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Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationWed, 22 Jan 2020 08:19:54 +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/2020/Jan/22/t15796821241tpcjeu7omgtfil.htm/, Retrieved Thu, 28 Mar 2024 13:56:51 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=319007, Retrieved Thu, 28 Mar 2024 13:56:51 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywordsregressietest system en usefulbness
Estimated Impact100
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Multiple Regression] [Examen 22/01] [2020-01-22 07:19:54] [6318fcabf82ddcca686e14c1c17254d8] [Current]
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Dataseries X:
10 10 36
8 9 32
8 12 33
9 14 39
5 6 34
10 13 39
8 12 36
9 13 33
8 6 30
7 12 39
10 10 37
10 9 37
9 12 35
4 7 32
4 10 36
8 11 36
9 15 41
10 10 36
8 12 37
5 10 29
10 12 39
8 11 37
7 11 32
8 12 36
8 15 43
9 12 30
8 11 33
6 9 28
8 11 30
8 11 28
5 9 39
9 15 34
8 12 34
8 9 29
8 12 32
6 12 33
6 9 27
9 9 35
8 11 38
9 12 40
10 12 34
8 12 34
8 12 26
7 6 39
7 11 34
10 12 39
8 9 26
7 11 30
10 9 34
7 10 34
7 10 29
9 9 41
9 12 43
8 11 31
6 9 33
8 9 34
9 12 30
2 6 23
6 10 29
8 12 35
8 11 40
7 14 27
8 8 30
6 9 27
10 10 29
10 10 33
10 10 32
8 11 33
8 10 36
7 12 34
10 14 45
5 10 30
3 8 22
2 8 24
3 7 25
4 11 26
2 6 27
6 9 27
8 12 35
8 12 36
5 12 32
10 9 35
9 15 35
8 15 36
9 13 37
8 9 33
5 12 25
7 9 35
9 15 37
8 11 36
4 11 35
7 6 29
8 14 35
7 11 31
7 8 30
9 10 37
6 10 36
7 9 35
4 8 32
6 9 34
10 10 37
9 11 36
10 14 39
8 12 37
4 9 31
8 13 40
5 8 38
8 12 35
9 14 38
8 9 32
4 10 41
8 12 28
10 12 40
6 9 25
7 9 28
10 12 37
9 15 37
8 12 40
3 11 26
8 8 30
7 11 32
7 11 31
8 10 28
8 12 34
7 9 39
7 11 33
9 15 43
9 14 37
9 6 31
4 9 31
6 9 34
6 8 32
6 7 27
8 10 34
3 6 28
8 9 32
8 9 39
6 7 28
10 11 39
2 9 32
9 12 36
6 9 31
6 10 39
5 11 23
4 7 25
7 12 32
5 8 32
8 13 36
6 11 39
9 11 31
6 12 32
4 11 28
7 12 34
2 3 28
8 10 38
9 13 35
6 10 32
5 6 26
7 11 32
8 12 28
4 9 31
9 10 33
9 15 38
9 9 38
7 6 36
5 9 31
7 15 36
9 15 43
8 9 37
6 11 28
9 9 35
8 11 34
7 10 40
7 9 31
7 6 41
8 12 35
10 13 38
6 12 37
6 12 31




Summary of computational transaction
Raw Input view raw input (R code)
Raw Outputview raw output of R engine
Computing time3 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 time3 seconds \tabularnewline
R ServerBig Analytics Cloud Computing Center \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=319007&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]3 seconds[/C][/ROW] [ROW]R Server[/C]Big Analytics Cloud Computing Center[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=319007&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=319007&T=0

As an alternative you can also use a 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 time3 seconds
R ServerBig Analytics Cloud Computing Center







Multiple Linear Regression - Estimated Regression Equation
Intention_to_Use[t] = -1.29083 + 0.280189Perceived_Usefulness[t] + 0.167725System_Quality[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Intention_to_Use[t] =  -1.29083 +  0.280189Perceived_Usefulness[t] +  0.167725System_Quality[t]  + e[t] \tabularnewline
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=319007&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Intention_to_Use[t] =  -1.29083 +  0.280189Perceived_Usefulness[t] +  0.167725System_Quality[t]  + e[t][/C][/ROW]
[ROW][C][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=319007&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=319007&T=1

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Estimated Regression Equation
Intention_to_Use[t] = -1.29083 + 0.280189Perceived_Usefulness[t] + 0.167725System_Quality[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)-1.291 0.8851-1.4580e+00 0.1465 0.07327
Perceived_Usefulness+0.2802 0.05684+4.9290e+00 1.899e-06 9.496e-07
System_Quality+0.1677 0.02828+5.9310e+00 1.56e-08 7.798e-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) & -1.291 &  0.8851 & -1.4580e+00 &  0.1465 &  0.07327 \tabularnewline
Perceived_Usefulness & +0.2802 &  0.05684 & +4.9290e+00 &  1.899e-06 &  9.496e-07 \tabularnewline
System_Quality & +0.1677 &  0.02828 & +5.9310e+00 &  1.56e-08 &  7.798e-09 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=319007&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]-1.291[/C][C] 0.8851[/C][C]-1.4580e+00[/C][C] 0.1465[/C][C] 0.07327[/C][/ROW]
[ROW][C]Perceived_Usefulness[/C][C]+0.2802[/C][C] 0.05684[/C][C]+4.9290e+00[/C][C] 1.899e-06[/C][C] 9.496e-07[/C][/ROW]
[ROW][C]System_Quality[/C][C]+0.1677[/C][C] 0.02828[/C][C]+5.9310e+00[/C][C] 1.56e-08[/C][C] 7.798e-09[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=319007&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=319007&T=2

As an alternative you can also use a 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)-1.291 0.8851-1.4580e+00 0.1465 0.07327
Perceived_Usefulness+0.2802 0.05684+4.9290e+00 1.899e-06 9.496e-07
System_Quality+0.1677 0.02828+5.9310e+00 1.56e-08 7.798e-09







Multiple Linear Regression - Regression Statistics
Multiple R 0.6089
R-squared 0.3708
Adjusted R-squared 0.3637
F-TEST (value) 51.86
F-TEST (DF numerator)2
F-TEST (DF denominator)176
p-value 0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation 1.566
Sum Squared Residuals 431.6

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R &  0.6089 \tabularnewline
R-squared &  0.3708 \tabularnewline
Adjusted R-squared &  0.3637 \tabularnewline
F-TEST (value) &  51.86 \tabularnewline
F-TEST (DF numerator) & 2 \tabularnewline
F-TEST (DF denominator) & 176 \tabularnewline
p-value &  0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation &  1.566 \tabularnewline
Sum Squared Residuals &  431.6 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=319007&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C] 0.6089[/C][/ROW]
[ROW][C]R-squared[/C][C] 0.3708[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C] 0.3637[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C] 51.86[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]2[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]176[/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] 1.566[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C] 431.6[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=319007&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=319007&T=3

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Regression Statistics
Multiple R 0.6089
R-squared 0.3708
Adjusted R-squared 0.3637
F-TEST (value) 51.86
F-TEST (DF numerator)2
F-TEST (DF denominator)176
p-value 0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation 1.566
Sum Squared Residuals 431.6







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=319007&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=319007&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=319007&T=4

As an alternative you can also use a 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 10 7.549 2.451
2 8 6.598 1.402
3 8 7.606 0.3936
4 9 9.173-0.1731
5 5 6.093-1.093
6 10 8.893 1.107
7 8 8.11-0.1096
8 9 7.887 1.113
9 8 5.422 2.578
10 7 8.613-1.613
11 10 7.717 2.283
12 10 7.437 2.563
13 9 7.942 1.058
14 4 6.038-2.038
15 4 7.549-3.549
16 8 7.829 0.1706
17 9 9.789-0.7887
18 10 7.549 2.451
19 8 8.277-0.2773
20 5 6.375-1.375
21 10 8.613 1.387
22 8 7.997 0.002912
23 7 7.158-0.1585
24 8 8.11-0.1096
25 8 10.12-2.124
26 9 7.103 1.897
27 8 7.326 0.6738
28 6 5.927 0.07282
29 8 6.823 1.177
30 8 6.488 1.512
31 5 7.772-2.772
32 9 8.615 0.3853
33 8 7.774 0.2259
34 8 6.095 1.905
35 8 7.439 0.5614
36 6 7.606-1.606
37 6 5.759 0.2405
38 9 7.101 1.899
39 8 8.165-0.1648
40 9 8.78 0.2195
41 10 7.774 2.226
42 8 7.774 0.2259
43 8 6.432 1.568
44 7 6.932 0.06841
45 7 7.494-0.4939
46 10 8.613 1.387
47 8 5.592 2.408
48 7 6.823 0.177
49 10 6.934 3.066
50 7 7.214-0.2137
51 7 6.375 0.6249
52 9 8.108 0.8924
53 9 9.284-0.2836
54 8 6.991 1.009
55 6 6.766-0.7658
56 8 6.934 1.066
57 9 7.103 1.897
58 2 4.248-2.248
59 6 6.375-0.3751
60 8 7.942 0.05817
61 8 8.5-0.5003
62 7 7.16-0.1604
63 8 5.982 2.018
64 6 5.759 0.2405
65 10 6.375 3.625
66 10 7.046 2.954
67 10 6.878 3.122
68 8 7.326 0.6738
69 8 7.549 0.4508
70 7 7.774-0.7741
71 10 10.18-0.1795
72 5 6.543-1.543
73 3 4.641-1.641
74 2 4.976-2.976
75 3 4.864-1.864
76 4 6.152-2.152
77 2 4.919-2.919
78 6 5.759 0.2405
79 8 7.942 0.05817
80 8 8.11-0.1096
81 5 7.439-2.439
82 10 7.101 2.899
83 9 8.782 0.2176
84 8 8.95-0.9501
85 9 8.557 0.4425
86 8 6.766 1.234
87 5 6.265-1.265
88 7 7.101-0.1013
89 9 9.118-0.1178
90 8 7.829 0.1706
91 4 7.662-3.662
92 7 5.254 1.746
93 8 8.502-0.5022
94 7 6.991 0.009265
95 7 5.982 1.018
96 9 7.717 1.283
97 6 7.549-1.549
98 7 7.101-0.1013
99 4 6.318-2.318
100 6 6.934-0.9335
101 10 7.717 2.283
102 9 7.829 1.171
103 10 9.173 0.8269
104 8 8.277-0.2773
105 4 6.43-2.43
106 8 9.061-1.061
107 5 7.324-2.324
108 8 7.942 0.05817
109 9 9.005-0.005379
110 8 6.598 1.402
111 4 8.388-4.388
112 8 6.768 1.232
113 10 8.78 1.22
114 6 5.424 0.576
115 7 5.927 1.073
116 10 8.277 1.723
117 9 9.118-0.1178
118 8 8.78-0.7805
119 3 6.152-3.152
120 8 5.982 2.018
121 7 7.158-0.1585
122 7 6.991 0.009265
123 8 6.207 1.793
124 8 7.774 0.2259
125 7 7.772-0.7722
126 7 7.326-0.3262
127 9 10.12-1.124
128 9 8.838 0.1623
129 9 5.59 3.41
130 4 6.43-2.43
131 6 6.934-0.9335
132 6 6.318-0.3179
133 6 5.199 0.8009
134 8 7.214 0.7863
135 3 5.087-2.087
136 8 6.598 1.402
137 8 7.772 0.2278
138 6 5.367 0.6332
139 10 8.333 1.667
140 2 6.598-4.598
141 9 8.11 0.8904
142 6 6.43-0.4304
143 6 8.052-2.052
144 5 5.649-0.6489
145 4 4.864-0.8636
146 7 7.439-0.4386
147 5 6.318-1.318
148 8 8.39-0.3897
149 6 8.333-2.333
150 9 6.991 2.009
151 6 7.439-1.439
152 4 6.488-2.488
153 7 7.774-0.7741
154 2 4.246-2.246
155 8 7.885 0.1154
156 9 8.222 0.778
157 6 6.878-0.8783
158 5 4.751 0.2488
159 7 7.158-0.1585
160 8 6.768 1.232
161 4 6.43-2.43
162 9 7.046 1.954
163 9 9.286-0.2856
164 9 7.604 1.396
165 7 6.428 0.5716
166 5 6.43-1.43
167 7 8.95-1.95
168 9 10.12-1.124
169 8 7.437 0.5633
170 6 6.488-0.4876
171 9 7.101 1.899
172 8 7.494 0.5061
173 7 8.22-1.22
174 7 6.43 0.5696
175 7 7.267-0.267
176 8 7.942 0.05817
177 10 8.725 1.275
178 6 8.277-2.277
179 6 7.271-1.271

