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R Software Module

rwasp_multipleregression.wasp

Title produced by software

Multiple Regression

Date of computation

Wed, 12 Dec 2018 10:55:10 +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/2018/Dec/12/t15446085593cmnq0j0jiznn9e.htm/, Retrieved Tue, 07 May 2024 03:42:57 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=315859, Retrieved Tue, 07 May 2024 03:42:57 +0000

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Estimated Impact

133

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-       [Multiple Regression] [paper] [2018-12-12 09:55:10] [a1c8a896a381c96cb9dd2943f54bdbd5] [Current]

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Dataseries X:

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Histogram

Boxplots

0.79 29.82 0.04 0.5
2.21 3.16 0.06 1.18
2.12 38.48 0.03 0.59
0.93 20.82 0.04 2.55
5.38 0.09 NA 0.94
3.14 41.09 0.1 6.92
2.23 2.97 0.07 0.89
11.88 0.1 NA 0.57
9.31 23.05 0.14 16.57
6.06 8.46 0.15 3.07
2.31 9.31 0.06 0.85
6.84 0.37 0.04 9.55
7.49 1.32 0.1 0.58
0.72 154.7 0.07 0.38
4.48 0.28 0.04 0.19
5.09 9.4 0.09 3.64
7.44 11.06 0.27 1.19
1.41 10.05 0.04 0.88
5.77 0.06 NA 0.13
4.84 0.74 0.25 5.27
2.96 10.5 0.06 16.73
3.12 3.83 0.02 1.63
3.83 2 0.02 3.47
3.11 198.66 0.09 9.08
2.86 0.03 NA 2.05
4.06 0.41 0.03 2.87
3.32 7.28 0.13 2.86
1.21 16.46 0.05 0.98
0.8 9.85 0.03 0.32
2.52 0.49 NA 0.62
1.21 14.86 NA 1.09
1.17 21.7 0.05 1.69
8.17 34.84 0.07 16.01
5.65 0.06 0 0.32
1.24 4.53 0.04 7.87
1.46 12.45 0.05 2.03
4.36 17.46 0.15 3.63
3.38 1408.04 0.12 0.94
1.87 47.7 0.09 3.6
1.03 0.72 0 0.32
1.29 4.34 0.03 10.91
0.82 65.7 0.05 3.07
2.84 4.8 0.1 1.53
1.27 19.84 0.08 1.78
3.92 4.31 0.06 2.8
1.95 11.27 0.03 0.76
4.21 1.13 0.05 0.34
5.19 10.66 0.13 2.46
5.51 5.6 0.25 4.78
2.19 0.86 NA 0.77
2.57 0.07 0 1.03
1.53 10.28 0.05 0.56
2.17 15.49 0.06 2.2
2.15 80.72 0.15 0.56
2.07 6.3 0.05 0.61
3.97 0.74 0.02 4.4
0.42 6.13 0.02 1.3
6.86 1.29 NA 10.53
1.02 91.73 0.06 0.58
2.9 0.88 0.04 2.37
5.87 5.41 NA 13.44
5.14 63.98 0.21 3.11
2.34 0.24 0 111.35
4.73 0.27 0 1.37
2.02 1.63 0.03 26.31
1.03 1.79 0.03 0.82
1.58 4.36 0.04 1.17
5.3 82.8 0.24 2.27
1.97 25.37 0.07 1.35
4.38 11.12 0.06 1.61
2.98 0.1 NA 1.96
3.23 0.46 0 0.45
1.89 15.08 0.07 0.99
1.41 11.45 0.04 2.09
1.53 1.66 0.06 3.03
3.07 0.8 0.06 66.58
0.61 10.17 0.03 0.27
1.68 7.94 0.06 1.77
2.92 9.98 0.13 2.17
1.16 1236.69 0.05 0.45
1.58 246.86 0.06 1.26
2.79 76.42 0.09 0.9
1.88 32.78 0.04 0.29
5.57 4.58 0.13 3.73
6.22 7.64 0.08 0.35
4.61 60.92 0.06 1.08
1.89 2.77 0.05 0.43
5.02 127.25 0.1 0.72
2.1 7.01 0.07 0.21
5.55 16.27 0.03 3.41
1.03 43.18 0.04 0.51
1.17 24.76 0.06 0.6
5.69 49 0.06 0.68
8.13 3.25 0.15 0.55
1.91 5.47 0.08 1.3
1.22 6.65 0.11 1.62
6.29 2.06 0.13 9.55
3.84 4.65 0.06 0.33
1.66 2.05 0.01 0.78
1.21 4.19 0.03 2.57
3.69 6.16 0.02 0.7
5.83 3.03 0.15 5.67
15.82 0.52 0.14 1.68
3.26 2.11 0.02 1.51
0.99 22.29 0.06 2.63
0.81 15.91 0.05 0.66
3.71 29.24 0.07 2.41
1.53 14.85 0.06 1.58
2.08 0.4 0.04 0.39
2.54 3.8 0.04 4.48
3.46 1.24 0 0.71
2.89 120.85 0.05 1.27
1.78 3.51 0.03 0.8
6.08 2.8 0.05 15.66
3.78 0.62 0.01 3.24
7.78 0 NA 1.36
1.68 32.52 0.03 0.71
0.87 25.2 0.05 2.06
1.43 52.8 0.11 1.84
2.48 2.26 0.02 6.88
2.94 0.01 NA 0.19
0.98 27.47 0.11 0.59
5.28 16.71 0.17 1.17
3.58 0.25 0 7.67
5.6 4.46 0.13 10.14
1.39 5.99 0.05 2.25
1.56 17.16 0.03 1.24
1.16 168.83 0.05 0.7
4.98 4.99 NA 8.18
7.52 3.31 0.19 1.92
0.79 179.16 0.03 0.35
2.79 3.8 0.02 2.94
1.91 7.17 0.13 3.92
4.16 6.69 0.14 10.52
2.28 29.99 0.07 3.97
1.1 96.71 0.05 0.54
4.44 38.21 0.09 2.08
3.88 10.6 0.05 1.51
10.8 2.05 0.06 1.24
3.65 0.86 0 0.18
2.71 21.76 0.12 2.32
5.69 143.17 0.03 6.79
0.87 11.46 0.05 0.54
4.94 0.05 0 0.62
2.45 0.18 0 0.34
3.11 0.11 NA 1.26
2.77 0.19 0 1.93
1.49 0.19 0 0.87
5.61 28.29 0.04 0.5
1.21 13.73 0.02 1.05
2.7 9.55 0.04 1.25
1.24 5.98 0.05 1.24
7.97 5.3 0.03 0.05
4.06 5.45 0.09 2.71
5.81 2.07 0.04 2.35
1.29 0.55 0.23 4.36
1.24 10.2 0.06 1.27
3.31 52.39 0.04 1.15
3.67 46.76 0.04 1.25
1.32 21.1 0.05 0.44
4.25 0.54 0.09 89.33
2.01 1.23 0.06 0.88
7.25 9.51 0.24 10.62
5.79 8 0.12 1.3
1.51 21.89 0.05 0.6
0.91 8.01 0.08 0.53
1.32 47.78 0.06 1.08
2.66 66.78 0.07 1.24
0.48 1.11 0.04 1.78
1.13 6.64 0.02 0.53
2.7 0.1 0 1.48
7.92 1.34 0 1.56
2.34 10.88 0.04 0.93
3.33 74 0.04 1.52
5.47 5.17 0.09 2.79
1.24 36.35 0.04 0.59
2.84 45.53 0.07 2.27
4.94 63.03 0.18 1.32
7.93 9.206 0 0.56
8.22 317.5 0.09 3.76
2.91 3.4 0.17 10.32
2.32 28.54 0.08 0.92
3.57 29.96 0.04 2.78
1.65 90.8 0.1 1
2.07 0.01 NA 1.51
1.03 23.85 0.04 0.5
0.99 14.08 0.04 2.23
1.37 13.72 0.02 0.62

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	15 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 time15 seconds \tabularnewline
R ServerBig Analytics Cloud Computing Center \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=315859&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]

15 seconds[/C][/ROW] [ROW]

R Server[/C]

Big Analytics Cloud Computing Center[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=315859&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=315859&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	15 seconds
R Server	Big Analytics Cloud Computing Center

Multiple Linear Regression - Estimated Regression Equation
Total_Ecological_Footprint[t] = + 2.18708 -0.00107868`Population_(millions)`[t] + 15.1658Urban_Land[t] + 0.0118292Total_Biocapacity[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Total_Ecological_Footprint[t] =  +  2.18708 -0.00107868`Population_(millions)`[t] +  15.1658Urban_Land[t] +  0.0118292Total_Biocapacity[t]  + e[t] \tabularnewline
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=315859&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Total_Ecological_Footprint[t] =  +  2.18708 -0.00107868`Population_(millions)`[t] +  15.1658Urban_Land[t] +  0.0118292Total_Biocapacity[t]  + e[t][/C][/ROW]
[ROW][C][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=315859&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=315859&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
Total_Ecological_Footprint[t] = + 2.18708 -0.00107868`Population_(millions)`[t] + 15.1658Urban_Land[t] + 0.0118292Total_Biocapacity[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)	+2.187	0.2683	+8.1500e+00	7.776e-14	3.888e-14
`Population_(millions)`	-0.001079	0.001131	-9.5390e-01	0.3415	0.1708
Urban_Land	+15.17	3.014	+5.0330e+00	1.229e-06	6.147e-07
Total_Biocapacity	+0.01183	0.01362	+8.6840e-01	0.3864	0.1932

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Ordinary Least Squares \tabularnewline
Variable & Parameter & S.D. & T-STATH0: parameter = 0 & 2-tail p-value & 1-tail p-value \tabularnewline
(Intercept) & +2.187 &  0.2683 & +8.1500e+00 &  7.776e-14 &  3.888e-14 \tabularnewline
`Population_(millions)` & -0.001079 &  0.001131 & -9.5390e-01 &  0.3415 &  0.1708 \tabularnewline
Urban_Land & +15.17 &  3.014 & +5.0330e+00 &  1.229e-06 &  6.147e-07 \tabularnewline
Total_Biocapacity & +0.01183 &  0.01362 & +8.6840e-01 &  0.3864 &  0.1932 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=315859&T=2

[TABLE]
[ROW][C]Multiple Linear Regression - Ordinary Least Squares[/C][/ROW]
[ROW][C]Variable[/C][C]Parameter[/C][C]S.D.[/C][C]T-STATH0: parameter = 0[/C][C]2-tail p-value[/C][C]1-tail p-value[/C][/ROW]
[ROW][C](Intercept)[/C][C]+2.187[/C][C] 0.2683[/C][C]+8.1500e+00[/C][C] 7.776e-14[/C][C] 3.888e-14[/C][/ROW]
[ROW][C]`Population_(millions)`[/C][C]-0.001079[/C][C] 0.001131[/C][C]-9.5390e-01[/C][C] 0.3415[/C][C] 0.1708[/C][/ROW]
[ROW][C]Urban_Land[/C][C]+15.17[/C][C] 3.014[/C][C]+5.0330e+00[/C][C] 1.229e-06[/C][C] 6.147e-07[/C][/ROW]
[ROW][C]Total_Biocapacity[/C][C]+0.01183[/C][C] 0.01362[/C][C]+8.6840e-01[/C][C] 0.3864[/C][C] 0.1932[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=315859&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=315859&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)	+2.187	0.2683	+8.1500e+00	7.776e-14	3.888e-14
`Population_(millions)`	-0.001079	0.001131	-9.5390e-01	0.3415	0.1708
Urban_Land	+15.17	3.014	+5.0330e+00	1.229e-06	6.147e-07
Total_Biocapacity	+0.01183	0.01362	+8.6840e-01	0.3864	0.1932

Multiple Linear Regression - Regression Statistics
Multiple R	0.3691
R-squared	0.1362
Adjusted R-squared	0.1209
F-TEST (value)	8.884
F-TEST (DF numerator)	3
F-TEST (DF denominator)	169
p-value	1.683e-05
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	2.163
Sum Squared Residuals	790.7

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R &  0.3691 \tabularnewline
R-squared &  0.1362 \tabularnewline
Adjusted R-squared &  0.1209 \tabularnewline
F-TEST (value) &  8.884 \tabularnewline
F-TEST (DF numerator) & 3 \tabularnewline
F-TEST (DF denominator) & 169 \tabularnewline
p-value &  1.683e-05 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation &  2.163 \tabularnewline
Sum Squared Residuals &  790.7 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=315859&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C] 0.3691[/C][/ROW]
[ROW][C]R-squared[/C][C] 0.1362[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C] 0.1209[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C] 8.884[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]3[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]169[/C][/ROW]
[ROW][C]p-value[/C][C] 1.683e-05[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C] 2.163[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C] 790.7[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=315859&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=315859&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	0.3691
R-squared	0.1362
Adjusted R-squared	0.1209
F-TEST (value)	8.884
F-TEST (DF numerator)	3
F-TEST (DF denominator)	169
p-value	1.683e-05
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	2.163
Sum Squared Residuals	790.7

