## Free Statistics

of Irreproducible Research!

Author's title
Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationTue, 28 Jan 2020 12:07:42 +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/28/t1580210238o6zzbuv2ckqrrvr.htm/, Retrieved Wed, 21 Apr 2021 09:04:27 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=319053, Retrieved Wed, 21 Apr 2021 09:04:27 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact37
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Multiple Regression] [vraag 10] [2020-01-28 11:07:42] [43eb2330ebca6ad52336dea971844457] [Current]
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Dataseries X:
10 36 21 10
8 32 22 15
8 33 17 14
9 39 21 14
5 34 19 8
10 39 23 19
8 36 21 17
9 33 22 18
8 30 11 10
7 39 20 15
10 37 18 16
10 37 16 12
9 35 18 13
4 32 13 10
4 36 17 14
8 36 20 15
9 41 20 20
10 36 15 9
8 37 18 12
5 29 15 13
10 39 19 16
8 37 19 12
7 32 19 14
8 36 20 15
8 43 20 19
9 30 16 16
8 33 18 16
6 28 17 14
8 30 18 14
8 28 13 14
5 39 20 13
9 34 21 18
8 34 17 15
8 29 19 15
8 32 20 15
6 33 15 13
6 27 15 14
9 35 19 15
8 38 18 14
9 40 22 19
10 34 20 16
8 34 18 16
8 26 14 12
7 39 15 10
7 34 17 11
10 39 16 13
8 26 17 14
7 30 15 11
10 34 17 11
7 34 18 16
7 29 16 9
9 41 18 16
9 43 22 19
8 31 16 13
6 33 16 15
8 34 20 14
9 30 18 15
2 23 16 11
6 29 16 14
8 35 20 15
8 40 21 17
7 27 18 16
8 30 15 13
6 27 18 15
10 29 18 14
10 33 20 15
10 32 18 14
8 33 16 12
8 36 19 12
7 34 20 15
10 45 22 17
5 30 18 13
3 22 8 5
2 24 13 7
3 25 13 10
4 26 18 15
2 27 12 9
6 27 16 9
8 35 21 15
8 36 20 14
5 32 18 11
10 35 22 18
9 35 23 20
8 36 23 20
9 37 21 16
8 33 16 15
5 25 14 14
7 35 18 13
9 37 22 18
8 36 20 14
4 35 18 12
7 29 12 9
8 35 17 19
7 31 15 13
7 30 18 12
9 37 18 14
6 36 15 6
7 35 16 14
4 32 15 11
6 34 16 11
10 37 19 14
9 36 19 12
10 39 23 19
8 37 20 13
4 31 18 14
8 40 21 17
5 38 19 12
8 35 18 16
9 38 19 15
8 32 17 15
4 41 21 15
8 28 19 16
10 40 24 15
6 25 12 12
7 28 15 13
10 37 18 14
9 37 19 17
8 40 22 14
3 26 19 14
8 30 16 14
7 32 19 15
7 31 18 11
8 28 18 11
8 34 19 16
7 39 21 12
7 33 19 12
9 43 22 19
9 37 23 18
9 31 17 16
4 31 18 16
6 34 19 13
6 32 15 11
6 27 14 10
8 34 18 14
3 28 17 14
8 32 19 14
8 39 16 16
6 28 14 10
10 39 20 16
2 32 16 7
9 36 18 16
6 31 16 15
6 39 21 17
5 23 16 11
4 25 14 11
7 32 16 10
5 32 19 13
8 36 19 14
6 39 19 13
9 31 18 13
6 32 16 12
4 28 14 10
7 34 19 15
2 28 11 6
8 38 18 15
9 35 18 15
6 32 16 11
5 26 20 14
7 32 18 14
8 28 20 16
4 31 16 12
9 33 18 15
9 38 19 20
9 38 19 12
7 36 15 9
5 31 17 13
7 36 21 15
9 43 24 19
8 37 16 11
6 28 13 11
9 35 21 17
8 34 16 15
7 40 17 14
7 31 17 15
7 41 18 11
8 35 18 12
10 38 23 15
6 37 20 16
6 31 20 16


 Summary of computational transaction Raw Input view raw input (R code) Raw Output view raw output of R engine Computing time 3 seconds R Server Big 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=319053&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=319053&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=319053&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 Output view raw output of R engine Computing time 3 seconds R Server Big Analytics Cloud Computing Center

 Multiple Linear Regression - Estimated Regression Equation Intention_to_Use[t] = -1.52273 + 0.151696System_Quality[t] + 0.0322698Information_Quality[t] + 0.226814Perceived_Ease_of_Use[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Intention_to_Use[t] =  -1.52273 +  0.151696System_Quality[t] +  0.0322698Information_Quality[t] +  0.226814Perceived_Ease_of_Use[t]  + e[t] \tabularnewline
\tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=319053&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Intention_to_Use[t] =  -1.52273 +  0.151696System_Quality[t] +  0.0322698Information_Quality[t] +  0.226814Perceived_Ease_of_Use[t]  + e[t][/C][/ROW]
[ROW][C][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=319053&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=319053&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] = -1.52273 + 0.151696System_Quality[t] + 0.0322698Information_Quality[t] + 0.226814Perceived_Ease_of_Use[t] + e[t]

 Multiple Linear Regression - Ordinary Least Squares Variable Parameter S.D. T-STATH0: parameter = 0 2-tail p-value 1-tail p-value (Intercept) -1.523 0.9064 -1.6800e+00 0.09473 0.04737 System_Quality +0.1517 0.0325 +4.6680e+00 6.029e-06 3.014e-06 Information_Quality +0.03227 0.06818 +4.7330e-01 0.6366 0.3183 Perceived_Ease_of_Use +0.2268 0.05703 +3.9770e+00 0.0001019 5.093e-05

\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) & -1.523 &  0.9064 & -1.6800e+00 &  0.09473 &  0.04737 \tabularnewline
System_Quality & +0.1517 &  0.0325 & +4.6680e+00 &  6.029e-06 &  3.014e-06 \tabularnewline
Information_Quality & +0.03227 &  0.06818 & +4.7330e-01 &  0.6366 &  0.3183 \tabularnewline
Perceived_Ease_of_Use & +0.2268 &  0.05703 & +3.9770e+00 &  0.0001019 &  5.093e-05 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=319053&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]-1.523[/C][C] 0.9064[/C][C]-1.6800e+00[/C][C] 0.09473[/C][C] 0.04737[/C][/ROW]
[ROW][C]System_Quality[/C][C]+0.1517[/C][C] 0.0325[/C][C]+4.6680e+00[/C][C] 6.029e-06[/C][C] 3.014e-06[/C][/ROW]
[ROW][C]Information_Quality[/C][C]+0.03227[/C][C] 0.06818[/C][C]+4.7330e-01[/C][C] 0.6366[/C][C] 0.3183[/C][/ROW]
[ROW][C]Perceived_Ease_of_Use[/C][C]+0.2268[/C][C] 0.05703[/C][C]+3.9770e+00[/C][C] 0.0001019[/C][C] 5.093e-05[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=319053&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=319053&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 Variable Parameter S.D. T-STATH0: parameter = 0 2-tail p-value 1-tail p-value (Intercept) -1.523 0.9064 -1.6800e+00 0.09473 0.04737 System_Quality +0.1517 0.0325 +4.6680e+00 6.029e-06 3.014e-06 Information_Quality +0.03227 0.06818 +4.7330e-01 0.6366 0.3183 Perceived_Ease_of_Use +0.2268 0.05703 +3.9770e+00 0.0001019 5.093e-05

 Multiple Linear Regression - Regression Statistics Multiple R 0.6205 R-squared 0.385 Adjusted R-squared 0.3745 F-TEST (value) 36.53 F-TEST (DF numerator) 3 F-TEST (DF denominator) 175 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 1.553 Sum Squared Residuals 421.9

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R &  0.6205 \tabularnewline
R-squared &  0.385 \tabularnewline
F-TEST (value) &  36.53 \tabularnewline
F-TEST (DF numerator) & 3 \tabularnewline
F-TEST (DF denominator) & 175 \tabularnewline
p-value &  0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation &  1.553 \tabularnewline
Sum Squared Residuals &  421.9 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=319053&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C] 0.6205[/C][/ROW]
[ROW][C]R-squared[/C][C] 0.385[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C] 36.53[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]3[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]175[/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] 1.553[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C] 421.9[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=319053&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=319053&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 0.6205 R-squared 0.385 Adjusted R-squared 0.3745 F-TEST (value) 36.53 F-TEST (DF numerator) 3 F-TEST (DF denominator) 175 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 1.553 Sum Squared Residuals 421.9

 Menu of Residual Diagnostics Description Link Histogram Compute Central Tendency Compute QQ Plot Compute Kernel Density Plot Compute Skewness/Kurtosis Test Compute Skewness-Kurtosis Plot Compute Harrell-Davis Plot Compute Bootstrap Plot -- Central Tendency Compute Blocked Bootstrap Plot -- Central Tendency Compute (Partial) Autocorrelation Plot Compute Spectral Analysis Compute Tukey lambda PPCC Plot Compute Box-Cox Normality Plot Compute Summary Statistics Compute

\begin{tabular}{lllllllll}
\hline
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=319053&T=4

[TABLE]
[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=319053&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=319053&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 Description Link Histogram Compute Central Tendency Compute QQ Plot Compute Kernel Density Plot Compute Skewness/Kurtosis Test Compute Skewness-Kurtosis Plot Compute Harrell-Davis Plot Compute Bootstrap Plot -- Central Tendency Compute Blocked Bootstrap Plot -- Central Tendency Compute (Partial) Autocorrelation Plot Compute Spectral Analysis Compute Tukey lambda PPCC Plot Compute Box-Cox Normality Plot Compute Summary Statistics Compute

 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals InterpolationForecast ResidualsPrediction Error 1 10 6.884 3.116 2 8 7.444 0.5563 3 8 7.207 0.7928 4 9 8.246 0.7535 5 5 6.063 -1.063 6 10 9.445 0.5549 7 8 8.472 -0.4718 8 9 8.276 0.7242 9 8 5.651 2.349 10 7 8.441 -1.441 11 10 8.3 1.7 12 10 7.328 2.672 13 9 7.316 1.684 14 4 6.019 -2.019 15 4 7.662 -3.662 16 8 7.986 0.01408 17 9 9.878 -0.8785 18 10 6.464 3.536 19 8 7.393 0.6074 20 5 6.309 -1.309 21 10 8.636 1.364 22 8 7.425 0.5751 23 7 7.12 -0.1201 24 8 7.986 0.01408 25 8 9.955 -1.955 26 9 7.173 1.827 27 8 7.693 0.3069 28 6 6.449 -0.4487 29 8 6.784 1.216 30 8 6.32 1.68 31 5 7.987 -2.987 32 9 8.395 0.6048 33 8 7.586 0.4143 34 8 6.892 1.108 35 8 7.379 0.6209 36 6 6.916 -0.9159 37 6 6.232 -0.2325 38 9 7.802 1.198 39 8 7.998 0.002045 40 9 9.564 -0.5645 41 10 7.909 2.091 42 8 7.845 0.1552 43 8 5.595 2.405 44 7 7.146 -0.1456 45 7 6.678 0.3215 46 10 7.858 2.142 47 8 6.145 1.855 48 7 6.007 0.9929 49 10 6.678 3.322 50 7 7.845 -0.8448 51 7 5.434 1.566 52 9 8.907 0.09333 53 9 10.02 -1.02 54 8 6.645 1.355 55 6 7.402 -1.402 56 8 7.456 0.5443 57 9 7.011 1.989 58 2 4.978 -2.978 59 6 6.568 -0.5682 60 8 7.834 0.1658 61 8 9.079 -1.079 62 7 6.783 0.2171 63 8 6.461 1.539 64 6 6.556 -0.5561 65 10 6.633 3.367 66 10 7.531 2.469 67 10 7.088 2.912 68 8 6.721 1.279 69 8 7.273 0.7268 70 7 7.683 -0.6825 71 10 9.869 0.1307 72 5 6.558 -1.558 73 3 3.207 -0.2068 74 2 4.125 -2.125 75 3 4.957 -1.957 76 4 6.404 -2.404 77 2 5.002 -3.002 78 6 5.131 0.8693 79 8 7.866 0.1335 80 8 7.759 0.2409 81 5 6.407 -1.407 82 10 8.579 1.421 83 9 9.065 -0.0651 84 8 9.217 -1.217 85 9 8.397 0.6033 86 8 7.402 0.5982 87 5 5.897 -0.8968 88 7 7.316 -0.3161 89 9 8.883 0.1174 90 8 7.759 0.2409 91 4 7.089 -3.089 92 7 5.305 1.695 93 8 8.645 -0.6447 94 7 6.612 0.3875 95 7 6.331 0.6692 96 9 7.846 1.154 97 6 5.783 0.2168 98 7 7.478 -0.4783 99 4 6.311 -2.311 100 6 6.646 -0.6462 101 10 7.879 2.121 102 9 7.273 1.727 103 10 9.445 0.5549 104 8 7.684 0.316 105 4 6.936 -2.936 106 8 9.079 -1.079 107 5 7.577 -2.577 108 8 7.996 0.003505 109 9 8.257 0.743 110 8 7.282 0.7177 111 4 8.777 -4.777 112 8 6.967 1.033 113 10 8.722 1.278 114 6 5.379 0.6213 115 7 6.157 0.8426 116 10 7.846 2.154 117 9 8.559 0.441 118 8 8.43 -0.4304 119 3 6.21 -3.21 120 8 6.72 1.28 121 7 7.347 -0.3469 122 7 6.256 0.7444 123 8 5.801 2.199 124 8 7.877 0.1229 125 7 7.793 -0.7928 126 7 6.818 0.1819 127 9 10.02 -1.02 128 9 8.915 0.08514 129 9 7.357 1.643 130 4 7.39 -3.39 131 6 7.197 -1.197 132 6 6.311 -0.3105 133 6 5.293 0.707 134 8 7.391 0.6088 135 3 6.449 -3.449 136 8 7.12 0.88 137 8 8.539 -0.5387 138 6 5.445 0.5553 139 10 8.668 1.332 140 2 5.436 -3.436 141 9 8.148 0.8518 142 6 7.098 -1.098 143 6 8.927 -2.927 144 5 4.978 0.02246 145 4 5.216 -1.216 146 7 6.116 0.884 147 5 6.893 -1.893 148 8 7.727 0.2732 149 6 7.955 -1.955 150 9 6.709 2.291 151 6 6.57 -0.5696 152 4 5.445 -1.445 153 7 7.65 -0.6503 154 2 4.441 -2.441 155 8 8.225 -0.2248 156 9 7.77 1.23 157 6 6.343 -0.3428 158 5 6.242 -1.242 159 7 7.088 -0.08778 160 8 6.999 1.001 161 4 6.418 -2.418 162 9 7.466 1.534 163 9 9.391 -0.3911 164 9 7.577 1.423 165 7 6.464 0.5363 166 5 6.677 -1.677 167 7 8.018 -1.018 168 9 10.08 -1.084 169 8 7.101 0.8987 170 6 5.639 0.3608 171 9 8.32 0.6799 172 8 7.553 0.4466 173 7 8.269 -1.269 174 7 7.131 -0.1306 175 7 7.773 -0.7726 176 8 7.089 0.9108 177 10 8.386 1.614 178 6 8.364 -2.364 179 6 7.454 -1.454

