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R Software Module

rwasp_multipleregression.wasp

Title produced by software

Multiple Regression

Date of computation

Sun, 20 Jan 2019 15:26:29 +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/2019/Jan/20/t1547994398jpe2j3dr9jcgxpa.htm/, Retrieved Fri, 03 May 2024 07:50:07 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=316485, Retrieved Fri, 03 May 2024 07:50:07 +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] [] [2019-01-20 14:26:29] [9172f81d29b60ad7d026eed068ac45c3] [Current]

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0.18 29.82 0.46 614.66 0.5
0.87 3.16 0.73 4534.37 1.18
1.14 38.48 0.73 5430.57 0.59
0.2 20.82 0.52 4665.91 2.55
NA 0.09 0.78 13205.1 0.94
1.08 41.09 0.83 13540 6.92
0.89 2.97 0.73 3426.39 0.89
NA 0.1 NA NA 0.57
4.85 23.05 0.93 66604.2 16.57
4.14 8.46 0.88 51274.1 3.07
1.25 9.31 0.75 7106.04 0.85
4.46 0.37 0.78 22647.3 9.55
6.19 1.32 0.82 24299 0.58
0.26 154.7 0.56 857.5 0.38
3.28 0.28 0.79 15722.8 0.19
2.57 9.4 0.8 6300.45 3.64
4.43 11.06 0.89 48053.3 1.19
0.51 10.05 0.48 746.83 0.88
NA 0.06 NA 70626.3 0.13
0.63 0.74 0.59 2395 5.27
0.67 10.5 0.65 2253.09 16.73
1.74 3.83 0.73 4708.85 1.63
2.36 2 0.69 7743.5 3.47
0.91 198.66 0.75 13237.6 9.08
NA 0.03 NA NA 2.05
3.24 0.41 0.85 47097.4 2.87
2.08 7.28 0.78 7615.28 2.86
0.12 16.46 0.39 671.07 0.98
0.04 9.85 0.39 276.69 0.32
NA 0.49 0.64 3801.45 0.62
NA 14.86 0.55 877.64 1.09
0.19 21.7 0.5 1271.21 1.69
5 34.84 0.91 52145.4 16.01
3.56 0.06 NA NA 0.32
0.08 4.53 0.37 495.04 7.87
0.01 12.45 0.39 1161.22 2.03
2.04 17.46 0.83 14525.8 3.63
2.32 1408.04 0.72 5560.94 0.94
0.67 47.7 0.72 7305.22 3.6
0.25 0.72 0.5 860.24 0.32
0.47 4.34 0.57 1943.69 10.91
0.07 65.7 0.42 338.63 3.07
1.37 4.8 0.76 8979.96 1.53
0.26 19.84 NA 1016.83 1.78
2.21 4.31 0.82 14522.8 2.8
1.23 11.27 0.77 5175.94 0.76
2.94 1.13 0.85 31454.7 0.34
3.42 10.66 0.87 21676.3 2.46
2.6 5.6 0.92 61413.6 4.78
NA 0.86 0.46 1433.17 0.77
1.47 0.07 0.72 7088.01 1.03
0.86 10.28 0.71 6085.89 0.56
1.08 15.49 0.73 5192.88 2.2
1.02 80.72 0.69 2930.33 0.56
0.84 6.3 0.66 3696.33 0.61
3.17 0.74 0.58 24064 4.4
0.03 6.13 0.39 439.73 1.3
NA 1.29 0.85 17304.4 10.53
0.07 91.73 0.43 379.38 0.58
1.06 0.88 0.72 4201.37 2.37
NA 5.41 0.88 50960.2 13.44
2.71 63.98 0.89 45430.3 3.11
1.58 0.24 NA NA 111.35
2.39 0.27 NA NA 1.37
0.43 1.63 0.67 11989 26.31
0.21 1.79 0.44 505.76 0.82
0.83 4.36 0.75 3710.7 1.17
3.28 82.8 0.91 46822.4 2.27
0.43 25.37 0.57 1627.9 1.35
2.58 11.12 0.86 25987.4 1.61
NA 0.1 0.74 7410.48 1.96
2.61 0.46 NA NA 0.45
0.7 15.08 0.62 3233.8 0.99
0.16 11.45 0.41 459.09 2.09
0.09 1.66 0.42 681.25 3.03
1.25 0.8 0.63 3269.46 66.58
0.15 10.17 0.48 749.13 0.27
0.6 7.94 0.61 2269.51 1.77
1.9 9.98 0.82 13964.2 2.17
0.61 1236.69 0.6 1513.85 0.45
0.64 246.86 0.68 3688.53 1.26
1.72 76.42 0.76 7511.1 0.9
1.36 32.78 0.65 5848.54 0.29
3.22 4.58 0.91 52853.6 3.73
4.59 7.64 0.89 33718.9 0.35
2.77 60.92 0.87 38412 1.08
1.09 2.77 0.72 5226.3 0.43
3.69 127.25 0.89 46201.6 0.72
1.09 7.01 0.75 4615.17 0.21
4.59 16.27 0.78 11278 3.41
0.2 43.18 0.54 1062.11 0.51
0.68 24.76 NA NA 0.6
4.17 49 0.89 24155.8 0.68
6.89 3.25 0.82 41830.5 0.55
0.95 5.47 0.65 1116.37 1.3
0.09 6.65 0.56 1236.24 1.62
1.66 2.06 0.81 13732 9.55
2.52 4.65 0.76 9143.86 0.33
0.51 2.05 0.48 1338.42 0.78
0.14 4.19 0.42 397.38 2.57
2.33 6.16 0.74 5859.43 0.7
2.15 3.03 0.83 14373.7 5.67
12.65 0.52 0.89 114665 1.68
2.06 2.11 0.74 5174.89 1.51
0.07 22.29 0.51 456.33 2.63
0.07 15.91 0.43 493.84 0.66
2.1 29.24 0.77 10252.6 2.41
0.1 14.85 0.41 741.22 1.58
1.73 0.4 NA NA 0.39
0.55 3.8 0.5 1524.39 4.48
1.99 1.24 0.77 8811.15 0.71
1.74 120.85 0.75 10123.9 1.27
1.03 3.51 0.68 1971.03 0.8
2.09 2.8 0.71 3736.07 15.66
2.13 0.62 0.8 7251.6 3.24
NA 0 NA NA 1.36
0.67 32.52 0.62 3149.43 0.71
0.17 25.2 0.41 538.82 2.06
0.09 52.8 0.53 1117.58 1.84
1.02 2.26 0.62 5880.8 6.88
NA 0.01 NA NA 0.19
0.16 27.47 0.54 700.07 0.59
3.23 16.71 0.92 53589.9 1.17
1.78 0.25 NA NA 7.67
2.84 4.46 0.91 37488.3 10.14
0.45 5.99 0.63 1626.85 2.25
0.1 17.16 0.34 410.91 1.24
0.21 168.83 0.5 2612.12 0.7
NA 4.99 0.94 100172 8.18
5.8 3.31 0.79 22622.8 1.92
0.38 179.16 0.53 1218.6 0.35
1.44 3.8 0.77 8410.77 2.94
0.35 7.17 0.5 1871.21 3.92
0.97 6.69 0.67 3557.31 10.52
0.67 29.99 0.73 5684.73 3.97
0.34 96.71 0.66 2379.44 0.54
2.64 38.21 0.84 13769.5 2.08
2.15 10.6 0.83 23217.3 1.51
9.57 2.05 0.85 99431.5 1.24
3.27 0.86 NA NA 0.18
1.46 21.76 0.79 9213.94 2.32
3.87 143.17 0.79 13320.2 6.79
0.07 11.46 0.48 628.08 0.54
3.34 0.05 0.74 12952.5 0.62
1.56 0.18 0.73 7737.2 0.34
NA 0.11 0.72 6171.48 1.26
0.96 0.19 0.7 4067.15 1.93
0.37 0.19 0.55 1384.53 0.87
4.21 28.29 0.83 23593.8 0.5
0.3 13.73 0.46 1079.27 1.05
1.66 9.55 0.76 6426.18 1.25
0.07 5.98 0.4 499.89 1.24
5.91 5.3 0.91 53122.4 0.05
2.82 5.45 0.84 18103.1 2.71
4.27 2.07 0.88 25040.5 2.35
0 0.55 0.5 1647.86 4.36
0.07 10.2 NA NA 1.27
2.34 52.39 0.66 8089.87 1.15
2.22 46.76 0.87 32008.7 1.25
0.52 21.1 0.75 2880.03 0.44
3.01 0.54 0.71 8190.7 89.33
0.67 1.23 0.53 4657.48 0.88
3.88 9.51 0.9 59381.9 10.62
4.26 8 0.93 88506.2 1.3
0.81 21.89 0.62 NA 0.6
0.13 8.01 0.62 836.17 0.53
0.17 47.78 0.51 765.33 1.08
1.54 66.78 0.72 5479.29 1.24
0.06 1.11 0.6 5167.86 1.78
0.31 6.64 0.47 580.86 0.53
0.88 0.1 0.72 4330.9 1.48
6.89 1.34 0.77 18310.8 1.56
1.11 10.88 0.72 4305.07 0.93
1.92 74 0.76 10437.7 1.52
4.13 5.17 0.68 5290.14 2.79
0.08 36.35 0.48 601.35 0.59
1.92 45.53 0.74 3589.63 2.27
3.14 63.03 0.9 40980.5 1.32
6.37 9.206 0.83 40817.4 0.56
5.9 317.5 0.91 49725 3.76
0.98 3.4 0.79 14238.1 10.32
1.41 28.54 0.67 1560.85 0.92
2.13 29.96 0.763846 10237.8 2.78
0.79 90.8 0.66 1532.31 1
NA 0.01 NA NA 1.51
0.42 23.85 0.5 1302.3 0.5
0.24 14.08 0.58 1740.64 2.23
0.53 13.72 0.49 865.91 0.62