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=315859&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=315859&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=315859&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	0.79	2.767	-1.977
2	2.21	3.108	-0.8976
3	2.12	2.608	-0.4875
4	0.93	2.801	-1.871
5	3.14	3.741	-0.6012
6	2.23	3.256	-1.026
7	9.31	4.481	4.829
8	6.06	4.489	1.571
9	2.31	3.097	-0.787
10	6.84	2.906	3.934
11	7.49	3.709	3.781
12	0.72	3.086	-2.366
13	4.48	2.796	1.684
14	5.09	3.585	1.505
15	7.44	6.284	1.156
16	1.41	2.793	-1.383
17	4.84	6.04	-1.2
18	2.96	3.284	-0.3236
19	3.12	2.506	0.6145
20	3.83	2.529	1.301
21	3.11	3.445	-0.3351
22	4.06	2.676	1.384
23	3.32	4.185	-0.8646
24	1.21	2.939	-1.729
25	0.8	2.635	-1.835
26	1.17	2.942	-1.772
27	8.17	3.4	4.77
28	5.65	2.191	3.459
29	1.24	2.882	-1.642
30	1.46	2.956	-1.496
31	4.36	4.486	-0.1261
32	3.38	2.499	0.8807
33	1.87	3.543	-1.673
34	1.03	2.19	-1.16
35	1.29	2.766	-1.476
36	0.82	2.911	-2.091
37	2.84	3.717	-0.8766
38	1.27	3.4	-2.13
39	3.92	3.126	0.7945
40	1.95	2.639	-0.6889
41	4.21	2.948	1.262
42	5.19	4.176	1.014
43	5.51	6.029	-0.519
44	2.57	2.199	0.3708
45	1.53	2.941	-1.411
46	2.17	3.106	-0.9363
47	2.15	4.382	-2.232
48	2.07	2.946	-0.8758
49	3.97	2.542	1.428
50	0.42	2.499	-2.079
51	1.02	3.005	-1.985
52	2.9	2.821	0.0792
53	5.14	5.34	-0.1997
54	2.34	3.504	-1.164
55	4.73	2.203	2.527
56	2.02	2.952	-0.9315
57	1.03	2.65	-1.62
58	1.58	2.803	-1.223
59	5.3	5.764	-0.4644
60	1.97	3.237	-1.267
61	4.38	3.104	1.276
62	3.23	2.192	1.038
63	1.89	3.244	-1.354
64	1.41	2.806	-1.396
65	1.53	3.131	-1.601
66	3.07	3.884	-0.8138
67	0.61	2.634	-2.024
68	1.68	3.109	-1.429
69	2.92	4.174	-1.254
70	1.16	1.617	-0.4567
71	1.58	2.846	-1.266
72	2.79	3.48	-0.6902
73	1.88	2.762	-0.8818
74	5.57	4.198	1.372
75	6.22	3.396	2.824
76	4.61	3.044	1.566
77	1.89	2.947	-1.057
78	5.02	3.575	1.445
79	2.1	3.244	-1.144
80	5.55	2.665	2.885
81	1.03	2.753	-1.723
82	1.17	3.077	-1.907
83	5.69	3.052	2.638
84	8.13	4.465	3.665
85	1.91	3.41	-1.5
86	1.22	3.867	-2.647
87	6.29	4.269	2.021
88	3.84	3.096	0.7441
89	1.66	2.346	-0.6858
90	1.21	2.668	-1.458
91	3.69	2.492	1.198
92	5.83	4.526	1.304
93	15.82	4.33	11.49
94	3.26	2.506	0.754
95	0.99	3.104	-2.114
96	0.81	2.936	-2.126
97	3.71	3.246	0.4643
98	1.53	3.1	-1.57
99	2.08	2.798	-0.7179
100	2.54	2.843	-0.3026
101	3.46	2.194	1.266
102	2.89	2.83	0.05996
103	1.78	2.648	-0.8677
104	6.08	3.128	2.952
105	3.78	2.376	1.404
106	1.68	2.615	-0.9354
107	0.87	2.943	-2.073
108	1.43	3.82	-2.39
109	2.48	2.569	-0.08934
110	0.98	3.833	-2.853
111	5.28	4.761	0.5189
112	3.58	2.278	1.302
113	5.6	4.274	1.326
114	1.39	2.966	-1.576
115	1.56	2.638	-1.078
116	1.16	2.772	-1.612
117	7.52	5.088	2.432
118	0.79	2.453	-1.663
119	2.79	2.521	0.2689
120	1.91	4.197	-2.287
121	4.16	4.428	-0.2675
122	2.28	3.263	-0.9833
123	1.1	2.847	-1.747
124	4.44	3.535	0.9046
125	3.88	2.952	0.9282
126	10.8	3.109	7.691
127	3.65	2.188	1.462
128	2.71	4.011	-1.301
129	5.69	2.568	3.122
130	0.87	2.939	-2.069
131	4.94	2.194	2.746
132	2.45	2.191	0.2591
133	2.77	2.21	0.5603
134	1.49	2.197	-0.7072
135	5.61	2.769	2.841
136	1.21	2.488	-1.278
137	2.7	2.798	-0.0982
138	1.24	2.954	-1.714
139	7.97	2.637	5.333
140	4.06	3.578	0.4818
141	5.81	2.819	2.991
142	1.29	5.726	-4.436
143	1.24	3.101	-1.861
144	3.31	2.751	0.5592
145	3.67	2.758	0.9119
146	1.32	2.928	-1.608
147	4.25	4.608	-0.3581
148	2.01	3.106	-1.096
149	7.25	5.942	1.308
150	5.79	4.014	1.776
151	1.51	2.929	-1.419
152	0.91	3.398	-2.488
153	1.32	3.058	-1.738
154	2.66	3.191	-0.5313
155	0.48	2.814	-2.334
156	1.13	2.49	-1.359
157	2.7	2.204	0.4955
158	7.92	2.204	5.716
159	2.34	2.793	-0.453
160	3.33	2.732	0.5981
161	5.47	3.579	1.891
162	1.24	2.761	-1.521
163	2.84	3.226	-0.3864
164	4.94	4.865	0.07544
165	7.93	2.184	5.746
166	8.22	3.254	4.966
167	2.91	4.884	-1.974
168	2.32	3.38	-1.06
169	3.57	2.794	0.7757
170	1.65	3.618	-1.968
171	1.03	2.774	-1.744
172	0.99	2.805	-1.815
173	1.37	2.483	-1.113

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 &  0.79 &  2.767 & -1.977 \tabularnewline
2 &  2.21 &  3.108 & -0.8976 \tabularnewline
3 &  2.12 &  2.608 & -0.4875 \tabularnewline
4 &  0.93 &  2.801 & -1.871 \tabularnewline
5 &  3.14 &  3.741 & -0.6012 \tabularnewline
6 &  2.23 &  3.256 & -1.026 \tabularnewline
7 &  9.31 &  4.481 &  4.829 \tabularnewline
8 &  6.06 &  4.489 &  1.571 \tabularnewline
9 &  2.31 &  3.097 & -0.787 \tabularnewline
10 &  6.84 &  2.906 &  3.934 \tabularnewline
11 &  7.49 &  3.709 &  3.781 \tabularnewline
12 &  0.72 &  3.086 & -2.366 \tabularnewline
13 &  4.48 &  2.796 &  1.684 \tabularnewline
14 &  5.09 &  3.585 &  1.505 \tabularnewline
15 &  7.44 &  6.284 &  1.156 \tabularnewline
16 &  1.41 &  2.793 & -1.383 \tabularnewline
17 &  4.84 &  6.04 & -1.2 \tabularnewline
18 &  2.96 &  3.284 & -0.3236 \tabularnewline
19 &  3.12 &  2.506 &  0.6145 \tabularnewline
20 &  3.83 &  2.529 &  1.301 \tabularnewline
21 &  3.11 &  3.445 & -0.3351 \tabularnewline
22 &  4.06 &  2.676 &  1.384 \tabularnewline
23 &  3.32 &  4.185 & -0.8646 \tabularnewline
24 &  1.21 &  2.939 & -1.729 \tabularnewline
25 &  0.8 &  2.635 & -1.835 \tabularnewline
26 &  1.17 &  2.942 & -1.772 \tabularnewline
27 &  8.17 &  3.4 &  4.77 \tabularnewline
28 &  5.65 &  2.191 &  3.459 \tabularnewline
29 &  1.24 &  2.882 & -1.642 \tabularnewline
30 &  1.46 &  2.956 & -1.496 \tabularnewline
31 &  4.36 &  4.486 & -0.1261 \tabularnewline
32 &  3.38 &  2.499 &  0.8807 \tabularnewline
33 &  1.87 &  3.543 & -1.673 \tabularnewline
34 &  1.03 &  2.19 & -1.16 \tabularnewline
35 &  1.29 &  2.766 & -1.476 \tabularnewline
36 &  0.82 &  2.911 & -2.091 \tabularnewline
37 &  2.84 &  3.717 & -0.8766 \tabularnewline
38 &  1.27 &  3.4 & -2.13 \tabularnewline
39 &  3.92 &  3.126 &  0.7945 \tabularnewline
40 &  1.95 &  2.639 & -0.6889 \tabularnewline
41 &  4.21 &  2.948 &  1.262 \tabularnewline
42 &  5.19 &  4.176 &  1.014 \tabularnewline
43 &  5.51 &  6.029 & -0.519 \tabularnewline
44 &  2.57 &  2.199 &  0.3708 \tabularnewline
45 &  1.53 &  2.941 & -1.411 \tabularnewline
46 &  2.17 &  3.106 & -0.9363 \tabularnewline
47 &  2.15 &  4.382 & -2.232 \tabularnewline
48 &  2.07 &  2.946 & -0.8758 \tabularnewline
49 &  3.97 &  2.542 &  1.428 \tabularnewline
50 &  0.42 &  2.499 & -2.079 \tabularnewline
51 &  1.02 &  3.005 & -1.985 \tabularnewline
52 &  2.9 &  2.821 &  0.0792 \tabularnewline
53 &  5.14 &  5.34 & -0.1997 \tabularnewline
54 &  2.34 &  3.504 & -1.164 \tabularnewline
55 &  4.73 &  2.203 &  2.527 \tabularnewline
56 &  2.02 &  2.952 & -0.9315 \tabularnewline
57 &  1.03 &  2.65 & -1.62 \tabularnewline
58 &  1.58 &  2.803 & -1.223 \tabularnewline
59 &  5.3 &  5.764 & -0.4644 \tabularnewline
60 &  1.97 &  3.237 & -1.267 \tabularnewline
61 &  4.38 &  3.104 &  1.276 \tabularnewline
62 &  3.23 &  2.192 &  1.038 \tabularnewline
63 &  1.89 &  3.244 & -1.354 \tabularnewline
64 &  1.41 &  2.806 & -1.396 \tabularnewline
65 &  1.53 &  3.131 & -1.601 \tabularnewline
66 &  3.07 &  3.884 & -0.8138 \tabularnewline
67 &  0.61 &  2.634 & -2.024 \tabularnewline
68 &  1.68 &  3.109 & -1.429 \tabularnewline
69 &  2.92 &  4.174 & -1.254 \tabularnewline
70 &  1.16 &  1.617 & -0.4567 \tabularnewline
71 &  1.58 &  2.846 & -1.266 \tabularnewline
72 &  2.79 &  3.48 & -0.6902 \tabularnewline
73 &  1.88 &  2.762 & -0.8818 \tabularnewline
74 &  5.57 &  4.198 &  1.372 \tabularnewline
75 &  6.22 &  3.396 &  2.824 \tabularnewline
76 &  4.61 &  3.044 &  1.566 \tabularnewline
77 &  1.89 &  2.947 & -1.057 \tabularnewline
78 &  5.02 &  3.575 &  1.445 \tabularnewline
79 &  2.1 &  3.244 & -1.144 \tabularnewline
80 &  5.55 &  2.665 &  2.885 \tabularnewline
81 &  1.03 &  2.753 & -1.723 \tabularnewline
82 &  1.17 &  3.077 & -1.907 \tabularnewline
83 &  5.69 &  3.052 &  2.638 \tabularnewline
84 &  8.13 &  4.465 &  3.665 \tabularnewline
85 &  1.91 &  3.41 & -1.5 \tabularnewline
86 &  1.22 &  3.867 & -2.647 \tabularnewline
87 &  6.29 &  4.269 &  2.021 \tabularnewline
88 &  3.84 &  3.096 &  0.7441 \tabularnewline
89 &  1.66 &  2.346 & -0.6858 \tabularnewline
90 &  1.21 &  2.668 & -1.458 \tabularnewline
91 &  3.69 &  2.492 &  1.198 \tabularnewline
92 &  5.83 &  4.526 &  1.304 \tabularnewline
93 &  15.82 &  4.33 &  11.49 \tabularnewline
94 &  3.26 &  2.506 &  0.754 \tabularnewline
95 &  0.99 &  3.104 & -2.114 \tabularnewline
96 &  0.81 &  2.936 & -2.126 \tabularnewline
97 &  3.71 &  3.246 &  0.4643 \tabularnewline
98 &  1.53 &  3.1 & -1.57 \tabularnewline
99 &  2.08 &  2.798 & -0.7179 \tabularnewline
100 &  2.54 &  2.843 & -0.3026 \tabularnewline
101 &  3.46 &  2.194 &  1.266 \tabularnewline
102 &  2.89 &  2.83 &  0.05996 \tabularnewline
103 &  1.78 &  2.648 & -0.8677 \tabularnewline
104 &  6.08 &  3.128 &  2.952 \tabularnewline
105 &  3.78 &  2.376 &  1.404 \tabularnewline
106 &  1.68 &  2.615 & -0.9354 \tabularnewline
107 &  0.87 &  2.943 & -2.073 \tabularnewline
108 &  1.43 &  3.82 & -2.39 \tabularnewline
109 &  2.48 &  2.569 & -0.08934 \tabularnewline
110 &  0.98 &  3.833 & -2.853 \tabularnewline
111 &  5.28 &  4.761 &  0.5189 \tabularnewline
112 &  3.58 &  2.278 &  1.302 \tabularnewline
113 &  5.6 &  4.274 &  1.326 \tabularnewline
114 &  1.39 &  2.966 & -1.576 \tabularnewline
115 &  1.56 &  2.638 & -1.078 \tabularnewline
116 &  1.16 &  2.772 & -1.612 \tabularnewline
117 &  7.52 &  5.088 &  2.432 \tabularnewline
118 &  0.79 &  2.453 & -1.663 \tabularnewline
119 &  2.79 &  2.521 &  0.2689 \tabularnewline
120 &  1.91 &  4.197 & -2.287 \tabularnewline
121 &  4.16 &  4.428 & -0.2675 \tabularnewline
122 &  2.28 &  3.263 & -0.9833 \tabularnewline
123 &  1.1 &  2.847 & -1.747 \tabularnewline
124 &  4.44 &  3.535 &  0.9046 \tabularnewline
125 &  3.88 &  2.952 &  0.9282 \tabularnewline
126 &  10.8 &  3.109 &  7.691 \tabularnewline
127 &  3.65 &  2.188 &  1.462 \tabularnewline
128 &  2.71 &  4.011 & -1.301 \tabularnewline
129 &  5.69 &  2.568 &  3.122 \tabularnewline
130 &  0.87 &  2.939 & -2.069 \tabularnewline
131 &  4.94 &  2.194 &  2.746 \tabularnewline
132 &  2.45 &  2.191 &  0.2591 \tabularnewline
133 &  2.77 &  2.21 &  0.5603 \tabularnewline
134 &  1.49 &  2.197 & -0.7072 \tabularnewline
135 &  5.61 &  2.769 &  2.841 \tabularnewline
136 &  1.21 &  2.488 & -1.278 \tabularnewline
137 &  2.7 &  2.798 & -0.0982 \tabularnewline
138 &  1.24 &  2.954 & -1.714 \tabularnewline
139 &  7.97 &  2.637 &  5.333 \tabularnewline
140 &  4.06 &  3.578 &  0.4818 \tabularnewline
141 &  5.81 &  2.819 &  2.991 \tabularnewline
142 &  1.29 &  5.726 & -4.436 \tabularnewline
143 &  1.24 &  3.101 & -1.861 \tabularnewline
144 &  3.31 &  2.751 &  0.5592 \tabularnewline
145 &  3.67 &  2.758 &  0.9119 \tabularnewline
146 &  1.32 &  2.928 & -1.608 \tabularnewline
147 &  4.25 &  4.608 & -0.3581 \tabularnewline
148 &  2.01 &  3.106 & -1.096 \tabularnewline
149 &  7.25 &  5.942 &  1.308 \tabularnewline
150 &  5.79 &  4.014 &  1.776 \tabularnewline
151 &  1.51 &  2.929 & -1.419 \tabularnewline
152 &  0.91 &  3.398 & -2.488 \tabularnewline
153 &  1.32 &  3.058 & -1.738 \tabularnewline
154 &  2.66 &  3.191 & -0.5313 \tabularnewline
155 &  0.48 &  2.814 & -2.334 \tabularnewline
156 &  1.13 &  2.49 & -1.359 \tabularnewline
157 &  2.7 &  2.204 &  0.4955 \tabularnewline
158 &  7.92 &  2.204 &  5.716 \tabularnewline
159 &  2.34 &  2.793 & -0.453 \tabularnewline
160 &  3.33 &  2.732 &  0.5981 \tabularnewline
161 &  5.47 &  3.579 &  1.891 \tabularnewline
162 &  1.24 &  2.761 & -1.521 \tabularnewline
163 &  2.84 &  3.226 & -0.3864 \tabularnewline
164 &  4.94 &  4.865 &  0.07544 \tabularnewline
165 &  7.93 &  2.184 &  5.746 \tabularnewline
166 &  8.22 &  3.254 &  4.966 \tabularnewline
167 &  2.91 &  4.884 & -1.974 \tabularnewline
168 &  2.32 &  3.38 & -1.06 \tabularnewline
169 &  3.57 &  2.794 &  0.7757 \tabularnewline
170 &  1.65 &  3.618 & -1.968 \tabularnewline
171 &  1.03 &  2.774 & -1.744 \tabularnewline
172 &  0.99 &  2.805 & -1.815 \tabularnewline
173 &  1.37 &  2.483 & -1.113 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=315859&T=5