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 &  10 &  6.884 &  3.116 \tabularnewline
2 &  8 &  7.444 &  0.5563 \tabularnewline
3 &  8 &  7.207 &  0.7928 \tabularnewline
4 &  9 &  8.246 &  0.7535 \tabularnewline
5 &  5 &  6.063 & -1.063 \tabularnewline
6 &  10 &  9.445 &  0.5549 \tabularnewline
7 &  8 &  8.472 & -0.4718 \tabularnewline
8 &  9 &  8.276 &  0.7242 \tabularnewline
9 &  8 &  5.651 &  2.349 \tabularnewline
10 &  7 &  8.441 & -1.441 \tabularnewline
11 &  10 &  8.3 &  1.7 \tabularnewline
12 &  10 &  7.328 &  2.672 \tabularnewline
13 &  9 &  7.316 &  1.684 \tabularnewline
14 &  4 &  6.019 & -2.019 \tabularnewline
15 &  4 &  7.662 & -3.662 \tabularnewline
16 &  8 &  7.986 &  0.01408 \tabularnewline
17 &  9 &  9.878 & -0.8785 \tabularnewline
18 &  10 &  6.464 &  3.536 \tabularnewline
19 &  8 &  7.393 &  0.6074 \tabularnewline
20 &  5 &  6.309 & -1.309 \tabularnewline
21 &  10 &  8.636 &  1.364 \tabularnewline
22 &  8 &  7.425 &  0.5751 \tabularnewline
23 &  7 &  7.12 & -0.1201 \tabularnewline
24 &  8 &  7.986 &  0.01408 \tabularnewline
25 &  8 &  9.955 & -1.955 \tabularnewline
26 &  9 &  7.173 &  1.827 \tabularnewline
27 &  8 &  7.693 &  0.3069 \tabularnewline
28 &  6 &  6.449 & -0.4487 \tabularnewline
29 &  8 &  6.784 &  1.216 \tabularnewline
30 &  8 &  6.32 &  1.68 \tabularnewline
31 &  5 &  7.987 & -2.987 \tabularnewline
32 &  9 &  8.395 &  0.6048 \tabularnewline
33 &  8 &  7.586 &  0.4143 \tabularnewline
34 &  8 &  6.892 &  1.108 \tabularnewline
35 &  8 &  7.379 &  0.6209 \tabularnewline
36 &  6 &  6.916 & -0.9159 \tabularnewline
37 &  6 &  6.232 & -0.2325 \tabularnewline
38 &  9 &  7.802 &  1.198 \tabularnewline
39 &  8 &  7.998 &  0.002045 \tabularnewline
40 &  9 &  9.564 & -0.5645 \tabularnewline
41 &  10 &  7.909 &  2.091 \tabularnewline
42 &  8 &  7.845 &  0.1552 \tabularnewline
43 &  8 &  5.595 &  2.405 \tabularnewline
44 &  7 &  7.146 & -0.1456 \tabularnewline
45 &  7 &  6.678 &  0.3215 \tabularnewline
46 &  10 &  7.858 &  2.142 \tabularnewline
47 &  8 &  6.145 &  1.855 \tabularnewline
48 &  7 &  6.007 &  0.9929 \tabularnewline
49 &  10 &  6.678 &  3.322 \tabularnewline
50 &  7 &  7.845 & -0.8448 \tabularnewline
51 &  7 &  5.434 &  1.566 \tabularnewline
52 &  9 &  8.907 &  0.09333 \tabularnewline
53 &  9 &  10.02 & -1.02 \tabularnewline
54 &  8 &  6.645 &  1.355 \tabularnewline
55 &  6 &  7.402 & -1.402 \tabularnewline
56 &  8 &  7.456 &  0.5443 \tabularnewline
57 &  9 &  7.011 &  1.989 \tabularnewline
58 &  2 &  4.978 & -2.978 \tabularnewline
59 &  6 &  6.568 & -0.5682 \tabularnewline
60 &  8 &  7.834 &  0.1658 \tabularnewline
61 &  8 &  9.079 & -1.079 \tabularnewline
62 &  7 &  6.783 &  0.2171 \tabularnewline
63 &  8 &  6.461 &  1.539 \tabularnewline
64 &  6 &  6.556 & -0.5561 \tabularnewline
65 &  10 &  6.633 &  3.367 \tabularnewline
66 &  10 &  7.531 &  2.469 \tabularnewline
67 &  10 &  7.088 &  2.912 \tabularnewline
68 &  8 &  6.721 &  1.279 \tabularnewline
69 &  8 &  7.273 &  0.7268 \tabularnewline
70 &  7 &  7.683 & -0.6825 \tabularnewline
71 &  10 &  9.869 &  0.1307 \tabularnewline
72 &  5 &  6.558 & -1.558 \tabularnewline
73 &  3 &  3.207 & -0.2068 \tabularnewline
74 &  2 &  4.125 & -2.125 \tabularnewline
75 &  3 &  4.957 & -1.957 \tabularnewline
76 &  4 &  6.404 & -2.404 \tabularnewline
77 &  2 &  5.002 & -3.002 \tabularnewline
78 &  6 &  5.131 &  0.8693 \tabularnewline
79 &  8 &  7.866 &  0.1335 \tabularnewline
80 &  8 &  7.759 &  0.2409 \tabularnewline
81 &  5 &  6.407 & -1.407 \tabularnewline
82 &  10 &  8.579 &  1.421 \tabularnewline
83 &  9 &  9.065 & -0.0651 \tabularnewline
84 &  8 &  9.217 & -1.217 \tabularnewline
85 &  9 &  8.397 &  0.6033 \tabularnewline
86 &  8 &  7.402 &  0.5982 \tabularnewline
87 &  5 &  5.897 & -0.8968 \tabularnewline
88 &  7 &  7.316 & -0.3161 \tabularnewline
89 &  9 &  8.883 &  0.1174 \tabularnewline
90 &  8 &  7.759 &  0.2409 \tabularnewline
91 &  4 &  7.089 & -3.089 \tabularnewline
92 &  7 &  5.305 &  1.695 \tabularnewline
93 &  8 &  8.645 & -0.6447 \tabularnewline
94 &  7 &  6.612 &  0.3875 \tabularnewline
95 &  7 &  6.331 &  0.6692 \tabularnewline
96 &  9 &  7.846 &  1.154 \tabularnewline
97 &  6 &  5.783 &  0.2168 \tabularnewline
98 &  7 &  7.478 & -0.4783 \tabularnewline
99 &  4 &  6.311 & -2.311 \tabularnewline
100 &  6 &  6.646 & -0.6462 \tabularnewline
101 &  10 &  7.879 &  2.121 \tabularnewline
102 &  9 &  7.273 &  1.727 \tabularnewline
103 &  10 &  9.445 &  0.5549 \tabularnewline
104 &  8 &  7.684 &  0.316 \tabularnewline
105 &  4 &  6.936 & -2.936 \tabularnewline
106 &  8 &  9.079 & -1.079 \tabularnewline
107 &  5 &  7.577 & -2.577 \tabularnewline
108 &  8 &  7.996 &  0.003505 \tabularnewline
109 &  9 &  8.257 &  0.743 \tabularnewline
110 &  8 &  7.282 &  0.7177 \tabularnewline
111 &  4 &  8.777 & -4.777 \tabularnewline
112 &  8 &  6.967 &  1.033 \tabularnewline
113 &  10 &  8.722 &  1.278 \tabularnewline
114 &  6 &  5.379 &  0.6213 \tabularnewline
115 &  7 &  6.157 &  0.8426 \tabularnewline
116 &  10 &  7.846 &  2.154 \tabularnewline
117 &  9 &  8.559 &  0.441 \tabularnewline
118 &  8 &  8.43 & -0.4304 \tabularnewline
119 &  3 &  6.21 & -3.21 \tabularnewline
120 &  8 &  6.72 &  1.28 \tabularnewline
121 &  7 &  7.347 & -0.3469 \tabularnewline
122 &  7 &  6.256 &  0.7444 \tabularnewline
123 &  8 &  5.801 &  2.199 \tabularnewline
124 &  8 &  7.877 &  0.1229 \tabularnewline
125 &  7 &  7.793 & -0.7928 \tabularnewline
126 &  7 &  6.818 &  0.1819 \tabularnewline
127 &  9 &  10.02 & -1.02 \tabularnewline
128 &  9 &  8.915 &  0.08514 \tabularnewline
129 &  9 &  7.357 &  1.643 \tabularnewline
130 &  4 &  7.39 & -3.39 \tabularnewline
131 &  6 &  7.197 & -1.197 \tabularnewline
132 &  6 &  6.311 & -0.3105 \tabularnewline
133 &  6 &  5.293 &  0.707 \tabularnewline
134 &  8 &  7.391 &  0.6088 \tabularnewline
135 &  3 &  6.449 & -3.449 \tabularnewline
136 &  8 &  7.12 &  0.88 \tabularnewline
137 &  8 &  8.539 & -0.5387 \tabularnewline
138 &  6 &  5.445 &  0.5553 \tabularnewline
139 &  10 &  8.668 &  1.332 \tabularnewline
140 &  2 &  5.436 & -3.436 \tabularnewline
141 &  9 &  8.148 &  0.8518 \tabularnewline
142 &  6 &  7.098 & -1.098 \tabularnewline
143 &  6 &  8.927 & -2.927 \tabularnewline
144 &  5 &  4.978 &  0.02246 \tabularnewline
145 &  4 &  5.216 & -1.216 \tabularnewline
146 &  7 &  6.116 &  0.884 \tabularnewline
147 &  5 &  6.893 & -1.893 \tabularnewline
148 &  8 &  7.727 &  0.2732 \tabularnewline
149 &  6 &  7.955 & -1.955 \tabularnewline
150 &  9 &  6.709 &  2.291 \tabularnewline
151 &  6 &  6.57 & -0.5696 \tabularnewline
152 &  4 &  5.445 & -1.445 \tabularnewline
153 &  7 &  7.65 & -0.6503 \tabularnewline
154 &  2 &  4.441 & -2.441 \tabularnewline
155 &  8 &  8.225 & -0.2248 \tabularnewline
156 &  9 &  7.77 &  1.23 \tabularnewline
157 &  6 &  6.343 & -0.3428 \tabularnewline
158 &  5 &  6.242 & -1.242 \tabularnewline
159 &  7 &  7.088 & -0.08778 \tabularnewline
160 &  8 &  6.999 &  1.001 \tabularnewline
161 &  4 &  6.418 & -2.418 \tabularnewline
162 &  9 &  7.466 &  1.534 \tabularnewline
163 &  9 &  9.391 & -0.3911 \tabularnewline
164 &  9 &  7.577 &  1.423 \tabularnewline
165 &  7 &  6.464 &  0.5363 \tabularnewline
166 &  5 &  6.677 & -1.677 \tabularnewline
167 &  7 &  8.018 & -1.018 \tabularnewline
168 &  9 &  10.08 & -1.084 \tabularnewline
169 &  8 &  7.101 &  0.8987 \tabularnewline
170 &  6 &  5.639 &  0.3608 \tabularnewline
171 &  9 &  8.32 &  0.6799 \tabularnewline
172 &  8 &  7.553 &  0.4466 \tabularnewline
173 &  7 &  8.269 & -1.269 \tabularnewline
174 &  7 &  7.131 & -0.1306 \tabularnewline
175 &  7 &  7.773 & -0.7726 \tabularnewline
176 &  8 &  7.089 &  0.9108 \tabularnewline
177 &  10 &  8.386 &  1.614 \tabularnewline
178 &  6 &  8.364 & -2.364 \tabularnewline
179 &  6 &  7.454 & -1.454 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=319053&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] 6.884[/C][C] 3.116[/C][/ROW]
[ROW][C]2[/C][C] 8[/C][C] 7.444[/C][C] 0.5563[/C][/ROW]
[ROW][C]3[/C][C] 8[/C][C] 7.207[/C][C] 0.7928[/C][/ROW]
[ROW][C]4[/C][C] 9[/C][C] 8.246[/C][C] 0.7535[/C][/ROW]
[ROW][C]5[/C][C] 5[/C][C] 6.063[/C][C]-1.063[/C][/ROW]
[ROW][C]6[/C][C] 10[/C][C] 9.445[/C][C] 0.5549[/C][/ROW]
[ROW][C]7[/C][C] 8[/C][C] 8.472[/C][C]-0.4718[/C][/ROW]
[ROW][C]8[/C][C] 9[/C][C] 8.276[/C][C] 0.7242[/C][/ROW]
[ROW][C]9[/C][C] 8[/C][C] 5.651[/C][C] 2.349[/C][/ROW]
[ROW][C]10[/C][C] 7[/C][C] 8.441[/C][C]-1.441[/C][/ROW]
[ROW][C]11[/C][C] 10[/C][C] 8.3[/C][C] 1.7[/C][/ROW]
[ROW][C]12[/C][C] 10[/C][C] 7.328[/C][C] 2.672[/C][/ROW]
[ROW][C]13[/C][C] 9[/C][C] 7.316[/C][C] 1.684[/C][/ROW]
[ROW][C]14[/C][C] 4[/C][C] 6.019[/C][C]-2.019[/C][/ROW]
[ROW][C]15[/C][C] 4[/C][C] 7.662[/C][C]-3.662[/C][/ROW]
[ROW][C]16[/C][C] 8[/C][C] 7.986[/C][C] 0.01408[/C][/ROW]
[ROW][C]17[/C][C] 9[/C][C] 9.878[/C][C]-0.8785[/C][/ROW]
[ROW][C]18[/C][C] 10[/C][C] 6.464[/C][C] 3.536[/C][/ROW]
[ROW][C]19[/C][C] 8[/C][C] 7.393[/C][C] 0.6074[/C][/ROW]
[ROW][C]20[/C][C] 5[/C][C] 6.309[/C][C]-1.309[/C][/ROW]
[ROW][C]21[/C][C] 10[/C][C] 8.636[/C][C] 1.364[/C][/ROW]
[ROW][C]22[/C][C] 8[/C][C] 7.425[/C][C] 0.5751[/C][/ROW]
[ROW][C]23[/C][C] 7[/C][C] 7.12[/C][C]-0.1201[/C][/ROW]
[ROW][C]24[/C][C] 8[/C][C] 7.986[/C][C] 0.01408[/C][/ROW]
[ROW][C]25[/C][C] 8[/C][C] 9.955[/C][C]-1.955[/C][/ROW]
[ROW][C]26[/C][C] 9[/C][C] 7.173[/C][C] 1.827[/C][/ROW]
[ROW][C]27[/C][C] 8[/C][C] 7.693[/C][C] 0.3069[/C][/ROW]
[ROW][C]28[/C][C] 6[/C][C] 6.449[/C][C]-0.4487[/C][/ROW]
[ROW][C]29[/C][C] 8[/C][C] 6.784[/C][C] 1.216[/C][/ROW]
[ROW][C]30[/C][C] 8[/C][C] 6.32[/C][C] 1.68[/C][/ROW]
[ROW][C]31[/C][C] 5[/C][C] 7.987[/C][C]-2.987[/C][/ROW]
[ROW][C]32[/C][C] 9[/C][C] 8.395[/C][C] 0.6048[/C][/ROW]
[ROW][C]33[/C][C] 8[/C][C] 7.586[/C][C] 0.4143[/C][/ROW]
[ROW][C]34[/C][C] 8[/C][C] 6.892[/C][C] 1.108[/C][/ROW]
[ROW][C]35[/C][C] 8[/C][C] 7.379[/C][C] 0.6209[/C][/ROW]
[ROW][C]36[/C][C] 6[/C][C] 6.916[/C][C]-0.9159[/C][/ROW]
[ROW][C]37[/C][C] 6[/C][C] 6.232[/C][C]-0.2325[/C][/ROW]
[ROW][C]38[/C][C] 9[/C][C] 7.802[/C][C] 1.198[/C][/ROW]
[ROW][C]39[/C][C] 8[/C][C] 7.998[/C][C] 0.002045[/C][/ROW]
[ROW][C]40[/C][C] 9[/C][C] 9.564[/C][C]-0.5645[/C][/ROW]
[ROW][C]41[/C][C] 10[/C][C] 7.909[/C][C] 2.091[/C][/ROW]
[ROW][C]42[/C][C] 8[/C][C] 7.845[/C][C] 0.1552[/C][/ROW]
[ROW][C]43[/C][C] 8[/C][C] 5.595[/C][C] 2.405[/C][/ROW]
[ROW][C]44[/C][C] 7[/C][C] 7.146[/C][C]-0.1456[/C][/ROW]
[ROW][C]45[/C][C] 7[/C][C] 6.678[/C][C] 0.3215[/C][/ROW]
[ROW][C]46[/C][C] 10[/C][C] 7.858[/C][C] 2.142[/C][/ROW]
[ROW][C]47[/C][C] 8[/C][C] 6.145[/C][C] 1.855[/C][/ROW]
[ROW][C]48[/C][C] 7[/C][C] 6.007[/C][C] 0.9929[/C][/ROW]
[ROW][C]49[/C][C] 10[/C][C] 6.678[/C][C] 3.322[/C][/ROW]
[ROW][C]50[/C][C] 7[/C][C] 7.845[/C][C]-0.8448[/C][/ROW]
[ROW][C]51[/C][C] 7[/C][C] 5.434[/C][C] 1.566[/C][/ROW]
[ROW][C]52[/C][C] 9[/C][C] 8.907[/C][C] 0.09333[/C][/ROW]
