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R Software Module

rwasp_multipleregression.wasp

Title produced by software

Multiple Regression

Date of computation

Wed, 19 Dec 2018 15:48:04 +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/19/t1545231100ym0dafl2cj7r95k.htm/, Retrieved Thu, 31 Oct 2024 23:05:51 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=316099, Retrieved Thu, 31 Oct 2024 23:05:51 +0000

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No (this computation is public)

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Estimated Impact

119

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

-       [Multiple Regression] [] [2018-12-19 14:48:04] [a77d3f185bf8346aeb8631871e5ed689] [Current]

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Dataseries X:

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Histogram

Boxplots

0.79 614.66 29.82 0.46
2.21 4534.37 3.16 0.73
2.12 5430.57 38.48 0.73
0.93 4665.91 20.82 0.52
5.38 13205.1 0.09 0.78
3.14 13540 41.09 0.83
2.23 3426.39 2.97 0.73
11.88 NA 0.1 NA
9.31 66604.2 23.05 0.93
6.06 51274.1 8.46 0.88
2.31 7106.04 9.31 0.75
6.84 22647.3 0.37 0.78
7.49 24299 1.32 0.82
0.72 857.5 154.7 0.56
4.48 15722.8 0.28 0.79
5.09 6300.45 9.4 0.8
7.44 48053.3 11.06 0.89
1.41 746.83 10.05 0.48
5.77 70626.3 0.06 NA
4.84 2395 0.74 0.59
2.96 2253.09 10.5 0.65
3.12 4708.85 3.83 0.73
3.83 7743.5 2 0.69
3.11 13237.6 198.66 0.75
2.86 NA 0.03 NA
4.06 47097.4 0.41 0.85
3.32 7615.28 7.28 0.78
1.21 671.07 16.46 0.39
0.8 276.69 9.85 0.39
2.52 3801.45 0.49 0.64
1.21 877.64 14.86 0.55
1.17 1271.21 21.7 0.5
8.17 52145.4 34.84 0.91
5.65 NA 0.06 NA
1.24 495.04 4.53 0.37
1.46 1161.22 12.45 0.39
4.36 14525.8 17.46 0.83
3.38 5560.94 1408.04 0.72
1.87 7305.22 47.7 0.72
1.03 860.24 0.72 0.5
1.29 1943.69 4.34 0.57
0.82 338.63 65.7 0.42
2.84 8979.96 4.8 0.76
1.27 1016.83 19.84 NA
3.92 14522.8 4.31 0.82
1.95 5175.94 11.27 0.77
4.21 31454.7 1.13 0.85
5.19 21676.3 10.66 0.87
5.51 61413.6 5.6 0.92
2.19 1433.17 0.86 0.46
2.57 7088.01 0.07 0.72
1.53 6085.89 10.28 0.71
2.17 5192.88 15.49 0.73
2.15 2930.33 80.72 0.69
2.07 3696.33 6.3 0.66
3.97 24064 0.74 0.58
0.42 439.73 6.13 0.39
6.86 17304.4 1.29 0.85
1.02 379.38 91.73 0.43
2.9 4201.37 0.88 0.72
5.87 50960.2 5.41 0.88
5.14 45430.3 63.98 0.89
2.34 NA 0.24 NA
4.73 NA 0.27 NA
2.02 11989 1.63 0.67
1.03 505.76 1.79 0.44
1.58 3710.7 4.36 0.75
5.3 46822.4 82.8 0.91
1.97 1627.9 25.37 0.57
4.38 25987.4 11.12 0.86
2.98 7410.48 0.1 0.74
3.23 NA 0.46 NA
1.89 3233.8 15.08 0.62
1.41 459.09 11.45 0.41
1.53 681.25 1.66 0.42
3.07 3269.46 0.8 0.63
0.61 749.13 10.17 0.48
1.68 2269.51 7.94 0.61
2.92 13964.2 9.98 0.82
1.16 1513.85 1236.69 0.6
1.58 3688.53 246.86 0.68
2.79 7511.1 76.42 0.76
1.88 5848.54 32.78 0.65
5.57 52853.6 4.58 0.91
6.22 33718.9 7.64 0.89
4.61 38412 60.92 0.87
1.89 5226.3 2.77 0.72
5.02 46201.6 127.25 0.89
2.1 4615.17 7.01 0.75
5.55 11278 16.27 0.78
1.03 1062.11 43.18 0.54
1.17 NA 24.76 NA
5.69 24155.8 49 0.89
8.13 41830.5 3.25 0.82
1.91 1116.37 5.47 0.65
1.22 1236.24 6.65 0.56
6.29 13732 2.06 0.81
3.84 9143.86 4.65 0.76
1.66 1338.42 2.05 0.48
1.21 397.38 4.19 0.42
3.69 5859.43 6.16 0.74
5.83 14373.7 3.03 0.83
15.82 114665 0.52 0.89
3.26 5174.89 2.11 0.74
0.99 456.33 22.29 0.51
0.81 493.84 15.91 0.43
3.71 10252.6 29.24 0.77
1.53 741.22 14.85 0.41
2.08 NA 0.4 NA
2.54 1524.39 3.8 0.5
3.46 8811.15 1.24 0.77
2.89 10123.9 120.85 0.75
1.78 1971.03 3.51 0.68
6.08 3736.07 2.8 0.71
3.78 7251.6 0.62 0.8
7.78 NA 0 NA
1.68 3149.43 32.52 0.62
0.87 538.82 25.2 0.41
1.43 1117.58 52.8 0.53
2.48 5880.8 2.26 0.62
2.94 NA 0.01 NA
0.98 700.07 27.47 0.54
5.28 53589.9 16.71 0.92
3.58 NA 0.25 NA
5.6 37488.3 4.46 0.91
1.39 1626.85 5.99 0.63
1.56 410.91 17.16 0.34
1.16 2612.12 168.83 0.5
4.98 100172 4.99 0.94
7.52 22622.8 3.31 0.79
0.79 1218.6 179.16 0.53
2.79 8410.77 3.8 0.77
1.91 1871.21 7.17 0.5
4.16 3557.31 6.69 0.67
2.28 5684.73 29.99 0.73
1.1 2379.44 96.71 0.66
4.44 13769.5 38.21 0.84
3.88 23217.3 10.6 0.83
10.8 99431.5 2.05 0.85
3.65 NA 0.86 NA
2.71 9213.94 21.76 0.79
5.69 13320.2 143.17 0.79
0.87 628.08 11.46 0.48
4.94 12952.5 0.05 0.74
2.45 7737.2 0.18 0.73
3.11 6171.48 0.11 0.72
2.77 4067.15 0.19 0.7
1.49 1384.53 0.19 0.55
5.61 23593.8 28.29 0.83
1.21 1079.27 13.73 0.46
2.7 6426.18 9.55 0.76
1.24 499.89 5.98 0.4
7.97 53122.4 5.3 0.91
4.06 18103.1 5.45 0.84
5.81 25040.5 2.07 0.88
1.29 1647.86 0.55 0.5
1.24 NA 10.2 NA
3.31 8089.87 52.39 0.66
3.67 32008.7 46.76 0.87
1.32 2880.03 21.1 0.75
4.25 8190.7 0.54 0.71
2.01 4657.48 1.23 0.53
7.25 59381.9 9.51 0.9
5.79 88506.2 8 0.93
1.51 NA 21.89 0.62
0.91 836.17 8.01 0.62
1.32 765.33 47.78 0.51
2.66 5479.29 66.78 0.72
0.48 5167.86 1.11 0.6
1.13 580.86 6.64 0.47
2.7 4330.9 0.1 0.72
7.92 18310.8 1.34 0.77
2.34 4305.07 10.88 0.72
3.33 10437.7 74 0.76
5.47 5290.14 5.17 0.68
1.24 601.35 36.35 0.48
2.84 3589.63 45.53 0.74
4.94 40980.5 63.03 0.9
7.93 40817.4 9.206 0.83
8.22 49725 317.5 0.91
2.91 14238.1 3.4 0.79
2.32 1560.85 28.54 0.67
3.57 10237.8 29.96 0.763846
1.65 1532.31 90.8 0.66
2.07 NA 0.01 NA
1.03 1302.3 23.85 0.5
0.99 1740.64 14.08 0.58
1.37 865.91 13.72 0.49

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	19 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 time19 seconds \tabularnewline
R ServerBig Analytics Cloud Computing Center \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=316099&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]

19 seconds[/C][/ROW] [ROW]

R Server[/C]

Big Analytics Cloud Computing Center[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=316099&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=316099&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	19 seconds
R Server	Big Analytics Cloud Computing Center

Multiple Linear Regression - Estimated Regression Equation
Total_Ecological_Footprint[t] = -1.30022 + 6.28387e-05GDP_per_Capita[t] -0.000423779`Population_(millions)`[t] + 5.40691HDI[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Total_Ecological_Footprint[t] =  -1.30022 +  6.28387e-05GDP_per_Capita[t] -0.000423779`Population_(millions)`[t] +  5.40691HDI[t]  + e[t] \tabularnewline
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=316099&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Total_Ecological_Footprint[t] =  -1.30022 +  6.28387e-05GDP_per_Capita[t] -0.000423779`Population_(millions)`[t] +  5.40691HDI[t]  + e[t][/C][/ROW]
[ROW][C][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=316099&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=316099&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] = -1.30022 + 6.28387e-05GDP_per_Capita[t] -0.000423779`Population_(millions)`[t] + 5.40691HDI[t] + e[t]

Multiple Linear Regression - Ordinary Least Squares
Variable	Parameter	S.D.	T-STATH0: parameter = 0	2-tail p-value	1-tail p-value
(Intercept)	-1.3	0.5279	-2.4630e+00	0.01479	0.007396
GDP_per_Capita	+6.284e-05	6.379e-06	+9.8500e+00	2.464e-18	1.232e-18
`Population_(millions)`	-0.0004238	0.0006559	-6.4620e-01	0.5191	0.2595
HDI	+5.407	0.8382	+6.4510e+00	1.154e-09	5.77e-10

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Ordinary Least Squares \tabularnewline
Variable & Parameter & S.D. & T-STATH0: parameter = 0 & 2-tail p-value & 1-tail p-value \tabularnewline
(Intercept) & -1.3 &  0.5279 & -2.4630e+00 &  0.01479 &  0.007396 \tabularnewline
GDP_per_Capita & +6.284e-05 &  6.379e-06 & +9.8500e+00 &  2.464e-18 &  1.232e-18 \tabularnewline
`Population_(millions)` & -0.0004238 &  0.0006559 & -6.4620e-01 &  0.5191 &  0.2595 \tabularnewline
HDI & +5.407 &  0.8382 & +6.4510e+00 &  1.154e-09 &  5.77e-10 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=316099&T=2

[TABLE]
[ROW][C]Multiple Linear Regression - Ordinary Least Squares[/C][/ROW]
[ROW][C]Variable[/C][C]Parameter[/C][C]S.D.[/C][C]T-STATH0: parameter = 0[/C][C]2-tail p-value[/C][C]1-tail p-value[/C][/ROW]
[ROW][C](Intercept)[/C][C]-1.3[/C][C] 0.5279[/C][C]-2.4630e+00[/C][C] 0.01479[/C][C] 0.007396[/C][/ROW]
[ROW][C]GDP_per_Capita[/C][C]+6.284e-05[/C][C] 6.379e-06[/C][C]+9.8500e+00[/C][C] 2.464e-18[/C][C] 1.232e-18[/C][/ROW]
[ROW][C]`Population_(millions)`[/C][C]-0.0004238[/C][C] 0.0006559[/C][C]-6.4620e-01[/C][C] 0.5191[/C][C] 0.2595[/C][/ROW]
[ROW][C]HDI[/C][C]+5.407[/C][C] 0.8382[/C][C]+6.4510e+00[/C][C] 1.154e-09[/C][C] 5.77e-10[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=316099&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=316099&T=2

As an alternative you can also use a QR Code:

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

Multiple Linear Regression - Ordinary Least Squares
Variable	Parameter	S.D.	T-STATH0: parameter = 0	2-tail p-value	1-tail p-value
(Intercept)	-1.3	0.5279	-2.4630e+00	0.01479	0.007396
GDP_per_Capita	+6.284e-05	6.379e-06	+9.8500e+00	2.464e-18	1.232e-18
`Population_(millions)`	-0.0004238	0.0006559	-6.4620e-01	0.5191	0.2595
HDI	+5.407	0.8382	+6.4510e+00	1.154e-09	5.77e-10

Multiple Linear Regression - Regression Statistics
Multiple R	0.8453
R-squared	0.7145
Adjusted R-squared	0.7093
F-TEST (value)	139.3
F-TEST (DF numerator)	3
F-TEST (DF denominator)	167
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	1.256
Sum Squared Residuals	263.6

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R &  0.8453 \tabularnewline
R-squared &  0.7145 \tabularnewline
Adjusted R-squared &  0.7093 \tabularnewline
F-TEST (value) &  139.3 \tabularnewline
F-TEST (DF numerator) & 3 \tabularnewline
F-TEST (DF denominator) & 167 \tabularnewline
p-value &  0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation &  1.256 \tabularnewline
Sum Squared Residuals &  263.6 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=316099&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C] 0.8453[/C][/ROW]
[ROW][C]R-squared[/C][C] 0.7145[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C] 0.7093[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C] 139.3[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]3[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]167[/C][/ROW]
[ROW][C]p-value[/C][C] 0[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C] 1.256[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C] 263.6[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=316099&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=316099&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	0.8453
R-squared	0.7145
Adjusted R-squared	0.7093
F-TEST (value)	139.3
F-TEST (DF numerator)	3
F-TEST (DF denominator)	167
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	1.256
Sum Squared Residuals	263.6