[TABLE]
[ROW][C]Multiple Linear Regression - Actuals, Interpolation, and Residuals[/C][/ROW]
[ROW][C]Time or Index[/C][C]Actuals[/C][C]InterpolationForecast[/C][C]ResidualsPrediction Error[/C][/ROW]
[ROW][C]1[/C][C] 0.79[/C][C] 2.767[/C][C]-1.977[/C][/ROW]
[ROW][C]2[/C][C] 2.21[/C][C] 3.108[/C][C]-0.8976[/C][/ROW]
[ROW][C]3[/C][C] 2.12[/C][C] 2.608[/C][C]-0.4875[/C][/ROW]
[ROW][C]4[/C][C] 0.93[/C][C] 2.801[/C][C]-1.871[/C][/ROW]
[ROW][C]5[/C][C] 3.14[/C][C] 3.741[/C][C]-0.6012[/C][/ROW]
[ROW][C]6[/C][C] 2.23[/C][C] 3.256[/C][C]-1.026[/C][/ROW]
[ROW][C]7[/C][C] 9.31[/C][C] 4.481[/C][C] 4.829[/C][/ROW]
[ROW][C]8[/C][C] 6.06[/C][C] 4.489[/C][C] 1.571[/C][/ROW]
[ROW][C]9[/C][C] 2.31[/C][C] 3.097[/C][C]-0.787[/C][/ROW]
[ROW][C]10[/C][C] 6.84[/C][C] 2.906[/C][C] 3.934[/C][/ROW]
[ROW][C]11[/C][C] 7.49[/C][C] 3.709[/C][C] 3.781[/C][/ROW]
[ROW][C]12[/C][C] 0.72[/C][C] 3.086[/C][C]-2.366[/C][/ROW]
[ROW][C]13[/C][C] 4.48[/C][C] 2.796[/C][C] 1.684[/C][/ROW]
[ROW][C]14[/C][C] 5.09[/C][C] 3.585[/C][C] 1.505[/C][/ROW]
[ROW][C]15[/C][C] 7.44[/C][C] 6.284[/C][C] 1.156[/C][/ROW]
[ROW][C]16[/C][C] 1.41[/C][C] 2.793[/C][C]-1.383[/C][/ROW]
[ROW][C]17[/C][C] 4.84[/C][C] 6.04[/C][C]-1.2[/C][/ROW]
[ROW][C]18[/C][C] 2.96[/C][C] 3.284[/C][C]-0.3236[/C][/ROW]
[ROW][C]19[/C][C] 3.12[/C][C] 2.506[/C][C] 0.6145[/C][/ROW]
[ROW][C]20[/C][C] 3.83[/C][C] 2.529[/C][C] 1.301[/C][/ROW]
[ROW][C]21[/C][C] 3.11[/C][C] 3.445[/C][C]-0.3351[/C][/ROW]
[ROW][C]22[/C][C] 4.06[/C][C] 2.676[/C][C] 1.384[/C][/ROW]
[ROW][C]23[/C][C] 3.32[/C][C] 4.185[/C][C]-0.8646[/C][/ROW]
[ROW][C]24[/C][C] 1.21[/C][C] 2.939[/C][C]-1.729[/C][/ROW]
[ROW][C]25[/C][C] 0.8[/C][C] 2.635[/C][C]-1.835[/C][/ROW]
[ROW][C]26[/C][C] 1.17[/C][C] 2.942[/C][C]-1.772[/C][/ROW]
[ROW][C]27[/C][C] 8.17[/C][C] 3.4[/C][C] 4.77[/C][/ROW]
[ROW][C]28[/C][C] 5.65[/C][C] 2.191[/C][C] 3.459[/C][/ROW]
[ROW][C]29[/C][C] 1.24[/C][C] 2.882[/C][C]-1.642[/C][/ROW]
[ROW][C]30[/C][C] 1.46[/C][C] 2.956[/C][C]-1.496[/C][/ROW]
[ROW][C]31[/C][C] 4.36[/C][C] 4.486[/C][C]-0.1261[/C][/ROW]
[ROW][C]32[/C][C] 3.38[/C][C] 2.499[/C][C] 0.8807[/C][/ROW]
[ROW][C]33[/C][C] 1.87[/C][C] 3.543[/C][C]-1.673[/C][/ROW]
[ROW][C]34[/C][C] 1.03[/C][C] 2.19[/C][C]-1.16[/C][/ROW]
[ROW][C]35[/C][C] 1.29[/C][C] 2.766[/C][C]-1.476[/C][/ROW]
[ROW][C]36[/C][C] 0.82[/C][C] 2.911[/C][C]-2.091[/C][/ROW]
[ROW][C]37[/C][C] 2.84[/C][C] 3.717[/C][C]-0.8766[/C][/ROW]
[ROW][C]38[/C][C] 1.27[/C][C] 3.4[/C][C]-2.13[/C][/ROW]
[ROW][C]39[/C][C] 3.92[/C][C] 3.126[/C][C] 0.7945[/C][/ROW]
[ROW][C]40[/C][C] 1.95[/C][C] 2.639[/C][C]-0.6889[/C][/ROW]
[ROW][C]41[/C][C] 4.21[/C][C] 2.948[/C][C] 1.262[/C][/ROW]
[ROW][C]42[/C][C] 5.19[/C][C] 4.176[/C][C] 1.014[/C][/ROW]
[ROW][C]43[/C][C] 5.51[/C][C] 6.029[/C][C]-0.519[/C][/ROW]
[ROW][C]44[/C][C] 2.57[/C][C] 2.199[/C][C] 0.3708[/C][/ROW]
[ROW][C]45[/C][C] 1.53[/C][C] 2.941[/C][C]-1.411[/C][/ROW]
[ROW][C]46[/C][C] 2.17[/C][C] 3.106[/C][C]-0.9363[/C][/ROW]
[ROW][C]47[/C][C] 2.15[/C][C] 4.382[/C][C]-2.232[/C][/ROW]
[ROW][C]48[/C][C] 2.07[/C][C] 2.946[/C][C]-0.8758[/C][/ROW]
[ROW][C]49[/C][C] 3.97[/C][C] 2.542[/C][C] 1.428[/C][/ROW]
[ROW][C]50[/C][C] 0.42[/C][C] 2.499[/C][C]-2.079[/C][/ROW]
[ROW][C]51[/C][C] 1.02[/C][C] 3.005[/C][C]-1.985[/C][/ROW]
[ROW][C]52[/C][C] 2.9[/C][C] 2.821[/C][C] 0.0792[/C][/ROW]
[ROW][C]53[/C][C] 5.14[/C][C] 5.34[/C][C]-0.1997[/C][/ROW]
[ROW][C]54[/C][C] 2.34[/C][C] 3.504[/C][C]-1.164[/C][/ROW]
[ROW][C]55[/C][C] 4.73[/C][C] 2.203[/C][C] 2.527[/C][/ROW]
[ROW][C]56[/C][C] 2.02[/C][C] 2.952[/C][C]-0.9315[/C][/ROW]
[ROW][C]57[/C][C] 1.03[/C][C] 2.65[/C][C]-1.62[/C][/ROW]
[ROW][C]58[/C][C] 1.58[/C][C] 2.803[/C][C]-1.223[/C][/ROW]
[ROW][C]59[/C][C] 5.3[/C][C] 5.764[/C][C]-0.4644[/C][/ROW]
[ROW][C]60[/C][C] 1.97[/C][C] 3.237[/C][C]-1.267[/C][/ROW]
[ROW][C]61[/C][C] 4.38[/C][C] 3.104[/C][C] 1.276[/C][/ROW]
[ROW][C]62[/C][C] 3.23[/C][C] 2.192[/C][C] 1.038[/C][/ROW]
[ROW][C]63[/C][C] 1.89[/C][C] 3.244[/C][C]-1.354[/C][/ROW]
[ROW][C]64[/C][C] 1.41[/C][C] 2.806[/C][C]-1.396[/C][/ROW]
[ROW][C]65[/C][C] 1.53[/C][C] 3.131[/C][C]-1.601[/C][/ROW]
[ROW][C]66[/C][C] 3.07[/C][C] 3.884[/C][C]-0.8138[/C][/ROW]
[ROW][C]67[/C][C] 0.61[/C][C] 2.634[/C][C]-2.024[/C][/ROW]
[ROW][C]68[/C][C] 1.68[/C][C] 3.109[/C][C]-1.429[/C][/ROW]
[ROW][C]69[/C][C] 2.92[/C][C] 4.174[/C][C]-1.254[/C][/ROW]
[ROW][C]70[/C][C] 1.16[/C][C] 1.617[/C][C]-0.4567[/C][/ROW]
[ROW][C]71[/C][C] 1.58[/C][C] 2.846[/C][C]-1.266[/C][/ROW]
[ROW][C]72[/C][C] 2.79[/C][C] 3.48[/C][C]-0.6902[/C][/ROW]
[ROW][C]73[/C][C] 1.88[/C][C] 2.762[/C][C]-0.8818[/C][/ROW]
[ROW][C]74[/C][C] 5.57[/C][C] 4.198[/C][C] 1.372[/C][/ROW]
[ROW][C]75[/C][C] 6.22[/C][C] 3.396[/C][C] 2.824[/C][/ROW]
[ROW][C]76[/C][C] 4.61[/C][C] 3.044[/C][C] 1.566[/C][/ROW]
[ROW][C]77[/C][C] 1.89[/C][C] 2.947[/C][C]-1.057[/C][/ROW]
[ROW][C]78[/C][C] 5.02[/C][C] 3.575[/C][C] 1.445[/C][/ROW]
[ROW][C]79[/C][C] 2.1[/C][C] 3.244[/C][C]-1.144[/C][/ROW]
[ROW][C]80[/C][C] 5.55[/C][C] 2.665[/C][C] 2.885[/C][/ROW]
[ROW][C]81[/C][C] 1.03[/C][C] 2.753[/C][C]-1.723[/C][/ROW]
[ROW][C]82[/C][C] 1.17[/C][C] 3.077[/C][C]-1.907[/C][/ROW]
[ROW][C]83[/C][C] 5.69[/C][C] 3.052[/C][C] 2.638[/C][/ROW]
[ROW][C]84[/C][C] 8.13[/C][C] 4.465[/C][C] 3.665[/C][/ROW]
[ROW][C]85[/C][C] 1.91[/C][C] 3.41[/C][C]-1.5[/C][/ROW]
[ROW][C]86[/C][C] 1.22[/C][C] 3.867[/C][C]-2.647[/C][/ROW]
[ROW][C]87[/C][C] 6.29[/C][C] 4.269[/C][C] 2.021[/C][/ROW]
[ROW][C]88[/C][C] 3.84[/C][C] 3.096[/C][C] 0.7441[/C][/ROW]
[ROW][C]89[/C][C] 1.66[/C][C] 2.346[/C][C]-0.6858[/C][/ROW]
[ROW][C]90[/C][C] 1.21[/C][C] 2.668[/C][C]-1.458[/C][/ROW]
[ROW][C]91[/C][C] 3.69[/C][C] 2.492[/C][C] 1.198[/C][/ROW]
[ROW][C]92[/C][C] 5.83[/C][C] 4.526[/C][C] 1.304[/C][/ROW]
[ROW][C]93[/C][C] 15.82[/C][C] 4.33[/C][C] 11.49[/C][/ROW]
[ROW][C]94[/C][C] 3.26[/C][C] 2.506[/C][C] 0.754[/C][/ROW]
[ROW][C]95[/C][C] 0.99[/C][C] 3.104[/C][C]-2.114[/C][/ROW]
[ROW][C]96[/C][C] 0.81[/C][C] 2.936[/C][C]-2.126[/C][/ROW]
[ROW][C]97[/C][C] 3.71[/C][C] 3.246[/C][C] 0.4643[/C][/ROW]
[ROW][C]98[/C][C] 1.53[/C][C] 3.1[/C][C]-1.57[/C][/ROW]
[ROW][C]99[/C][C] 2.08[/C][C] 2.798[/C][C]-0.7179[/C][/ROW]
[ROW][C]100[/C][C] 2.54[/C][C] 2.843[/C][C]-0.3026[/C][/ROW]
[ROW][C]101[/C][C] 3.46[/C][C] 2.194[/C][C] 1.266[/C][/ROW]
[ROW][C]102[/C][C] 2.89[/C][C] 2.83[/C][C] 0.05996[/C][/ROW]
[ROW][C]103[/C][C] 1.78[/C][C] 2.648[/C][C]-0.8677[/C][/ROW]
[ROW][C]104[/C][C] 6.08[/C][C] 3.128[/C][C] 2.952[/C][/ROW]
[ROW][C]105[/C][C] 3.78[/C][C] 2.376[/C][C] 1.404[/C][/ROW]
[ROW][C]106[/C][C] 1.68[/C][C] 2.615[/C][C]-0.9354[/C][/ROW]
[ROW][C]107[/C][C] 0.87[/C][C] 2.943[/C][C]-2.073[/C][/ROW]
[ROW][C]108[/C][C] 1.43[/C][C] 3.82[/C][C]-2.39[/C][/ROW]
[ROW][C]109[/C][C] 2.48[/C][C] 2.569[/C][C]-0.08934[/C][/ROW]
[ROW][C]110[/C][C] 0.98[/C][C] 3.833[/C][C]-2.853[/C][/ROW]
[ROW][C]111[/C][C] 5.28[/C][C] 4.761[/C][C] 0.5189[/C][/ROW]
[ROW][C]112[/C][C] 3.58[/C][C] 2.278[/C][C] 1.302[/C][/ROW]
[ROW][C]113[/C][C] 5.6[/C][C] 4.274[/C][C] 1.326[/C][/ROW]
[ROW][C]114[/C][C] 1.39[/C][C] 2.966[/C][C]-1.576[/C][/ROW]
[ROW][C]115[/C][C] 1.56[/C][C] 2.638[/C][C]-1.078[/C][/ROW]
[ROW][C]116[/C][C] 1.16[/C][C] 2.772[/C][C]-1.612[/C][/ROW]
[ROW][C]117[/C][C] 7.52[/C][C] 5.088[/C][C] 2.432[/C][/ROW]
[ROW][C]118[/C][C] 0.79[/C][C] 2.453[/C][C]-1.663[/C][/ROW]