[ROW][C]53[/C][C] 9[/C][C] 10.02[/C][C]-1.02[/C][/ROW]
[ROW][C]54[/C][C] 8[/C][C] 6.645[/C][C] 1.355[/C][/ROW]
[ROW][C]55[/C][C] 6[/C][C] 7.402[/C][C]-1.402[/C][/ROW]
[ROW][C]56[/C][C] 8[/C][C] 7.456[/C][C] 0.5443[/C][/ROW]
[ROW][C]57[/C][C] 9[/C][C] 7.011[/C][C] 1.989[/C][/ROW]
[ROW][C]58[/C][C] 2[/C][C] 4.978[/C][C]-2.978[/C][/ROW]
[ROW][C]59[/C][C] 6[/C][C] 6.568[/C][C]-0.5682[/C][/ROW]
[ROW][C]60[/C][C] 8[/C][C] 7.834[/C][C] 0.1658[/C][/ROW]
[ROW][C]61[/C][C] 8[/C][C] 9.079[/C][C]-1.079[/C][/ROW]
[ROW][C]62[/C][C] 7[/C][C] 6.783[/C][C] 0.2171[/C][/ROW]
[ROW][C]63[/C][C] 8[/C][C] 6.461[/C][C] 1.539[/C][/ROW]
[ROW][C]64[/C][C] 6[/C][C] 6.556[/C][C]-0.5561[/C][/ROW]
[ROW][C]65[/C][C] 10[/C][C] 6.633[/C][C] 3.367[/C][/ROW]
[ROW][C]66[/C][C] 10[/C][C] 7.531[/C][C] 2.469[/C][/ROW]
[ROW][C]67[/C][C] 10[/C][C] 7.088[/C][C] 2.912[/C][/ROW]
[ROW][C]68[/C][C] 8[/C][C] 6.721[/C][C] 1.279[/C][/ROW]
[ROW][C]69[/C][C] 8[/C][C] 7.273[/C][C] 0.7268[/C][/ROW]
[ROW][C]70[/C][C] 7[/C][C] 7.683[/C][C]-0.6825[/C][/ROW]
[ROW][C]71[/C][C] 10[/C][C] 9.869[/C][C] 0.1307[/C][/ROW]
[ROW][C]72[/C][C] 5[/C][C] 6.558[/C][C]-1.558[/C][/ROW]
[ROW][C]73[/C][C] 3[/C][C] 3.207[/C][C]-0.2068[/C][/ROW]
[ROW][C]74[/C][C] 2[/C][C] 4.125[/C][C]-2.125[/C][/ROW]
[ROW][C]75[/C][C] 3[/C][C] 4.957[/C][C]-1.957[/C][/ROW]
[ROW][C]76[/C][C] 4[/C][C] 6.404[/C][C]-2.404[/C][/ROW]
[ROW][C]77[/C][C] 2[/C][C] 5.002[/C][C]-3.002[/C][/ROW]
[ROW][C]78[/C][C] 6[/C][C] 5.131[/C][C] 0.8693[/C][/ROW]
[ROW][C]79[/C][C] 8[/C][C] 7.866[/C][C] 0.1335[/C][/ROW]
[ROW][C]80[/C][C] 8[/C][C] 7.759[/C][C] 0.2409[/C][/ROW]
[ROW][C]81[/C][C] 5[/C][C] 6.407[/C][C]-1.407[/C][/ROW]
[ROW][C]82[/C][C] 10[/C][C] 8.579[/C][C] 1.421[/C][/ROW]
[ROW][C]83[/C][C] 9[/C][C] 9.065[/C][C]-0.0651[/C][/ROW]
[ROW][C]84[/C][C] 8[/C][C] 9.217[/C][C]-1.217[/C][/ROW]
[ROW][C]85[/C][C] 9[/C][C] 8.397[/C][C] 0.6033[/C][/ROW]
[ROW][C]86[/C][C] 8[/C][C] 7.402[/C][C] 0.5982[/C][/ROW]
[ROW][C]87[/C][C] 5[/C][C] 5.897[/C][C]-0.8968[/C][/ROW]
[ROW][C]88[/C][C] 7[/C][C] 7.316[/C][C]-0.3161[/C][/ROW]
[ROW][C]89[/C][C] 9[/C][C] 8.883[/C][C] 0.1174[/C][/ROW]
[ROW][C]90[/C][C] 8[/C][C] 7.759[/C][C] 0.2409[/C][/ROW]
[ROW][C]91[/C][C] 4[/C][C] 7.089[/C][C]-3.089[/C][/ROW]
[ROW][C]92[/C][C] 7[/C][C] 5.305[/C][C] 1.695[/C][/ROW]
[ROW][C]93[/C][C] 8[/C][C] 8.645[/C][C]-0.6447[/C][/ROW]
[ROW][C]94[/C][C] 7[/C][C] 6.612[/C][C] 0.3875[/C][/ROW]
[ROW][C]95[/C][C] 7[/C][C] 6.331[/C][C] 0.6692[/C][/ROW]
[ROW][C]96[/C][C] 9[/C][C] 7.846[/C][C] 1.154[/C][/ROW]
[ROW][C]97[/C][C] 6[/C][C] 5.783[/C][C] 0.2168[/C][/ROW]
[ROW][C]98[/C][C] 7[/C][C] 7.478[/C][C]-0.4783[/C][/ROW]
[ROW][C]99[/C][C] 4[/C][C] 6.311[/C][C]-2.311[/C][/ROW]
[ROW][C]100[/C][C] 6[/C][C] 6.646[/C][C]-0.6462[/C][/ROW]
[ROW][C]101[/C][C] 10[/C][C] 7.879[/C][C] 2.121[/C][/ROW]
[ROW][C]102[/C][C] 9[/C][C] 7.273[/C][C] 1.727[/C][/ROW]
[ROW][C]103[/C][C] 10[/C][C] 9.445[/C][C] 0.5549[/C][/ROW]
[ROW][C]104[/C][C] 8[/C][C] 7.684[/C][C] 0.316[/C][/ROW]
[ROW][C]105[/C][C] 4[/C][C] 6.936[/C][C]-2.936[/C][/ROW]
[ROW][C]106[/C][C] 8[/C][C] 9.079[/C][C]-1.079[/C][/ROW]
[ROW][C]107[/C][C] 5[/C][C] 7.577[/C][C]-2.577[/C][/ROW]
[ROW][C]108[/C][C] 8[/C][C] 7.996[/C][C] 0.003505[/C][/ROW]
[ROW][C]109[/C][C] 9[/C][C] 8.257[/C][C] 0.743[/C][/ROW]
[ROW][C]110[/C][C] 8[/C][C] 7.282[/C][C] 0.7177[/C][/ROW]
[ROW][C]111[/C][C] 4[/C][C] 8.777[/C][C]-4.777[/C][/ROW]
[ROW][C]112[/C][C] 8[/C][C] 6.967[/C][C] 1.033[/C][/ROW]
[ROW][C]113[/C][C] 10[/C][C] 8.722[/C][C] 1.278[/C][/ROW]
[ROW][C]114[/C][C] 6[/C][C] 5.379[/C][C] 0.6213[/C][/ROW]
[ROW][C]115[/C][C] 7[/C][C] 6.157[/C][C] 0.8426[/C][/ROW]
[ROW][C]116[/C][C] 10[/C][C] 7.846[/C][C] 2.154[/C][/ROW]
[ROW][C]117[/C][C] 9[/C][C] 8.559[/C][C] 0.441[/C][/ROW]
[ROW][C]118[/C][C] 8[/C][C] 8.43[/C][C]-0.4304[/C][/ROW]
[ROW][C]119[/C][C] 3[/C][C] 6.21[/C][C]-3.21[/C][/ROW]
[ROW][C]120[/C][C] 8[/C][C] 6.72[/C][C] 1.28[/C][/ROW]
[ROW][C]121[/C][C] 7[/C][C] 7.347[/C][C]-0.3469[/C][/ROW]
[ROW][C]122[/C][C] 7[/C][C] 6.256[/C][C] 0.7444[/C][/ROW]
[ROW][C]123[/C][C] 8[/C][C] 5.801[/C][C] 2.199[/C][/ROW]
[ROW][C]124[/C][C] 8[/C][C] 7.877[/C][C] 0.1229[/C][/ROW]
[ROW][C]125[/C][C] 7[/C][C] 7.793[/C][C]-0.7928[/C][/ROW]
[ROW][C]126[/C][C] 7[/C][C] 6.818[/C][C] 0.1819[/C][/ROW]
[ROW][C]127[/C][C] 9[/C][C] 10.02[/C][C]-1.02[/C][/ROW]
[ROW][C]128[/C][C] 9[/C][C] 8.915[/C][C] 0.08514[/C][/ROW]
[ROW][C]129[/C][C] 9[/C][C] 7.357[/C][C] 1.643[/C][/ROW]
[ROW][C]130[/C][C] 4[/C][C] 7.39[/C][C]-3.39[/C][/ROW]
[ROW][C]131[/C][C] 6[/C][C] 7.197[/C][C]-1.197[/C][/ROW]
[ROW][C]132[/C][C] 6[/C][C] 6.311[/C][C]-0.3105[/C][/ROW]
[ROW][C]133[/C][C] 6[/C][C] 5.293[/C][C] 0.707[/C][/ROW]
[ROW][C]134[/C][C] 8[/C][C] 7.391[/C][C] 0.6088[/C][/ROW]
[ROW][C]135[/C][C] 3[/C][C] 6.449[/C][C]-3.449[/C][/ROW]
[ROW][C]136[/C][C] 8[/C][C] 7.12[/C][C] 0.88[/C][/ROW]
[ROW][C]137[/C][C] 8[/C][C] 8.539[/C][C]-0.5387[/C][/ROW]
[ROW][C]138[/C][C] 6[/C][C] 5.445[/C][C] 0.5553[/C][/ROW]
[ROW][C]139[/C][C] 10[/C][C] 8.668[/C][C] 1.332[/C][/ROW]
[ROW][C]140[/C][C] 2[/C][C] 5.436[/C][C]-3.436[/C][/ROW]
[ROW][C]141[/C][C] 9[/C][C] 8.148[/C][C] 0.8518[/C][/ROW]
[ROW][C]142[/C][C] 6[/C][C] 7.098[/C][C]-1.098[/C][/ROW]
[ROW][C]143[/C][C] 6[/C][C] 8.927[/C][C]-2.927[/C][/ROW]
[ROW][C]144[/C][C] 5[/C][C] 4.978[/C][C] 0.02246[/C][/ROW]
[ROW][C]145[/C][C] 4[/C][C] 5.216[/C][C]-1.216[/C][/ROW]
[ROW][C]146[/C][C] 7[/C][C] 6.116[/C][C] 0.884[/C][/ROW]
[ROW][C]147[/C][C] 5[/C][C] 6.893[/C][C]-1.893[/C][/ROW]
[ROW][C]148[/C][C] 8[/C][C] 7.727[/C][C] 0.2732[/C][/ROW]
[ROW][C]149[/C][C] 6[/C][C] 7.955[/C][C]-1.955[/C][/ROW]
[ROW][C]150[/C][C] 9[/C][C] 6.709[/C][C] 2.291[/C][/ROW]
[ROW][C]151[/C][C] 6[/C][C] 6.57[/C][C]-0.5696[/C][/ROW]
[ROW][C]152[/C][C] 4[/C][C] 5.445[/C][C]-1.445[/C][/ROW]
[ROW][C]153[/C][C] 7[/C][C] 7.65[/C][C]-0.6503[/C][/ROW]
[ROW][C]154[/C][C] 2[/C][C] 4.441[/C][C]-2.441[/C][/ROW]
[ROW][C]155[/C][C] 8[/C][C] 8.225[/C][C]-0.2248[/C][/ROW]
[ROW][C]156[/C][C] 9[/C][C] 7.77[/C][C] 1.23[/C][/ROW]
[ROW][C]157[/C][C] 6[/C][C] 6.343[/C][C]-0.3428[/C][/ROW]
[ROW][C]158[/C][C] 5[/C][C] 6.242[/C][C]-1.242[/C][/ROW]
[ROW][C]159[/C][C] 7[/C][C] 7.088[/C][C]-0.08778[/C][/ROW]
[ROW][C]160[/C][C] 8[/C][C] 6.999[/C][C] 1.001[/C][/ROW]
[ROW][C]161[/C][C] 4[/C][C] 6.418[/C][C]-2.418[/C][/ROW]
[ROW][C]162[/C][C] 9[/C][C] 7.466[/C][C] 1.534[/C][/ROW]
[ROW][C]163[/C][C] 9[/C][C] 9.391[/C][C]-0.3911[/C][/ROW]
[ROW][C]164[/C][C] 9[/C][C] 7.577[/C][C] 1.423[/C][/ROW]
[ROW][C]165[/C][C] 7[/C][C] 6.464[/C][C] 0.5363[/C][/ROW]
[ROW][C]166[/C][C] 5[/C][C] 6.677[/C][C]-1.677[/C][/ROW]
[ROW][C]167[/C][C] 7[/C][C] 8.018[/C][C]-1.018[/C][/ROW]
[ROW][C]168[/C][C] 9[/C][C] 10.08[/C][C]-1.084[/C][/ROW]
[ROW][C]169[/C][C] 8[/C][C] 7.101[/C][C] 0.8987[/C][/ROW]
[ROW][C]170[/C][C] 6[/C][C] 5.639[/C][C] 0.3608[/C][/ROW]
[ROW][C]171[/C][C] 9[/C][C] 8.32[/C][C] 0.6799[/C][/ROW]
[ROW][C]172[/C][C] 8[/C][C] 7.553[/C][C] 0.4466[/C][/ROW]
[ROW][C]173[/C][C] 7[/C][C] 8.269[/C][C]-1.269[/C][/ROW]
[ROW][C]174[/C][C] 7[/C][C] 7.131[/C][C]-0.1306[/C][/ROW]
[ROW][C]175[/C][C] 7[/C][C] 7.773[/C][C]-0.7726[/C][/ROW]
[ROW][C]176[/C][C] 8[/C][C] 7.089[/C][C] 0.9108[/C][/ROW]
[ROW][C]177[/C][C] 10[/C][C] 8.386[/C][C] 1.614[/C][/ROW]
[ROW][C]178[/C][C] 6[/C][C] 8.364[/C][C]-2.364[/C][/ROW]
[ROW][C]179[/C][C] 6[/C][C] 7.454[/C][C]-1.454[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=319053&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=319053&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 Index Actuals InterpolationForecast ResidualsPrediction Error 1 10 6.884 3.116 2 8 7.444 0.5563 3 8 7.207 0.7928 4 9 8.246 0.7535 5 5 6.063 -1.063 6 10 9.445 0.5549 7 8 8.472 -0.4718 8 9 8.276 0.7242 9 8 5.651 2.349 10 7 8.441 -1.441 11 10 8.3 1.7 12 10 7.328 2.672 13 9 7.316 1.684 14 4 6.019 -2.019 15 4 7.662 -3.662 16 8 7.986 0.01408 17 9 9.878 -0.8785 18 10 6.464 3.536 19 8 7.393 0.6074 20 5 6.309 -1.309 21 10 8.636 1.364 22 8 7.425 0.5751 23 7 7.12 -0.1201 24 8 7.986 0.01408 25 8 9.955 -1.955 26 9 7.173 1.827 27 8 7.693 0.3069 28 6 6.449 -0.4487 29 8 6.784 1.216 30 8 6.32 1.68 31 5 7.987 -2.987 32 9 8.395 0.6048 33 8 7.586 0.4143 34 8 6.892 1.108 35 8 7.379 0.6209 36 6 6.916 -0.9159 37 6 6.232 -0.2325 38 9 7.802 1.198 39 8 7.998 0.002045 40 9 9.564 -0.5645 41 10 7.909 2.091 42 8 7.845 0.1552 43 8 5.595 2.405 44 7 7.146 -0.1456 45 7 6.678 0.3215 46 10 7.858 2.142 47 8 6.145 1.855 48 7 6.007 0.9929 49 10 6.678 3.322 50 7 7.845 -0.8448 51 7 5.434 1.566 52 9 8.907 0.09333 53 9 10.02 -1.02 54 8 6.645 1.355 55 6 7.402 -1.402 56 8 7.456 0.5443 57 9 7.011 1.989 58 2 4.978 -2.978 59 6 6.568 -0.5682 60 8 7.834 0.1658 61 8 9.079 -1.079 62 7 6.783 0.2171 63 8 6.461 1.539 64 6 6.556 -0.5561 65 10 6.633 3.367 66 10 7.531 2.469 67 10 7.088 2.912 68 8 6.721 1.279 69 8 7.273 0.7268 70 7 7.683 -0.6825 71 10 9.869 0.1307 72 5 6.558 -1.558 73 3 3.207 -0.2068 74 2 4.125 -2.125 75 3 4.957 -1.957 76 4 6.404 -2.404 77 2 5.002 -3.002 78 6 5.131 0.8693 79 8 7.866 0.1335 80 8 7.759 0.2409 81 5 6.407 -1.407 82 10 8.579 1.421 83 9 9.065 -0.0651 84 8 9.217 -1.217 85 9 8.397 0.6033 86 8 7.402 0.5982 87 5 5.897 -0.8968 88 7 7.316 -0.3161 89 9 8.883 0.1174 90 8 7.759 0.2409 91 4 7.089 -3.089 92 7 5.305 1.695 93 8 8.645 -0.6447 94 7 6.612 0.3875 95 7 6.331 0.6692 96 9 7.846 1.154 97 6 5.783 0.2168 98 7 7.478 -0.4783 99 4 6.311 -2.311 100 6 6.646 -0.6462 101 10 7.879 2.121 102 9 7.273 1.727 103 10 9.445 0.5549 104 8 7.684 0.316 105 4 6.936 -2.936 106 8 9.079 -1.079 107 5 7.577 -2.577 108 8 7.996 0.003505 109 9 8.257 0.743 110 8 7.282 0.7177 111 4 8.777 -4.777 112 8 6.967 1.033 113 10 8.722 1.278 114 6 5.379 0.6213 115 7 6.157 0.8426 116 10 7.846 2.154 117 9 8.559 0.441 118 8 8.43 -0.4304 119 3 6.21 -3.21 120 8 6.72 1.28 121 7 7.347 -0.3469 122 7 6.256 0.7444 123 8 5.801 2.199 124 8 7.877 0.1229 125 7 7.793 -0.7928 126 7 6.818 0.1819 127 9 10.02 -1.02 128 9 8.915 0.08514 129 9 7.357 1.643 130 4 7.39 -3.39 131 6 7.197 -1.197 132 6 6.311 -0.3105 133 6 5.293 0.707 134 8 7.391 0.6088 135 3 6.449 -3.449 136 8 7.12 0.88 137 8 8.539 -0.5387 138 6 5.445 0.5553 139 10 8.668 1.332 140 2 5.436 -3.436 141 9 8.148 0.8518 142 6 7.098 -1.098 143 6 8.927 -2.927 144 5 4.978 0.02246 145 4 5.216 -1.216 146 7 6.116 0.884 147 5 6.893 -1.893 148 8 7.727 0.2732 149 6 7.955 -1.955 150 9 6.709 2.291 151 6 6.57 -0.5696 152 4 5.445 -1.445 153 7 7.65 -0.6503 154 2 4.441 -2.441 155 8 8.225 -0.2248 156 9 7.77 1.23 157 6 6.343 -0.3428 158 5 6.242 -1.242 159 7 7.088 -0.08778 160 8 6.999 1.001 161 4 6.418 -2.418 162 9 7.466 1.534 163 9 9.391 -0.3911 164 9 7.577 1.423 165 7 6.464 0.5363 166 5 6.677 -1.677 167 7 8.018 -1.018 168 9 10.08 -1.084 169 8 7.101 0.8987 170 6 5.639 0.3608 171 9 8.32 0.6799 172 8 7.553 0.4466 173 7 8.269 -1.269 174 7 7.131 -0.1306 175 7 7.773 -0.7726 176 8 7.089 0.9108 177 10 8.386 1.614 178 6 8.364 -2.364 179 6 7.454 -1.454