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=316099&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=316099&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=316099&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	1.213	-0.4229
2	2.21	2.93	-0.7204
3	2.12	2.972	-0.8518
4	0.93	1.796	-0.8658
5	5.38	3.747	1.633
6	3.14	4.021	-0.8809
7	2.23	2.861	-0.6309
8	9.31	7.904	1.406
9	6.06	6.676	-0.6163
10	2.31	3.198	-0.8876
11	6.84	4.34	2.5
12	7.49	4.66	2.83
13	0.72	1.716	-0.996
14	4.48	3.959	0.5209
15	5.09	3.417	1.673
16	7.44	6.527	0.9131
17	1.41	1.338	0.07223
18	4.84	2.04	2.8
19	2.96	2.351	0.6086
20	3.12	2.941	0.1789
21	3.83	2.916	0.9137
22	3.11	3.503	-0.3926
23	4.06	6.255	-2.195
24	3.32	3.393	-0.07262
25	1.21	0.8437	0.3663
26	0.8	0.8217	-0.02169
27	2.52	2.399	0.1211
28	1.21	1.722	-0.5124
29	1.17	1.474	-0.3039
30	8.17	6.882	1.288
31	1.24	0.7295	0.5105
32	1.46	0.8762	0.5838
33	4.36	4.093	0.2671
34	3.38	2.345	1.034
35	1.87	3.032	-1.162
36	1.03	1.457	-0.427
37	1.29	1.902	-0.612
38	0.82	0.9641	-0.1441
39	2.84	3.371	-0.5313
40	3.92	4.044	-0.1242
41	1.95	3.184	-1.234
42	4.21	5.272	-1.062
43	5.19	4.761	0.4286
44	5.51	7.531	-2.021
45	2.19	1.277	0.9133
46	2.57	3.038	-0.4681
47	1.53	2.917	-1.387
48	2.17	2.967	-0.7966
49	2.15	2.58	-0.4305
50	2.07	2.498	-0.4279
51	3.97	3.348	0.6224
52	0.42	0.8335	-0.4135
53	6.86	4.383	2.478
54	1.02	1.01	0.01028
55	2.9	2.856	0.04361
56	5.87	6.658	-0.7878
57	5.14	6.34	-1.2
58	2.02	3.075	-1.055
59	1.03	1.11	-0.07985
60	1.58	2.986	-1.406
61	5.3	6.527	-1.227
62	1.97	1.873	0.09673
63	4.38	4.978	-0.598
64	2.98	3.167	-0.1865
65	1.89	2.249	-0.3589
66	1.41	0.9406	0.4694
67	1.53	1.013	0.5172
68	3.07	2.311	0.7588
69	0.61	1.338	-0.7279
70	1.68	2.137	-0.4572
71	2.92	4.007	-1.087
72	1.16	1.515	-0.355
73	1.58	2.504	-0.9237
74	2.79	3.249	-0.4586
75	1.88	2.568	-0.6879
76	5.57	6.939	-1.369
77	6.22	5.628	0.5925
78	4.61	5.792	-1.182
79	1.89	2.92	-1.03
80	5.02	6.361	-1.341
81	2.1	3.042	-0.942
82	5.55	3.619	1.931
83	1.03	1.668	-0.638
84	5.69	5.009	0.6809
85	8.13	5.761	2.369
86	1.91	2.282	-0.3721
87	1.22	1.803	-0.5825
88	6.29	3.941	2.349
89	3.84	3.382	0.4583
90	1.66	1.378	0.2817
91	1.21	0.9939	0.2161
92	3.69	3.066	0.6235
93	5.83	4.089	1.741
94	15.82	10.72	5.103
95	3.26	3.025	0.2348
96	0.99	1.477	-0.4865
97	0.81	1.049	-0.239
98	3.71	3.495	0.215
99	1.53	0.9569	0.5731
100	2.54	1.497	1.043
101	3.46	3.416	0.04374
102	2.89	3.34	-0.4499
103	1.78	2.499	-0.7189
104	6.08	2.772	3.308
105	3.78	3.481	0.2993
106	1.68	2.236	-0.5562
107	0.87	0.9398	-0.0698
108	1.43	1.613	-0.1833
109	2.48	2.421	0.05935
110	0.98	1.652	-0.6719
111	5.28	7.035	-1.755
112	5.6	5.974	-0.3739
113	1.39	2.206	-0.8158
114	1.56	0.5567	1.003
115	1.16	1.496	-0.3358
116	4.98	10.07	-5.095
117	7.52	4.391	3.129
118	0.79	1.566	-0.7761
119	2.79	3.39	-0.6
120	1.91	1.518	0.3922
121	4.16	2.543	1.617
122	2.28	2.991	-0.7113
123	1.1	2.377	-1.277
124	4.44	4.091	0.3493
125	3.88	4.642	-0.762
126	10.8	9.543	1.257
127	2.71	3.541	-0.831
128	5.69	3.748	1.942
129	0.87	1.33	-0.4597
130	4.94	3.515	1.425
131	2.45	3.133	-0.6829
132	3.11	2.981	0.1295
133	2.77	2.74	0.02989
134	1.49	1.761	-0.2705
135	5.61	4.658	0.9519
136	1.21	1.249	-0.03896
137	2.7	3.209	-0.5088
138	1.24	0.8914	0.3486
139	7.97	6.956	1.014
140	4.06	4.377	-0.3169
141	5.81	5.03	0.7795
142	1.29	1.507	-0.2166
143	3.31	2.755	0.5555
144	3.67	5.395	-1.725
145	1.32	2.927	-1.607
146	4.25	3.053	1.197
147	2.01	1.858	0.1524
148	7.25	7.293	-0.04345
149	5.79	9.286	-3.496
150	0.91	2.101	-1.191
151	1.32	1.485	-0.1652
152	2.66	2.909	-0.2488
153	0.48	2.268	-1.788
154	1.13	1.275	-0.1447
155	2.7	2.865	-0.1649
156	7.92	4.013	3.907
157	2.34	2.859	-0.5187
158	3.33	3.434	-0.1036
159	5.47	2.707	2.763
160	1.24	1.317	-0.07748
161	2.84	2.907	-0.06717
162	4.94	6.114	-1.174
163	7.93	5.749	2.181
164	8.22	6.61	1.61
165	2.91	3.865	-0.9545
166	2.32	2.408	-0.0884
167	3.57	3.46	0.1095
168	1.65	2.326	-0.6762
169	1.03	1.475	-0.445
170	0.99	1.939	-0.9492
171	1.37	1.398	-0.02777