[ROW][C]119[/C][C] 2.79[/C][C] 2.521[/C][C] 0.2689[/C][/ROW]
[ROW][C]120[/C][C] 1.91[/C][C] 4.197[/C][C]-2.287[/C][/ROW]
[ROW][C]121[/C][C] 4.16[/C][C] 4.428[/C][C]-0.2675[/C][/ROW]
[ROW][C]122[/C][C] 2.28[/C][C] 3.263[/C][C]-0.9833[/C][/ROW]
[ROW][C]123[/C][C] 1.1[/C][C] 2.847[/C][C]-1.747[/C][/ROW]
[ROW][C]124[/C][C] 4.44[/C][C] 3.535[/C][C] 0.9046[/C][/ROW]
[ROW][C]125[/C][C] 3.88[/C][C] 2.952[/C][C] 0.9282[/C][/ROW]
[ROW][C]126[/C][C] 10.8[/C][C] 3.109[/C][C] 7.691[/C][/ROW]
[ROW][C]127[/C][C] 3.65[/C][C] 2.188[/C][C] 1.462[/C][/ROW]
[ROW][C]128[/C][C] 2.71[/C][C] 4.011[/C][C]-1.301[/C][/ROW]
[ROW][C]129[/C][C] 5.69[/C][C] 2.568[/C][C] 3.122[/C][/ROW]
[ROW][C]130[/C][C] 0.87[/C][C] 2.939[/C][C]-2.069[/C][/ROW]
[ROW][C]131[/C][C] 4.94[/C][C] 2.194[/C][C] 2.746[/C][/ROW]
[ROW][C]132[/C][C] 2.45[/C][C] 2.191[/C][C] 0.2591[/C][/ROW]
[ROW][C]133[/C][C] 2.77[/C][C] 2.21[/C][C] 0.5603[/C][/ROW]
[ROW][C]134[/C][C] 1.49[/C][C] 2.197[/C][C]-0.7072[/C][/ROW]
[ROW][C]135[/C][C] 5.61[/C][C] 2.769[/C][C] 2.841[/C][/ROW]
[ROW][C]136[/C][C] 1.21[/C][C] 2.488[/C][C]-1.278[/C][/ROW]
[ROW][C]137[/C][C] 2.7[/C][C] 2.798[/C][C]-0.0982[/C][/ROW]
[ROW][C]138[/C][C] 1.24[/C][C] 2.954[/C][C]-1.714[/C][/ROW]
[ROW][C]139[/C][C] 7.97[/C][C] 2.637[/C][C] 5.333[/C][/ROW]
[ROW][C]140[/C][C] 4.06[/C][C] 3.578[/C][C] 0.4818[/C][/ROW]
[ROW][C]141[/C][C] 5.81[/C][C] 2.819[/C][C] 2.991[/C][/ROW]
[ROW][C]142[/C][C] 1.29[/C][C] 5.726[/C][C]-4.436[/C][/ROW]
[ROW][C]143[/C][C] 1.24[/C][C] 3.101[/C][C]-1.861[/C][/ROW]
[ROW][C]144[/C][C] 3.31[/C][C] 2.751[/C][C] 0.5592[/C][/ROW]
[ROW][C]145[/C][C] 3.67[/C][C] 2.758[/C][C] 0.9119[/C][/ROW]
[ROW][C]146[/C][C] 1.32[/C][C] 2.928[/C][C]-1.608[/C][/ROW]
[ROW][C]147[/C][C] 4.25[/C][C] 4.608[/C][C]-0.3581[/C][/ROW]
[ROW][C]148[/C][C] 2.01[/C][C] 3.106[/C][C]-1.096[/C][/ROW]
[ROW][C]149[/C][C] 7.25[/C][C] 5.942[/C][C] 1.308[/C][/ROW]
[ROW][C]150[/C][C] 5.79[/C][C] 4.014[/C][C] 1.776[/C][/ROW]
[ROW][C]151[/C][C] 1.51[/C][C] 2.929[/C][C]-1.419[/C][/ROW]
[ROW][C]152[/C][C] 0.91[/C][C] 3.398[/C][C]-2.488[/C][/ROW]
[ROW][C]153[/C][C] 1.32[/C][C] 3.058[/C][C]-1.738[/C][/ROW]
[ROW][C]154[/C][C] 2.66[/C][C] 3.191[/C][C]-0.5313[/C][/ROW]
[ROW][C]155[/C][C] 0.48[/C][C] 2.814[/C][C]-2.334[/C][/ROW]
[ROW][C]156[/C][C] 1.13[/C][C] 2.49[/C][C]-1.359[/C][/ROW]
[ROW][C]157[/C][C] 2.7[/C][C] 2.204[/C][C] 0.4955[/C][/ROW]
[ROW][C]158[/C][C] 7.92[/C][C] 2.204[/C][C] 5.716[/C][/ROW]
[ROW][C]159[/C][C] 2.34[/C][C] 2.793[/C][C]-0.453[/C][/ROW]
[ROW][C]160[/C][C] 3.33[/C][C] 2.732[/C][C] 0.5981[/C][/ROW]
[ROW][C]161[/C][C] 5.47[/C][C] 3.579[/C][C] 1.891[/C][/ROW]
[ROW][C]162[/C][C] 1.24[/C][C] 2.761[/C][C]-1.521[/C][/ROW]
[ROW][C]163[/C][C] 2.84[/C][C] 3.226[/C][C]-0.3864[/C][/ROW]
[ROW][C]164[/C][C] 4.94[/C][C] 4.865[/C][C] 0.07544[/C][/ROW]
[ROW][C]165[/C][C] 7.93[/C][C] 2.184[/C][C] 5.746[/C][/ROW]
[ROW][C]166[/C][C] 8.22[/C][C] 3.254[/C][C] 4.966[/C][/ROW]
[ROW][C]167[/C][C] 2.91[/C][C] 4.884[/C][C]-1.974[/C][/ROW]
[ROW][C]168[/C][C] 2.32[/C][C] 3.38[/C][C]-1.06[/C][/ROW]
[ROW][C]169[/C][C] 3.57[/C][C] 2.794[/C][C] 0.7757[/C][/ROW]
[ROW][C]170[/C][C] 1.65[/C][C] 3.618[/C][C]-1.968[/C][/ROW]
[ROW][C]171[/C][C] 1.03[/C][C] 2.774[/C][C]-1.744[/C][/ROW]
[ROW][C]172[/C][C] 0.99[/C][C] 2.805[/C][C]-1.815[/C][/ROW]
[ROW][C]173[/C][C] 1.37[/C][C] 2.483[/C][C]-1.113[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=315859&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=315859&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	0.79	2.767	-1.977
2	2.21	3.108	-0.8976
3	2.12	2.608	-0.4875
4	0.93	2.801	-1.871
5	3.14	3.741	-0.6012
6	2.23	3.256	-1.026
7	9.31	4.481	4.829
8	6.06	4.489	1.571
9	2.31	3.097	-0.787
10	6.84	2.906	3.934
11	7.49	3.709	3.781
12	0.72	3.086	-2.366
13	4.48	2.796	1.684
14	5.09	3.585	1.505
15	7.44	6.284	1.156
16	1.41	2.793	-1.383
17	4.84	6.04	-1.2
18	2.96	3.284	-0.3236
19	3.12	2.506	0.6145
20	3.83	2.529	1.301
21	3.11	3.445	-0.3351
22	4.06	2.676	1.384
23	3.32	4.185	-0.8646
24	1.21	2.939	-1.729
25	0.8	2.635	-1.835
26	1.17	2.942	-1.772
27	8.17	3.4	4.77
28	5.65	2.191	3.459
29	1.24	2.882	-1.642
30	1.46	2.956	-1.496
31	4.36	4.486	-0.1261
32	3.38	2.499	0.8807
33	1.87	3.543	-1.673
34	1.03	2.19	-1.16
35	1.29	2.766	-1.476
36	0.82	2.911	-2.091
37	2.84	3.717	-0.8766
38	1.27	3.4	-2.13
39	3.92	3.126	0.7945
40	1.95	2.639	-0.6889
41	4.21	2.948	1.262
42	5.19	4.176	1.014
43	5.51	6.029	-0.519
44	2.57	2.199	0.3708
45	1.53	2.941	-1.411
46	2.17	3.106	-0.9363
47	2.15	4.382	-2.232
48	2.07	2.946	-0.8758
49	3.97	2.542	1.428
50	0.42	2.499	-2.079
51	1.02	3.005	-1.985
52	2.9	2.821	0.0792
53	5.14	5.34	-0.1997
54	2.34	3.504	-1.164
55	4.73	2.203	2.527
56	2.02	2.952	-0.9315
57	1.03	2.65	-1.62
58	1.58	2.803	-1.223
59	5.3	5.764	-0.4644
60	1.97	3.237	-1.267
61	4.38	3.104	1.276
62	3.23	2.192	1.038
63	1.89	3.244	-1.354
64	1.41	2.806	-1.396
65	1.53	3.131	-1.601
66	3.07	3.884	-0.8138
67	0.61	2.634	-2.024
68	1.68	3.109	-1.429
69	2.92	4.174	-1.254
70	1.16	1.617	-0.4567
71	1.58	2.846	-1.266
72	2.79	3.48	-0.6902
73	1.88	2.762	-0.8818
74	5.57	4.198	1.372
75	6.22	3.396	2.824
76	4.61	3.044	1.566
77	1.89	2.947	-1.057
78	5.02	3.575	1.445
79	2.1	3.244	-1.144
80	5.55	2.665	2.885
81	1.03	2.753	-1.723
82	1.17	3.077	-1.907
83	5.69	3.052	2.638
84	8.13	4.465	3.665
85	1.91	3.41	-1.5
86	1.22	3.867	-2.647
87	6.29	4.269	2.021
88	3.84	3.096	0.7441
89	1.66	2.346	-0.6858
90	1.21	2.668	-1.458
91	3.69	2.492	1.198
92	5.83	4.526	1.304
93	15.82	4.33	11.49
94	3.26	2.506	0.754
95	0.99	3.104	-2.114
96	0.81	2.936	-2.126
97	3.71	3.246	0.4643
98	1.53	3.1	-1.57
99	2.08	2.798	-0.7179
100	2.54	2.843	-0.3026
101	3.46	2.194	1.266
102	2.89	2.83	0.05996
103	1.78	2.648	-0.8677
104	6.08	3.128	2.952
105	3.78	2.376	1.404
106	1.68	2.615	-0.9354
107	0.87	2.943	-2.073
108	1.43	3.82	-2.39
109	2.48	2.569	-0.08934
110	0.98	3.833	-2.853
111	5.28	4.761	0.5189
112	3.58	2.278	1.302
113	5.6	4.274	1.326
114	1.39	2.966	-1.576
115	1.56	2.638	-1.078
116	1.16	2.772	-1.612
117	7.52	5.088	2.432
118	0.79	2.453	-1.663
119	2.79	2.521	0.2689
120	1.91	4.197	-2.287
121	4.16	4.428	-0.2675
122	2.28	3.263	-0.9833
123	1.1	2.847	-1.747
124	4.44	3.535	0.9046
125	3.88	2.952	0.9282
126	10.8	3.109	7.691
127	3.65	2.188	1.462
128	2.71	4.011	-1.301
129	5.69	2.568	3.122
130	0.87	2.939	-2.069
131	4.94	2.194	2.746
132	2.45	2.191	0.2591
133	2.77	2.21	0.5603
134	1.49	2.197	-0.7072
135	5.61	2.769	2.841
136	1.21	2.488	-1.278
137	2.7	2.798	-0.0982
138	1.24	2.954	-1.714
139	7.97	2.637	5.333
140	4.06	3.578	0.4818
141	5.81	2.819	2.991
142	1.29	5.726	-4.436
143	1.24	3.101	-1.861
144	3.31	2.751	0.5592
145	3.67	2.758	0.9119
146	1.32	2.928	-1.608
147	4.25	4.608	-0.3581
148	2.01	3.106	-1.096
149	7.25	5.942	1.308
150	5.79	4.014	1.776
151	1.51	2.929	-1.419
152	0.91	3.398	-2.488
153	1.32	3.058	-1.738
154	2.66	3.191	-0.5313
155	0.48	2.814	-2.334
156	1.13	2.49	-1.359
157	2.7	2.204	0.4955
158	7.92	2.204	5.716
159	2.34	2.793	-0.453
160	3.33	2.732	0.5981
161	5.47	3.579	1.891
162	1.24	2.761	-1.521
163	2.84	3.226	-0.3864
164	4.94	4.865	0.07544
165	7.93	2.184	5.746
166	8.22	3.254	4.966
167	2.91	4.884	-1.974
168	2.32	3.38	-1.06
169	3.57	2.794	0.7757
170	1.65	3.618	-1.968
171	1.03	2.774	-1.744
172	0.99	2.805	-1.815
173	1.37	2.483	-1.113