 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 7 0.7195 0.561 0.2805 8 0.5766 0.8468 0.4234 9 0.5462 0.9077 0.4538 10 0.5939 0.8122 0.4061 11 0.5434 0.9132 0.4566 12 0.54 0.92 0.46 13 0.4566 0.9133 0.5434 14 0.7585 0.483 0.2415 15 0.9561 0.08785 0.04392 16 0.9346 0.1307 0.06535 17 0.9093 0.1814 0.09071 18 0.9511 0.09786 0.04893 19 0.9303 0.1393 0.06965 20 0.9225 0.1549 0.07747 21 0.9066 0.1869 0.09344 22 0.8771 0.2459 0.1229 23 0.8398 0.3204 0.1602 24 0.7972 0.4056 0.2028 25 0.8118 0.3765 0.1882 26 0.8319 0.3362 0.1681 27 0.7902 0.4195 0.2098 28 0.7542 0.4915 0.2458 29 0.7204 0.5592 0.2796 30 0.7057 0.5887 0.2943 31 0.838 0.324 0.162 32 0.8054 0.3891 0.1946 33 0.7644 0.4711 0.2356 34 0.7265 0.547 0.2735 35 0.68 0.6399 0.32 36 0.6653 0.6695 0.3347 37 0.6301 0.7398 0.3699 38 0.6003 0.7994 0.3997 39 0.5475 0.905 0.4525 40 0.4962 0.9925 0.5038 41 0.5213 0.9573 0.4787 42 0.469 0.938 0.531 43 0.4787 0.9575 0.5213 44 0.4295 0.859 0.5705 45 0.3823 0.7647 0.6177 46 0.4278 0.8556 0.5722 47 0.4095 0.819 0.5905 48 0.3689 0.7378 0.6311 49 0.4886 0.9772 0.5114 50 0.4629 0.9257 0.5371 51 0.4356 0.8713 0.5644 52 0.3902 0.7804 0.6098 53 0.3529 0.7058 0.6471 54 0.3263 0.6527 0.6737 55 0.3369 0.6737 0.6631 56 0.2973 0.5945 0.7027 57 0.3027 0.6054 0.6973 58 0.5662 0.8676 0.4338 59 0.5376 0.9248 0.4624 60 0.4922 0.9844 0.5078 61 0.4662 0.9324 0.5338 62 0.4219 0.8438 0.5781 63 0.4087 0.8173 0.5913 64 0.3791 0.7583 0.6209 65 0.5217 0.9566 0.4783 66 0.5805 0.839 0.4195 67 0.6735 0.6531 0.3265 68 0.6541 0.6918 0.3459 69 0.6202 0.7596 0.3798 70 0.5929 0.8143 0.4071 71 0.5511 0.8979 0.4489 72 0.5794 0.8412 0.4206 73 0.573 0.854 0.427 74 0.6555 0.6889 0.3445 75 0.6942 0.6116 0.3058 76 0.7515 0.4971 0.2485 77 0.8409 0.3183 0.1591 78 0.8222 0.3556 0.1778 79 0.7939 0.4122 0.2061 80 0.7636 0.4729 0.2364 81 0.7626 0.4748 0.2374 82 0.7567 0.4866 0.2433 83 0.7223 0.5555 0.2777 84 0.7084 0.5831 0.2916 85 0.6767 0.6466 0.3233 86 0.6434 0.7133 0.3566 87 0.6145 0.7709 0.3855 88 0.5764 0.8473 0.4236 89 0.5347 0.9306 0.4653 90 0.4944 0.9889 0.5056 91 0.625 0.75 0.375 92 0.6341 0.7317 0.3659 93 0.5987 0.8026 0.4013 94 0.56 0.8799 0.44 95 0.5273 0.9454 0.4727 96 0.5097 0.9805 0.4903 97 0.4737 0.9474 0.5263 98 0.4347 0.8694 0.5653 99 0.4835 0.9671 0.5165 100 0.4478 0.8955 0.5522 101 0.4907 0.9813 0.5093 102 0.5104 0.9792 0.4896 103 0.4742 0.9485 0.5258 104 0.4379 0.8758 0.5621 105 0.5456 0.9088 0.4544 106 0.521 0.9581 0.479 107 0.5867 0.8266 0.4133 108 0.5432 0.9136 0.4568 109 0.5129 0.9743 0.4871 110 0.4799 0.9598 0.5201 111 0.8044 0.3912 0.1956 112 0.791 0.4181 0.209 113 0.7869 0.4263 0.2131 114 0.7607 0.4787 0.2393 115 0.741 0.518 0.259 116 0.7843 0.4313 0.2157 117 0.7547 0.4906 0.2453 118 0.7189 0.5621 0.2811 119 0.8239 0.3522 0.1761 120 0.8229 0.3542 0.1771 121 0.7915 0.417 0.2085 122 0.7697 0.4605 0.2303 123 0.8249 0.3502 0.1751 124 0.7942 0.4116 0.2058 125 0.7642 0.4716 0.2358 126 0.7305 0.5389 0.2695 127 0.706 0.588 0.294 128 0.6636 0.6728 0.3364 129 0.6895 0.6209 0.3105 130 0.8169 0.3662 0.1831 131 0.7962 0.4076 0.2038 132 0.7598 0.4804 0.2402 133 0.7456 0.5087 0.2544 134 0.7177 0.5647 0.2823 135 0.8469 0.3062 0.1531 136 0.8327 0.3345 0.1673 137 0.8004 0.3991 0.1996 138 0.7818 0.4364 0.2182 139 0.7811 0.4377 0.2189 140 0.8834 0.2331 0.1166 141 0.8754 0.2493 0.1246 142 0.8506 0.2987 0.1494 143 0.9197 0.1606 0.08029 144 0.8983 0.2034 0.1017 145 0.8773 0.2454 0.1227 146 0.8688 0.2624 0.1312 147 0.8798 0.2405 0.1202 148 0.8491 0.3018 0.1509 149 0.8706 0.2588 0.1294 150 0.9304 0.1392 0.0696 151 0.9061 0.1878 0.09389 152 0.8872 0.2256 0.1128 153 0.8551 0.2899 0.1449 154 0.8981 0.2038 0.1019 155 0.8627 0.2745 0.1373 156 0.8644 0.2713 0.1356 157 0.8218 0.3564 0.1782 158 0.8112 0.3776 0.1888 159 0.7541 0.4918 0.2459 160 0.7298 0.5403 0.2702 161 0.8397 0.3206 0.1603 162 0.8694 0.2613 0.1306 163 0.8536 0.2927 0.1464 164 0.8356 0.3287 0.1644 165 0.7678 0.4644 0.2322 166 0.8112 0.3775 0.1888 167 0.778 0.4439 0.222 168 0.6855 0.6289 0.3145 169 0.6057 0.7886 0.3943 170 0.4746 0.9492 0.5254 171 0.4236 0.8471 0.5764 172 0.4825 0.9649 0.5175

