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R Software Module

rwasp_multipleregression.wasp

Title produced by software

Multiple Regression

Date of computation

Wed, 19 Dec 2018 16:50:41 +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/t1545237062nio0e6o5y7z5voo.htm/, Retrieved Mon, 29 Apr 2024 23:08:00 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=316108, Retrieved Mon, 29 Apr 2024 23:08:00 +0000

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

-       [Multiple Regression] [] [2018-12-19 15:50:41] [a77d3f185bf8346aeb8631871e5ed689] [Current]

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0.18 29.82 0.46 614.66
0.87 3.16 0.73 4534.37
1.14 38.48 0.73 5430.57
0.2 20.82 0.52 4665.91
NA 0.09 0.78 13205.1
1.08 41.09 0.83 13540
0.89 2.97 0.73 3426.39
NA 0.1 NA NA
4.85 23.05 0.93 66604.2
4.14 8.46 0.88 51274.1
1.25 9.31 0.75 7106.04
4.46 0.37 0.78 22647.3
6.19 1.32 0.82 24299
0.26 154.7 0.56 857.5
3.28 0.28 0.79 15722.8
2.57 9.4 0.8 6300.45
4.43 11.06 0.89 48053.3
0.51 10.05 0.48 746.83
NA 0.06 NA 70626.3
0.63 0.74 0.59 2395
0.67 10.5 0.65 2253.09
1.74 3.83 0.73 4708.85
2.36 2 0.69 7743.5
0.91 198.66 0.75 13237.6
NA 0.03 NA NA
3.24 0.41 0.85 47097.4
2.08 7.28 0.78 7615.28
0.12 16.46 0.39 671.07
0.04 9.85 0.39 276.69
NA 0.49 0.64 3801.45
NA 14.86 0.55 877.64
0.19 21.7 0.5 1271.21
5 34.84 0.91 52145.4
3.56 0.06 NA NA
0.08 4.53 0.37 495.04
0.01 12.45 0.39 1161.22
2.04 17.46 0.83 14525.8
2.32 1408.04 0.72 5560.94
0.67 47.7 0.72 7305.22
0.25 0.72 0.5 860.24
0.47 4.34 0.57 1943.69
0.07 65.7 0.42 338.63
1.37 4.8 0.76 8979.96
0.26 19.84 NA 1016.83
2.21 4.31 0.82 14522.8
1.23 11.27 0.77 5175.94
2.94 1.13 0.85 31454.7
3.42 10.66 0.87 21676.3
2.6 5.6 0.92 61413.6
NA 0.86 0.46 1433.17
1.47 0.07 0.72 7088.01
0.86 10.28 0.71 6085.89
1.08 15.49 0.73 5192.88
1.02 80.72 0.69 2930.33
0.84 6.3 0.66 3696.33
3.17 0.74 0.58 24064
0.03 6.13 0.39 439.73
NA 1.29 0.85 17304.4
0.07 91.73 0.43 379.38
1.06 0.88 0.72 4201.37
NA 5.41 0.88 50960.2
2.71 63.98 0.89 45430.3
1.58 0.24 NA NA
2.39 0.27 NA NA
0.43 1.63 0.67 11989
0.21 1.79 0.44 505.76
0.83 4.36 0.75 3710.7
3.28 82.8 0.91 46822.4
0.43 25.37 0.57 1627.9
2.58 11.12 0.86 25987.4
NA 0.1 0.74 7410.48
2.61 0.46 NA NA
0.7 15.08 0.62 3233.8
0.16 11.45 0.41 459.09
0.09 1.66 0.42 681.25
1.25 0.8 0.63 3269.46
0.15 10.17 0.48 749.13
0.6 7.94 0.61 2269.51
1.9 9.98 0.82 13964.2
0.61 1236.69 0.6 1513.85
0.64 246.86 0.68 3688.53
1.72 76.42 0.76 7511.1
1.36 32.78 0.65 5848.54
3.22 4.58 0.91 52853.6
4.59 7.64 0.89 33718.9
2.77 60.92 0.87 38412
1.09 2.77 0.72 5226.3
3.69 127.25 0.89 46201.6
1.09 7.01 0.75 4615.17
4.59 16.27 0.78 11278
0.2 43.18 0.54 1062.11
0.68 24.76 NA NA
4.17 49 0.89 24155.8
6.89 3.25 0.82 41830.5
0.95 5.47 0.65 1116.37
0.09 6.65 0.56 1236.24
1.66 2.06 0.81 13732
2.52 4.65 0.76 9143.86
0.51 2.05 0.48 1338.42
0.14 4.19 0.42 397.38
2.33 6.16 0.74 5859.43
2.15 3.03 0.83 14373.7
12.65 0.52 0.89 114665
2.06 2.11 0.74 5174.89
0.07 22.29 0.51 456.33
0.07 15.91 0.43 493.84
2.1 29.24 0.77 10252.6
0.1 14.85 0.41 741.22
1.73 0.4 NA NA
0.55 3.8 0.5 1524.39
1.99 1.24 0.77 8811.15
1.74 120.85 0.75 10123.9
1.03 3.51 0.68 1971.03
2.09 2.8 0.71 3736.07
2.13 0.62 0.8 7251.6
NA 0 NA NA
0.67 32.52 0.62 3149.43
0.17 25.2 0.41 538.82
0.09 52.8 0.53 1117.58
1.02 2.26 0.62 5880.8
NA 0.01 NA NA
0.16 27.47 0.54 700.07
3.23 16.71 0.92 53589.9
1.78 0.25 NA NA
2.84 4.46 0.91 37488.3
0.45 5.99 0.63 1626.85
0.1 17.16 0.34 410.91
0.21 168.83 0.5 2612.12
NA 4.99 0.94 100172
5.8 3.31 0.79 22622.8
0.38 179.16 0.53 1218.6
1.44 3.8 0.77 8410.77
0.35 7.17 0.5 1871.21
0.97 6.69 0.67 3557.31
0.67 29.99 0.73 5684.73
0.34 96.71 0.66 2379.44
2.64 38.21 0.84 13769.5
2.15 10.6 0.83 23217.3
9.57 2.05 0.85 99431.5
3.27 0.86 NA NA
1.46 21.76 0.79 9213.94
3.87 143.17 0.79 13320.2
0.07 11.46 0.48 628.08
3.34 0.05 0.74 12952.5
1.56 0.18 0.73 7737.2
NA 0.11 0.72 6171.48
0.96 0.19 0.7 4067.15
0.37 0.19 0.55 1384.53
4.21 28.29 0.83 23593.8
0.3 13.73 0.46 1079.27
1.66 9.55 0.76 6426.18
0.07 5.98 0.4 499.89
5.91 5.3 0.91 53122.4
2.82 5.45 0.84 18103.1
4.27 2.07 0.88 25040.5
0 0.55 0.5 1647.86
0.07 10.2 NA NA
2.34 52.39 0.66 8089.87
2.22 46.76 0.87 32008.7
0.52 21.1 0.75 2880.03
3.01 0.54 0.71 8190.7
0.67 1.23 0.53 4657.48
3.88 9.51 0.9 59381.9
4.26 8 0.93 88506.2
0.81 21.89 0.62 NA
0.13 8.01 0.62 836.17
0.17 47.78 0.51 765.33
1.54 66.78 0.72 5479.29
0.06 1.11 0.6 5167.86
0.31 6.64 0.47 580.86
0.88 0.1 0.72 4330.9
6.89 1.34 0.77 18310.8
1.11 10.88 0.72 4305.07
1.92 74 0.76 10437.7
4.13 5.17 0.68 5290.14
0.08 36.35 0.48 601.35
1.92 45.53 0.74 3589.63
3.14 63.03 0.9 40980.5
6.37 9.206 0.83 40817.4
5.9 317.5 0.91 49725
0.98 3.4 0.79 14238.1
1.41 28.54 0.67 1560.85
2.13 29.96 0.763846 10237.8
0.79 90.8 0.66 1532.31
NA 0.01 NA NA
0.42 23.85 0.5 1302.3
0.24 14.08 0.58 1740.64
0.53 13.72 0.49 865.91

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
R Framework error message	Warning: there are blank lines in the 'Data X' field. Please, use NA for missing data - blank lines are simply deleted and are NOT treated as missing values.

\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
R Framework error message & Warning: there are blank lines in the 'Data X' field.
Please, use NA for missing data - blank lines are simply
 deleted and are NOT treated as missing values. \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=316108&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] [ROW]

R Framework error message[/C][C]

Warning: there are blank lines in the 'Data X' field.
Please, use NA for missing data - blank lines are simply
 deleted and are NOT treated as missing values.

[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=316108&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=316108&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
R Framework error message	Warning: there are blank lines in the 'Data X' field. Please, use NA for missing data - blank lines are simply deleted and are NOT treated as missing values.

Multiple Linear Regression - Estimated Regression Equation
Carbon_Footprint[t] = -1.2628 + 0.000107097`Population_(millions)`[t] + 3.22652HDI[t] + 6.37398e-05GDP_per_Capita[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Carbon_Footprint[t] =  -1.2628 +  0.000107097`Population_(millions)`[t] +  3.22652HDI[t] +  6.37398e-05GDP_per_Capita[t]  + e[t] \tabularnewline
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=316108&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Carbon_Footprint[t] =  -1.2628 +  0.000107097`Population_(millions)`[t] +  3.22652HDI[t] +  6.37398e-05GDP_per_Capita[t]  + e[t][/C][/ROW]
[ROW][C][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=316108&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=316108&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
Carbon_Footprint[t] = -1.2628 + 0.000107097`Population_(millions)`[t] + 3.22652HDI[t] + 6.37398e-05GDP_per_Capita[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)	-1.263	0.4485	-2.8160e+00	0.005484	0.002742
`Population_(millions)`	+0.0001071	0.0005463	+1.9610e-01	0.8448	0.4224
HDI	+3.227	0.7154	+4.5100e+00	1.256e-05	6.279e-06
GDP_per_Capita	+6.374e-05	5.685e-06	+1.1210e+01	7.847e-22	3.924e-22

\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) & -1.263 &  0.4485 & -2.8160e+00 &  0.005484 &  0.002742 \tabularnewline
`Population_(millions)` & +0.0001071 &  0.0005463 & +1.9610e-01 &  0.8448 &  0.4224 \tabularnewline
HDI & +3.227 &  0.7154 & +4.5100e+00 &  1.256e-05 &  6.279e-06 \tabularnewline
GDP_per_Capita & +6.374e-05 &  5.685e-06 & +1.1210e+01 &  7.847e-22 &  3.924e-22 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=316108&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]-1.263[/C][C] 0.4485[/C][C]-2.8160e+00[/C][C] 0.005484[/C][C] 0.002742[/C][/ROW]
[ROW][C]`Population_(millions)`[/C][C]+0.0001071[/C][C] 0.0005463[/C][C]+1.9610e-01[/C][C] 0.8448[/C][C] 0.4224[/C][/ROW]
[ROW][C]HDI[/C][C]+3.227[/C][C] 0.7154[/C][C]+4.5100e+00[/C][C] 1.256e-05[/C][C] 6.279e-06[/C][/ROW]
[ROW][C]GDP_per_Capita[/C][C]+6.374e-05[/C][C] 5.685e-06[/C][C]+1.1210e+01[/C][C] 7.847e-22[/C][C] 3.924e-22[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=316108&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=316108&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)	-1.263	0.4485	-2.8160e+00	0.005484	0.002742
`Population_(millions)`	+0.0001071	0.0005463	+1.9610e-01	0.8448	0.4224
HDI	+3.227	0.7154	+4.5100e+00	1.256e-05	6.279e-06
GDP_per_Capita	+6.374e-05	5.685e-06	+1.1210e+01	7.847e-22	3.924e-22

Multiple Linear Regression - Regression Statistics
Multiple R	0.8459
R-squared	0.7155
Adjusted R-squared	0.7101
F-TEST (value)	132.4
F-TEST (DF numerator)	3
F-TEST (DF denominator)	158
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	1.045
Sum Squared Residuals	172.4

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R &  0.8459 \tabularnewline
R-squared &  0.7155 \tabularnewline
Adjusted R-squared &  0.7101 \tabularnewline
F-TEST (value) &  132.4 \tabularnewline
F-TEST (DF numerator) & 3 \tabularnewline
F-TEST (DF denominator) & 158 \tabularnewline
p-value &  0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation &  1.045 \tabularnewline
Sum Squared Residuals &  172.4 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=316108&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C] 0.8459[/C][/ROW]
[ROW][C]R-squared[/C][C] 0.7155[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C] 0.7101[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C] 132.4[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]3[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]158[/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] 1.045[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C] 172.4[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=316108&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=316108&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	0.8459
R-squared	0.7155
Adjusted R-squared	0.7101
F-TEST (value)	132.4
F-TEST (DF numerator)	3
F-TEST (DF denominator)	158
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	1.045
Sum Squared Residuals	172.4

