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R Software Module

rwasp_multipleregression.wasp

Title produced by software

Multiple Regression

Date of computation

Fri, 15 Dec 2017 10:24:56 +0100

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Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2017/Dec/15/t1513349844mr8titidugo6m81.htm/, Retrieved Wed, 15 May 2024 02:31:33 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=309794, Retrieved Wed, 15 May 2024 02:31:33 +0000

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

-       [Multiple Regression] [MP seasonal] [2017-12-15 09:24:56] [10ffd28249f7eed11c347be075080a78] [Current]

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Dataseries X:

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53.1 58.4 97.7
64.1 64.8 88.9
75.3 73.8 96.5
66 65 89.5
73.6 73 85.4
73.2 71.1 84.3
53.5 58.2 83.7
60.6 64 86.2
73 75 90.7
72.4 74.9 95.7
75.8 75 95.6
79.6 68.3 97
77.8 72.5 97.2
75.7 72.4 86.6
88.5 79.6 88.4
72.9 70.7 81.4
80.8 76.4 86.9
86.6 79.7 84.9
63.8 64.2 83.7
69.2 67.9 86.8
76.5 74.1 88.3
77.1 78.5 92.5
75.3 73.4 94.7
69.5 65.4 94.5
64.3 69.9 98.7
66.7 69.6 88.6
77.3 76.8 95.2
75.3 75.6 91.3
73.4 74 91.7
78 76 89.3
61 68.1 88.7
58.4 65.5 91.2
73.4 76.9 88.6
82.3 81.7 94.6
72.2 73.6 96
76 68.7 94.3
64.3 73.3 102
70.8 71.5 93.4
74 78.3 96.7
71.4 76.5 93.7
70.1 71.8 91.6
77.6 77.6 89.6
61.2 70 92.9
52.1 64 94.1
74.4 81.3 92
73.1 82.5 97.5
70.9 73.1 92.7
80.7 78.1 100.7
62.9 70.7 105.9
69.3 74.9 95.3
82.3 88 99.8
76.2 81.3 91.3
70.8 75.7 90.8
87.3 89.8 87.1
62 74.6 91.4
66.9 74.9 86.1
84.4 90 87.1
82.6 88.1 92.6
77.7 84.9 96.6
87 87.7 105.3
76 80.5 102.4
76.3 79 98.2
88.8 89.9 98.6
81.2 86.3 92.6
74.5 81.1 87.9
98.1 92.4 84.1
63.3 71.8 86.7
67.7 76.1 84.4
85.8 92.5 86
78.6 87 90.4
87.2 89.5 92.9
106.4 88.7 105.8
75 83.8 106
80.4 84.9 99.1
94.8 99 99.9
77 84.6 88.1
91 92.7 87.8
96.7 97.6 87.1
69.2 78 85.9
69.5 81.9 86.5
93.7 96.5 84.1
98.5 99.9 92.1
93.3 96.2 93.3
100.4 90.5 98.9
87.4 91.4 103
89 89.7 98.4
106.1 102.7 100.7
92.5 91.5 92.3
96.6 96.2 89
113.3 104.5 88.9
87.6 90.3 85.5
89.2 90.3 90.1
115.6 100.4 87
133.2 111.3 97.1
111.1 101.3 101.5
113.1 94.4 103
102 100.4 106.1
109.3 102 96.1
111.1 104.3 94.2
116.8 108.8 89.1
107.5 101.3 85.2
120.5 108.9 86.5
95.5 98.5 88
87.9 88.8 88.4
118.6 111.8 87.9
116.3 109.6 95.7
98.8 92.5 94.8
102.9 94.5 105.2
80.4 80.8 108.7
87 83.7 96.1
97.4 94.2 98.3
87.2 86.2 88.6
110.6 89 90.8
101.1 94.7 88.1
69.1 81.9 91.9
77.4 80.2 98.5
95 96.5 98.6
93.2 95.6 100.3
96.3 91.9 98.7
93.9 89.9 110.7
78.5 86.3 115.4
90 94 105.4
109.2 108 108
94.3 96.3 94.5
93.1 94.6 96.5
114.5 111.7 91
78.5 92 94.1
88.3 91.9 96.4
114.8 109.2 93.1
112.2 106.8 97.5
106.9 105.8 102.5
119.7 103.6 105.7
97.1 97.6 109.1
106.3 102.8 97.2
131.7 124.8 100.3
106.7 103.9 91.3
124 112.2 94.3
117.2 108.5 89.5
87.8 92.4 89.3
91.9 101.1 93.4
125.1 114.9 91.9
115.4 106.4 92.9
117.7 104 93.7
124.3 101.6 100.1
104.8 99.4 105.5
109.6 102.3 110.5
139.5 121.3 89.5
105.3 99.3 90.4
112.4 102.9 89.9
128.9 111.4 84.6
91.6 98.5 86.2
98.7 98.5 83.4
117.8 108.5 82.9
117.4 112.1 81.8
110.5 105.3 87.6
103.1 95.2 94.6
95.8 98.2 99.6
98.2 96.6 96.7
117.2 109.6 99.8
108.5 108 83.8
113.2 106.7 82.4
120.2 111.5 86.8
102.8 104.5 91
89.4 94.3 85.3
119.8 109.6 83.6
126.9 116.4 94
114.4 106.5 100.3
117.4 100.5 107.1
109.4 101.7 100.7
111.1 104.1 95.5
121 112.3 92.9
116.6 111.2 79.2
119.5 108.2 82
121.2 115.1 79.3
101 102.3 81.5
92.7 93.6 76
125.5 120.6 73.1
123.4 118.4 80.4
110.3 106.6 82.1
118.8 105.3 90.5
97.1 101.5 98.1
107.6 100.1 89.5
131 119.5 86.5
117.9 111.2 77
111 103.7 74.7
131.4 117.8 73.4
101.8 101.7 72.5
93.9 97.4 69.3
138.5 120 75.2
131.1 117 83.5
124.9 110.6 90.5
126.6 105.3 92.2
102.7 100.9 110.5
121.6 108.1 101.8
132.8 119.3 107.4
123 113 95.5
116 108.6 84.5
135 123.3 81.1
93.7 101.4 86.2
98.4 103.5 91.5
129.8 119.4 84.7
121.9 113.1 92.2
124.8 112 99.2
126.9 115.8 104.5
102 105.4 113
117.7 110.9 100.4
144.8 128.5 101
113.3 109 84.8
129.3 117.2 86.5
135.7 124.4 91.7
94.3 104.7 94.8
106 108.6 95

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	12 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 time12 seconds \tabularnewline
R ServerBig Analytics Cloud Computing Center \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=309794&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]

12 seconds[/C][/ROW] [ROW]

R Server[/C]

Big Analytics Cloud Computing Center[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=309794&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=309794&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	12 seconds
R Server	Big Analytics Cloud Computing Center

Multiple Linear Regression - Estimated Regression Equation
X14[t] = + 27.309 + 0.607043X58[t] -0.0389223X64[t] + 0.0176725`X58(t-1)`[t] -0.0237601`X58(t-2)`[t] + 0.0180732`X58(t-3)`[t] + 0.130289`X58(t-4)`[t] -0.0154737`X58(t-1s)`[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
X14[t] =  +  27.309 +  0.607043X58[t] -0.0389223X64[t] +  0.0176725`X58(t-1)`[t] -0.0237601`X58(t-2)`[t] +  0.0180732`X58(t-3)`[t] +  0.130289`X58(t-4)`[t] -0.0154737`X58(t-1s)`[t]  + e[t] \tabularnewline
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=309794&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]X14[t] =  +  27.309 +  0.607043X58[t] -0.0389223X64[t] +  0.0176725`X58(t-1)`[t] -0.0237601`X58(t-2)`[t] +  0.0180732`X58(t-3)`[t] +  0.130289`X58(t-4)`[t] -0.0154737`X58(t-1s)`[t]  + e[t][/C][/ROW]
[ROW][C][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=309794&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=309794&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
X14[t] = + 27.309 + 0.607043X58[t] -0.0389223X64[t] + 0.0176725`X58(t-1)`[t] -0.0237601`X58(t-2)`[t] + 0.0180732`X58(t-3)`[t] + 0.130289`X58(t-4)`[t] -0.0154737`X58(t-1s)`[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)	+27.31	3.688	+7.4050e+00	4.287e-12	2.144e-12
X58	+0.607	0.03061	+1.9830e+01	5.712e-48	2.856e-48
X64	-0.03892	0.03425	-1.1360e+00	0.2572	0.1286
`X58(t-1)`	+0.01767	0.02582	+6.8440e-01	0.4946	0.2473
`X58(t-2)`	-0.02376	0.02193	-1.0830e+00	0.2801	0.14
`X58(t-3)`	+0.01807	0.02625	+6.8840e-01	0.492	0.246
`X58(t-4)`	+0.1303	0.02368	+5.5020e+00	1.214e-07	6.069e-08
`X58(t-1s)`	-0.01547	0.02748	-5.6300e-01	0.5741	0.287

\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) & +27.31 &  3.688 & +7.4050e+00 &  4.287e-12 &  2.144e-12 \tabularnewline
X58 & +0.607 &  0.03061 & +1.9830e+01 &  5.712e-48 &  2.856e-48 \tabularnewline
X64 & -0.03892 &  0.03425 & -1.1360e+00 &  0.2572 &  0.1286 \tabularnewline
`X58(t-1)` & +0.01767 &  0.02582 & +6.8440e-01 &  0.4946 &  0.2473 \tabularnewline
`X58(t-2)` & -0.02376 &  0.02193 & -1.0830e+00 &  0.2801 &  0.14 \tabularnewline
`X58(t-3)` & +0.01807 &  0.02625 & +6.8840e-01 &  0.492 &  0.246 \tabularnewline
`X58(t-4)` & +0.1303 &  0.02368 & +5.5020e+00 &  1.214e-07 &  6.069e-08 \tabularnewline
`X58(t-1s)` & -0.01547 &  0.02748 & -5.6300e-01 &  0.5741 &  0.287 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=309794&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]+27.31[/C][C] 3.688[/C][C]+7.4050e+00[/C][C] 4.287e-12[/C][C] 2.144e-12[/C][/ROW]
[ROW][C]X58[/C][C]+0.607[/C][C] 0.03061[/C][C]+1.9830e+01[/C][C] 5.712e-48[/C][C] 2.856e-48[/C][/ROW]
[ROW][C]X64[/C][C]-0.03892[/C][C] 0.03425[/C][C]-1.1360e+00[/C][C] 0.2572[/C][C] 0.1286[/C][/ROW]
[ROW][C]`X58(t-1)`[/C][C]+0.01767[/C][C] 0.02582[/C][C]+6.8440e-01[/C][C] 0.4946[/C][C] 0.2473[/C][/ROW]
[ROW][C]`X58(t-2)`[/C][C]-0.02376[/C][C] 0.02193[/C][C]-1.0830e+00[/C][C] 0.2801[/C][C] 0.14[/C][/ROW]
[ROW][C]`X58(t-3)`[/C][C]+0.01807[/C][C] 0.02625[/C][C]+6.8840e-01[/C][C] 0.492[/C][C] 0.246[/C][/ROW]
[ROW][C]`X58(t-4)`[/C][C]+0.1303[/C][C] 0.02368[/C][C]+5.5020e+00[/C][C] 1.214e-07[/C][C] 6.069e-08[/C][/ROW]
[ROW][C]`X58(t-1s)`[/C][C]-0.01547[/C][C] 0.02748[/C][C]-5.6300e-01[/C][C] 0.5741[/C][C] 0.287[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=309794&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=309794&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)	+27.31	3.688	+7.4050e+00	4.287e-12	2.144e-12
X58	+0.607	0.03061	+1.9830e+01	5.712e-48	2.856e-48
X64	-0.03892	0.03425	-1.1360e+00	0.2572	0.1286
`X58(t-1)`	+0.01767	0.02582	+6.8440e-01	0.4946	0.2473
`X58(t-2)`	-0.02376	0.02193	-1.0830e+00	0.2801	0.14
`X58(t-3)`	+0.01807	0.02625	+6.8840e-01	0.492	0.246
`X58(t-4)`	+0.1303	0.02368	+5.5020e+00	1.214e-07	6.069e-08
`X58(t-1s)`	-0.01547	0.02748	-5.6300e-01	0.5741	0.287

Multiple Linear Regression - Regression Statistics
Multiple R	0.9669
R-squared	0.9349
Adjusted R-squared	0.9325
F-TEST (value)	385.9
F-TEST (DF numerator)	7
F-TEST (DF denominator)	188
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	3.864
Sum Squared Residuals	2807

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R &  0.9669 \tabularnewline
R-squared &  0.9349 \tabularnewline
Adjusted R-squared &  0.9325 \tabularnewline
F-TEST (value) &  385.9 \tabularnewline
F-TEST (DF numerator) & 7 \tabularnewline
F-TEST (DF denominator) & 188 \tabularnewline
p-value &  0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation &  3.864 \tabularnewline
Sum Squared Residuals &  2807 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=309794&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C] 0.9669[/C][/ROW]
[ROW][C]R-squared[/C][C] 0.9349[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C] 0.9325[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C] 385.9[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]7[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]188[/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] 3.864[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C] 2807[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=309794&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=309794&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	0.9669
R-squared	0.9349
Adjusted R-squared	0.9325
F-TEST (value)	385.9
F-TEST (DF numerator)	7
F-TEST (DF denominator)	188
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	3.864
Sum Squared Residuals	2807