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 &  10 &  7.549 &  2.451 \tabularnewline
2 &  8 &  6.598 &  1.402 \tabularnewline
3 &  8 &  7.606 &  0.3936 \tabularnewline
4 &  9 &  9.173 & -0.1731 \tabularnewline
5 &  5 &  6.093 & -1.093 \tabularnewline
6 &  10 &  8.893 &  1.107 \tabularnewline
7 &  8 &  8.11 & -0.1096 \tabularnewline
8 &  9 &  7.887 &  1.113 \tabularnewline
9 &  8 &  5.422 &  2.578 \tabularnewline
10 &  7 &  8.613 & -1.613 \tabularnewline
11 &  10 &  7.717 &  2.283 \tabularnewline
12 &  10 &  7.437 &  2.563 \tabularnewline
13 &  9 &  7.942 &  1.058 \tabularnewline
14 &  4 &  6.038 & -2.038 \tabularnewline
15 &  4 &  7.549 & -3.549 \tabularnewline
16 &  8 &  7.829 &  0.1706 \tabularnewline
17 &  9 &  9.789 & -0.7887 \tabularnewline
18 &  10 &  7.549 &  2.451 \tabularnewline
19 &  8 &  8.277 & -0.2773 \tabularnewline
20 &  5 &  6.375 & -1.375 \tabularnewline
21 &  10 &  8.613 &  1.387 \tabularnewline
22 &  8 &  7.997 &  0.002912 \tabularnewline
23 &  7 &  7.158 & -0.1585 \tabularnewline
24 &  8 &  8.11 & -0.1096 \tabularnewline
25 &  8 &  10.12 & -2.124 \tabularnewline
26 &  9 &  7.103 &  1.897 \tabularnewline
27 &  8 &  7.326 &  0.6738 \tabularnewline
28 &  6 &  5.927 &  0.07282 \tabularnewline
29 &  8 &  6.823 &  1.177 \tabularnewline
30 &  8 &  6.488 &  1.512 \tabularnewline
31 &  5 &  7.772 & -2.772 \tabularnewline
32 &  9 &  8.615 &  0.3853 \tabularnewline
33 &  8 &  7.774 &  0.2259 \tabularnewline
34 &  8 &  6.095 &  1.905 \tabularnewline
35 &  8 &  7.439 &  0.5614 \tabularnewline
36 &  6 &  7.606 & -1.606 \tabularnewline
37 &  6 &  5.759 &  0.2405 \tabularnewline
38 &  9 &  7.101 &  1.899 \tabularnewline
39 &  8 &  8.165 & -0.1648 \tabularnewline
40 &  9 &  8.78 &  0.2195 \tabularnewline
41 &  10 &  7.774 &  2.226 \tabularnewline
42 &  8 &  7.774 &  0.2259 \tabularnewline
43 &  8 &  6.432 &  1.568 \tabularnewline
44 &  7 &  6.932 &  0.06841 \tabularnewline
45 &  7 &  7.494 & -0.4939 \tabularnewline
46 &  10 &  8.613 &  1.387 \tabularnewline
47 &  8 &  5.592 &  2.408 \tabularnewline
48 &  7 &  6.823 &  0.177 \tabularnewline
49 &  10 &  6.934 &  3.066 \tabularnewline
50 &  7 &  7.214 & -0.2137 \tabularnewline
51 &  7 &  6.375 &  0.6249 \tabularnewline
52 &  9 &  8.108 &  0.8924 \tabularnewline
53 &  9 &  9.284 & -0.2836 \tabularnewline
54 &  8 &  6.991 &  1.009 \tabularnewline
55 &  6 &  6.766 & -0.7658 \tabularnewline
56 &  8 &  6.934 &  1.066 \tabularnewline
57 &  9 &  7.103 &  1.897 \tabularnewline
58 &  2 &  4.248 & -2.248 \tabularnewline
59 &  6 &  6.375 & -0.3751 \tabularnewline
60 &  8 &  7.942 &  0.05817 \tabularnewline
61 &  8 &  8.5 & -0.5003 \tabularnewline
62 &  7 &  7.16 & -0.1604 \tabularnewline
63 &  8 &  5.982 &  2.018 \tabularnewline
64 &  6 &  5.759 &  0.2405 \tabularnewline
65 &  10 &  6.375 &  3.625 \tabularnewline
66 &  10 &  7.046 &  2.954 \tabularnewline
67 &  10 &  6.878 &  3.122 \tabularnewline
68 &  8 &  7.326 &  0.6738 \tabularnewline
69 &  8 &  7.549 &  0.4508 \tabularnewline
70 &  7 &  7.774 & -0.7741 \tabularnewline
71 &  10 &  10.18 & -0.1795 \tabularnewline
72 &  5 &  6.543 & -1.543 \tabularnewline
73 &  3 &  4.641 & -1.641 \tabularnewline
74 &  2 &  4.976 & -2.976 \tabularnewline
75 &  3 &  4.864 & -1.864 \tabularnewline
76 &  4 &  6.152 & -2.152 \tabularnewline
77 &  2 &  4.919 & -2.919 \tabularnewline
78 &  6 &  5.759 &  0.2405 \tabularnewline
79 &  8 &  7.942 &  0.05817 \tabularnewline
80 &  8 &  8.11 & -0.1096 \tabularnewline
81 &  5 &  7.439 & -2.439 \tabularnewline
82 &  10 &  7.101 &  2.899 \tabularnewline
83 &  9 &  8.782 &  0.2176 \tabularnewline
84 &  8 &  8.95 & -0.9501 \tabularnewline
85 &  9 &  8.557 &  0.4425 \tabularnewline
86 &  8 &  6.766 &  1.234 \tabularnewline
87 &  5 &  6.265 & -1.265 \tabularnewline
88 &  7 &  7.101 & -0.1013 \tabularnewline
89 &  9 &  9.118 & -0.1178 \tabularnewline
90 &  8 &  7.829 &  0.1706 \tabularnewline
91 &  4 &  7.662 & -3.662 \tabularnewline
92 &  7 &  5.254 &  1.746 \tabularnewline
93 &  8 &  8.502 & -0.5022 \tabularnewline
94 &  7 &  6.991 &  0.009265 \tabularnewline
95 &  7 &  5.982 &  1.018 \tabularnewline
96 &  9 &  7.717 &  1.283 \tabularnewline
97 &  6 &  7.549 & -1.549 \tabularnewline
98 &  7 &  7.101 & -0.1013 \tabularnewline
99 &  4 &  6.318 & -2.318 \tabularnewline
100 &  6 &  6.934 & -0.9335 \tabularnewline
101 &  10 &  7.717 &  2.283 \tabularnewline
102 &  9 &  7.829 &  1.171 \tabularnewline
103 &  10 &  9.173 &  0.8269 \tabularnewline
104 &  8 &  8.277 & -0.2773 \tabularnewline
105 &  4 &  6.43 & -2.43 \tabularnewline
106 &  8 &  9.061 & -1.061 \tabularnewline
107 &  5 &  7.324 & -2.324 \tabularnewline
108 &  8 &  7.942 &  0.05817 \tabularnewline
109 &  9 &  9.005 & -0.005379 \tabularnewline
110 &  8 &  6.598 &  1.402 \tabularnewline
111 &  4 &  8.388 & -4.388 \tabularnewline
112 &  8 &  6.768 &  1.232 \tabularnewline
113 &  10 &  8.78 &  1.22 \tabularnewline
114 &  6 &  5.424 &  0.576 \tabularnewline
115 &  7 &  5.927 &  1.073 \tabularnewline
116 &  10 &  8.277 &  1.723 \tabularnewline
117 &  9 &  9.118 & -0.1178 \tabularnewline
118 &  8 &  8.78 & -0.7805 \tabularnewline
119 &  3 &  6.152 & -3.152 \tabularnewline
120 &  8 &  5.982 &  2.018 \tabularnewline
121 &  7 &  7.158 & -0.1585 \tabularnewline
122 &  7 &  6.991 &  0.009265 \tabularnewline
123 &  8 &  6.207 &  1.793 \tabularnewline
124 &  8 &  7.774 &  0.2259 \tabularnewline
125 &  7 &  7.772 & -0.7722 \tabularnewline
126 &  7 &  7.326 & -0.3262 \tabularnewline
127 &  9 &  10.12 & -1.124 \tabularnewline
128 &  9 &  8.838 &  0.1623 \tabularnewline
129 &  9 &  5.59 &  3.41 \tabularnewline
130 &  4 &  6.43 & -2.43 \tabularnewline
131 &  6 &  6.934 & -0.9335 \tabularnewline
132 &  6 &  6.318 & -0.3179 \tabularnewline
133 &  6 &  5.199 &  0.8009 \tabularnewline
134 &  8 &  7.214 &  0.7863 \tabularnewline
135 &  3 &  5.087 & -2.087 \tabularnewline
136 &  8 &  6.598 &  1.402 \tabularnewline
137 &  8 &  7.772 &  0.2278 \tabularnewline
138 &  6 &  5.367 &  0.6332 \tabularnewline
139 &  10 &  8.333 &  1.667 \tabularnewline
140 &  2 &  6.598 & -4.598 \tabularnewline
141 &  9 &  8.11 &  0.8904 \tabularnewline
142 &  6 &  6.43 & -0.4304 \tabularnewline
143 &  6 &  8.052 & -2.052 \tabularnewline
144 &  5 &  5.649 & -0.6489 \tabularnewline
145 &  4 &  4.864 & -0.8636 \tabularnewline
146 &  7 &  7.439 & -0.4386 \tabularnewline
147 &  5 &  6.318 & -1.318 \tabularnewline
148 &  8 &  8.39 & -0.3897 \tabularnewline
149 &  6 &  8.333 & -2.333 \tabularnewline
150 &  9 &  6.991 &  2.009 \tabularnewline
151 &  6 &  7.439 & -1.439 \tabularnewline
152 &  4 &  6.488 & -2.488 \tabularnewline
153 &  7 &  7.774 & -0.7741 \tabularnewline
154 &  2 &  4.246 & -2.246 \tabularnewline
155 &  8 &  7.885 &  0.1154 \tabularnewline
156 &  9 &  8.222 &  0.778 \tabularnewline
157 &  6 &  6.878 & -0.8783 \tabularnewline
158 &  5 &  4.751 &  0.2488 \tabularnewline
159 &  7 &  7.158 & -0.1585 \tabularnewline
160 &  8 &  6.768 &  1.232 \tabularnewline
161 &  4 &  6.43 & -2.43 \tabularnewline
162 &  9 &  7.046 &  1.954 \tabularnewline
163 &  9 &  9.286 & -0.2856 \tabularnewline
164 &  9 &  7.604 &  1.396 \tabularnewline
165 &  7 &  6.428 &  0.5716 \tabularnewline
166 &  5 &  6.43 & -1.43 \tabularnewline
167 &  7 &  8.95 & -1.95 \tabularnewline
168 &  9 &  10.12 & -1.124 \tabularnewline
169 &  8 &  7.437 &  0.5633 \tabularnewline
170 &  6 &  6.488 & -0.4876 \tabularnewline
171 &  9 &  7.101 &  1.899 \tabularnewline
172 &  8 &  7.494 &  0.5061 \tabularnewline
173 &  7 &  8.22 & -1.22 \tabularnewline
174 &  7 &  6.43 &  0.5696 \tabularnewline
175 &  7 &  7.267 & -0.267 \tabularnewline
176 &  8 &  7.942 &  0.05817 \tabularnewline
177 &  10 &  8.725 &  1.275 \tabularnewline
178 &  6 &  8.277 & -2.277 \tabularnewline
179 &  6 &  7.271 & -1.271 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=319007&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] 10[/C][C] 7.549[/C][C] 2.451[/C][/ROW]
[ROW][C]2[/C][C] 8[/C][C] 6.598[/C][C] 1.402[/C][/ROW]
[ROW][C]3[/C][C] 8[/C][C] 7.606[/C][C] 0.3936[/C][/ROW]
[ROW][C]4[/C][C] 9[/C][C] 9.173[/C][C]-0.1731[/C][/ROW]
[ROW][C]5[/C][C] 5[/C][C] 6.093[/C][C]-1.093[/C][/ROW]
[ROW][C]6[/C][C] 10[/C][C] 8.893[/C][C] 1.107[/C][/ROW]
[ROW][C]7[/C][C] 8[/C][C] 8.11[/C][C]-0.1096[/C][/ROW]
[ROW][C]8[/C][C] 9[/C][C] 7.887[/C][C] 1.113[/C][/ROW]
[ROW][C]9[/C][C] 8[/C][C] 5.422[/C][C] 2.578[/C][/ROW]
[ROW][C]10[/C][C] 7[/C][C] 8.613[/C][C]-1.613[/C][/ROW]
[ROW][C]11[/C][C] 10[/C][C] 7.717[/C][C] 2.283[/C][/ROW]
[ROW][C]12[/C][C] 10[/C][C] 7.437[/C][C] 2.563[/C][/ROW]
[ROW][C]13[/C][C] 9[/C][C] 7.942[/C][C] 1.058[/C][/ROW]
[ROW][C]14[/C][C] 4[/C][C] 6.038[/C][C]-2.038[/C][/ROW]
[ROW][C]15[/C][C] 4[/C][C] 7.549[/C][C]-3.549[/C][/ROW]
[ROW][C]16[/C][C] 8[/C][C] 7.829[/C][C] 0.1706[/C][/ROW]
[ROW][C]17[/C][C] 9[/C][C] 9.789[/C][C]-0.7887[/C][/ROW]
[ROW][C]18[/C][C] 10[/C][C] 7.549[/C][C] 2.451[/C][/ROW]
[ROW][C]19[/C][C] 8[/C][C] 8.277[/C][C]-0.2773[/C][/ROW]
[ROW][C]20[/C][C] 5[/C][C] 6.375[/C][C]-1.375[/C][/ROW]
[ROW][C]21[/C][C] 10[/C][C] 8.613[/C][C] 1.387[/C][/ROW]
[ROW][C]22[/C][C] 8[/C][C] 7.997[/C][C] 0.002912[/C][/ROW]
[ROW][C]23[/C][C] 7[/C][C] 7.158[/C][C]-0.1585[/C][/ROW]
[ROW][C]24[/C][C] 8[/C][C] 8.11[/C][C]-0.1096[/C][/ROW]
[ROW][C]25[/C][C] 8[/C][C] 10.12[/C][C]-2.124[/C][/ROW]
[ROW][C]26[/C][C] 9[/C][C] 7.103[/C][C] 1.897[/C][/ROW]
[ROW][C]27[/C][C] 8[/C][C] 7.326[/C][C] 0.6738[/C][/ROW]
[ROW][C]28[/C][C] 6[/C][C] 5.927[/C][C] 0.07282[/C][/ROW]
[ROW][C]29[/C][C] 8[/C][C] 6.823[/C][C] 1.177[/C][/ROW]
[ROW][C]30[/C][C] 8[/C][C] 6.488[/C][C] 1.512[/C][/ROW]
[ROW][C]31[/C][C] 5[/C][C] 7.772[/C][C]-2.772[/C][/ROW]
[ROW][C]32[/C][C] 9[/C][C] 8.615[/C][C] 0.3853[/C][/ROW]
[ROW][C]33[/C][C] 8[/C][C] 7.774[/C][C] 0.2259[/C][/ROW]
[ROW][C]34[/C][C] 8[/C][C] 6.095[/C][C] 1.905[/C][/ROW]
[ROW][C]35[/C][C] 8[/C][C] 7.439[/C][C] 0.5614[/C][/ROW]
[ROW][C]36[/C][C] 6[/C][C] 7.606[/C][C]-1.606[/C][/ROW]
[ROW][C]37[/C][C] 6[/C][C] 5.759[/C][C] 0.2405[/C][/ROW]
[ROW][C]38[/C][C] 9[/C][C] 7.101[/C][C] 1.899[/C][/ROW]
[ROW][C]39[/C][C] 8[/C][C] 8.165[/C][C]-0.1648[/C][/ROW]
[ROW][C]40[/C][C] 9[/C][C] 8.78[/C][C] 0.2195[/C][/ROW]
[ROW][C]41[/C][C] 10[/C][C] 7.774[/C][C] 2.226[/C][/ROW]
[ROW][C]42[/C][C] 8[/C][C] 7.774[/C][C] 0.2259[/C][/ROW]
[ROW][C]43[/C][C] 8[/C][C] 6.432[/C][C] 1.568[/C][/ROW]
[ROW][C]44[/C][C] 7[/C][C] 6.932[/C][C] 0.06841[/C][/ROW]
[ROW][C]45[/C][C] 7[/C][C] 7.494[/C][C]-0.4939[/C][/ROW]
[ROW][C]46[/C][C] 10[/C][C] 8.613[/C][C] 1.387[/C][/ROW]
[ROW][C]47[/C][C] 8[/C][C] 5.592[/C][C] 2.408[/C][/ROW]
[ROW][C]48[/C][C] 7[/C][C] 6.823[/C][C] 0.177[/C][/ROW]
[ROW][C]49[/C][C] 10[/C][C] 6.934[/C][C] 3.066[/C][/ROW]
[ROW][C]50[/C][C] 7[/C][C] 7.214[/C][C]-0.2137[/C][/ROW]
[ROW][C]51[/C][C] 7[/C][C] 6.375[/C][C] 0.6249[/C][/ROW]
[ROW][C]52[/C][C] 9[/C][C] 8.108[/C][C] 0.8924[/C][/ROW]
[ROW][C]53[/C][C] 9[/C][C] 9.284[/C][C]-0.2836[/C][/ROW]
[ROW][C]54[/C][C] 8[/C][C] 6.991[/C][C] 1.009[/C][/ROW]
[ROW][C]55[/C][C] 6[/C][C] 6.766[/C][C]-0.7658[/C][/ROW]
[ROW][C]56[/C][C] 8[/C][C] 6.934[/C][C] 1.066[/C][/ROW]
[ROW][C]57[/C][C] 9[/C][C] 7.103[/C][C] 1.897[/C][/ROW]
[ROW][C]58[/C][C] 2[/C][C] 4.248[/C][C]-2.248[/C][/ROW]
[ROW][C]59[/C][C] 6[/C][C] 6.375[/C][C]-0.3751[/C][/ROW]
[ROW][C]60[/C][C] 8[/C][C] 7.942[/C][C] 0.05817[/C][/ROW]
[ROW][C]61[/C][C] 8[/C][C] 8.5[/C][C]-0.5003[/C][/ROW]
[ROW][C]62[/C][C] 7[/C][C] 7.16[/C][C]-0.1604[/C][/ROW]
[ROW][C]63[/C][C] 8[/C][C] 5.982[/C][C] 2.018[/C][/ROW]
[ROW][C]64[/C][C] 6[/C][C] 5.759[/C][C] 0.2405[/C][/ROW]
[ROW][C]65[/C][C] 10[/C][C] 6.375[/C][C] 3.625[/C][/ROW]
[ROW][C]66[/C][C] 10[/C][C] 7.046[/C][C] 2.954[/C][/ROW]
[ROW][C]67[/C][C] 10[/C][C] 6.878[/C][C] 3.122[/C][/ROW]
[ROW][C]68[/C][C] 8[/C][C] 7.326[/C][C] 0.6738[/C][/ROW]
[ROW][C]69[/C][C] 8[/C][C] 7.549[/C][C] 0.4508[/C][/ROW]
[ROW][C]70[/C][C] 7[/C][C] 7.774[/C][C]-0.7741[/C][/ROW]
[ROW][C]71[/C][C] 10[/C][C] 10.18[/C][C]-0.1795[/C][/ROW]
[ROW][C]72[/C][C] 5[/C][C] 6.543[/C][C]-1.543[/C][/ROW]
[ROW][C]73[/C][C] 3[/C][C] 4.641[/C][C]-1.641[/C][/ROW]
[ROW][C]74[/C][C] 2[/C][C] 4.976[/C][C]-2.976[/C][/ROW]
[ROW][C]75[/C][C] 3[/C][C] 4.864[/C][C]-1.864[/C][/ROW]
[ROW][C]76[/C][C] 4[/C][C] 6.152[/C][C]-2.152[/C][/ROW]
[ROW][C]77[/C][C] 2[/C][C] 4.919[/C][C]-2.919[/C][/ROW]
[ROW][C]78[/C][C] 6[/C][C] 5.759[/C][C] 0.2405[/C][/ROW]
[ROW][C]79[/C][C] 8[/C][C] 7.942[/C][C] 0.05817[/C][/ROW]
[ROW][C]80[/C][C] 8[/C][C] 8.11[/C][C]-0.1096[/C][/ROW]
[ROW][C]81[/C][C] 5[/C][C] 7.439[/C][C]-2.439[/C][/ROW]
[ROW][C]82[/C][C] 10[/C][C] 7.101[/C][C] 2.899[/C][/ROW]
[ROW][C]83[/C][C] 9[/C][C] 8.782[/C][C] 0.2176[/C][/ROW]
[ROW][C]84[/C][C] 8[/C][C] 8.95[/C][C]-0.9501[/C][/ROW]
[ROW][C]85[/C][C] 9[/C][C] 8.557[/C][C] 0.4425[/C][/ROW]
[ROW][C]86[/C][C] 8[/C][C] 6.766[/C][C] 1.234[/C][/ROW]
[ROW][C]87[/C][C] 5[/C][C] 6.265[/C][C]-1.265[/C][/ROW]
[ROW][C]88[/C][C] 7[/C][C] 7.101[/C][C]-0.1013[/C][/ROW]
[ROW][C]89[/C][C] 9[/C][C] 9.118[/C][C]-0.1178[/C][/ROW]
[ROW][C]90[/C][C] 8[/C][C] 7.829[/C][C] 0.1706[/C][/ROW]
[ROW][C]91[/C][C] 4[/C][C] 7.662[/C][C]-3.662[/C][/ROW]
[ROW][C]92[/C][C] 7[/C][C] 5.254[/C][C] 1.746[/C][/ROW]
[ROW][C]93[/C][C] 8[/C][C] 8.502[/C][C]-0.5022[/C][/ROW]
[ROW][C]94[/C][C] 7[/C][C] 6.991[/C][C] 0.009265[/C][/ROW]
[ROW][C]95[/C][C] 7[/C][C] 5.982[/C][C] 1.018[/C][/ROW]
[ROW][C]96[/C][C] 9[/C][C] 7.717[/C][C] 1.283[/C][/ROW]
[ROW][C]97[/C][C] 6[/C][C] 7.549[/C][C]-1.549[/C][/ROW]
[ROW][C]98[/C][C] 7[/C][C] 7.101[/C][C]-0.1013[/C][/ROW]
[ROW][C]99[/C][C] 4[/C][C] 6.318[/C][C]-2.318[/C][/ROW]
[ROW][C]100[/C][C] 6[/C][C] 6.934[/C][C]-0.9335[/C][/ROW]
[ROW][C]101[/C][C] 10[/C][C] 7.717[/C][C] 2.283[/C][/ROW]
[ROW][C]102[/C][C] 9[/C][C] 7.829[/C][C] 1.171[/C][/ROW]
[ROW][C]103[/C][C] 10[/C][C] 9.173[/C][C] 0.8269[/C][/ROW]
[ROW][C]104[/C][C] 8[/C][C] 8.277[/C][C]-0.2773[/C][/ROW]
[ROW][C]105[/C][C] 4[/C][C] 6.43[/C][C]-2.43[/C][/ROW]
[ROW][C]106[/C][C] 8[/C][C] 9.061[/C][C]-1.061[/C][/ROW]
[ROW][C]107[/C][C] 5[/C][C] 7.324[/C][C]-2.324[/C][/ROW]
[ROW][C]108[/C][C] 8[/C][C] 7.942[/C][C] 0.05817[/C][/ROW]
[ROW][C]109[/C][C] 9[/C][C] 9.005[/C][C]-0.005379[/C][/ROW]
[ROW][C]110[/C][C] 8[/C][C] 6.598[/C][C] 1.402[/C][/ROW]
[ROW][C]111[/C][C] 4[/C][C] 8.388[/C][C]-4.388[/C][/ROW]
[ROW][C]112[/C][C] 8[/C][C] 6.768[/C][C] 1.232[/C][/ROW]
[ROW][C]113[/C][C] 10[/C][C] 8.78[/C][C] 1.22[/C][/ROW]
[ROW][C]114[/C][C] 6[/C][C] 5.424[/C][C] 0.576[/C][/ROW]
[ROW][C]115[/C][C] 7[/C][C] 5.927[/C][C] 1.073[/C][/ROW]
[ROW][C]116[/C][C] 10[/C][C] 8.277[/C][C] 1.723[/C][/ROW]
[ROW][C]117[/C][C] 9[/C][C] 9.118[/C][C]-0.1178[/C][/ROW]
[ROW][C]118[/C][C] 8[/C][C] 8.78[/C][C]-0.7805[/C][/ROW]
[ROW][C]119[/C][C] 3[/C][C] 6.152[/C][C]-3.152[/C][/ROW]
[ROW][C]120[/C][C] 8[/C][C] 5.982[/C][C] 2.018[/C][/ROW]
[ROW][C]121[/C][C] 7[/C][C] 7.158[/C][C]-0.1585[/C][/ROW]
[ROW][C]122[/C][C] 7[/C][C] 6.991[/C][C] 0.009265[/C][/ROW]
[ROW][C]123[/C][C] 8[/C][C] 6.207[/C][C] 1.793[/C][/ROW]
[ROW][C]124[/C][C] 8[/C][C] 7.774[/C][C] 0.2259[/C][/ROW]
[ROW][C]125[/C][C] 7[/C][C] 7.772[/C][C]-0.7722[/C][/ROW]
[ROW][C]126[/C][C] 7[/C][C] 7.326[/C][C]-0.3262[/C][/ROW]
[ROW][C]127[/C][C] 9[/C][C] 10.12[/C][C]-1.124[/C][/ROW]
[ROW][C]128[/C][C] 9[/C][C] 8.838[/C][C] 0.1623[/C][/ROW]
[ROW][C]129[/C][C] 9[/C][C] 5.59[/C][C] 3.41[/C][/ROW]
[ROW][C]130[/C][C] 4[/C][C] 6.43[/C][C]-2.43[/C][/ROW]
[ROW][C]131[/C][C] 6[/C][C] 6.934[/C][C]-0.9335[/C][/ROW]
[ROW][C]132[/C][C] 6[/C][C] 6.318[/C][C]-0.3179[/C][/ROW]
[ROW][C]133[/C][C] 6[/C][C] 5.199[/C][C] 0.8009[/C][/ROW]
[ROW][C]134[/C][C] 8[/C][C] 7.214[/C][C] 0.7863[/C][/ROW]
[ROW][C]135[/C][C] 3[/C][C] 5.087[/C][C]-2.087[/C][/ROW]
[ROW][C]136[/C][C] 8[/C][C] 6.598[/C][C] 1.402[/C][/ROW]
[ROW][C]137[/C][C] 8[/C][C] 7.772[/C][C] 0.2278[/C][/ROW]
[ROW][C]138[/C][C] 6[/C][C] 5.367[/C][C] 0.6332[/C][/ROW]
[ROW][C]139[/C][C] 10[/C][C] 8.333[/C][C] 1.667[/C][/ROW]
[ROW][C]140[/C][C] 2[/C][C] 6.598[/C][C]-4.598[/C][/ROW]
[ROW][C]141[/C][C] 9[/C][C] 8.11[/C][C] 0.8904[/C][/ROW]
[ROW][C]142[/C][C] 6[/C][C] 6.43[/C][C]-0.4304[/C][/ROW]
[ROW][C]143[/C][C] 6[/C][C] 8.052[/C][C]-2.052[/C][/ROW]
[ROW][C]144[/C][C] 5[/C][C] 5.649[/C][C]-0.6489[/C][/ROW]
[ROW][C]145[/C][C] 4[/C][C] 4.864[/C][C]-0.8636[/C][/ROW]
[ROW][C]146[/C][C] 7[/C][C] 7.439[/C][C]-0.4386[/C][/ROW]
[ROW][C]147[/C][C] 5[/C][C] 6.318[/C][C]-1.318[/C][/ROW]
[ROW][C]148[/C][C] 8[/C][C] 8.39[/C][C]-0.3897[/C][/ROW]
[ROW][C]149[/C][C] 6[/C][C] 8.333[/C][C]-2.333[/C][/ROW]
[ROW][C]150[/C][C] 9[/C][C] 6.991[/C][C] 2.009[/C][/ROW]
[ROW][C]151[/C][C] 6[/C][C] 7.439[/C][C]-1.439[/C][/ROW]
[ROW][C]152[/C][C] 4[/C][C] 6.488[/C][C]-2.488[/C][/ROW]
[ROW][C]153[/C][C] 7[/C][C] 7.774[/C][C]-0.7741[/C][/ROW]
[ROW][C]154[/C][C] 2[/C][C] 4.246[/C][C]-2.246[/C][/ROW]
[ROW][C]155[/C][C] 8[/C][C] 7.885[/C][C] 0.1154[/C][/ROW]
[ROW][C]156[/C][C] 9[/C][C] 8.222[/C][C] 0.778[/C][/ROW]
[ROW][C]157[/C][C] 6[/C][C] 6.878[/C][C]-0.8783[/C][/ROW]
[ROW][C]158[/C][C] 5[/C][C] 4.751[/C][C] 0.2488[/C][/ROW]
[ROW][C]159[/C][C] 7[/C][C] 7.158[/C][C]-0.1585[/C][/ROW]
[ROW][C]160[/C][C] 8[/C][C] 6.768[/C][C] 1.232[/C][/ROW]
[ROW][C]161[/C][C] 4[/C][C] 6.43[/C][C]-2.43[/C][/ROW]
[ROW][C]162[/C][C] 9[/C][C] 7.046[/C][C] 1.954[/C][/ROW]
[ROW][C]163[/C][C] 9[/C][C] 9.286[/C][C]-0.2856[/C][/ROW]
[ROW][C]164[/C][C] 9[/C][C] 7.604[/C][C] 1.396[/C][/ROW]
[ROW][C]165[/C][C] 7[/C][C] 6.428[/C][C] 0.5716[/C][/ROW]
[ROW][C]166[/C][C] 5[/C][C] 6.43[/C][C]-1.43[/C][/ROW]
[ROW][C]167[/C][C] 7[/C][C] 8.95[/C][C]-1.95[/C][/ROW]
[ROW][C]168[/C][C] 9[/C][C] 10.12[/C][C]-1.124[/C][/ROW]
[ROW][C]169[/C][C] 8[/C][C] 7.437[/C][C] 0.5633[/C][/ROW]
[ROW][C]170[/C][C] 6[/C][C] 6.488[/C][C]-0.4876[/C][/ROW]
[ROW][C]171[/C][C] 9[/C][C] 7.101[/C][C] 1.899[/C][/ROW]
[ROW][C]172[/C][C] 8[/C][C] 7.494[/C][C] 0.5061[/C][/ROW]
[ROW][C]173[/C][C] 7[/C][C] 8.22[/C][C]-1.22[/C][/ROW]
[ROW][C]174[/C][C] 7[/C][C] 6.43[/C][C] 0.5696[/C][/ROW]
[ROW][C]175[/C][C] 7[/C][C] 7.267[/C][C]-0.267[/C][/ROW]
[ROW][C]176[/C][C] 8[/C][C] 7.942[/C][C] 0.05817[/C][/ROW]
[ROW][C]177[/C][C] 10[/C][C] 8.725[/C][C] 1.275[/C][/ROW]
[ROW][C]178[/C][C] 6[/C][C] 8.277[/C][C]-2.277[/C][/ROW]
[ROW][C]179[/C][C] 6[/C][C] 7.271[/C][C]-1.271[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=319007&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=319007&T=5