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=316108&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=316108&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=316108&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	0.18	0.2638	-0.08377
2	0.87	1.382	-0.5119
3	1.14	1.443	-0.3028
4	0.2	0.7146	-0.5146
5	1.08	2.283	-1.203
6	0.89	1.311	-0.4213
7	4.85	5.986	-1.136
8	4.14	4.846	-0.7056
9	1.25	1.611	-0.361
10	4.46	2.697	1.763
11	6.19	2.932	3.258
12	0.26	0.6153	-0.3553
13	3.28	2.288	0.9916
14	2.57	1.721	0.849
15	4.43	4.673	-0.2429
16	0.51	0.3346	0.1754
17	0.63	0.7936	-0.1636
18	0.67	0.9792	-0.3092
19	1.74	1.393	0.3469
20	2.36	1.457	0.9027
21	0.91	2.022	-1.112
22	3.24	4.482	-1.242
23	2.08	1.74	0.3399
24	0.12	0.04008	0.07992
25	0.04	0.01424	0.02576
26	0.19	0.4338	-0.2438
27	5	5.001	-0.0008036
28	0.08	-0.03695	0.1169
29	0.01	0.0709	-0.0609
30	2.04	2.343	-0.303
31	2.32	1.566	0.7545
32	0.67	1.531	-0.861
33	0.25	0.4054	-0.1554
34	0.47	0.7007	-0.2307
35	0.07	0.121	-0.05096
36	1.37	1.762	-0.3923
37	2.21	2.309	-0.09909
38	1.23	1.553	-0.3227
39	2.94	3.485	-0.5448
40	3.42	2.927	0.4929
41	2.6	5.621	-3.021
42	1.47	1.512	-0.04209
43	0.86	1.417	-0.557
44	1.08	1.425	-0.3452
45	1.02	1.159	-0.1389
46	0.84	1.103	-0.263
47	3.17	2.143	1.028
48	0.03	0.02423	0.005769
49	0.07	0.1586	-0.08861
50	1.06	1.328	-0.2682
51	2.71	4.511	-1.801
52	0.43	1.663	-1.233
53	0.21	0.1893	0.0207
54	0.83	1.394	-0.5641
55	3.28	4.667	-1.387
56	0.43	0.6828	-0.2528
57	2.58	3.17	-0.5896
58	0.7	0.9454	-0.2454
59	0.16	0.09057	0.06943
60	0.09	0.1359	-0.04594
61	1.25	0.9784	0.2716
62	0.15	0.3348	-0.1848
63	0.6	0.8509	-0.2509
64	1.9	2.274	-0.3741
65	0.61	0.9021	-0.2921
66	0.64	1.193	-0.5528
67	1.72	1.676	0.0437
68	1.36	1.211	0.1493
69	3.22	5.043	-1.823
70	4.59	3.759	0.8311
71	2.77	3.999	-1.229
72	1.09	1.394	-0.3037
73	3.69	4.567	-0.8773
74	1.09	1.452	-0.362
75	4.59	1.974	2.616
76	0.2	0.5518	-0.3518
77	4.17	3.154	1.016
78	6.89	4.05	2.84
79	0.95	0.9062	0.04382
80	0.09	0.6236	-0.5336
81	1.66	2.226	-0.5662
82	2.52	1.773	0.7473
83	0.51	0.3715	0.1385
84	0.14	0.1181	0.02188
85	2.33	1.499	0.831
86	2.15	2.332	-0.1817
87	12.65	8.918	3.732
88	2.06	1.455	0.6051
89	0.07	0.4142	-0.3442
90	0.07	0.1578	-0.08779
91	2.1	1.878	0.2217
92	0.1	0.1089	-0.008912
93	0.55	0.448	0.102
94	1.99	1.783	0.2066
95	1.74	1.815	-0.07533
96	1.03	1.057	-0.02725
97	2.09	1.266	0.8235
98	2.13	1.781	0.3493
99	0.67	0.9419	-0.2719
100	0.17	0.09712	0.07288
101	0.09	0.5241	-0.4341
102	1.02	1.113	-0.09273
103	0.16	0.5271	-0.3671
104	3.23	5.123	-1.893
105	2.84	4.063	-1.223
106	0.45	0.8742	-0.4242
107	0.1	-0.1378	0.2377
108	0.21	0.535	-0.325
109	5.8	2.728	3.072
110	0.38	0.5441	-0.1641
111	1.44	1.758	-0.3181
112	0.35	0.4705	-0.1205
113	0.97	1.126	-0.1564
114	0.67	1.458	-0.7881
115	0.34	1.029	-0.6887
116	2.64	2.329	0.3108
117	2.15	2.896	-0.7462
118	9.57	7.818	1.752
119	1.46	1.876	-0.4158
120	3.87	2.151	1.719
121	0.07	0.3272	-0.2572
122	3.34	1.95	1.39
123	1.56	1.586	-0.02575
124	0.96	1.255	-0.295
125	0.37	0.6001	-0.2301
126	4.21	2.922	1.288
127	0.3	0.2917	0.008335
128	1.66	1.6	0.06002
129	0.07	0.06031	0.009685
130	5.91	5.06	0.8501
131	2.82	2.602	0.218
132	4.27	3.173	1.097
133	0	0.4556	-0.4556
134	2.34	1.388	0.952
135	2.22	3.59	-1.37
136	0.52	1.343	-0.8229
137	3.01	1.55	1.46
138	0.67	0.7443	-0.07426
139	3.88	5.427	-1.547
140	4.26	7.38	-3.12
141	0.13	0.7918	-0.6618
142	0.17	0.4366	-0.2666
143	1.54	1.417	0.1233
144	0.06	1.003	-0.9426
145	0.31	0.2914	0.0186
146	0.88	1.336	-0.4564
147	6.89	2.389	4.501
148	1.11	1.336	-0.2259
149	1.92	1.863	0.05742
150	4.13	1.269	2.861
151	0.08	0.3282	-0.2482
152	1.92	1.359	0.5615
153	3.14	4.26	-1.12
154	6.37	4.018	2.352
155	5.9	4.877	1.023
156	0.98	2.194	-1.214
157	1.41	1.002	0.4085
158	2.13	1.858	0.2725
159	0.79	0.9741	-0.1841
160	0.42	0.436	-0.01603
161	0.24	0.721	-0.481
162	0.53	0.3749	0.1551