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
7 &  0.7195 &  0.561 &  0.2805 \tabularnewline
8 &  0.5766 &  0.8468 &  0.4234 \tabularnewline
9 &  0.5462 &  0.9077 &  0.4538 \tabularnewline
10 &  0.5939 &  0.8122 &  0.4061 \tabularnewline
11 &  0.5434 &  0.9132 &  0.4566 \tabularnewline
12 &  0.54 &  0.92 &  0.46 \tabularnewline
13 &  0.4566 &  0.9133 &  0.5434 \tabularnewline
14 &  0.7585 &  0.483 &  0.2415 \tabularnewline
15 &  0.9561 &  0.08785 &  0.04392 \tabularnewline
16 &  0.9346 &  0.1307 &  0.06535 \tabularnewline
17 &  0.9093 &  0.1814 &  0.09071 \tabularnewline
18 &  0.9511 &  0.09786 &  0.04893 \tabularnewline
19 &  0.9303 &  0.1393 &  0.06965 \tabularnewline
20 &  0.9225 &  0.1549 &  0.07747 \tabularnewline
21 &  0.9066 &  0.1869 &  0.09344 \tabularnewline
22 &  0.8771 &  0.2459 &  0.1229 \tabularnewline
23 &  0.8398 &  0.3204 &  0.1602 \tabularnewline
24 &  0.7972 &  0.4056 &  0.2028 \tabularnewline
25 &  0.8118 &  0.3765 &  0.1882 \tabularnewline
26 &  0.8319 &  0.3362 &  0.1681 \tabularnewline
27 &  0.7902 &  0.4195 &  0.2098 \tabularnewline
28 &  0.7542 &  0.4915 &  0.2458 \tabularnewline
29 &  0.7204 &  0.5592 &  0.2796 \tabularnewline
30 &  0.7057 &  0.5887 &  0.2943 \tabularnewline
31 &  0.838 &  0.324 &  0.162 \tabularnewline
32 &  0.8054 &  0.3891 &  0.1946 \tabularnewline
33 &  0.7644 &  0.4711 &  0.2356 \tabularnewline
34 &  0.7265 &  0.547 &  0.2735 \tabularnewline
35 &  0.68 &  0.6399 &  0.32 \tabularnewline
36 &  0.6653 &  0.6695 &  0.3347 \tabularnewline
37 &  0.6301 &  0.7398 &  0.3699 \tabularnewline
38 &  0.6003 &  0.7994 &  0.3997 \tabularnewline
39 &  0.5475 &  0.905 &  0.4525 \tabularnewline
40 &  0.4962 &  0.9925 &  0.5038 \tabularnewline
41 &  0.5213 &  0.9573 &  0.4787 \tabularnewline
42 &  0.469 &  0.938 &  0.531 \tabularnewline
43 &  0.4787 &  0.9575 &  0.5213 \tabularnewline
44 &  0.4295 &  0.859 &  0.5705 \tabularnewline
45 &  0.3823 &  0.7647 &  0.6177 \tabularnewline
46 &  0.4278 &  0.8556 &  0.5722 \tabularnewline
47 &  0.4095 &  0.819 &  0.5905 \tabularnewline
48 &  0.3689 &  0.7378 &  0.6311 \tabularnewline
49 &  0.4886 &  0.9772 &  0.5114 \tabularnewline
50 &  0.4629 &  0.9257 &  0.5371 \tabularnewline
51 &  0.4356 &  0.8713 &  0.5644 \tabularnewline
52 &  0.3902 &  0.7804 &  0.6098 \tabularnewline
53 &  0.3529 &  0.7058 &  0.6471 \tabularnewline
54 &  0.3263 &  0.6527 &  0.6737 \tabularnewline
55 &  0.3369 &  0.6737 &  0.6631 \tabularnewline
56 &  0.2973 &  0.5945 &  0.7027 \tabularnewline
57 &  0.3027 &  0.6054 &  0.6973 \tabularnewline
58 &  0.5662 &  0.8676 &  0.4338 \tabularnewline
59 &  0.5376 &  0.9248 &  0.4624 \tabularnewline
60 &  0.4922 &  0.9844 &  0.5078 \tabularnewline
61 &  0.4662 &  0.9324 &  0.5338 \tabularnewline
62 &  0.4219 &  0.8438 &  0.5781 \tabularnewline
63 &  0.4087 &  0.8173 &  0.5913 \tabularnewline
64 &  0.3791 &  0.7583 &  0.6209 \tabularnewline
65 &  0.5217 &  0.9566 &  0.4783 \tabularnewline
66 &  0.5805 &  0.839 &  0.4195 \tabularnewline
67 &  0.6735 &  0.6531 &  0.3265 \tabularnewline
68 &  0.6541 &  0.6918 &  0.3459 \tabularnewline
69 &  0.6202 &  0.7596 &  0.3798 \tabularnewline
70 &  0.5929 &  0.8143 &  0.4071 \tabularnewline
71 &  0.5511 &  0.8979 &  0.4489 \tabularnewline
72 &  0.5794 &  0.8412 &  0.4206 \tabularnewline
73 &  0.573 &  0.854 &  0.427 \tabularnewline
74 &  0.6555 &  0.6889 &  0.3445 \tabularnewline
75 &  0.6942 &  0.6116 &  0.3058 \tabularnewline
76 &  0.7515 &  0.4971 &  0.2485 \tabularnewline
77 &  0.8409 &  0.3183 &  0.1591 \tabularnewline
78 &  0.8222 &  0.3556 &  0.1778 \tabularnewline
79 &  0.7939 &  0.4122 &  0.2061 \tabularnewline
80 &  0.7636 &  0.4729 &  0.2364 \tabularnewline
81 &  0.7626 &  0.4748 &  0.2374 \tabularnewline
82 &  0.7567 &  0.4866 &  0.2433 \tabularnewline
83 &  0.7223 &  0.5555 &  0.2777 \tabularnewline
84 &  0.7084 &  0.5831 &  0.2916 \tabularnewline
85 &  0.6767 &  0.6466 &  0.3233 \tabularnewline
86 &  0.6434 &  0.7133 &  0.3566 \tabularnewline
87 &  0.6145 &  0.7709 &  0.3855 \tabularnewline
88 &  0.5764 &  0.8473 &  0.4236 \tabularnewline
89 &  0.5347 &  0.9306 &  0.4653 \tabularnewline
90 &  0.4944 &  0.9889 &  0.5056 \tabularnewline
91 &  0.625 &  0.75 &  0.375 \tabularnewline
92 &  0.6341 &  0.7317 &  0.3659 \tabularnewline
93 &  0.5987 &  0.8026 &  0.4013 \tabularnewline
94 &  0.56 &  0.8799 &  0.44 \tabularnewline
95 &  0.5273 &  0.9454 &  0.4727 \tabularnewline
96 &  0.5097 &  0.9805 &  0.4903 \tabularnewline
97 &  0.4737 &  0.9474 &  0.5263 \tabularnewline
98 &  0.4347 &  0.8694 &  0.5653 \tabularnewline
99 &  0.4835 &  0.9671 &  0.5165 \tabularnewline
100 &  0.4478 &  0.8955 &  0.5522 \tabularnewline
101 &  0.4907 &  0.9813 &  0.5093 \tabularnewline
102 &  0.5104 &  0.9792 &  0.4896 \tabularnewline
103 &  0.4742 &  0.9485 &  0.5258 \tabularnewline
104 &  0.4379 &  0.8758 &  0.5621 \tabularnewline
105 &  0.5456 &  0.9088 &  0.4544 \tabularnewline
106 &  0.521 &  0.9581 &  0.479 \tabularnewline
107 &  0.5867 &  0.8266 &  0.4133 \tabularnewline
108 &  0.5432 &  0.9136 &  0.4568 \tabularnewline
109 &  0.5129 &  0.9743 &  0.4871 \tabularnewline
110 &  0.4799 &  0.9598 &  0.5201 \tabularnewline
111 &  0.8044 &  0.3912 &  0.1956 \tabularnewline
112 &  0.791 &  0.4181 &  0.209 \tabularnewline
113 &  0.7869 &  0.4263 &  0.2131 \tabularnewline
114 &  0.7607 &  0.4787 &  0.2393 \tabularnewline
115 &  0.741 &  0.518 &  0.259 \tabularnewline
116 &  0.7843 &  0.4313 &  0.2157 \tabularnewline
117 &  0.7547 &  0.4906 &  0.2453 \tabularnewline
118 &  0.7189 &  0.5621 &  0.2811 \tabularnewline
119 &  0.8239 &  0.3522 &  0.1761 \tabularnewline
120 &  0.8229 &  0.3542 &  0.1771 \tabularnewline
121 &  0.7915 &  0.417 &  0.2085 \tabularnewline
122 &  0.7697 &  0.4605 &  0.2303 \tabularnewline
123 &  0.8249 &  0.3502 &  0.1751 \tabularnewline
124 &  0.7942 &  0.4116 &  0.2058 \tabularnewline
125 &  0.7642 &  0.4716 &  0.2358 \tabularnewline
126 &  0.7305 &  0.5389 &  0.2695 \tabularnewline
127 &  0.706 &  0.588 &  0.294 \tabularnewline
128 &  0.6636 &  0.6728 &  0.3364 \tabularnewline
129 &  0.6895 &  0.6209 &  0.3105 \tabularnewline
130 &  0.8169 &  0.3662 &  0.1831 \tabularnewline
131 &  0.7962 &  0.4076 &  0.2038 \tabularnewline
132 &  0.7598 &  0.4804 &  0.2402 \tabularnewline
133 &  0.7456 &  0.5087 &  0.2544 \tabularnewline
134 &  0.7177 &  0.5647 &  0.2823 \tabularnewline
135 &  0.8469 &  0.3062 &  0.1531 \tabularnewline
136 &  0.8327 &  0.3345 &  0.1673 \tabularnewline
137 &  0.8004 &  0.3991 &  0.1996 \tabularnewline
138 &  0.7818 &  0.4364 &  0.2182 \tabularnewline
139 &  0.7811 &  0.4377 &  0.2189 \tabularnewline
140 &  0.8834 &  0.2331 &  0.1166 \tabularnewline
141 &  0.8754 &  0.2493 &  0.1246 \tabularnewline
142 &  0.8506 &  0.2987 &  0.1494 \tabularnewline
143 &  0.9197 &  0.1606 &  0.08029 \tabularnewline
144 &  0.8983 &  0.2034 &  0.1017 \tabularnewline
145 &  0.8773 &  0.2454 &  0.1227 \tabularnewline
146 &  0.8688 &  0.2624 &  0.1312 \tabularnewline
147 &  0.8798 &  0.2405 &  0.1202 \tabularnewline
148 &  0.8491 &  0.3018 &  0.1509 \tabularnewline
149 &  0.8706 &  0.2588 &  0.1294 \tabularnewline
150 &  0.9304 &  0.1392 &  0.0696 \tabularnewline
151 &  0.9061 &  0.1878 &  0.09389 \tabularnewline
152 &  0.8872 &  0.2256 &  0.1128 \tabularnewline
153 &  0.8551 &  0.2899 &  0.1449 \tabularnewline
154 &  0.8981 &  0.2038 &  0.1019 \tabularnewline
155 &  0.8627 &  0.2745 &  0.1373 \tabularnewline
156 &  0.8644 &  0.2713 &  0.1356 \tabularnewline
157 &  0.8218 &  0.3564 &  0.1782 \tabularnewline
158 &  0.8112 &  0.3776 &  0.1888 \tabularnewline
159 &  0.7541 &  0.4918 &  0.2459 \tabularnewline
160 &  0.7298 &  0.5403 &  0.2702 \tabularnewline
161 &  0.8397 &  0.3206 &  0.1603 \tabularnewline