As an alternative you can also use a 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 10 7.549 2.451
2 8 6.598 1.402
3 8 7.606 0.3936
4 9 9.173-0.1731
5 5 6.093-1.093
6 10 8.893 1.107
7 8 8.11-0.1096
8 9 7.887 1.113
9 8 5.422 2.578
10 7 8.613-1.613
11 10 7.717 2.283
12 10 7.437 2.563
13 9 7.942 1.058
14 4 6.038-2.038
15 4 7.549-3.549
16 8 7.829 0.1706
17 9 9.789-0.7887
18 10 7.549 2.451
19 8 8.277-0.2773
20 5 6.375-1.375
21 10 8.613 1.387
22 8 7.997 0.002912
23 7 7.158-0.1585
24 8 8.11-0.1096
25 8 10.12-2.124
26 9 7.103 1.897
27 8 7.326 0.6738
28 6 5.927 0.07282
29 8 6.823 1.177
30 8 6.488 1.512
31 5 7.772-2.772
32 9 8.615 0.3853
33 8 7.774 0.2259
34 8 6.095 1.905
35 8 7.439 0.5614
36 6 7.606-1.606
37 6 5.759 0.2405
38 9 7.101 1.899
39 8 8.165-0.1648
40 9 8.78 0.2195
41 10 7.774 2.226
42 8 7.774 0.2259
43 8 6.432 1.568
44 7 6.932 0.06841
45 7 7.494-0.4939
46 10 8.613 1.387
47 8 5.592 2.408
48 7 6.823 0.177
49 10 6.934 3.066
50 7 7.214-0.2137
51 7 6.375 0.6249
52 9 8.108 0.8924
53 9 9.284-0.2836
54 8 6.991 1.009
55 6 6.766-0.7658
56 8 6.934 1.066
57 9 7.103 1.897
58 2 4.248-2.248
59 6 6.375-0.3751
60 8 7.942 0.05817
61 8 8.5-0.5003
62 7 7.16-0.1604
63 8 5.982 2.018
64 6 5.759 0.2405
65 10 6.375 3.625
66 10 7.046 2.954
67 10 6.878 3.122
68 8 7.326 0.6738
69 8 7.549 0.4508
70 7 7.774-0.7741
71 10 10.18-0.1795
72 5 6.543-1.543
73 3 4.641-1.641
74 2 4.976-2.976
75 3 4.864-1.864
76 4 6.152-2.152
77 2 4.919-2.919
78 6 5.759 0.2405
79 8 7.942 0.05817
80 8 8.11-0.1096
81 5 7.439-2.439
82 10 7.101 2.899
83 9 8.782 0.2176
84 8 8.95-0.9501
85 9 8.557 0.4425
86 8 6.766 1.234
87 5 6.265-1.265
88 7 7.101-0.1013
89 9 9.118-0.1178
90 8 7.829 0.1706
91 4 7.662-3.662
92 7 5.254 1.746
93 8 8.502-0.5022
94 7 6.991 0.009265
95 7 5.982 1.018
96 9 7.717 1.283
97 6 7.549-1.549
98 7 7.101-0.1013
99 4 6.318-2.318
100 6 6.934-0.9335
101 10 7.717 2.283
102 9 7.829 1.171
103 10 9.173 0.8269
104 8 8.277-0.2773
105 4 6.43-2.43
106 8 9.061-1.061
107 5 7.324-2.324
108 8 7.942 0.05817
109 9 9.005-0.005379
110 8 6.598 1.402
111 4 8.388-4.388
112 8 6.768 1.232
113 10 8.78 1.22
114 6 5.424 0.576
115 7 5.927 1.073
116 10 8.277 1.723
117 9 9.118-0.1178
118 8 8.78-0.7805
119 3 6.152-3.152
120 8 5.982 2.018
121 7 7.158-0.1585
122 7 6.991 0.009265
123 8 6.207 1.793
124 8 7.774 0.2259
125 7 7.772-0.7722
126 7 7.326-0.3262
127 9 10.12-1.124
128 9 8.838 0.1623
129 9 5.59 3.41
130 4 6.43-2.43
131 6 6.934-0.9335
132 6 6.318-0.3179
133 6 5.199 0.8009
134 8 7.214 0.7863
135 3 5.087-2.087
136 8 6.598 1.402
137 8 7.772 0.2278
138 6 5.367 0.6332
139 10 8.333 1.667
140 2 6.598-4.598
141 9 8.11 0.8904
142 6 6.43-0.4304
143 6 8.052-2.052
144 5 5.649-0.6489
145 4 4.864-0.8636
146 7 7.439-0.4386
147 5 6.318-1.318
148 8 8.39-0.3897
149 6 8.333-2.333
150 9 6.991 2.009
151 6 7.439-1.439
152 4 6.488-2.488
153 7 7.774-0.7741
154 2 4.246-2.246
155 8 7.885 0.1154
156 9 8.222 0.778
157 6 6.878-0.8783
158 5 4.751 0.2488
159 7 7.158-0.1585
160 8 6.768 1.232
161 4 6.43-2.43
162 9 7.046 1.954
163 9 9.286-0.2856
164 9 7.604 1.396
165 7 6.428 0.5716
166 5 6.43-1.43
167 7 8.95-1.95
168 9 10.12-1.124
169 8 7.437 0.5633
170 6 6.488-0.4876
171 9 7.101 1.899
172 8 7.494 0.5061
173 7 8.22-1.22
174 7 6.43 0.5696
175 7 7.267-0.267
176 8 7.942 0.05817
177 10 8.725 1.275
178 6 8.277-2.277
179 6 7.271-1.271