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 &  0.18 &  0.2638 & -0.08377 \tabularnewline
2 &  0.87 &  1.382 & -0.5119 \tabularnewline
3 &  1.14 &  1.443 & -0.3028 \tabularnewline
4 &  0.2 &  0.7146 & -0.5146 \tabularnewline
5 &  1.08 &  2.283 & -1.203 \tabularnewline
6 &  0.89 &  1.311 & -0.4213 \tabularnewline
7 &  4.85 &  5.986 & -1.136 \tabularnewline
8 &  4.14 &  4.846 & -0.7056 \tabularnewline
9 &  1.25 &  1.611 & -0.361 \tabularnewline
10 &  4.46 &  2.697 &  1.763 \tabularnewline
11 &  6.19 &  2.932 &  3.258 \tabularnewline
12 &  0.26 &  0.6153 & -0.3553 \tabularnewline
13 &  3.28 &  2.288 &  0.9916 \tabularnewline
14 &  2.57 &  1.721 &  0.849 \tabularnewline
15 &  4.43 &  4.673 & -0.2429 \tabularnewline
16 &  0.51 &  0.3346 &  0.1754 \tabularnewline
17 &  0.63 &  0.7936 & -0.1636 \tabularnewline
18 &  0.67 &  0.9792 & -0.3092 \tabularnewline
19 &  1.74 &  1.393 &  0.3469 \tabularnewline
20 &  2.36 &  1.457 &  0.9027 \tabularnewline
21 &  0.91 &  2.022 & -1.112 \tabularnewline
22 &  3.24 &  4.482 & -1.242 \tabularnewline
23 &  2.08 &  1.74 &  0.3399 \tabularnewline
24 &  0.12 &  0.04008 &  0.07992 \tabularnewline
25 &  0.04 &  0.01424 &  0.02576 \tabularnewline
26 &  0.19 &  0.4338 & -0.2438 \tabularnewline
27 &  5 &  5.001 & -0.0008036 \tabularnewline
28 &  0.08 & -0.03695 &  0.1169 \tabularnewline
29 &  0.01 &  0.0709 & -0.0609 \tabularnewline
30 &  2.04 &  2.343 & -0.303 \tabularnewline
31 &  2.32 &  1.566 &  0.7545 \tabularnewline
32 &  0.67 &  1.531 & -0.861 \tabularnewline
33 &  0.25 &  0.4054 & -0.1554 \tabularnewline
34 &  0.47 &  0.7007 & -0.2307 \tabularnewline
35 &  0.07 &  0.121 & -0.05096 \tabularnewline
36 &  1.37 &  1.762 & -0.3923 \tabularnewline
37 &  2.21 &  2.309 & -0.09909 \tabularnewline
38 &  1.23 &  1.553 & -0.3227 \tabularnewline
39 &  2.94 &  3.485 & -0.5448 \tabularnewline
40 &  3.42 &  2.927 &  0.4929 \tabularnewline
41 &  2.6 &  5.621 & -3.021 \tabularnewline
42 &  1.47 &  1.512 & -0.04209 \tabularnewline
43 &  0.86 &  1.417 & -0.557 \tabularnewline
44 &  1.08 &  1.425 & -0.3452 \tabularnewline
45 &  1.02 &  1.159 & -0.1389 \tabularnewline
46 &  0.84 &  1.103 & -0.263 \tabularnewline
47 &  3.17 &  2.143 &  1.028 \tabularnewline
48 &  0.03 &  0.02423 &  0.005769 \tabularnewline
49 &  0.07 &  0.1586 & -0.08861 \tabularnewline
50 &  1.06 &  1.328 & -0.2682 \tabularnewline
51 &  2.71 &  4.511 & -1.801 \tabularnewline
52 &  0.43 &  1.663 & -1.233 \tabularnewline
53 &  0.21 &  0.1893 &  0.0207 \tabularnewline
54 &  0.83 &  1.394 & -0.5641 \tabularnewline
55 &  3.28 &  4.667 & -1.387 \tabularnewline
56 &  0.43 &  0.6828 & -0.2528 \tabularnewline
57 &  2.58 &  3.17 & -0.5896 \tabularnewline
58 &  0.7 &  0.9454 & -0.2454 \tabularnewline
59 &  0.16 &  0.09057 &  0.06943 \tabularnewline
60 &  0.09 &  0.1359 & -0.04594 \tabularnewline
61 &  1.25 &  0.9784 &  0.2716 \tabularnewline
62 &  0.15 &  0.3348 & -0.1848 \tabularnewline
63 &  0.6 &  0.8509 & -0.2509 \tabularnewline
64 &  1.9 &  2.274 & -0.3741 \tabularnewline
65 &  0.61 &  0.9021 & -0.2921 \tabularnewline
66 &  0.64 &  1.193 & -0.5528 \tabularnewline
67 &  1.72 &  1.676 &  0.0437 \tabularnewline
68 &  1.36 &  1.211 &  0.1493 \tabularnewline
69 &  3.22 &  5.043 & -1.823 \tabularnewline
70 &  4.59 &  3.759 &  0.8311 \tabularnewline
71 &  2.77 &  3.999 & -1.229 \tabularnewline
72 &  1.09 &  1.394 & -0.3037 \tabularnewline
73 &  3.69 &  4.567 & -0.8773 \tabularnewline
74 &  1.09 &  1.452 & -0.362 \tabularnewline
75 &  4.59 &  1.974 &  2.616 \tabularnewline
76 &  0.2 &  0.5518 & -0.3518 \tabularnewline
77 &  4.17 &  3.154 &  1.016 \tabularnewline
78 &  6.89 &  4.05 &  2.84 \tabularnewline
79 &  0.95 &  0.9062 &  0.04382 \tabularnewline
80 &  0.09 &  0.6236 & -0.5336 \tabularnewline
81 &  1.66 &  2.226 & -0.5662 \tabularnewline
82 &  2.52 &  1.773 &  0.7473 \tabularnewline
83 &  0.51 &  0.3715 &  0.1385 \tabularnewline
84 &  0.14 &  0.1181 &  0.02188 \tabularnewline
85 &  2.33 &  1.499 &  0.831 \tabularnewline
86 &  2.15 &  2.332 & -0.1817 \tabularnewline
87 &  12.65 &  8.918 &  3.732 \tabularnewline
88 &  2.06 &  1.455 &  0.6051 \tabularnewline
89 &  0.07 &  0.4142 & -0.3442 \tabularnewline
90 &  0.07 &  0.1578 & -0.08779 \tabularnewline
91 &  2.1 &  1.878 &  0.2217 \tabularnewline
92 &  0.1 &  0.1089 & -0.008912 \tabularnewline
93 &  0.55 &  0.448 &  0.102 \tabularnewline
94 &  1.99 &  1.783 &  0.2066 \tabularnewline
95 &  1.74 &  1.815 & -0.07533 \tabularnewline
96 &  1.03 &  1.057 & -0.02725 \tabularnewline
97 &  2.09 &  1.266 &  0.8235 \tabularnewline
98 &  2.13 &  1.781 &  0.3493 \tabularnewline
99 &  0.67 &  0.9419 & -0.2719 \tabularnewline
100 &  0.17 &  0.09712 &  0.07288 \tabularnewline
101 &  0.09 &  0.5241 & -0.4341 \tabularnewline
102 &  1.02 &  1.113 & -0.09273 \tabularnewline
103 &  0.16 &  0.5271 & -0.3671 \tabularnewline
104 &  3.23 &  5.123 & -1.893 \tabularnewline
105 &  2.84 &  4.063 & -1.223 \tabularnewline
106 &  0.45 &  0.8742 & -0.4242 \tabularnewline
107 &  0.1 & -0.1378 &  0.2377 \tabularnewline
108 &  0.21 &  0.535 & -0.325 \tabularnewline
109 &  5.8 &  2.728 &  3.072 \tabularnewline
110 &  0.38 &  0.5441 & -0.1641 \tabularnewline
111 &  1.44 &  1.758 & -0.3181 \tabularnewline
112 &  0.35 &  0.4705 & -0.1205 \tabularnewline
113 &  0.97 &  1.126 & -0.1564 \tabularnewline
114 &  0.67 &  1.458 & -0.7881 \tabularnewline
115 &  0.34 &  1.029 & -0.6887 \tabularnewline
116 &  2.64 &  2.329 &  0.3108 \tabularnewline
117 &  2.15 &  2.896 & -0.7462 \tabularnewline
118 &  9.57 &  7.818 &  1.752 \tabularnewline
119 &  1.46 &  1.876 & -0.4158 \tabularnewline
120 &  3.87 &  2.151 &  1.719 \tabularnewline
121 &  0.07 &  0.3272 & -0.2572 \tabularnewline
122 &  3.34 &  1.95 &  1.39 \tabularnewline
123 &  1.56 &  1.586 & -0.02575 \tabularnewline
124 &  0.96 &  1.255 & -0.295 \tabularnewline
125 &  0.37 &  0.6001 & -0.2301 \tabularnewline
126 &  4.21 &  2.922 &  1.288 \tabularnewline
127 &  0.3 &  0.2917 &  0.008335 \tabularnewline
128 &  1.66 &  1.6 &  0.06002 \tabularnewline
129 &  0.07 &  0.06031 &  0.009685 \tabularnewline
130 &  5.91 &  5.06 &  0.8501 \tabularnewline
131 &  2.82 &  2.602 &  0.218 \tabularnewline
132 &  4.27 &  3.173 &  1.097 \tabularnewline
133 &  0 &  0.4556 & -0.4556 \tabularnewline
134 &  2.34 &  1.388 &  0.952 \tabularnewline
135 &  2.22 &  3.59 & -1.37 \tabularnewline
136 &  0.52 &  1.343 & -0.8229 \tabularnewline
137 &  3.01 &  1.55 &  1.46 \tabularnewline
138 &  0.67 &  0.7443 & -0.07426 \tabularnewline
139 &  3.88 &  5.427 & -1.547 \tabularnewline
140 &  4.26 &  7.38 & -3.12 \tabularnewline
141 &  0.13 &  0.7918 & -0.6618 \tabularnewline
142 &  0.17 &  0.4366 & -0.2666 \tabularnewline
143 &  1.54 &  1.417 &  0.1233 \tabularnewline
144 &  0.06 &  1.003 & -0.9426 \tabularnewline
145 &  0.31 &  0.2914 &  0.0186 \tabularnewline
146 &  0.88 &  1.336 & -0.4564 \tabularnewline
147 &  6.89 &  2.389 &  4.501 \tabularnewline
148 &  1.11 &  1.336 & -0.2259 \tabularnewline
149 &  1.92 &  1.863 &  0.05742 \tabularnewline
150 &  4.13 &  1.269 &  2.861 \tabularnewline
151 &  0.08 &  0.3282 & -0.2482 \tabularnewline
152 &  1.92 &  1.359 &  0.5615 \tabularnewline
153 &  3.14 &  4.26 & -1.12 \tabularnewline
154 &  6.37 &  4.018 &  2.352 \tabularnewline
155 &  5.9 &  4.877 &  1.023 \tabularnewline
156 &  0.98 &  2.194 & -1.214 \tabularnewline
157 &  1.41 &  1.002 &  0.4085 \tabularnewline
158 &  2.13 &  1.858 &  0.2725 \tabularnewline
159 &  0.79 &  0.9741 & -0.1841 \tabularnewline
160 &  0.42 &  0.436 & -0.01603 \tabularnewline
161 &  0.24 &  0.721 & -0.481 \tabularnewline
162 &  0.53 &  0.3749 &  0.1551 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=316108&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] 0.18[/C][C] 0.2638[/C][C]-0.08377[/C][/ROW]
[ROW][C]2[/C][C] 0.87[/C][C] 1.382[/C][C]-0.5119[/C][/ROW]
[ROW][C]3[/C][C] 1.14[/C][C] 1.443[/C][C]-0.3028[/C][/ROW]
[ROW][C]4[/C][C] 0.2[/C][C] 0.7146[/C][C]-0.5146[/C][/ROW]
[ROW][C]5[/C][C] 1.08[/C][C] 2.283[/C][C]-1.203[/C][/ROW]
[ROW][C]6[/C][C] 0.89[/C][C] 1.311[/C][C]-0.4213[/C][/ROW]
[ROW][C]7[/C][C] 4.85[/C][C] 5.986[/C][C]-1.136[/C][/ROW]
[ROW][C]8[/C][C] 4.14[/C][C] 4.846[/C][C]-0.7056[/C][/ROW]
[ROW][C]9[/C][C] 1.25[/C][C] 1.611[/C][C]-0.361[/C][/ROW]
[ROW][C]10[/C][C] 4.46[/C][C] 2.697[/C][C] 1.763[/C][/ROW]
[ROW][C]11[/C][C] 6.19[/C][C] 2.932[/C][C] 3.258[/C][/ROW]
[ROW][C]12[/C][C] 0.26[/C][C] 0.6153[/C][C]-0.3553[/C][/ROW]
[ROW][C]13[/C][C] 3.28[/C][C] 2.288[/C][C] 0.9916[/C][/ROW]
[ROW][C]14[/C][C] 2.57[/C][C] 1.721[/C][C] 0.849[/C][/ROW]
[ROW][C]15[/C][C] 4.43[/C][C] 4.673[/C][C]-0.2429[/C][/ROW]
[ROW][C]16[/C][C] 0.51[/C][C] 0.3346[/C][C] 0.1754[/C][/ROW]
[ROW][C]17[/C][C] 0.63[/C][C] 0.7936[/C][C]-0.1636[/C][/ROW]
[ROW][C]18[/C][C] 0.67[/C][C] 0.9792[/C][C]-0.3092[/C][/ROW]
[ROW][C]19[/C][C] 1.74[/C][C] 1.393[/C][C] 0.3469[/C][/ROW]
[ROW][C]20[/C][C] 2.36[/C][C] 1.457[/C][C] 0.9027[/C][/ROW]
[ROW][C]21[/C][C] 0.91[/C][C] 2.022[/C][C]-1.112[/C][/ROW]
[ROW][C]22[/C][C] 3.24[/C][C] 4.482[/C][C]-1.242[/C][/ROW]
[ROW][C]23[/C][C] 2.08[/C][C] 1.74[/C][C] 0.3399[/C][/ROW]
[ROW][C]24[/C][C] 0.12[/C][C] 0.04008[/C][C] 0.07992[/C][/ROW]
[ROW][C]25[/C][C] 0.04[/C][C] 0.01424[/C][C] 0.02576[/C][/ROW]
[ROW][C]26[/C][C] 0.19[/C][C] 0.4338[/C][C]-0.2438[/C][/ROW]
[ROW][C]27[/C][C] 5[/C][C] 5.001[/C][C]-0.0008036[/C][/ROW]
[ROW][C]28[/C][C] 0.08[/C][C]-0.03695[/C][C] 0.1169[/C][/ROW]
[ROW][C]29[/C][C] 0.01[/C][C] 0.0709[/C][C]-0.0609[/C][/ROW]
[ROW][C]30[/C][C] 2.04[/C][C] 2.343[/C][C]-0.303[/C][/ROW]
[ROW][C]31[/C][C] 2.32[/C][C] 1.566[/C][C] 0.7545[/C][/ROW]
[ROW][C]32[/C][C] 0.67[/C][C] 1.531[/C][C]-0.861[/C][/ROW]
[ROW][C]33[/C][C] 0.25[/C][C] 0.4054[/C][C]-0.1554[/C][/ROW]
[ROW][C]34[/C][C] 0.47[/C][C] 0.7007[/C][C]-0.2307[/C][/ROW]
[ROW][C]35[/C][C] 0.07[/C][C] 0.121[/C][C]-0.05096[/C][/ROW]
[ROW][C]36[/C][C] 1.37[/C][C] 1.762[/C][C]-0.3923[/C][/ROW]
[ROW][C]37[/C][C] 2.21[/C][C] 2.309[/C][C]-0.09909[/C][/ROW]
[ROW][C]38[/C][C] 1.23[/C][C] 1.553[/C][C]-0.3227[/C][/ROW]
[ROW][C]39[/C][C] 2.94[/C][C] 3.485[/C][C]-0.5448[/C][/ROW]
[ROW][C]40[/C][C] 3.42[/C][C] 2.927[/C][C] 0.4929[/C][/ROW]
[ROW][C]41[/C][C] 2.6[/C][C] 5.621[/C][C]-3.021[/C][/ROW]
[ROW][C]42[/C][C] 1.47[/C][C] 1.512[/C][C]-0.04209[/C][/ROW]
[ROW][C]43[/C][C] 0.86[/C][C] 1.417[/C][C]-0.557[/C][/ROW]
[ROW][C]44[/C][C] 1.08[/C][C] 1.425[/C][C]-0.3452[/C][/ROW]
[ROW][C]45[/C][C] 1.02[/C][C] 1.159[/C][C]-0.1389[/C][/ROW]
[ROW][C]46[/C][C] 0.84[/C][C] 1.103[/C][C]-0.263[/C][/ROW]
[ROW][C]47[/C][C] 3.17[/C][C] 2.143[/C][C] 1.028[/C][/ROW]
[ROW][C]48[/C][C] 0.03[/C][C] 0.02423[/C][C] 0.005769[/C][/ROW]
[ROW][C]49[/C][C] 0.07[/C][C] 0.1586[/C][C]-0.08861[/C][/ROW]