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	13 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 time13 seconds \tabularnewline
R ServerBig Analytics Cloud Computing Center \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=316485&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]

13 seconds[/C][/ROW] [ROW]

R Server[/C]

Big Analytics Cloud Computing Center[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=316485&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=316485&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	13 seconds
R Server	Big Analytics Cloud Computing Center

Multiple Linear Regression - Estimated Regression Equation
Carbon_Footprint[t] = -1.26537 + 0.000122018`Population_(millions)`[t] + 3.20593HDI[t] + 6.38132e-05GDP_per_Capita[t] + 0.00416326Total_Biocapacity[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Carbon_Footprint[t] =  -1.26537 +  0.000122018`Population_(millions)`[t] +  3.20593HDI[t] +  6.38132e-05GDP_per_Capita[t] +  0.00416326Total_Biocapacity[t]  + e[t] \tabularnewline
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=316485&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Carbon_Footprint[t] =  -1.26537 +  0.000122018`Population_(millions)`[t] +  3.20593HDI[t] +  6.38132e-05GDP_per_Capita[t] +  0.00416326Total_Biocapacity[t]  + e[t][/C][/ROW]
[ROW][C][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=316485&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=316485&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] = -1.26537 + 0.000122018`Population_(millions)`[t] + 3.20593HDI[t] + 6.38132e-05GDP_per_Capita[t] + 0.00416326Total_Biocapacity[t] + e[t]

Multiple Linear Regression - Ordinary Least Squares
Variable	Parameter	S.D.	T-STATH0: parameter = 0	2-tail p-value	1-tail p-value
(Intercept)	-1.265	0.4496	-2.8140e+00	0.005512	0.002756
`Population_(millions)`	+0.000122	0.0005486	+2.2240e-01	0.8243	0.4121
HDI	+3.206	0.7185	+4.4620e+00	1.542e-05	7.711e-06
GDP_per_Capita	+6.381e-05	5.702e-06	+1.1190e+01	9.532e-22	4.766e-22
Total_Biocapacity	+0.004163	0.009001	+4.6250e-01	0.6444	0.3222

\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) & -1.265 &  0.4496 & -2.8140e+00 &  0.005512 &  0.002756 \tabularnewline
`Population_(millions)` & +0.000122 &  0.0005486 & +2.2240e-01 &  0.8243 &  0.4121 \tabularnewline
HDI & +3.206 &  0.7185 & +4.4620e+00 &  1.542e-05 &  7.711e-06 \tabularnewline
GDP_per_Capita & +6.381e-05 &  5.702e-06 & +1.1190e+01 &  9.532e-22 &  4.766e-22 \tabularnewline
Total_Biocapacity & +0.004163 &  0.009001 & +4.6250e-01 &  0.6444 &  0.3222 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=316485&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]-1.265[/C][C] 0.4496[/C][C]-2.8140e+00[/C][C] 0.005512[/C][C] 0.002756[/C][/ROW]
[ROW][C]`Population_(millions)`[/C][C]+0.000122[/C][C] 0.0005486[/C][C]+2.2240e-01[/C][C] 0.8243[/C][C] 0.4121[/C][/ROW]
[ROW][C]HDI[/C][C]+3.206[/C][C] 0.7185[/C][C]+4.4620e+00[/C][C] 1.542e-05[/C][C] 7.711e-06[/C][/ROW]
[ROW][C]GDP_per_Capita[/C][C]+6.381e-05[/C][C] 5.702e-06[/C][C]+1.1190e+01[/C][C] 9.532e-22[/C][C] 4.766e-22[/C][/ROW]
[ROW][C]Total_Biocapacity[/C][C]+0.004163[/C][C] 0.009001[/C][C]+4.6250e-01[/C][C] 0.6444[/C][C] 0.3222[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=316485&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=316485&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)	-1.265	0.4496	-2.8140e+00	0.005512	0.002756
`Population_(millions)`	+0.000122	0.0005486	+2.2240e-01	0.8243	0.4121
HDI	+3.206	0.7185	+4.4620e+00	1.542e-05	7.711e-06
GDP_per_Capita	+6.381e-05	5.702e-06	+1.1190e+01	9.532e-22	4.766e-22
Total_Biocapacity	+0.004163	0.009001	+4.6250e-01	0.6444	0.3222