162 &  0.8694 &  0.2613 &  0.1306 \tabularnewline
163 &  0.8536 &  0.2927 &  0.1464 \tabularnewline
164 &  0.8356 &  0.3287 &  0.1644 \tabularnewline
165 &  0.7678 &  0.4644 &  0.2322 \tabularnewline
166 &  0.8112 &  0.3775 &  0.1888 \tabularnewline
167 &  0.778 &  0.4439 &  0.222 \tabularnewline
168 &  0.6855 &  0.6289 &  0.3145 \tabularnewline
169 &  0.6057 &  0.7886 &  0.3943 \tabularnewline
170 &  0.4746 &  0.9492 &  0.5254 \tabularnewline
171 &  0.4236 &  0.8471 &  0.5764 \tabularnewline
172 &  0.4825 &  0.9649 &  0.5175 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=319053&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]7[/C][C] 0.7195[/C][C] 0.561[/C][C] 0.2805[/C][/ROW]
[ROW][C]8[/C][C] 0.5766[/C][C] 0.8468[/C][C] 0.4234[/C][/ROW]
[ROW][C]9[/C][C] 0.5462[/C][C] 0.9077[/C][C] 0.4538[/C][/ROW]
[ROW][C]10[/C][C] 0.5939[/C][C] 0.8122[/C][C] 0.4061[/C][/ROW]
[ROW][C]11[/C][C] 0.5434[/C][C] 0.9132[/C][C] 0.4566[/C][/ROW]
[ROW][C]12[/C][C] 0.54[/C][C] 0.92[/C][C] 0.46[/C][/ROW]
[ROW][C]13[/C][C] 0.4566[/C][C] 0.9133[/C][C] 0.5434[/C][/ROW]
[ROW][C]14[/C][C] 0.7585[/C][C] 0.483[/C][C] 0.2415[/C][/ROW]
[ROW][C]15[/C][C] 0.9561[/C][C] 0.08785[/C][C] 0.04392[/C][/ROW]
[ROW][C]16[/C][C] 0.9346[/C][C] 0.1307[/C][C] 0.06535[/C][/ROW]
[ROW][C]17[/C][C] 0.9093[/C][C] 0.1814[/C][C] 0.09071[/C][/ROW]
[ROW][C]18[/C][C] 0.9511[/C][C] 0.09786[/C][C] 0.04893[/C][/ROW]
[ROW][C]19[/C][C] 0.9303[/C][C] 0.1393[/C][C] 0.06965[/C][/ROW]
[ROW][C]20[/C][C] 0.9225[/C][C] 0.1549[/C][C] 0.07747[/C][/ROW]
[ROW][C]21[/C][C] 0.9066[/C][C] 0.1869[/C][C] 0.09344[/C][/ROW]
[ROW][C]22[/C][C] 0.8771[/C][C] 0.2459[/C][C] 0.1229[/C][/ROW]
[ROW][C]23[/C][C] 0.8398[/C][C] 0.3204[/C][C] 0.1602[/C][/ROW]
[ROW][C]24[/C][C] 0.7972[/C][C] 0.4056[/C][C] 0.2028[/C][/ROW]
[ROW][C]25[/C][C] 0.8118[/C][C] 0.3765[/C][C] 0.1882[/C][/ROW]
[ROW][C]26[/C][C] 0.8319[/C][C] 0.3362[/C][C] 0.1681[/C][/ROW]
[ROW][C]27[/C][C] 0.7902[/C][C] 0.4195[/C][C] 0.2098[/C][/ROW]
[ROW][C]28[/C][C] 0.7542[/C][C] 0.4915[/C][C] 0.2458[/C][/ROW]
[ROW][C]29[/C][C] 0.7204[/C][C] 0.5592[/C][C] 0.2796[/C][/ROW]
[ROW][C]30[/C][C] 0.7057[/C][C] 0.5887[/C][C] 0.2943[/C][/ROW]
[ROW][C]31[/C][C] 0.838[/C][C] 0.324[/C][C] 0.162[/C][/ROW]
[ROW][C]32[/C][C] 0.8054[/C][C] 0.3891[/C][C] 0.1946[/C][/ROW]
[ROW][C]33[/C][C] 0.7644[/C][C] 0.4711[/C][C] 0.2356[/C][/ROW]
[ROW][C]34[/C][C] 0.7265[/C][C] 0.547[/C][C] 0.2735[/C][/ROW]
[ROW][C]35[/C][C] 0.68[/C][C] 0.6399[/C][C] 0.32[/C][/ROW]
[ROW][C]36[/C][C] 0.6653[/C][C] 0.6695[/C][C] 0.3347[/C][/ROW]
[ROW][C]37[/C][C] 0.6301[/C][C] 0.7398[/C][C] 0.3699[/C][/ROW]
[ROW][C]38[/C][C] 0.6003[/C][C] 0.7994[/C][C] 0.3997[/C][/ROW]
[ROW][C]39[/C][C] 0.5475[/C][C] 0.905[/C][C] 0.4525[/C][/ROW]
[ROW][C]40[/C][C] 0.4962[/C][C] 0.9925[/C][C] 0.5038[/C][/ROW]
[ROW][C]41[/C][C] 0.5213[/C][C] 0.9573[/C][C] 0.4787[/C][/ROW]
[ROW][C]42[/C][C] 0.469[/C][C] 0.938[/C][C] 0.531[/C][/ROW]
[ROW][C]43[/C][C] 0.4787[/C][C] 0.9575[/C][C] 0.5213[/C][/ROW]
[ROW][C]44[/C][C] 0.4295[/C][C] 0.859[/C][C] 0.5705[/C][/ROW]
[ROW][C]45[/C][C] 0.3823[/C][C] 0.7647[/C][C] 0.6177[/C][/ROW]
[ROW][C]46[/C][C] 0.4278[/C][C] 0.8556[/C][C] 0.5722[/C][/ROW]
[ROW][C]47[/C][C] 0.4095[/C][C] 0.819[/C][C] 0.5905[/C][/ROW]
[ROW][C]48[/C][C] 0.3689[/C][C] 0.7378[/C][C] 0.6311[/C][/ROW]
[ROW][C]49[/C][C] 0.4886[/C][C] 0.9772[/C][C] 0.5114[/C][/ROW]
[ROW][C]50[/C][C] 0.4629[/C][C] 0.9257[/C][C] 0.5371[/C][/ROW]
[ROW][C]51[/C][C] 0.4356[/C][C] 0.8713[/C][C] 0.5644[/C][/ROW]
[ROW][C]52[/C][C] 0.3902[/C][C] 0.7804[/C][C] 0.6098[/C][/ROW]
[ROW][C]53[/C][C] 0.3529[/C][C] 0.7058[/C][C] 0.6471[/C][/ROW]
[ROW][C]54[/C][C] 0.3263[/C][C] 0.6527[/C][C] 0.6737[/C][/ROW]
[ROW][C]55[/C][C] 0.3369[/C][C] 0.6737[/C][C] 0.6631[/C][/ROW]
[ROW][C]56[/C][C] 0.2973[/C][C] 0.5945[/C][C] 0.7027[/C][/ROW]
[ROW][C]57[/C][C] 0.3027[/C][C] 0.6054[/C][C] 0.6973[/C][/ROW]
[ROW][C]58[/C][C] 0.5662[/C][C] 0.8676[/C][C] 0.4338[/C][/ROW]
[ROW][C]59[/C][C] 0.5376[/C][C] 0.9248[/C][C] 0.4624[/C][/ROW]
[ROW][C]60[/C][C] 0.4922[/C][C] 0.9844[/C][C] 0.5078[/C][/ROW]
[ROW][C]61[/C][C] 0.4662[/C][C] 0.9324[/C][C] 0.5338[/C][/ROW]
[ROW][C]62[/C][C] 0.4219[/C][C] 0.8438[/C][C] 0.5781[/C][/ROW]
[ROW][C]63[/C][C] 0.4087[/C][C] 0.8173[/C][C] 0.5913[/C][/ROW]
[ROW][C]64[/C][C] 0.3791[/C][C] 0.7583[/C][C] 0.6209[/C][/ROW]
[ROW][C]65[/C][C] 0.5217[/C][C] 0.9566[/C][C] 0.4783[/C][/ROW]
[ROW][C]66[/C][C] 0.5805[/C][C] 0.839[/C][C] 0.4195[/C][/ROW]
[ROW][C]67[/C][C] 0.6735[/C][C] 0.6531[/C][C] 0.3265[/C][/ROW]
[ROW][C]68[/C][C] 0.6541[/C][C] 0.6918[/C][C] 0.3459[/C][/ROW]
[ROW][C]69[/C][C] 0.6202[/C][C] 0.7596[/C][C] 0.3798[/C][/ROW]
[ROW][C]70[/C][C] 0.5929[/C][C] 0.8143[/C][C] 0.4071[/C][/ROW]
[ROW][C]71[/C][C] 0.5511[/C][C] 0.8979[/C][C] 0.4489[/C][/ROW]
[ROW][C]72[/C][C] 0.5794[/C][C] 0.8412[/C][C] 0.4206[/C][/ROW]
[ROW][C]73[/C][C] 0.573[/C][C] 0.854[/C][C] 0.427[/C][/ROW]
[ROW][C]74[/C][C] 0.6555[/C][C] 0.6889[/C][C] 0.3445[/C][/ROW]
[ROW][C]75[/C][C] 0.6942[/C][C] 0.6116[/C][C] 0.3058[/C][/ROW]
[ROW][C]76[/C][C] 0.7515[/C][C] 0.4971[/C][C] 0.2485[/C][/ROW]
[ROW][C]77[/C][C] 0.8409[/C][C] 0.3183[/C][C] 0.1591[/C][/ROW]
[ROW][C]78[/C][C] 0.8222[/C][C] 0.3556[/C][C] 0.1778[/C][/ROW]
[ROW][C]79[/C][C] 0.7939[/C][C] 0.4122[/C][C] 0.2061[/C][/ROW]
[ROW][C]80[/C][C] 0.7636[/C][C] 0.4729[/C][C] 0.2364[/C][/ROW]
[ROW][C]81[/C][C] 0.7626[/C][C] 0.4748[/C][C] 0.2374[/C][/ROW]
[ROW][C]82[/C][C] 0.7567[/C][C] 0.4866[/C][C] 0.2433[/C][/ROW]
[ROW][C]83[/C][C] 0.7223[/C][C] 0.5555[/C][C] 0.2777[/C][/ROW]
[ROW][C]84[/C][C] 0.7084[/C][C] 0.5831[/C][C] 0.2916[/C][/ROW]
[ROW][C]85[/C][C] 0.6767[/C][C] 0.6466[/C][C] 0.3233[/C][/ROW]
[ROW][C]86[/C][C] 0.6434[/C][C] 0.7133[/C][C] 0.3566[/C][/ROW]
[ROW][C]87[/C][C] 0.6145[/C][C] 0.7709[/C][C] 0.3855[/C][/ROW]
[ROW][C]88[/C][C] 0.5764[/C][C] 0.8473[/C][C] 0.4236[/C][/ROW]
[ROW][C]89[/C][C] 0.5347[/C][C] 0.9306[/C][C] 0.4653[/C][/ROW]
[ROW][C]90[/C][C] 0.4944[/C][C] 0.9889[/C][C] 0.5056[/C][/ROW]
[ROW][C]91[/C][C] 0.625[/C][C] 0.75[/C][C] 0.375[/C][/ROW]
[ROW][C]92[/C][C] 0.6341[/C][C] 0.7317[/C][C] 0.3659[/C][/ROW]
[ROW][C]93[/C][C] 0.5987[/C][C] 0.8026[/C][C] 0.4013[/C][/ROW]
[ROW][C]94[/C][C] 0.56[/C][C] 0.8799[/C][C] 0.44[/C][/ROW]
[ROW][C]95[/C][C] 0.5273[/C][C] 0.9454[/C][C] 0.4727[/C][/ROW]
[ROW][C]96[/C][C] 0.5097[/C][C] 0.9805[/C][C] 0.4903[/C][/ROW]
[ROW][C]97[/C][C] 0.4737[/C][C] 0.9474[/C][C] 0.5263[/C][/ROW]
[ROW][C]98[/C][C] 0.4347[/C][C] 0.8694[/C][C] 0.5653[/C][/ROW]
[ROW][C]99[/C][C] 0.4835[/C][C] 0.9671[/C][C] 0.5165[/C][/ROW]
[ROW][C]100[/C][C] 0.4478[/C][C] 0.8955[/C][C] 0.5522[/C][/ROW]
[ROW][C]101[/C][C] 0.4907[/C][C] 0.9813[/C][C] 0.5093[/C][/ROW]
[ROW][C]102[/C][C] 0.5104[/C][C] 0.9792[/C][C] 0.4896[/C][/ROW]
[ROW][C]103[/C][C] 0.4742[/C][C] 0.9485[/C][C] 0.5258[/C][/ROW]
[ROW][C]104[/C][C] 0.4379[/C][C] 0.8758[/C][C] 0.5621[/C][/ROW]
[ROW][C]105[/C][C] 0.5456[/C][C] 0.9088[/C][C] 0.4544[/C][/ROW]
[ROW][C]106[/C][C] 0.521[/C][C] 0.9581[/C][C] 0.479[/C][/ROW]
[ROW][C]107[/C][C] 0.5867[/C][C] 0.8266[/C][C] 0.4133[/C][/ROW]
[ROW][C]108[/C][C] 0.5432[/C][C] 0.9136[/C][C] 0.4568[/C][/ROW]
[ROW][C]109[/C][C] 0.5129[/C][C] 0.9743[/C][C] 0.4871[/C][/ROW]
[ROW][C]110[/C][C] 0.4799[/C][C] 0.9598[/C][C] 0.5201[/C][/ROW]
[ROW][C]111[/C][C] 0.8044[/C][C] 0.3912[/C][C] 0.1956[/C][/ROW]
[ROW][C]112[/C][C] 0.791[/C][C] 0.4181[/C][C] 0.209[/C][/ROW]
[ROW][C]113[/C][C] 0.7869[/C][C] 0.4263[/C][C] 0.2131[/C][/ROW]
[ROW][C]114[/C][C] 0.7607[/C][C] 0.4787[/C][C] 0.2393[/C][/ROW]
[ROW][C]115[/C][C] 0.741[/C][C] 0.518[/C][C] 0.259[/C][/ROW]
[ROW][C]116[/C][C] 0.7843[/C][C] 0.4313[/C][C] 0.2157[/C][/ROW]
[ROW][C]117[/C][C] 0.7547[/C][C] 0.4906[/C][C] 0.2453[/C][/ROW]
[ROW][C]118[/C][C] 0.7189[/C][C] 0.5621[/C][C] 0.2811[/C][/ROW]