[ROW][C]50[/C][C] 1.06[/C][C] 1.328[/C][C]-0.2682[/C][/ROW]
[ROW][C]51[/C][C] 2.71[/C][C] 4.511[/C][C]-1.801[/C][/ROW]
[ROW][C]52[/C][C] 0.43[/C][C] 1.663[/C][C]-1.233[/C][/ROW]
[ROW][C]53[/C][C] 0.21[/C][C] 0.1893[/C][C] 0.0207[/C][/ROW]
[ROW][C]54[/C][C] 0.83[/C][C] 1.394[/C][C]-0.5641[/C][/ROW]
[ROW][C]55[/C][C] 3.28[/C][C] 4.667[/C][C]-1.387[/C][/ROW]
[ROW][C]56[/C][C] 0.43[/C][C] 0.6828[/C][C]-0.2528[/C][/ROW]
[ROW][C]57[/C][C] 2.58[/C][C] 3.17[/C][C]-0.5896[/C][/ROW]
[ROW][C]58[/C][C] 0.7[/C][C] 0.9454[/C][C]-0.2454[/C][/ROW]
[ROW][C]59[/C][C] 0.16[/C][C] 0.09057[/C][C] 0.06943[/C][/ROW]
[ROW][C]60[/C][C] 0.09[/C][C] 0.1359[/C][C]-0.04594[/C][/ROW]
[ROW][C]61[/C][C] 1.25[/C][C] 0.9784[/C][C] 0.2716[/C][/ROW]
[ROW][C]62[/C][C] 0.15[/C][C] 0.3348[/C][C]-0.1848[/C][/ROW]
[ROW][C]63[/C][C] 0.6[/C][C] 0.8509[/C][C]-0.2509[/C][/ROW]
[ROW][C]64[/C][C] 1.9[/C][C] 2.274[/C][C]-0.3741[/C][/ROW]
[ROW][C]65[/C][C] 0.61[/C][C] 0.9021[/C][C]-0.2921[/C][/ROW]
[ROW][C]66[/C][C] 0.64[/C][C] 1.193[/C][C]-0.5528[/C][/ROW]
[ROW][C]67[/C][C] 1.72[/C][C] 1.676[/C][C] 0.0437[/C][/ROW]
[ROW][C]68[/C][C] 1.36[/C][C] 1.211[/C][C] 0.1493[/C][/ROW]
[ROW][C]69[/C][C] 3.22[/C][C] 5.043[/C][C]-1.823[/C][/ROW]
[ROW][C]70[/C][C] 4.59[/C][C] 3.759[/C][C] 0.8311[/C][/ROW]
[ROW][C]71[/C][C] 2.77[/C][C] 3.999[/C][C]-1.229[/C][/ROW]
[ROW][C]72[/C][C] 1.09[/C][C] 1.394[/C][C]-0.3037[/C][/ROW]
[ROW][C]73[/C][C] 3.69[/C][C] 4.567[/C][C]-0.8773[/C][/ROW]
[ROW][C]74[/C][C] 1.09[/C][C] 1.452[/C][C]-0.362[/C][/ROW]
[ROW][C]75[/C][C] 4.59[/C][C] 1.974[/C][C] 2.616[/C][/ROW]
[ROW][C]76[/C][C] 0.2[/C][C] 0.5518[/C][C]-0.3518[/C][/ROW]
[ROW][C]77[/C][C] 4.17[/C][C] 3.154[/C][C] 1.016[/C][/ROW]
[ROW][C]78[/C][C] 6.89[/C][C] 4.05[/C][C] 2.84[/C][/ROW]
[ROW][C]79[/C][C] 0.95[/C][C] 0.9062[/C][C] 0.04382[/C][/ROW]
[ROW][C]80[/C][C] 0.09[/C][C] 0.6236[/C][C]-0.5336[/C][/ROW]
[ROW][C]81[/C][C] 1.66[/C][C] 2.226[/C][C]-0.5662[/C][/ROW]
[ROW][C]82[/C][C] 2.52[/C][C] 1.773[/C][C] 0.7473[/C][/ROW]
[ROW][C]83[/C][C] 0.51[/C][C] 0.3715[/C][C] 0.1385[/C][/ROW]
[ROW][C]84[/C][C] 0.14[/C][C] 0.1181[/C][C] 0.02188[/C][/ROW]
[ROW][C]85[/C][C] 2.33[/C][C] 1.499[/C][C] 0.831[/C][/ROW]
[ROW][C]86[/C][C] 2.15[/C][C] 2.332[/C][C]-0.1817[/C][/ROW]
[ROW][C]87[/C][C] 12.65[/C][C] 8.918[/C][C] 3.732[/C][/ROW]
[ROW][C]88[/C][C] 2.06[/C][C] 1.455[/C][C] 0.6051[/C][/ROW]
[ROW][C]89[/C][C] 0.07[/C][C] 0.4142[/C][C]-0.3442[/C][/ROW]
[ROW][C]90[/C][C] 0.07[/C][C] 0.1578[/C][C]-0.08779[/C][/ROW]
[ROW][C]91[/C][C] 2.1[/C][C] 1.878[/C][C] 0.2217[/C][/ROW]
[ROW][C]92[/C][C] 0.1[/C][C] 0.1089[/C][C]-0.008912[/C][/ROW]
[ROW][C]93[/C][C] 0.55[/C][C] 0.448[/C][C] 0.102[/C][/ROW]
[ROW][C]94[/C][C] 1.99[/C][C] 1.783[/C][C] 0.2066[/C][/ROW]
[ROW][C]95[/C][C] 1.74[/C][C] 1.815[/C][C]-0.07533[/C][/ROW]
[ROW][C]96[/C][C] 1.03[/C][C] 1.057[/C][C]-0.02725[/C][/ROW]
[ROW][C]97[/C][C] 2.09[/C][C] 1.266[/C][C] 0.8235[/C][/ROW]
[ROW][C]98[/C][C] 2.13[/C][C] 1.781[/C][C] 0.3493[/C][/ROW]
[ROW][C]99[/C][C] 0.67[/C][C] 0.9419[/C][C]-0.2719[/C][/ROW]
[ROW][C]100[/C][C] 0.17[/C][C] 0.09712[/C][C] 0.07288[/C][/ROW]
[ROW][C]101[/C][C] 0.09[/C][C] 0.5241[/C][C]-0.4341[/C][/ROW]
[ROW][C]102[/C][C] 1.02[/C][C] 1.113[/C][C]-0.09273[/C][/ROW]
[ROW][C]103[/C][C] 0.16[/C][C] 0.5271[/C][C]-0.3671[/C][/ROW]
[ROW][C]104[/C][C] 3.23[/C][C] 5.123[/C][C]-1.893[/C][/ROW]
[ROW][C]105[/C][C] 2.84[/C][C] 4.063[/C][C]-1.223[/C][/ROW]
[ROW][C]106[/C][C] 0.45[/C][C] 0.8742[/C][C]-0.4242[/C][/ROW]
[ROW][C]107[/C][C] 0.1[/C][C]-0.1378[/C][C] 0.2377[/C][/ROW]
[ROW][C]108[/C][C] 0.21[/C][C] 0.535[/C][C]-0.325[/C][/ROW]
[ROW][C]109[/C][C] 5.8[/C][C] 2.728[/C][C] 3.072[/C][/ROW]
[ROW][C]110[/C][C] 0.38[/C][C] 0.5441[/C][C]-0.1641[/C][/ROW]
[ROW][C]111[/C][C] 1.44[/C][C] 1.758[/C][C]-0.3181[/C][/ROW]
[ROW][C]112[/C][C] 0.35[/C][C] 0.4705[/C][C]-0.1205[/C][/ROW]
[ROW][C]113[/C][C] 0.97[/C][C] 1.126[/C][C]-0.1564[/C][/ROW]
[ROW][C]114[/C][C] 0.67[/C][C] 1.458[/C][C]-0.7881[/C][/ROW]
[ROW][C]115[/C][C] 0.34[/C][C] 1.029[/C][C]-0.6887[/C][/ROW]
[ROW][C]116[/C][C] 2.64[/C][C] 2.329[/C][C] 0.3108[/C][/ROW]
[ROW][C]117[/C][C] 2.15[/C][C] 2.896[/C][C]-0.7462[/C][/ROW]
[ROW][C]118[/C][C] 9.57[/C][C] 7.818[/C][C] 1.752[/C][/ROW]
[ROW][C]119[/C][C] 1.46[/C][C] 1.876[/C][C]-0.4158[/C][/ROW]
[ROW][C]120[/C][C] 3.87[/C][C] 2.151[/C][C] 1.719[/C][/ROW]
[ROW][C]121[/C][C] 0.07[/C][C] 0.3272[/C][C]-0.2572[/C][/ROW]
[ROW][C]122[/C][C] 3.34[/C][C] 1.95[/C][C] 1.39[/C][/ROW]
[ROW][C]123[/C][C] 1.56[/C][C] 1.586[/C][C]-0.02575[/C][/ROW]
[ROW][C]124[/C][C] 0.96[/C][C] 1.255[/C][C]-0.295[/C][/ROW]
[ROW][C]125[/C][C] 0.37[/C][C] 0.6001[/C][C]-0.2301[/C][/ROW]
[ROW][C]126[/C][C] 4.21[/C][C] 2.922[/C][C] 1.288[/C][/ROW]
[ROW][C]127[/C][C] 0.3[/C][C] 0.2917[/C][C] 0.008335[/C][/ROW]
[ROW][C]128[/C][C] 1.66[/C][C] 1.6[/C][C] 0.06002[/C][/ROW]
[ROW][C]129[/C][C] 0.07[/C][C] 0.06031[/C][C] 0.009685[/C][/ROW]
[ROW][C]130[/C][C] 5.91[/C][C] 5.06[/C][C] 0.8501[/C][/ROW]
[ROW][C]131[/C][C] 2.82[/C][C] 2.602[/C][C] 0.218[/C][/ROW]
[ROW][C]132[/C][C] 4.27[/C][C] 3.173[/C][C] 1.097[/C][/ROW]
[ROW][C]133[/C][C] 0[/C][C] 0.4556[/C][C]-0.4556[/C][/ROW]
[ROW][C]134[/C][C] 2.34[/C][C] 1.388[/C][C] 0.952[/C][/ROW]
[ROW][C]135[/C][C] 2.22[/C][C] 3.59[/C][C]-1.37[/C][/ROW]
[ROW][C]136[/C][C] 0.52[/C][C] 1.343[/C][C]-0.8229[/C][/ROW]
[ROW][C]137[/C][C] 3.01[/C][C] 1.55[/C][C] 1.46[/C][/ROW]
[ROW][C]138[/C][C] 0.67[/C][C] 0.7443[/C][C]-0.07426[/C][/ROW]
[ROW][C]139[/C][C] 3.88[/C][C] 5.427[/C][C]-1.547[/C][/ROW]
[ROW][C]140[/C][C] 4.26[/C][C] 7.38[/C][C]-3.12[/C][/ROW]
[ROW][C]141[/C][C] 0.13[/C][C] 0.7918[/C][C]-0.6618[/C][/ROW]
[ROW][C]142[/C][C] 0.17[/C][C] 0.4366[/C][C]-0.2666[/C][/ROW]
[ROW][C]143[/C][C] 1.54[/C][C] 1.417[/C][C] 0.1233[/C][/ROW]
[ROW][C]144[/C][C] 0.06[/C][C] 1.003[/C][C]-0.9426[/C][/ROW]
[ROW][C]145[/C][C] 0.31[/C][C] 0.2914[/C][C] 0.0186[/C][/ROW]
[ROW][C]146[/C][C] 0.88[/C][C] 1.336[/C][C]-0.4564[/C][/ROW]
[ROW][C]147[/C][C] 6.89[/C][C] 2.389[/C][C] 4.501[/C][/ROW]
[ROW][C]148[/C][C] 1.11[/C][C] 1.336[/C][C]-0.2259[/C][/ROW]
[ROW][C]149[/C][C] 1.92[/C][C] 1.863[/C][C] 0.05742[/C][/ROW]
[ROW][C]150[/C][C] 4.13[/C][C] 1.269[/C][C] 2.861[/C][/ROW]
[ROW][C]151[/C][C] 0.08[/C][C] 0.3282[/C][C]-0.2482[/C][/ROW]
[ROW][C]152[/C][C] 1.92[/C][C] 1.359[/C][C] 0.5615[/C][/ROW]
[ROW][C]153[/C][C] 3.14[/C][C] 4.26[/C][C]-1.12[/C][/ROW]
[ROW][C]154[/C][C] 6.37[/C][C] 4.018[/C][C] 2.352[/C][/ROW]
[ROW][C]155[/C][C] 5.9[/C][C] 4.877[/C][C] 1.023[/C][/ROW]
[ROW][C]156[/C][C] 0.98[/C][C] 2.194[/C][C]-1.214[/C][/ROW]
[ROW][C]157[/C][C] 1.41[/C][C] 1.002[/C][C] 0.4085[/C][/ROW]
[ROW][C]158[/C][C] 2.13[/C][C] 1.858[/C][C] 0.2725[/C][/ROW]
[ROW][C]159[/C][C] 0.79[/C][C] 0.9741[/C][C]-0.1841[/C][/ROW]
[ROW][C]160[/C][C] 0.42[/C][C] 0.436[/C][C]-0.01603[/C][/ROW]
[ROW][C]161[/C][C] 0.24[/C][C] 0.721[/C][C]-0.481[/C][/ROW]
[ROW][C]162[/C][C] 0.53[/C][C] 0.3749[/C][C] 0.1551[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=316108&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=316108&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	0.18	0.2638	-0.08377
2	0.87	1.382	-0.5119
3	1.14	1.443	-0.3028
4	0.2	0.7146	-0.5146
5	1.08	2.283	-1.203
6	0.89	1.311	-0.4213
7	4.85	5.986	-1.136
8	4.14	4.846	-0.7056
9	1.25	1.611	-0.361
10	4.46	2.697	1.763
11	6.19	2.932	3.258
12	0.26	0.6153	-0.3553
13	3.28	2.288	0.9916
14	2.57	1.721	0.849
15	4.43	4.673	-0.2429
16	0.51	0.3346	0.1754
17	0.63	0.7936	-0.1636
18	0.67	0.9792	-0.3092
19	1.74	1.393	0.3469
20	2.36	1.457	0.9027
21	0.91	2.022	-1.112
22	3.24	4.482	-1.242
23	2.08	1.74	0.3399
24	0.12	0.04008	0.07992
25	0.04	0.01424	0.02576
26	0.19	0.4338	-0.2438
27	5	5.001	-0.0008036
28	0.08	-0.03695	0.1169
29	0.01	0.0709	-0.0609
30	2.04	2.343	-0.303
31	2.32	1.566	0.7545
32	0.67	1.531	-0.861
33	0.25	0.4054	-0.1554
34	0.47	0.7007	-0.2307
35	0.07	0.121	-0.05096
36	1.37	1.762	-0.3923
37	2.21	2.309	-0.09909
38	1.23	1.553	-0.3227
39	2.94	3.485	-0.5448
40	3.42	2.927	0.4929
41	2.6	5.621	-3.021
42	1.47	1.512	-0.04209
43	0.86	1.417	-0.557
44	1.08	1.425	-0.3452
45	1.02	1.159	-0.1389
46	0.84	1.103	-0.263
47	3.17	2.143	1.028
48	0.03	0.02423	0.005769
49	0.07	0.1586	-0.08861
50	1.06	1.328	-0.2682
51	2.71	4.511	-1.801
52	0.43	1.663	-1.233
53	0.21	0.1893	0.0207
54	0.83	1.394	-0.5641
55	3.28	4.667	-1.387
56	0.43	0.6828	-0.2528
57	2.58	3.17	-0.5896
58	0.7	0.9454	-0.2454
59	0.16	0.09057	0.06943
60	0.09	0.1359	-0.04594
61	1.25	0.9784	0.2716
62	0.15	0.3348	-0.1848
63	0.6	0.8509	-0.2509
64	1.9	2.274	-0.3741
65	0.61	0.9021	-0.2921
66	0.64	1.193	-0.5528
67	1.72	1.676	0.0437
68	1.36	1.211	0.1493
69	3.22	5.043	-1.823
70	4.59	3.759	0.8311
71	2.77	3.999	-1.229
72	1.09	1.394	-0.3037
73	3.69	4.567	-0.8773
74	1.09	1.452	-0.362
75	4.59	1.974	2.616
76	0.2	0.5518	-0.3518
77	4.17	3.154	1.016
78	6.89	4.05	2.84
79	0.95	0.9062	0.04382
80	0.09	0.6236	-0.5336
81	1.66	2.226	-0.5662
82	2.52	1.773	0.7473
83	0.51	0.3715	0.1385
84	0.14	0.1181	0.02188
85	2.33	1.499	0.831
86	2.15	2.332	-0.1817
87	12.65	8.918	3.732
88	2.06	1.455	0.6051
89	0.07	0.4142	-0.3442
90	0.07	0.1578	-0.08779
91	2.1	1.878	0.2217
92	0.1	0.1089	-0.008912
93	0.55	0.448	0.102
94	1.99	1.783	0.2066
95	1.74	1.815	-0.07533
96	1.03	1.057	-0.02725
97	2.09	1.266	0.8235
98	2.13	1.781	0.3493
99	0.67	0.9419	-0.2719
100	0.17	0.09712	0.07288
101	0.09	0.5241	-0.4341
102	1.02	1.113	-0.09273
103	0.16	0.5271	-0.3671
104	3.23	5.123	-1.893
105	2.84	4.063	-1.223
106	0.45	0.8742	-0.4242
107	0.1	-0.1378	0.2377
108	0.21	0.535	-0.325
109	5.8	2.728	3.072
110	0.38	0.5441	-0.1641
111	1.44	1.758	-0.3181
112	0.35	0.4705	-0.1205
113	0.97	1.126	-0.1564
114	0.67	1.458	-0.7881
115	0.34	1.029	-0.6887
116	2.64	2.329	0.3108
117	2.15	2.896	-0.7462
118	9.57	7.818	1.752
119	1.46	1.876	-0.4158
120	3.87	2.151	1.719
121	0.07	0.3272	-0.2572
122	3.34	1.95	1.39
123	1.56	1.586	-0.02575
124	0.96	1.255	-0.295
125	0.37	0.6001	-0.2301
126	4.21	2.922	1.288
127	0.3	0.2917	0.008335
128	1.66	1.6	0.06002
129	0.07	0.06031	0.009685
130	5.91	5.06	0.8501
131	2.82	2.602	0.218
132	4.27	3.173	1.097
133	0	0.4556	-0.4556
134	2.34	1.388	0.952
135	2.22	3.59	-1.37
136	0.52	1.343	-0.8229
137	3.01	1.55	1.46
138	0.67	0.7443	-0.07426
139	3.88	5.427	-1.547
140	4.26	7.38	-3.12
141	0.13	0.7918	-0.6618
142	0.17	0.4366	-0.2666
143	1.54	1.417	0.1233
144	0.06	1.003	-0.9426
145	0.31	0.2914	0.0186
146	0.88	1.336	-0.4564
147	6.89	2.389	4.501
148	1.11	1.336	-0.2259
149	1.92	1.863	0.05742
150	4.13	1.269	2.861
151	0.08	0.3282	-0.2482
152	1.92	1.359	0.5615
153	3.14	4.26	-1.12
154	6.37	4.018	2.352
155	5.9	4.877	1.023
156	0.98	2.194	-1.214
157	1.41	1.002	0.4085
158	2.13	1.858	0.2725
159	0.79	0.9741	-0.1841
160	0.42	0.436	-0.01603
161	0.24	0.721	-0.481
162	0.53	0.3749	0.1551