Multiple Linear Regression - Regression Statistics
Multiple R	0.8461
R-squared	0.7159
Adjusted R-squared	0.7086
F-TEST (value)	98.89
F-TEST (DF numerator)	4
F-TEST (DF denominator)	157
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	1.047
Sum Squared Residuals	172.1

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R &  0.8461 \tabularnewline
R-squared &  0.7159 \tabularnewline
Adjusted R-squared &  0.7086 \tabularnewline
F-TEST (value) &  98.89 \tabularnewline
F-TEST (DF numerator) & 4 \tabularnewline
F-TEST (DF denominator) & 157 \tabularnewline
p-value &  0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation &  1.047 \tabularnewline
Sum Squared Residuals &  172.1 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=316485&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C] 0.8461[/C][/ROW]
[ROW][C]R-squared[/C][C] 0.7159[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C] 0.7086[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C] 98.89[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]4[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]157[/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] 1.047[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C] 172.1[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=316485&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=316485&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	0.8461
R-squared	0.7159
Adjusted R-squared	0.7086
F-TEST (value)	98.89
F-TEST (DF numerator)	4
F-TEST (DF denominator)	157
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	1.047
Sum Squared Residuals	172.1

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=316485&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=316485&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=316485&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	0.2543	-0.0743
2	0.87	1.37	-0.4996
3	1.14	1.429	-0.2887
4	0.2	0.7126	-0.5126
5	1.08	2.293	-1.213
6	0.89	1.298	-0.4077
7	4.85	6.038	-1.188
8	4.14	4.842	-0.7016
9	1.25	1.597	-0.3472
10	4.46	2.72	1.74
11	6.19	2.917	3.273
12	0.26	0.6051	-0.3451
13	3.28	2.271	1.009
14	2.57	1.718	0.8523
15	4.43	4.661	-0.2306
16	0.51	0.326	0.184
17	0.63	0.801	-0.171
18	0.67	1.033	-0.3632
19	1.74	1.383	0.3573
20	2.36	1.456	0.9045
21	0.91	2.046	-1.136
22	3.24	4.477	-1.237
23	2.08	1.734	0.346
24	0.12	0.03385	0.08615
25	0.04	0.005131	0.03487
26	0.19	0.4284	-0.2384
27	5	5.051	-0.0505
28	0.08	-0.01427	0.09427
29	0.01	0.06901	-0.05901
30	2.04	2.34	-0.2997
31	2.32	1.573	0.7465
32	0.67	1.53	-0.8599
33	0.25	0.3939	-0.1439
34	0.47	0.732	-0.262
35	0.07	0.1235	-0.05353
36	1.37	1.751	-0.3811
37	2.21	2.302	-0.09242
38	1.23	1.538	-0.308
39	2.94	3.468	-0.5284
40	3.42	2.919	0.5014
41	2.6	5.624	-3.024
42	1.47	1.5	-0.0295
43	0.86	1.403	-0.5428
44	1.08	1.417	-0.3374
45	1.02	1.146	-0.1259
46	0.84	1.09	-0.2497
47	3.17	2.148	1.022
48	0.03	0.01916	0.01084
49	0.07	0.151	-0.08099
50	1.06	1.321	-0.261
51	2.71	4.508	-1.798
52	0.43	1.757	-1.327
53	0.21	0.1811	0.02886
54	0.83	1.381	-0.5513
55	3.28	4.659	-1.379
56	0.43	0.6746	-0.2446
57	2.58	3.158	-0.5781
58	0.7	0.9346	-0.2346
59	0.16	0.08845	0.07155
60	0.09	0.1374	-0.04741
61	1.25	1.24	0.009714
62	0.15	0.3236	-0.1736
63	0.6	0.8434	-0.2434
64	1.9	2.265	-0.3648
65	0.61	0.9076	-0.2976
66	0.64	1.185	-0.5454
67	1.72	1.664	0.05649
68	1.36	1.197	0.1631
69	3.22	5.041	-1.821
70	4.59	3.742	0.848
71	2.77	3.987	-1.217
72	1.09	1.379	-0.2885
73	3.69	4.555	-0.8647
74	1.09	1.435	-0.3453
75	4.59	1.971	2.619
76	0.2	0.541	-0.341
77	4.17	3.138	1.032
78	6.89	4.036	2.854
79	0.95	0.8958	0.0542
80	0.09	0.6164	-0.5264
81	1.66	2.248	-0.5877
82	2.52	1.757	0.7634
83	0.51	0.3624	0.1476
84	0.14	0.1177	0.02231
85	2.33	1.485	0.8454
86	2.15	2.337	-0.1868
87	12.65	8.912	3.738
88	2.06	1.444	0.6162
89	0.07	0.4124	-0.3424
90	0.07	0.1494	-0.07938
91	2.1	1.871	0.229
92	0.1	0.1047	-0.004748
93	0.55	0.454	0.09602
94	1.99	1.769	0.2214
95	1.74	1.805	-0.06515
96	1.03	1.044	-0.0142
97	2.09	1.315	0.7752
98	2.13	1.776	0.3543
99	0.67	0.9302	-0.2602
100	0.17	0.09509	0.07491
101	0.09	0.5192	-0.4292
102	1.02	1.127	-0.1065
103	0.16	0.5163	-0.3563
104	3.23	5.111	-1.881
105	2.84	4.087	-1.247
106	0.45	0.8683	-0.4183
107	0.1	-0.1419	0.2419
108	0.21	0.5278	-0.3178
109	5.8	2.719	3.081
110	0.38	0.5349	-0.1549
111	1.44	1.753	-0.3126
112	0.35	0.4742	-0.1242
113	0.97	1.154	-0.1842
114	0.67	1.458	-0.7879
115	0.34	1.016	-0.6764
116	2.64	2.32	0.3204
117	2.15	2.885	-0.7347
118	9.57	7.81	1.76
119	1.46	1.868	-0.4076
120	3.87	2.163	1.707
121	0.07	0.3172	-0.2472
122	3.34	1.936	1.404
123	1.56	1.57	-0.01013
124	0.96	1.246	-0.2864
125	0.37	0.5899	-0.2199
126	4.21	2.907	1.303
127	0.3	0.2843	0.01573
128	1.66	1.588	0.07242
129	0.07	0.05479	0.01521
130	5.91	5.043	0.8672
131	2.82	2.595	0.2252
132	4.27	3.164	1.106
133	0	0.461	-0.461
134	2.34	1.378	0.962
135	2.22	3.577	-1.357
136	0.52	1.327	-0.8073
137	3.01	1.905	1.105
138	0.67	0.7348	-0.06479
139	3.88	5.455	-1.575
140	4.26	7.37	-3.11
141	0.13	0.7788	-0.6488
142	0.17	0.4288	-0.2588
143	1.54	1.406	0.1341
144	0.06	0.9955	-0.9355
145	0.31	0.2815	0.0285
146	0.88	1.325	-0.4454
147	6.89	2.378	4.512
148	1.11	1.323	-0.2128
149	1.92	1.853	0.06744
150	4.13	1.264	2.866
151	0.08	0.3187	-0.2387
152	1.92	1.351	0.5689
153	3.14	4.248	-1.108
154	6.37	4.004	2.366
155	5.9	4.88	1.02
156	0.98	2.219	-1.239
157	1.41	0.9895	0.4205
158	2.13	1.852	0.278
159	0.79	0.9636	-0.1736
160	0.42	0.4257	-0.005688
161	0.24	0.7161	-0.4761
162	0.53	0.365	0.165