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
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=309794&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=309794&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=309794&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	76.4	82.53	-6.127
2	79.7	86.6	-6.9
3	64.2	74.41	-10.21
4	67.9	75.03	-7.128
5	74.1	80.98	-6.881
6	78.5	81.54	-3.035
7	73.4	77.27	-3.868
8	65.4	74.49	-9.086
9	69.9	72.1	-2.196
10	69.6	74.07	-4.47
11	76.8	79.88	-3.076
12	75.6	78.34	-2.736
13	74	76.12	-2.124
14	76	79.44	-3.438
15	68.1	70.97	-2.866
16	65.5	68.5	-3.002
17	76.9	77.79	-0.8893
18	81.7	83.57	-1.868
19	73.6	74.95	-1.349
20	68.7	76.95	-8.254
21	73.3	72.06	1.245
22	71.5	76.98	-5.478
23	78.3	77.77	0.5259
24	76.5	76.53	-0.0293
25	71.8	74.32	-2.522
26	77.6	79.83	-2.225
27	70	70.54	-0.5379
28	64	64.18	-0.177
29	81.3	77.76	3.541
30	82.5	77.91	4.591
31	73.1	74.06	-0.9625
32	78.1	78.85	-0.7507
33	70.7	71.13	-0.4314
34	74.9	74.57	0.3281
35	88	82.67	5.335
36	81.3	80.37	0.9338
37	75.7	74.51	1.192
38	89.8	85.67	4.13
39	74.6	72.4	2.198
40	74.9	73.99	0.9082
41	90	84.51	5.487
42	88.1	85.11	2.988
43	84.9	78.36	6.539
44	87.7	84.43	3.273
45	80.5	80.67	-0.1659
46	79	80.17	-1.174
47	89.9	87.34	2.558
48	86.3	84.28	2.017
49	81.1	78.62	2.477
50	92.4	93.17	-0.7689
51	71.8	74.4	-2.602
52	76.1	74.8	1.301
53	92.5	85.91	6.588
54	87	84.06	2.941
55	89.5	84.25	5.254
56	88.7	96.48	-7.779
57	83.8	79.94	3.857
58	84.9	81.69	3.209
59	99	92.52	6.483
60	84.6	84.35	0.2509
61	92.7	88.31	4.387
62	97.6	93.07	4.531
63	78	78.28	-0.2833
64	81.9	75.69	6.214
65	96.5	92.78	3.724
66	99.9	96.16	3.744
67	96.2	88.75	7.448
68	90.5	92.82	-2.317
69	91.4	88.74	2.659
70	89.7	89.94	-0.2405
71	102.7	99.8	2.903
72	91.5	93.1	-1.597
73	96.2	93.19	3.013
74	104.5	104.2	0.3469
75	90.3	91.29	-0.9898
76	90.3	89.53	0.7714
77	100.4	106.8	-6.376
78	111.3	119.1	-7.832
79	101.3	102	-0.6899
80	94.4	102.9	-8.513
81	100.4	100.6	-0.1731
82	102	107	-5.019
83	104.3	105.5	-1.171
84	108.8	109.3	-0.4579
85	101.3	102.4	-1.144
86	108.9	110.7	-1.811
87	98.5	96.66	1.838
88	88.8	91.83	-3.032
89	111.8	109.6	2.238
90	109.6	109.6	0.0446
91	92.5	95.14	-2.644
92	94.5	96.51	-2.008
93	80.8	87.33	-6.531
94	83.7	90.6	-6.904
95	94.2	95.25	-1.049
96	86.2	89.5	-3.301
97	89	100.5	-11.52
98	94.7	96.37	-1.666
99	81.9	77.63	4.274
100	80.2	81.28	-1.079
101	96.5	95.27	1.232
102	95.6	92.44	3.157
103	91.9	90.19	1.712
104	89.9	89.7	0.2019
105	86.3	82.66	3.641
106	94	89.53	4.466
107	108	101.9	6.143
108	96.3	92.97	3.33
109	94.6	89.28	5.317
110	111.7	104.8	6.887
111	92	85.97	6.027
112	91.9	88.6	3.303
113	109.2	105.8	3.402
114	106.8	106.5	0.3502
115	105.8	97.8	7.999
116	103.6	107.2	-3.608
117	97.6	97.35	0.2478
118	102.8	102.1	0.7158
119	124.8	117.3	7.474
120	103.9	104.2	-0.32
121	112.2	110.8	1.4
122	108.5	109.1	-0.5853
123	92.4	94.13	-1.729
124	101.1	93	8.096
125	114.9	115.7	-0.8087
126	106.4	108.9	-2.494
127	104	105.6	-1.624
128	101.6	110.6	-8.989
129	99.4	103.1	-3.703
130	102.3	104	-1.656
131	121.3	123.5	-2.198
132	99.3	104	-4.711
133	102.9	104.3	-1.404
134	111.4	116.7	-5.336
135	98.5	97.89	0.6138
136	98.5	96.86	1.637
137	108.5	110.2	-1.698
138	112.1	111.8	0.3072
139	105.3	102.2	3.15
140	95.2	98.44	-3.242
141	98.2	96.63	1.568
142	96.6	98	-1.397
143	109.6	108.1	1.469
144	108	103.2	4.816
145	106.7	104.5	2.231
146	111.5	109.2	2.262
147	104.5	101.4	3.081
148	94.3	91.87	2.426
149	109.6	111	-1.415
150	116.4	116.4	0.02088
151	106.5	105.5	0.9534
152	100.5	105.6	-5.131
153	101.7	105.6	-3.876
154	104.1	107.3	-3.16
155	112.3	111.7	0.5773
156	111.2	110.1	1.1
157	108.2	110.4	-2.155
158	115.1	111.9	3.16
159	102.3	101	1.268
160	93.6	95.5	-1.897
161	120.6	115.8	4.808
162	118.4	114.8	3.643
163	106.6	103.3	3.266
164	105.3	107.5	-2.15
165	101.5	98.8	2.698
166	100.1	104.4	-4.289
167	119.5	117.7	1.795
168	111.2	111.1	0.1301
169	103.7	103.5	0.1991
170	117.8	117.9	-0.08915
171	101.7	103.6	-1.905
172	97.4	96.22	1.177
173	120	122.6	-2.593
174	117	120.9	-3.909
175	110.6	111.9	-1.286
176	105.3	112.6	-7.263
177	100.9	103.5	-2.633
178	108.1	113.6	-5.543
179	119.3	120	-0.687
180	113	114.2	-1.242
181	108.6	107.3	1.284
182	123.3	121.4	1.859
183	101.4	98.41	2.986
184	103.5	98.6	4.902
185	119.4	117.7	1.671
186	113.1	114.9	-1.829
187	112	110.3	1.669
188	115.8	112.8	3.008
189	105.4	101.6	3.768
190	110.9	109.9	1.006
191	128.5	127.4	1.067
192	109	109	-0.02265
193	117.2	114.6	2.584
194	124.4	121.6	2.828
195	104.7	99.65	5.047
196	108.6	102	6.624