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
6 0.5975 0.8051 0.4025
7 0.4764 0.9528 0.5236
8 0.3327 0.6653 0.6673
9 0.3542 0.7085 0.6458
10 0.3628 0.7257 0.6372
11 0.4503 0.9007 0.5497
12 0.5054 0.9891 0.4946
13 0.4121 0.8242 0.5879
14 0.6787 0.6425 0.3213
15 0.9195 0.161 0.08049
16 0.8861 0.2279 0.1139
17 0.8524 0.2951 0.1476
18 0.8778 0.2444 0.1222
19 0.8416 0.3168 0.1584
20 0.8571 0.2858 0.1429
21 0.8335 0.3331 0.1665
22 0.7898 0.4203 0.2102
23 0.7404 0.5192 0.2596
24 0.6865 0.627 0.3135
25 0.723 0.5539 0.277
26 0.7184 0.5632 0.2816
27 0.6659 0.6682 0.3341
28 0.6194 0.7613 0.3806
29 0.5723 0.8553 0.4277
30 0.5313 0.9374 0.4687
31 0.6324 0.7353 0.3676
32 0.578 0.8439 0.4219
33 0.5221 0.9557 0.4779
34 0.4992 0.9985 0.5008
35 0.4454 0.8908 0.5546
36 0.4846 0.9692 0.5154
37 0.4437 0.8874 0.5563
38 0.4574 0.9148 0.5426
39 0.4047 0.8093 0.5953
40 0.3577 0.7153 0.6423
41 0.3875 0.7749 0.6125
42 0.3392 0.6783 0.6608
43 0.3074 0.6149 0.6926
44 0.2642 0.5285 0.7358
45 0.2351 0.4702 0.7649
46 0.2324 0.4647 0.7676
47 0.2381 0.4762 0.7619
48 0.2073 0.4146 0.7927
49 0.2977 0.5954 0.7023
50 0.2631 0.5263 0.7369
51 0.2294 0.4587 0.7706
52 0.2088 0.4175 0.7912
53 0.1759 0.3518 0.8241
54 0.1518 0.3037 0.8482
55 0.1431 0.2861 0.8569
56 0.1243 0.2485 0.8757
57 0.123 0.246 0.877
58 0.2264 0.4528 0.7736
59 0.2045 0.409 0.7955
60 0.1742 0.3484 0.8258
61 0.1491 0.2982 0.8509
62 0.1317 0.2633 0.8683
63 0.1385 0.277 0.8615
64 0.1177 0.2354 0.8823
65 0.2238 0.4477 0.7762
66 0.3068 0.6135 0.6932
67 0.4165 0.8329 0.5835
68 0.3805 0.761 0.6195
69 0.3417 0.6835 0.6583
70 0.3207 0.6413 0.6793
71 0.2823 0.5646 0.7177
72 0.3072 0.6145 0.6928
73 0.3529 0.7057 0.6471
74 0.5035 0.9929 0.4965
75 0.5348 0.9305 0.4652
76 0.5828 0.8345 0.4172
77 0.6873 0.6255 0.3127
78 0.649 0.7019 0.351
79 0.6097 0.7806 0.3903
80 0.5698 0.8604 0.4302
81 0.6348 0.7305 0.3652
82 0.7257 0.5485 0.2743
83 0.6908 0.6183 0.3092
84 0.6681 0.6638 0.3319
85 0.6327 0.7346 0.3673
86 0.6171 0.7659 0.3829
87 0.6011 0.7977 0.3989
88 0.5609 0.8782 0.4391
89 0.5195 0.961 0.4805
90 0.4785 0.957 0.5215
91 0.6668 0.6663 0.3332
92 0.6758 0.6484 0.3242
93 0.6398 0.7204 0.3602
94 0.5991 0.8018 0.4009
95 0.5761 0.8478 0.4239
96 0.5648 0.8704 0.4352
97 0.5646 0.8708 0.4354
98 0.5234 0.9531 0.4766
99 0.5736 0.8527 0.4264
100 0.5457 0.9086 0.4543
101 0.5997 0.8007 0.4003
102 0.5854 0.8293 0.4146
103 0.5592 0.8817 0.4408
104 0.5178 0.9644 0.4822
105 0.5752 0.8495 0.4248
106 0.5491 0.9018 0.4509
107 0.595 0.81 0.405
108 0.5528 0.8944 0.4472
109 0.5102 0.9797 0.4898
110 0.5054 0.9893 0.4946
111 0.7716 0.4568 0.2284
112 0.766 0.468 0.234
113 0.7563 0.4874 0.2437
114 0.7287 0.5425 0.2713
115 0.7155 0.569 0.2845
116 0.7336 0.5327 0.2664
117 0.6969 0.6062 0.3031
118 0.6633 0.6734 0.3367
119 0.7642 0.4716 0.2358
120 0.7959 0.4082 0.2041
121 0.7618 0.4764 0.2382
122 0.7253 0.5493 0.2747
123 0.7541 0.4917 0.2459
124 0.72 0.5599 0.28
125 0.6872 0.6256 0.3128
126 0.6447 0.7105 0.3553
127 0.6169 0.7663 0.3831
128 0.574 0.8521 0.426
129 0.7701 0.4598 0.2299
130 0.8056 0.3887 0.1944
131 0.7782 0.4436 0.2218
132 0.7401 0.5198 0.2599
133 0.7255 0.5489 0.2745
134 0.7031 0.5938 0.2969
135 0.7148 0.5704 0.2852
136 0.726 0.5479 0.274
137 0.6846 0.6308 0.3154
138 0.6631 0.6739 0.3369
139 0.6895 0.6209 0.3105
140 0.9256 0.1488 0.07438
141 0.9192 0.1615 0.08076
142 0.8964 0.2071 0.1036
143 0.9092 0.1816 0.09081
144 0.8844 0.2313 0.1156
145 0.857 0.2859 0.143
146 0.8218 0.3564 0.1782
147 0.8018 0.3964 0.1982
148 0.7579 0.4842 0.2421
149 0.8083 0.3834 0.1917
150 0.8703 0.2594 0.1297
151 0.852 0.2961 0.148
152 0.8852 0.2296 0.1148
153 0.8554 0.2892 0.1446
154 0.9138 0.1724 0.08618
155 0.8833 0.2334 0.1167
156 0.8703 0.2594 0.1297
157 0.8419 0.3162 0.1581
158 0.7948 0.4104 0.2052
159 0.7372 0.5257 0.2628
160 0.7506 0.4988 0.2494
161 0.8569 0.2862 0.1431
162 0.8986 0.2028 0.1014
163 0.8659 0.2683 0.1341
164 0.8623 0.2753 0.1377
165 0.8039 0.3923 0.1961
166 0.819 0.3619 0.181
167 0.7849 0.4301 0.2151
168 0.6993 0.6015 0.3007
169 0.6067 0.7865 0.3933
170 0.5131 0.9738 0.4869
171 0.5806 0.8388 0.4194
172 0.4746 0.9492 0.5254
173 0.3537 0.7074 0.6463