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 &  0.79 &  1.213 & -0.4229 \tabularnewline
2 &  2.21 &  2.93 & -0.7204 \tabularnewline
3 &  2.12 &  2.972 & -0.8518 \tabularnewline
4 &  0.93 &  1.796 & -0.8658 \tabularnewline
5 &  5.38 &  3.747 &  1.633 \tabularnewline
6 &  3.14 &  4.021 & -0.8809 \tabularnewline
7 &  2.23 &  2.861 & -0.6309 \tabularnewline
8 &  9.31 &  7.904 &  1.406 \tabularnewline
9 &  6.06 &  6.676 & -0.6163 \tabularnewline
10 &  2.31 &  3.198 & -0.8876 \tabularnewline
11 &  6.84 &  4.34 &  2.5 \tabularnewline
12 &  7.49 &  4.66 &  2.83 \tabularnewline
13 &  0.72 &  1.716 & -0.996 \tabularnewline
14 &  4.48 &  3.959 &  0.5209 \tabularnewline
15 &  5.09 &  3.417 &  1.673 \tabularnewline
16 &  7.44 &  6.527 &  0.9131 \tabularnewline
17 &  1.41 &  1.338 &  0.07223 \tabularnewline
18 &  4.84 &  2.04 &  2.8 \tabularnewline
19 &  2.96 &  2.351 &  0.6086 \tabularnewline
20 &  3.12 &  2.941 &  0.1789 \tabularnewline
21 &  3.83 &  2.916 &  0.9137 \tabularnewline
22 &  3.11 &  3.503 & -0.3926 \tabularnewline
23 &  4.06 &  6.255 & -2.195 \tabularnewline
24 &  3.32 &  3.393 & -0.07262 \tabularnewline
25 &  1.21 &  0.8437 &  0.3663 \tabularnewline
26 &  0.8 &  0.8217 & -0.02169 \tabularnewline
27 &  2.52 &  2.399 &  0.1211 \tabularnewline
28 &  1.21 &  1.722 & -0.5124 \tabularnewline
29 &  1.17 &  1.474 & -0.3039 \tabularnewline
30 &  8.17 &  6.882 &  1.288 \tabularnewline
31 &  1.24 &  0.7295 &  0.5105 \tabularnewline
32 &  1.46 &  0.8762 &  0.5838 \tabularnewline
33 &  4.36 &  4.093 &  0.2671 \tabularnewline
34 &  3.38 &  2.345 &  1.034 \tabularnewline
35 &  1.87 &  3.032 & -1.162 \tabularnewline
36 &  1.03 &  1.457 & -0.427 \tabularnewline
37 &  1.29 &  1.902 & -0.612 \tabularnewline
38 &  0.82 &  0.9641 & -0.1441 \tabularnewline
39 &  2.84 &  3.371 & -0.5313 \tabularnewline
40 &  3.92 &  4.044 & -0.1242 \tabularnewline
41 &  1.95 &  3.184 & -1.234 \tabularnewline
42 &  4.21 &  5.272 & -1.062 \tabularnewline
43 &  5.19 &  4.761 &  0.4286 \tabularnewline
44 &  5.51 &  7.531 & -2.021 \tabularnewline
45 &  2.19 &  1.277 &  0.9133 \tabularnewline
46 &  2.57 &  3.038 & -0.4681 \tabularnewline
47 &  1.53 &  2.917 & -1.387 \tabularnewline
48 &  2.17 &  2.967 & -0.7966 \tabularnewline
49 &  2.15 &  2.58 & -0.4305 \tabularnewline
50 &  2.07 &  2.498 & -0.4279 \tabularnewline
51 &  3.97 &  3.348 &  0.6224 \tabularnewline
52 &  0.42 &  0.8335 & -0.4135 \tabularnewline
53 &  6.86 &  4.383 &  2.478 \tabularnewline
54 &  1.02 &  1.01 &  0.01028 \tabularnewline
55 &  2.9 &  2.856 &  0.04361 \tabularnewline
56 &  5.87 &  6.658 & -0.7878 \tabularnewline
57 &  5.14 &  6.34 & -1.2 \tabularnewline
58 &  2.02 &  3.075 & -1.055 \tabularnewline
59 &  1.03 &  1.11 & -0.07985 \tabularnewline
60 &  1.58 &  2.986 & -1.406 \tabularnewline
61 &  5.3 &  6.527 & -1.227 \tabularnewline
62 &  1.97 &  1.873 &  0.09673 \tabularnewline
63 &  4.38 &  4.978 & -0.598 \tabularnewline
64 &  2.98 &  3.167 & -0.1865 \tabularnewline
65 &  1.89 &  2.249 & -0.3589 \tabularnewline
66 &  1.41 &  0.9406 &  0.4694 \tabularnewline
67 &  1.53 &  1.013 &  0.5172 \tabularnewline
68 &  3.07 &  2.311 &  0.7588 \tabularnewline
69 &  0.61 &  1.338 & -0.7279 \tabularnewline
70 &  1.68 &  2.137 & -0.4572 \tabularnewline
71 &  2.92 &  4.007 & -1.087 \tabularnewline
72 &  1.16 &  1.515 & -0.355 \tabularnewline
73 &  1.58 &  2.504 & -0.9237 \tabularnewline
74 &  2.79 &  3.249 & -0.4586 \tabularnewline
75 &  1.88 &  2.568 & -0.6879 \tabularnewline
76 &  5.57 &  6.939 & -1.369 \tabularnewline
77 &  6.22 &  5.628 &  0.5925 \tabularnewline
78 &  4.61 &  5.792 & -1.182 \tabularnewline
79 &  1.89 &  2.92 & -1.03 \tabularnewline
80 &  5.02 &  6.361 & -1.341 \tabularnewline
81 &  2.1 &  3.042 & -0.942 \tabularnewline
82 &  5.55 &  3.619 &  1.931 \tabularnewline
83 &  1.03 &  1.668 & -0.638 \tabularnewline
84 &  5.69 &  5.009 &  0.6809 \tabularnewline
85 &  8.13 &  5.761 &  2.369 \tabularnewline
86 &  1.91 &  2.282 & -0.3721 \tabularnewline
87 &  1.22 &  1.803 & -0.5825 \tabularnewline
88 &  6.29 &  3.941 &  2.349 \tabularnewline
89 &  3.84 &  3.382 &  0.4583 \tabularnewline
90 &  1.66 &  1.378 &  0.2817 \tabularnewline
91 &  1.21 &  0.9939 &  0.2161 \tabularnewline
92 &  3.69 &  3.066 &  0.6235 \tabularnewline
93 &  5.83 &  4.089 &  1.741 \tabularnewline
94 &  15.82 &  10.72 &  5.103 \tabularnewline
95 &  3.26 &  3.025 &  0.2348 \tabularnewline
96 &  0.99 &  1.477 & -0.4865 \tabularnewline
97 &  0.81 &  1.049 & -0.239 \tabularnewline
98 &  3.71 &  3.495 &  0.215 \tabularnewline
99 &  1.53 &  0.9569 &  0.5731 \tabularnewline
100 &  2.54 &  1.497 &  1.043 \tabularnewline
101 &  3.46 &  3.416 &  0.04374 \tabularnewline
102 &  2.89 &  3.34 & -0.4499 \tabularnewline
103 &  1.78 &  2.499 & -0.7189 \tabularnewline
104 &  6.08 &  2.772 &  3.308 \tabularnewline
105 &  3.78 &  3.481 &  0.2993 \tabularnewline
106 &  1.68 &  2.236 & -0.5562 \tabularnewline
107 &  0.87 &  0.9398 & -0.0698 \tabularnewline
108 &  1.43 &  1.613 & -0.1833 \tabularnewline
109 &  2.48 &  2.421 &  0.05935 \tabularnewline
110 &  0.98 &  1.652 & -0.6719 \tabularnewline
111 &  5.28 &  7.035 & -1.755 \tabularnewline
112 &  5.6 &  5.974 & -0.3739 \tabularnewline
113 &  1.39 &  2.206 & -0.8158 \tabularnewline
114 &  1.56 &  0.5567 &  1.003 \tabularnewline
115 &  1.16 &  1.496 & -0.3358 \tabularnewline
116 &  4.98 &  10.07 & -5.095 \tabularnewline
117 &  7.52 &  4.391 &  3.129 \tabularnewline
118 &  0.79 &  1.566 & -0.7761 \tabularnewline
119 &  2.79 &  3.39 & -0.6 \tabularnewline
120 &  1.91 &  1.518 &  0.3922 \tabularnewline
121 &  4.16 &  2.543 &  1.617 \tabularnewline
122 &  2.28 &  2.991 & -0.7113 \tabularnewline
123 &  1.1 &  2.377 & -1.277 \tabularnewline
124 &  4.44 &  4.091 &  0.3493 \tabularnewline
125 &  3.88 &  4.642 & -0.762 \tabularnewline
126 &  10.8 &  9.543 &  1.257 \tabularnewline
127 &  2.71 &  3.541 & -0.831 \tabularnewline
128 &  5.69 &  3.748 &  1.942 \tabularnewline
129 &  0.87 &  1.33 & -0.4597 \tabularnewline
130 &  4.94 &  3.515 &  1.425 \tabularnewline
131 &  2.45 &  3.133 & -0.6829 \tabularnewline
132 &  3.11 &  2.981 &  0.1295 \tabularnewline
133 &  2.77 &  2.74 &  0.02989 \tabularnewline
134 &  1.49 &  1.761 & -0.2705 \tabularnewline
135 &  5.61 &  4.658 &  0.9519 \tabularnewline
136 &  1.21 &  1.249 & -0.03896 \tabularnewline
137 &  2.7 &  3.209 & -0.5088 \tabularnewline
138 &  1.24 &  0.8914 &  0.3486 \tabularnewline
139 &  7.97 &  6.956 &  1.014 \tabularnewline
140 &  4.06 &  4.377 & -0.3169 \tabularnewline
141 &  5.81 &  5.03 &  0.7795 \tabularnewline
142 &  1.29 &  1.507 & -0.2166 \tabularnewline
143 &  3.31 &  2.755 &  0.5555 \tabularnewline
144 &  3.67 &  5.395 & -1.725 \tabularnewline
145 &  1.32 &  2.927 & -1.607 \tabularnewline
146 &  4.25 &  3.053 &  1.197 \tabularnewline
147 &  2.01 &  1.858 &  0.1524 \tabularnewline
148 &  7.25 &  7.293 & -0.04345 \tabularnewline
149 &  5.79 &  9.286 & -3.496 \tabularnewline
150 &  0.91 &  2.101 & -1.191 \tabularnewline
151 &  1.32 &  1.485 & -0.1652 \tabularnewline
152 &  2.66 &  2.909 & -0.2488 \tabularnewline
153 &  0.48 &  2.268 & -1.788 \tabularnewline
154 &  1.13 &  1.275 & -0.1447 \tabularnewline
155 &  2.7 &  2.865 & -0.1649 \tabularnewline
156 &  7.92 &  4.013 &  3.907 \tabularnewline
157 &  2.34 &  2.859 & -0.5187 \tabularnewline
158 &  3.33 &  3.434 & -0.1036 \tabularnewline
159 &  5.47 &  2.707 &  2.763 \tabularnewline
160 &  1.24 &  1.317 & -0.07748 \tabularnewline
161 &  2.84 &  2.907 & -0.06717 \tabularnewline
162 &  4.94 &  6.114 & -1.174 \tabularnewline
163 &  7.93 &  5.749 &  2.181 \tabularnewline
164 &  8.22 &  6.61 &  1.61 \tabularnewline
165 &  2.91 &  3.865 & -0.9545 \tabularnewline
166 &  2.32 &  2.408 & -0.0884 \tabularnewline
167 &  3.57 &  3.46 &  0.1095 \tabularnewline
168 &  1.65 &  2.326 & -0.6762 \tabularnewline
169 &  1.03 &  1.475 & -0.445 \tabularnewline
170 &  0.99 &  1.939 & -0.9492 \tabularnewline
171 &  1.37 &  1.398 & -0.02777 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=316099&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] 1.213[/C][C]-0.4229[/C][/ROW]
[ROW][C]2[/C][C] 2.21[/C][C] 2.93[/C][C]-0.7204[/C][/ROW]
[ROW][C]3[/C][C] 2.12[/C][C] 2.972[/C][C]-0.8518[/C][/ROW]
[ROW][C]4[/C][C] 0.93[/C][C] 1.796[/C][C]-0.8658[/C][/ROW]
[ROW][C]5[/C][C] 5.38[/C][C] 3.747[/C][C] 1.633[/C][/ROW]
[ROW][C]6[/C][C] 3.14[/C][C] 4.021[/C][C]-0.8809[/C][/ROW]
[ROW][C]7[/C][C] 2.23[/C][C] 2.861[/C][C]-0.6309[/C][/ROW]
[ROW][C]8[/C][C] 9.31[/C][C] 7.904[/C][C] 1.406[/C][/ROW]
[ROW][C]9[/C][C] 6.06[/C][C] 6.676[/C][C]-0.6163[/C][/ROW]
[ROW][C]10[/C][C] 2.31[/C][C] 3.198[/C][C]-0.8876[/C][/ROW]
[ROW][C]11[/C][C] 6.84[/C][C] 4.34[/C][C] 2.5[/C][/ROW]
[ROW][C]12[/C][C] 7.49[/C][C] 4.66[/C][C] 2.83[/C][/ROW]
[ROW][C]13[/C][C] 0.72[/C][C] 1.716[/C][C]-0.996[/C][/ROW]
[ROW][C]14[/C][C] 4.48[/C][C] 3.959[/C][C] 0.5209[/C][/ROW]
[ROW][C]15[/C][C] 5.09[/C][C] 3.417[/C][C] 1.673[/C][/ROW]
[ROW][C]16[/C][C] 7.44[/C][C] 6.527[/C][C] 0.9131[/C][/ROW]
[ROW][C]17[/C][C] 1.41[/C][C] 1.338[/C][C] 0.07223[/C][/ROW]
[ROW][C]18[/C][C] 4.84[/C][C] 2.04[/C][C] 2.8[/C][/ROW]
[ROW][C]19[/C][C] 2.96[/C][C] 2.351[/C][C] 0.6086[/C][/ROW]
[ROW][C]20[/C][C] 3.12[/C][C] 2.941[/C][C] 0.1789[/C][/ROW]
[ROW][C]21[/C][C] 3.83[/C][C] 2.916[/C][C] 0.9137[/C][/ROW]
[ROW][C]22[/C][C] 3.11[/C][C] 3.503[/C][C]-0.3926[/C][/ROW]
[ROW][C]23[/C][C] 4.06[/C][C] 6.255[/C][C]-2.195[/C][/ROW]
[ROW][C]24[/C][C] 3.32[/C][C] 3.393[/C][C]-0.07262[/C][/ROW]
[ROW][C]25[/C][C] 1.21[/C][C] 0.8437[/C][C] 0.3663[/C][/ROW]
[ROW][C]26[/C][C] 0.8[/C][C] 0.8217[/C][C]-0.02169[/C][/ROW]
[ROW][C]27[/C][C] 2.52[/C][C] 2.399[/C][C] 0.1211[/C][/ROW]
[ROW][C]28[/C][C] 1.21[/C][C] 1.722[/C][C]-0.5124[/C][/ROW]
[ROW][C]29[/C][C] 1.17[/C][C] 1.474[/C][C]-0.3039[/C][/ROW]
[ROW][C]30[/C][C] 8.17[/C][C] 6.882[/C][C] 1.288[/C][/ROW]
[ROW][C]31[/C][C] 1.24[/C][C] 0.7295[/C][C] 0.5105[/C][/ROW]
[ROW][C]32[/C][C] 1.46[/C][C] 0.8762[/C][C] 0.5838[/C][/ROW]
[ROW][C]33[/C][C] 4.36[/C][C] 4.093[/C][C] 0.2671[/C][/ROW]
[ROW][C]34[/C][C] 3.38[/C][C] 2.345[/C][C] 1.034[/C][/ROW]
[ROW][C]35[/C][C] 1.87[/C][C] 3.032[/C][C]-1.162[/C][/ROW]
[ROW][C]36[/C][C] 1.03[/C][C] 1.457[/C][C]-0.427[/C][/ROW]
[ROW][C]37[/C][C] 1.29[/C][C] 1.902[/C][C]-0.612[/C][/ROW]
[ROW][C]38[/C][C] 0.82[/C][C] 0.9641[/C][C]-0.1441[/C][/ROW]
[ROW][C]39[/C][C] 2.84[/C][C] 3.371[/C][C]-0.5313[/C][/ROW]
[ROW][C]40[/C][C] 3.92[/C][C] 4.044[/C][C]-0.1242[/C][/ROW]
[ROW][C]41[/C][C] 1.95[/C][C] 3.184[/C][C]-1.234[/C][/ROW]
[ROW][C]42[/C][C] 4.21[/C][C] 5.272[/C][C]-1.062[/C][/ROW]
[ROW][C]43[/C][C] 5.19[/C][C] 4.761[/C][C] 0.4286[/C][/ROW]
[ROW][C]44[/C][C] 5.51[/C][C] 7.531[/C][C]-2.021[/C][/ROW]
[ROW][C]45[/C][C] 2.19[/C][C] 1.277[/C][C] 0.9133[/C][/ROW]
[ROW][C]46[/C][C] 2.57[/C][C] 3.038[/C][C]-0.4681[/C][/ROW]
[ROW][C]47[/C][C] 1.53[/C][C] 2.917[/C][C]-1.387[/C][/ROW]
[ROW][C]48[/C][C] 2.17[/C][C] 2.967[/C][C]-0.7966[/C][/ROW]
[ROW][C]49[/C][C] 2.15[/C][C] 2.58[/C][C]-0.4305[/C][/ROW]
[ROW][C]50[/C][C] 2.07[/C][C] 2.498[/C][C]-0.4279[/C][/ROW]
[ROW][C]51[/C][C] 3.97[/C][C] 3.348[/C][C] 0.6224[/C][/ROW]