[ROW][C]119[/C][C] 0.8239[/C][C] 0.3522[/C][C] 0.1761[/C][/ROW]
[ROW][C]120[/C][C] 0.8229[/C][C] 0.3542[/C][C] 0.1771[/C][/ROW]
[ROW][C]121[/C][C] 0.7915[/C][C] 0.417[/C][C] 0.2085[/C][/ROW]
[ROW][C]122[/C][C] 0.7697[/C][C] 0.4605[/C][C] 0.2303[/C][/ROW]
[ROW][C]123[/C][C] 0.8249[/C][C] 0.3502[/C][C] 0.1751[/C][/ROW]
[ROW][C]124[/C][C] 0.7942[/C][C] 0.4116[/C][C] 0.2058[/C][/ROW]
[ROW][C]125[/C][C] 0.7642[/C][C] 0.4716[/C][C] 0.2358[/C][/ROW]
[ROW][C]126[/C][C] 0.7305[/C][C] 0.5389[/C][C] 0.2695[/C][/ROW]
[ROW][C]127[/C][C] 0.706[/C][C] 0.588[/C][C] 0.294[/C][/ROW]
[ROW][C]128[/C][C] 0.6636[/C][C] 0.6728[/C][C] 0.3364[/C][/ROW]
[ROW][C]129[/C][C] 0.6895[/C][C] 0.6209[/C][C] 0.3105[/C][/ROW]
[ROW][C]130[/C][C] 0.8169[/C][C] 0.3662[/C][C] 0.1831[/C][/ROW]
[ROW][C]131[/C][C] 0.7962[/C][C] 0.4076[/C][C] 0.2038[/C][/ROW]
[ROW][C]132[/C][C] 0.7598[/C][C] 0.4804[/C][C] 0.2402[/C][/ROW]
[ROW][C]133[/C][C] 0.7456[/C][C] 0.5087[/C][C] 0.2544[/C][/ROW]
[ROW][C]134[/C][C] 0.7177[/C][C] 0.5647[/C][C] 0.2823[/C][/ROW]
[ROW][C]135[/C][C] 0.8469[/C][C] 0.3062[/C][C] 0.1531[/C][/ROW]
[ROW][C]136[/C][C] 0.8327[/C][C] 0.3345[/C][C] 0.1673[/C][/ROW]
[ROW][C]137[/C][C] 0.8004[/C][C] 0.3991[/C][C] 0.1996[/C][/ROW]
[ROW][C]138[/C][C] 0.7818[/C][C] 0.4364[/C][C] 0.2182[/C][/ROW]
[ROW][C]139[/C][C] 0.7811[/C][C] 0.4377[/C][C] 0.2189[/C][/ROW]
[ROW][C]140[/C][C] 0.8834[/C][C] 0.2331[/C][C] 0.1166[/C][/ROW]
[ROW][C]141[/C][C] 0.8754[/C][C] 0.2493[/C][C] 0.1246[/C][/ROW]
[ROW][C]142[/C][C] 0.8506[/C][C] 0.2987[/C][C] 0.1494[/C][/ROW]
[ROW][C]143[/C][C] 0.9197[/C][C] 0.1606[/C][C] 0.08029[/C][/ROW]
[ROW][C]144[/C][C] 0.8983[/C][C] 0.2034[/C][C] 0.1017[/C][/ROW]
[ROW][C]145[/C][C] 0.8773[/C][C] 0.2454[/C][C] 0.1227[/C][/ROW]
[ROW][C]146[/C][C] 0.8688[/C][C] 0.2624[/C][C] 0.1312[/C][/ROW]
[ROW][C]147[/C][C] 0.8798[/C][C] 0.2405[/C][C] 0.1202[/C][/ROW]
[ROW][C]148[/C][C] 0.8491[/C][C] 0.3018[/C][C] 0.1509[/C][/ROW]
[ROW][C]149[/C][C] 0.8706[/C][C] 0.2588[/C][C] 0.1294[/C][/ROW]
[ROW][C]150[/C][C] 0.9304[/C][C] 0.1392[/C][C] 0.0696[/C][/ROW]
[ROW][C]151[/C][C] 0.9061[/C][C] 0.1878[/C][C] 0.09389[/C][/ROW]
[ROW][C]152[/C][C] 0.8872[/C][C] 0.2256[/C][C] 0.1128[/C][/ROW]
[ROW][C]153[/C][C] 0.8551[/C][C] 0.2899[/C][C] 0.1449[/C][/ROW]
[ROW][C]154[/C][C] 0.8981[/C][C] 0.2038[/C][C] 0.1019[/C][/ROW]
[ROW][C]155[/C][C] 0.8627[/C][C] 0.2745[/C][C] 0.1373[/C][/ROW]
[ROW][C]156[/C][C] 0.8644[/C][C] 0.2713[/C][C] 0.1356[/C][/ROW]
[ROW][C]157[/C][C] 0.8218[/C][C] 0.3564[/C][C] 0.1782[/C][/ROW]
[ROW][C]158[/C][C] 0.8112[/C][C] 0.3776[/C][C] 0.1888[/C][/ROW]
[ROW][C]159[/C][C] 0.7541[/C][C] 0.4918[/C][C] 0.2459[/C][/ROW]
[ROW][C]160[/C][C] 0.7298[/C][C] 0.5403[/C][C] 0.2702[/C][/ROW]
[ROW][C]161[/C][C] 0.8397[/C][C] 0.3206[/C][C] 0.1603[/C][/ROW]
[ROW][C]162[/C][C] 0.8694[/C][C] 0.2613[/C][C] 0.1306[/C][/ROW]
[ROW][C]163[/C][C] 0.8536[/C][C] 0.2927[/C][C] 0.1464[/C][/ROW]
[ROW][C]164[/C][C] 0.8356[/C][C] 0.3287[/C][C] 0.1644[/C][/ROW]
[ROW][C]165[/C][C] 0.7678[/C][C] 0.4644[/C][C] 0.2322[/C][/ROW]
[ROW][C]166[/C][C] 0.8112[/C][C] 0.3775[/C][C] 0.1888[/C][/ROW]
[ROW][C]167[/C][C] 0.778[/C][C] 0.4439[/C][C] 0.222[/C][/ROW]
[ROW][C]168[/C][C] 0.6855[/C][C] 0.6289[/C][C] 0.3145[/C][/ROW]
[ROW][C]169[/C][C] 0.6057[/C][C] 0.7886[/C][C] 0.3943[/C][/ROW]
[ROW][C]170[/C][C] 0.4746[/C][C] 0.9492[/C][C] 0.5254[/C][/ROW]
[ROW][C]171[/C][C] 0.4236[/C][C] 0.8471[/C][C] 0.5764[/C][/ROW]
[ROW][C]172[/C][C] 0.4825[/C][C] 0.9649[/C][C] 0.5175[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=319053&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=319053&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-values Alternative Hypothesis breakpoint index greater 2-sided less 7 0.7195 0.561 0.2805 8 0.5766 0.8468 0.4234 9 0.5462 0.9077 0.4538 10 0.5939 0.8122 0.4061 11 0.5434 0.9132 0.4566 12 0.54 0.92 0.46 13 0.4566 0.9133 0.5434 14 0.7585 0.483 0.2415 15 0.9561 0.08785 0.04392 16 0.9346 0.1307 0.06535 17 0.9093 0.1814 0.09071 18 0.9511 0.09786 0.04893 19 0.9303 0.1393 0.06965 20 0.9225 0.1549 0.07747 21 0.9066 0.1869 0.09344 22 0.8771 0.2459 0.1229 23 0.8398 0.3204 0.1602 24 0.7972 0.4056 0.2028 25 0.8118 0.3765 0.1882 26 0.8319 0.3362 0.1681 27 0.7902 0.4195 0.2098 28 0.7542 0.4915 0.2458 29 0.7204 0.5592 0.2796 30 0.7057 0.5887 0.2943 31 0.838 0.324 0.162 32 0.8054 0.3891 0.1946 33 0.7644 0.4711 0.2356 34 0.7265 0.547 0.2735 35 0.68 0.6399 0.32 36 0.6653 0.6695 0.3347 37 0.6301 0.7398 0.3699 38 0.6003 0.7994 0.3997 39 0.5475 0.905 0.4525 40 0.4962 0.9925 0.5038 41 0.5213 0.9573 0.4787 42 0.469 0.938 0.531 43 0.4787 0.9575 0.5213 44 0.4295 0.859 0.5705 45 0.3823 0.7647 0.6177 46 0.4278 0.8556 0.5722 47 0.4095 0.819 0.5905 48 0.3689 0.7378 0.6311 49 0.4886 0.9772 0.5114 50 0.4629 0.9257 0.5371 51 0.4356 0.8713 0.5644 52 0.3902 0.7804 0.6098 53 0.3529 0.7058 0.6471 54 0.3263 0.6527 0.6737 55 0.3369 0.6737 0.6631 56 0.2973 0.5945 0.7027 57 0.3027 0.6054 0.6973 58 0.5662 0.8676 0.4338 59 0.5376 0.9248 0.4624 60 0.4922 0.9844 0.5078 61 0.4662 0.9324 0.5338 62 0.4219 0.8438 0.5781 63 0.4087 0.8173 0.5913 64 0.3791 0.7583 0.6209 65 0.5217 0.9566 0.4783 66 0.5805 0.839 0.4195 67 0.6735 0.6531 0.3265 68 0.6541 0.6918 0.3459 69 0.6202 0.7596 0.3798 70 0.5929 0.8143 0.4071 71 0.5511 0.8979 0.4489 72 0.5794 0.8412 0.4206 73 0.573 0.854 0.427 74 0.6555 0.6889 0.3445 75 0.6942 0.6116 0.3058 76 0.7515 0.4971 0.2485 77 0.8409 0.3183 0.1591 78 0.8222 0.3556 0.1778 79 0.7939 0.4122 0.2061 80 0.7636 0.4729 0.2364 81 0.7626 0.4748 0.2374 82 0.7567 0.4866 0.2433 83 0.7223 0.5555 0.2777 84 0.7084 0.5831 0.2916 85 0.6767 0.6466 0.3233 86 0.6434 0.7133 0.3566 87 0.6145 0.7709 0.3855 88 0.5764 0.8473 0.4236 89 0.5347 0.9306 0.4653 90 0.4944 0.9889 0.5056 91 0.625 0.75 0.375 92 0.6341 0.7317 0.3659 93 0.5987 0.8026 0.4013 94 0.56 0.8799 0.44 95 0.5273 0.9454 0.4727 96 0.5097 0.9805 0.4903 97 0.4737 0.9474 0.5263 98 0.4347 0.8694 0.5653 99 0.4835 0.9671 0.5165 100 0.4478 0.8955 0.5522 101 0.4907 0.9813 0.5093 102 0.5104 0.9792 0.4896 103 0.4742 0.9485 0.5258 104 0.4379 0.8758 0.5621 105 0.5456 0.9088 0.4544 106 0.521 0.9581 0.479 107 0.5867 0.8266 0.4133 108 0.5432 0.9136 0.4568 109 0.5129 0.9743 0.4871 110 0.4799 0.9598 0.5201 111 0.8044 0.3912 0.1956 112 0.791 0.4181 0.209 113 0.7869 0.4263 0.2131 114 0.7607 0.4787 0.2393 115 0.741 0.518 0.259 116 0.7843 0.4313 0.2157 117 0.7547 0.4906 0.2453 118 0.7189 0.5621 0.2811 119 0.8239 0.3522 0.1761 120 0.8229 0.3542 0.1771 121 0.7915 0.417 0.2085 122 0.7697 0.4605 0.2303 123 0.8249 0.3502 0.1751 124 0.7942 0.4116 0.2058 125 0.7642 0.4716 0.2358 126 0.7305 0.5389 0.2695 127 0.706 0.588 0.294 128 0.6636 0.6728 0.3364 129 0.6895 0.6209 0.3105 130 0.8169 0.3662 0.1831 131 0.7962 0.4076 0.2038 132 0.7598 0.4804 0.2402 133 0.7456 0.5087 0.2544 134 0.7177 0.5647 0.2823 135 0.8469 0.3062 0.1531 136 0.8327 0.3345 0.1673 137 0.8004 0.3991 0.1996 138 0.7818 0.4364 0.2182 139 0.7811 0.4377 0.2189 140 0.8834 0.2331 0.1166 141 0.8754 0.2493 0.1246 142 0.8506 0.2987 0.1494 143 0.9197 0.1606 0.08029 144 0.8983 0.2034 0.1017 145 0.8773 0.2454 0.1227 146 0.8688 0.2624 0.1312 147 0.8798 0.2405 0.1202 148 0.8491 0.3018 0.1509 149 0.8706 0.2588 0.1294 150 0.9304 0.1392 0.0696 151 0.9061 0.1878 0.09389 152 0.8872 0.2256 0.1128 153 0.8551 0.2899 0.1449 154 0.8981 0.2038 0.1019 155 0.8627 0.2745 0.1373 156 0.8644 0.2713 0.1356 157 0.8218 0.3564 0.1782 158 0.8112 0.3776 0.1888 159 0.7541 0.4918 0.2459 160 0.7298 0.5403 0.2702 161 0.8397 0.3206 0.1603 162 0.8694 0.2613 0.1306 163 0.8536 0.2927 0.1464 164 0.8356 0.3287 0.1644 165 0.7678 0.4644 0.2322 166 0.8112 0.3775 0.1888 167 0.778 0.4439 0.222 168 0.6855 0.6289 0.3145 169 0.6057 0.7886 0.3943 170 0.4746 0.9492 0.5254 171 0.4236 0.8471 0.5764 172 0.4825 0.9649 0.5175