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
7	0.03881	0.07762	0.9612
8	0.01215	0.0243	0.9879
9	0.003387	0.006773	0.9966
10	0.3072	0.6144	0.6928
11	0.9006	0.1988	0.09939
12	0.9465	0.1069	0.05347
13	0.9291	0.1419	0.07095
14	0.9007	0.1987	0.09935
15	0.8584	0.2832	0.1416
16	0.8059	0.3882	0.1941
17	0.7538	0.4924	0.2462
18	0.7019	0.5961	0.2981
19	0.6319	0.7362	0.3681
20	0.5865	0.827	0.4135
21	0.5222	0.9557	0.4778
22	0.5311	0.9377	0.4689
23	0.4611	0.9223	0.5389
24	0.3958	0.7916	0.6042
25	0.3313	0.6625	0.6687
26	0.2749	0.5497	0.7251
27	0.2307	0.4614	0.7693
28	0.1847	0.3693	0.8153
29	0.1442	0.2885	0.8558
30	0.1186	0.2372	0.8814
31	0.1854	0.3708	0.8146
32	0.1816	0.3632	0.8184
33	0.1445	0.289	0.8555
34	0.1144	0.2287	0.8856
35	0.08793	0.1759	0.9121
36	0.07067	0.1413	0.9293
37	0.05307	0.1061	0.9469
38	0.04111	0.08222	0.9589
39	0.03178	0.06357	0.9682
40	0.02515	0.05031	0.9748
41	0.119	0.238	0.881
42	0.09394	0.1879	0.9061
43	0.08045	0.1609	0.9195
44	0.06497	0.1299	0.935
45	0.05018	0.1004	0.9498
46	0.03866	0.07732	0.9613
47	0.04981	0.09962	0.9502
48	0.03766	0.07533	0.9623
49	0.02812	0.05625	0.9719
50	0.02131	0.04261	0.9787
51	0.0298	0.05959	0.9702
52	0.03307	0.06614	0.9669
53	0.02473	0.04945	0.9753
54	0.0204	0.04081	0.9796
55	0.02072	0.04143	0.9793
56	0.01551	0.03103	0.9845
57	0.01199	0.02398	0.988
58	0.008777	0.01755	0.9912
59	0.006255	0.01251	0.9937
60	0.004382	0.008763	0.9956
61	0.003137	0.006274	0.9969
62	0.00217	0.004339	0.9978
63	0.001505	0.00301	0.9985
64	0.001049	0.002098	0.999
65	0.0007871	0.001574	0.9992
66	0.0005801	0.00116	0.9994
67	0.0003789	0.0007578	0.9996
68	0.0002483	0.0004966	0.9998
69	0.000414	0.0008281	0.9996
70	0.0005459	0.001092	0.9995
71	0.0005521	0.001104	0.9994
72	0.0003801	0.0007602	0.9996
73	0.0003068	0.0006137	0.9997
74	0.000214	0.000428	0.9998
75	0.003102	0.006203	0.9969
76	0.002265	0.004529	0.9977
77	0.00258	0.00516	0.9974
78	0.03896	0.07791	0.961
79	0.03018	0.06035	0.9698
80	0.02495	0.0499	0.975
81	0.0209	0.0418	0.9791
82	0.01812	0.03624	0.9819
83	0.0137	0.0274	0.9863
84	0.01017	0.02034	0.9898
85	0.00892	0.01784	0.9911
86	0.006617	0.01323	0.9934
87	0.1728	0.3455	0.8272
88	0.1545	0.3089	0.8455
89	0.1312	0.2624	0.8688
90	0.1085	0.2169	0.8915
91	0.08974	0.1795	0.9103
92	0.07259	0.1452	0.9274
93	0.05816	0.1163	0.9418
94	0.04659	0.09318	0.9534
95	0.03676	0.07352	0.9632
96	0.02853	0.05705	0.9715
97	0.0256	0.0512	0.9744
98	0.02008	0.04016	0.9799
99	0.01542	0.03084	0.9846
100	0.01149	0.02298	0.9885
101	0.008885	0.01777	0.9911
102	0.006462	0.01292	0.9935
103	0.00482	0.00964	0.9952
104	0.009843	0.01969	0.9902
105	0.01146	0.02293	0.9885
106	0.008895	0.01779	0.9911
107	0.006676	0.01335	0.9933
108	0.004905	0.009811	0.9951
109	0.03853	0.07705	0.9615
110	0.0301	0.06019	0.9699
111	0.0237	0.0474	0.9763
112	0.01772	0.03545	0.9823
113	0.01322	0.02644	0.9868
114	0.01196	0.02393	0.988
115	0.01062	0.02123	0.9894
116	0.007798	0.0156	0.9922
117	0.006892	0.01378	0.9931
118	0.02325	0.04651	0.9767
119	0.02042	0.04084	0.9796
120	0.02448	0.04896	0.9755
121	0.01812	0.03624	0.9819
122	0.02018	0.04037	0.9798
123	0.01502	0.03004	0.985
124	0.01177	0.02355	0.9882
125	0.008423	0.01685	0.9916
126	0.008626	0.01725	0.9914
127	0.005987	0.01197	0.994
128	0.004251	0.008503	0.9957
129	0.002948	0.005896	0.9971
130	0.003169	0.006338	0.9968
131	0.002089	0.004179	0.9979
132	0.00186	0.003719	0.9981
133	0.001227	0.002454	0.9988
134	0.0009688	0.001938	0.999
135	0.001212	0.002423	0.9988
136	0.001466	0.002932	0.9985
137	0.00156	0.00312	0.9984
138	0.0009483	0.001897	0.9991
139	0.0008835	0.001767	0.9991
140	0.01476	0.02952	0.9852
141	0.01152	0.02303	0.9885
142	0.007398	0.0148	0.9926
143	0.004536	0.009072	0.9955
144	0.00469	0.00938	0.9953
145	0.002788	0.005576	0.9972
146	0.002062	0.004123	0.9979
147	0.1799	0.3597	0.8201
148	0.1329	0.2659	0.8671
149	0.09094	0.1819	0.9091
150	0.4642	0.9284	0.5358
151	0.3739	0.7478	0.6261
152	0.3525	0.705	0.6475
153	0.6925	0.6149	0.3075
154	0.8653	0.2694	0.1347
155	0.791	0.418	0.209