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
7	0.1717	0.3433	0.8283
8	0.1208	0.2417	0.8792
9	0.05527	0.1105	0.9447
10	0.06327	0.1265	0.9367
11	0.2725	0.545	0.7275
12	0.3081	0.6163	0.6919
13	0.3143	0.6285	0.6857
14	0.234	0.4679	0.766
15	0.1804	0.3609	0.8196
16	0.1449	0.2898	0.8551
17	0.2493	0.4987	0.7507
18	0.3855	0.7711	0.6145
19	0.3155	0.6311	0.6845
20	0.2601	0.5202	0.7399
21	0.2233	0.4466	0.7767
22	0.183	0.3661	0.8169
23	0.1568	0.3135	0.8432
24	0.1413	0.2827	0.8587
25	0.1258	0.2517	0.8742
26	0.1113	0.2226	0.8887
27	0.1337	0.2673	0.8663
28	0.2357	0.4713	0.7643
29	0.2886	0.5772	0.7114
30	0.2628	0.5256	0.7372
31	0.2163	0.4326	0.7837
32	0.2714	0.5428	0.7286
33	0.2591	0.5182	0.7409
34	0.2184	0.4367	0.7816
35	0.2421	0.4842	0.7579
36	0.2374	0.4749	0.7626
37	0.199	0.3979	0.801
38	0.1902	0.3804	0.8098
39	0.1626	0.3253	0.8374
40	0.1315	0.2629	0.8685
41	0.125	0.2499	0.875
42	0.1057	0.2114	0.8943
43	0.0876	0.1752	0.9124
44	0.07091	0.1418	0.9291
45	0.05825	0.1165	0.9417
46	0.04598	0.09196	0.954
47	0.04387	0.08773	0.9561
48	0.03355	0.06711	0.9664
49	0.02864	0.05728	0.9714
50	0.0265	0.053	0.9735
51	0.02341	0.04682	0.9766
52	0.01739	0.03478	0.9826
53	0.01263	0.02525	0.9874
54	0.0456	0.0912	0.9544
55	0.05482	0.1096	0.9452
56	0.04438	0.08875	0.9556
57	0.03897	0.07794	0.961
58	0.03202	0.06404	0.968
59	0.02471	0.04941	0.9753
60	0.02022	0.04044	0.9798
61	0.01732	0.03464	0.9827
62	0.01424	0.02849	0.9858
63	0.01172	0.02344	0.9883
64	0.009629	0.01926	0.9904
65	0.00823	0.01646	0.9918
66	0.006245	0.01249	0.9938
67	0.005873	0.01175	0.9941
68	0.004778	0.009556	0.9952
69	0.003769	0.007539	0.9962
70	0.002679	0.005358	0.9973
71	0.002104	0.004208	0.9979
72	0.001498	0.002997	0.9985
73	0.001074	0.002148	0.9989
74	0.0008992	0.001798	0.9991
75	0.001386	0.002771	0.9986
76	0.00123	0.002459	0.9988
77	0.0009053	0.001811	0.9991
78	0.0007546	0.001509	0.9992
79	0.0005608	0.001122	0.9994
80	0.0008822	0.001764	0.9991
81	0.0007601	0.00152	0.9992
82	0.0006919	0.001384	0.9993
83	0.0009177	0.001835	0.9991
84	0.001985	0.00397	0.998
85	0.001638	0.003277	0.9984
86	0.001975	0.00395	0.998
87	0.001924	0.003847	0.9981
88	0.001422	0.002844	0.9986
89	0.001016	0.002031	0.999
90	0.0008175	0.001635	0.9992
91	0.0006491	0.001298	0.9994
92	0.00051	0.00102	0.9995
93	0.424	0.848	0.576
94	0.387	0.774	0.613
95	0.3837	0.7673	0.6163
96	0.3809	0.7617	0.6191
97	0.3414	0.6829	0.6586
98	0.321	0.6421	0.679
99	0.2862	0.5725	0.7138
100	0.2506	0.5011	0.7494
101	0.2282	0.4563	0.7718
102	0.1965	0.393	0.8035
103	0.172	0.344	0.828
104	0.1952	0.3903	0.8048
105	0.1771	0.3542	0.8229
106	0.1554	0.3108	0.8446
107	0.1535	0.3071	0.8465
108	0.1577	0.3153	0.8423
109	0.1325	0.2651	0.8675
110	0.1476	0.2953	0.8524
111	0.1272	0.2544	0.8728
112	0.1114	0.2227	0.8886
113	0.1009	0.2019	0.8991
114	0.09158	0.1832	0.9084
115	0.07921	0.1584	0.9208
116	0.07904	0.1581	0.921
117	0.09974	0.1995	0.9003
118	0.1166	0.2332	0.8834
119	0.09578	0.1916	0.9042
120	0.09101	0.182	0.909
121	0.0744	0.1488	0.9256
122	0.06225	0.1245	0.9378
123	0.06699	0.134	0.933
124	0.05487	0.1097	0.9451
125	0.0445	0.08901	0.9555
126	0.414	0.828	0.586
127	0.3819	0.7638	0.6181
128	0.3425	0.685	0.6575
129	0.3335	0.667	0.6665
130	0.3283	0.6565	0.6717
131	0.3432	0.6865	0.6568
132	0.2971	0.5943	0.7029
133	0.2546	0.5093	0.7454
134	0.2223	0.4445	0.7777
135	0.24	0.48	0.76
136	0.2184	0.4369	0.7816
137	0.1805	0.361	0.8195
138	0.165	0.3301	0.835
139	0.3785	0.7569	0.6215
140	0.3375	0.675	0.6625
141	0.4031	0.8061	0.5969
142	0.4569	0.9137	0.5431
143	0.4282	0.8564	0.5718
144	0.3703	0.7406	0.6297
145	0.3192	0.6384	0.6808
146	0.2892	0.5783	0.7108
147	0.3092	0.6184	0.6908
148	0.2579	0.5158	0.7421
149	0.2588	0.5177	0.7412
150	0.3535	0.707	0.6465
151	0.3044	0.6088	0.6956
152	0.266	0.532	0.734
153	0.2428	0.4855	0.7572
154	0.1934	0.3868	0.8066
155	0.1979	0.3958	0.8021
156	0.1798	0.3597	0.8202
157	0.1374	0.2749	0.8626
158	0.3613	0.7226	0.6387
159	0.282	0.564	0.718
160	0.2107	0.4214	0.7893
161	0.248	0.4959	0.752
162	0.2109	0.4217	0.7891
163	0.1418	0.2836	0.8582
164	0.2728	0.5455	0.7272
165	0.9302	0.1396	0.06981
166	0.8462	0.3075	0.1538