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 &  76.4 &  82.53 & -6.127 \tabularnewline
2 &  79.7 &  86.6 & -6.9 \tabularnewline
3 &  64.2 &  74.41 & -10.21 \tabularnewline
4 &  67.9 &  75.03 & -7.128 \tabularnewline
5 &  74.1 &  80.98 & -6.881 \tabularnewline
6 &  78.5 &  81.54 & -3.035 \tabularnewline
7 &  73.4 &  77.27 & -3.868 \tabularnewline
8 &  65.4 &  74.49 & -9.086 \tabularnewline
9 &  69.9 &  72.1 & -2.196 \tabularnewline
10 &  69.6 &  74.07 & -4.47 \tabularnewline
11 &  76.8 &  79.88 & -3.076 \tabularnewline
12 &  75.6 &  78.34 & -2.736 \tabularnewline
13 &  74 &  76.12 & -2.124 \tabularnewline
14 &  76 &  79.44 & -3.438 \tabularnewline
15 &  68.1 &  70.97 & -2.866 \tabularnewline
16 &  65.5 &  68.5 & -3.002 \tabularnewline
17 &  76.9 &  77.79 & -0.8893 \tabularnewline
18 &  81.7 &  83.57 & -1.868 \tabularnewline
19 &  73.6 &  74.95 & -1.349 \tabularnewline
20 &  68.7 &  76.95 & -8.254 \tabularnewline
21 &  73.3 &  72.06 &  1.245 \tabularnewline
22 &  71.5 &  76.98 & -5.478 \tabularnewline
23 &  78.3 &  77.77 &  0.5259 \tabularnewline
24 &  76.5 &  76.53 & -0.0293 \tabularnewline
25 &  71.8 &  74.32 & -2.522 \tabularnewline
26 &  77.6 &  79.83 & -2.225 \tabularnewline
27 &  70 &  70.54 & -0.5379 \tabularnewline
28 &  64 &  64.18 & -0.177 \tabularnewline
29 &  81.3 &  77.76 &  3.541 \tabularnewline
30 &  82.5 &  77.91 &  4.591 \tabularnewline
31 &  73.1 &  74.06 & -0.9625 \tabularnewline
32 &  78.1 &  78.85 & -0.7507 \tabularnewline
33 &  70.7 &  71.13 & -0.4314 \tabularnewline
34 &  74.9 &  74.57 &  0.3281 \tabularnewline
35 &  88 &  82.67 &  5.335 \tabularnewline
36 &  81.3 &  80.37 &  0.9338 \tabularnewline
37 &  75.7 &  74.51 &  1.192 \tabularnewline
38 &  89.8 &  85.67 &  4.13 \tabularnewline
39 &  74.6 &  72.4 &  2.198 \tabularnewline
40 &  74.9 &  73.99 &  0.9082 \tabularnewline
41 &  90 &  84.51 &  5.487 \tabularnewline
42 &  88.1 &  85.11 &  2.988 \tabularnewline
43 &  84.9 &  78.36 &  6.539 \tabularnewline
44 &  87.7 &  84.43 &  3.273 \tabularnewline
45 &  80.5 &  80.67 & -0.1659 \tabularnewline
46 &  79 &  80.17 & -1.174 \tabularnewline
47 &  89.9 &  87.34 &  2.558 \tabularnewline
48 &  86.3 &  84.28 &  2.017 \tabularnewline
49 &  81.1 &  78.62 &  2.477 \tabularnewline
50 &  92.4 &  93.17 & -0.7689 \tabularnewline
51 &  71.8 &  74.4 & -2.602 \tabularnewline
52 &  76.1 &  74.8 &  1.301 \tabularnewline
53 &  92.5 &  85.91 &  6.588 \tabularnewline
54 &  87 &  84.06 &  2.941 \tabularnewline
55 &  89.5 &  84.25 &  5.254 \tabularnewline
56 &  88.7 &  96.48 & -7.779 \tabularnewline
57 &  83.8 &  79.94 &  3.857 \tabularnewline
58 &  84.9 &  81.69 &  3.209 \tabularnewline
59 &  99 &  92.52 &  6.483 \tabularnewline
60 &  84.6 &  84.35 &  0.2509 \tabularnewline
61 &  92.7 &  88.31 &  4.387 \tabularnewline
62 &  97.6 &  93.07 &  4.531 \tabularnewline
63 &  78 &  78.28 & -0.2833 \tabularnewline
64 &  81.9 &  75.69 &  6.214 \tabularnewline
65 &  96.5 &  92.78 &  3.724 \tabularnewline
66 &  99.9 &  96.16 &  3.744 \tabularnewline
67 &  96.2 &  88.75 &  7.448 \tabularnewline
68 &  90.5 &  92.82 & -2.317 \tabularnewline
69 &  91.4 &  88.74 &  2.659 \tabularnewline
70 &  89.7 &  89.94 & -0.2405 \tabularnewline
71 &  102.7 &  99.8 &  2.903 \tabularnewline
72 &  91.5 &  93.1 & -1.597 \tabularnewline
73 &  96.2 &  93.19 &  3.013 \tabularnewline
74 &  104.5 &  104.2 &  0.3469 \tabularnewline
75 &  90.3 &  91.29 & -0.9898 \tabularnewline
76 &  90.3 &  89.53 &  0.7714 \tabularnewline
77 &  100.4 &  106.8 & -6.376 \tabularnewline
78 &  111.3 &  119.1 & -7.832 \tabularnewline
79 &  101.3 &  102 & -0.6899 \tabularnewline
80 &  94.4 &  102.9 & -8.513 \tabularnewline
81 &  100.4 &  100.6 & -0.1731 \tabularnewline
82 &  102 &  107 & -5.019 \tabularnewline
83 &  104.3 &  105.5 & -1.171 \tabularnewline
84 &  108.8 &  109.3 & -0.4579 \tabularnewline
85 &  101.3 &  102.4 & -1.144 \tabularnewline
86 &  108.9 &  110.7 & -1.811 \tabularnewline
87 &  98.5 &  96.66 &  1.838 \tabularnewline
88 &  88.8 &  91.83 & -3.032 \tabularnewline
89 &  111.8 &  109.6 &  2.238 \tabularnewline
90 &  109.6 &  109.6 &  0.0446 \tabularnewline
91 &  92.5 &  95.14 & -2.644 \tabularnewline
92 &  94.5 &  96.51 & -2.008 \tabularnewline
93 &  80.8 &  87.33 & -6.531 \tabularnewline
94 &  83.7 &  90.6 & -6.904 \tabularnewline
95 &  94.2 &  95.25 & -1.049 \tabularnewline
96 &  86.2 &  89.5 & -3.301 \tabularnewline
97 &  89 &  100.5 & -11.52 \tabularnewline
98 &  94.7 &  96.37 & -1.666 \tabularnewline
99 &  81.9 &  77.63 &  4.274 \tabularnewline
100 &  80.2 &  81.28 & -1.079 \tabularnewline
101 &  96.5 &  95.27 &  1.232 \tabularnewline
102 &  95.6 &  92.44 &  3.157 \tabularnewline
103 &  91.9 &  90.19 &  1.712 \tabularnewline
104 &  89.9 &  89.7 &  0.2019 \tabularnewline
105 &  86.3 &  82.66 &  3.641 \tabularnewline
106 &  94 &  89.53 &  4.466 \tabularnewline
107 &  108 &  101.9 &  6.143 \tabularnewline
108 &  96.3 &  92.97 &  3.33 \tabularnewline
109 &  94.6 &  89.28 &  5.317 \tabularnewline
110 &  111.7 &  104.8 &  6.887 \tabularnewline
111 &  92 &  85.97 &  6.027 \tabularnewline
112 &  91.9 &  88.6 &  3.303 \tabularnewline
113 &  109.2 &  105.8 &  3.402 \tabularnewline
114 &  106.8 &  106.5 &  0.3502 \tabularnewline
115 &  105.8 &  97.8 &  7.999 \tabularnewline
116 &  103.6 &  107.2 & -3.608 \tabularnewline
117 &  97.6 &  97.35 &  0.2478 \tabularnewline
118 &  102.8 &  102.1 &  0.7158 \tabularnewline
119 &  124.8 &  117.3 &  7.474 \tabularnewline
120 &  103.9 &  104.2 & -0.32 \tabularnewline
121 &  112.2 &  110.8 &  1.4 \tabularnewline
122 &  108.5 &  109.1 & -0.5853 \tabularnewline
123 &  92.4 &  94.13 & -1.729 \tabularnewline
124 &  101.1 &  93 &  8.096 \tabularnewline
125 &  114.9 &  115.7 & -0.8087 \tabularnewline
126 &  106.4 &  108.9 & -2.494 \tabularnewline
127 &  104 &  105.6 & -1.624 \tabularnewline
128 &  101.6 &  110.6 & -8.989 \tabularnewline
129 &  99.4 &  103.1 & -3.703 \tabularnewline
130 &  102.3 &  104 & -1.656 \tabularnewline
131 &  121.3 &  123.5 & -2.198 \tabularnewline
132 &  99.3 &  104 & -4.711 \tabularnewline
133 &  102.9 &  104.3 & -1.404 \tabularnewline
134 &  111.4 &  116.7 & -5.336 \tabularnewline
135 &  98.5 &  97.89 &  0.6138 \tabularnewline
136 &  98.5 &  96.86 &  1.637 \tabularnewline
137 &  108.5 &  110.2 & -1.698 \tabularnewline
138 &  112.1 &  111.8 &  0.3072 \tabularnewline
139 &  105.3 &  102.2 &  3.15 \tabularnewline
140 &  95.2 &  98.44 & -3.242 \tabularnewline
141 &  98.2 &  96.63 &  1.568 \tabularnewline
142 &  96.6 &  98 & -1.397 \tabularnewline
143 &  109.6 &  108.1 &  1.469 \tabularnewline
144 &  108 &  103.2 &  4.816 \tabularnewline
145 &  106.7 &  104.5 &  2.231 \tabularnewline
146 &  111.5 &  109.2 &  2.262 \tabularnewline
147 &  104.5 &  101.4 &  3.081 \tabularnewline
148 &  94.3 &  91.87 &  2.426 \tabularnewline
149 &  109.6 &  111 & -1.415 \tabularnewline
150 &  116.4 &  116.4 &  0.02088 \tabularnewline
151 &  106.5 &  105.5 &  0.9534 \tabularnewline
152 &  100.5 &  105.6 & -5.131 \tabularnewline
153 &  101.7 &  105.6 & -3.876 \tabularnewline
154 &  104.1 &  107.3 & -3.16 \tabularnewline
155 &  112.3 &  111.7 &  0.5773 \tabularnewline
156 &  111.2 &  110.1 &  1.1 \tabularnewline
157 &  108.2 &  110.4 & -2.155 \tabularnewline
158 &  115.1 &  111.9 &  3.16 \tabularnewline
159 &  102.3 &  101 &  1.268 \tabularnewline
160 &  93.6 &  95.5 & -1.897 \tabularnewline
161 &  120.6 &  115.8 &  4.808 \tabularnewline
162 &  118.4 &  114.8 &  3.643 \tabularnewline
163 &  106.6 &  103.3 &  3.266 \tabularnewline
164 &  105.3 &  107.5 & -2.15 \tabularnewline
165 &  101.5 &  98.8 &  2.698 \tabularnewline
166 &  100.1 &  104.4 & -4.289 \tabularnewline
167 &  119.5 &  117.7 &  1.795 \tabularnewline
168 &  111.2 &  111.1 &  0.1301 \tabularnewline
169 &  103.7 &  103.5 &  0.1991 \tabularnewline
170 &  117.8 &  117.9 & -0.08915 \tabularnewline
171 &  101.7 &  103.6 & -1.905 \tabularnewline
172 &  97.4 &  96.22 &  1.177 \tabularnewline
173 &  120 &  122.6 & -2.593 \tabularnewline
174 &  117 &  120.9 & -3.909 \tabularnewline
175 &  110.6 &  111.9 & -1.286 \tabularnewline
176 &  105.3 &  112.6 & -7.263 \tabularnewline
177 &  100.9 &  103.5 & -2.633 \tabularnewline
178 &  108.1 &  113.6 & -5.543 \tabularnewline
179 &  119.3 &  120 & -0.687 \tabularnewline
180 &  113 &  114.2 & -1.242 \tabularnewline
181 &  108.6 &  107.3 &  1.284 \tabularnewline
182 &  123.3 &  121.4 &  1.859 \tabularnewline
183 &  101.4 &  98.41 &  2.986 \tabularnewline
184 &  103.5 &  98.6 &  4.902 \tabularnewline
185 &  119.4 &  117.7 &  1.671 \tabularnewline
186 &  113.1 &  114.9 & -1.829 \tabularnewline
187 &  112 &  110.3 &  1.669 \tabularnewline
188 &  115.8 &  112.8 &  3.008 \tabularnewline
189 &  105.4 &  101.6 &  3.768 \tabularnewline
190 &  110.9 &  109.9 &  1.006 \tabularnewline
191 &  128.5 &  127.4 &  1.067 \tabularnewline
192 &  109 &  109 & -0.02265 \tabularnewline
193 &  117.2 &  114.6 &  2.584 \tabularnewline
194 &  124.4 &  121.6 &  2.828 \tabularnewline
195 &  104.7 &  99.65 &  5.047 \tabularnewline
196 &  108.6 &  102 &  6.624 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=309794&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] 76.4[/C][C] 82.53[/C][C]-6.127[/C][/ROW]
[ROW][C]2[/C][C] 79.7[/C][C] 86.6[/C][C]-6.9[/C][/ROW]
[ROW][C]3[/C][C] 64.2[/C][C] 74.41[/C][C]-10.21[/C][/ROW]
[ROW][C]4[/C][C] 67.9[/C][C] 75.03[/C][C]-7.128[/C][/ROW]
[ROW][C]5[/C][C] 74.1[/C][C] 80.98[/C][C]-6.881[/C][/ROW]
[ROW][C]6[/C][C] 78.5[/C][C] 81.54[/C][C]-3.035[/C][/ROW]
[ROW][C]7[/C][C] 73.4[/C][C] 77.27[/C][C]-3.868[/C][/ROW]
[ROW][C]8[/C][C] 65.4[/C][C] 74.49[/C][C]-9.086[/C][/ROW]
[ROW][C]9[/C][C] 69.9[/C][C] 72.1[/C][C]-2.196[/C][/ROW]
[ROW][C]10[/C][C] 69.6[/C][C] 74.07[/C][C]-4.47[/C][/ROW]
[ROW][C]11[/C][C] 76.8[/C][C] 79.88[/C][C]-3.076[/C][/ROW]
[ROW][C]12[/C][C] 75.6[/C][C] 78.34[/C][C]-2.736[/C][/ROW]
[ROW][C]13[/C][C] 74[/C][C] 76.12[/C][C]-2.124[/C][/ROW]
[ROW][C]14[/C][C] 76[/C][C] 79.44[/C][C]-3.438[/C][/ROW]
[ROW][C]15[/C][C] 68.1[/C][C] 70.97[/C][C]-2.866[/C][/ROW]
[ROW][C]16[/C][C] 65.5[/C][C] 68.5[/C][C]-3.002[/C][/ROW]
[ROW][C]17[/C][C] 76.9[/C][C] 77.79[/C][C]-0.8893[/C][/ROW]
[ROW][C]18[/C][C] 81.7[/C][C] 83.57[/C][C]-1.868[/C][/ROW]
[ROW][C]19[/C][C] 73.6[/C][C] 74.95[/C][C]-1.349[/C][/ROW]
[ROW][C]20[/C][C] 68.7[/C][C] 76.95[/C][C]-8.254[/C][/ROW]
[ROW][C]21[/C][C] 73.3[/C][C] 72.06[/C][C] 1.245[/C][/ROW]
[ROW][C]22[/C][C] 71.5[/C][C] 76.98[/C][C]-5.478[/C][/ROW]
[ROW][C]23[/C][C] 78.3[/C][C] 77.77[/C][C] 0.5259[/C][/ROW]
[ROW][C]24[/C][C] 76.5[/C][C] 76.53[/C][C]-0.0293[/C][/ROW]
[ROW][C]25[/C][C] 71.8[/C][C] 74.32[/C][C]-2.522[/C][/ROW]
[ROW][C]26[/C][C] 77.6[/C][C] 79.83[/C][C]-2.225[/C][/ROW]
[ROW][C]27[/C][C] 70[/C][C] 70.54[/C][C]-0.5379[/C][/ROW]
[ROW][C]28[/C][C] 64[/C][C] 64.18[/C][C]-0.177[/C][/ROW]
[ROW][C]29[/C][C] 81.3[/C][C] 77.76[/C][C] 3.541[/C][/ROW]
[ROW][C]30[/C][C] 82.5[/C][C] 77.91[/C][C] 4.591[/C][/ROW]
[ROW][C]31[/C][C] 73.1[/C][C] 74.06[/C][C]-0.9625[/C][/ROW]
[ROW][C]32[/C][C] 78.1[/C][C] 78.85[/C][C]-0.7507[/C][/ROW]
[ROW][C]33[/C][C] 70.7[/C][C] 71.13[/C][C]-0.4314[/C][/ROW]
[ROW][C]34[/C][C] 74.9[/C][C] 74.57[/C][C] 0.3281[/C][/ROW]
[ROW][C]35[/C][C] 88[/C][C] 82.67[/C][C] 5.335[/C][/ROW]
[ROW][C]36[/C][C] 81.3[/C][C] 80.37[/C][C] 0.9338[/C][/ROW]
[ROW][C]37[/C][C] 75.7[/C][C] 74.51[/C][C] 1.192[/C][/ROW]
[ROW][C]38[/C][C] 89.8[/C][C] 85.67[/C][C] 4.13[/C][/ROW]
[ROW][C]39[/C][C] 74.6[/C][C] 72.4[/C][C] 2.198[/C][/ROW]
[ROW][C]40[/C][C] 74.9[/C][C] 73.99[/C][C] 0.9082[/C][/ROW]
[ROW][C]41[/C][C] 90[/C][C] 84.51[/C][C] 5.487[/C][/ROW]
[ROW][C]42[/C][C] 88.1[/C][C] 85.11[/C][C] 2.988[/C][/ROW]
[ROW][C]43[/C][C] 84.9[/C][C] 78.36[/C][C] 6.539[/C][/ROW]
[ROW][C]44[/C][C] 87.7[/C][C] 84.43[/C][C] 3.273[/C][/ROW]
[ROW][C]45[/C][C] 80.5[/C][C] 80.67[/C][C]-0.1659[/C][/ROW]
[ROW][C]46[/C][C] 79[/C][C] 80.17[/C][C]-1.174[/C][/ROW]
[ROW][C]47[/C][C] 89.9[/C][C] 87.34[/C][C] 2.558[/C][/ROW]
[ROW][C]48[/C][C] 86.3[/C][C] 84.28[/C][C] 2.017[/C][/ROW]
[ROW][C]49[/C][C] 81.1[/C][C] 78.62[/C][C] 2.477[/C][/ROW]
[ROW][C]50[/C][C] 92.4[/C][C] 93.17[/C][C]-0.7689[/C][/ROW]
[ROW][C]51[/C][C] 71.8[/C][C] 74.4[/C][C]-2.602[/C][/ROW]
[ROW][C]52[/C][C] 76.1[/C][C] 74.8[/C][C] 1.301[/C][/ROW]
[ROW][C]53[/C][C] 92.5[/C][C] 85.91[/C][C] 6.588[/C][/ROW]
[ROW][C]54[/C][C] 87[/C][C] 84.06[/C][C] 2.941[/C][/ROW]
[ROW][C]55[/C][C] 89.5[/C][C] 84.25[/C][C] 5.254[/C][/ROW]
[ROW][C]56[/C][C] 88.7[/C][C] 96.48[/C][C]-7.779[/C][/ROW]
[ROW][C]57[/C][C] 83.8[/C][C] 79.94[/C][C] 3.857[/C][/ROW]
[ROW][C]58[/C][C] 84.9[/C][C] 81.69[/C][C] 3.209[/C][/ROW]
[ROW][C]59[/C][C] 99[/C][C] 92.52[/C][C] 6.483[/C][/ROW]