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
7 &  0.03881 &  0.07762 &  0.9612 \tabularnewline
8 &  0.01215 &  0.0243 &  0.9879 \tabularnewline
9 &  0.003387 &  0.006773 &  0.9966 \tabularnewline
10 &  0.3072 &  0.6144 &  0.6928 \tabularnewline
11 &  0.9006 &  0.1988 &  0.09939 \tabularnewline
12 &  0.9465 &  0.1069 &  0.05347 \tabularnewline
13 &  0.9291 &  0.1419 &  0.07095 \tabularnewline
14 &  0.9007 &  0.1987 &  0.09935 \tabularnewline
15 &  0.8584 &  0.2832 &  0.1416 \tabularnewline
16 &  0.8059 &  0.3882 &  0.1941 \tabularnewline
17 &  0.7538 &  0.4924 &  0.2462 \tabularnewline
18 &  0.7019 &  0.5961 &  0.2981 \tabularnewline
19 &  0.6319 &  0.7362 &  0.3681 \tabularnewline
20 &  0.5865 &  0.827 &  0.4135 \tabularnewline
21 &  0.5222 &  0.9557 &  0.4778 \tabularnewline
22 &  0.5311 &  0.9377 &  0.4689 \tabularnewline
23 &  0.4611 &  0.9223 &  0.5389 \tabularnewline
24 &  0.3958 &  0.7916 &  0.6042 \tabularnewline
25 &  0.3313 &  0.6625 &  0.6687 \tabularnewline
26 &  0.2749 &  0.5497 &  0.7251 \tabularnewline
27 &  0.2307 &  0.4614 &  0.7693 \tabularnewline
28 &  0.1847 &  0.3693 &  0.8153 \tabularnewline
29 &  0.1442 &  0.2885 &  0.8558 \tabularnewline
30 &  0.1186 &  0.2372 &  0.8814 \tabularnewline
31 &  0.1854 &  0.3708 &  0.8146 \tabularnewline
32 &  0.1816 &  0.3632 &  0.8184 \tabularnewline
33 &  0.1445 &  0.289 &  0.8555 \tabularnewline
34 &  0.1144 &  0.2287 &  0.8856 \tabularnewline
35 &  0.08793 &  0.1759 &  0.9121 \tabularnewline
36 &  0.07067 &  0.1413 &  0.9293 \tabularnewline
37 &  0.05307 &  0.1061 &  0.9469 \tabularnewline
38 &  0.04111 &  0.08222 &  0.9589 \tabularnewline
39 &  0.03178 &  0.06357 &  0.9682 \tabularnewline
40 &  0.02515 &  0.05031 &  0.9748 \tabularnewline
41 &  0.119 &  0.238 &  0.881 \tabularnewline
42 &  0.09394 &  0.1879 &  0.9061 \tabularnewline
43 &  0.08045 &  0.1609 &  0.9195 \tabularnewline
44 &  0.06497 &  0.1299 &  0.935 \tabularnewline
45 &  0.05018 &  0.1004 &  0.9498 \tabularnewline
46 &  0.03866 &  0.07732 &  0.9613 \tabularnewline
47 &  0.04981 &  0.09962 &  0.9502 \tabularnewline
48 &  0.03766 &  0.07533 &  0.9623 \tabularnewline
49 &  0.02812 &  0.05625 &  0.9719 \tabularnewline
50 &  0.02131 &  0.04261 &  0.9787 \tabularnewline
51 &  0.0298 &  0.05959 &  0.9702 \tabularnewline
52 &  0.03307 &  0.06614 &  0.9669 \tabularnewline
53 &  0.02473 &  0.04945 &  0.9753 \tabularnewline
54 &  0.0204 &  0.04081 &  0.9796 \tabularnewline
55 &  0.02072 &  0.04143 &  0.9793 \tabularnewline
56 &  0.01551 &  0.03103 &  0.9845 \tabularnewline
57 &  0.01199 &  0.02398 &  0.988 \tabularnewline
58 &  0.008777 &  0.01755 &  0.9912 \tabularnewline
59 &  0.006255 &  0.01251 &  0.9937 \tabularnewline
60 &  0.004382 &  0.008763 &  0.9956 \tabularnewline
61 &  0.003137 &  0.006274 &  0.9969 \tabularnewline
62 &  0.00217 &  0.004339 &  0.9978 \tabularnewline
63 &  0.001505 &  0.00301 &  0.9985 \tabularnewline
64 &  0.001049 &  0.002098 &  0.999 \tabularnewline
65 &  0.0007871 &  0.001574 &  0.9992 \tabularnewline
66 &  0.0005801 &  0.00116 &  0.9994 \tabularnewline
67 &  0.0003789 &  0.0007578 &  0.9996 \tabularnewline
68 &  0.0002483 &  0.0004966 &  0.9998 \tabularnewline
69 &  0.000414 &  0.0008281 &  0.9996 \tabularnewline
70 &  0.0005459 &  0.001092 &  0.9995 \tabularnewline
71 &  0.0005521 &  0.001104 &  0.9994 \tabularnewline
72 &  0.0003801 &  0.0007602 &  0.9996 \tabularnewline
73 &  0.0003068 &  0.0006137 &  0.9997 \tabularnewline
74 &  0.000214 &  0.000428 &  0.9998 \tabularnewline
75 &  0.003102 &  0.006203 &  0.9969 \tabularnewline
76 &  0.002265 &  0.004529 &  0.9977 \tabularnewline
77 &  0.00258 &  0.00516 &  0.9974 \tabularnewline
78 &  0.03896 &  0.07791 &  0.961 \tabularnewline
79 &  0.03018 &  0.06035 &  0.9698 \tabularnewline
80 &  0.02495 &  0.0499 &  0.975 \tabularnewline
81 &  0.0209 &  0.0418 &  0.9791 \tabularnewline
82 &  0.01812 &  0.03624 &  0.9819 \tabularnewline
83 &  0.0137 &  0.0274 &  0.9863 \tabularnewline
84 &  0.01017 &  0.02034 &  0.9898 \tabularnewline
85 &  0.00892 &  0.01784 &  0.9911 \tabularnewline
86 &  0.006617 &  0.01323 &  0.9934 \tabularnewline
87 &  0.1728 &  0.3455 &  0.8272 \tabularnewline
88 &  0.1545 &  0.3089 &  0.8455 \tabularnewline
89 &  0.1312 &  0.2624 &  0.8688 \tabularnewline
90 &  0.1085 &  0.2169 &  0.8915 \tabularnewline
91 &  0.08974 &  0.1795 &  0.9103 \tabularnewline
92 &  0.07259 &  0.1452 &  0.9274 \tabularnewline
93 &  0.05816 &  0.1163 &  0.9418 \tabularnewline
94 &  0.04659 &  0.09318 &  0.9534 \tabularnewline
95 &  0.03676 &  0.07352 &  0.9632 \tabularnewline
96 &  0.02853 &  0.05705 &  0.9715 \tabularnewline
97 &  0.0256 &  0.0512 &  0.9744 \tabularnewline
98 &  0.02008 &  0.04016 &  0.9799 \tabularnewline
99 &  0.01542 &  0.03084 &  0.9846 \tabularnewline
100 &  0.01149 &  0.02298 &  0.9885 \tabularnewline
101 &  0.008885 &  0.01777 &  0.9911 \tabularnewline
102 &  0.006462 &  0.01292 &  0.9935 \tabularnewline
103 &  0.00482 &  0.00964 &  0.9952 \tabularnewline
104 &  0.009843 &  0.01969 &  0.9902 \tabularnewline
105 &  0.01146 &  0.02293 &  0.9885 \tabularnewline
106 &  0.008895 &  0.01779 &  0.9911 \tabularnewline
107 &  0.006676 &  0.01335 &  0.9933 \tabularnewline
108 &  0.004905 &  0.009811 &  0.9951 \tabularnewline
109 &  0.03853 &  0.07705 &  0.9615 \tabularnewline
110 &  0.0301 &  0.06019 &  0.9699 \tabularnewline
111 &  0.0237 &  0.0474 &  0.9763 \tabularnewline
112 &  0.01772 &  0.03545 &  0.9823 \tabularnewline
113 &  0.01322 &  0.02644 &  0.9868 \tabularnewline
114 &  0.01196 &  0.02393 &  0.988 \tabularnewline
115 &  0.01062 &  0.02123 &  0.9894 \tabularnewline
116 &  0.007798 &  0.0156 &  0.9922 \tabularnewline
117 &  0.006892 &  0.01378 &  0.9931 \tabularnewline
118 &  0.02325 &  0.04651 &  0.9767 \tabularnewline
119 &  0.02042 &  0.04084 &  0.9796 \tabularnewline
120 &  0.02448 &  0.04896 &  0.9755 \tabularnewline
121 &  0.01812 &  0.03624 &  0.9819 \tabularnewline
122 &  0.02018 &  0.04037 &  0.9798 \tabularnewline
123 &  0.01502 &  0.03004 &  0.985 \tabularnewline
124 &  0.01177 &  0.02355 &  0.9882 \tabularnewline
125 &  0.008423 &  0.01685 &  0.9916 \tabularnewline
126 &  0.008626 &  0.01725 &  0.9914 \tabularnewline
127 &  0.005987 &  0.01197 &  0.994 \tabularnewline
128 &  0.004251 &  0.008503 &  0.9957 \tabularnewline
129 &  0.002948 &  0.005896 &  0.9971 \tabularnewline
130 &  0.003169 &  0.006338 &  0.9968 \tabularnewline
131 &  0.002089 &  0.004179 &  0.9979 \tabularnewline
132 &  0.00186 &  0.003719 &  0.9981 \tabularnewline
133 &  0.001227 &  0.002454 &  0.9988 \tabularnewline
134 &  0.0009688 &  0.001938 &  0.999 \tabularnewline
135 &  0.001212 &  0.002423 &  0.9988 \tabularnewline
136 &  0.001466 &  0.002932 &  0.9985 \tabularnewline
137 &  0.00156 &  0.00312 &  0.9984 \tabularnewline
138 &  0.0009483 &  0.001897 &  0.9991 \tabularnewline
139 &  0.0008835 &  0.001767 &  0.9991 \tabularnewline
140 &  0.01476 &  0.02952 &  0.9852 \tabularnewline
141 &  0.01152 &  0.02303 &  0.9885 \tabularnewline
142 &  0.007398 &  0.0148 &  0.9926 \tabularnewline
143 &  0.004536 &  0.009072 &  0.9955 \tabularnewline
144 &  0.00469 &  0.00938 &  0.9953 \tabularnewline
145 &  0.002788 &  0.005576 &  0.9972 \tabularnewline
146 &  0.002062 &  0.004123 &  0.9979 \tabularnewline
147 &  0.1799 &  0.3597 &  0.8201 \tabularnewline
148 &  0.1329 &  0.2659 &  0.8671 \tabularnewline
149 &  0.09094 &  0.1819 &  0.9091 \tabularnewline
150 &  0.4642 &  0.9284 &  0.5358 \tabularnewline
151 &  0.3739 &  0.7478 &  0.6261 \tabularnewline
152 &  0.3525 &  0.705 &  0.6475 \tabularnewline
153 &  0.6925 &  0.6149 &  0.3075 \tabularnewline
154 &  0.8653 &  0.2694 &  0.1347 \tabularnewline
155 &  0.791 &  0.418 &  0.209 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=316108&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] 0.03881[/C][C] 0.07762[/C][C] 0.9612[/C][/ROW]
[ROW][C]8[/C][C] 0.01215[/C][C] 0.0243[/C][C] 0.9879[/C][/ROW]
[ROW][C]9[/C][C] 0.003387[/C][C] 0.006773[/C][C] 0.9966[/C][/ROW]
[ROW][C]10[/C][C] 0.3072[/C][C] 0.6144[/C][C] 0.6928[/C][/ROW]
[ROW][C]11[/C][C] 0.9006[/C][C] 0.1988[/C][C] 0.09939[/C][/ROW]
[ROW][C]12[/C][C] 0.9465[/C][C] 0.1069[/C][C] 0.05347[/C][/ROW]
[ROW][C]13[/C][C] 0.9291[/C][C] 0.1419[/C][C] 0.07095[/C][/ROW]
[ROW][C]14[/C][C] 0.9007[/C][C] 0.1987[/C][C] 0.09935[/C][/ROW]
[ROW][C]15[/C][C] 0.8584[/C][C] 0.2832[/C][C] 0.1416[/C][/ROW]
[ROW][C]16[/C][C] 0.8059[/C][C] 0.3882[/C][C] 0.1941[/C][/ROW]
[ROW][C]17[/C][C] 0.7538[/C][C] 0.4924[/C][C] 0.2462[/C][/ROW]
[ROW][C]18[/C][C] 0.7019[/C][C] 0.5961[/C][C] 0.2981[/C][/ROW]
[ROW][C]19[/C][C] 0.6319[/C][C] 0.7362[/C][C] 0.3681[/C][/ROW]
[ROW][C]20[/C][C] 0.5865[/C][C] 0.827[/C][C] 0.4135[/C][/ROW]
[ROW][C]21[/C][C] 0.5222[/C][C] 0.9557[/C][C] 0.4778[/C][/ROW]
[ROW][C]22[/C][C] 0.5311[/C][C] 0.9377[/C][C] 0.4689[/C][/ROW]
[ROW][C]23[/C][C] 0.4611[/C][C] 0.9223[/C][C] 0.5389[/C][/ROW]
[ROW][C]24[/C][C] 0.3958[/C][C] 0.7916[/C][C] 0.6042[/C][/ROW]
[ROW][C]25[/C][C] 0.3313[/C][C] 0.6625[/C][C] 0.6687[/C][/ROW]
[ROW][C]26[/C][C] 0.2749[/C][C] 0.5497[/C][C] 0.7251[/C][/ROW]
[ROW][C]27[/C][C] 0.2307[/C][C] 0.4614[/C][C] 0.7693[/C][/ROW]
[ROW][C]28[/C][C] 0.1847[/C][C] 0.3693[/C][C] 0.8153[/C][/ROW]
[ROW][C]29[/C][C] 0.1442[/C][C] 0.2885[/C][C] 0.8558[/C][/ROW]
[ROW][C]30[/C][C] 0.1186[/C][C] 0.2372[/C][C] 0.8814[/C][/ROW]
[ROW][C]31[/C][C] 0.1854[/C][C] 0.3708[/C][C] 0.8146[/C][/ROW]
[ROW][C]32[/C][C] 0.1816[/C][C] 0.3632[/C][C] 0.8184[/C][/ROW]
[ROW][C]33[/C][C] 0.1445[/C][C] 0.289[/C][C] 0.8555[/C][/ROW]
[ROW][C]34[/C][C] 0.1144[/C][C] 0.2287[/C][C] 0.8856[/C][/ROW]
[ROW][C]35[/C][C] 0.08793[/C][C] 0.1759[/C][C] 0.9121[/C][/ROW]
[ROW][C]36[/C][C] 0.07067[/C][C] 0.1413[/C][C] 0.9293[/C][/ROW]
[ROW][C]37[/C][C] 0.05307[/C][C] 0.1061[/C][C] 0.9469[/C][/ROW]
[ROW][C]38[/C][C] 0.04111[/C][C] 0.08222[/C][C] 0.9589[/C][/ROW]
[ROW][C]39[/C][C] 0.03178[/C][C] 0.06357[/C][C] 0.9682[/C][/ROW]
[ROW][C]40[/C][C] 0.02515[/C][C] 0.05031[/C][C] 0.9748[/C][/ROW]
[ROW][C]41[/C][C] 0.119[/C][C] 0.238[/C][C] 0.881[/C][/ROW]
[ROW][C]42[/C][C] 0.09394[/C][C] 0.1879[/C][C] 0.9061[/C][/ROW]
[ROW][C]43[/C][C] 0.08045[/C][C] 0.1609[/C][C] 0.9195[/C][/ROW]
[ROW][C]44[/C][C] 0.06497[/C][C] 0.1299[/C][C] 0.935[/C][/ROW]
[ROW][C]45[/C][C] 0.05018[/C][C] 0.1004[/C][C] 0.9498[/C][/ROW]
[ROW][C]46[/C][C] 0.03866[/C][C] 0.07732[/C][C] 0.9613[/C][/ROW]
[ROW][C]47[/C][C] 0.04981[/C][C] 0.09962[/C][C] 0.9502[/C][/ROW]
[ROW][C]48[/C][C] 0.03766[/C][C] 0.07533[/C][C] 0.9623[/C][/ROW]
[ROW][C]49[/C][C] 0.02812[/C][C] 0.05625[/C][C] 0.9719[/C][/ROW]
[ROW][C]50[/C][C] 0.02131[/C][C] 0.04261[/C][C] 0.9787[/C][/ROW]
[ROW][C]51[/C][C] 0.0298[/C][C] 0.05959[/C][C] 0.9702[/C][/ROW]
[ROW][C]52[/C][C] 0.03307[/C][C] 0.06614[/C][C] 0.9669[/C][/ROW]
[ROW][C]53[/C][C] 0.02473[/C][C] 0.04945[/C][C] 0.9753[/C][/ROW]