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
6 &  0.5975 &  0.8051 &  0.4025 \tabularnewline
7 &  0.4764 &  0.9528 &  0.5236 \tabularnewline
8 &  0.3327 &  0.6653 &  0.6673 \tabularnewline
9 &  0.3542 &  0.7085 &  0.6458 \tabularnewline
10 &  0.3628 &  0.7257 &  0.6372 \tabularnewline
11 &  0.4503 &  0.9007 &  0.5497 \tabularnewline
12 &  0.5054 &  0.9891 &  0.4946 \tabularnewline
13 &  0.4121 &  0.8242 &  0.5879 \tabularnewline
14 &  0.6787 &  0.6425 &  0.3213 \tabularnewline
15 &  0.9195 &  0.161 &  0.08049 \tabularnewline
16 &  0.8861 &  0.2279 &  0.1139 \tabularnewline
17 &  0.8524 &  0.2951 &  0.1476 \tabularnewline
18 &  0.8778 &  0.2444 &  0.1222 \tabularnewline
19 &  0.8416 &  0.3168 &  0.1584 \tabularnewline
20 &  0.8571 &  0.2858 &  0.1429 \tabularnewline
21 &  0.8335 &  0.3331 &  0.1665 \tabularnewline
22 &  0.7898 &  0.4203 &  0.2102 \tabularnewline
23 &  0.7404 &  0.5192 &  0.2596 \tabularnewline
24 &  0.6865 &  0.627 &  0.3135 \tabularnewline
25 &  0.723 &  0.5539 &  0.277 \tabularnewline
26 &  0.7184 &  0.5632 &  0.2816 \tabularnewline
27 &  0.6659 &  0.6682 &  0.3341 \tabularnewline
28 &  0.6194 &  0.7613 &  0.3806 \tabularnewline
29 &  0.5723 &  0.8553 &  0.4277 \tabularnewline
30 &  0.5313 &  0.9374 &  0.4687 \tabularnewline
31 &  0.6324 &  0.7353 &  0.3676 \tabularnewline
32 &  0.578 &  0.8439 &  0.4219 \tabularnewline
33 &  0.5221 &  0.9557 &  0.4779 \tabularnewline
34 &  0.4992 &  0.9985 &  0.5008 \tabularnewline
35 &  0.4454 &  0.8908 &  0.5546 \tabularnewline
36 &  0.4846 &  0.9692 &  0.5154 \tabularnewline
37 &  0.4437 &  0.8874 &  0.5563 \tabularnewline
38 &  0.4574 &  0.9148 &  0.5426 \tabularnewline
39 &  0.4047 &  0.8093 &  0.5953 \tabularnewline
40 &  0.3577 &  0.7153 &  0.6423 \tabularnewline
41 &  0.3875 &  0.7749 &  0.6125 \tabularnewline
42 &  0.3392 &  0.6783 &  0.6608 \tabularnewline
43 &  0.3074 &  0.6149 &  0.6926 \tabularnewline
44 &  0.2642 &  0.5285 &  0.7358 \tabularnewline
45 &  0.2351 &  0.4702 &  0.7649 \tabularnewline
46 &  0.2324 &  0.4647 &  0.7676 \tabularnewline
47 &  0.2381 &  0.4762 &  0.7619 \tabularnewline
48 &  0.2073 &  0.4146 &  0.7927 \tabularnewline
49 &  0.2977 &  0.5954 &  0.7023 \tabularnewline
50 &  0.2631 &  0.5263 &  0.7369 \tabularnewline
51 &  0.2294 &  0.4587 &  0.7706 \tabularnewline
52 &  0.2088 &  0.4175 &  0.7912 \tabularnewline
53 &  0.1759 &  0.3518 &  0.8241 \tabularnewline
54 &  0.1518 &  0.3037 &  0.8482 \tabularnewline
55 &  0.1431 &  0.2861 &  0.8569 \tabularnewline
56 &  0.1243 &  0.2485 &  0.8757 \tabularnewline
57 &  0.123 &  0.246 &  0.877 \tabularnewline
58 &  0.2264 &  0.4528 &  0.7736 \tabularnewline
59 &  0.2045 &  0.409 &  0.7955 \tabularnewline
60 &  0.1742 &  0.3484 &  0.8258 \tabularnewline
61 &  0.1491 &  0.2982 &  0.8509 \tabularnewline
62 &  0.1317 &  0.2633 &  0.8683 \tabularnewline
63 &  0.1385 &  0.277 &  0.8615 \tabularnewline
64 &  0.1177 &  0.2354 &  0.8823 \tabularnewline
65 &  0.2238 &  0.4477 &  0.7762 \tabularnewline
66 &  0.3068 &  0.6135 &  0.6932 \tabularnewline
67 &  0.4165 &  0.8329 &  0.5835 \tabularnewline
68 &  0.3805 &  0.761 &  0.6195 \tabularnewline
69 &  0.3417 &  0.6835 &  0.6583 \tabularnewline
70 &  0.3207 &  0.6413 &  0.6793 \tabularnewline
71 &  0.2823 &  0.5646 &  0.7177 \tabularnewline
72 &  0.3072 &  0.6145 &  0.6928 \tabularnewline
73 &  0.3529 &  0.7057 &  0.6471 \tabularnewline
74 &  0.5035 &  0.9929 &  0.4965 \tabularnewline
75 &  0.5348 &  0.9305 &  0.4652 \tabularnewline
76 &  0.5828 &  0.8345 &  0.4172 \tabularnewline
77 &  0.6873 &  0.6255 &  0.3127 \tabularnewline
78 &  0.649 &  0.7019 &  0.351 \tabularnewline
79 &  0.6097 &  0.7806 &  0.3903 \tabularnewline
80 &  0.5698 &  0.8604 &  0.4302 \tabularnewline
81 &  0.6348 &  0.7305 &  0.3652 \tabularnewline
82 &  0.7257 &  0.5485 &  0.2743 \tabularnewline
83 &  0.6908 &  0.6183 &  0.3092 \tabularnewline
84 &  0.6681 &  0.6638 &  0.3319 \tabularnewline
85 &  0.6327 &  0.7346 &  0.3673 \tabularnewline
86 &  0.6171 &  0.7659 &  0.3829 \tabularnewline
87 &  0.6011 &  0.7977 &  0.3989 \tabularnewline
88 &  0.5609 &  0.8782 &  0.4391 \tabularnewline
89 &  0.5195 &  0.961 &  0.4805 \tabularnewline
90 &  0.4785 &  0.957 &  0.5215 \tabularnewline
91 &  0.6668 &  0.6663 &  0.3332 \tabularnewline
92 &  0.6758 &  0.6484 &  0.3242 \tabularnewline
93 &  0.6398 &  0.7204 &  0.3602 \tabularnewline
94 &  0.5991 &  0.8018 &  0.4009 \tabularnewline
95 &  0.5761 &  0.8478 &  0.4239 \tabularnewline
96 &  0.5648 &  0.8704 &  0.4352 \tabularnewline
97 &  0.5646 &  0.8708 &  0.4354 \tabularnewline
98 &  0.5234 &  0.9531 &  0.4766 \tabularnewline
99 &  0.5736 &  0.8527 &  0.4264 \tabularnewline
100 &  0.5457 &  0.9086 &  0.4543 \tabularnewline
101 &  0.5997 &  0.8007 &  0.4003 \tabularnewline
102 &  0.5854 &  0.8293 &  0.4146 \tabularnewline
103 &  0.5592 &  0.8817 &  0.4408 \tabularnewline
104 &  0.5178 &  0.9644 &  0.4822 \tabularnewline
105 &  0.5752 &  0.8495 &  0.4248 \tabularnewline
106 &  0.5491 &  0.9018 &  0.4509 \tabularnewline
107 &  0.595 &  0.81 &  0.405 \tabularnewline
108 &  0.5528 &  0.8944 &  0.4472 \tabularnewline
109 &  0.5102 &  0.9797 &  0.4898 \tabularnewline
110 &  0.5054 &  0.9893 &  0.4946 \tabularnewline
111 &  0.7716 &  0.4568 &  0.2284 \tabularnewline
112 &  0.766 &  0.468 &  0.234 \tabularnewline
113 &  0.7563 &  0.4874 &  0.2437 \tabularnewline
114 &  0.7287 &  0.5425 &  0.2713 \tabularnewline
115 &  0.7155 &  0.569 &  0.2845 \tabularnewline
116 &  0.7336 &  0.5327 &  0.2664 \tabularnewline
117 &  0.6969 &  0.6062 &  0.3031 \tabularnewline
118 &  0.6633 &  0.6734 &  0.3367 \tabularnewline
119 &  0.7642 &  0.4716 &  0.2358 \tabularnewline
120 &  0.7959 &  0.4082 &  0.2041 \tabularnewline
121 &  0.7618 &  0.4764 &  0.2382 \tabularnewline
122 &  0.7253 &  0.5493 &  0.2747 \tabularnewline
123 &  0.7541 &  0.4917 &  0.2459 \tabularnewline
124 &  0.72 &  0.5599 &  0.28 \tabularnewline
125 &  0.6872 &  0.6256 &  0.3128 \tabularnewline
126 &  0.6447 &  0.7105 &  0.3553 \tabularnewline
127 &  0.6169 &  0.7663 &  0.3831 \tabularnewline
128 &  0.574 &  0.8521 &  0.426 \tabularnewline
129 &  0.7701 &  0.4598 &  0.2299 \tabularnewline
130 &  0.8056 &  0.3887 &  0.1944 \tabularnewline
131 &  0.7782 &  0.4436 &  0.2218 \tabularnewline
132 &  0.7401 &  0.5198 &  0.2599 \tabularnewline
133 &  0.7255 &  0.5489 &  0.2745 \tabularnewline
134 &  0.7031 &  0.5938 &  0.2969 \tabularnewline
135 &  0.7148 &  0.5704 &  0.2852 \tabularnewline
136 &  0.726 &  0.5479 &  0.274 \tabularnewline
137 &  0.6846 &  0.6308 &  0.3154 \tabularnewline
138 &  0.6631 &  0.6739 &  0.3369 \tabularnewline
139 &  0.6895 &  0.6209 &  0.3105 \tabularnewline
140 &  0.9256 &  0.1488 &  0.07438 \tabularnewline
141 &  0.9192 &  0.1615 &  0.08076 \tabularnewline
142 &  0.8964 &  0.2071 &  0.1036 \tabularnewline
143 &  0.9092 &  0.1816 &  0.09081 \tabularnewline
144 &  0.8844 &  0.2313 &  0.1156 \tabularnewline
145 &  0.857 &  0.2859 &  0.143 \tabularnewline
146 &  0.8218 &  0.3564 &  0.1782 \tabularnewline
147 &  0.8018 &  0.3964 &  0.1982 \tabularnewline
148 &  0.7579 &  0.4842 &  0.2421 \tabularnewline
149 &  0.8083 &  0.3834 &  0.1917 \tabularnewline
150 &  0.8703 &  0.2594 &  0.1297 \tabularnewline
151 &  0.852 &  0.2961 &  0.148 \tabularnewline
152 &  0.8852 &  0.2296 &  0.1148 \tabularnewline
153 &  0.8554 &  0.2892 &  0.1446 \tabularnewline
154 &  0.9138 &  0.1724 &  0.08618 \tabularnewline
155 &  0.8833 &  0.2334 &  0.1167 \tabularnewline
156 &  0.8703 &  0.2594 &  0.1297 \tabularnewline
157 &  0.8419 &  0.3162 &  0.1581 \tabularnewline
158 &  0.7948 &  0.4104 &  0.2052 \tabularnewline
159 &  0.7372 &  0.5257 &  0.2628 \tabularnewline
160 &  0.7506 &  0.4988 &  0.2494 \tabularnewline
161 &  0.8569 &  0.2862 &  0.1431 \tabularnewline
162 &  0.8986 &  0.2028 &  0.1014 \tabularnewline
163 &  0.8659 &  0.2683 &  0.1341 \tabularnewline
164 &  0.8623 &  0.2753 &  0.1377 \tabularnewline
165 &  0.8039 &  0.3923 &  0.1961 \tabularnewline
166 &  0.819 &  0.3619 &  0.181 \tabularnewline
167 &  0.7849 &  0.4301 &  0.2151 \tabularnewline
168 &  0.6993 &  0.6015 &  0.3007 \tabularnewline
169 &  0.6067 &  0.7865 &  0.3933 \tabularnewline
170 &  0.5131 &  0.9738 &  0.4869 \tabularnewline
171 &  0.5806 &  0.8388 &  0.4194 \tabularnewline
172 &  0.4746 &  0.9492 &  0.5254 \tabularnewline
173 &  0.3537 &  0.7074 &  0.6463 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=319007&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] 0.5975[/C][C] 0.8051[/C][C] 0.4025[/C][/ROW]
[ROW][C]7[/C][C] 0.4764[/C][C] 0.9528[/C][C] 0.5236[/C][/ROW]
[ROW][C]8[/C][C] 0.3327[/C][C] 0.6653[/C][C] 0.6673[/C][/ROW]
[ROW][C]9[/C][C] 0.3542[/C][C] 0.7085[/C][C] 0.6458[/C][/ROW]
[ROW][C]10[/C][C] 0.3628[/C][C] 0.7257[/C][C] 0.6372[/C][/ROW]
[ROW][C]11[/C][C] 0.4503[/C][C] 0.9007[/C][C] 0.5497[/C][/ROW]
[ROW][C]12[/C][C] 0.5054[/C][C] 0.9891[/C][C] 0.4946[/C][/ROW]
[ROW][C]13[/C][C] 0.4121[/C][C] 0.8242[/C][C] 0.5879[/C][/ROW]
[ROW][C]14[/C][C] 0.6787[/C][C] 0.6425[/C][C] 0.3213[/C][/ROW]
[ROW][C]15[/C][C] 0.9195[/C][C] 0.161[/C][C] 0.08049[/C][/ROW]
[ROW][C]16[/C][C] 0.8861[/C][C] 0.2279[/C][C] 0.1139[/C][/ROW]
[ROW][C]17[/C][C] 0.8524[/C][C] 0.2951[/C][C] 0.1476[/C][/ROW]
[ROW][C]18[/C][C] 0.8778[/C][C] 0.2444[/C][C] 0.1222[/C][/ROW]
[ROW][C]19[/C][C] 0.8416[/C][C] 0.3168[/C][C] 0.1584[/C][/ROW]
[ROW][C]20[/C][C] 0.8571[/C][C] 0.2858[/C][C] 0.1429[/C][/ROW]
[ROW][C]21[/C][C] 0.8335[/C][C] 0.3331[/C][C] 0.1665[/C][/ROW]
[ROW][C]22[/C][C] 0.7898[/C][C] 0.4203[/C][C] 0.2102[/C][/ROW]
[ROW][C]23[/C][C] 0.7404[/C][C] 0.5192[/C][C] 0.2596[/C][/ROW]
[ROW][C]24[/C][C] 0.6865[/C][C] 0.627[/C][C] 0.3135[/C][/ROW]