[ROW][C]60[/C][C] 84.6[/C][C] 84.35[/C][C] 0.2509[/C][/ROW]
[ROW][C]61[/C][C] 92.7[/C][C] 88.31[/C][C] 4.387[/C][/ROW]
[ROW][C]62[/C][C] 97.6[/C][C] 93.07[/C][C] 4.531[/C][/ROW]
[ROW][C]63[/C][C] 78[/C][C] 78.28[/C][C]-0.2833[/C][/ROW]
[ROW][C]64[/C][C] 81.9[/C][C] 75.69[/C][C] 6.214[/C][/ROW]
[ROW][C]65[/C][C] 96.5[/C][C] 92.78[/C][C] 3.724[/C][/ROW]
[ROW][C]66[/C][C] 99.9[/C][C] 96.16[/C][C] 3.744[/C][/ROW]
[ROW][C]67[/C][C] 96.2[/C][C] 88.75[/C][C] 7.448[/C][/ROW]
[ROW][C]68[/C][C] 90.5[/C][C] 92.82[/C][C]-2.317[/C][/ROW]
[ROW][C]69[/C][C] 91.4[/C][C] 88.74[/C][C] 2.659[/C][/ROW]
[ROW][C]70[/C][C] 89.7[/C][C] 89.94[/C][C]-0.2405[/C][/ROW]
[ROW][C]71[/C][C] 102.7[/C][C] 99.8[/C][C] 2.903[/C][/ROW]
[ROW][C]72[/C][C] 91.5[/C][C] 93.1[/C][C]-1.597[/C][/ROW]
[ROW][C]73[/C][C] 96.2[/C][C] 93.19[/C][C] 3.013[/C][/ROW]
[ROW][C]74[/C][C] 104.5[/C][C] 104.2[/C][C] 0.3469[/C][/ROW]
[ROW][C]75[/C][C] 90.3[/C][C] 91.29[/C][C]-0.9898[/C][/ROW]
[ROW][C]76[/C][C] 90.3[/C][C] 89.53[/C][C] 0.7714[/C][/ROW]
[ROW][C]77[/C][C] 100.4[/C][C] 106.8[/C][C]-6.376[/C][/ROW]
[ROW][C]78[/C][C] 111.3[/C][C] 119.1[/C][C]-7.832[/C][/ROW]
[ROW][C]79[/C][C] 101.3[/C][C] 102[/C][C]-0.6899[/C][/ROW]
[ROW][C]80[/C][C] 94.4[/C][C] 102.9[/C][C]-8.513[/C][/ROW]
[ROW][C]81[/C][C] 100.4[/C][C] 100.6[/C][C]-0.1731[/C][/ROW]
[ROW][C]82[/C][C] 102[/C][C] 107[/C][C]-5.019[/C][/ROW]
[ROW][C]83[/C][C] 104.3[/C][C] 105.5[/C][C]-1.171[/C][/ROW]
[ROW][C]84[/C][C] 108.8[/C][C] 109.3[/C][C]-0.4579[/C][/ROW]
[ROW][C]85[/C][C] 101.3[/C][C] 102.4[/C][C]-1.144[/C][/ROW]
[ROW][C]86[/C][C] 108.9[/C][C] 110.7[/C][C]-1.811[/C][/ROW]
[ROW][C]87[/C][C] 98.5[/C][C] 96.66[/C][C] 1.838[/C][/ROW]
[ROW][C]88[/C][C] 88.8[/C][C] 91.83[/C][C]-3.032[/C][/ROW]
[ROW][C]89[/C][C] 111.8[/C][C] 109.6[/C][C] 2.238[/C][/ROW]
[ROW][C]90[/C][C] 109.6[/C][C] 109.6[/C][C] 0.0446[/C][/ROW]
[ROW][C]91[/C][C] 92.5[/C][C] 95.14[/C][C]-2.644[/C][/ROW]
[ROW][C]92[/C][C] 94.5[/C][C] 96.51[/C][C]-2.008[/C][/ROW]
[ROW][C]93[/C][C] 80.8[/C][C] 87.33[/C][C]-6.531[/C][/ROW]
[ROW][C]94[/C][C] 83.7[/C][C] 90.6[/C][C]-6.904[/C][/ROW]
[ROW][C]95[/C][C] 94.2[/C][C] 95.25[/C][C]-1.049[/C][/ROW]
[ROW][C]96[/C][C] 86.2[/C][C] 89.5[/C][C]-3.301[/C][/ROW]
[ROW][C]97[/C][C] 89[/C][C] 100.5[/C][C]-11.52[/C][/ROW]
[ROW][C]98[/C][C] 94.7[/C][C] 96.37[/C][C]-1.666[/C][/ROW]
[ROW][C]99[/C][C] 81.9[/C][C] 77.63[/C][C] 4.274[/C][/ROW]
[ROW][C]100[/C][C] 80.2[/C][C] 81.28[/C][C]-1.079[/C][/ROW]
[ROW][C]101[/C][C] 96.5[/C][C] 95.27[/C][C] 1.232[/C][/ROW]
[ROW][C]102[/C][C] 95.6[/C][C] 92.44[/C][C] 3.157[/C][/ROW]
[ROW][C]103[/C][C] 91.9[/C][C] 90.19[/C][C] 1.712[/C][/ROW]
[ROW][C]104[/C][C] 89.9[/C][C] 89.7[/C][C] 0.2019[/C][/ROW]
[ROW][C]105[/C][C] 86.3[/C][C] 82.66[/C][C] 3.641[/C][/ROW]
[ROW][C]106[/C][C] 94[/C][C] 89.53[/C][C] 4.466[/C][/ROW]
[ROW][C]107[/C][C] 108[/C][C] 101.9[/C][C] 6.143[/C][/ROW]
[ROW][C]108[/C][C] 96.3[/C][C] 92.97[/C][C] 3.33[/C][/ROW]
[ROW][C]109[/C][C] 94.6[/C][C] 89.28[/C][C] 5.317[/C][/ROW]
[ROW][C]110[/C][C] 111.7[/C][C] 104.8[/C][C] 6.887[/C][/ROW]
[ROW][C]111[/C][C] 92[/C][C] 85.97[/C][C] 6.027[/C][/ROW]
[ROW][C]112[/C][C] 91.9[/C][C] 88.6[/C][C] 3.303[/C][/ROW]
[ROW][C]113[/C][C] 109.2[/C][C] 105.8[/C][C] 3.402[/C][/ROW]
[ROW][C]114[/C][C] 106.8[/C][C] 106.5[/C][C] 0.3502[/C][/ROW]
[ROW][C]115[/C][C] 105.8[/C][C] 97.8[/C][C] 7.999[/C][/ROW]
[ROW][C]116[/C][C] 103.6[/C][C] 107.2[/C][C]-3.608[/C][/ROW]
[ROW][C]117[/C][C] 97.6[/C][C] 97.35[/C][C] 0.2478[/C][/ROW]
[ROW][C]118[/C][C] 102.8[/C][C] 102.1[/C][C] 0.7158[/C][/ROW]
[ROW][C]119[/C][C] 124.8[/C][C] 117.3[/C][C] 7.474[/C][/ROW]
[ROW][C]120[/C][C] 103.9[/C][C] 104.2[/C][C]-0.32[/C][/ROW]
[ROW][C]121[/C][C] 112.2[/C][C] 110.8[/C][C] 1.4[/C][/ROW]
[ROW][C]122[/C][C] 108.5[/C][C] 109.1[/C][C]-0.5853[/C][/ROW]
[ROW][C]123[/C][C] 92.4[/C][C] 94.13[/C][C]-1.729[/C][/ROW]
[ROW][C]124[/C][C] 101.1[/C][C] 93[/C][C] 8.096[/C][/ROW]
[ROW][C]125[/C][C] 114.9[/C][C] 115.7[/C][C]-0.8087[/C][/ROW]
[ROW][C]126[/C][C] 106.4[/C][C] 108.9[/C][C]-2.494[/C][/ROW]
[ROW][C]127[/C][C] 104[/C][C] 105.6[/C][C]-1.624[/C][/ROW]
[ROW][C]128[/C][C] 101.6[/C][C] 110.6[/C][C]-8.989[/C][/ROW]
[ROW][C]129[/C][C] 99.4[/C][C] 103.1[/C][C]-3.703[/C][/ROW]
[ROW][C]130[/C][C] 102.3[/C][C] 104[/C][C]-1.656[/C][/ROW]
[ROW][C]131[/C][C] 121.3[/C][C] 123.5[/C][C]-2.198[/C][/ROW]
[ROW][C]132[/C][C] 99.3[/C][C] 104[/C][C]-4.711[/C][/ROW]
[ROW][C]133[/C][C] 102.9[/C][C] 104.3[/C][C]-1.404[/C][/ROW]
[ROW][C]134[/C][C] 111.4[/C][C] 116.7[/C][C]-5.336[/C][/ROW]
[ROW][C]135[/C][C] 98.5[/C][C] 97.89[/C][C] 0.6138[/C][/ROW]
[ROW][C]136[/C][C] 98.5[/C][C] 96.86[/C][C] 1.637[/C][/ROW]
[ROW][C]137[/C][C] 108.5[/C][C] 110.2[/C][C]-1.698[/C][/ROW]
[ROW][C]138[/C][C] 112.1[/C][C] 111.8[/C][C] 0.3072[/C][/ROW]
[ROW][C]139[/C][C] 105.3[/C][C] 102.2[/C][C] 3.15[/C][/ROW]
[ROW][C]140[/C][C] 95.2[/C][C] 98.44[/C][C]-3.242[/C][/ROW]
[ROW][C]141[/C][C] 98.2[/C][C] 96.63[/C][C] 1.568[/C][/ROW]
[ROW][C]142[/C][C] 96.6[/C][C] 98[/C][C]-1.397[/C][/ROW]
[ROW][C]143[/C][C] 109.6[/C][C] 108.1[/C][C] 1.469[/C][/ROW]
[ROW][C]144[/C][C] 108[/C][C] 103.2[/C][C] 4.816[/C][/ROW]
[ROW][C]145[/C][C] 106.7[/C][C] 104.5[/C][C] 2.231[/C][/ROW]
[ROW][C]146[/C][C] 111.5[/C][C] 109.2[/C][C] 2.262[/C][/ROW]
[ROW][C]147[/C][C] 104.5[/C][C] 101.4[/C][C] 3.081[/C][/ROW]
[ROW][C]148[/C][C] 94.3[/C][C] 91.87[/C][C] 2.426[/C][/ROW]
[ROW][C]149[/C][C] 109.6[/C][C] 111[/C][C]-1.415[/C][/ROW]
[ROW][C]150[/C][C] 116.4[/C][C] 116.4[/C][C] 0.02088[/C][/ROW]
[ROW][C]151[/C][C] 106.5[/C][C] 105.5[/C][C] 0.9534[/C][/ROW]
[ROW][C]152[/C][C] 100.5[/C][C] 105.6[/C][C]-5.131[/C][/ROW]
[ROW][C]153[/C][C] 101.7[/C][C] 105.6[/C][C]-3.876[/C][/ROW]
[ROW][C]154[/C][C] 104.1[/C][C] 107.3[/C][C]-3.16[/C][/ROW]
[ROW][C]155[/C][C] 112.3[/C][C] 111.7[/C][C] 0.5773[/C][/ROW]
[ROW][C]156[/C][C] 111.2[/C][C] 110.1[/C][C] 1.1[/C][/ROW]
[ROW][C]157[/C][C] 108.2[/C][C] 110.4[/C][C]-2.155[/C][/ROW]
[ROW][C]158[/C][C] 115.1[/C][C] 111.9[/C][C] 3.16[/C][/ROW]
[ROW][C]159[/C][C] 102.3[/C][C] 101[/C][C] 1.268[/C][/ROW]
[ROW][C]160[/C][C] 93.6[/C][C] 95.5[/C][C]-1.897[/C][/ROW]
[ROW][C]161[/C][C] 120.6[/C][C] 115.8[/C][C] 4.808[/C][/ROW]
[ROW][C]162[/C][C] 118.4[/C][C] 114.8[/C][C] 3.643[/C][/ROW]
[ROW][C]163[/C][C] 106.6[/C][C] 103.3[/C][C] 3.266[/C][/ROW]
[ROW][C]164[/C][C] 105.3[/C][C] 107.5[/C][C]-2.15[/C][/ROW]
[ROW][C]165[/C][C] 101.5[/C][C] 98.8[/C][C] 2.698[/C][/ROW]
[ROW][C]166[/C][C] 100.1[/C][C] 104.4[/C][C]-4.289[/C][/ROW]
[ROW][C]167[/C][C] 119.5[/C][C] 117.7[/C][C] 1.795[/C][/ROW]
[ROW][C]168[/C][C] 111.2[/C][C] 111.1[/C][C] 0.1301[/C][/ROW]
[ROW][C]169[/C][C] 103.7[/C][C] 103.5[/C][C] 0.1991[/C][/ROW]
[ROW][C]170[/C][C] 117.8[/C][C] 117.9[/C][C]-0.08915[/C][/ROW]
[ROW][C]171[/C][C] 101.7[/C][C] 103.6[/C][C]-1.905[/C][/ROW]
[ROW][C]172[/C][C] 97.4[/C][C] 96.22[/C][C] 1.177[/C][/ROW]
[ROW][C]173[/C][C] 120[/C][C] 122.6[/C][C]-2.593[/C][/ROW]
[ROW][C]174[/C][C] 117[/C][C] 120.9[/C][C]-3.909[/C][/ROW]
[ROW][C]175[/C][C] 110.6[/C][C] 111.9[/C][C]-1.286[/C][/ROW]
[ROW][C]176[/C][C] 105.3[/C][C] 112.6[/C][C]-7.263[/C][/ROW]
[ROW][C]177[/C][C] 100.9[/C][C] 103.5[/C][C]-2.633[/C][/ROW]
[ROW][C]178[/C][C] 108.1[/C][C] 113.6[/C][C]-5.543[/C][/ROW]
[ROW][C]179[/C][C] 119.3[/C][C] 120[/C][C]-0.687[/C][/ROW]
[ROW][C]180[/C][C] 113[/C][C] 114.2[/C][C]-1.242[/C][/ROW]
[ROW][C]181[/C][C] 108.6[/C][C] 107.3[/C][C] 1.284[/C][/ROW]
[ROW][C]182[/C][C] 123.3[/C][C] 121.4[/C][C] 1.859[/C][/ROW]
[ROW][C]183[/C][C] 101.4[/C][C] 98.41[/C][C] 2.986[/C][/ROW]
[ROW][C]184[/C][C] 103.5[/C][C] 98.6[/C][C] 4.902[/C][/ROW]
[ROW][C]185[/C][C] 119.4[/C][C] 117.7[/C][C] 1.671[/C][/ROW]
[ROW][C]186[/C][C] 113.1[/C][C] 114.9[/C][C]-1.829[/C][/ROW]
[ROW][C]187[/C][C] 112[/C][C] 110.3[/C][C] 1.669[/C][/ROW]
[ROW][C]188[/C][C] 115.8[/C][C] 112.8[/C][C] 3.008[/C][/ROW]
[ROW][C]189[/C][C] 105.4[/C][C] 101.6[/C][C] 3.768[/C][/ROW]
[ROW][C]190[/C][C] 110.9[/C][C] 109.9[/C][C] 1.006[/C][/ROW]
[ROW][C]191[/C][C] 128.5[/C][C] 127.4[/C][C] 1.067[/C][/ROW]
[ROW][C]192[/C][C] 109[/C][C] 109[/C][C]-0.02265[/C][/ROW]
[ROW][C]193[/C][C] 117.2[/C][C] 114.6[/C][C] 2.584[/C][/ROW]
[ROW][C]194[/C][C] 124.4[/C][C] 121.6[/C][C] 2.828[/C][/ROW]
[ROW][C]195[/C][C] 104.7[/C][C] 99.65[/C][C] 5.047[/C][/ROW]
[ROW][C]196[/C][C] 108.6[/C][C] 102[/C][C] 6.624[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=309794&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=309794&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	76.4	82.53	-6.127
2	79.7	86.6	-6.9
3	64.2	74.41	-10.21
4	67.9	75.03	-7.128
5	74.1	80.98	-6.881
6	78.5	81.54	-3.035
7	73.4	77.27	-3.868
8	65.4	74.49	-9.086
9	69.9	72.1	-2.196
10	69.6	74.07	-4.47
11	76.8	79.88	-3.076
12	75.6	78.34	-2.736
13	74	76.12	-2.124
14	76	79.44	-3.438
15	68.1	70.97	-2.866
16	65.5	68.5	-3.002
17	76.9	77.79	-0.8893
18	81.7	83.57	-1.868
19	73.6	74.95	-1.349
20	68.7	76.95	-8.254
21	73.3	72.06	1.245
22	71.5	76.98	-5.478
23	78.3	77.77	0.5259
24	76.5	76.53	-0.0293
25	71.8	74.32	-2.522
26	77.6	79.83	-2.225
27	70	70.54	-0.5379
28	64	64.18	-0.177
29	81.3	77.76	3.541
30	82.5	77.91	4.591
31	73.1	74.06	-0.9625
32	78.1	78.85	-0.7507
33	70.7	71.13	-0.4314
34	74.9	74.57	0.3281
35	88	82.67	5.335
36	81.3	80.37	0.9338
37	75.7	74.51	1.192
38	89.8	85.67	4.13
39	74.6	72.4	2.198
40	74.9	73.99	0.9082
41	90	84.51	5.487
42	88.1	85.11	2.988
43	84.9	78.36	6.539
44	87.7	84.43	3.273
45	80.5	80.67	-0.1659
46	79	80.17	-1.174
47	89.9	87.34	2.558
48	86.3	84.28	2.017
49	81.1	78.62	2.477
50	92.4	93.17	-0.7689
51	71.8	74.4	-2.602
52	76.1	74.8	1.301
53	92.5	85.91	6.588
54	87	84.06	2.941
55	89.5	84.25	5.254
56	88.7	96.48	-7.779
57	83.8	79.94	3.857
58	84.9	81.69	3.209
59	99	92.52	6.483
60	84.6	84.35	0.2509
61	92.7	88.31	4.387
62	97.6	93.07	4.531
63	78	78.28	-0.2833
64	81.9	75.69	6.214
65	96.5	92.78	3.724
66	99.9	96.16	3.744
67	96.2	88.75	7.448
68	90.5	92.82	-2.317
69	91.4	88.74	2.659
70	89.7	89.94	-0.2405
71	102.7	99.8	2.903
72	91.5	93.1	-1.597
73	96.2	93.19	3.013
74	104.5	104.2	0.3469
75	90.3	91.29	-0.9898
76	90.3	89.53	0.7714
77	100.4	106.8	-6.376
78	111.3	119.1	-7.832
79	101.3	102	-0.6899
80	94.4	102.9	-8.513
81	100.4	100.6	-0.1731
82	102	107	-5.019
83	104.3	105.5	-1.171
84	108.8	109.3	-0.4579
85	101.3	102.4	-1.144
86	108.9	110.7	-1.811
87	98.5	96.66	1.838
88	88.8	91.83	-3.032
89	111.8	109.6	2.238
90	109.6	109.6	0.0446
91	92.5	95.14	-2.644
92	94.5	96.51	-2.008
93	80.8	87.33	-6.531
94	83.7	90.6	-6.904
95	94.2	95.25	-1.049
96	86.2	89.5	-3.301
97	89	100.5	-11.52
98	94.7	96.37	-1.666
99	81.9	77.63	4.274
100	80.2	81.28	-1.079
101	96.5	95.27	1.232
102	95.6	92.44	3.157
103	91.9	90.19	1.712
104	89.9	89.7	0.2019
105	86.3	82.66	3.641
106	94	89.53	4.466
107	108	101.9	6.143
108	96.3	92.97	3.33
109	94.6	89.28	5.317
110	111.7	104.8	6.887
111	92	85.97	6.027
112	91.9	88.6	3.303
113	109.2	105.8	3.402
114	106.8	106.5	0.3502
115	105.8	97.8	7.999
116	103.6	107.2	-3.608
117	97.6	97.35	0.2478
118	102.8	102.1	0.7158
119	124.8	117.3	7.474
120	103.9	104.2	-0.32
121	112.2	110.8	1.4
122	108.5	109.1	-0.5853
123	92.4	94.13	-1.729
124	101.1	93	8.096
125	114.9	115.7	-0.8087
126	106.4	108.9	-2.494
127	104	105.6	-1.624
128	101.6	110.6	-8.989
129	99.4	103.1	-3.703
130	102.3	104	-1.656
131	121.3	123.5	-2.198
132	99.3	104	-4.711
133	102.9	104.3	-1.404
134	111.4	116.7	-5.336
135	98.5	97.89	0.6138
136	98.5	96.86	1.637
137	108.5	110.2	-1.698
138	112.1	111.8	0.3072
139	105.3	102.2	3.15
140	95.2	98.44	-3.242
141	98.2	96.63	1.568
142	96.6	98	-1.397
143	109.6	108.1	1.469
144	108	103.2	4.816
145	106.7	104.5	2.231
146	111.5	109.2	2.262
147	104.5	101.4	3.081
148	94.3	91.87	2.426
149	109.6	111	-1.415
150	116.4	116.4	0.02088
151	106.5	105.5	0.9534
152	100.5	105.6	-5.131
153	101.7	105.6	-3.876
154	104.1	107.3	-3.16
155	112.3	111.7	0.5773
156	111.2	110.1	1.1
157	108.2	110.4	-2.155
158	115.1	111.9	3.16
159	102.3	101	1.268
160	93.6	95.5	-1.897
161	120.6	115.8	4.808
162	118.4	114.8	3.643
163	106.6	103.3	3.266
164	105.3	107.5	-2.15
165	101.5	98.8	2.698
166	100.1	104.4	-4.289
167	119.5	117.7	1.795
168	111.2	111.1	0.1301
169	103.7	103.5	0.1991
170	117.8	117.9	-0.08915
171	101.7	103.6	-1.905
172	97.4	96.22	1.177
173	120	122.6	-2.593
174	117	120.9	-3.909
175	110.6	111.9	-1.286
176	105.3	112.6	-7.263
177	100.9	103.5	-2.633
178	108.1	113.6	-5.543
179	119.3	120	-0.687
180	113	114.2	-1.242
181	108.6	107.3	1.284
182	123.3	121.4	1.859
183	101.4	98.41	2.986
184	103.5	98.6	4.902
185	119.4	117.7	1.671
186	113.1	114.9	-1.829
187	112	110.3	1.669
188	115.8	112.8	3.008
189	105.4	101.6	3.768
190	110.9	109.9	1.006
191	128.5	127.4	1.067
192	109	109	-0.02265
193	117.2	114.6	2.584
194	124.4	121.6	2.828
195	104.7	99.65	5.047
196	108.6	102	6.624