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 &  0.18 &  0.2543 & -0.0743 \tabularnewline
2 &  0.87 &  1.37 & -0.4996 \tabularnewline
3 &  1.14 &  1.429 & -0.2887 \tabularnewline
4 &  0.2 &  0.7126 & -0.5126 \tabularnewline
5 &  1.08 &  2.293 & -1.213 \tabularnewline
6 &  0.89 &  1.298 & -0.4077 \tabularnewline
7 &  4.85 &  6.038 & -1.188 \tabularnewline
8 &  4.14 &  4.842 & -0.7016 \tabularnewline
9 &  1.25 &  1.597 & -0.3472 \tabularnewline
10 &  4.46 &  2.72 &  1.74 \tabularnewline
11 &  6.19 &  2.917 &  3.273 \tabularnewline
12 &  0.26 &  0.6051 & -0.3451 \tabularnewline
13 &  3.28 &  2.271 &  1.009 \tabularnewline
14 &  2.57 &  1.718 &  0.8523 \tabularnewline
15 &  4.43 &  4.661 & -0.2306 \tabularnewline
16 &  0.51 &  0.326 &  0.184 \tabularnewline
17 &  0.63 &  0.801 & -0.171 \tabularnewline
18 &  0.67 &  1.033 & -0.3632 \tabularnewline
19 &  1.74 &  1.383 &  0.3573 \tabularnewline
20 &  2.36 &  1.456 &  0.9045 \tabularnewline
21 &  0.91 &  2.046 & -1.136 \tabularnewline
22 &  3.24 &  4.477 & -1.237 \tabularnewline
23 &  2.08 &  1.734 &  0.346 \tabularnewline
24 &  0.12 &  0.03385 &  0.08615 \tabularnewline
25 &  0.04 &  0.005131 &  0.03487 \tabularnewline
26 &  0.19 &  0.4284 & -0.2384 \tabularnewline
27 &  5 &  5.051 & -0.0505 \tabularnewline
28 &  0.08 & -0.01427 &  0.09427 \tabularnewline
29 &  0.01 &  0.06901 & -0.05901 \tabularnewline
30 &  2.04 &  2.34 & -0.2997 \tabularnewline
31 &  2.32 &  1.573 &  0.7465 \tabularnewline
32 &  0.67 &  1.53 & -0.8599 \tabularnewline
33 &  0.25 &  0.3939 & -0.1439 \tabularnewline
34 &  0.47 &  0.732 & -0.262 \tabularnewline
35 &  0.07 &  0.1235 & -0.05353 \tabularnewline
36 &  1.37 &  1.751 & -0.3811 \tabularnewline
37 &  2.21 &  2.302 & -0.09242 \tabularnewline
38 &  1.23 &  1.538 & -0.308 \tabularnewline
39 &  2.94 &  3.468 & -0.5284 \tabularnewline
40 &  3.42 &  2.919 &  0.5014 \tabularnewline
41 &  2.6 &  5.624 & -3.024 \tabularnewline
42 &  1.47 &  1.5 & -0.0295 \tabularnewline
43 &  0.86 &  1.403 & -0.5428 \tabularnewline
44 &  1.08 &  1.417 & -0.3374 \tabularnewline
45 &  1.02 &  1.146 & -0.1259 \tabularnewline
46 &  0.84 &  1.09 & -0.2497 \tabularnewline
47 &  3.17 &  2.148 &  1.022 \tabularnewline
48 &  0.03 &  0.01916 &  0.01084 \tabularnewline
49 &  0.07 &  0.151 & -0.08099 \tabularnewline
50 &  1.06 &  1.321 & -0.261 \tabularnewline
51 &  2.71 &  4.508 & -1.798 \tabularnewline
52 &  0.43 &  1.757 & -1.327 \tabularnewline
53 &  0.21 &  0.1811 &  0.02886 \tabularnewline
54 &  0.83 &  1.381 & -0.5513 \tabularnewline
55 &  3.28 &  4.659 & -1.379 \tabularnewline
56 &  0.43 &  0.6746 & -0.2446 \tabularnewline
57 &  2.58 &  3.158 & -0.5781 \tabularnewline
58 &  0.7 &  0.9346 & -0.2346 \tabularnewline
59 &  0.16 &  0.08845 &  0.07155 \tabularnewline
60 &  0.09 &  0.1374 & -0.04741 \tabularnewline
61 &  1.25 &  1.24 &  0.009714 \tabularnewline
62 &  0.15 &  0.3236 & -0.1736 \tabularnewline
63 &  0.6 &  0.8434 & -0.2434 \tabularnewline
64 &  1.9 &  2.265 & -0.3648 \tabularnewline
65 &  0.61 &  0.9076 & -0.2976 \tabularnewline
66 &  0.64 &  1.185 & -0.5454 \tabularnewline
67 &  1.72 &  1.664 &  0.05649 \tabularnewline
68 &  1.36 &  1.197 &  0.1631 \tabularnewline
69 &  3.22 &  5.041 & -1.821 \tabularnewline
70 &  4.59 &  3.742 &  0.848 \tabularnewline
71 &  2.77 &  3.987 & -1.217 \tabularnewline
72 &  1.09 &  1.379 & -0.2885 \tabularnewline
73 &  3.69 &  4.555 & -0.8647 \tabularnewline
74 &  1.09 &  1.435 & -0.3453 \tabularnewline
75 &  4.59 &  1.971 &  2.619 \tabularnewline
76 &  0.2 &  0.541 & -0.341 \tabularnewline
77 &  4.17 &  3.138 &  1.032 \tabularnewline
78 &  6.89 &  4.036 &  2.854 \tabularnewline
79 &  0.95 &  0.8958 &  0.0542 \tabularnewline
80 &  0.09 &  0.6164 & -0.5264 \tabularnewline
81 &  1.66 &  2.248 & -0.5877 \tabularnewline