[ROW][C]25[/C][C] 0.723[/C][C] 0.5539[/C][C] 0.277[/C][/ROW]
[ROW][C]26[/C][C] 0.7184[/C][C] 0.5632[/C][C] 0.2816[/C][/ROW]
[ROW][C]27[/C][C] 0.6659[/C][C] 0.6682[/C][C] 0.3341[/C][/ROW]
[ROW][C]28[/C][C] 0.6194[/C][C] 0.7613[/C][C] 0.3806[/C][/ROW]
[ROW][C]29[/C][C] 0.5723[/C][C] 0.8553[/C][C] 0.4277[/C][/ROW]
[ROW][C]30[/C][C] 0.5313[/C][C] 0.9374[/C][C] 0.4687[/C][/ROW]
[ROW][C]31[/C][C] 0.6324[/C][C] 0.7353[/C][C] 0.3676[/C][/ROW]
[ROW][C]32[/C][C] 0.578[/C][C] 0.8439[/C][C] 0.4219[/C][/ROW]
[ROW][C]33[/C][C] 0.5221[/C][C] 0.9557[/C][C] 0.4779[/C][/ROW]
[ROW][C]34[/C][C] 0.4992[/C][C] 0.9985[/C][C] 0.5008[/C][/ROW]
[ROW][C]35[/C][C] 0.4454[/C][C] 0.8908[/C][C] 0.5546[/C][/ROW]
[ROW][C]36[/C][C] 0.4846[/C][C] 0.9692[/C][C] 0.5154[/C][/ROW]
[ROW][C]37[/C][C] 0.4437[/C][C] 0.8874[/C][C] 0.5563[/C][/ROW]
[ROW][C]38[/C][C] 0.4574[/C][C] 0.9148[/C][C] 0.5426[/C][/ROW]
[ROW][C]39[/C][C] 0.4047[/C][C] 0.8093[/C][C] 0.5953[/C][/ROW]
[ROW][C]40[/C][C] 0.3577[/C][C] 0.7153[/C][C] 0.6423[/C][/ROW]
[ROW][C]41[/C][C] 0.3875[/C][C] 0.7749[/C][C] 0.6125[/C][/ROW]
[ROW][C]42[/C][C] 0.3392[/C][C] 0.6783[/C][C] 0.6608[/C][/ROW]
[ROW][C]43[/C][C] 0.3074[/C][C] 0.6149[/C][C] 0.6926[/C][/ROW]
[ROW][C]44[/C][C] 0.2642[/C][C] 0.5285[/C][C] 0.7358[/C][/ROW]
[ROW][C]45[/C][C] 0.2351[/C][C] 0.4702[/C][C] 0.7649[/C][/ROW]
[ROW][C]46[/C][C] 0.2324[/C][C] 0.4647[/C][C] 0.7676[/C][/ROW]
[ROW][C]47[/C][C] 0.2381[/C][C] 0.4762[/C][C] 0.7619[/C][/ROW]
[ROW][C]48[/C][C] 0.2073[/C][C] 0.4146[/C][C] 0.7927[/C][/ROW]
[ROW][C]49[/C][C] 0.2977[/C][C] 0.5954[/C][C] 0.7023[/C][/ROW]
[ROW][C]50[/C][C] 0.2631[/C][C] 0.5263[/C][C] 0.7369[/C][/ROW]
[ROW][C]51[/C][C] 0.2294[/C][C] 0.4587[/C][C] 0.7706[/C][/ROW]
[ROW][C]52[/C][C] 0.2088[/C][C] 0.4175[/C][C] 0.7912[/C][/ROW]
[ROW][C]53[/C][C] 0.1759[/C][C] 0.3518[/C][C] 0.8241[/C][/ROW]
[ROW][C]54[/C][C] 0.1518[/C][C] 0.3037[/C][C] 0.8482[/C][/ROW]
[ROW][C]55[/C][C] 0.1431[/C][C] 0.2861[/C][C] 0.8569[/C][/ROW]
[ROW][C]56[/C][C] 0.1243[/C][C] 0.2485[/C][C] 0.8757[/C][/ROW]
[ROW][C]57[/C][C] 0.123[/C][C] 0.246[/C][C] 0.877[/C][/ROW]
[ROW][C]58[/C][C] 0.2264[/C][C] 0.4528[/C][C] 0.7736[/C][/ROW]
[ROW][C]59[/C][C] 0.2045[/C][C] 0.409[/C][C] 0.7955[/C][/ROW]
[ROW][C]60[/C][C] 0.1742[/C][C] 0.3484[/C][C] 0.8258[/C][/ROW]
[ROW][C]61[/C][C] 0.1491[/C][C] 0.2982[/C][C] 0.8509[/C][/ROW]
[ROW][C]62[/C][C] 0.1317[/C][C] 0.2633[/C][C] 0.8683[/C][/ROW]
[ROW][C]63[/C][C] 0.1385[/C][C] 0.277[/C][C] 0.8615[/C][/ROW]
[ROW][C]64[/C][C] 0.1177[/C][C] 0.2354[/C][C] 0.8823[/C][/ROW]
[ROW][C]65[/C][C] 0.2238[/C][C] 0.4477[/C][C] 0.7762[/C][/ROW]
[ROW][C]66[/C][C] 0.3068[/C][C] 0.6135[/C][C] 0.6932[/C][/ROW]
[ROW][C]67[/C][C] 0.4165[/C][C] 0.8329[/C][C] 0.5835[/C][/ROW]
[ROW][C]68[/C][C] 0.3805[/C][C] 0.761[/C][C] 0.6195[/C][/ROW]
[ROW][C]69[/C][C] 0.3417[/C][C] 0.6835[/C][C] 0.6583[/C][/ROW]
[ROW][C]70[/C][C] 0.3207[/C][C] 0.6413[/C][C] 0.6793[/C][/ROW]
[ROW][C]71[/C][C] 0.2823[/C][C] 0.5646[/C][C] 0.7177[/C][/ROW]
[ROW][C]72[/C][C] 0.3072[/C][C] 0.6145[/C][C] 0.6928[/C][/ROW]
[ROW][C]73[/C][C] 0.3529[/C][C] 0.7057[/C][C] 0.6471[/C][/ROW]
[ROW][C]74[/C][C] 0.5035[/C][C] 0.9929[/C][C] 0.4965[/C][/ROW]
[ROW][C]75[/C][C] 0.5348[/C][C] 0.9305[/C][C] 0.4652[/C][/ROW]
[ROW][C]76[/C][C] 0.5828[/C][C] 0.8345[/C][C] 0.4172[/C][/ROW]
[ROW][C]77[/C][C] 0.6873[/C][C] 0.6255[/C][C] 0.3127[/C][/ROW]
[ROW][C]78[/C][C] 0.649[/C][C] 0.7019[/C][C] 0.351[/C][/ROW]
[ROW][C]79[/C][C] 0.6097[/C][C] 0.7806[/C][C] 0.3903[/C][/ROW]
[ROW][C]80[/C][C] 0.5698[/C][C] 0.8604[/C][C] 0.4302[/C][/ROW]
[ROW][C]81[/C][C] 0.6348[/C][C] 0.7305[/C][C] 0.3652[/C][/ROW]
[ROW][C]82[/C][C] 0.7257[/C][C] 0.5485[/C][C] 0.2743[/C][/ROW]
[ROW][C]83[/C][C] 0.6908[/C][C] 0.6183[/C][C] 0.3092[/C][/ROW]
[ROW][C]84[/C][C] 0.6681[/C][C] 0.6638[/C][C] 0.3319[/C][/ROW]
[ROW][C]85[/C][C] 0.6327[/C][C] 0.7346[/C][C] 0.3673[/C][/ROW]
[ROW][C]86[/C][C] 0.6171[/C][C] 0.7659[/C][C] 0.3829[/C][/ROW]
[ROW][C]87[/C][C] 0.6011[/C][C] 0.7977[/C][C] 0.3989[/C][/ROW]
[ROW][C]88[/C][C] 0.5609[/C][C] 0.8782[/C][C] 0.4391[/C][/ROW]
[ROW][C]89[/C][C] 0.5195[/C][C] 0.961[/C][C] 0.4805[/C][/ROW]
[ROW][C]90[/C][C] 0.4785[/C][C] 0.957[/C][C] 0.5215[/C][/ROW]
[ROW][C]91[/C][C] 0.6668[/C][C] 0.6663[/C][C] 0.3332[/C][/ROW]
[ROW][C]92[/C][C] 0.6758[/C][C] 0.6484[/C][C] 0.3242[/C][/ROW]
[ROW][C]93[/C][C] 0.6398[/C][C] 0.7204[/C][C] 0.3602[/C][/ROW]
[ROW][C]94[/C][C] 0.5991[/C][C] 0.8018[/C][C] 0.4009[/C][/ROW]
[ROW][C]95[/C][C] 0.5761[/C][C] 0.8478[/C][C] 0.4239[/C][/ROW]
[ROW][C]96[/C][C] 0.5648[/C][C] 0.8704[/C][C] 0.4352[/C][/ROW]
[ROW][C]97[/C][C] 0.5646[/C][C] 0.8708[/C][C] 0.4354[/C][/ROW]
[ROW][C]98[/C][C] 0.5234[/C][C] 0.9531[/C][C] 0.4766[/C][/ROW]
[ROW][C]99[/C][C] 0.5736[/C][C] 0.8527[/C][C] 0.4264[/C][/ROW]
[ROW][C]100[/C][C] 0.5457[/C][C] 0.9086[/C][C] 0.4543[/C][/ROW]
[ROW][C]101[/C][C] 0.5997[/C][C] 0.8007[/C][C] 0.4003[/C][/ROW]
[ROW][C]102[/C][C] 0.5854[/C][C] 0.8293[/C][C] 0.4146[/C][/ROW]
[ROW][C]103[/C][C] 0.5592[/C][C] 0.8817[/C][C] 0.4408[/C][/ROW]
[ROW][C]104[/C][C] 0.5178[/C][C] 0.9644[/C][C] 0.4822[/C][/ROW]
[ROW][C]105[/C][C] 0.5752[/C][C] 0.8495[/C][C] 0.4248[/C][/ROW]
[ROW][C]106[/C][C] 0.5491[/C][C] 0.9018[/C][C] 0.4509[/C][/ROW]
[ROW][C]107[/C][C] 0.595[/C][C] 0.81[/C][C] 0.405[/C][/ROW]
[ROW][C]108[/C][C] 0.5528[/C][C] 0.8944[/C][C] 0.4472[/C][/ROW]
[ROW][C]109[/C][C] 0.5102[/C][C] 0.9797[/C][C] 0.4898[/C][/ROW]
[ROW][C]110[/C][C] 0.5054[/C][C] 0.9893[/C][C] 0.4946[/C][/ROW]
[ROW][C]111[/C][C] 0.7716[/C][C] 0.4568[/C][C] 0.2284[/C][/ROW]
[ROW][C]112[/C][C] 0.766[/C][C] 0.468[/C][C] 0.234[/C][/ROW]
[ROW][C]113[/C][C] 0.7563[/C][C] 0.4874[/C][C] 0.2437[/C][/ROW]
[ROW][C]114[/C][C] 0.7287[/C][C] 0.5425[/C][C] 0.2713[/C][/ROW]
[ROW][C]115[/C][C] 0.7155[/C][C] 0.569[/C][C] 0.2845[/C][/ROW]
[ROW][C]116[/C][C] 0.7336[/C][C] 0.5327[/C][C] 0.2664[/C][/ROW]
[ROW][C]117[/C][C] 0.6969[/C][C] 0.6062[/C][C] 0.3031[/C][/ROW]
[ROW][C]118[/C][C] 0.6633[/C][C] 0.6734[/C][C] 0.3367[/C][/ROW]
[ROW][C]119[/C][C] 0.7642[/C][C] 0.4716[/C][C] 0.2358[/C][/ROW]
[ROW][C]120[/C][C] 0.7959[/C][C] 0.4082[/C][C] 0.2041[/C][/ROW]
[ROW][C]121[/C][C] 0.7618[/C][C] 0.4764[/C][C] 0.2382[/C][/ROW]
[ROW][C]122[/C][C] 0.7253[/C][C] 0.5493[/C][C] 0.2747[/C][/ROW]
[ROW][C]123[/C][C] 0.7541[/C][C] 0.4917[/C][C] 0.2459[/C][/ROW]
[ROW][C]124[/C][C] 0.72[/C][C] 0.5599[/C][C] 0.28[/C][/ROW]
[ROW][C]125[/C][C] 0.6872[/C][C] 0.6256[/C][C] 0.3128[/C][/ROW]
[ROW][C]126[/C][C] 0.6447[/C][C] 0.7105[/C][C] 0.3553[/C][/ROW]
[ROW][C]127[/C][C] 0.6169[/C][C] 0.7663[/C][C] 0.3831[/C][/ROW]
[ROW][C]128[/C][C] 0.574[/C][C] 0.8521[/C][C] 0.426[/C][/ROW]
[ROW][C]129[/C][C] 0.7701[/C][C] 0.4598[/C][C] 0.2299[/C][/ROW]
[ROW][C]130[/C][C] 0.8056[/C][C] 0.3887[/C][C] 0.1944[/C][/ROW]
[ROW][C]131[/C][C] 0.7782[/C][C] 0.4436[/C][C] 0.2218[/C][/ROW]
[ROW][C]132[/C][C] 0.7401[/C][C] 0.5198[/C][C] 0.2599[/C][/ROW]
[ROW][C]133[/C][C] 0.7255[/C][C] 0.5489[/C][C] 0.2745[/C][/ROW]
[ROW][C]134[/C][C] 0.7031[/C][C] 0.5938[/C][C] 0.2969[/C][/ROW]
[ROW][C]135[/C][C] 0.7148[/C][C] 0.5704[/C][C] 0.2852[/C][/ROW]
[ROW][C]136[/C][C] 0.726[/C][C] 0.5479[/C][C] 0.274[/C][/ROW]
[ROW][C]137[/C][C] 0.6846[/C][C] 0.6308[/C][C] 0.3154[/C][/ROW]
[ROW][C]138[/C][C] 0.6631[/C][C] 0.6739[/C][C] 0.3369[/C][/ROW]
[ROW][C]139[/C][C] 0.6895[/C][C] 0.6209[/C][C] 0.3105[/C][/ROW]
[ROW][C]140[/C][C] 0.9256[/C][C] 0.1488[/C][C] 0.07438[/C][/ROW]
[ROW][C]141[/C][C] 0.9192[/C][C] 0.1615[/C][C] 0.08076[/C][/ROW]
[ROW][C]142[/C][C] 0.8964[/C][C] 0.2071[/C][C] 0.1036[/C][/ROW]
[ROW][C]143[/C][C] 0.9092[/C][C] 0.1816[/C][C] 0.09081[/C][/ROW]
[ROW][C]144[/C][C] 0.8844[/C][C] 0.2313[/C][C] 0.1156[/C][/ROW]
[ROW][C]145[/C][C] 0.857[/C][C] 0.2859[/C][C] 0.143[/C][/ROW]
[ROW][C]146[/C][C] 0.8218[/C][C] 0.3564[/C][C] 0.1782[/C][/ROW]
[ROW][C]147[/C][C] 0.8018[/C][C] 0.3964[/C][C] 0.1982[/C][/ROW]
[ROW][C]148[/C][C] 0.7579[/C][C] 0.4842[/C][C] 0.2421[/C][/ROW]
[ROW][C]149[/C][C] 0.8083[/C][C] 0.3834[/C][C] 0.1917[/C][/ROW]
[ROW][C]150[/C][C] 0.8703[/C][C] 0.2594[/C][C] 0.1297[/C][/ROW]
[ROW][C]151[/C][C] 0.852[/C][C] 0.2961[/C][C] 0.148[/C][/ROW]
[ROW][C]152[/C][C] 0.8852[/C][C] 0.2296[/C][C] 0.1148[/C][/ROW]
[ROW][C]153[/C][C] 0.8554[/C][C] 0.2892[/C][C] 0.1446[/C][/ROW]
[ROW][C]154[/C][C] 0.9138[/C][C] 0.1724[/C][C] 0.08618[/C][/ROW]
[ROW][C]155[/C][C] 0.8833[/C][C] 0.2334[/C][C] 0.1167[/C][/ROW]
[ROW][C]156[/C][C] 0.8703[/C][C] 0.2594[/C][C] 0.1297[/C][/ROW]
[ROW][C]157[/C][C] 0.8419[/C][C] 0.3162[/C][C] 0.1581[/C][/ROW]
[ROW][C]158[/C][C] 0.7948[/C][C] 0.4104[/C][C] 0.2052[/C][/ROW]
[ROW][C]159[/C][C] 0.7372[/C][C] 0.5257[/C][C] 0.2628[/C][/ROW]
[ROW][C]160[/C][C] 0.7506[/C][C] 0.4988[/C][C] 0.2494[/C][/ROW]
[ROW][C]161[/C][C] 0.8569[/C][C] 0.2862[/C][C] 0.1431[/C][/ROW]
[ROW][C]162[/C][C] 0.8986[/C][C] 0.2028[/C][C] 0.1014[/C][/ROW]
[ROW][C]163[/C][C] 0.8659[/C][C] 0.2683[/C][C] 0.1341[/C][/ROW]
[ROW][C]164[/C][C] 0.8623[/C][C] 0.2753[/C][C] 0.1377[/C][/ROW]
[ROW][C]165[/C][C] 0.8039[/C][C] 0.3923[/C][C] 0.1961[/C][/ROW]
[ROW][C]166[/C][C] 0.819[/C][C] 0.3619[/C][C] 0.181[/C][/ROW]
[ROW][C]167[/C][C] 0.7849[/C][C] 0.4301[/C][C] 0.2151[/C][/ROW]
[ROW][C]168[/C][C] 0.6993[/C][C] 0.6015[/C][C] 0.3007[/C][/ROW]
[ROW][C]169[/C][C] 0.6067[/C][C] 0.7865[/C][C] 0.3933[/C][/ROW]
[ROW][C]170[/C][C] 0.5131[/C][C] 0.9738[/C][C] 0.4869[/C][/ROW]
[ROW][C]171[/C][C] 0.5806[/C][C] 0.8388[/C][C] 0.4194[/C][/ROW]
[ROW][C]172[/C][C] 0.4746[/C][C] 0.9492[/C][C] 0.5254[/C][/ROW]
[ROW][C]173[/C][C] 0.3537[/C][C] 0.7074[/C][C] 0.6463[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=319007&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=319007&T=6