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
11	0.2925	0.585	0.7075
12	0.1802	0.3605	0.8198
13	0.1567	0.3133	0.8433
14	0.1043	0.2086	0.8957
15	0.1523	0.3047	0.8477
16	0.09815	0.1963	0.9018
17	0.06393	0.1279	0.9361
18	0.03757	0.07515	0.9624
19	0.02121	0.04242	0.9788
20	0.02951	0.05902	0.9705
21	0.08701	0.174	0.913
22	0.06957	0.1391	0.9304
23	0.05566	0.1113	0.9443
24	0.05104	0.1021	0.949
25	0.03627	0.07254	0.9637
26	0.02979	0.05958	0.9702
27	0.02311	0.04621	0.9769
28	0.01637	0.03274	0.9836
29	0.01874	0.03749	0.9813
30	0.01475	0.02951	0.9852
31	0.009735	0.01947	0.9903
32	0.006261	0.01252	0.9937
33	0.003996	0.007992	0.996
34	0.004891	0.009782	0.9951
35	0.01065	0.0213	0.9893
36	0.01561	0.03122	0.9844
37	0.03965	0.07929	0.9604
38	0.1947	0.3893	0.8053
39	0.2717	0.5433	0.7283
40	0.3028	0.6057	0.6972
41	0.3603	0.7205	0.6397
42	0.3736	0.7472	0.6264
43	0.5757	0.8486	0.4243
44	0.5524	0.8952	0.4476
45	0.5054	0.9892	0.4946
46	0.467	0.9339	0.533
47	0.4421	0.8843	0.5579
48	0.46	0.9201	0.54
49	0.5608	0.8784	0.4392
50	0.5258	0.9484	0.4742
51	0.5169	0.9662	0.4831
52	0.5895	0.8209	0.4105
53	0.6897	0.6206	0.3103
54	0.6792	0.6415	0.3208
55	0.7008	0.5985	0.2992
56	0.9062	0.1875	0.09377
57	0.9076	0.1848	0.09241
58	0.9022	0.1956	0.09779
59	0.9071	0.1857	0.09287
60	0.8887	0.2226	0.1113
61	0.8907	0.2186	0.1093
62	0.8823	0.2355	0.1177
63	0.8659	0.2681	0.1341
64	0.8973	0.2054	0.1027
65	0.8797	0.2406	0.1203
66	0.8618	0.2764	0.1382
67	0.8926	0.2149	0.1074
68	0.9068	0.1865	0.09323
69	0.8887	0.2225	0.1113
70	0.876	0.248	0.124
71	0.858	0.284	0.142
72	0.8505	0.299	0.1495
73	0.828	0.344	0.172
74	0.8089	0.3822	0.1911
75	0.7862	0.4276	0.2138
76	0.7536	0.4928	0.2464
77	0.8549	0.2901	0.1451
78	0.9386	0.1229	0.06144
79	0.9263	0.1475	0.07375
80	0.9699	0.06013	0.03007
81	0.9619	0.07615	0.03807
82	0.9663	0.06749	0.03375
83	0.9583	0.0834	0.0417
84	0.9495	0.1011	0.05053
85	0.9398	0.1205	0.06024
86	0.9283	0.1434	0.07171
87	0.9173	0.1654	0.08271
88	0.9117	0.1766	0.08832
89	0.8989	0.2022	0.1011
90	0.8795	0.2409	0.1205
91	0.866	0.268	0.134
92	0.8471	0.3059	0.1529
93	0.8818	0.2364	0.1182
94	0.9188	0.1624	0.08122
95	0.9061	0.1878	0.09391
96	0.9098	0.1805	0.09025
97	0.993	0.01409	0.007045
98	0.9921	0.01573	0.007863
99	0.9931	0.01389	0.006945
100	0.9935	0.01293	0.006464
101	0.9924	0.01522	0.007612
102	0.9914	0.01716	0.00858
103	0.99	0.01997	0.009985
104	0.9888	0.02236	0.01118
105	0.987	0.02603	0.01301
106	0.9857	0.02858	0.01429
107	0.9864	0.0271	0.01355
108	0.9838	0.03242	0.01621
109	0.9843	0.0314	0.0157
110	0.9894	0.02128	0.01064
111	0.9909	0.01822	0.009108
112	0.9894	0.02117	0.01058
113	0.9875	0.02501	0.01251
114	0.9835	0.03291	0.01646
115	0.9933	0.01332	0.00666
116	0.9929	0.01411	0.007057
117	0.9906	0.01883	0.009414
118	0.9876	0.02487	0.01243
119	0.9969	0.006125	0.003062
120	0.9957	0.008524	0.004262
121	0.9951	0.009802	0.004901
122	0.9933	0.01338	0.006688
123	0.9919	0.0162	0.008101
124	0.9979	0.004237	0.002118
125	0.997	0.006002	0.003001
126	0.9962	0.007658	0.003829
127	0.9947	0.01051	0.005257
128	0.9987	0.002642	0.001321
129	0.9986	0.002883	0.001442
130	0.998	0.003951	0.001975
131	0.9973	0.005367	0.002684
132	0.9981	0.003775	0.001887
133	0.998	0.00408	0.00204
134	0.9983	0.003479	0.00174
135	0.9975	0.004953	0.002477
136	0.9966	0.006736	0.003368
137	0.9958	0.008497	0.004248
138	0.9941	0.01186	0.005931
139	0.9931	0.01385	0.006925
140	0.9953	0.00942	0.00471
141	0.9934	0.01323	0.006614
142	0.9929	0.01414	0.007068
143	0.991	0.01791	0.008955
144	0.9934	0.01321	0.006605
145	0.9914	0.01715	0.008577
146	0.9884	0.02316	0.01158
147	0.9905	0.01895	0.009477
148	0.9872	0.02569	0.01284
149	0.9838	0.03232	0.01616
150	0.9795	0.04095	0.02048
151	0.9744	0.05112	0.02556
152	0.9725	0.05506	0.02753
153	0.9676	0.06485	0.03242
154	0.9624	0.07525	0.03763
155	0.9492	0.1016	0.05082
156	0.936	0.128	0.06398
157	0.9232	0.1537	0.07684
158	0.914	0.172	0.08601
159	0.889	0.2219	0.111
160	0.8825	0.235	0.1175
161	0.8992	0.2015	0.1008
162	0.9082	0.1835	0.09175
163	0.8973	0.2054	0.1027
164	0.8876	0.2248	0.1124
165	0.8557	0.2886	0.1443
166	0.9194	0.1612	0.0806
167	0.9188	0.1625	0.08123
168	0.8964	0.2072	0.1036
169	0.8778	0.2444	0.1222
170	0.8393	0.3214	0.1607
171	0.7968	0.4065	0.2032
172	0.756	0.488	0.244
173	0.6989	0.6021	0.3011
174	0.6327	0.7347	0.3673
175	0.5523	0.8953	0.4477
176	0.9106	0.1787	0.08935
177	0.9436	0.1128	0.05638
178	0.9995	0.000902	0.000451
179	0.9994	0.001261	0.0006306
180	0.9993	0.001497	0.0007486
181	0.9998	0.0004344	0.0002172
182	0.9991	0.001812	0.0009061
183	0.9997	0.000666	0.000333
184	0.9983	0.003316	0.001658
185	0.9902	0.01956	0.009778