82 &  2.52 &  1.757 &  0.7634 \tabularnewline
83 &  0.51 &  0.3624 &  0.1476 \tabularnewline
84 &  0.14 &  0.1177 &  0.02231 \tabularnewline
85 &  2.33 &  1.485 &  0.8454 \tabularnewline
86 &  2.15 &  2.337 & -0.1868 \tabularnewline
87 &  12.65 &  8.912 &  3.738 \tabularnewline
88 &  2.06 &  1.444 &  0.6162 \tabularnewline
89 &  0.07 &  0.4124 & -0.3424 \tabularnewline
90 &  0.07 &  0.1494 & -0.07938 \tabularnewline
91 &  2.1 &  1.871 &  0.229 \tabularnewline
92 &  0.1 &  0.1047 & -0.004748 \tabularnewline
93 &  0.55 &  0.454 &  0.09602 \tabularnewline
94 &  1.99 &  1.769 &  0.2214 \tabularnewline
95 &  1.74 &  1.805 & -0.06515 \tabularnewline
96 &  1.03 &  1.044 & -0.0142 \tabularnewline
97 &  2.09 &  1.315 &  0.7752 \tabularnewline
98 &  2.13 &  1.776 &  0.3543 \tabularnewline
99 &  0.67 &  0.9302 & -0.2602 \tabularnewline
100 &  0.17 &  0.09509 &  0.07491 \tabularnewline
101 &  0.09 &  0.5192 & -0.4292 \tabularnewline
102 &  1.02 &  1.127 & -0.1065 \tabularnewline
103 &  0.16 &  0.5163 & -0.3563 \tabularnewline
104 &  3.23 &  5.111 & -1.881 \tabularnewline
105 &  2.84 &  4.087 & -1.247 \tabularnewline
106 &  0.45 &  0.8683 & -0.4183 \tabularnewline
107 &  0.1 & -0.1419 &  0.2419 \tabularnewline
108 &  0.21 &  0.5278 & -0.3178 \tabularnewline
109 &  5.8 &  2.719 &  3.081 \tabularnewline
110 &  0.38 &  0.5349 & -0.1549 \tabularnewline
111 &  1.44 &  1.753 & -0.3126 \tabularnewline
112 &  0.35 &  0.4742 & -0.1242 \tabularnewline
113 &  0.97 &  1.154 & -0.1842 \tabularnewline
114 &  0.67 &  1.458 & -0.7879 \tabularnewline
115 &  0.34 &  1.016 & -0.6764 \tabularnewline
116 &  2.64 &  2.32 &  0.3204 \tabularnewline
117 &  2.15 &  2.885 & -0.7347 \tabularnewline
118 &  9.57 &  7.81 &  1.76 \tabularnewline
119 &  1.46 &  1.868 & -0.4076 \tabularnewline
120 &  3.87 &  2.163 &  1.707 \tabularnewline
121 &  0.07 &  0.3172 & -0.2472 \tabularnewline
122 &  3.34 &  1.936 &  1.404 \tabularnewline
123 &  1.56 &  1.57 & -0.01013 \tabularnewline
124 &  0.96 &  1.246 & -0.2864 \tabularnewline
125 &  0.37 &  0.5899 & -0.2199 \tabularnewline
126 &  4.21 &  2.907 &  1.303 \tabularnewline
127 &  0.3 &  0.2843 &  0.01573 \tabularnewline
128 &  1.66 &  1.588 &  0.07242 \tabularnewline
129 &  0.07 &  0.05479 &  0.01521 \tabularnewline
130 &  5.91 &  5.043 &  0.8672 \tabularnewline
131 &  2.82 &  2.595 &  0.2252 \tabularnewline
132 &  4.27 &  3.164 &  1.106 \tabularnewline
133 &  0 &  0.461 & -0.461 \tabularnewline
134 &  2.34 &  1.378 &  0.962 \tabularnewline
135 &  2.22 &  3.577 & -1.357 \tabularnewline
136 &  0.52 &  1.327 & -0.8073 \tabularnewline
137 &  3.01 &  1.905 &  1.105 \tabularnewline
138 &  0.67 &  0.7348 & -0.06479 \tabularnewline
139 &  3.88 &  5.455 & -1.575 \tabularnewline
140 &  4.26 &  7.37 & -3.11 \tabularnewline
141 &  0.13 &  0.7788 & -0.6488 \tabularnewline
142 &  0.17 &  0.4288 & -0.2588 \tabularnewline
143 &  1.54 &  1.406 &  0.1341 \tabularnewline
144 &  0.06 &  0.9955 & -0.9355 \tabularnewline
145 &  0.31 &  0.2815 &  0.0285 \tabularnewline
146 &  0.88 &  1.325 & -0.4454 \tabularnewline
147 &  6.89 &  2.378 &  4.512 \tabularnewline
148 &  1.11 &  1.323 & -0.2128 \tabularnewline
149 &  1.92 &  1.853 &  0.06744 \tabularnewline
150 &  4.13 &  1.264 &  2.866 \tabularnewline
151 &  0.08 &  0.3187 & -0.2387 \tabularnewline
152 &  1.92 &  1.351 &  0.5689 \tabularnewline
153 &  3.14 &  4.248 & -1.108 \tabularnewline
154 &  6.37 &  4.004 &  2.366 \tabularnewline
155 &  5.9 &  4.88 &  1.02 \tabularnewline
156 &  0.98 &  2.219 & -1.239 \tabularnewline
157 &  1.41 &  0.9895 &  0.4205 \tabularnewline
158 &  2.13 &  1.852 &  0.278 \tabularnewline
159 &  0.79 &  0.9636 & -0.1736 \tabularnewline
160 &  0.42 &  0.4257 & -0.005688 \tabularnewline
161 &  0.24 &  0.7161 & -0.4761 \tabularnewline