As an alternative you can also use a 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 0.5975 0.8051 0.4025
7 0.4764 0.9528 0.5236
8 0.3327 0.6653 0.6673
9 0.3542 0.7085 0.6458
10 0.3628 0.7257 0.6372
11 0.4503 0.9007 0.5497
12 0.5054 0.9891 0.4946
13 0.4121 0.8242 0.5879
14 0.6787 0.6425 0.3213
15 0.9195 0.161 0.08049
16 0.8861 0.2279 0.1139
17 0.8524 0.2951 0.1476
18 0.8778 0.2444 0.1222
19 0.8416 0.3168 0.1584
20 0.8571 0.2858 0.1429
21 0.8335 0.3331 0.1665
22 0.7898 0.4203 0.2102
23 0.7404 0.5192 0.2596
24 0.6865 0.627 0.3135
25 0.723 0.5539 0.277
26 0.7184 0.5632 0.2816
27 0.6659 0.6682 0.3341
28 0.6194 0.7613 0.3806
29 0.5723 0.8553 0.4277
30 0.5313 0.9374 0.4687
31 0.6324 0.7353 0.3676
32 0.578 0.8439 0.4219
33 0.5221 0.9557 0.4779
34 0.4992 0.9985 0.5008
35 0.4454 0.8908 0.5546
36 0.4846 0.9692 0.5154
37 0.4437 0.8874 0.5563
38 0.4574 0.9148 0.5426
39 0.4047 0.8093 0.5953
40 0.3577 0.7153 0.6423
41 0.3875 0.7749 0.6125
42 0.3392 0.6783 0.6608
43 0.3074 0.6149 0.6926
44 0.2642 0.5285 0.7358
45 0.2351 0.4702 0.7649
46 0.2324 0.4647 0.7676
47 0.2381 0.4762 0.7619
48 0.2073 0.4146 0.7927
49 0.2977 0.5954 0.7023
50 0.2631 0.5263 0.7369
51 0.2294 0.4587 0.7706
52 0.2088 0.4175 0.7912
53 0.1759 0.3518 0.8241
54 0.1518 0.3037 0.8482
55 0.1431 0.2861 0.8569
56 0.1243 0.2485 0.8757
57 0.123 0.246 0.877
58 0.2264 0.4528 0.7736
59 0.2045 0.409 0.7955
60 0.1742 0.3484 0.8258
61 0.1491 0.2982 0.8509
62 0.1317 0.2633 0.8683
63 0.1385 0.277 0.8615
64 0.1177 0.2354 0.8823
65 0.2238 0.4477 0.7762
66 0.3068 0.6135 0.6932
67 0.4165 0.8329 0.5835
68 0.3805 0.761 0.6195
69 0.3417 0.6835 0.6583
70 0.3207 0.6413 0.6793
71 0.2823 0.5646 0.7177
72 0.3072 0.6145 0.6928
73 0.3529 0.7057 0.6471
74 0.5035 0.9929 0.4965
75 0.5348 0.9305 0.4652
76 0.5828 0.8345 0.4172
77 0.6873 0.6255 0.3127
78 0.649 0.7019 0.351
79 0.6097 0.7806 0.3903
80 0.5698 0.8604 0.4302
81 0.6348 0.7305 0.3652
82 0.7257 0.5485 0.2743
83 0.6908 0.6183 0.3092
84 0.6681 0.6638 0.3319
85 0.6327 0.7346 0.3673
86 0.6171 0.7659 0.3829
87 0.6011 0.7977 0.3989
88 0.5609 0.8782 0.4391
89 0.5195 0.961 0.4805
90 0.4785 0.957 0.5215
91 0.6668 0.6663 0.3332
92 0.6758 0.6484 0.3242
93 0.6398 0.7204 0.3602
94 0.5991 0.8018 0.4009
95 0.5761 0.8478 0.4239
96 0.5648 0.8704 0.4352
97 0.5646 0.8708 0.4354
98 0.5234 0.9531 0.4766
99 0.5736 0.8527 0.4264
100 0.5457 0.9086 0.4543
101 0.5997 0.8007 0.4003
102 0.5854 0.8293 0.4146
103 0.5592 0.8817 0.4408
104 0.5178 0.9644 0.4822
105 0.5752 0.8495 0.4248
106 0.5491 0.9018 0.4509
107 0.595 0.81 0.405
108 0.5528 0.8944 0.4472
109 0.5102 0.9797 0.4898
110 0.5054 0.9893 0.4946
111 0.7716 0.4568 0.2284
112 0.766 0.468 0.234
113 0.7563 0.4874 0.2437
114 0.7287 0.5425 0.2713
115 0.7155 0.569 0.2845
116 0.7336 0.5327 0.2664
117 0.6969 0.6062 0.3031
118 0.6633 0.6734 0.3367
119 0.7642 0.4716 0.2358
120 0.7959 0.4082 0.2041
121 0.7618 0.4764 0.2382
122 0.7253 0.5493 0.2747
123 0.7541 0.4917 0.2459
124 0.72 0.5599 0.28
125 0.6872 0.6256 0.3128
126 0.6447 0.7105 0.3553
127 0.6169 0.7663 0.3831
128 0.574 0.8521 0.426
129 0.7701 0.4598 0.2299
130 0.8056 0.3887 0.1944
131 0.7782 0.4436 0.2218
132 0.7401 0.5198 0.2599
133 0.7255 0.5489 0.2745
134 0.7031 0.5938 0.2969
135 0.7148 0.5704 0.2852
136 0.726 0.5479 0.274
137 0.6846 0.6308 0.3154
138 0.6631 0.6739 0.3369
139 0.6895 0.6209 0.3105
140 0.9256 0.1488 0.07438
141 0.9192 0.1615 0.08076
142 0.8964 0.2071 0.1036
143 0.9092 0.1816 0.09081
144 0.8844 0.2313 0.1156
145 0.857 0.2859 0.143
146 0.8218 0.3564 0.1782
147 0.8018 0.3964 0.1982
148 0.7579 0.4842 0.2421
149 0.8083 0.3834 0.1917
150 0.8703 0.2594 0.1297
151 0.852 0.2961 0.148
152 0.8852 0.2296 0.1148
153 0.8554 0.2892 0.1446
154 0.9138 0.1724 0.08618
155 0.8833 0.2334 0.1167
156 0.8703 0.2594 0.1297
157 0.8419 0.3162 0.1581
158 0.7948 0.4104 0.2052
159 0.7372 0.5257 0.2628
160 0.7506 0.4988 0.2494
161 0.8569 0.2862 0.1431
162 0.8986 0.2028 0.1014
163 0.8659 0.2683 0.1341
164 0.8623 0.2753 0.1377
165 0.8039 0.3923 0.1961
166 0.819 0.3619 0.181
167 0.7849 0.4301 0.2151
168 0.6993 0.6015 0.3007
169 0.6067 0.7865 0.3933
170 0.5131 0.9738 0.4869
171 0.5806 0.8388 0.4194
172 0.4746 0.9492 0.5254
173 0.3537 0.7074 0.6463