[ROW][C]52[/C][C] 0.42[/C][C] 0.8335[/C][C]-0.4135[/C][/ROW]
[ROW][C]53[/C][C] 6.86[/C][C] 4.383[/C][C] 2.478[/C][/ROW]
[ROW][C]54[/C][C] 1.02[/C][C] 1.01[/C][C] 0.01028[/C][/ROW]
[ROW][C]55[/C][C] 2.9[/C][C] 2.856[/C][C] 0.04361[/C][/ROW]
[ROW][C]56[/C][C] 5.87[/C][C] 6.658[/C][C]-0.7878[/C][/ROW]
[ROW][C]57[/C][C] 5.14[/C][C] 6.34[/C][C]-1.2[/C][/ROW]
[ROW][C]58[/C][C] 2.02[/C][C] 3.075[/C][C]-1.055[/C][/ROW]
[ROW][C]59[/C][C] 1.03[/C][C] 1.11[/C][C]-0.07985[/C][/ROW]
[ROW][C]60[/C][C] 1.58[/C][C] 2.986[/C][C]-1.406[/C][/ROW]
[ROW][C]61[/C][C] 5.3[/C][C] 6.527[/C][C]-1.227[/C][/ROW]
[ROW][C]62[/C][C] 1.97[/C][C] 1.873[/C][C] 0.09673[/C][/ROW]
[ROW][C]63[/C][C] 4.38[/C][C] 4.978[/C][C]-0.598[/C][/ROW]
[ROW][C]64[/C][C] 2.98[/C][C] 3.167[/C][C]-0.1865[/C][/ROW]
[ROW][C]65[/C][C] 1.89[/C][C] 2.249[/C][C]-0.3589[/C][/ROW]
[ROW][C]66[/C][C] 1.41[/C][C] 0.9406[/C][C] 0.4694[/C][/ROW]
[ROW][C]67[/C][C] 1.53[/C][C] 1.013[/C][C] 0.5172[/C][/ROW]
[ROW][C]68[/C][C] 3.07[/C][C] 2.311[/C][C] 0.7588[/C][/ROW]
[ROW][C]69[/C][C] 0.61[/C][C] 1.338[/C][C]-0.7279[/C][/ROW]
[ROW][C]70[/C][C] 1.68[/C][C] 2.137[/C][C]-0.4572[/C][/ROW]
[ROW][C]71[/C][C] 2.92[/C][C] 4.007[/C][C]-1.087[/C][/ROW]
[ROW][C]72[/C][C] 1.16[/C][C] 1.515[/C][C]-0.355[/C][/ROW]
[ROW][C]73[/C][C] 1.58[/C][C] 2.504[/C][C]-0.9237[/C][/ROW]
[ROW][C]74[/C][C] 2.79[/C][C] 3.249[/C][C]-0.4586[/C][/ROW]
[ROW][C]75[/C][C] 1.88[/C][C] 2.568[/C][C]-0.6879[/C][/ROW]
[ROW][C]76[/C][C] 5.57[/C][C] 6.939[/C][C]-1.369[/C][/ROW]
[ROW][C]77[/C][C] 6.22[/C][C] 5.628[/C][C] 0.5925[/C][/ROW]
[ROW][C]78[/C][C] 4.61[/C][C] 5.792[/C][C]-1.182[/C][/ROW]
[ROW][C]79[/C][C] 1.89[/C][C] 2.92[/C][C]-1.03[/C][/ROW]
[ROW][C]80[/C][C] 5.02[/C][C] 6.361[/C][C]-1.341[/C][/ROW]
[ROW][C]81[/C][C] 2.1[/C][C] 3.042[/C][C]-0.942[/C][/ROW]
[ROW][C]82[/C][C] 5.55[/C][C] 3.619[/C][C] 1.931[/C][/ROW]
[ROW][C]83[/C][C] 1.03[/C][C] 1.668[/C][C]-0.638[/C][/ROW]
[ROW][C]84[/C][C] 5.69[/C][C] 5.009[/C][C] 0.6809[/C][/ROW]
[ROW][C]85[/C][C] 8.13[/C][C] 5.761[/C][C] 2.369[/C][/ROW]
[ROW][C]86[/C][C] 1.91[/C][C] 2.282[/C][C]-0.3721[/C][/ROW]
[ROW][C]87[/C][C] 1.22[/C][C] 1.803[/C][C]-0.5825[/C][/ROW]
[ROW][C]88[/C][C] 6.29[/C][C] 3.941[/C][C] 2.349[/C][/ROW]
[ROW][C]89[/C][C] 3.84[/C][C] 3.382[/C][C] 0.4583[/C][/ROW]
[ROW][C]90[/C][C] 1.66[/C][C] 1.378[/C][C] 0.2817[/C][/ROW]
[ROW][C]91[/C][C] 1.21[/C][C] 0.9939[/C][C] 0.2161[/C][/ROW]
[ROW][C]92[/C][C] 3.69[/C][C] 3.066[/C][C] 0.6235[/C][/ROW]
[ROW][C]93[/C][C] 5.83[/C][C] 4.089[/C][C] 1.741[/C][/ROW]
[ROW][C]94[/C][C] 15.82[/C][C] 10.72[/C][C] 5.103[/C][/ROW]
[ROW][C]95[/C][C] 3.26[/C][C] 3.025[/C][C] 0.2348[/C][/ROW]
[ROW][C]96[/C][C] 0.99[/C][C] 1.477[/C][C]-0.4865[/C][/ROW]
[ROW][C]97[/C][C] 0.81[/C][C] 1.049[/C][C]-0.239[/C][/ROW]
[ROW][C]98[/C][C] 3.71[/C][C] 3.495[/C][C] 0.215[/C][/ROW]
[ROW][C]99[/C][C] 1.53[/C][C] 0.9569[/C][C] 0.5731[/C][/ROW]
[ROW][C]100[/C][C] 2.54[/C][C] 1.497[/C][C] 1.043[/C][/ROW]
[ROW][C]101[/C][C] 3.46[/C][C] 3.416[/C][C] 0.04374[/C][/ROW]
[ROW][C]102[/C][C] 2.89[/C][C] 3.34[/C][C]-0.4499[/C][/ROW]
[ROW][C]103[/C][C] 1.78[/C][C] 2.499[/C][C]-0.7189[/C][/ROW]
[ROW][C]104[/C][C] 6.08[/C][C] 2.772[/C][C] 3.308[/C][/ROW]
[ROW][C]105[/C][C] 3.78[/C][C] 3.481[/C][C] 0.2993[/C][/ROW]
[ROW][C]106[/C][C] 1.68[/C][C] 2.236[/C][C]-0.5562[/C][/ROW]
[ROW][C]107[/C][C] 0.87[/C][C] 0.9398[/C][C]-0.0698[/C][/ROW]
[ROW][C]108[/C][C] 1.43[/C][C] 1.613[/C][C]-0.1833[/C][/ROW]
[ROW][C]109[/C][C] 2.48[/C][C] 2.421[/C][C] 0.05935[/C][/ROW]
[ROW][C]110[/C][C] 0.98[/C][C] 1.652[/C][C]-0.6719[/C][/ROW]
[ROW][C]111[/C][C] 5.28[/C][C] 7.035[/C][C]-1.755[/C][/ROW]
[ROW][C]112[/C][C] 5.6[/C][C] 5.974[/C][C]-0.3739[/C][/ROW]
[ROW][C]113[/C][C] 1.39[/C][C] 2.206[/C][C]-0.8158[/C][/ROW]
[ROW][C]114[/C][C] 1.56[/C][C] 0.5567[/C][C] 1.003[/C][/ROW]
[ROW][C]115[/C][C] 1.16[/C][C] 1.496[/C][C]-0.3358[/C][/ROW]
[ROW][C]116[/C][C] 4.98[/C][C] 10.07[/C][C]-5.095[/C][/ROW]
[ROW][C]117[/C][C] 7.52[/C][C] 4.391[/C][C] 3.129[/C][/ROW]
[ROW][C]118[/C][C] 0.79[/C][C] 1.566[/C][C]-0.7761[/C][/ROW]
[ROW][C]119[/C][C] 2.79[/C][C] 3.39[/C][C]-0.6[/C][/ROW]
[ROW][C]120[/C][C] 1.91[/C][C] 1.518[/C][C] 0.3922[/C][/ROW]
[ROW][C]121[/C][C] 4.16[/C][C] 2.543[/C][C] 1.617[/C][/ROW]
[ROW][C]122[/C][C] 2.28[/C][C] 2.991[/C][C]-0.7113[/C][/ROW]
[ROW][C]123[/C][C] 1.1[/C][C] 2.377[/C][C]-1.277[/C][/ROW]
[ROW][C]124[/C][C] 4.44[/C][C] 4.091[/C][C] 0.3493[/C][/ROW]
[ROW][C]125[/C][C] 3.88[/C][C] 4.642[/C][C]-0.762[/C][/ROW]
[ROW][C]126[/C][C] 10.8[/C][C] 9.543[/C][C] 1.257[/C][/ROW]
[ROW][C]127[/C][C] 2.71[/C][C] 3.541[/C][C]-0.831[/C][/ROW]
[ROW][C]128[/C][C] 5.69[/C][C] 3.748[/C][C] 1.942[/C][/ROW]
[ROW][C]129[/C][C] 0.87[/C][C] 1.33[/C][C]-0.4597[/C][/ROW]
[ROW][C]130[/C][C] 4.94[/C][C] 3.515[/C][C] 1.425[/C][/ROW]
[ROW][C]131[/C][C] 2.45[/C][C] 3.133[/C][C]-0.6829[/C][/ROW]
[ROW][C]132[/C][C] 3.11[/C][C] 2.981[/C][C] 0.1295[/C][/ROW]
[ROW][C]133[/C][C] 2.77[/C][C] 2.74[/C][C] 0.02989[/C][/ROW]
[ROW][C]134[/C][C] 1.49[/C][C] 1.761[/C][C]-0.2705[/C][/ROW]
[ROW][C]135[/C][C] 5.61[/C][C] 4.658[/C][C] 0.9519[/C][/ROW]
[ROW][C]136[/C][C] 1.21[/C][C] 1.249[/C][C]-0.03896[/C][/ROW]
[ROW][C]137[/C][C] 2.7[/C][C] 3.209[/C][C]-0.5088[/C][/ROW]
[ROW][C]138[/C][C] 1.24[/C][C] 0.8914[/C][C] 0.3486[/C][/ROW]
[ROW][C]139[/C][C] 7.97[/C][C] 6.956[/C][C] 1.014[/C][/ROW]
[ROW][C]140[/C][C] 4.06[/C][C] 4.377[/C][C]-0.3169[/C][/ROW]
[ROW][C]141[/C][C] 5.81[/C][C] 5.03[/C][C] 0.7795[/C][/ROW]
[ROW][C]142[/C][C] 1.29[/C][C] 1.507[/C][C]-0.2166[/C][/ROW]
[ROW][C]143[/C][C] 3.31[/C][C] 2.755[/C][C] 0.5555[/C][/ROW]
[ROW][C]144[/C][C] 3.67[/C][C] 5.395[/C][C]-1.725[/C][/ROW]
[ROW][C]145[/C][C] 1.32[/C][C] 2.927[/C][C]-1.607[/C][/ROW]
[ROW][C]146[/C][C] 4.25[/C][C] 3.053[/C][C] 1.197[/C][/ROW]
[ROW][C]147[/C][C] 2.01[/C][C] 1.858[/C][C] 0.1524[/C][/ROW]
[ROW][C]148[/C][C] 7.25[/C][C] 7.293[/C][C]-0.04345[/C][/ROW]
[ROW][C]149[/C][C] 5.79[/C][C] 9.286[/C][C]-3.496[/C][/ROW]
[ROW][C]150[/C][C] 0.91[/C][C] 2.101[/C][C]-1.191[/C][/ROW]
[ROW][C]151[/C][C] 1.32[/C][C] 1.485[/C][C]-0.1652[/C][/ROW]
[ROW][C]152[/C][C] 2.66[/C][C] 2.909[/C][C]-0.2488[/C][/ROW]
[ROW][C]153[/C][C] 0.48[/C][C] 2.268[/C][C]-1.788[/C][/ROW]
[ROW][C]154[/C][C] 1.13[/C][C] 1.275[/C][C]-0.1447[/C][/ROW]
[ROW][C]155[/C][C] 2.7[/C][C] 2.865[/C][C]-0.1649[/C][/ROW]
[ROW][C]156[/C][C] 7.92[/C][C] 4.013[/C][C] 3.907[/C][/ROW]
[ROW][C]157[/C][C] 2.34[/C][C] 2.859[/C][C]-0.5187[/C][/ROW]
[ROW][C]158[/C][C] 3.33[/C][C] 3.434[/C][C]-0.1036[/C][/ROW]
[ROW][C]159[/C][C] 5.47[/C][C] 2.707[/C][C] 2.763[/C][/ROW]
[ROW][C]160[/C][C] 1.24[/C][C] 1.317[/C][C]-0.07748[/C][/ROW]
[ROW][C]161[/C][C] 2.84[/C][C] 2.907[/C][C]-0.06717[/C][/ROW]
[ROW][C]162[/C][C] 4.94[/C][C] 6.114[/C][C]-1.174[/C][/ROW]
[ROW][C]163[/C][C] 7.93[/C][C] 5.749[/C][C] 2.181[/C][/ROW]
[ROW][C]164[/C][C] 8.22[/C][C] 6.61[/C][C] 1.61[/C][/ROW]
[ROW][C]165[/C][C] 2.91[/C][C] 3.865[/C][C]-0.9545[/C][/ROW]
[ROW][C]166[/C][C] 2.32[/C][C] 2.408[/C][C]-0.0884[/C][/ROW]
[ROW][C]167[/C][C] 3.57[/C][C] 3.46[/C][C] 0.1095[/C][/ROW]
[ROW][C]168[/C][C] 1.65[/C][C] 2.326[/C][C]-0.6762[/C][/ROW]
[ROW][C]169[/C][C] 1.03[/C][C] 1.475[/C][C]-0.445[/C][/ROW]
[ROW][C]170[/C][C] 0.99[/C][C] 1.939[/C][C]-0.9492[/C][/ROW]
[ROW][C]171[/C][C] 1.37[/C][C] 1.398[/C][C]-0.02777[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=316099&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=316099&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	1.213	-0.4229
2	2.21	2.93	-0.7204
3	2.12	2.972	-0.8518
4	0.93	1.796	-0.8658
5	5.38	3.747	1.633
6	3.14	4.021	-0.8809
7	2.23	2.861	-0.6309
8	9.31	7.904	1.406
9	6.06	6.676	-0.6163
10	2.31	3.198	-0.8876
11	6.84	4.34	2.5
12	7.49	4.66	2.83
13	0.72	1.716	-0.996
14	4.48	3.959	0.5209
15	5.09	3.417	1.673
16	7.44	6.527	0.9131
17	1.41	1.338	0.07223
18	4.84	2.04	2.8
19	2.96	2.351	0.6086
20	3.12	2.941	0.1789
21	3.83	2.916	0.9137
22	3.11	3.503	-0.3926
23	4.06	6.255	-2.195
24	3.32	3.393	-0.07262
25	1.21	0.8437	0.3663
26	0.8	0.8217	-0.02169
27	2.52	2.399	0.1211
28	1.21	1.722	-0.5124
29	1.17	1.474	-0.3039
30	8.17	6.882	1.288
31	1.24	0.7295	0.5105
32	1.46	0.8762	0.5838
33	4.36	4.093	0.2671
34	3.38	2.345	1.034
35	1.87	3.032	-1.162
36	1.03	1.457	-0.427
37	1.29	1.902	-0.612
38	0.82	0.9641	-0.1441
39	2.84	3.371	-0.5313
40	3.92	4.044	-0.1242
41	1.95	3.184	-1.234
42	4.21	5.272	-1.062
43	5.19	4.761	0.4286
44	5.51	7.531	-2.021
45	2.19	1.277	0.9133
46	2.57	3.038	-0.4681
47	1.53	2.917	-1.387
48	2.17	2.967	-0.7966
49	2.15	2.58	-0.4305
50	2.07	2.498	-0.4279
51	3.97	3.348	0.6224
52	0.42	0.8335	-0.4135
53	6.86	4.383	2.478
54	1.02	1.01	0.01028
55	2.9	2.856	0.04361
56	5.87	6.658	-0.7878
57	5.14	6.34	-1.2
58	2.02	3.075	-1.055
59	1.03	1.11	-0.07985
60	1.58	2.986	-1.406
61	5.3	6.527	-1.227
62	1.97	1.873	0.09673
63	4.38	4.978	-0.598
64	2.98	3.167	-0.1865
65	1.89	2.249	-0.3589
66	1.41	0.9406	0.4694
67	1.53	1.013	0.5172
68	3.07	2.311	0.7588
69	0.61	1.338	-0.7279
70	1.68	2.137	-0.4572
71	2.92	4.007	-1.087
72	1.16	1.515	-0.355
73	1.58	2.504	-0.9237
74	2.79	3.249	-0.4586
75	1.88	2.568	-0.6879
76	5.57	6.939	-1.369
77	6.22	5.628	0.5925
78	4.61	5.792	-1.182
79	1.89	2.92	-1.03
80	5.02	6.361	-1.341
81	2.1	3.042	-0.942
82	5.55	3.619	1.931
83	1.03	1.668	-0.638
84	5.69	5.009	0.6809
85	8.13	5.761	2.369
86	1.91	2.282	-0.3721
87	1.22	1.803	-0.5825
88	6.29	3.941	2.349
89	3.84	3.382	0.4583
90	1.66	1.378	0.2817
91	1.21	0.9939	0.2161
92	3.69	3.066	0.6235
93	5.83	4.089	1.741
94	15.82	10.72	5.103
95	3.26	3.025	0.2348
96	0.99	1.477	-0.4865
97	0.81	1.049	-0.239
98	3.71	3.495	0.215
99	1.53	0.9569	0.5731
100	2.54	1.497	1.043
101	3.46	3.416	0.04374
102	2.89	3.34	-0.4499
103	1.78	2.499	-0.7189
104	6.08	2.772	3.308
105	3.78	3.481	0.2993
106	1.68	2.236	-0.5562
107	0.87	0.9398	-0.0698
108	1.43	1.613	-0.1833
109	2.48	2.421	0.05935
110	0.98	1.652	-0.6719
111	5.28	7.035	-1.755
112	5.6	5.974	-0.3739
113	1.39	2.206	-0.8158
114	1.56	0.5567	1.003
115	1.16	1.496	-0.3358
116	4.98	10.07	-5.095
117	7.52	4.391	3.129
118	0.79	1.566	-0.7761
119	2.79	3.39	-0.6
120	1.91	1.518	0.3922
121	4.16	2.543	1.617
122	2.28	2.991	-0.7113
123	1.1	2.377	-1.277
124	4.44	4.091	0.3493
125	3.88	4.642	-0.762
126	10.8	9.543	1.257
127	2.71	3.541	-0.831
128	5.69	3.748	1.942
129	0.87	1.33	-0.4597
130	4.94	3.515	1.425
131	2.45	3.133	-0.6829
132	3.11	2.981	0.1295
133	2.77	2.74	0.02989
134	1.49	1.761	-0.2705
135	5.61	4.658	0.9519
136	1.21	1.249	-0.03896
137	2.7	3.209	-0.5088
138	1.24	0.8914	0.3486
139	7.97	6.956	1.014
140	4.06	4.377	-0.3169
141	5.81	5.03	0.7795
142	1.29	1.507	-0.2166
143	3.31	2.755	0.5555
144	3.67	5.395	-1.725
145	1.32	2.927	-1.607
146	4.25	3.053	1.197
147	2.01	1.858	0.1524
148	7.25	7.293	-0.04345
149	5.79	9.286	-3.496
150	0.91	2.101	-1.191
151	1.32	1.485	-0.1652
152	2.66	2.909	-0.2488
153	0.48	2.268	-1.788
154	1.13	1.275	-0.1447
155	2.7	2.865	-0.1649
156	7.92	4.013	3.907
157	2.34	2.859	-0.5187
158	3.33	3.434	-0.1036
159	5.47	2.707	2.763
160	1.24	1.317	-0.07748
161	2.84	2.907	-0.06717
162	4.94	6.114	-1.174
163	7.93	5.749	2.181
164	8.22	6.61	1.61
165	2.91	3.865	-0.9545
166	2.32	2.408	-0.0884
167	3.57	3.46	0.1095
168	1.65	2.326	-0.6762
169	1.03	1.475	-0.445
170	0.99	1.939	-0.9492
171	1.37	1.398	-0.02777