162 &  0.53 &  0.365 &  0.165 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=316485&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] 0.2543[/C][C]-0.0743[/C][/ROW]
[ROW][C]2[/C][C] 0.87[/C][C] 1.37[/C][C]-0.4996[/C][/ROW]
[ROW][C]3[/C][C] 1.14[/C][C] 1.429[/C][C]-0.2887[/C][/ROW]
[ROW][C]4[/C][C] 0.2[/C][C] 0.7126[/C][C]-0.5126[/C][/ROW]
[ROW][C]5[/C][C] 1.08[/C][C] 2.293[/C][C]-1.213[/C][/ROW]
[ROW][C]6[/C][C] 0.89[/C][C] 1.298[/C][C]-0.4077[/C][/ROW]
[ROW][C]7[/C][C] 4.85[/C][C] 6.038[/C][C]-1.188[/C][/ROW]
[ROW][C]8[/C][C] 4.14[/C][C] 4.842[/C][C]-0.7016[/C][/ROW]
[ROW][C]9[/C][C] 1.25[/C][C] 1.597[/C][C]-0.3472[/C][/ROW]
[ROW][C]10[/C][C] 4.46[/C][C] 2.72[/C][C] 1.74[/C][/ROW]
[ROW][C]11[/C][C] 6.19[/C][C] 2.917[/C][C] 3.273[/C][/ROW]
[ROW][C]12[/C][C] 0.26[/C][C] 0.6051[/C][C]-0.3451[/C][/ROW]
[ROW][C]13[/C][C] 3.28[/C][C] 2.271[/C][C] 1.009[/C][/ROW]
[ROW][C]14[/C][C] 2.57[/C][C] 1.718[/C][C] 0.8523[/C][/ROW]
[ROW][C]15[/C][C] 4.43[/C][C] 4.661[/C][C]-0.2306[/C][/ROW]
[ROW][C]16[/C][C] 0.51[/C][C] 0.326[/C][C] 0.184[/C][/ROW]
[ROW][C]17[/C][C] 0.63[/C][C] 0.801[/C][C]-0.171[/C][/ROW]
[ROW][C]18[/C][C] 0.67[/C][C] 1.033[/C][C]-0.3632[/C][/ROW]
[ROW][C]19[/C][C] 1.74[/C][C] 1.383[/C][C] 0.3573[/C][/ROW]
[ROW][C]20[/C][C] 2.36[/C][C] 1.456[/C][C] 0.9045[/C][/ROW]
[ROW][C]21[/C][C] 0.91[/C][C] 2.046[/C][C]-1.136[/C][/ROW]
[ROW][C]22[/C][C] 3.24[/C][C] 4.477[/C][C]-1.237[/C][/ROW]
[ROW][C]23[/C][C] 2.08[/C][C] 1.734[/C][C] 0.346[/C][/ROW]
[ROW][C]24[/C][C] 0.12[/C][C] 0.03385[/C][C] 0.08615[/C][/ROW]
[ROW][C]25[/C][C] 0.04[/C][C] 0.005131[/C][C] 0.03487[/C][/ROW]
[ROW][C]26[/C][C] 0.19[/C][C] 0.4284[/C][C]-0.2384[/C][/ROW]
[ROW][C]27[/C][C] 5[/C][C] 5.051[/C][C]-0.0505[/C][/ROW]
[ROW][C]28[/C][C] 0.08[/C][C]-0.01427[/C][C] 0.09427[/C][/ROW]
[ROW][C]29[/C][C] 0.01[/C][C] 0.06901[/C][C]-0.05901[/C][/ROW]
[ROW][C]30[/C][C] 2.04[/C][C] 2.34[/C][C]-0.2997[/C][/ROW]
[ROW][C]31[/C][C] 2.32[/C][C] 1.573[/C][C] 0.7465[/C][/ROW]
[ROW][C]32[/C][C] 0.67[/C][C] 1.53[/C][C]-0.8599[/C][/ROW]
[ROW][C]33[/C][C] 0.25[/C][C] 0.3939[/C][C]-0.1439[/C][/ROW]
[ROW][C]34[/C][C] 0.47[/C][C] 0.732[/C][C]-0.262[/C][/ROW]
[ROW][C]35[/C][C] 0.07[/C][C] 0.1235[/C][C]-0.05353[/C][/ROW]
[ROW][C]36[/C][C] 1.37[/C][C] 1.751[/C][C]-0.3811[/C][/ROW]
[ROW][C]37[/C][C] 2.21[/C][C] 2.302[/C][C]-0.09242[/C][/ROW]
[ROW][C]38[/C][C] 1.23[/C][C] 1.538[/C][C]-0.308[/C][/ROW]
[ROW][C]39[/C][C] 2.94[/C][C] 3.468[/C][C]-0.5284[/C][/ROW]
[ROW][C]40[/C][C] 3.42[/C][C] 2.919[/C][C] 0.5014[/C][/ROW]
[ROW][C]41[/C][C] 2.6[/C][C] 5.624[/C][C]-3.024[/C][/ROW]
[ROW][C]42[/C][C] 1.47[/C][C] 1.5[/C][C]-0.0295[/C][/ROW]
[ROW][C]43[/C][C] 0.86[/C][C] 1.403[/C][C]-0.5428[/C][/ROW]
[ROW][C]44[/C][C] 1.08[/C][C] 1.417[/C][C]-0.3374[/C][/ROW]
[ROW][C]45[/C][C] 1.02[/C][C] 1.146[/C][C]-0.1259[/C][/ROW]
[ROW][C]46[/C][C] 0.84[/C][C] 1.09[/C][C]-0.2497[/C][/ROW]
[ROW][C]47[/C][C] 3.17[/C][C] 2.148[/C][C] 1.022[/C][/ROW]
[ROW][C]48[/C][C] 0.03[/C][C] 0.01916[/C][C] 0.01084[/C][/ROW]
[ROW][C]49[/C][C] 0.07[/C][C] 0.151[/C][C]-0.08099[/C][/ROW]
[ROW][C]50[/C][C] 1.06[/C][C] 1.321[/C][C]-0.261[/C][/ROW]
[ROW][C]51[/C][C] 2.71[/C][C] 4.508[/C][C]-1.798[/C][/ROW]
[ROW][C]52[/C][C] 0.43[/C][C] 1.757[/C][C]-1.327[/C][/ROW]
[ROW][C]53[/C][C] 0.21[/C][C] 0.1811[/C][C] 0.02886[/C][/ROW]
[ROW][C]54[/C][C] 0.83[/C][C] 1.381[/C][C]-0.5513[/C][/ROW]
[ROW][C]55[/C][C] 3.28[/C][C] 4.659[/C][C]-1.379[/C][/ROW]
[ROW][C]56[/C][C] 0.43[/C][C] 0.6746[/C][C]-0.2446[/C][/ROW]
[ROW][C]57[/C][C] 2.58[/C][C] 3.158[/C][C]-0.5781[/C][/ROW]
[ROW][C]58[/C][C] 0.7[/C][C] 0.9346[/C][C]-0.2346[/C][/ROW]
[ROW][C]59[/C][C] 0.16[/C][C] 0.08845[/C][C] 0.07155[/C][/ROW]
[ROW][C]60[/C][C] 0.09[/C][C] 0.1374[/C][C]-0.04741[/C][/ROW]
[ROW][C]61[/C][C] 1.25[/C][C] 1.24[/C][C] 0.009714[/C][/ROW]