[ROW][C]54[/C][C] 0.0204[/C][C] 0.04081[/C][C] 0.9796[/C][/ROW]
[ROW][C]55[/C][C] 0.02072[/C][C] 0.04143[/C][C] 0.9793[/C][/ROW]
[ROW][C]56[/C][C] 0.01551[/C][C] 0.03103[/C][C] 0.9845[/C][/ROW]
[ROW][C]57[/C][C] 0.01199[/C][C] 0.02398[/C][C] 0.988[/C][/ROW]
[ROW][C]58[/C][C] 0.008777[/C][C] 0.01755[/C][C] 0.9912[/C][/ROW]
[ROW][C]59[/C][C] 0.006255[/C][C] 0.01251[/C][C] 0.9937[/C][/ROW]
[ROW][C]60[/C][C] 0.004382[/C][C] 0.008763[/C][C] 0.9956[/C][/ROW]
[ROW][C]61[/C][C] 0.003137[/C][C] 0.006274[/C][C] 0.9969[/C][/ROW]
[ROW][C]62[/C][C] 0.00217[/C][C] 0.004339[/C][C] 0.9978[/C][/ROW]
[ROW][C]63[/C][C] 0.001505[/C][C] 0.00301[/C][C] 0.9985[/C][/ROW]
[ROW][C]64[/C][C] 0.001049[/C][C] 0.002098[/C][C] 0.999[/C][/ROW]
[ROW][C]65[/C][C] 0.0007871[/C][C] 0.001574[/C][C] 0.9992[/C][/ROW]
[ROW][C]66[/C][C] 0.0005801[/C][C] 0.00116[/C][C] 0.9994[/C][/ROW]
[ROW][C]67[/C][C] 0.0003789[/C][C] 0.0007578[/C][C] 0.9996[/C][/ROW]
[ROW][C]68[/C][C] 0.0002483[/C][C] 0.0004966[/C][C] 0.9998[/C][/ROW]
[ROW][C]69[/C][C] 0.000414[/C][C] 0.0008281[/C][C] 0.9996[/C][/ROW]
[ROW][C]70[/C][C] 0.0005459[/C][C] 0.001092[/C][C] 0.9995[/C][/ROW]
[ROW][C]71[/C][C] 0.0005521[/C][C] 0.001104[/C][C] 0.9994[/C][/ROW]
[ROW][C]72[/C][C] 0.0003801[/C][C] 0.0007602[/C][C] 0.9996[/C][/ROW]
[ROW][C]73[/C][C] 0.0003068[/C][C] 0.0006137[/C][C] 0.9997[/C][/ROW]
[ROW][C]74[/C][C] 0.000214[/C][C] 0.000428[/C][C] 0.9998[/C][/ROW]
[ROW][C]75[/C][C] 0.003102[/C][C] 0.006203[/C][C] 0.9969[/C][/ROW]
[ROW][C]76[/C][C] 0.002265[/C][C] 0.004529[/C][C] 0.9977[/C][/ROW]
[ROW][C]77[/C][C] 0.00258[/C][C] 0.00516[/C][C] 0.9974[/C][/ROW]
[ROW][C]78[/C][C] 0.03896[/C][C] 0.07791[/C][C] 0.961[/C][/ROW]
[ROW][C]79[/C][C] 0.03018[/C][C] 0.06035[/C][C] 0.9698[/C][/ROW]
[ROW][C]80[/C][C] 0.02495[/C][C] 0.0499[/C][C] 0.975[/C][/ROW]
[ROW][C]81[/C][C] 0.0209[/C][C] 0.0418[/C][C] 0.9791[/C][/ROW]
[ROW][C]82[/C][C] 0.01812[/C][C] 0.03624[/C][C] 0.9819[/C][/ROW]
[ROW][C]83[/C][C] 0.0137[/C][C] 0.0274[/C][C] 0.9863[/C][/ROW]
[ROW][C]84[/C][C] 0.01017[/C][C] 0.02034[/C][C] 0.9898[/C][/ROW]
[ROW][C]85[/C][C] 0.00892[/C][C] 0.01784[/C][C] 0.9911[/C][/ROW]
[ROW][C]86[/C][C] 0.006617[/C][C] 0.01323[/C][C] 0.9934[/C][/ROW]
[ROW][C]87[/C][C] 0.1728[/C][C] 0.3455[/C][C] 0.8272[/C][/ROW]
[ROW][C]88[/C][C] 0.1545[/C][C] 0.3089[/C][C] 0.8455[/C][/ROW]
[ROW][C]89[/C][C] 0.1312[/C][C] 0.2624[/C][C] 0.8688[/C][/ROW]
[ROW][C]90[/C][C] 0.1085[/C][C] 0.2169[/C][C] 0.8915[/C][/ROW]
[ROW][C]91[/C][C] 0.08974[/C][C] 0.1795[/C][C] 0.9103[/C][/ROW]
[ROW][C]92[/C][C] 0.07259[/C][C] 0.1452[/C][C] 0.9274[/C][/ROW]
[ROW][C]93[/C][C] 0.05816[/C][C] 0.1163[/C][C] 0.9418[/C][/ROW]
[ROW][C]94[/C][C] 0.04659[/C][C] 0.09318[/C][C] 0.9534[/C][/ROW]
[ROW][C]95[/C][C] 0.03676[/C][C] 0.07352[/C][C] 0.9632[/C][/ROW]
[ROW][C]96[/C][C] 0.02853[/C][C] 0.05705[/C][C] 0.9715[/C][/ROW]
[ROW][C]97[/C][C] 0.0256[/C][C] 0.0512[/C][C] 0.9744[/C][/ROW]
[ROW][C]98[/C][C] 0.02008[/C][C] 0.04016[/C][C] 0.9799[/C][/ROW]
[ROW][C]99[/C][C] 0.01542[/C][C] 0.03084[/C][C] 0.9846[/C][/ROW]
[ROW][C]100[/C][C] 0.01149[/C][C] 0.02298[/C][C] 0.9885[/C][/ROW]
[ROW][C]101[/C][C] 0.008885[/C][C] 0.01777[/C][C] 0.9911[/C][/ROW]
[ROW][C]102[/C][C] 0.006462[/C][C] 0.01292[/C][C] 0.9935[/C][/ROW]
[ROW][C]103[/C][C] 0.00482[/C][C] 0.00964[/C][C] 0.9952[/C][/ROW]
[ROW][C]104[/C][C] 0.009843[/C][C] 0.01969[/C][C] 0.9902[/C][/ROW]
[ROW][C]105[/C][C] 0.01146[/C][C] 0.02293[/C][C] 0.9885[/C][/ROW]
[ROW][C]106[/C][C] 0.008895[/C][C] 0.01779[/C][C] 0.9911[/C][/ROW]
[ROW][C]107[/C][C] 0.006676[/C][C] 0.01335[/C][C] 0.9933[/C][/ROW]
[ROW][C]108[/C][C] 0.004905[/C][C] 0.009811[/C][C] 0.9951[/C][/ROW]
[ROW][C]109[/C][C] 0.03853[/C][C] 0.07705[/C][C] 0.9615[/C][/ROW]
[ROW][C]110[/C][C] 0.0301[/C][C] 0.06019[/C][C] 0.9699[/C][/ROW]
[ROW][C]111[/C][C] 0.0237[/C][C] 0.0474[/C][C] 0.9763[/C][/ROW]
[ROW][C]112[/C][C] 0.01772[/C][C] 0.03545[/C][C] 0.9823[/C][/ROW]
[ROW][C]113[/C][C] 0.01322[/C][C] 0.02644[/C][C] 0.9868[/C][/ROW]
[ROW][C]114[/C][C] 0.01196[/C][C] 0.02393[/C][C] 0.988[/C][/ROW]
[ROW][C]115[/C][C] 0.01062[/C][C] 0.02123[/C][C] 0.9894[/C][/ROW]
[ROW][C]116[/C][C] 0.007798[/C][C] 0.0156[/C][C] 0.9922[/C][/ROW]
[ROW][C]117[/C][C] 0.006892[/C][C] 0.01378[/C][C] 0.9931[/C][/ROW]
[ROW][C]118[/C][C] 0.02325[/C][C] 0.04651[/C][C] 0.9767[/C][/ROW]
[ROW][C]119[/C][C] 0.02042[/C][C] 0.04084[/C][C] 0.9796[/C][/ROW]
[ROW][C]120[/C][C] 0.02448[/C][C] 0.04896[/C][C] 0.9755[/C][/ROW]
[ROW][C]121[/C][C] 0.01812[/C][C] 0.03624[/C][C] 0.9819[/C][/ROW]
[ROW][C]122[/C][C] 0.02018[/C][C] 0.04037[/C][C] 0.9798[/C][/ROW]
[ROW][C]123[/C][C] 0.01502[/C][C] 0.03004[/C][C] 0.985[/C][/ROW]
[ROW][C]124[/C][C] 0.01177[/C][C] 0.02355[/C][C] 0.9882[/C][/ROW]
[ROW][C]125[/C][C] 0.008423[/C][C] 0.01685[/C][C] 0.9916[/C][/ROW]
[ROW][C]126[/C][C] 0.008626[/C][C] 0.01725[/C][C] 0.9914[/C][/ROW]
[ROW][C]127[/C][C] 0.005987[/C][C] 0.01197[/C][C] 0.994[/C][/ROW]
[ROW][C]128[/C][C] 0.004251[/C][C] 0.008503[/C][C] 0.9957[/C][/ROW]
[ROW][C]129[/C][C] 0.002948[/C][C] 0.005896[/C][C] 0.9971[/C][/ROW]
[ROW][C]130[/C][C] 0.003169[/C][C] 0.006338[/C][C] 0.9968[/C][/ROW]
[ROW][C]131[/C][C] 0.002089[/C][C] 0.004179[/C][C] 0.9979[/C][/ROW]
[ROW][C]132[/C][C] 0.00186[/C][C] 0.003719[/C][C] 0.9981[/C][/ROW]
[ROW][C]133[/C][C] 0.001227[/C][C] 0.002454[/C][C] 0.9988[/C][/ROW]
[ROW][C]134[/C][C] 0.0009688[/C][C] 0.001938[/C][C] 0.999[/C][/ROW]
[ROW][C]135[/C][C] 0.001212[/C][C] 0.002423[/C][C] 0.9988[/C][/ROW]
[ROW][C]136[/C][C] 0.001466[/C][C] 0.002932[/C][C] 0.9985[/C][/ROW]
[ROW][C]137[/C][C] 0.00156[/C][C] 0.00312[/C][C] 0.9984[/C][/ROW]
[ROW][C]138[/C][C] 0.0009483[/C][C] 0.001897[/C][C] 0.9991[/C][/ROW]
[ROW][C]139[/C][C] 0.0008835[/C][C] 0.001767[/C][C] 0.9991[/C][/ROW]
[ROW][C]140[/C][C] 0.01476[/C][C] 0.02952[/C][C] 0.9852[/C][/ROW]
[ROW][C]141[/C][C] 0.01152[/C][C] 0.02303[/C][C] 0.9885[/C][/ROW]
[ROW][C]142[/C][C] 0.007398[/C][C] 0.0148[/C][C] 0.9926[/C][/ROW]
[ROW][C]143[/C][C] 0.004536[/C][C] 0.009072[/C][C] 0.9955[/C][/ROW]
[ROW][C]144[/C][C] 0.00469[/C][C] 0.00938[/C][C] 0.9953[/C][/ROW]
[ROW][C]145[/C][C] 0.002788[/C][C] 0.005576[/C][C] 0.9972[/C][/ROW]
[ROW][C]146[/C][C] 0.002062[/C][C] 0.004123[/C][C] 0.9979[/C][/ROW]
[ROW][C]147[/C][C] 0.1799[/C][C] 0.3597[/C][C] 0.8201[/C][/ROW]
[ROW][C]148[/C][C] 0.1329[/C][C] 0.2659[/C][C] 0.8671[/C][/ROW]
[ROW][C]149[/C][C] 0.09094[/C][C] 0.1819[/C][C] 0.9091[/C][/ROW]
[ROW][C]150[/C][C] 0.4642[/C][C] 0.9284[/C][C] 0.5358[/C][/ROW]
[ROW][C]151[/C][C] 0.3739[/C][C] 0.7478[/C][C] 0.6261[/C][/ROW]
[ROW][C]152[/C][C] 0.3525[/C][C] 0.705[/C][C] 0.6475[/C][/ROW]
[ROW][C]153[/C][C] 0.6925[/C][C] 0.6149[/C][C] 0.3075[/C][/ROW]
[ROW][C]154[/C][C] 0.8653[/C][C] 0.2694[/C][C] 0.1347[/C][/ROW]
[ROW][C]155[/C][C] 0.791[/C][C] 0.418[/C][C] 0.209[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=316108&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=316108&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	0.03881	0.07762	0.9612
8	0.01215	0.0243	0.9879
9	0.003387	0.006773	0.9966
10	0.3072	0.6144	0.6928
11	0.9006	0.1988	0.09939
12	0.9465	0.1069	0.05347
13	0.9291	0.1419	0.07095
14	0.9007	0.1987	0.09935
15	0.8584	0.2832	0.1416
16	0.8059	0.3882	0.1941
17	0.7538	0.4924	0.2462
18	0.7019	0.5961	0.2981
19	0.6319	0.7362	0.3681
20	0.5865	0.827	0.4135
21	0.5222	0.9557	0.4778
22	0.5311	0.9377	0.4689
23	0.4611	0.9223	0.5389
24	0.3958	0.7916	0.6042
25	0.3313	0.6625	0.6687
26	0.2749	0.5497	0.7251
27	0.2307	0.4614	0.7693
28	0.1847	0.3693	0.8153
29	0.1442	0.2885	0.8558
30	0.1186	0.2372	0.8814
31	0.1854	0.3708	0.8146
32	0.1816	0.3632	0.8184
33	0.1445	0.289	0.8555
34	0.1144	0.2287	0.8856
35	0.08793	0.1759	0.9121
36	0.07067	0.1413	0.9293
37	0.05307	0.1061	0.9469
38	0.04111	0.08222	0.9589
39	0.03178	0.06357	0.9682
40	0.02515	0.05031	0.9748
41	0.119	0.238	0.881
42	0.09394	0.1879	0.9061
43	0.08045	0.1609	0.9195
44	0.06497	0.1299	0.935
45	0.05018	0.1004	0.9498
46	0.03866	0.07732	0.9613
47	0.04981	0.09962	0.9502
48	0.03766	0.07533	0.9623
49	0.02812	0.05625	0.9719
50	0.02131	0.04261	0.9787
51	0.0298	0.05959	0.9702
52	0.03307	0.06614	0.9669
53	0.02473	0.04945	0.9753
54	0.0204	0.04081	0.9796
55	0.02072	0.04143	0.9793
56	0.01551	0.03103	0.9845
57	0.01199	0.02398	0.988
58	0.008777	0.01755	0.9912
59	0.006255	0.01251	0.9937
60	0.004382	0.008763	0.9956
61	0.003137	0.006274	0.9969
62	0.00217	0.004339	0.9978
63	0.001505	0.00301	0.9985
64	0.001049	0.002098	0.999
65	0.0007871	0.001574	0.9992
66	0.0005801	0.00116	0.9994
67	0.0003789	0.0007578	0.9996
68	0.0002483	0.0004966	0.9998
69	0.000414	0.0008281	0.9996
70	0.0005459	0.001092	0.9995
71	0.0005521	0.001104	0.9994
72	0.0003801	0.0007602	0.9996
73	0.0003068	0.0006137	0.9997
74	0.000214	0.000428	0.9998
75	0.003102	0.006203	0.9969
76	0.002265	0.004529	0.9977
77	0.00258	0.00516	0.9974
78	0.03896	0.07791	0.961
79	0.03018	0.06035	0.9698
80	0.02495	0.0499	0.975
81	0.0209	0.0418	0.9791
82	0.01812	0.03624	0.9819
83	0.0137	0.0274	0.9863
84	0.01017	0.02034	0.9898
85	0.00892	0.01784	0.9911
86	0.006617	0.01323	0.9934
87	0.1728	0.3455	0.8272
88	0.1545	0.3089	0.8455
89	0.1312	0.2624	0.8688
90	0.1085	0.2169	0.8915
91	0.08974	0.1795	0.9103
92	0.07259	0.1452	0.9274
93	0.05816	0.1163	0.9418
94	0.04659	0.09318	0.9534
95	0.03676	0.07352	0.9632
96	0.02853	0.05705	0.9715
97	0.0256	0.0512	0.9744
98	0.02008	0.04016	0.9799
99	0.01542	0.03084	0.9846
100	0.01149	0.02298	0.9885
101	0.008885	0.01777	0.9911
102	0.006462	0.01292	0.9935
103	0.00482	0.00964	0.9952
104	0.009843	0.01969	0.9902
105	0.01146	0.02293	0.9885
106	0.008895	0.01779	0.9911
107	0.006676	0.01335	0.9933
108	0.004905	0.009811	0.9951
109	0.03853	0.07705	0.9615
110	0.0301	0.06019	0.9699
111	0.0237	0.0474	0.9763
112	0.01772	0.03545	0.9823
113	0.01322	0.02644	0.9868
114	0.01196	0.02393	0.988
115	0.01062	0.02123	0.9894
116	0.007798	0.0156	0.9922
117	0.006892	0.01378	0.9931
118	0.02325	0.04651	0.9767
119	0.02042	0.04084	0.9796
120	0.02448	0.04896	0.9755
121	0.01812	0.03624	0.9819
122	0.02018	0.04037	0.9798
123	0.01502	0.03004	0.985
124	0.01177	0.02355	0.9882
125	0.008423	0.01685	0.9916
126	0.008626	0.01725	0.9914
127	0.005987	0.01197	0.994
128	0.004251	0.008503	0.9957
129	0.002948	0.005896	0.9971
130	0.003169	0.006338	0.9968
131	0.002089	0.004179	0.9979
132	0.00186	0.003719	0.9981
133	0.001227	0.002454	0.9988
134	0.0009688	0.001938	0.999
135	0.001212	0.002423	0.9988
136	0.001466	0.002932	0.9985
137	0.00156	0.00312	0.9984
138	0.0009483	0.001897	0.9991
139	0.0008835	0.001767	0.9991
140	0.01476	0.02952	0.9852
141	0.01152	0.02303	0.9885
142	0.007398	0.0148	0.9926
143	0.004536	0.009072	0.9955
144	0.00469	0.00938	0.9953
145	0.002788	0.005576	0.9972
146	0.002062	0.004123	0.9979
147	0.1799	0.3597	0.8201
148	0.1329	0.2659	0.8671
149	0.09094	0.1819	0.9091
150	0.4642	0.9284	0.5358
151	0.3739	0.7478	0.6261
152	0.3525	0.705	0.6475
153	0.6925	0.6149	0.3075
154	0.8653	0.2694	0.1347
155	0.791	0.418	0.209