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
7 &  0.1717 &  0.3433 &  0.8283 \tabularnewline
8 &  0.1208 &  0.2417 &  0.8792 \tabularnewline
9 &  0.05527 &  0.1105 &  0.9447 \tabularnewline
10 &  0.06327 &  0.1265 &  0.9367 \tabularnewline
11 &  0.2725 &  0.545 &  0.7275 \tabularnewline
12 &  0.3081 &  0.6163 &  0.6919 \tabularnewline
13 &  0.3143 &  0.6285 &  0.6857 \tabularnewline
14 &  0.234 &  0.4679 &  0.766 \tabularnewline
15 &  0.1804 &  0.3609 &  0.8196 \tabularnewline
16 &  0.1449 &  0.2898 &  0.8551 \tabularnewline
17 &  0.2493 &  0.4987 &  0.7507 \tabularnewline
18 &  0.3855 &  0.7711 &  0.6145 \tabularnewline
19 &  0.3155 &  0.6311 &  0.6845 \tabularnewline
20 &  0.2601 &  0.5202 &  0.7399 \tabularnewline
21 &  0.2233 &  0.4466 &  0.7767 \tabularnewline
22 &  0.183 &  0.3661 &  0.8169 \tabularnewline
23 &  0.1568 &  0.3135 &  0.8432 \tabularnewline
24 &  0.1413 &  0.2827 &  0.8587 \tabularnewline
25 &  0.1258 &  0.2517 &  0.8742 \tabularnewline
26 &  0.1113 &  0.2226 &  0.8887 \tabularnewline
27 &  0.1337 &  0.2673 &  0.8663 \tabularnewline
28 &  0.2357 &  0.4713 &  0.7643 \tabularnewline
29 &  0.2886 &  0.5772 &  0.7114 \tabularnewline
30 &  0.2628 &  0.5256 &  0.7372 \tabularnewline
31 &  0.2163 &  0.4326 &  0.7837 \tabularnewline
32 &  0.2714 &  0.5428 &  0.7286 \tabularnewline
33 &  0.2591 &  0.5182 &  0.7409 \tabularnewline
34 &  0.2184 &  0.4367 &  0.7816 \tabularnewline
35 &  0.2421 &  0.4842 &  0.7579 \tabularnewline
36 &  0.2374 &  0.4749 &  0.7626 \tabularnewline
37 &  0.199 &  0.3979 &  0.801 \tabularnewline
38 &  0.1902 &  0.3804 &  0.8098 \tabularnewline
39 &  0.1626 &  0.3253 &  0.8374 \tabularnewline
40 &  0.1315 &  0.2629 &  0.8685 \tabularnewline
41 &  0.125 &  0.2499 &  0.875 \tabularnewline
42 &  0.1057 &  0.2114 &  0.8943 \tabularnewline
43 &  0.0876 &  0.1752 &  0.9124 \tabularnewline
44 &  0.07091 &  0.1418 &  0.9291 \tabularnewline
45 &  0.05825 &  0.1165 &  0.9417 \tabularnewline
46 &  0.04598 &  0.09196 &  0.954 \tabularnewline
47 &  0.04387 &  0.08773 &  0.9561 \tabularnewline
48 &  0.03355 &  0.06711 &  0.9664 \tabularnewline
49 &  0.02864 &  0.05728 &  0.9714 \tabularnewline
50 &  0.0265 &  0.053 &  0.9735 \tabularnewline
51 &  0.02341 &  0.04682 &  0.9766 \tabularnewline
52 &  0.01739 &  0.03478 &  0.9826 \tabularnewline
53 &  0.01263 &  0.02525 &  0.9874 \tabularnewline
54 &  0.0456 &  0.0912 &  0.9544 \tabularnewline
55 &  0.05482 &  0.1096 &  0.9452 \tabularnewline
56 &  0.04438 &  0.08875 &  0.9556 \tabularnewline
57 &  0.03897 &  0.07794 &  0.961 \tabularnewline
58 &  0.03202 &  0.06404 &  0.968 \tabularnewline
59 &  0.02471 &  0.04941 &  0.9753 \tabularnewline
60 &  0.02022 &  0.04044 &  0.9798 \tabularnewline
61 &  0.01732 &  0.03464 &  0.9827 \tabularnewline
62 &  0.01424 &  0.02849 &  0.9858 \tabularnewline
63 &  0.01172 &  0.02344 &  0.9883 \tabularnewline
64 &  0.009629 &  0.01926 &  0.9904 \tabularnewline
65 &  0.00823 &  0.01646 &  0.9918 \tabularnewline
66 &  0.006245 &  0.01249 &  0.9938 \tabularnewline
67 &  0.005873 &  0.01175 &  0.9941 \tabularnewline
68 &  0.004778 &  0.009556 &  0.9952 \tabularnewline
69 &  0.003769 &  0.007539 &  0.9962 \tabularnewline
70 &  0.002679 &  0.005358 &  0.9973 \tabularnewline
71 &  0.002104 &  0.004208 &  0.9979 \tabularnewline
72 &  0.001498 &  0.002997 &  0.9985 \tabularnewline
73 &  0.001074 &  0.002148 &  0.9989 \tabularnewline
74 &  0.0008992 &  0.001798 &  0.9991 \tabularnewline
75 &  0.001386 &  0.002771 &  0.9986 \tabularnewline
76 &  0.00123 &  0.002459 &  0.9988 \tabularnewline
77 &  0.0009053 &  0.001811 &  0.9991 \tabularnewline
78 &  0.0007546 &  0.001509 &  0.9992 \tabularnewline
79 &  0.0005608 &  0.001122 &  0.9994 \tabularnewline
80 &  0.0008822 &  0.001764 &  0.9991 \tabularnewline
81 &  0.0007601 &  0.00152 &  0.9992 \tabularnewline
82 &  0.0006919 &  0.001384 &  0.9993 \tabularnewline
83 &  0.0009177 &  0.001835 &  0.9991 \tabularnewline
84 &  0.001985 &  0.00397 &  0.998 \tabularnewline
85 &  0.001638 &  0.003277 &  0.9984 \tabularnewline
86 &  0.001975 &  0.00395 &  0.998 \tabularnewline
87 &  0.001924 &  0.003847 &  0.9981 \tabularnewline
88 &  0.001422 &  0.002844 &  0.9986 \tabularnewline
89 &  0.001016 &  0.002031 &  0.999 \tabularnewline
90 &  0.0008175 &  0.001635 &  0.9992 \tabularnewline
91 &  0.0006491 &  0.001298 &  0.9994 \tabularnewline
92 &  0.00051 &  0.00102 &  0.9995 \tabularnewline
93 &  0.424 &  0.848 &  0.576 \tabularnewline
94 &  0.387 &  0.774 &  0.613 \tabularnewline
95 &  0.3837 &  0.7673 &  0.6163 \tabularnewline
96 &  0.3809 &  0.7617 &  0.6191 \tabularnewline
97 &  0.3414 &  0.6829 &  0.6586 \tabularnewline
98 &  0.321 &  0.6421 &  0.679 \tabularnewline
99 &  0.2862 &  0.5725 &  0.7138 \tabularnewline
100 &  0.2506 &  0.5011 &  0.7494 \tabularnewline
101 &  0.2282 &  0.4563 &  0.7718 \tabularnewline
102 &  0.1965 &  0.393 &  0.8035 \tabularnewline
103 &  0.172 &  0.344 &  0.828 \tabularnewline
104 &  0.1952 &  0.3903 &  0.8048 \tabularnewline
105 &  0.1771 &  0.3542 &  0.8229 \tabularnewline
106 &  0.1554 &  0.3108 &  0.8446 \tabularnewline
107 &  0.1535 &  0.3071 &  0.8465 \tabularnewline
108 &  0.1577 &  0.3153 &  0.8423 \tabularnewline
109 &  0.1325 &  0.2651 &  0.8675 \tabularnewline
110 &  0.1476 &  0.2953 &  0.8524 \tabularnewline
111 &  0.1272 &  0.2544 &  0.8728 \tabularnewline
112 &  0.1114 &  0.2227 &  0.8886 \tabularnewline
113 &  0.1009 &  0.2019 &  0.8991 \tabularnewline
114 &  0.09158 &  0.1832 &  0.9084 \tabularnewline
115 &  0.07921 &  0.1584 &  0.9208 \tabularnewline
116 &  0.07904 &  0.1581 &  0.921 \tabularnewline
117 &  0.09974 &  0.1995 &  0.9003 \tabularnewline
118 &  0.1166 &  0.2332 &  0.8834 \tabularnewline
119 &  0.09578 &  0.1916 &  0.9042 \tabularnewline
120 &  0.09101 &  0.182 &  0.909 \tabularnewline
121 &  0.0744 &  0.1488 &  0.9256 \tabularnewline
122 &  0.06225 &  0.1245 &  0.9378 \tabularnewline
123 &  0.06699 &  0.134 &  0.933 \tabularnewline
124 &  0.05487 &  0.1097 &  0.9451 \tabularnewline
125 &  0.0445 &  0.08901 &  0.9555 \tabularnewline
126 &  0.414 &  0.828 &  0.586 \tabularnewline
127 &  0.3819 &  0.7638 &  0.6181 \tabularnewline
128 &  0.3425 &  0.685 &  0.6575 \tabularnewline
129 &  0.3335 &  0.667 &  0.6665 \tabularnewline
130 &  0.3283 &  0.6565 &  0.6717 \tabularnewline
131 &  0.3432 &  0.6865 &  0.6568 \tabularnewline
132 &  0.2971 &  0.5943 &  0.7029 \tabularnewline
133 &  0.2546 &  0.5093 &  0.7454 \tabularnewline
134 &  0.2223 &  0.4445 &  0.7777 \tabularnewline
135 &  0.24 &  0.48 &  0.76 \tabularnewline
136 &  0.2184 &  0.4369 &  0.7816 \tabularnewline
137 &  0.1805 &  0.361 &  0.8195 \tabularnewline
138 &  0.165 &  0.3301 &  0.835 \tabularnewline
139 &  0.3785 &  0.7569 &  0.6215 \tabularnewline
140 &  0.3375 &  0.675 &  0.6625 \tabularnewline
141 &  0.4031 &  0.8061 &  0.5969 \tabularnewline
142 &  0.4569 &  0.9137 &  0.5431 \tabularnewline
143 &  0.4282 &  0.8564 &  0.5718 \tabularnewline
144 &  0.3703 &  0.7406 &  0.6297 \tabularnewline
145 &  0.3192 &  0.6384 &  0.6808 \tabularnewline
146 &  0.2892 &  0.5783 &  0.7108 \tabularnewline
147 &  0.3092 &  0.6184 &  0.6908 \tabularnewline
148 &  0.2579 &  0.5158 &  0.7421 \tabularnewline
149 &  0.2588 &  0.5177 &  0.7412 \tabularnewline
150 &  0.3535 &  0.707 &  0.6465 \tabularnewline
151 &  0.3044 &  0.6088 &  0.6956 \tabularnewline
152 &  0.266 &  0.532 &  0.734 \tabularnewline
153 &  0.2428 &  0.4855 &  0.7572 \tabularnewline
154 &  0.1934 &  0.3868 &  0.8066 \tabularnewline
155 &  0.1979 &  0.3958 &  0.8021 \tabularnewline
156 &  0.1798 &  0.3597 &  0.8202 \tabularnewline
157 &  0.1374 &  0.2749 &  0.8626 \tabularnewline
158 &  0.3613 &  0.7226 &  0.6387 \tabularnewline
159 &  0.282 &  0.564 &  0.718 \tabularnewline
160 &  0.2107 &  0.4214 &  0.7893 \tabularnewline
161 &  0.248 &  0.4959 &  0.752 \tabularnewline
162 &  0.2109 &  0.4217 &  0.7891 \tabularnewline
163 &  0.1418 &  0.2836 &  0.8582 \tabularnewline
164 &  0.2728 &  0.5455 &  0.7272 \tabularnewline
165 &  0.9302 &  0.1396 &  0.06981 \tabularnewline
166 &  0.8462 &  0.3075 &  0.1538 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=315859&T=6