[ROW][C]62[/C][C] 0.15[/C][C] 0.3236[/C][C]-0.1736[/C][/ROW]
[ROW][C]63[/C][C] 0.6[/C][C] 0.8434[/C][C]-0.2434[/C][/ROW]
[ROW][C]64[/C][C] 1.9[/C][C] 2.265[/C][C]-0.3648[/C][/ROW]
[ROW][C]65[/C][C] 0.61[/C][C] 0.9076[/C][C]-0.2976[/C][/ROW]
[ROW][C]66[/C][C] 0.64[/C][C] 1.185[/C][C]-0.5454[/C][/ROW]
[ROW][C]67[/C][C] 1.72[/C][C] 1.664[/C][C] 0.05649[/C][/ROW]
[ROW][C]68[/C][C] 1.36[/C][C] 1.197[/C][C] 0.1631[/C][/ROW]
[ROW][C]69[/C][C] 3.22[/C][C] 5.041[/C][C]-1.821[/C][/ROW]
[ROW][C]70[/C][C] 4.59[/C][C] 3.742[/C][C] 0.848[/C][/ROW]
[ROW][C]71[/C][C] 2.77[/C][C] 3.987[/C][C]-1.217[/C][/ROW]
[ROW][C]72[/C][C] 1.09[/C][C] 1.379[/C][C]-0.2885[/C][/ROW]
[ROW][C]73[/C][C] 3.69[/C][C] 4.555[/C][C]-0.8647[/C][/ROW]
[ROW][C]74[/C][C] 1.09[/C][C] 1.435[/C][C]-0.3453[/C][/ROW]
[ROW][C]75[/C][C] 4.59[/C][C] 1.971[/C][C] 2.619[/C][/ROW]
[ROW][C]76[/C][C] 0.2[/C][C] 0.541[/C][C]-0.341[/C][/ROW]
[ROW][C]77[/C][C] 4.17[/C][C] 3.138[/C][C] 1.032[/C][/ROW]
[ROW][C]78[/C][C] 6.89[/C][C] 4.036[/C][C] 2.854[/C][/ROW]
[ROW][C]79[/C][C] 0.95[/C][C] 0.8958[/C][C] 0.0542[/C][/ROW]
[ROW][C]80[/C][C] 0.09[/C][C] 0.6164[/C][C]-0.5264[/C][/ROW]
[ROW][C]81[/C][C] 1.66[/C][C] 2.248[/C][C]-0.5877[/C][/ROW]
[ROW][C]82[/C][C] 2.52[/C][C] 1.757[/C][C] 0.7634[/C][/ROW]
[ROW][C]83[/C][C] 0.51[/C][C] 0.3624[/C][C] 0.1476[/C][/ROW]
[ROW][C]84[/C][C] 0.14[/C][C] 0.1177[/C][C] 0.02231[/C][/ROW]
[ROW][C]85[/C][C] 2.33[/C][C] 1.485[/C][C] 0.8454[/C][/ROW]
[ROW][C]86[/C][C] 2.15[/C][C] 2.337[/C][C]-0.1868[/C][/ROW]
[ROW][C]87[/C][C] 12.65[/C][C] 8.912[/C][C] 3.738[/C][/ROW]
[ROW][C]88[/C][C] 2.06[/C][C] 1.444[/C][C] 0.6162[/C][/ROW]
[ROW][C]89[/C][C] 0.07[/C][C] 0.4124[/C][C]-0.3424[/C][/ROW]
[ROW][C]90[/C][C] 0.07[/C][C] 0.1494[/C][C]-0.07938[/C][/ROW]
[ROW][C]91[/C][C] 2.1[/C][C] 1.871[/C][C] 0.229[/C][/ROW]
[ROW][C]92[/C][C] 0.1[/C][C] 0.1047[/C][C]-0.004748[/C][/ROW]
[ROW][C]93[/C][C] 0.55[/C][C] 0.454[/C][C] 0.09602[/C][/ROW]
[ROW][C]94[/C][C] 1.99[/C][C] 1.769[/C][C] 0.2214[/C][/ROW]
[ROW][C]95[/C][C] 1.74[/C][C] 1.805[/C][C]-0.06515[/C][/ROW]
[ROW][C]96[/C][C] 1.03[/C][C] 1.044[/C][C]-0.0142[/C][/ROW]
[ROW][C]97[/C][C] 2.09[/C][C] 1.315[/C][C] 0.7752[/C][/ROW]
[ROW][C]98[/C][C] 2.13[/C][C] 1.776[/C][C] 0.3543[/C][/ROW]
[ROW][C]99[/C][C] 0.67[/C][C] 0.9302[/C][C]-0.2602[/C][/ROW]
[ROW][C]100[/C][C] 0.17[/C][C] 0.09509[/C][C] 0.07491[/C][/ROW]
[ROW][C]101[/C][C] 0.09[/C][C] 0.5192[/C][C]-0.4292[/C][/ROW]
[ROW][C]102[/C][C] 1.02[/C][C] 1.127[/C][C]-0.1065[/C][/ROW]
[ROW][C]103[/C][C] 0.16[/C][C] 0.5163[/C][C]-0.3563[/C][/ROW]
[ROW][C]104[/C][C] 3.23[/C][C] 5.111[/C][C]-1.881[/C][/ROW]
[ROW][C]105[/C][C] 2.84[/C][C] 4.087[/C][C]-1.247[/C][/ROW]
[ROW][C]106[/C][C] 0.45[/C][C] 0.8683[/C][C]-0.4183[/C][/ROW]
[ROW][C]107[/C][C] 0.1[/C][C]-0.1419[/C][C] 0.2419[/C][/ROW]
[ROW][C]108[/C][C] 0.21[/C][C] 0.5278[/C][C]-0.3178[/C][/ROW]
[ROW][C]109[/C][C] 5.8[/C][C] 2.719[/C][C] 3.081[/C][/ROW]
[ROW][C]110[/C][C] 0.38[/C][C] 0.5349[/C][C]-0.1549[/C][/ROW]
[ROW][C]111[/C][C] 1.44[/C][C] 1.753[/C][C]-0.3126[/C][/ROW]
[ROW][C]112[/C][C] 0.35[/C][C] 0.4742[/C][C]-0.1242[/C][/ROW]
[ROW][C]113[/C][C] 0.97[/C][C] 1.154[/C][C]-0.1842[/C][/ROW]
[ROW][C]114[/C][C] 0.67[/C][C] 1.458[/C][C]-0.7879[/C][/ROW]
[ROW][C]115[/C][C] 0.34[/C][C] 1.016[/C][C]-0.6764[/C][/ROW]
[ROW][C]116[/C][C] 2.64[/C][C] 2.32[/C][C] 0.3204[/C][/ROW]
[ROW][C]117[/C][C] 2.15[/C][C] 2.885[/C][C]-0.7347[/C][/ROW]
[ROW][C]118[/C][C] 9.57[/C][C] 7.81[/C][C] 1.76[/C][/ROW]
[ROW][C]119[/C][C] 1.46[/C][C] 1.868[/C][C]-0.4076[/C][/ROW]
[ROW][C]120[/C][C] 3.87[/C][C] 2.163[/C][C] 1.707[/C][/ROW]
[ROW][C]121[/C][C] 0.07[/C][C] 0.3172[/C][C]-0.2472[/C][/ROW]
[ROW][C]122[/C][C] 3.34[/C][C] 1.936[/C][C] 1.404[/C][/ROW]
[ROW][C]123[/C][C] 1.56[/C][C] 1.57[/C][C]-0.01013[/C][/ROW]