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	37	0.2483	NOK
5% type I error level	82	0.550336	NOK
10% type I error level	100	0.671141	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 & 37 &  0.2483 & NOK \tabularnewline
5% type I error level & 82 & 0.550336 & NOK \tabularnewline
10% type I error level & 100 & 0.671141 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=316108&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]37[/C][C] 0.2483[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]82[/C][C]0.550336[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]100[/C][C]0.671141[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=316108&T=7

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=316108&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	37	0.2483	NOK
5% type I error level	82	0.550336	NOK
10% type I error level	100	0.671141	NOK

Ramsey RESET F-Test for powers (2 and 3) of fitted values
> reset_test_fitted RESET test data: mylm RESET = 27.973, df1 = 2, df2 = 156, p-value = 4.152e-11
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors RESET test data: mylm RESET = 14.261, df1 = 6, df2 = 152, p-value = 7.432e-13
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components RESET test data: mylm RESET = 32.691, df1 = 2, df2 = 156, p-value = 1.389e-12

\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 = 27.973, df1 = 2, df2 = 156, p-value = 4.152e-11
 \tabularnewline
Ramsey RESET F-Test for powers (2 and 3) of regressors \tabularnewline
> reset_test_regressors
	RESET test
data:  mylm
RESET = 14.261, df1 = 6, df2 = 152, p-value = 7.432e-13
 \tabularnewline
Ramsey RESET F-Test for powers (2 and 3) of principal components \tabularnewline
> reset_test_principal_components
	RESET test
data:  mylm
RESET = 32.691, df1 = 2, df2 = 156, p-value = 1.389e-12
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=316108&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 = 27.973, df1 = 2, df2 = 156, p-value = 4.152e-11
[/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 = 14.261, df1 = 6, df2 = 152, p-value = 7.432e-13
[/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 = 32.691, df1 = 2, df2 = 156, p-value = 1.389e-12
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=316108&T=8

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=316108&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 = 27.973, df1 = 2, df2 = 156, p-value = 4.152e-11
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors RESET test data: mylm RESET = 14.261, df1 = 6, df2 = 152, p-value = 7.432e-13
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components RESET test data: mylm RESET = 32.691, df1 = 2, df2 = 156, p-value = 1.389e-12

Variance Inflation Factors (Multicollinearity)
> vif `Population_(millions)` HDI GDP_per_Capita 1.003850 1.850481 1.854204

\begin{tabular}{lllllllll}
\hline
Variance Inflation Factors (Multicollinearity) \tabularnewline
> vif
`Population_(millions)`                     HDI          GDP_per_Capita 
               1.003850                1.850481                1.854204 
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=316108&T=9

[TABLE]
[ROW][C]Variance Inflation Factors (Multicollinearity)[/C][/ROW]
[ROW][C]> vif
`Population_(millions)`                     HDI          GDP_per_Capita 
               1.003850                1.850481                1.854204 
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=316108&T=9

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=316108&T=9

As an alternative you can also use a QR Code:

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

Variance Inflation Factors (Multicollinearity)
> vif `Population_(millions)` HDI GDP_per_Capita 1.003850 1.850481 1.854204

Figure 1

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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):

par6 <- '12'
par5 <- ''
par4 <- ''
par3 <- 'No Linear Trend'
par2 <- 'Do not include Seasonal Dummies'
par1 <- '1'
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