[TABLE]
[ROW][C]Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]p-values[/C][C]Alternative Hypothesis[/C][/ROW]
[ROW][C]breakpoint index[/C][C]greater[/C][C]2-sided[/C][C]less[/C][/ROW]
[ROW][C]7[/C][C] 0.1717[/C][C] 0.3433[/C][C] 0.8283[/C][/ROW]
[ROW][C]8[/C][C] 0.1208[/C][C] 0.2417[/C][C] 0.8792[/C][/ROW]
[ROW][C]9[/C][C] 0.05527[/C][C] 0.1105[/C][C] 0.9447[/C][/ROW]
[ROW][C]10[/C][C] 0.06327[/C][C] 0.1265[/C][C] 0.9367[/C][/ROW]
[ROW][C]11[/C][C] 0.2725[/C][C] 0.545[/C][C] 0.7275[/C][/ROW]
[ROW][C]12[/C][C] 0.3081[/C][C] 0.6163[/C][C] 0.6919[/C][/ROW]
[ROW][C]13[/C][C] 0.3143[/C][C] 0.6285[/C][C] 0.6857[/C][/ROW]
[ROW][C]14[/C][C] 0.234[/C][C] 0.4679[/C][C] 0.766[/C][/ROW]
[ROW][C]15[/C][C] 0.1804[/C][C] 0.3609[/C][C] 0.8196[/C][/ROW]
[ROW][C]16[/C][C] 0.1449[/C][C] 0.2898[/C][C] 0.8551[/C][/ROW]
[ROW][C]17[/C][C] 0.2493[/C][C] 0.4987[/C][C] 0.7507[/C][/ROW]
[ROW][C]18[/C][C] 0.3855[/C][C] 0.7711[/C][C] 0.6145[/C][/ROW]
[ROW][C]19[/C][C] 0.3155[/C][C] 0.6311[/C][C] 0.6845[/C][/ROW]
[ROW][C]20[/C][C] 0.2601[/C][C] 0.5202[/C][C] 0.7399[/C][/ROW]
[ROW][C]21[/C][C] 0.2233[/C][C] 0.4466[/C][C] 0.7767[/C][/ROW]
[ROW][C]22[/C][C] 0.183[/C][C] 0.3661[/C][C] 0.8169[/C][/ROW]
[ROW][C]23[/C][C] 0.1568[/C][C] 0.3135[/C][C] 0.8432[/C][/ROW]
[ROW][C]24[/C][C] 0.1413[/C][C] 0.2827[/C][C] 0.8587[/C][/ROW]
[ROW][C]25[/C][C] 0.1258[/C][C] 0.2517[/C][C] 0.8742[/C][/ROW]
[ROW][C]26[/C][C] 0.1113[/C][C] 0.2226[/C][C] 0.8887[/C][/ROW]
[ROW][C]27[/C][C] 0.1337[/C][C] 0.2673[/C][C] 0.8663[/C][/ROW]
[ROW][C]28[/C][C] 0.2357[/C][C] 0.4713[/C][C] 0.7643[/C][/ROW]
[ROW][C]29[/C][C] 0.2886[/C][C] 0.5772[/C][C] 0.7114[/C][/ROW]
[ROW][C]30[/C][C] 0.2628[/C][C] 0.5256[/C][C] 0.7372[/C][/ROW]
[ROW][C]31[/C][C] 0.2163[/C][C] 0.4326[/C][C] 0.7837[/C][/ROW]
[ROW][C]32[/C][C] 0.2714[/C][C] 0.5428[/C][C] 0.7286[/C][/ROW]
[ROW][C]33[/C][C] 0.2591[/C][C] 0.5182[/C][C] 0.7409[/C][/ROW]
[ROW][C]34[/C][C] 0.2184[/C][C] 0.4367[/C][C] 0.7816[/C][/ROW]
[ROW][C]35[/C][C] 0.2421[/C][C] 0.4842[/C][C] 0.7579[/C][/ROW]
[ROW][C]36[/C][C] 0.2374[/C][C] 0.4749[/C][C] 0.7626[/C][/ROW]
[ROW][C]37[/C][C] 0.199[/C][C] 0.3979[/C][C] 0.801[/C][/ROW]
[ROW][C]38[/C][C] 0.1902[/C][C] 0.3804[/C][C] 0.8098[/C][/ROW]
[ROW][C]39[/C][C] 0.1626[/C][C] 0.3253[/C][C] 0.8374[/C][/ROW]
[ROW][C]40[/C][C] 0.1315[/C][C] 0.2629[/C][C] 0.8685[/C][/ROW]
[ROW][C]41[/C][C] 0.125[/C][C] 0.2499[/C][C] 0.875[/C][/ROW]
[ROW][C]42[/C][C] 0.1057[/C][C] 0.2114[/C][C] 0.8943[/C][/ROW]
[ROW][C]43[/C][C] 0.0876[/C][C] 0.1752[/C][C] 0.9124[/C][/ROW]
[ROW][C]44[/C][C] 0.07091[/C][C] 0.1418[/C][C] 0.9291[/C][/ROW]
[ROW][C]45[/C][C] 0.05825[/C][C] 0.1165[/C][C] 0.9417[/C][/ROW]
[ROW][C]46[/C][C] 0.04598[/C][C] 0.09196[/C][C] 0.954[/C][/ROW]
[ROW][C]47[/C][C] 0.04387[/C][C] 0.08773[/C][C] 0.9561[/C][/ROW]
[ROW][C]48[/C][C] 0.03355[/C][C] 0.06711[/C][C] 0.9664[/C][/ROW]
[ROW][C]49[/C][C] 0.02864[/C][C] 0.05728[/C][C] 0.9714[/C][/ROW]
[ROW][C]50[/C][C] 0.0265[/C][C] 0.053[/C][C] 0.9735[/C][/ROW]
[ROW][C]51[/C][C] 0.02341[/C][C] 0.04682[/C][C] 0.9766[/C][/ROW]
[ROW][C]52[/C][C] 0.01739[/C][C] 0.03478[/C][C] 0.9826[/C][/ROW]
[ROW][C]53[/C][C] 0.01263[/C][C] 0.02525[/C][C] 0.9874[/C][/ROW]
[ROW][C]54[/C][C] 0.0456[/C][C] 0.0912[/C][C] 0.9544[/C][/ROW]
[ROW][C]55[/C][C] 0.05482[/C][C] 0.1096[/C][C] 0.9452[/C][/ROW]
[ROW][C]56[/C][C] 0.04438[/C][C] 0.08875[/C][C] 0.9556[/C][/ROW]
[ROW][C]57[/C][C] 0.03897[/C][C] 0.07794[/C][C] 0.961[/C][/ROW]
[ROW][C]58[/C][C] 0.03202[/C][C] 0.06404[/C][C] 0.968[/C][/ROW]
[ROW][C]59[/C][C] 0.02471[/C][C] 0.04941[/C][C] 0.9753[/C][/ROW]
[ROW][C]60[/C][C] 0.02022[/C][C] 0.04044[/C][C] 0.9798[/C][/ROW]
[ROW][C]61[/C][C] 0.01732[/C][C] 0.03464[/C][C] 0.9827[/C][/ROW]
[ROW][C]62[/C][C] 0.01424[/C][C] 0.02849[/C][C] 0.9858[/C][/ROW]
[ROW][C]63[/C][C] 0.01172[/C][C] 0.02344[/C][C] 0.9883[/C][/ROW]
[ROW][C]64[/C][C] 0.009629[/C][C] 0.01926[/C][C] 0.9904[/C][/ROW]
[ROW][C]65[/C][C] 0.00823[/C][C] 0.01646[/C][C] 0.9918[/C][/ROW]
[ROW][C]66[/C][C] 0.006245[/C][C] 0.01249[/C][C] 0.9938[/C][/ROW]
[ROW][C]67[/C][C] 0.005873[/C][C] 0.01175[/C][C] 0.9941[/C][/ROW]
[ROW][C]68[/C][C] 0.004778[/C][C] 0.009556[/C][C] 0.9952[/C][/ROW]
[ROW][C]69[/C][C] 0.003769[/C][C] 0.007539[/C][C] 0.9962[/C][/ROW]
[ROW][C]70[/C][C] 0.002679[/C][C] 0.005358[/C][C] 0.9973[/C][/ROW]
[ROW][C]71[/C][C] 0.002104[/C][C] 0.004208[/C][C] 0.9979[/C][/ROW]
[ROW][C]72[/C][C] 0.001498[/C][C] 0.002997[/C][C] 0.9985[/C][/ROW]
[ROW][C]73[/C][C] 0.001074[/C][C] 0.002148[/C][C] 0.9989[/C][/ROW]
[ROW][C]74[/C][C] 0.0008992[/C][C] 0.001798[/C][C] 0.9991[/C][/ROW]
[ROW][C]75[/C][C] 0.001386[/C][C] 0.002771[/C][C] 0.9986[/C][/ROW]
[ROW][C]76[/C][C] 0.00123[/C][C] 0.002459[/C][C] 0.9988[/C][/ROW]
[ROW][C]77[/C][C] 0.0009053[/C][C] 0.001811[/C][C] 0.9991[/C][/ROW]
[ROW][C]78[/C][C] 0.0007546[/C][C] 0.001509[/C][C] 0.9992[/C][/ROW]
[ROW][C]79[/C][C] 0.0005608[/C][C] 0.001122[/C][C] 0.9994[/C][/ROW]
[ROW][C]80[/C][C] 0.0008822[/C][C] 0.001764[/C][C] 0.9991[/C][/ROW]
[ROW][C]81[/C][C] 0.0007601[/C][C] 0.00152[/C][C] 0.9992[/C][/ROW]
[ROW][C]82[/C][C] 0.0006919[/C][C] 0.001384[/C][C] 0.9993[/C][/ROW]
[ROW][C]83[/C][C] 0.0009177[/C][C] 0.001835[/C][C] 0.9991[/C][/ROW]
[ROW][C]84[/C][C] 0.001985[/C][C] 0.00397[/C][C] 0.998[/C][/ROW]
[ROW][C]85[/C][C] 0.001638[/C][C] 0.003277[/C][C] 0.9984[/C][/ROW]
[ROW][C]86[/C][C] 0.001975[/C][C] 0.00395[/C][C] 0.998[/C][/ROW]
[ROW][C]87[/C][C] 0.001924[/C][C] 0.003847[/C][C] 0.9981[/C][/ROW]
[ROW][C]88[/C][C] 0.001422[/C][C] 0.002844[/C][C] 0.9986[/C][/ROW]
[ROW][C]89[/C][C] 0.001016[/C][C] 0.002031[/C][C] 0.999[/C][/ROW]
[ROW][C]90[/C][C] 0.0008175[/C][C] 0.001635[/C][C] 0.9992[/C][/ROW]
[ROW][C]91[/C][C] 0.0006491[/C][C] 0.001298[/C][C] 0.9994[/C][/ROW]
[ROW][C]92[/C][C] 0.00051[/C][C] 0.00102[/C][C] 0.9995[/C][/ROW]
[ROW][C]93[/C][C] 0.424[/C][C] 0.848[/C][C] 0.576[/C][/ROW]
[ROW][C]94[/C][C] 0.387[/C][C] 0.774[/C][C] 0.613[/C][/ROW]
[ROW][C]95[/C][C] 0.3837[/C][C] 0.7673[/C][C] 0.6163[/C][/ROW]
[ROW][C]96[/C][C] 0.3809[/C][C] 0.7617[/C][C] 0.6191[/C][/ROW]
[ROW][C]97[/C][C] 0.3414[/C][C] 0.6829[/C][C] 0.6586[/C][/ROW]
[ROW][C]98[/C][C] 0.321[/C][C] 0.6421[/C][C] 0.679[/C][/ROW]
[ROW][C]99[/C][C] 0.2862[/C][C] 0.5725[/C][C] 0.7138[/C][/ROW]
[ROW][C]100[/C][C] 0.2506[/C][C] 0.5011[/C][C] 0.7494[/C][/ROW]
[ROW][C]101[/C][C] 0.2282[/C][C] 0.4563[/C][C] 0.7718[/C][/ROW]
[ROW][C]102[/C][C] 0.1965[/C][C] 0.393[/C][C] 0.8035[/C][/ROW]
[ROW][C]103[/C][C] 0.172[/C][C] 0.344[/C][C] 0.828[/C][/ROW]
[ROW][C]104[/C][C] 0.1952[/C][C] 0.3903[/C][C] 0.8048[/C][/ROW]
[ROW][C]105[/C][C] 0.1771[/C][C] 0.3542[/C][C] 0.8229[/C][/ROW]
[ROW][C]106[/C][C] 0.1554[/C][C] 0.3108[/C][C] 0.8446[/C][/ROW]
[ROW][C]107[/C][C] 0.1535[/C][C] 0.3071[/C][C] 0.8465[/C][/ROW]
[ROW][C]108[/C][C] 0.1577[/C][C] 0.3153[/C][C] 0.8423[/C][/ROW]
[ROW][C]109[/C][C] 0.1325[/C][C] 0.2651[/C][C] 0.8675[/C][/ROW]
[ROW][C]110[/C][C] 0.1476[/C][C] 0.2953[/C][C] 0.8524[/C][/ROW]
[ROW][C]111[/C][C] 0.1272[/C][C] 0.2544[/C][C] 0.8728[/C][/ROW]
[ROW][C]112[/C][C] 0.1114[/C][C] 0.2227[/C][C] 0.8886[/C][/ROW]
[ROW][C]113[/C][C] 0.1009[/C][C] 0.2019[/C][C] 0.8991[/C][/ROW]
[ROW][C]114[/C][C] 0.09158[/C][C] 0.1832[/C][C] 0.9084[/C][/ROW]
[ROW][C]115[/C][C] 0.07921[/C][C] 0.1584[/C][C] 0.9208[/C][/ROW]
[ROW][C]116[/C][C] 0.07904[/C][C] 0.1581[/C][C] 0.921[/C][/ROW]
[ROW][C]117[/C][C] 0.09974[/C][C] 0.1995[/C][C] 0.9003[/C][/ROW]
[ROW][C]118[/C][C] 0.1166[/C][C] 0.2332[/C][C] 0.8834[/C][/ROW]
[ROW][C]119[/C][C] 0.09578[/C][C] 0.1916[/C][C] 0.9042[/C][/ROW]
[ROW][C]120[/C][C] 0.09101[/C][C] 0.182[/C][C] 0.909[/C][/ROW]
[ROW][C]121[/C][C] 0.0744[/C][C] 0.1488[/C][C] 0.9256[/C][/ROW]
[ROW][C]122[/C][C] 0.06225[/C][C] 0.1245[/C][C] 0.9378[/C][/ROW]
[ROW][C]123[/C][C] 0.06699[/C][C] 0.134[/C][C] 0.933[/C][/ROW]
[ROW][C]124[/C][C] 0.05487[/C][C] 0.1097[/C][C] 0.9451[/C][/ROW]
[ROW][C]125[/C][C] 0.0445[/C][C] 0.08901[/C][C] 0.9555[/C][/ROW]
[ROW][C]126[/C][C] 0.414[/C][C] 0.828[/C][C] 0.586[/C][/ROW]
[ROW][C]127[/C][C] 0.3819[/C][C] 0.7638[/C][C] 0.6181[/C][/ROW]
[ROW][C]128[/C][C] 0.3425[/C][C] 0.685[/C][C] 0.6575[/C][/ROW]
[ROW][C]129[/C][C] 0.3335[/C][C] 0.667[/C][C] 0.6665[/C][/ROW]
[ROW][C]130[/C][C] 0.3283[/C][C] 0.6565[/C][C] 0.6717[/C][/ROW]
[ROW][C]131[/C][C] 0.3432[/C][C] 0.6865[/C][C] 0.6568[/C][/ROW]
[ROW][C]132[/C][C] 0.2971[/C][C] 0.5943[/C][C] 0.7029[/C][/ROW]
[ROW][C]133[/C][C] 0.2546[/C][C] 0.5093[/C][C] 0.7454[/C][/ROW]
[ROW][C]134[/C][C] 0.2223[/C][C] 0.4445[/C][C] 0.7777[/C][/ROW]
[ROW][C]135[/C][C] 0.24[/C][C] 0.48[/C][C] 0.76[/C][/ROW]
[ROW][C]136[/C][C] 0.2184[/C][C] 0.4369[/C][C] 0.7816[/C][/ROW]
[ROW][C]137[/C][C] 0.1805[/C][C] 0.361[/C][C] 0.8195[/C][/ROW]
[ROW][C]138[/C][C] 0.165[/C][C] 0.3301[/C][C] 0.835[/C][/ROW]
[ROW][C]139[/C][C] 0.3785[/C][C] 0.7569[/C][C] 0.6215[/C][/ROW]
[ROW][C]140[/C][C] 0.3375[/C][C] 0.675[/C][C] 0.6625[/C][/ROW]
[ROW][C]141[/C][C] 0.4031[/C][C] 0.8061[/C][C] 0.5969[/C][/ROW]
[ROW][C]142[/C][C] 0.4569[/C][C] 0.9137[/C][C] 0.5431[/C][/ROW]
[ROW][C]143[/C][C] 0.4282[/C][C] 0.8564[/C][C] 0.5718[/C][/ROW]
[ROW][C]144[/C][C] 0.3703[/C][C] 0.7406[/C][C] 0.6297[/C][/ROW]
[ROW][C]145[/C][C] 0.3192[/C][C] 0.6384[/C][C] 0.6808[/C][/ROW]
[ROW][C]146[/C][C] 0.2892[/C][C] 0.5783[/C][C] 0.7108[/C][/ROW]
[ROW][C]147[/C][C] 0.3092[/C][C] 0.6184[/C][C] 0.6908[/C][/ROW]
[ROW][C]148[/C][C] 0.2579[/C][C] 0.5158[/C][C] 0.7421[/C][/ROW]
[ROW][C]149[/C][C] 0.2588[/C][C] 0.5177[/C][C] 0.7412[/C][/ROW]
[ROW][C]150[/C][C] 0.3535[/C][C] 0.707[/C][C] 0.6465[/C][/ROW]
[ROW][C]151[/C][C] 0.3044[/C][C] 0.6088[/C][C] 0.6956[/C][/ROW]
[ROW][C]152[/C][C] 0.266[/C][C] 0.532[/C][C] 0.734[/C][/ROW]
[ROW][C]153[/C][C] 0.2428[/C][C] 0.4855[/C][C] 0.7572[/C][/ROW]
[ROW][C]154[/C][C] 0.1934[/C][C] 0.3868[/C][C] 0.8066[/C][/ROW]
[ROW][C]155[/C][C] 0.1979[/C][C] 0.3958[/C][C] 0.8021[/C][/ROW]
[ROW][C]156[/C][C] 0.1798[/C][C] 0.3597[/C][C] 0.8202[/C][/ROW]
[ROW][C]157[/C][C] 0.1374[/C][C] 0.2749[/C][C] 0.8626[/C][/ROW]
[ROW][C]158[/C][C] 0.3613[/C][C] 0.7226[/C][C] 0.6387[/C][/ROW]
[ROW][C]159[/C][C] 0.282[/C][C] 0.564[/C][C] 0.718[/C][/ROW]
[ROW][C]160[/C][C] 0.2107[/C][C] 0.4214[/C][C] 0.7893[/C][/ROW]
[ROW][C]161[/C][C] 0.248[/C][C] 0.4959[/C][C] 0.752[/C][/ROW]
[ROW][C]162[/C][C] 0.2109[/C][C] 0.4217[/C][C] 0.7891[/C][/ROW]
[ROW][C]163[/C][C] 0.1418[/C][C] 0.2836[/C][C] 0.8582[/C][/ROW]
[ROW][C]164[/C][C] 0.2728[/C][C] 0.5455[/C][C] 0.7272[/C][/ROW]
[ROW][C]165[/C][C] 0.9302[/C][C] 0.1396[/C][C] 0.06981[/C][/ROW]
[ROW][C]166[/C][C] 0.8462[/C][C] 0.3075[/C][C] 0.1538[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=315859&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=315859&T=6