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
7	0.2141	0.4281	0.7859
8	0.2208	0.4415	0.7792
9	0.3505	0.701	0.6495
10	0.2597	0.5193	0.7403
11	0.5091	0.9818	0.4909
12	0.6772	0.6457	0.3228
13	0.7377	0.5245	0.2623
14	0.6569	0.6863	0.3431
15	0.6712	0.6577	0.3288
16	0.5938	0.8125	0.4062
17	0.5169	0.9662	0.4831
18	0.7352	0.5295	0.2648
19	0.672	0.6561	0.328
20	0.6057	0.7886	0.3943
21	0.5421	0.9158	0.4579
22	0.5231	0.9538	0.4769
23	0.7734	0.4533	0.2266
24	0.7297	0.5406	0.2703
25	0.6766	0.6469	0.3234
26	0.6162	0.7676	0.3838
27	0.5559	0.8882	0.4441
28	0.5095	0.981	0.4905
29	0.4508	0.9015	0.5492
30	0.4268	0.8535	0.5732
31	0.3771	0.7543	0.6229
32	0.3311	0.6622	0.6689
33	0.2798	0.5595	0.7202
34	0.3337	0.6675	0.6663
35	0.3443	0.6886	0.6557
36	0.3003	0.6006	0.6997
37	0.2662	0.5324	0.7338
38	0.2224	0.4449	0.7776
39	0.1946	0.3892	0.8054
40	0.1609	0.3219	0.8391
41	0.1646	0.3293	0.8354
42	0.1696	0.3392	0.8304
43	0.1398	0.2795	0.8602
44	0.2246	0.4493	0.7754
45	0.2063	0.4127	0.7937
46	0.1768	0.3536	0.8232
47	0.1868	0.3737	0.8132
48	0.1668	0.3336	0.8332
49	0.1401	0.2802	0.8599
50	0.1163	0.2326	0.8837
51	0.09766	0.1953	0.9023
52	0.08026	0.1605	0.9197
53	0.1536	0.3072	0.8464
54	0.1262	0.2523	0.8738
55	0.1025	0.2049	0.8975
56	0.09223	0.1845	0.9078
57	0.09234	0.1847	0.9077
58	0.08708	0.1742	0.9129
59	0.06954	0.1391	0.9305
60	0.07438	0.1488	0.9256
61	0.07355	0.1471	0.9264
62	0.05846	0.1169	0.9415
63	0.04821	0.09642	0.9518
64	0.03765	0.07531	0.9623
65	0.02955	0.05911	0.9704
66	0.02345	0.04689	0.9766
67	0.01858	0.03715	0.9814
68	0.01564	0.03127	0.9844
69	0.01297	0.02594	0.987
70	0.009964	0.01993	0.99
71	0.009162	0.01832	0.9908
72	0.007609	0.01522	0.9924
73	0.006348	0.0127	0.9937
74	0.004723	0.009445	0.9953
75	0.003708	0.007416	0.9963
76	0.003885	0.00777	0.9961
77	0.003094	0.006189	0.9969
78	0.002886	0.005772	0.9971
79	0.002583	0.005166	0.9974
80	0.002562	0.005124	0.9974
81	0.002199	0.004398	0.9978
82	0.00391	0.00782	0.9961
83	0.003036	0.006072	0.997
84	0.002462	0.004924	0.9975
85	0.006483	0.01297	0.9935
86	0.004841	0.009682	0.9952
87	0.003734	0.007469	0.9963
88	0.008582	0.01716	0.9914
89	0.006579	0.01316	0.9934
90	0.004872	0.009743	0.9951
91	0.003544	0.007088	0.9965
92	0.002745	0.005489	0.9973
93	0.003736	0.007472	0.9963
94	0.1604	0.3208	0.8396
95	0.1363	0.2727	0.8637
96	0.1166	0.2332	0.8834
97	0.09679	0.1936	0.9032
98	0.0797	0.1594	0.9203
99	0.06776	0.1355	0.9322
100	0.06414	0.1283	0.9359
101	0.05142	0.1028	0.9486
102	0.04245	0.0849	0.9576
103	0.03622	0.07244	0.9638
104	0.1322	0.2644	0.8678
105	0.1109	0.2218	0.8891
106	0.09464	0.1893	0.9054
107	0.07707	0.1542	0.9229
108	0.06216	0.1243	0.9378
109	0.0494	0.0988	0.9506
110	0.04142	0.08284	0.9586
111	0.05159	0.1032	0.9484
112	0.0414	0.08279	0.9586
113	0.03574	0.07149	0.9643
114	0.03429	0.06859	0.9657
115	0.02697	0.05394	0.973
116	0.3758	0.7516	0.6242
117	0.6094	0.7813	0.3906
118	0.5887	0.8226	0.4113
119	0.5508	0.8983	0.4492
120	0.5089	0.9822	0.4911
121	0.5441	0.9117	0.4559
122	0.5105	0.979	0.4895
123	0.5248	0.9503	0.4752
124	0.4769	0.9537	0.5231
125	0.4448	0.8895	0.5552
126	0.4876	0.9753	0.5124
127	0.4684	0.9367	0.5316
128	0.4927	0.9854	0.5073
129	0.4438	0.8877	0.5562
130	0.4585	0.9169	0.5415
131	0.4218	0.8435	0.5782
132	0.3702	0.7405	0.6298
133	0.3204	0.6407	0.6796
134	0.274	0.5481	0.726
135	0.2525	0.5049	0.7475
136	0.211	0.4219	0.789
137	0.1804	0.3608	0.8196
138	0.1555	0.311	0.8445
139	0.1602	0.3204	0.8398
140	0.1297	0.2594	0.8703
141	0.1117	0.2235	0.8883
142	0.08637	0.1727	0.9136
143	0.06818	0.1364	0.9318
144	0.07993	0.1599	0.9201
145	0.1076	0.2152	0.8924
146	0.0984	0.1968	0.9016
147	0.07761	0.1552	0.9224
148	0.06149	0.123	0.9385
149	0.2757	0.5514	0.7243
150	0.2532	0.5063	0.7468
151	0.1994	0.3987	0.8006
152	0.1525	0.305	0.8475
153	0.1979	0.3959	0.8021
154	0.1489	0.2978	0.8511
155	0.1086	0.2173	0.8914
156	0.5142	0.9717	0.4858
157	0.4287	0.8574	0.5713
158	0.3377	0.6754	0.6623
159	0.8178	0.3644	0.1822
160	0.7292	0.5416	0.2708
161	0.6545	0.6911	0.3455
162	0.9451	0.1097	0.05486
163	0.9419	0.1162	0.05812
164	0.8746	0.2509	0.1254