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
11 &  0.2925 &  0.585 &  0.7075 \tabularnewline
12 &  0.1802 &  0.3605 &  0.8198 \tabularnewline
13 &  0.1567 &  0.3133 &  0.8433 \tabularnewline
14 &  0.1043 &  0.2086 &  0.8957 \tabularnewline
15 &  0.1523 &  0.3047 &  0.8477 \tabularnewline
16 &  0.09815 &  0.1963 &  0.9018 \tabularnewline
17 &  0.06393 &  0.1279 &  0.9361 \tabularnewline
18 &  0.03757 &  0.07515 &  0.9624 \tabularnewline
19 &  0.02121 &  0.04242 &  0.9788 \tabularnewline
20 &  0.02951 &  0.05902 &  0.9705 \tabularnewline
21 &  0.08701 &  0.174 &  0.913 \tabularnewline
22 &  0.06957 &  0.1391 &  0.9304 \tabularnewline
23 &  0.05566 &  0.1113 &  0.9443 \tabularnewline
24 &  0.05104 &  0.1021 &  0.949 \tabularnewline
25 &  0.03627 &  0.07254 &  0.9637 \tabularnewline
26 &  0.02979 &  0.05958 &  0.9702 \tabularnewline
27 &  0.02311 &  0.04621 &  0.9769 \tabularnewline
28 &  0.01637 &  0.03274 &  0.9836 \tabularnewline
29 &  0.01874 &  0.03749 &  0.9813 \tabularnewline
30 &  0.01475 &  0.02951 &  0.9852 \tabularnewline
31 &  0.009735 &  0.01947 &  0.9903 \tabularnewline
32 &  0.006261 &  0.01252 &  0.9937 \tabularnewline
33 &  0.003996 &  0.007992 &  0.996 \tabularnewline
34 &  0.004891 &  0.009782 &  0.9951 \tabularnewline
35 &  0.01065 &  0.0213 &  0.9893 \tabularnewline
36 &  0.01561 &  0.03122 &  0.9844 \tabularnewline
37 &  0.03965 &  0.07929 &  0.9604 \tabularnewline
38 &  0.1947 &  0.3893 &  0.8053 \tabularnewline
39 &  0.2717 &  0.5433 &  0.7283 \tabularnewline
40 &  0.3028 &  0.6057 &  0.6972 \tabularnewline
41 &  0.3603 &  0.7205 &  0.6397 \tabularnewline
42 &  0.3736 &  0.7472 &  0.6264 \tabularnewline
43 &  0.5757 &  0.8486 &  0.4243 \tabularnewline
44 &  0.5524 &  0.8952 &  0.4476 \tabularnewline
45 &  0.5054 &  0.9892 &  0.4946 \tabularnewline
46 &  0.467 &  0.9339 &  0.533 \tabularnewline
47 &  0.4421 &  0.8843 &  0.5579 \tabularnewline
48 &  0.46 &  0.9201 &  0.54 \tabularnewline
49 &  0.5608 &  0.8784 &  0.4392 \tabularnewline
50 &  0.5258 &  0.9484 &  0.4742 \tabularnewline
51 &  0.5169 &  0.9662 &  0.4831 \tabularnewline
52 &  0.5895 &  0.8209 &  0.4105 \tabularnewline
53 &  0.6897 &  0.6206 &  0.3103 \tabularnewline
54 &  0.6792 &  0.6415 &  0.3208 \tabularnewline
55 &  0.7008 &  0.5985 &  0.2992 \tabularnewline
56 &  0.9062 &  0.1875 &  0.09377 \tabularnewline
57 &  0.9076 &  0.1848 &  0.09241 \tabularnewline
58 &  0.9022 &  0.1956 &  0.09779 \tabularnewline
59 &  0.9071 &  0.1857 &  0.09287 \tabularnewline
60 &  0.8887 &  0.2226 &  0.1113 \tabularnewline
61 &  0.8907 &  0.2186 &  0.1093 \tabularnewline
62 &  0.8823 &  0.2355 &  0.1177 \tabularnewline
63 &  0.8659 &  0.2681 &  0.1341 \tabularnewline
64 &  0.8973 &  0.2054 &  0.1027 \tabularnewline
65 &  0.8797 &  0.2406 &  0.1203 \tabularnewline
66 &  0.8618 &  0.2764 &  0.1382 \tabularnewline
67 &  0.8926 &  0.2149 &  0.1074 \tabularnewline
68 &  0.9068 &  0.1865 &  0.09323 \tabularnewline
69 &  0.8887 &  0.2225 &  0.1113 \tabularnewline
70 &  0.876 &  0.248 &  0.124 \tabularnewline
71 &  0.858 &  0.284 &  0.142 \tabularnewline
72 &  0.8505 &  0.299 &  0.1495 \tabularnewline
73 &  0.828 &  0.344 &  0.172 \tabularnewline
74 &  0.8089 &  0.3822 &  0.1911 \tabularnewline
75 &  0.7862 &  0.4276 &  0.2138 \tabularnewline
76 &  0.7536 &  0.4928 &  0.2464 \tabularnewline
77 &  0.8549 &  0.2901 &  0.1451 \tabularnewline
78 &  0.9386 &  0.1229 &  0.06144 \tabularnewline
79 &  0.9263 &  0.1475 &  0.07375 \tabularnewline
80 &  0.9699 &  0.06013 &  0.03007 \tabularnewline
81 &  0.9619 &  0.07615 &  0.03807 \tabularnewline
82 &  0.9663 &  0.06749 &  0.03375 \tabularnewline
83 &  0.9583 &  0.0834 &  0.0417 \tabularnewline
84 &  0.9495 &  0.1011 &  0.05053 \tabularnewline
85 &  0.9398 &  0.1205 &  0.06024 \tabularnewline
86 &  0.9283 &  0.1434 &  0.07171 \tabularnewline
87 &  0.9173 &  0.1654 &  0.08271 \tabularnewline
88 &  0.9117 &  0.1766 &  0.08832 \tabularnewline
89 &  0.8989 &  0.2022 &  0.1011 \tabularnewline
90 &  0.8795 &  0.2409 &  0.1205 \tabularnewline
91 &  0.866 &  0.268 &  0.134 \tabularnewline
92 &  0.8471 &  0.3059 &  0.1529 \tabularnewline
93 &  0.8818 &  0.2364 &  0.1182 \tabularnewline
94 &  0.9188 &  0.1624 &  0.08122 \tabularnewline
95 &  0.9061 &  0.1878 &  0.09391 \tabularnewline
96 &  0.9098 &  0.1805 &  0.09025 \tabularnewline
97 &  0.993 &  0.01409 &  0.007045 \tabularnewline
98 &  0.9921 &  0.01573 &  0.007863 \tabularnewline
99 &  0.9931 &  0.01389 &  0.006945 \tabularnewline
100 &  0.9935 &  0.01293 &  0.006464 \tabularnewline
101 &  0.9924 &  0.01522 &  0.007612 \tabularnewline
102 &  0.9914 &  0.01716 &  0.00858 \tabularnewline
103 &  0.99 &  0.01997 &  0.009985 \tabularnewline
104 &  0.9888 &  0.02236 &  0.01118 \tabularnewline
105 &  0.987 &  0.02603 &  0.01301 \tabularnewline
106 &  0.9857 &  0.02858 &  0.01429 \tabularnewline
107 &  0.9864 &  0.0271 &  0.01355 \tabularnewline
108 &  0.9838 &  0.03242 &  0.01621 \tabularnewline
109 &  0.9843 &  0.0314 &  0.0157 \tabularnewline
110 &  0.9894 &  0.02128 &  0.01064 \tabularnewline
111 &  0.9909 &  0.01822 &  0.009108 \tabularnewline
112 &  0.9894 &  0.02117 &  0.01058 \tabularnewline
113 &  0.9875 &  0.02501 &  0.01251 \tabularnewline
114 &  0.9835 &  0.03291 &  0.01646 \tabularnewline
115 &  0.9933 &  0.01332 &  0.00666 \tabularnewline
116 &  0.9929 &  0.01411 &  0.007057 \tabularnewline
117 &  0.9906 &  0.01883 &  0.009414 \tabularnewline
118 &  0.9876 &  0.02487 &  0.01243 \tabularnewline
119 &  0.9969 &  0.006125 &  0.003062 \tabularnewline
120 &  0.9957 &  0.008524 &  0.004262 \tabularnewline
121 &  0.9951 &  0.009802 &  0.004901 \tabularnewline
122 &  0.9933 &  0.01338 &  0.006688 \tabularnewline
123 &  0.9919 &  0.0162 &  0.008101 \tabularnewline
124 &  0.9979 &  0.004237 &  0.002118 \tabularnewline
125 &  0.997 &  0.006002 &  0.003001 \tabularnewline
126 &  0.9962 &  0.007658 &  0.003829 \tabularnewline
127 &  0.9947 &  0.01051 &  0.005257 \tabularnewline
128 &  0.9987 &  0.002642 &  0.001321 \tabularnewline
129 &  0.9986 &  0.002883 &  0.001442 \tabularnewline
130 &  0.998 &  0.003951 &  0.001975 \tabularnewline
131 &  0.9973 &  0.005367 &  0.002684 \tabularnewline
132 &  0.9981 &  0.003775 &  0.001887 \tabularnewline
133 &  0.998 &  0.00408 &  0.00204 \tabularnewline
134 &  0.9983 &  0.003479 &  0.00174 \tabularnewline
135 &  0.9975 &  0.004953 &  0.002477 \tabularnewline
136 &  0.9966 &  0.006736 &  0.003368 \tabularnewline
137 &  0.9958 &  0.008497 &  0.004248 \tabularnewline
138 &  0.9941 &  0.01186 &  0.005931 \tabularnewline
139 &  0.9931 &  0.01385 &  0.006925 \tabularnewline
140 &  0.9953 &  0.00942 &  0.00471 \tabularnewline
141 &  0.9934 &  0.01323 &  0.006614 \tabularnewline
142 &  0.9929 &  0.01414 &  0.007068 \tabularnewline
143 &  0.991 &  0.01791 &  0.008955 \tabularnewline
144 &  0.9934 &  0.01321 &  0.006605 \tabularnewline
145 &  0.9914 &  0.01715 &  0.008577 \tabularnewline
146 &  0.9884 &  0.02316 &  0.01158 \tabularnewline
147 &  0.9905 &  0.01895 &  0.009477 \tabularnewline
148 &  0.9872 &  0.02569 &  0.01284 \tabularnewline
149 &  0.9838 &  0.03232 &  0.01616 \tabularnewline
150 &  0.9795 &  0.04095 &  0.02048 \tabularnewline
151 &  0.9744 &  0.05112 &  0.02556 \tabularnewline
152 &  0.9725 &  0.05506 &  0.02753 \tabularnewline
153 &  0.9676 &  0.06485 &  0.03242 \tabularnewline
154 &  0.9624 &  0.07525 &  0.03763 \tabularnewline
155 &  0.9492 &  0.1016 &  0.05082 \tabularnewline
156 &  0.936 &  0.128 &  0.06398 \tabularnewline
157 &  0.9232 &  0.1537 &  0.07684 \tabularnewline
158 &  0.914 &  0.172 &  0.08601 \tabularnewline
159 &  0.889 &  0.2219 &  0.111 \tabularnewline
160 &  0.8825 &  0.235 &  0.1175 \tabularnewline
161 &  0.8992 &  0.2015 &  0.1008 \tabularnewline
162 &  0.9082 &  0.1835 &  0.09175 \tabularnewline
163 &  0.8973 &  0.2054 &  0.1027 \tabularnewline
164 &  0.8876 &  0.2248 &  0.1124 \tabularnewline
165 &  0.8557 &  0.2886 &  0.1443 \tabularnewline
166 &  0.9194 &  0.1612 &  0.0806 \tabularnewline
167 &  0.9188 &  0.1625 &  0.08123 \tabularnewline
168 &  0.8964 &  0.2072 &  0.1036 \tabularnewline
169 &  0.8778 &  0.2444 &  0.1222 \tabularnewline
170 &  0.8393 &  0.3214 &  0.1607 \tabularnewline
171 &  0.7968 &  0.4065 &  0.2032 \tabularnewline
172 &  0.756 &  0.488 &  0.244 \tabularnewline
173 &  0.6989 &  0.6021 &  0.3011 \tabularnewline
174 &  0.6327 &  0.7347 &  0.3673 \tabularnewline
175 &  0.5523 &  0.8953 &  0.4477 \tabularnewline
176 &  0.9106 &  0.1787 &  0.08935 \tabularnewline
177 &  0.9436 &  0.1128 &  0.05638 \tabularnewline
178 &  0.9995 &  0.000902 &  0.000451 \tabularnewline
179 &  0.9994 &  0.001261 &  0.0006306 \tabularnewline
180 &  0.9993 &  0.001497 &  0.0007486 \tabularnewline
181 &  0.9998 &  0.0004344 &  0.0002172 \tabularnewline
182 &  0.9991 &  0.001812 &  0.0009061 \tabularnewline
183 &  0.9997 &  0.000666 &  0.000333 \tabularnewline
184 &  0.9983 &  0.003316 &  0.001658 \tabularnewline
185 &  0.9902 &  0.01956 &  0.009778 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=309794&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]11[/C][C] 0.2925[/C][C] 0.585[/C][C] 0.7075[/C][/ROW]
[ROW][C]12[/C][C] 0.1802[/C][C] 0.3605[/C][C] 0.8198[/C][/ROW]
[ROW][C]13[/C][C] 0.1567[/C][C] 0.3133[/C][C] 0.8433[/C][/ROW]
[ROW][C]14[/C][C] 0.1043[/C][C] 0.2086[/C][C] 0.8957[/C][/ROW]
[ROW][C]15[/C][C] 0.1523[/C][C] 0.3047[/C][C] 0.8477[/C][/ROW]
[ROW][C]16[/C][C] 0.09815[/C][C] 0.1963[/C][C] 0.9018[/C][/ROW]
[ROW][C]17[/C][C] 0.06393[/C][C] 0.1279[/C][C] 0.9361[/C][/ROW]
[ROW][C]18[/C][C] 0.03757[/C][C] 0.07515[/C][C] 0.9624[/C][/ROW]
[ROW][C]19[/C][C] 0.02121[/C][C] 0.04242[/C][C] 0.9788[/C][/ROW]
[ROW][C]20[/C][C] 0.02951[/C][C] 0.05902[/C][C] 0.9705[/C][/ROW]
[ROW][C]21[/C][C] 0.08701[/C][C] 0.174[/C][C] 0.913[/C][/ROW]
[ROW][C]22[/C][C] 0.06957[/C][C] 0.1391[/C][C] 0.9304[/C][/ROW]
[ROW][C]23[/C][C] 0.05566[/C][C] 0.1113[/C][C] 0.9443[/C][/ROW]
[ROW][C]24[/C][C] 0.05104[/C][C] 0.1021[/C][C] 0.949[/C][/ROW]
[ROW][C]25[/C][C] 0.03627[/C][C] 0.07254[/C][C] 0.9637[/C][/ROW]
[ROW][C]26[/C][C] 0.02979[/C][C] 0.05958[/C][C] 0.9702[/C][/ROW]
[ROW][C]27[/C][C] 0.02311[/C][C] 0.04621[/C][C] 0.9769[/C][/ROW]
[ROW][C]28[/C][C] 0.01637[/C][C] 0.03274[/C][C] 0.9836[/C][/ROW]
[ROW][C]29[/C][C] 0.01874[/C][C] 0.03749[/C][C] 0.9813[/C][/ROW]
[ROW][C]30[/C][C] 0.01475[/C][C] 0.02951[/C][C] 0.9852[/C][/ROW]
[ROW][C]31[/C][C] 0.009735[/C][C] 0.01947[/C][C] 0.9903[/C][/ROW]
[ROW][C]32[/C][C] 0.006261[/C][C] 0.01252[/C][C] 0.9937[/C][/ROW]
[ROW][C]33[/C][C] 0.003996[/C][C] 0.007992[/C][C] 0.996[/C][/ROW]
[ROW][C]34[/C][C] 0.004891[/C][C] 0.009782[/C][C] 0.9951[/C][/ROW]
[ROW][C]35[/C][C] 0.01065[/C][C] 0.0213[/C][C] 0.9893[/C][/ROW]
[ROW][C]36[/C][C] 0.01561[/C][C] 0.03122[/C][C] 0.9844[/C][/ROW]
[ROW][C]37[/C][C] 0.03965[/C][C] 0.07929[/C][C] 0.9604[/C][/ROW]
[ROW][C]38[/C][C] 0.1947[/C][C] 0.3893[/C][C] 0.8053[/C][/ROW]
[ROW][C]39[/C][C] 0.2717[/C][C] 0.5433[/C][C] 0.7283[/C][/ROW]
[ROW][C]40[/C][C] 0.3028[/C][C] 0.6057[/C][C] 0.6972[/C][/ROW]
[ROW][C]41[/C][C] 0.3603[/C][C] 0.7205[/C][C] 0.6397[/C][/ROW]
[ROW][C]42[/C][C] 0.3736[/C][C] 0.7472[/C][C] 0.6264[/C][/ROW]
[ROW][C]43[/C][C] 0.5757[/C][C] 0.8486[/C][C] 0.4243[/C][/ROW]
[ROW][C]44[/C][C] 0.5524[/C][C] 0.8952[/C][C] 0.4476[/C][/ROW]
[ROW][C]45[/C][C] 0.5054[/C][C] 0.9892[/C][C] 0.4946[/C][/ROW]
[ROW][C]46[/C][C] 0.467[/C][C] 0.9339[/C][C] 0.533[/C][/ROW]
[ROW][C]47[/C][C] 0.4421[/C][C] 0.8843[/C][C] 0.5579[/C][/ROW]
[ROW][C]48[/C][C] 0.46[/C][C] 0.9201[/C][C] 0.54[/C][/ROW]
[ROW][C]49[/C][C] 0.5608[/C][C] 0.8784[/C][C] 0.4392[/C][/ROW]
[ROW][C]50[/C][C] 0.5258[/C][C] 0.9484[/C][C] 0.4742[/C][/ROW]
[ROW][C]51[/C][C] 0.5169[/C][C] 0.9662[/C][C] 0.4831[/C][/ROW]
[ROW][C]52[/C][C] 0.5895[/C][C] 0.8209[/C][C] 0.4105[/C][/ROW]
[ROW][C]53[/C][C] 0.6897[/C][C] 0.6206[/C][C] 0.3103[/C][/ROW]
[ROW][C]54[/C][C] 0.6792[/C][C] 0.6415[/C][C] 0.3208[/C][/ROW]
[ROW][C]55[/C][C] 0.7008[/C][C] 0.5985[/C][C] 0.2992[/C][/ROW]
[ROW][C]56[/C][C] 0.9062[/C][C] 0.1875[/C][C] 0.09377[/C][/ROW]
[ROW][C]57[/C][C] 0.9076[/C][C] 0.1848[/C][C] 0.09241[/C][/ROW]
[ROW][C]58[/C][C] 0.9022[/C][C] 0.1956[/C][C] 0.09779[/C][/ROW]
[ROW][C]59[/C][C] 0.9071[/C][C] 0.1857[/C][C] 0.09287[/C][/ROW]
[ROW][C]60[/C][C] 0.8887[/C][C] 0.2226[/C][C] 0.1113[/C][/ROW]
[ROW][C]61[/C][C] 0.8907[/C][C] 0.2186[/C][C] 0.1093[/C][/ROW]
[ROW][C]62[/C][C] 0.8823[/C][C] 0.2355[/C][C] 0.1177[/C][/ROW]
[ROW][C]63[/C][C] 0.8659[/C][C] 0.2681[/C][C] 0.1341[/C][/ROW]
[ROW][C]64[/C][C] 0.8973[/C][C] 0.2054[/C][C] 0.1027[/C][/ROW]
[ROW][C]65[/C][C] 0.8797[/C][C] 0.2406[/C][C] 0.1203[/C][/ROW]
