009008 0.9955
138 0.006021 0.01204 0.994
139 0.04204 0.08407 0.958
140 0.1571 0.3143 0.8429
141 0.1764 0.3528 0.8236
142 0.1471 0.2942 0.8529
143 0.123 0.246 0.877
144 0.1018 0.2037 0.8982
145 0.09485 0.1897 0.9052
146 0.08771 0.1754 0.9123
147 0.1007 0.2013 0.8993
148 0.08266 0.1653 0.9173
149 0.08028 0.1606 0.9197
150 0.1032 0.2064 0.8968
151 0.0919 0.1838 0.9081
152 0.1312 0.2624 0.8688
153 0.4091 0.8182 0.5909
154 0.7131 0.5739 0.2869
155 0.666 0.668 0.334
156 0.6431 0.7137 0.3569
157 0.6459 0.7082 0.3541
158 0.6306 0.7388 0.3694
159 0.5801 0.8397 0.4199
160 0.6252 0.7495 0.3748
161 0.8332 0.3336 0.1668
162 0.7791 0.4419 0.2209
163 0.7117 0.5766 0.2883
164 0.6558 0.6884 0.3442
165 0.7576 0.4848 0.2424
166 0.9169 0.1663 0.08314
167 0.8665 0.267 0.1335
168 0.8114 0.3773 0.1886
169 0.7612 0.4776 0.2388
170 0.8806 0.2389 0.1194
171 0.8813 0.2374 0.1187
172 0.8212 0.3576 0.1788






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level26 0.1557NOK
5% type I error level630.377246NOK
10% type I error level810.48503NOK

\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 & 26 &  0.1557 & NOK \tabularnewline
5% type I error level & 63 & 0.377246 & NOK \tabularnewline
10% type I error level & 81 & 0.48503 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=309919&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]26[/C][C] 0.1557[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]63[/C][C]0.377246[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]81[/C][C]0.48503[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=309919&T=7

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=309919&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 level26 0.1557NOK
5% type I error level630.377246NOK
10% type I error level810.48503NOK






Ramsey RESET F-Test for powers (2 and 3) of fitted values
> reset_test_fitted
	RESET test
data:  mylm
RESET = 0.32655, df1 = 2, df2 = 173, p-value = 0.7219
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors
	RESET test
data:  mylm
RESET = 0.31016, df1 = 4, df2 = 171, p-value = 0.8709
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components
	RESET test
data:  mylm
RESET = 1.8399, df1 = 2, df2 = 173, p-value = 0.1619


\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 = 0.32655, df1 = 2, df2 = 173, p-value = 0.7219

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

> reset_test_regressors
	RESET test
data:  mylm
RESET = 0.31016, df1 = 4, df2 = 171, p-value = 0.8709

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

> reset_test_principal_components
	RESET test
data:  mylm
RESET = 1.8399, df1 = 2, df2 = 173, p-value = 0.1619

 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=309919&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 = 0.32655, df1 = 2, df2 = 173, p-value = 0.7219

[/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 = 0.31016, df1 = 4, df2 = 171, p-value = 0.8709

[/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 = 1.8399, df1 = 2, df2 = 173, p-value = 0.1619

[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=309919&T=8

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=309919&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 = 0.32655, df1 = 2, df2 = 173, p-value = 0.7219
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors
	RESET test
data:  mylm
RESET = 0.31016, df1 = 4, df2 = 171, p-value = 0.8709
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components
	RESET test
data:  mylm
RESET = 1.8399, df1 = 2, df2 = 173, p-value = 0.1619






Variance Inflation Factors (Multicollinearity)
> vif
`(1-B)genderB`  `(1-B)groupB` 
      1.008988       1.008988 


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

> vif
`(1-B)genderB`  `(1-B)groupB` 
      1.008988       1.008988 

 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=309919&T=9

[TABLE]
[ROW][C]Variance Inflation Factors (Multicollinearity)[/C][/ROW]
[ROW][C]
> vif
`(1-B)genderB`  `(1-B)groupB` 
      1.008988       1.008988 

[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=309919&T=9

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=309919&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
`(1-B)genderB`  `(1-B)groupB` 
      1.008988       1.008988


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):
Parameters (R input):
par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = First Differences ; par4 = 0 ; par5 = 0 ; par6 = 12 ;
R code (references can be found in the software module):
par6 <- '12'
par5 <- '1'
par4 <- '1'
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')