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level0 0OK
5% type I error level00OK
10% type I error level00OK

\begin{tabular}{lllllllll}
\hline
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
Description & # significant tests & % significant tests & OK/NOK \tabularnewline
1% type I error level & 0 &  0 & OK \tabularnewline
5% type I error level & 0 & 0 & OK \tabularnewline
10% type I error level & 0 & 0 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=319007&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]0[/C][C] 0[/C][C]OK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=319007&T=7

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=319007&T=7

As an alternative you can also use a 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 level0 0OK
5% type I error level00OK
10% type I error level00OK







Ramsey RESET F-Test for powers (2 and 3) of fitted values
> reset_test_fitted
	RESET test
data:  mylm
RESET = 5.2468, df1 = 2, df2 = 174, p-value = 0.006129
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors
	RESET test
data:  mylm
RESET = 3.2213, df1 = 4, df2 = 172, p-value = 0.01401
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components
	RESET test
data:  mylm
RESET = 6.0918, df1 = 2, df2 = 174, p-value = 0.002773

\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 = 5.2468, df1 = 2, df2 = 174, p-value = 0.006129
\tabularnewline Ramsey RESET F-Test for powers (2 and 3) of regressors \tabularnewline
> reset_test_regressors
	RESET test
data:  mylm
RESET = 3.2213, df1 = 4, df2 = 172, p-value = 0.01401
\tabularnewline Ramsey RESET F-Test for powers (2 and 3) of principal components \tabularnewline
> reset_test_principal_components
	RESET test
data:  mylm
RESET = 6.0918, df1 = 2, df2 = 174, p-value = 0.002773
\tabularnewline \hline \end{tabular} %Source: https://freestatistics.org/blog/index.php?pk=319007&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 = 5.2468, df1 = 2, df2 = 174, p-value = 0.006129
[/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 = 3.2213, df1 = 4, df2 = 172, p-value = 0.01401
[/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 = 6.0918, df1 = 2, df2 = 174, p-value = 0.002773
[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=319007&T=8

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=319007&T=8

As an alternative you can also use a 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 = 5.2468, df1 = 2, df2 = 174, p-value = 0.006129
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors
	RESET test
data:  mylm
RESET = 3.2213, df1 = 4, df2 = 172, p-value = 0.01401
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components
	RESET test
data:  mylm
RESET = 6.0918, df1 = 2, df2 = 174, p-value = 0.002773







Variance Inflation Factors (Multicollinearity)
> vif
Perceived_Usefulness       System_Quality 
            1.226313             1.226313 

\begin{tabular}{lllllllll}
\hline
Variance Inflation Factors (Multicollinearity) \tabularnewline
> vif
Perceived_Usefulness       System_Quality 
            1.226313             1.226313 
\tabularnewline \hline \end{tabular} %Source: https://freestatistics.org/blog/index.php?pk=319007&T=9

[TABLE]
[ROW][C]Variance Inflation Factors (Multicollinearity)[/C][/ROW]
[ROW][C]
> vif
Perceived_Usefulness       System_Quality 
            1.226313             1.226313 
[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=319007&T=9

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=319007&T=9

As an alternative you can also use a QR Code:  

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

Variance Inflation Factors (Multicollinearity)
> vif
Perceived_Usefulness       System_Quality 
            1.226313             1.226313 



Parameters (Session):
par1 = two.sided ; par2 = 0.97 ; par3 = 14 ;
Parameters (R input):
par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ; par4 = 0 ; par5 = 0 ; par6 = 12 ;
R code (references can be found in the software module):
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')