[ROW][C]66[/C][C] 0.8618[/C][C] 0.2764[/C][C] 0.1382[/C][/ROW]
[ROW][C]67[/C][C] 0.8926[/C][C] 0.2149[/C][C] 0.1074[/C][/ROW]
[ROW][C]68[/C][C] 0.9068[/C][C] 0.1865[/C][C] 0.09323[/C][/ROW]
[ROW][C]69[/C][C] 0.8887[/C][C] 0.2225[/C][C] 0.1113[/C][/ROW]
[ROW][C]70[/C][C] 0.876[/C][C] 0.248[/C][C] 0.124[/C][/ROW]
[ROW][C]71[/C][C] 0.858[/C][C] 0.284[/C][C] 0.142[/C][/ROW]
[ROW][C]72[/C][C] 0.8505[/C][C] 0.299[/C][C] 0.1495[/C][/ROW]
[ROW][C]73[/C][C] 0.828[/C][C] 0.344[/C][C] 0.172[/C][/ROW]
[ROW][C]74[/C][C] 0.8089[/C][C] 0.3822[/C][C] 0.1911[/C][/ROW]
[ROW][C]75[/C][C] 0.7862[/C][C] 0.4276[/C][C] 0.2138[/C][/ROW]
[ROW][C]76[/C][C] 0.7536[/C][C] 0.4928[/C][C] 0.2464[/C][/ROW]
[ROW][C]77[/C][C] 0.8549[/C][C] 0.2901[/C][C] 0.1451[/C][/ROW]
[ROW][C]78[/C][C] 0.9386[/C][C] 0.1229[/C][C] 0.06144[/C][/ROW]
[ROW][C]79[/C][C] 0.9263[/C][C] 0.1475[/C][C] 0.07375[/C][/ROW]
[ROW][C]80[/C][C] 0.9699[/C][C] 0.06013[/C][C] 0.03007[/C][/ROW]
[ROW][C]81[/C][C] 0.9619[/C][C] 0.07615[/C][C] 0.03807[/C][/ROW]
[ROW][C]82[/C][C] 0.9663[/C][C] 0.06749[/C][C] 0.03375[/C][/ROW]
[ROW][C]83[/C][C] 0.9583[/C][C] 0.0834[/C][C] 0.0417[/C][/ROW]
[ROW][C]84[/C][C] 0.9495[/C][C] 0.1011[/C][C] 0.05053[/C][/ROW]
[ROW][C]85[/C][C] 0.9398[/C][C] 0.1205[/C][C] 0.06024[/C][/ROW]
[ROW][C]86[/C][C] 0.9283[/C][C] 0.1434[/C][C] 0.07171[/C][/ROW]
[ROW][C]87[/C][C] 0.9173[/C][C] 0.1654[/C][C] 0.08271[/C][/ROW]
[ROW][C]88[/C][C] 0.9117[/C][C] 0.1766[/C][C] 0.08832[/C][/ROW]
[ROW][C]89[/C][C] 0.8989[/C][C] 0.2022[/C][C] 0.1011[/C][/ROW]
[ROW][C]90[/C][C] 0.8795[/C][C] 0.2409[/C][C] 0.1205[/C][/ROW]
[ROW][C]91[/C][C] 0.866[/C][C] 0.268[/C][C] 0.134[/C][/ROW]
[ROW][C]92[/C][C] 0.8471[/C][C] 0.3059[/C][C] 0.1529[/C][/ROW]
[ROW][C]93[/C][C] 0.8818[/C][C] 0.2364[/C][C] 0.1182[/C][/ROW]
[ROW][C]94[/C][C] 0.9188[/C][C] 0.1624[/C][C] 0.08122[/C][/ROW]
[ROW][C]95[/C][C] 0.9061[/C][C] 0.1878[/C][C] 0.09391[/C][/ROW]
[ROW][C]96[/C][C] 0.9098[/C][C] 0.1805[/C][C] 0.09025[/C][/ROW]
[ROW][C]97[/C][C] 0.993[/C][C] 0.01409[/C][C] 0.007045[/C][/ROW]
[ROW][C]98[/C][C] 0.9921[/C][C] 0.01573[/C][C] 0.007863[/C][/ROW]
[ROW][C]99[/C][C] 0.9931[/C][C] 0.01389[/C][C] 0.006945[/C][/ROW]
[ROW][C]100[/C][C] 0.9935[/C][C] 0.01293[/C][C] 0.006464[/C][/ROW]
[ROW][C]101[/C][C] 0.9924[/C][C] 0.01522[/C][C] 0.007612[/C][/ROW]
[ROW][C]102[/C][C] 0.9914[/C][C] 0.01716[/C][C] 0.00858[/C][/ROW]
[ROW][C]103[/C][C] 0.99[/C][C] 0.01997[/C][C] 0.009985[/C][/ROW]
[ROW][C]104[/C][C] 0.9888[/C][C] 0.02236[/C][C] 0.01118[/C][/ROW]
[ROW][C]105[/C][C] 0.987[/C][C] 0.02603[/C][C] 0.01301[/C][/ROW]
[ROW][C]106[/C][C] 0.9857[/C][C] 0.02858[/C][C] 0.01429[/C][/ROW]
[ROW][C]107[/C][C] 0.9864[/C][C] 0.0271[/C][C] 0.01355[/C][/ROW]
[ROW][C]108[/C][C] 0.9838[/C][C] 0.03242[/C][C] 0.01621[/C][/ROW]
[ROW][C]109[/C][C] 0.9843[/C][C] 0.0314[/C][C] 0.0157[/C][/ROW]
[ROW][C]110[/C][C] 0.9894[/C][C] 0.02128[/C][C] 0.01064[/C][/ROW]
[ROW][C]111[/C][C] 0.9909[/C][C] 0.01822[/C][C] 0.009108[/C][/ROW]
[ROW][C]112[/C][C] 0.9894[/C][C] 0.02117[/C][C] 0.01058[/C][/ROW]
[ROW][C]113[/C][C] 0.9875[/C][C] 0.02501[/C][C] 0.01251[/C][/ROW]
[ROW][C]114[/C][C] 0.9835[/C][C] 0.03291[/C][C] 0.01646[/C][/ROW]
[ROW][C]115[/C][C] 0.9933[/C][C] 0.01332[/C][C] 0.00666[/C][/ROW]
[ROW][C]116[/C][C] 0.9929[/C][C] 0.01411[/C][C] 0.007057[/C][/ROW]
[ROW][C]117[/C][C] 0.9906[/C][C] 0.01883[/C][C] 0.009414[/C][/ROW]
[ROW][C]118[/C][C] 0.9876[/C][C] 0.02487[/C][C] 0.01243[/C][/ROW]
[ROW][C]119[/C][C] 0.9969[/C][C] 0.006125[/C][C] 0.003062[/C][/ROW]
[ROW][C]120[/C][C] 0.9957[/C][C] 0.008524[/C][C] 0.004262[/C][/ROW]
[ROW][C]121[/C][C] 0.9951[/C][C] 0.009802[/C][C] 0.004901[/C][/ROW]
[ROW][C]122[/C][C] 0.9933[/C][C] 0.01338[/C][C] 0.006688[/C][/ROW]
[ROW][C]123[/C][C] 0.9919[/C][C] 0.0162[/C][C] 0.008101[/C][/ROW]
[ROW][C]124[/C][C] 0.9979[/C][C] 0.004237[/C][C] 0.002118[/C][/ROW]
[ROW][C]125[/C][C] 0.997[/C][C] 0.006002[/C][C] 0.003001[/C][/ROW]
[ROW][C]126[/C][C] 0.9962[/C][C] 0.007658[/C][C] 0.003829[/C][/ROW]
[ROW][C]127[/C][C] 0.9947[/C][C] 0.01051[/C][C] 0.005257[/C][/ROW]
[ROW][C]128[/C][C] 0.9987[/C][C] 0.002642[/C][C] 0.001321[/C][/ROW]
[ROW][C]129[/C][C] 0.9986[/C][C] 0.002883[/C][C] 0.001442[/C][/ROW]
[ROW][C]130[/C][C] 0.998[/C][C] 0.003951[/C][C] 0.001975[/C][/ROW]
[ROW][C]131[/C][C] 0.9973[/C][C] 0.005367[/C][C] 0.002684[/C][/ROW]
[ROW][C]132[/C][C] 0.9981[/C][C] 0.003775[/C][C] 0.001887[/C][/ROW]
[ROW][C]133[/C][C] 0.998[/C][C] 0.00408[/C][C] 0.00204[/C][/ROW]
[ROW][C]134[/C][C] 0.9983[/C][C] 0.003479[/C][C] 0.00174[/C][/ROW]
[ROW][C]135[/C][C] 0.9975[/C][C] 0.004953[/C][C] 0.002477[/C][/ROW]
[ROW][C]136[/C][C] 0.9966[/C][C] 0.006736[/C][C] 0.003368[/C][/ROW]
[ROW][C]137[/C][C] 0.9958[/C][C] 0.008497[/C][C] 0.004248[/C][/ROW]
[ROW][C]138[/C][C] 0.9941[/C][C] 0.01186[/C][C] 0.005931[/C][/ROW]
[ROW][C]139[/C][C] 0.9931[/C][C] 0.01385[/C][C] 0.006925[/C][/ROW]
[ROW][C]140[/C][C] 0.9953[/C][C] 0.00942[/C][C] 0.00471[/C][/ROW]
[ROW][C]141[/C][C] 0.9934[/C][C] 0.01323[/C][C] 0.006614[/C][/ROW]
[ROW][C]142[/C][C] 0.9929[/C][C] 0.01414[/C][C] 0.007068[/C][/ROW]
[ROW][C]143[/C][C] 0.991[/C][C] 0.01791[/C][C] 0.008955[/C][/ROW]
[ROW][C]144[/C][C] 0.9934[/C][C] 0.01321[/C][C] 0.006605[/C][/ROW]
[ROW][C]145[/C][C] 0.9914[/C][C] 0.01715[/C][C] 0.008577[/C][/ROW]
[ROW][C]146[/C][C] 0.9884[/C][C] 0.02316[/C][C] 0.01158[/C][/ROW]
[ROW][C]147[/C][C] 0.9905[/C][C] 0.01895[/C][C] 0.009477[/C][/ROW]
[ROW][C]148[/C][C] 0.9872[/C][C] 0.02569[/C][C] 0.01284[/C][/ROW]
[ROW][C]149[/C][C] 0.9838[/C][C] 0.03232[/C][C] 0.01616[/C][/ROW]
[ROW][C]150[/C][C] 0.9795[/C][C] 0.04095[/C][C] 0.02048[/C][/ROW]
[ROW][C]151[/C][C] 0.9744[/C][C] 0.05112[/C][C] 0.02556[/C][/ROW]
[ROW][C]152[/C][C] 0.9725[/C][C] 0.05506[/C][C] 0.02753[/C][/ROW]
[ROW][C]153[/C][C] 0.9676[/C][C] 0.06485[/C][C] 0.03242[/C][/ROW]
[ROW][C]154[/C][C] 0.9624[/C][C] 0.07525[/C][C] 0.03763[/C][/ROW]
[ROW][C]155[/C][C] 0.9492[/C][C] 0.1016[/C][C] 0.05082[/C][/ROW]
[ROW][C]156[/C][C] 0.936[/C][C] 0.128[/C][C] 0.06398[/C][/ROW]
[ROW][C]157[/C][C] 0.9232[/C][C] 0.1537[/C][C] 0.07684[/C][/ROW]
[ROW][C]158[/C][C] 0.914[/C][C] 0.172[/C][C] 0.08601[/C][/ROW]
[ROW][C]159[/C][C] 0.889[/C][C] 0.2219[/C][C] 0.111[/C][/ROW]
[ROW][C]160[/C][C] 0.8825[/C][C] 0.235[/C][C] 0.1175[/C][/ROW]
[ROW][C]161[/C][C] 0.8992[/C][C] 0.2015[/C][C] 0.1008[/C][/ROW]
[ROW][C]162[/C][C] 0.9082[/C][C] 0.1835[/C][C] 0.09175[/C][/ROW]
[ROW][C]163[/C][C] 0.8973[/C][C] 0.2054[/C][C] 0.1027[/C][/ROW]
[ROW][C]164[/C][C] 0.8876[/C][C] 0.2248[/C][C] 0.1124[/C][/ROW]
[ROW][C]165[/C][C] 0.8557[/C][C] 0.2886[/C][C] 0.1443[/C][/ROW]
[ROW][C]166[/C][C] 0.9194[/C][C] 0.1612[/C][C] 0.0806[/C][/ROW]
[ROW][C]167[/C][C] 0.9188[/C][C] 0.1625[/C][C] 0.08123[/C][/ROW]
[ROW][C]168[/C][C] 0.8964[/C][C] 0.2072[/C][C] 0.1036[/C][/ROW]
[ROW][C]169[/C][C] 0.8778[/C][C] 0.2444[/C][C] 0.1222[/C][/ROW]
[ROW][C]170[/C][C] 0.8393[/C][C] 0.3214[/C][C] 0.1607[/C][/ROW]
[ROW][C]171[/C][C] 0.7968[/C][C] 0.4065[/C][C] 0.2032[/C][/ROW]
[ROW][C]172[/C][C] 0.756[/C][C] 0.488[/C][C] 0.244[/C][/ROW]
[ROW][C]173[/C][C] 0.6989[/C][C] 0.6021[/C][C] 0.3011[/C][/ROW]
[ROW][C]174[/C][C] 0.6327[/C][C] 0.7347[/C][C] 0.3673[/C][/ROW]
[ROW][C]175[/C][C] 0.5523[/C][C] 0.8953[/C][C] 0.4477[/C][/ROW]
[ROW][C]176[/C][C] 0.9106[/C][C] 0.1787[/C][C] 0.08935[/C][/ROW]
[ROW][C]177[/C][C] 0.9436[/C][C] 0.1128[/C][C] 0.05638[/C][/ROW]
[ROW][C]178[/C][C] 0.9995[/C][C] 0.000902[/C][C] 0.000451[/C][/ROW]
[ROW][C]179[/C][C] 0.9994[/C][C] 0.001261[/C][C] 0.0006306[/C][/ROW]
[ROW][C]180[/C][C] 0.9993[/C][C] 0.001497[/C][C] 0.0007486[/C][/ROW]
[ROW][C]181[/C][C] 0.9998[/C][C] 0.0004344[/C][C] 0.0002172[/C][/ROW]
[ROW][C]182[/C][C] 0.9991[/C][C] 0.001812[/C][C] 0.0009061[/C][/ROW]
[ROW][C]183[/C][C] 0.9997[/C][C] 0.000666[/C][C] 0.000333[/C][/ROW]
[ROW][C]184[/C][C] 0.9983[/C][C] 0.003316[/C][C] 0.001658[/C][/ROW]
[ROW][C]185[/C][C] 0.9902[/C][C] 0.01956[/C][C] 0.009778[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=309794&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=309794&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
11	0.2925	0.585	0.7075
12	0.1802	0.3605	0.8198
13	0.1567	0.3133	0.8433
14	0.1043	0.2086	0.8957
15	0.1523	0.3047	0.8477
16	0.09815	0.1963	0.9018
17	0.06393	0.1279	0.9361
18	0.03757	0.07515	0.9624
19	0.02121	0.04242	0.9788
20	0.02951	0.05902	0.9705
21	0.08701	0.174	0.913
22	0.06957	0.1391	0.9304
23	0.05566	0.1113	0.9443
24	0.05104	0.1021	0.949
25	0.03627	0.07254	0.9637
26	0.02979	0.05958	0.9702
27	0.02311	0.04621	0.9769
28	0.01637	0.03274	0.9836
29	0.01874	0.03749	0.9813
30	0.01475	0.02951	0.9852
31	0.009735	0.01947	0.9903
32	0.006261	0.01252	0.9937
33	0.003996	0.007992	0.996
34	0.004891	0.009782	0.9951
35	0.01065	0.0213	0.9893
36	0.01561	0.03122	0.9844
37	0.03965	0.07929	0.9604
38	0.1947	0.3893	0.8053
39	0.2717	0.5433	0.7283
40	0.3028	0.6057	0.6972
41	0.3603	0.7205	0.6397
42	0.3736	0.7472	0.6264
43	0.5757	0.8486	0.4243
44	0.5524	0.8952	0.4476
45	0.5054	0.9892	0.4946
46	0.467	0.9339	0.533
47	0.4421	0.8843	0.5579
48	0.46	0.9201	0.54
49	0.5608	0.8784	0.4392
50	0.5258	0.9484	0.4742
51	0.5169	0.9662	0.4831
52	0.5895	0.8209	0.4105
53	0.6897	0.6206	0.3103
54	0.6792	0.6415	0.3208
55	0.7008	0.5985	0.2992
56	0.9062	0.1875	0.09377
57	0.9076	0.1848	0.09241
58	0.9022	0.1956	0.09779
59	0.9071	0.1857	0.09287
60	0.8887	0.2226	0.1113
61	0.8907	0.2186	0.1093
62	0.8823	0.2355	0.1177
63	0.8659	0.2681	0.1341
64	0.8973	0.2054	0.1027
65	0.8797	0.2406	0.1203
66	0.8618	0.2764	0.1382
67	0.8926	0.2149	0.1074
68	0.9068	0.1865	0.09323
69	0.8887	0.2225	0.1113
70	0.876	0.248	0.124
71	0.858	0.284	0.142
72	0.8505	0.299	0.1495
73	0.828	0.344	0.172
74	0.8089	0.3822	0.1911
75	0.7862	0.4276	0.2138
76	0.7536	0.4928	0.2464
77	0.8549	0.2901	0.1451
78	0.9386	0.1229	0.06144
79	0.9263	0.1475	0.07375
80	0.9699	0.06013	0.03007
81	0.9619	0.07615	0.03807
82	0.9663	0.06749	0.03375
83	0.9583	0.0834	0.0417
84	0.9495	0.1011	0.05053
85	0.9398	0.1205	0.06024
86	0.9283	0.1434	0.07171
87	0.9173	0.1654	0.08271
88	0.9117	0.1766	0.08832
89	0.8989	0.2022	0.1011
90	0.8795	0.2409	0.1205
91	0.866	0.268	0.134
92	0.8471	0.3059	0.1529
93	0.8818	0.2364	0.1182
94	0.9188	0.1624	0.08122
95	0.9061	0.1878	0.09391
96	0.9098	0.1805	0.09025
97	0.993	0.01409	0.007045
98	0.9921	0.01573	0.007863
99	0.9931	0.01389	0.006945
100	0.9935	0.01293	0.006464
101	0.9924	0.01522	0.007612
102	0.9914	0.01716	0.00858
103	0.99	0.01997	0.009985
104	0.9888	0.02236	0.01118
105	0.987	0.02603	0.01301
106	0.9857	0.02858	0.01429
107	0.9864	0.0271	0.01355
108	0.9838	0.03242	0.01621
109	0.9843	0.0314	0.0157
110	0.9894	0.02128	0.01064
111	0.9909	0.01822	0.009108
112	0.9894	0.02117	0.01058
113	0.9875	0.02501	0.01251
114	0.9835	0.03291	0.01646
115	0.9933	0.01332	0.00666
116	0.9929	0.01411	0.007057
117	0.9906	0.01883	0.009414
118	0.9876	0.02487	0.01243
119	0.9969	0.006125	0.003062
120	0.9957	0.008524	0.004262
121	0.9951	0.009802	0.004901
122	0.9933	0.01338	0.006688
123	0.9919	0.0162	0.008101
124	0.9979	0.004237	0.002118
125	0.997	0.006002	0.003001
126	0.9962	0.007658	0.003829
127	0.9947	0.01051	0.005257
128	0.9987	0.002642	0.001321
129	0.9986	0.002883	0.001442
130	0.998	0.003951	0.001975
131	0.9973	0.005367	0.002684
132	0.9981	0.003775	0.001887
133	0.998	0.00408	0.00204
134	0.9983	0.003479	0.00174
135	0.9975	0.004953	0.002477
136	0.9966	0.006736	0.003368
137	0.9958	0.008497	0.004248
138	0.9941	0.01186	0.005931
139	0.9931	0.01385	0.006925
140	0.9953	0.00942	0.00471
141	0.9934	0.01323	0.006614
142	0.9929	0.01414	0.007068
143	0.991	0.01791	0.008955
144	0.9934	0.01321	0.006605
145	0.9914	0.01715	0.008577
146	0.9884	0.02316	0.01158
147	0.9905	0.01895	0.009477
148	0.9872	0.02569	0.01284
149	0.9838	0.03232	0.01616
150	0.9795	0.04095	0.02048
151	0.9744	0.05112	0.02556
152	0.9725	0.05506	0.02753
153	0.9676	0.06485	0.03242
154	0.9624	0.07525	0.03763
155	0.9492	0.1016	0.05082
156	0.936	0.128	0.06398
157	0.9232	0.1537	0.07684
158	0.914	0.172	0.08601
159	0.889	0.2219	0.111
160	0.8825	0.235	0.1175
161	0.8992	0.2015	0.1008
162	0.9082	0.1835	0.09175
163	0.8973	0.2054	0.1027
164	0.8876	0.2248	0.1124
165	0.8557	0.2886	0.1443
166	0.9194	0.1612	0.0806
167	0.9188	0.1625	0.08123
168	0.8964	0.2072	0.1036
169	0.8778	0.2444	0.1222
170	0.8393	0.3214	0.1607
171	0.7968	0.4065	0.2032
172	0.756	0.488	0.244
173	0.6989	0.6021	0.3011
174	0.6327	0.7347	0.3673
175	0.5523	0.8953	0.4477
176	0.9106	0.1787	0.08935
177	0.9436	0.1128	0.05638
178	0.9995	0.000902	0.000451
179	0.9994	0.001261	0.0006306
180	0.9993	0.001497	0.0007486
181	0.9998	0.0004344	0.0002172
182	0.9991	0.001812	0.0009061
183	0.9997	0.000666	0.000333
184	0.9983	0.003316	0.001658
185	0.9902	0.01956	0.009778