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
7 &  0.2141 &  0.4281 &  0.7859 \tabularnewline
8 &  0.2208 &  0.4415 &  0.7792 \tabularnewline
9 &  0.3505 &  0.701 &  0.6495 \tabularnewline
10 &  0.2597 &  0.5193 &  0.7403 \tabularnewline
11 &  0.5091 &  0.9818 &  0.4909 \tabularnewline
12 &  0.6772 &  0.6457 &  0.3228 \tabularnewline
13 &  0.7377 &  0.5245 &  0.2623 \tabularnewline
14 &  0.6569 &  0.6863 &  0.3431 \tabularnewline
15 &  0.6712 &  0.6577 &  0.3288 \tabularnewline
16 &  0.5938 &  0.8125 &  0.4062 \tabularnewline
17 &  0.5169 &  0.9662 &  0.4831 \tabularnewline
18 &  0.7352 &  0.5295 &  0.2648 \tabularnewline
19 &  0.672 &  0.6561 &  0.328 \tabularnewline
20 &  0.6057 &  0.7886 &  0.3943 \tabularnewline
21 &  0.5421 &  0.9158 &  0.4579 \tabularnewline
22 &  0.5231 &  0.9538 &  0.4769 \tabularnewline
23 &  0.7734 &  0.4533 &  0.2266 \tabularnewline
24 &  0.7297 &  0.5406 &  0.2703 \tabularnewline
25 &  0.6766 &  0.6469 &  0.3234 \tabularnewline
26 &  0.6162 &  0.7676 &  0.3838 \tabularnewline
27 &  0.5559 &  0.8882 &  0.4441 \tabularnewline
28 &  0.5095 &  0.981 &  0.4905 \tabularnewline
29 &  0.4508 &  0.9015 &  0.5492 \tabularnewline
30 &  0.4268 &  0.8535 &  0.5732 \tabularnewline
31 &  0.3771 &  0.7543 &  0.6229 \tabularnewline
32 &  0.3311 &  0.6622 &  0.6689 \tabularnewline
33 &  0.2798 &  0.5595 &  0.7202 \tabularnewline
34 &  0.3337 &  0.6675 &  0.6663 \tabularnewline
35 &  0.3443 &  0.6886 &  0.6557 \tabularnewline
36 &  0.3003 &  0.6006 &  0.6997 \tabularnewline
37 &  0.2662 &  0.5324 &  0.7338 \tabularnewline
38 &  0.2224 &  0.4449 &  0.7776 \tabularnewline
39 &  0.1946 &  0.3892 &  0.8054 \tabularnewline
40 &  0.1609 &  0.3219 &  0.8391 \tabularnewline
41 &  0.1646 &  0.3293 &  0.8354 \tabularnewline
42 &  0.1696 &  0.3392 &  0.8304 \tabularnewline
43 &  0.1398 &  0.2795 &  0.8602 \tabularnewline
44 &  0.2246 &  0.4493 &  0.7754 \tabularnewline
45 &  0.2063 &  0.4127 &  0.7937 \tabularnewline
46 &  0.1768 &  0.3536 &  0.8232 \tabularnewline
47 &  0.1868 &  0.3737 &  0.8132 \tabularnewline
48 &  0.1668 &  0.3336 &  0.8332 \tabularnewline
49 &  0.1401 &  0.2802 &  0.8599 \tabularnewline
50 &  0.1163 &  0.2326 &  0.8837 \tabularnewline
51 &  0.09766 &  0.1953 &  0.9023 \tabularnewline
52 &  0.08026 &  0.1605 &  0.9197 \tabularnewline
53 &  0.1536 &  0.3072 &  0.8464 \tabularnewline
54 &  0.1262 &  0.2523 &  0.8738 \tabularnewline
55 &  0.1025 &  0.2049 &  0.8975 \tabularnewline
56 &  0.09223 &  0.1845 &  0.9078 \tabularnewline
57 &  0.09234 &  0.1847 &  0.9077 \tabularnewline
58 &  0.08708 &  0.1742 &  0.9129 \tabularnewline
59 &  0.06954 &  0.1391 &  0.9305 \tabularnewline
60 &  0.07438 &  0.1488 &  0.9256 \tabularnewline
61 &  0.07355 &  0.1471 &  0.9264 \tabularnewline
62 &  0.05846 &  0.1169 &  0.9415 \tabularnewline
63 &  0.04821 &  0.09642 &  0.9518 \tabularnewline
64 &  0.03765 &  0.07531 &  0.9623 \tabularnewline
65 &  0.02955 &  0.05911 &  0.9704 \tabularnewline
66 &  0.02345 &  0.04689 &  0.9766 \tabularnewline
67 &  0.01858 &  0.03715 &  0.9814 \tabularnewline
68 &  0.01564 &  0.03127 &  0.9844 \tabularnewline
69 &  0.01297 &  0.02594 &  0.987 \tabularnewline
70 &  0.009964 &  0.01993 &  0.99 \tabularnewline
71 &  0.009162 &  0.01832 &  0.9908 \tabularnewline
72 &  0.007609 &  0.01522 &  0.9924 \tabularnewline
73 &  0.006348 &  0.0127 &  0.9937 \tabularnewline
74 &  0.004723 &  0.009445 &  0.9953 \tabularnewline
75 &  0.003708 &  0.007416 &  0.9963 \tabularnewline
76 &  0.003885 &  0.00777 &  0.9961 \tabularnewline
77 &  0.003094 &  0.006189 &  0.9969 \tabularnewline
78 &  0.002886 &  0.005772 &  0.9971 \tabularnewline
79 &  0.002583 &  0.005166 &  0.9974 \tabularnewline
80 &  0.002562 &  0.005124 &  0.9974 \tabularnewline
81 &  0.002199 &  0.004398 &  0.9978 \tabularnewline
82 &  0.00391 &  0.00782 &  0.9961 \tabularnewline
83 &  0.003036 &  0.006072 &  0.997 \tabularnewline
84 &  0.002462 &  0.004924 &  0.9975 \tabularnewline
85 &  0.006483 &  0.01297 &  0.9935 \tabularnewline
86 &  0.004841 &  0.009682 &  0.9952 \tabularnewline
87 &  0.003734 &  0.007469 &  0.9963 \tabularnewline
88 &  0.008582 &  0.01716 &  0.9914 \tabularnewline
89 &  0.006579 &  0.01316 &  0.9934 \tabularnewline
90 &  0.004872 &  0.009743 &  0.9951 \tabularnewline
91 &  0.003544 &  0.007088 &  0.9965 \tabularnewline
92 &  0.002745 &  0.005489 &  0.9973 \tabularnewline
93 &  0.003736 &  0.007472 &  0.9963 \tabularnewline
94 &  0.1604 &  0.3208 &  0.8396 \tabularnewline
95 &  0.1363 &  0.2727 &  0.8637 \tabularnewline
96 &  0.1166 &  0.2332 &  0.8834 \tabularnewline
97 &  0.09679 &  0.1936 &  0.9032 \tabularnewline
98 &  0.0797 &  0.1594 &  0.9203 \tabularnewline
99 &  0.06776 &  0.1355 &  0.9322 \tabularnewline
100 &  0.06414 &  0.1283 &  0.9359 \tabularnewline
101 &  0.05142 &  0.1028 &  0.9486 \tabularnewline
102 &  0.04245 &  0.0849 &  0.9576 \tabularnewline
103 &  0.03622 &  0.07244 &  0.9638 \tabularnewline
104 &  0.1322 &  0.2644 &  0.8678 \tabularnewline
105 &  0.1109 &  0.2218 &  0.8891 \tabularnewline
106 &  0.09464 &  0.1893 &  0.9054 \tabularnewline
107 &  0.07707 &  0.1542 &  0.9229 \tabularnewline
108 &  0.06216 &  0.1243 &  0.9378 \tabularnewline
109 &  0.0494 &  0.0988 &  0.9506 \tabularnewline
110 &  0.04142 &  0.08284 &  0.9586 \tabularnewline
111 &  0.05159 &  0.1032 &  0.9484 \tabularnewline
112 &  0.0414 &  0.08279 &  0.9586 \tabularnewline
113 &  0.03574 &  0.07149 &  0.9643 \tabularnewline
114 &  0.03429 &  0.06859 &  0.9657 \tabularnewline
115 &  0.02697 &  0.05394 &  0.973 \tabularnewline
116 &  0.3758 &  0.7516 &  0.6242 \tabularnewline
117 &  0.6094 &  0.7813 &  0.3906 \tabularnewline
118 &  0.5887 &  0.8226 &  0.4113 \tabularnewline
119 &  0.5508 &  0.8983 &  0.4492 \tabularnewline
120 &  0.5089 &  0.9822 &  0.4911 \tabularnewline
121 &  0.5441 &  0.9117 &  0.4559 \tabularnewline
122 &  0.5105 &  0.979 &  0.4895 \tabularnewline
123 &  0.5248 &  0.9503 &  0.4752 \tabularnewline
124 &  0.4769 &  0.9537 &  0.5231 \tabularnewline
125 &  0.4448 &  0.8895 &  0.5552 \tabularnewline
126 &  0.4876 &  0.9753 &  0.5124 \tabularnewline
127 &  0.4684 &  0.9367 &  0.5316 \tabularnewline
128 &  0.4927 &  0.9854 &  0.5073 \tabularnewline
129 &  0.4438 &  0.8877 &  0.5562 \tabularnewline
130 &  0.4585 &  0.9169 &  0.5415 \tabularnewline
131 &  0.4218 &  0.8435 &  0.5782 \tabularnewline
132 &  0.3702 &  0.7405 &  0.6298 \tabularnewline
133 &  0.3204 &  0.6407 &  0.6796 \tabularnewline
134 &  0.274 &  0.5481 &  0.726 \tabularnewline
135 &  0.2525 &  0.5049 &  0.7475 \tabularnewline
136 &  0.211 &  0.4219 &  0.789 \tabularnewline
137 &  0.1804 &  0.3608 &  0.8196 \tabularnewline
138 &  0.1555 &  0.311 &  0.8445 \tabularnewline
139 &  0.1602 &  0.3204 &  0.8398 \tabularnewline
140 &  0.1297 &  0.2594 &  0.8703 \tabularnewline
141 &  0.1117 &  0.2235 &  0.8883 \tabularnewline
142 &  0.08637 &  0.1727 &  0.9136 \tabularnewline
143 &  0.06818 &  0.1364 &  0.9318 \tabularnewline
144 &  0.07993 &  0.1599 &  0.9201 \tabularnewline
145 &  0.1076 &  0.2152 &  0.8924 \tabularnewline
146 &  0.0984 &  0.1968 &  0.9016 \tabularnewline
147 &  0.07761 &  0.1552 &  0.9224 \tabularnewline
148 &  0.06149 &  0.123 &  0.9385 \tabularnewline
149 &  0.2757 &  0.5514 &  0.7243 \tabularnewline
150 &  0.2532 &  0.5063 &  0.7468 \tabularnewline
151 &  0.1994 &  0.3987 &  0.8006 \tabularnewline
152 &  0.1525 &  0.305 &  0.8475 \tabularnewline
153 &  0.1979 &  0.3959 &  0.8021 \tabularnewline
154 &  0.1489 &  0.2978 &  0.8511 \tabularnewline
155 &  0.1086 &  0.2173 &  0.8914 \tabularnewline
156 &  0.5142 &  0.9717 &  0.4858 \tabularnewline
157 &  0.4287 &  0.8574 &  0.5713 \tabularnewline
158 &  0.3377 &  0.6754 &  0.6623 \tabularnewline
159 &  0.8178 &  0.3644 &  0.1822 \tabularnewline
160 &  0.7292 &  0.5416 &  0.2708 \tabularnewline
161 &  0.6545 &  0.6911 &  0.3455 \tabularnewline
162 &  0.9451 &  0.1097 &  0.05486 \tabularnewline
163 &  0.9419 &  0.1162 &  0.05812 \tabularnewline
164 &  0.8746 &  0.2509 &  0.1254 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=316099&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] 0.2141[/C][C] 0.4281[/C][C] 0.7859[/C][/ROW]
[ROW][C]8[/C][C] 0.2208[/C][C] 0.4415[/C][C] 0.7792[/C][/ROW]
[ROW][C]9[/C][C] 0.3505[/C][C] 0.701[/C][C] 0.6495[/C][/ROW]
[ROW][C]10[/C][C] 0.2597[/C][C] 0.5193[/C][C] 0.7403[/C][/ROW]
[ROW][C]11[/C][C] 0.5091[/C][C] 0.9818[/C][C] 0.4909[/C][/ROW]
[ROW][C]12[/C][C] 0.6772[/C][C] 0.6457[/C][C] 0.3228[/C][/ROW]
[ROW][C]13[/C][C] 0.7377[/C][C] 0.5245[/C][C] 0.2623[/C][/ROW]
[ROW][C]14[/C][C] 0.6569[/C][C] 0.6863[/C][C] 0.3431[/C][/ROW]
[ROW][C]15[/C][C] 0.6712[/C][C] 0.6577[/C][C] 0.3288[/C][/ROW]
[ROW][C]16[/C][C] 0.5938[/C][C] 0.8125[/C][C] 0.4062[/C][/ROW]
[ROW][C]17[/C][C] 0.5169[/C][C] 0.9662[/C][C] 0.4831[/C][/ROW]
[ROW][C]18[/C][C] 0.7352[/C][C] 0.5295[/C][C] 0.2648[/C][/ROW]
[ROW][C]19[/C][C] 0.672[/C][C] 0.6561[/C][C] 0.328[/C][/ROW]
[ROW][C]20[/C][C] 0.6057[/C][C] 0.7886[/C][C] 0.3943[/C][/ROW]
[ROW][C]21[/C][C] 0.5421[/C][C] 0.9158[/C][C] 0.4579[/C][/ROW]
[ROW][C]22[/C][C] 0.5231[/C][C] 0.9538[/C][C] 0.4769[/C][/ROW]
[ROW][C]23[/C][C] 0.7734[/C][C] 0.4533[/C][C] 0.2266[/C][/ROW]
[ROW][C]24[/C][C] 0.7297[/C][C] 0.5406[/C][C] 0.2703[/C][/ROW]
[ROW][C]25[/C][C] 0.6766[/C][C] 0.6469[/C][C] 0.3234[/C][/ROW]
[ROW][C]26[/C][C] 0.6162[/C][C] 0.7676[/C][C] 0.3838[/C][/ROW]
[ROW][C]27[/C][C] 0.5559[/C][C] 0.8882[/C][C] 0.4441[/C][/ROW]
[ROW][C]28[/C][C] 0.5095[/C][C] 0.981[/C][C] 0.4905[/C][/ROW]
[ROW][C]29[/C][C] 0.4508[/C][C] 0.9015[/C][C] 0.5492[/C][/ROW]
[ROW][C]30[/C][C] 0.4268[/C][C] 0.8535[/C][C] 0.5732[/C][/ROW]
[ROW][C]31[/C][C] 0.3771[/C][C] 0.7543[/C][C] 0.6229[/C][/ROW]
[ROW][C]32[/C][C] 0.3311[/C][C] 0.6622[/C][C] 0.6689[/C][/ROW]
[ROW][C]33[/C][C] 0.2798[/C][C] 0.5595[/C][C] 0.7202[/C][/ROW]
[ROW][C]34[/C][C] 0.3337[/C][C] 0.6675[/C][C] 0.6663[/C][/ROW]
[ROW][C]35[/C][C] 0.3443[/C][C] 0.6886[/C][C] 0.6557[/C][/ROW]
[ROW][C]36[/C][C] 0.3003[/C][C] 0.6006[/C][C] 0.6997[/C][/ROW]
[ROW][C]37[/C][C] 0.2662[/C][C] 0.5324[/C][C] 0.7338[/C][/ROW]
[ROW][C]38[/C][C] 0.2224[/C][C] 0.4449[/C][C] 0.7776[/C][/ROW]
[ROW][C]39[/C][C] 0.1946[/C][C] 0.3892[/C][C] 0.8054[/C][/ROW]
[ROW][C]40[/C][C] 0.1609[/C][C] 0.3219[/C][C] 0.8391[/C][/ROW]
[ROW][C]41[/C][C] 0.1646[/C][C] 0.3293[/C][C] 0.8354[/C][/ROW]
[ROW][C]42[/C][C] 0.1696[/C][C] 0.3392[/C][C] 0.8304[/C][/ROW]
[ROW][C]43[/C][C] 0.1398[/C][C] 0.2795[/C][C] 0.8602[/C][/ROW]
[ROW][C]44[/C][C] 0.2246[/C][C] 0.4493[/C][C] 0.7754[/C][/ROW]
[ROW][C]45[/C][C] 0.2063[/C][C] 0.4127[/C][C] 0.7937[/C][/ROW]
[ROW][C]46[/C][C] 0.1768[/C][C] 0.3536[/C][C] 0.8232[/C][/ROW]
[ROW][C]47[/C][C] 0.1868[/C][C] 0.3737[/C][C] 0.8132[/C][/ROW]
[ROW][C]48[/C][C] 0.1668[/C][C] 0.3336[/C][C] 0.8332[/C][/ROW]
[ROW][C]49[/C][C] 0.1401[/C][C] 0.2802[/C][C] 0.8599[/C][/ROW]
[ROW][C]50[/C][C] 0.1163[/C][C] 0.2326[/C][C] 0.8837[/C][/ROW]
[ROW][C]51[/C][C] 0.09766[/C][C] 0.1953[/C][C] 0.9023[/C][/ROW]
[ROW][C]52[/C][C] 0.08026[/C][C] 0.1605[/C][C] 0.9197[/C][/ROW]
[ROW][C]53[/C][C] 0.1536[/C][C] 0.3072[/C][C] 0.8464[/C][/ROW]
[ROW][C]54[/C][C] 0.1262[/C][C] 0.2523[/C][C] 0.8738[/C][/ROW]
[ROW][C]55[/C][C] 0.1025[/C][C] 0.2049[/C][C] 0.8975[/C][/ROW]