As an alternative you can also use a QR Code:

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

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
7	0.1717	0.3433	0.8283
8	0.1208	0.2417	0.8792
9	0.05527	0.1105	0.9447
10	0.06327	0.1265	0.9367
11	0.2725	0.545	0.7275
12	0.3081	0.6163	0.6919
13	0.3143	0.6285	0.6857
14	0.234	0.4679	0.766
15	0.1804	0.3609	0.8196
16	0.1449	0.2898	0.8551
17	0.2493	0.4987	0.7507
18	0.3855	0.7711	0.6145
19	0.3155	0.6311	0.6845
20	0.2601	0.5202	0.7399
21	0.2233	0.4466	0.7767
22	0.183	0.3661	0.8169
23	0.1568	0.3135	0.8432
24	0.1413	0.2827	0.8587
25	0.1258	0.2517	0.8742
26	0.1113	0.2226	0.8887
27	0.1337	0.2673	0.8663
28	0.2357	0.4713	0.7643
29	0.2886	0.5772	0.7114
30	0.2628	0.5256	0.7372
31	0.2163	0.4326	0.7837
32	0.2714	0.5428	0.7286
33	0.2591	0.5182	0.7409
34	0.2184	0.4367	0.7816
35	0.2421	0.4842	0.7579
36	0.2374	0.4749	0.7626
37	0.199	0.3979	0.801
38	0.1902	0.3804	0.8098
39	0.1626	0.3253	0.8374
40	0.1315	0.2629	0.8685
41	0.125	0.2499	0.875
42	0.1057	0.2114	0.8943
43	0.0876	0.1752	0.9124
44	0.07091	0.1418	0.9291
45	0.05825	0.1165	0.9417
46	0.04598	0.09196	0.954
47	0.04387	0.08773	0.9561
48	0.03355	0.06711	0.9664
49	0.02864	0.05728	0.9714
50	0.0265	0.053	0.9735
51	0.02341	0.04682	0.9766
52	0.01739	0.03478	0.9826
53	0.01263	0.02525	0.9874
54	0.0456	0.0912	0.9544
55	0.05482	0.1096	0.9452
56	0.04438	0.08875	0.9556
57	0.03897	0.07794	0.961
58	0.03202	0.06404	0.968
59	0.02471	0.04941	0.9753
60	0.02022	0.04044	0.9798
61	0.01732	0.03464	0.9827
62	0.01424	0.02849	0.9858
63	0.01172	0.02344	0.9883
64	0.009629	0.01926	0.9904
65	0.00823	0.01646	0.9918
66	0.006245	0.01249	0.9938
67	0.005873	0.01175	0.9941
68	0.004778	0.009556	0.9952
69	0.003769	0.007539	0.9962
70	0.002679	0.005358	0.9973
71	0.002104	0.004208	0.9979
72	0.001498	0.002997	0.9985
73	0.001074	0.002148	0.9989
74	0.0008992	0.001798	0.9991
75	0.001386	0.002771	0.9986
76	0.00123	0.002459	0.9988
77	0.0009053	0.001811	0.9991
78	0.0007546	0.001509	0.9992
79	0.0005608	0.001122	0.9994
80	0.0008822	0.001764	0.9991
81	0.0007601	0.00152	0.9992
82	0.0006919	0.001384	0.9993
83	0.0009177	0.001835	0.9991
84	0.001985	0.00397	0.998
85	0.001638	0.003277	0.9984
86	0.001975	0.00395	0.998
87	0.001924	0.003847	0.9981
88	0.001422	0.002844	0.9986
89	0.001016	0.002031	0.999
90	0.0008175	0.001635	0.9992
91	0.0006491	0.001298	0.9994
92	0.00051	0.00102	0.9995
93	0.424	0.848	0.576
94	0.387	0.774	0.613
95	0.3837	0.7673	0.6163
96	0.3809	0.7617	0.6191
97	0.3414	0.6829	0.6586
98	0.321	0.6421	0.679
99	0.2862	0.5725	0.7138
100	0.2506	0.5011	0.7494
101	0.2282	0.4563	0.7718
102	0.1965	0.393	0.8035
103	0.172	0.344	0.828
104	0.1952	0.3903	0.8048
105	0.1771	0.3542	0.8229
106	0.1554	0.3108	0.8446
107	0.1535	0.3071	0.8465
108	0.1577	0.3153	0.8423
109	0.1325	0.2651	0.8675
110	0.1476	0.2953	0.8524
111	0.1272	0.2544	0.8728
112	0.1114	0.2227	0.8886
113	0.1009	0.2019	0.8991
114	0.09158	0.1832	0.9084
115	0.07921	0.1584	0.9208
116	0.07904	0.1581	0.921
117	0.09974	0.1995	0.9003
118	0.1166	0.2332	0.8834
119	0.09578	0.1916	0.9042
120	0.09101	0.182	0.909
121	0.0744	0.1488	0.9256
122	0.06225	0.1245	0.9378
123	0.06699	0.134	0.933
124	0.05487	0.1097	0.9451
125	0.0445	0.08901	0.9555
126	0.414	0.828	0.586
127	0.3819	0.7638	0.6181
128	0.3425	0.685	0.6575
129	0.3335	0.667	0.6665
130	0.3283	0.6565	0.6717
131	0.3432	0.6865	0.6568
132	0.2971	0.5943	0.7029
133	0.2546	0.5093	0.7454
134	0.2223	0.4445	0.7777
135	0.24	0.48	0.76
136	0.2184	0.4369	0.7816
137	0.1805	0.361	0.8195
138	0.165	0.3301	0.835
139	0.3785	0.7569	0.6215
140	0.3375	0.675	0.6625
141	0.4031	0.8061	0.5969
142	0.4569	0.9137	0.5431
143	0.4282	0.8564	0.5718
144	0.3703	0.7406	0.6297
145	0.3192	0.6384	0.6808
146	0.2892	0.5783	0.7108
147	0.3092	0.6184	0.6908
148	0.2579	0.5158	0.7421
149	0.2588	0.5177	0.7412
150	0.3535	0.707	0.6465
151	0.3044	0.6088	0.6956
152	0.266	0.532	0.734
153	0.2428	0.4855	0.7572
154	0.1934	0.3868	0.8066
155	0.1979	0.3958	0.8021
156	0.1798	0.3597	0.8202
157	0.1374	0.2749	0.8626
158	0.3613	0.7226	0.6387
159	0.282	0.564	0.718
160	0.2107	0.4214	0.7893
161	0.248	0.4959	0.752
162	0.2109	0.4217	0.7891
163	0.1418	0.2836	0.8582
164	0.2728	0.5455	0.7272
165	0.9302	0.1396	0.06981
166	0.8462	0.3075	0.1538

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	25	0.1562	NOK
5% type I error level	37	0.23125	NOK
10% type I error level	47	0.29375	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 & 25 &  0.1562 & NOK \tabularnewline
5% type I error level & 37 & 0.23125 & NOK \tabularnewline
10% type I error level & 47 & 0.29375 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=315859&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]25[/C][C] 0.1562[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]37[/C][C]0.23125[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]47[/C][C]0.29375[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=315859&T=7

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=315859&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	25	0.1562	NOK
5% type I error level	37	0.23125	NOK
10% type I error level	47	0.29375	NOK

Ramsey RESET F-Test for powers (2 and 3) of fitted values
> reset_test_fitted RESET test data: mylm RESET = 4.8734, df1 = 2, df2 = 167, p-value = 0.008769
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors RESET test data: mylm RESET = 3.1856, df1 = 6, df2 = 163, p-value = 0.005541
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components RESET test data: mylm RESET = 0.8252, df1 = 2, df2 = 167, p-value = 0.4399

\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 = 4.8734, df1 = 2, df2 = 167, p-value = 0.008769
 \tabularnewline
Ramsey RESET F-Test for powers (2 and 3) of regressors \tabularnewline
> reset_test_regressors
	RESET test
data:  mylm
RESET = 3.1856, df1 = 6, df2 = 163, p-value = 0.005541
 \tabularnewline
Ramsey RESET F-Test for powers (2 and 3) of principal components \tabularnewline
> reset_test_principal_components
	RESET test
data:  mylm
RESET = 0.8252, df1 = 2, df2 = 167, p-value = 0.4399
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=315859&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 = 4.8734, df1 = 2, df2 = 167, p-value = 0.008769
[/C][/ROW]
[ROW][C]Ramsey RESET F-Test for powers (2 and 3) of regressors[/C][/ROW]
[ROW][C]> reset_test_regressors
	RESET test
data:  mylm
RESET = 3.1856, df1 = 6, df2 = 163, p-value = 0.005541
[/C][/ROW]
[ROW][C]Ramsey RESET F-Test for powers (2 and 3) of principal components[/C][/ROW]
[ROW][C]> reset_test_principal_components
	RESET test
data:  mylm
RESET = 0.8252, df1 = 2, df2 = 167, p-value = 0.4399
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=315859&T=8

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=315859&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 = 4.8734, df1 = 2, df2 = 167, p-value = 0.008769
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors RESET test data: mylm RESET = 3.1856, df1 = 6, df2 = 163, p-value = 0.005541
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components RESET test data: mylm RESET = 0.8252, df1 = 2, df2 = 167, p-value = 0.4399

Variance Inflation Factors (Multicollinearity)
> vif `Population_(millions)` Urban_Land Total_Biocapacity 1.006998 1.004147 1.003033

\begin{tabular}{lllllllll}
\hline
Variance Inflation Factors (Multicollinearity) \tabularnewline
> vif
`Population_(millions)`              Urban_Land       Total_Biocapacity 
               1.006998                1.004147                1.003033 
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=315859&T=9

[TABLE]
[ROW][C]Variance Inflation Factors (Multicollinearity)[/C][/ROW]
[ROW][C]> vif
`Population_(millions)`              Urban_Land       Total_Biocapacity 
               1.006998                1.004147                1.003033 
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=315859&T=9

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=315859&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 `Population_(millions)` Urban_Land Total_Biocapacity 1.006998 1.004147 1.003033

Figure 1

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Figure 3

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Figure 4

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Figure 5

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Figure 6

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Figure 7

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Figure 8

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Figure 9

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Figure 10

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Parameters (Session):

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

Parameters (R input):

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

R code (references can be found in the software module):

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