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	26	0.1486	NOK
5% type I error level	73	0.417143	NOK
10% type I error level	86	0.491429	NOK

\begin{tabular}{lllllllll}
\hline
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
Description & # significant tests & % significant tests & OK/NOK \tabularnewline
1% type I error level & 26 &  0.1486 & NOK \tabularnewline
5% type I error level & 73 & 0.417143 & NOK \tabularnewline
10% type I error level & 86 & 0.491429 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=309794&T=7

[TABLE]
[ROW][C]Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]Description[/C][C]# significant tests[/C][C]% significant tests[/C][C]OK/NOK[/C][/ROW]
[ROW][C]1% type I error level[/C][C]26[/C][C] 0.1486[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]73[/C][C]0.417143[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]86[/C][C]0.491429[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=309794&T=7

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=309794&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	26	0.1486	NOK
5% type I error level	73	0.417143	NOK
10% type I error level	86	0.491429	NOK

Ramsey RESET F-Test for powers (2 and 3) of fitted values
> reset_test_fitted RESET test data: mylm RESET = 4.3823, df1 = 2, df2 = 186, p-value = 0.01381
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors RESET test data: mylm RESET = 1.1842, df1 = 14, df2 = 174, p-value = 0.2911
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components RESET test data: mylm RESET = 4.7131, df1 = 2, df2 = 186, p-value = 0.01008

\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 = 4.3823, df1 = 2, df2 = 186, p-value = 0.01381
 \tabularnewline
Ramsey RESET F-Test for powers (2 and 3) of regressors \tabularnewline
> reset_test_regressors
	RESET test
data:  mylm
RESET = 1.1842, df1 = 14, df2 = 174, p-value = 0.2911
 \tabularnewline
Ramsey RESET F-Test for powers (2 and 3) of principal components \tabularnewline
> reset_test_principal_components
	RESET test
data:  mylm
RESET = 4.7131, df1 = 2, df2 = 186, p-value = 0.01008
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=309794&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 = 4.3823, df1 = 2, df2 = 186, p-value = 0.01381
[/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 = 1.1842, df1 = 14, df2 = 174, p-value = 0.2911
[/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 = 4.7131, df1 = 2, df2 = 186, p-value = 0.01008
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=309794&T=8

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=309794&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 = 4.3823, df1 = 2, df2 = 186, p-value = 0.01381
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors RESET test data: mylm RESET = 1.1842, df1 = 14, df2 = 174, p-value = 0.2911
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components RESET test data: mylm RESET = 4.7131, df1 = 2, df2 = 186, p-value = 0.01008

Variance Inflation Factors (Multicollinearity)
> vif X58 X64 `X58(t-1)` `X58(t-2)` `X58(t-3)` `X58(t-4)` 5.231837 1.036580 3.749806 2.707794 3.834359 3.095035 `X58(t-1s)` 4.127368

\begin{tabular}{lllllllll}
\hline
Variance Inflation Factors (Multicollinearity) \tabularnewline
> vif
        X58         X64  `X58(t-1)`  `X58(t-2)`  `X58(t-3)`  `X58(t-4)` 
   5.231837    1.036580    3.749806    2.707794    3.834359    3.095035 
`X58(t-1s)` 
   4.127368 
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=309794&T=9

[TABLE]
[ROW][C]Variance Inflation Factors (Multicollinearity)[/C][/ROW]
[ROW][C]> vif
        X58         X64  `X58(t-1)`  `X58(t-2)`  `X58(t-3)`  `X58(t-4)` 
   5.231837    1.036580    3.749806    2.707794    3.834359    3.095035 
`X58(t-1s)` 
   4.127368 
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=309794&T=9

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=309794&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 X58 X64 `X58(t-1)` `X58(t-2)` `X58(t-3)` `X58(t-4)` 5.231837 1.036580 3.749806 2.707794 3.834359 3.095035 `X58(t-1s)` 4.127368

Figure 1

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Figure 9

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Figure 10

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Parameters (Session):

par1 = 2 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ; par4 = 4 ; par5 = 1 ; par6 = 12 ;

Parameters (R input):

par1 = 2 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ; par4 = 4 ; par5 = 1 ; 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')

Free Statistics

Description of Statistical Computation

Tree of Dependent Computations

Dataset

Tables (Output of Computation)

Figures (Output of Computation)

Input Parameters & R Code