[ROW][C]56[/C][C] 0.09223[/C][C] 0.1845[/C][C] 0.9078[/C][/ROW]
[ROW][C]57[/C][C] 0.09234[/C][C] 0.1847[/C][C] 0.9077[/C][/ROW]
[ROW][C]58[/C][C] 0.08708[/C][C] 0.1742[/C][C] 0.9129[/C][/ROW]
[ROW][C]59[/C][C] 0.06954[/C][C] 0.1391[/C][C] 0.9305[/C][/ROW]
[ROW][C]60[/C][C] 0.07438[/C][C] 0.1488[/C][C] 0.9256[/C][/ROW]
[ROW][C]61[/C][C] 0.07355[/C][C] 0.1471[/C][C] 0.9264[/C][/ROW]
[ROW][C]62[/C][C] 0.05846[/C][C] 0.1169[/C][C] 0.9415[/C][/ROW]
[ROW][C]63[/C][C] 0.04821[/C][C] 0.09642[/C][C] 0.9518[/C][/ROW]
[ROW][C]64[/C][C] 0.03765[/C][C] 0.07531[/C][C] 0.9623[/C][/ROW]
[ROW][C]65[/C][C] 0.02955[/C][C] 0.05911[/C][C] 0.9704[/C][/ROW]
[ROW][C]66[/C][C] 0.02345[/C][C] 0.04689[/C][C] 0.9766[/C][/ROW]
[ROW][C]67[/C][C] 0.01858[/C][C] 0.03715[/C][C] 0.9814[/C][/ROW]
[ROW][C]68[/C][C] 0.01564[/C][C] 0.03127[/C][C] 0.9844[/C][/ROW]
[ROW][C]69[/C][C] 0.01297[/C][C] 0.02594[/C][C] 0.987[/C][/ROW]
[ROW][C]70[/C][C] 0.009964[/C][C] 0.01993[/C][C] 0.99[/C][/ROW]
[ROW][C]71[/C][C] 0.009162[/C][C] 0.01832[/C][C] 0.9908[/C][/ROW]
[ROW][C]72[/C][C] 0.007609[/C][C] 0.01522[/C][C] 0.9924[/C][/ROW]
[ROW][C]73[/C][C] 0.006348[/C][C] 0.0127[/C][C] 0.9937[/C][/ROW]
[ROW][C]74[/C][C] 0.004723[/C][C] 0.009445[/C][C] 0.9953[/C][/ROW]
[ROW][C]75[/C][C] 0.003708[/C][C] 0.007416[/C][C] 0.9963[/C][/ROW]
[ROW][C]76[/C][C] 0.003885[/C][C] 0.00777[/C][C] 0.9961[/C][/ROW]
[ROW][C]77[/C][C] 0.003094[/C][C] 0.006189[/C][C] 0.9969[/C][/ROW]
[ROW][C]78[/C][C] 0.002886[/C][C] 0.005772[/C][C] 0.9971[/C][/ROW]
[ROW][C]79[/C][C] 0.002583[/C][C] 0.005166[/C][C] 0.9974[/C][/ROW]
[ROW][C]80[/C][C] 0.002562[/C][C] 0.005124[/C][C] 0.9974[/C][/ROW]
[ROW][C]81[/C][C] 0.002199[/C][C] 0.004398[/C][C] 0.9978[/C][/ROW]
[ROW][C]82[/C][C] 0.00391[/C][C] 0.00782[/C][C] 0.9961[/C][/ROW]
[ROW][C]83[/C][C] 0.003036[/C][C] 0.006072[/C][C] 0.997[/C][/ROW]
[ROW][C]84[/C][C] 0.002462[/C][C] 0.004924[/C][C] 0.9975[/C][/ROW]
[ROW][C]85[/C][C] 0.006483[/C][C] 0.01297[/C][C] 0.9935[/C][/ROW]
[ROW][C]86[/C][C] 0.004841[/C][C] 0.009682[/C][C] 0.9952[/C][/ROW]
[ROW][C]87[/C][C] 0.003734[/C][C] 0.007469[/C][C] 0.9963[/C][/ROW]
[ROW][C]88[/C][C] 0.008582[/C][C] 0.01716[/C][C] 0.9914[/C][/ROW]
[ROW][C]89[/C][C] 0.006579[/C][C] 0.01316[/C][C] 0.9934[/C][/ROW]
[ROW][C]90[/C][C] 0.004872[/C][C] 0.009743[/C][C] 0.9951[/C][/ROW]
[ROW][C]91[/C][C] 0.003544[/C][C] 0.007088[/C][C] 0.9965[/C][/ROW]
[ROW][C]92[/C][C] 0.002745[/C][C] 0.005489[/C][C] 0.9973[/C][/ROW]
[ROW][C]93[/C][C] 0.003736[/C][C] 0.007472[/C][C] 0.9963[/C][/ROW]
[ROW][C]94[/C][C] 0.1604[/C][C] 0.3208[/C][C] 0.8396[/C][/ROW]
[ROW][C]95[/C][C] 0.1363[/C][C] 0.2727[/C][C] 0.8637[/C][/ROW]
[ROW][C]96[/C][C] 0.1166[/C][C] 0.2332[/C][C] 0.8834[/C][/ROW]
[ROW][C]97[/C][C] 0.09679[/C][C] 0.1936[/C][C] 0.9032[/C][/ROW]
[ROW][C]98[/C][C] 0.0797[/C][C] 0.1594[/C][C] 0.9203[/C][/ROW]
[ROW][C]99[/C][C] 0.06776[/C][C] 0.1355[/C][C] 0.9322[/C][/ROW]
[ROW][C]100[/C][C] 0.06414[/C][C] 0.1283[/C][C] 0.9359[/C][/ROW]
[ROW][C]101[/C][C] 0.05142[/C][C] 0.1028[/C][C] 0.9486[/C][/ROW]
[ROW][C]102[/C][C] 0.04245[/C][C] 0.0849[/C][C] 0.9576[/C][/ROW]
[ROW][C]103[/C][C] 0.03622[/C][C] 0.07244[/C][C] 0.9638[/C][/ROW]
[ROW][C]104[/C][C] 0.1322[/C][C] 0.2644[/C][C] 0.8678[/C][/ROW]
[ROW][C]105[/C][C] 0.1109[/C][C] 0.2218[/C][C] 0.8891[/C][/ROW]
[ROW][C]106[/C][C] 0.09464[/C][C] 0.1893[/C][C] 0.9054[/C][/ROW]
[ROW][C]107[/C][C] 0.07707[/C][C] 0.1542[/C][C] 0.9229[/C][/ROW]
[ROW][C]108[/C][C] 0.06216[/C][C] 0.1243[/C][C] 0.9378[/C][/ROW]
[ROW][C]109[/C][C] 0.0494[/C][C] 0.0988[/C][C] 0.9506[/C][/ROW]
[ROW][C]110[/C][C] 0.04142[/C][C] 0.08284[/C][C] 0.9586[/C][/ROW]
[ROW][C]111[/C][C] 0.05159[/C][C] 0.1032[/C][C] 0.9484[/C][/ROW]
[ROW][C]112[/C][C] 0.0414[/C][C] 0.08279[/C][C] 0.9586[/C][/ROW]
[ROW][C]113[/C][C] 0.03574[/C][C] 0.07149[/C][C] 0.9643[/C][/ROW]
[ROW][C]114[/C][C] 0.03429[/C][C] 0.06859[/C][C] 0.9657[/C][/ROW]
[ROW][C]115[/C][C] 0.02697[/C][C] 0.05394[/C][C] 0.973[/C][/ROW]
[ROW][C]116[/C][C] 0.3758[/C][C] 0.7516[/C][C] 0.6242[/C][/ROW]
[ROW][C]117[/C][C] 0.6094[/C][C] 0.7813[/C][C] 0.3906[/C][/ROW]
[ROW][C]118[/C][C] 0.5887[/C][C] 0.8226[/C][C] 0.4113[/C][/ROW]
[ROW][C]119[/C][C] 0.5508[/C][C] 0.8983[/C][C] 0.4492[/C][/ROW]
[ROW][C]120[/C][C] 0.5089[/C][C] 0.9822[/C][C] 0.4911[/C][/ROW]
[ROW][C]121[/C][C] 0.5441[/C][C] 0.9117[/C][C] 0.4559[/C][/ROW]
[ROW][C]122[/C][C] 0.5105[/C][C] 0.979[/C][C] 0.4895[/C][/ROW]
[ROW][C]123[/C][C] 0.5248[/C][C] 0.9503[/C][C] 0.4752[/C][/ROW]
[ROW][C]124[/C][C] 0.4769[/C][C] 0.9537[/C][C] 0.5231[/C][/ROW]
[ROW][C]125[/C][C] 0.4448[/C][C] 0.8895[/C][C] 0.5552[/C][/ROW]
[ROW][C]126[/C][C] 0.4876[/C][C] 0.9753[/C][C] 0.5124[/C][/ROW]
[ROW][C]127[/C][C] 0.4684[/C][C] 0.9367[/C][C] 0.5316[/C][/ROW]
[ROW][C]128[/C][C] 0.4927[/C][C] 0.9854[/C][C] 0.5073[/C][/ROW]
[ROW][C]129[/C][C] 0.4438[/C][C] 0.8877[/C][C] 0.5562[/C][/ROW]
[ROW][C]130[/C][C] 0.4585[/C][C] 0.9169[/C][C] 0.5415[/C][/ROW]
[ROW][C]131[/C][C] 0.4218[/C][C] 0.8435[/C][C] 0.5782[/C][/ROW]
[ROW][C]132[/C][C] 0.3702[/C][C] 0.7405[/C][C] 0.6298[/C][/ROW]
[ROW][C]133[/C][C] 0.3204[/C][C] 0.6407[/C][C] 0.6796[/C][/ROW]
[ROW][C]134[/C][C] 0.274[/C][C] 0.5481[/C][C] 0.726[/C][/ROW]
[ROW][C]135[/C][C] 0.2525[/C][C] 0.5049[/C][C] 0.7475[/C][/ROW]
[ROW][C]136[/C][C] 0.211[/C][C] 0.4219[/C][C] 0.789[/C][/ROW]
[ROW][C]137[/C][C] 0.1804[/C][C] 0.3608[/C][C] 0.8196[/C][/ROW]
[ROW][C]138[/C][C] 0.1555[/C][C] 0.311[/C][C] 0.8445[/C][/ROW]
[ROW][C]139[/C][C] 0.1602[/C][C] 0.3204[/C][C] 0.8398[/C][/ROW]
[ROW][C]140[/C][C] 0.1297[/C][C] 0.2594[/C][C] 0.8703[/C][/ROW]
[ROW][C]141[/C][C] 0.1117[/C][C] 0.2235[/C][C] 0.8883[/C][/ROW]
[ROW][C]142[/C][C] 0.08637[/C][C] 0.1727[/C][C] 0.9136[/C][/ROW]
[ROW][C]143[/C][C] 0.06818[/C][C] 0.1364[/C][C] 0.9318[/C][/ROW]
[ROW][C]144[/C][C] 0.07993[/C][C] 0.1599[/C][C] 0.9201[/C][/ROW]
[ROW][C]145[/C][C] 0.1076[/C][C] 0.2152[/C][C] 0.8924[/C][/ROW]
[ROW][C]146[/C][C] 0.0984[/C][C] 0.1968[/C][C] 0.9016[/C][/ROW]
[ROW][C]147[/C][C] 0.07761[/C][C] 0.1552[/C][C] 0.9224[/C][/ROW]
[ROW][C]148[/C][C] 0.06149[/C][C] 0.123[/C][C] 0.9385[/C][/ROW]
[ROW][C]149[/C][C] 0.2757[/C][C] 0.5514[/C][C] 0.7243[/C][/ROW]
[ROW][C]150[/C][C] 0.2532[/C][C] 0.5063[/C][C] 0.7468[/C][/ROW]
[ROW][C]151[/C][C] 0.1994[/C][C] 0.3987[/C][C] 0.8006[/C][/ROW]
[ROW][C]152[/C][C] 0.1525[/C][C] 0.305[/C][C] 0.8475[/C][/ROW]
[ROW][C]153[/C][C] 0.1979[/C][C] 0.3959[/C][C] 0.8021[/C][/ROW]
[ROW][C]154[/C][C] 0.1489[/C][C] 0.2978[/C][C] 0.8511[/C][/ROW]
[ROW][C]155[/C][C] 0.1086[/C][C] 0.2173[/C][C] 0.8914[/C][/ROW]
[ROW][C]156[/C][C] 0.5142[/C][C] 0.9717[/C][C] 0.4858[/C][/ROW]
[ROW][C]157[/C][C] 0.4287[/C][C] 0.8574[/C][C] 0.5713[/C][/ROW]
[ROW][C]158[/C][C] 0.3377[/C][C] 0.6754[/C][C] 0.6623[/C][/ROW]
[ROW][C]159[/C][C] 0.8178[/C][C] 0.3644[/C][C] 0.1822[/C][/ROW]
[ROW][C]160[/C][C] 0.7292[/C][C] 0.5416[/C][C] 0.2708[/C][/ROW]
[ROW][C]161[/C][C] 0.6545[/C][C] 0.6911[/C][C] 0.3455[/C][/ROW]
[ROW][C]162[/C][C] 0.9451[/C][C] 0.1097[/C][C] 0.05486[/C][/ROW]
[ROW][C]163[/C][C] 0.9419[/C][C] 0.1162[/C][C] 0.05812[/C][/ROW]
[ROW][C]164[/C][C] 0.8746[/C][C] 0.2509[/C][C] 0.1254[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=316099&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=316099&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	0.2141	0.4281	0.7859
8	0.2208	0.4415	0.7792
9	0.3505	0.701	0.6495
10	0.2597	0.5193	0.7403
11	0.5091	0.9818	0.4909
12	0.6772	0.6457	0.3228
13	0.7377	0.5245	0.2623
14	0.6569	0.6863	0.3431
15	0.6712	0.6577	0.3288
16	0.5938	0.8125	0.4062
17	0.5169	0.9662	0.4831
18	0.7352	0.5295	0.2648
19	0.672	0.6561	0.328
20	0.6057	0.7886	0.3943
21	0.5421	0.9158	0.4579
22	0.5231	0.9538	0.4769
23	0.7734	0.4533	0.2266
24	0.7297	0.5406	0.2703
25	0.6766	0.6469	0.3234
26	0.6162	0.7676	0.3838
27	0.5559	0.8882	0.4441
28	0.5095	0.981	0.4905
29	0.4508	0.9015	0.5492
30	0.4268	0.8535	0.5732
31	0.3771	0.7543	0.6229
32	0.3311	0.6622	0.6689
33	0.2798	0.5595	0.7202
34	0.3337	0.6675	0.6663
35	0.3443	0.6886	0.6557
36	0.3003	0.6006	0.6997
37	0.2662	0.5324	0.7338
38	0.2224	0.4449	0.7776
39	0.1946	0.3892	0.8054
40	0.1609	0.3219	0.8391
41	0.1646	0.3293	0.8354
42	0.1696	0.3392	0.8304
43	0.1398	0.2795	0.8602
44	0.2246	0.4493	0.7754
45	0.2063	0.4127	0.7937
46	0.1768	0.3536	0.8232
47	0.1868	0.3737	0.8132
48	0.1668	0.3336	0.8332
49	0.1401	0.2802	0.8599
50	0.1163	0.2326	0.8837
51	0.09766	0.1953	0.9023
52	0.08026	0.1605	0.9197
53	0.1536	0.3072	0.8464
54	0.1262	0.2523	0.8738
55	0.1025	0.2049	0.8975
56	0.09223	0.1845	0.9078
57	0.09234	0.1847	0.9077
58	0.08708	0.1742	0.9129
59	0.06954	0.1391	0.9305
60	0.07438	0.1488	0.9256
61	0.07355	0.1471	0.9264
62	0.05846	0.1169	0.9415
63	0.04821	0.09642	0.9518
64	0.03765	0.07531	0.9623
65	0.02955	0.05911	0.9704
66	0.02345	0.04689	0.9766
67	0.01858	0.03715	0.9814
68	0.01564	0.03127	0.9844
69	0.01297	0.02594	0.987
70	0.009964	0.01993	0.99
71	0.009162	0.01832	0.9908
72	0.007609	0.01522	0.9924
73	0.006348	0.0127	0.9937
74	0.004723	0.009445	0.9953
75	0.003708	0.007416	0.9963
76	0.003885	0.00777	0.9961
77	0.003094	0.006189	0.9969
78	0.002886	0.005772	0.9971
79	0.002583	0.005166	0.9974
80	0.002562	0.005124	0.9974
81	0.002199	0.004398	0.9978
82	0.00391	0.00782	0.9961
83	0.003036	0.006072	0.997
84	0.002462	0.004924	0.9975
85	0.006483	0.01297	0.9935
86	0.004841	0.009682	0.9952
87	0.003734	0.007469	0.9963
88	0.008582	0.01716	0.9914
89	0.006579	0.01316	0.9934
90	0.004872	0.009743	0.9951
91	0.003544	0.007088	0.9965
92	0.002745	0.005489	0.9973
93	0.003736	0.007472	0.9963
94	0.1604	0.3208	0.8396
95	0.1363	0.2727	0.8637
96	0.1166	0.2332	0.8834
97	0.09679	0.1936	0.9032
98	0.0797	0.1594	0.9203
99	0.06776	0.1355	0.9322
100	0.06414	0.1283	0.9359
101	0.05142	0.1028	0.9486
102	0.04245	0.0849	0.9576
103	0.03622	0.07244	0.9638
104	0.1322	0.2644	0.8678
105	0.1109	0.2218	0.8891
106	0.09464	0.1893	0.9054
107	0.07707	0.1542	0.9229
108	0.06216	0.1243	0.9378
109	0.0494	0.0988	0.9506
110	0.04142	0.08284	0.9586
111	0.05159	0.1032	0.9484
112	0.0414	0.08279	0.9586
113	0.03574	0.07149	0.9643
114	0.03429	0.06859	0.9657
115	0.02697	0.05394	0.973
116	0.3758	0.7516	0.6242
117	0.6094	0.7813	0.3906
118	0.5887	0.8226	0.4113
119	0.5508	0.8983	0.4492
120	0.5089	0.9822	0.4911
121	0.5441	0.9117	0.4559
122	0.5105	0.979	0.4895
123	0.5248	0.9503	0.4752
124	0.4769	0.9537	0.5231
125	0.4448	0.8895	0.5552
126	0.4876	0.9753	0.5124
127	0.4684	0.9367	0.5316
128	0.4927	0.9854	0.5073
129	0.4438	0.8877	0.5562
130	0.4585	0.9169	0.5415
131	0.4218	0.8435	0.5782
132	0.3702	0.7405	0.6298
133	0.3204	0.6407	0.6796
134	0.274	0.5481	0.726
135	0.2525	0.5049	0.7475
136	0.211	0.4219	0.789
137	0.1804	0.3608	0.8196
138	0.1555	0.311	0.8445
139	0.1602	0.3204	0.8398
140	0.1297	0.2594	0.8703
141	0.1117	0.2235	0.8883
142	0.08637	0.1727	0.9136
143	0.06818	0.1364	0.9318
144	0.07993	0.1599	0.9201
145	0.1076	0.2152	0.8924
146	0.0984	0.1968	0.9016
147	0.07761	0.1552	0.9224
148	0.06149	0.123	0.9385
149	0.2757	0.5514	0.7243
150	0.2532	0.5063	0.7468
151	0.1994	0.3987	0.8006
152	0.1525	0.305	0.8475
153	0.1979	0.3959	0.8021
154	0.1489	0.2978	0.8511
155	0.1086	0.2173	0.8914
156	0.5142	0.9717	0.4858
157	0.4287	0.8574	0.5713
158	0.3377	0.6754	0.6623
159	0.8178	0.3644	0.1822
160	0.7292	0.5416	0.2708
161	0.6545	0.6911	0.3455
162	0.9451	0.1097	0.05486
163	0.9419	0.1162	0.05812
164	0.8746	0.2509	0.1254