 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 0 0 OK 5% type I error level 0 0 OK 10% type I error level 2 0.0120482 OK

\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 & 2 & 0.0120482 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=319053&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]2[/C][C]0.0120482[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=319053&T=7

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=319053&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 tests OK/NOK 1% type I error level 0 0 OK 5% type I error level 0 0 OK 10% type I error level 2 0.0120482 OK

 Ramsey RESET F-Test for powers (2 and 3) of fitted values > reset_test_fitted RESET test data: mylm RESET = 4.6851, df1 = 2, df2 = 173, p-value = 0.01043  Ramsey RESET F-Test for powers (2 and 3) of regressors > reset_test_regressors RESET test data: mylm RESET = 2.3794, df1 = 6, df2 = 169, p-value = 0.03117  Ramsey RESET F-Test for powers (2 and 3) of principal components > reset_test_principal_components RESET test data: mylm RESET = 4.6541, df1 = 2, df2 = 173, p-value = 0.01075 

\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 = 4.6851, df1 = 2, df2 = 173, p-value = 0.01043
\tabularnewline
Ramsey RESET F-Test for powers (2 and 3) of regressors \tabularnewline
> reset_test_regressors
RESET test
data:  mylm
RESET = 2.3794, df1 = 6, df2 = 169, p-value = 0.03117
\tabularnewline
Ramsey RESET F-Test for powers (2 and 3) of principal components \tabularnewline
> reset_test_principal_components
RESET test
data:  mylm
RESET = 4.6541, df1 = 2, df2 = 173, p-value = 0.01075
\tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=319053&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 = 4.6851, df1 = 2, df2 = 173, p-value = 0.01043
[/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 = 2.3794, df1 = 6, df2 = 169, p-value = 0.03117
[/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 = 4.6541, df1 = 2, df2 = 173, p-value = 0.01075
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=319053&T=8

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=319053&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 = 4.6851, df1 = 2, df2 = 173, p-value = 0.01043  Ramsey RESET F-Test for powers (2 and 3) of regressors > reset_test_regressors RESET test data: mylm RESET = 2.3794, df1 = 6, df2 = 169, p-value = 0.03117  Ramsey RESET F-Test for powers (2 and 3) of principal components > reset_test_principal_components RESET test data: mylm RESET = 4.6541, df1 = 2, df2 = 173, p-value = 0.01075 

 Variance Inflation Factors (Multicollinearity) > vif System_Quality Information_Quality Perceived_Ease_of_Use 1.647271 2.586342 1.972540 

\begin{tabular}{lllllllll}
\hline
Variance Inflation Factors (Multicollinearity) \tabularnewline
> vif
System_Quality   Information_Quality Perceived_Ease_of_Use
1.647271              2.586342              1.972540
\tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=319053&T=9

[TABLE]
[ROW][C]Variance Inflation Factors (Multicollinearity)[/C][/ROW]
[ROW][C]> vif
System_Quality   Information_Quality Perceived_Ease_of_Use
1.647271              2.586342              1.972540
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=319053&T=9

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=319053&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 System_Quality Information_Quality Perceived_Ease_of_Use 1.647271 2.586342 1.972540 

library(lattice)library(lmtest)library(car)library(MASS)n25 <- 25 #minimum number of obs. for Goldfeld-Quandt testmywarning <- ''par6 <- as.numeric(par6)if(is.na(par6)) {par6 <- 12mywarning = 'Warning: you did not specify the seasonality. The seasonal period was set to s = 12.'}par1 <- as.numeric(par1)if(is.na(par1)) {par1 <- 1mywarning = 'Warning: you did not specify the column number of the endogenous series! The first column was selected by default.'}if (par4=='') par4 <- 0par4 <- as.numeric(par4)if (!is.numeric(par4)) par4 <- 0if (par5=='') par5 <- 0par5 <- as.numeric(par5)if (!is.numeric(par5)) par5 <- 0x <- 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 <- x1if (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 + 3nmkm3 <- n - k - 3gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))numgqtests <- 0numsignificant1 <- 0numsignificant5 <- 0numsignificant10 <- 0for (mypoint in kp3:nmkm3) {j <- 0numgqtests <- numgqtests + 1for (myalt in c('greater', 'two.sided', 'less')) {j <- j + 1gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value}if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1}gqarr}bitmap(file='test0.png')plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')points(x[,1]-mysum$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$resid))bitmap(file='test5.png')dum <- cbind(lag(myerror,k=1),myerror)dumdum1 <- dum[2:length(myerror),]dum1z <- as.data.frame(dum1)print(z)plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')lines(lowess(z))abline(lm(z))grid()dev.off()bitmap(file='test6.png')acf(mysum$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-STATH0: 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$resid)myra <-table.start()a <- table.row.start(a)a <- table.element(a,'Menu of Residual Diagnostics',2,TRUE)a <- table.row.end(a)a <- table.row.start(a)a <- table.element(a,'Description',1,TRUE)a <- table.element(a,'Link',1,TRUE)a <- table.row.end(a)a <- table.row.start(a)a <-table.element(a,'Histogram',1,header=TRUE)a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_histogram.wasp?convertgetintopost=1&data=',paste(as.character(mysum$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$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)a <- table.row.end(a)a<-table.end(a)table.save(a,file='mytable7.tab')if(n < 200) {a<-table.start()a<-table.row.start(a)a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)a<-table.row.end(a)a<-table.row.start(a)a<-table.element(a, 'Time or Index', 1, TRUE)a<-table.element(a, 'Actuals', 1, TRUE)a<-table.element(a, 'InterpolationForecast', 1, TRUE)a<-table.element(a, 'ResidualsPrediction Error', 1, TRUE)a<-table.row.end(a)for (i in 1:n) {a<-table.row.start(a)a<-table.element(a,i, 1, TRUE)a<-table.element(a,formatC(signif(x[i],6),format='g',flag=' '))a<-table.element(a,formatC(signif(x[i]-mysum$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')