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	17	0.1076	NOK
5% type I error level	28	0.177215	NOK
10% type I error level	39	0.246835	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 & 17 &  0.1076 & NOK \tabularnewline
5% type I error level & 28 & 0.177215 & NOK \tabularnewline
10% type I error level & 39 & 0.246835 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=316099&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]17[/C][C] 0.1076[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]28[/C][C]0.177215[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]39[/C][C]0.246835[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=316099&T=7

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=316099&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	17	0.1076	NOK
5% type I error level	28	0.177215	NOK
10% type I error level	39	0.246835	NOK

Ramsey RESET F-Test for powers (2 and 3) of fitted values
> reset_test_fitted RESET test data: mylm RESET = 6.6093, df1 = 2, df2 = 165, p-value = 0.001733
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors RESET test data: mylm RESET = 8.5874, df1 = 6, df2 = 161, p-value = 4.189e-08
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components RESET test data: mylm RESET = 21.18, df1 = 2, df2 = 165, p-value = 6.497e-09

\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 = 6.6093, df1 = 2, df2 = 165, p-value = 0.001733
 \tabularnewline
Ramsey RESET F-Test for powers (2 and 3) of regressors \tabularnewline
> reset_test_regressors
	RESET test
data:  mylm
RESET = 8.5874, df1 = 6, df2 = 161, p-value = 4.189e-08
 \tabularnewline
Ramsey RESET F-Test for powers (2 and 3) of principal components \tabularnewline
> reset_test_principal_components
	RESET test
data:  mylm
RESET = 21.18, df1 = 2, df2 = 165, p-value = 6.497e-09
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=316099&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 = 6.6093, df1 = 2, df2 = 165, p-value = 0.001733
[/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 = 8.5874, df1 = 6, df2 = 161, p-value = 4.189e-08
[/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 = 21.18, df1 = 2, df2 = 165, p-value = 6.497e-09
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=316099&T=8

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=316099&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 = 6.6093, df1 = 2, df2 = 165, p-value = 0.001733
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors RESET test data: mylm RESET = 8.5874, df1 = 6, df2 = 161, p-value = 4.189e-08
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components RESET test data: mylm RESET = 21.18, df1 = 2, df2 = 165, p-value = 6.497e-09

Variance Inflation Factors (Multicollinearity)
> vif GDP_per_Capita `Population_(millions)` HDI 1.855189 1.003786 1.851080

\begin{tabular}{lllllllll}
\hline
Variance Inflation Factors (Multicollinearity) \tabularnewline
> vif
         GDP_per_Capita `Population_(millions)`                     HDI 
               1.855189                1.003786                1.851080 
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=316099&T=9

[TABLE]
[ROW][C]Variance Inflation Factors (Multicollinearity)[/C][/ROW]
[ROW][C]> vif
         GDP_per_Capita `Population_(millions)`                     HDI 
               1.855189                1.003786                1.851080 
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=316099&T=9

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=316099&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 GDP_per_Capita `Population_(millions)` HDI 1.855189 1.003786 1.851080

Figure 1

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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