[ROW][C]124[/C][C] 0.96[/C][C] 1.246[/C][C]-0.2864[/C][/ROW]
[ROW][C]125[/C][C] 0.37[/C][C] 0.5899[/C][C]-0.2199[/C][/ROW]
[ROW][C]126[/C][C] 4.21[/C][C] 2.907[/C][C] 1.303[/C][/ROW]
[ROW][C]127[/C][C] 0.3[/C][C] 0.2843[/C][C] 0.01573[/C][/ROW]
[ROW][C]128[/C][C] 1.66[/C][C] 1.588[/C][C] 0.07242[/C][/ROW]
[ROW][C]129[/C][C] 0.07[/C][C] 0.05479[/C][C] 0.01521[/C][/ROW]
[ROW][C]130[/C][C] 5.91[/C][C] 5.043[/C][C] 0.8672[/C][/ROW]
[ROW][C]131[/C][C] 2.82[/C][C] 2.595[/C][C] 0.2252[/C][/ROW]
[ROW][C]132[/C][C] 4.27[/C][C] 3.164[/C][C] 1.106[/C][/ROW]
[ROW][C]133[/C][C] 0[/C][C] 0.461[/C][C]-0.461[/C][/ROW]
[ROW][C]134[/C][C] 2.34[/C][C] 1.378[/C][C] 0.962[/C][/ROW]
[ROW][C]135[/C][C] 2.22[/C][C] 3.577[/C][C]-1.357[/C][/ROW]
[ROW][C]136[/C][C] 0.52[/C][C] 1.327[/C][C]-0.8073[/C][/ROW]
[ROW][C]137[/C][C] 3.01[/C][C] 1.905[/C][C] 1.105[/C][/ROW]
[ROW][C]138[/C][C] 0.67[/C][C] 0.7348[/C][C]-0.06479[/C][/ROW]
[ROW][C]139[/C][C] 3.88[/C][C] 5.455[/C][C]-1.575[/C][/ROW]
[ROW][C]140[/C][C] 4.26[/C][C] 7.37[/C][C]-3.11[/C][/ROW]
[ROW][C]141[/C][C] 0.13[/C][C] 0.7788[/C][C]-0.6488[/C][/ROW]
[ROW][C]142[/C][C] 0.17[/C][C] 0.4288[/C][C]-0.2588[/C][/ROW]
[ROW][C]143[/C][C] 1.54[/C][C] 1.406[/C][C] 0.1341[/C][/ROW]
[ROW][C]144[/C][C] 0.06[/C][C] 0.9955[/C][C]-0.9355[/C][/ROW]
[ROW][C]145[/C][C] 0.31[/C][C] 0.2815[/C][C] 0.0285[/C][/ROW]
[ROW][C]146[/C][C] 0.88[/C][C] 1.325[/C][C]-0.4454[/C][/ROW]
[ROW][C]147[/C][C] 6.89[/C][C] 2.378[/C][C] 4.512[/C][/ROW]
[ROW][C]148[/C][C] 1.11[/C][C] 1.323[/C][C]-0.2128[/C][/ROW]
[ROW][C]149[/C][C] 1.92[/C][C] 1.853[/C][C] 0.06744[/C][/ROW]
[ROW][C]150[/C][C] 4.13[/C][C] 1.264[/C][C] 2.866[/C][/ROW]
[ROW][C]151[/C][C] 0.08[/C][C] 0.3187[/C][C]-0.2387[/C][/ROW]
[ROW][C]152[/C][C] 1.92[/C][C] 1.351[/C][C] 0.5689[/C][/ROW]
[ROW][C]153[/C][C] 3.14[/C][C] 4.248[/C][C]-1.108[/C][/ROW]
[ROW][C]154[/C][C] 6.37[/C][C] 4.004[/C][C] 2.366[/C][/ROW]
[ROW][C]155[/C][C] 5.9[/C][C] 4.88[/C][C] 1.02[/C][/ROW]
[ROW][C]156[/C][C] 0.98[/C][C] 2.219[/C][C]-1.239[/C][/ROW]
[ROW][C]157[/C][C] 1.41[/C][C] 0.9895[/C][C] 0.4205[/C][/ROW]
[ROW][C]158[/C][C] 2.13[/C][C] 1.852[/C][C] 0.278[/C][/ROW]
[ROW][C]159[/C][C] 0.79[/C][C] 0.9636[/C][C]-0.1736[/C][/ROW]
[ROW][C]160[/C][C] 0.42[/C][C] 0.4257[/C][C]-0.005688[/C][/ROW]
[ROW][C]161[/C][C] 0.24[/C][C] 0.7161[/C][C]-0.4761[/C][/ROW]
[ROW][C]162[/C][C] 0.53[/C][C] 0.365[/C][C] 0.165[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=316485&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=316485&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	0.2543	-0.0743
2	0.87	1.37	-0.4996
3	1.14	1.429	-0.2887
4	0.2	0.7126	-0.5126
5	1.08	2.293	-1.213
6	0.89	1.298	-0.4077
7	4.85	6.038	-1.188
8	4.14	4.842	-0.7016
9	1.25	1.597	-0.3472
10	4.46	2.72	1.74
11	6.19	2.917	3.273
12	0.26	0.6051	-0.3451
13	3.28	2.271	1.009
14	2.57	1.718	0.8523
15	4.43	4.661	-0.2306
16	0.51	0.326	0.184
17	0.63	0.801	-0.171
18	0.67	1.033	-0.3632
19	1.74	1.383	0.3573
20	2.36	1.456	0.9045
21	0.91	2.046	-1.136
22	3.24	4.477	-1.237
23	2.08	1.734	0.346
24	0.12	0.03385	0.08615
25	0.04	0.005131	0.03487
26	0.19	0.4284	-0.2384
27	5	5.051	-0.0505
28	0.08	-0.01427	0.09427
29	0.01	0.06901	-0.05901
30	2.04	2.34	-0.2997
31	2.32	1.573	0.7465
32	0.67	1.53	-0.8599
33	0.25	0.3939	-0.1439
34	0.47	0.732	-0.262
35	0.07	0.1235	-0.05353
36	1.37	1.751	-0.3811
37	2.21	2.302	-0.09242
38	1.23	1.538	-0.308
39	2.94	3.468	-0.5284
40	3.42	2.919	0.5014
41	2.6	5.624	-3.024
42	1.47	1.5	-0.0295
43	0.86	1.403	-0.5428
44	1.08	1.417	-0.3374
45	1.02	1.146	-0.1259
46	0.84	1.09	-0.2497
47	3.17	2.148	1.022
48	0.03	0.01916	0.01084
49	0.07	0.151	-0.08099
50	1.06	1.321	-0.261
51	2.71	4.508	-1.798
52	0.43	1.757	-1.327
53	0.21	0.1811	0.02886
54	0.83	1.381	-0.5513
55	3.28	4.659	-1.379
56	0.43	0.6746	-0.2446
57	2.58	3.158	-0.5781
58	0.7	0.9346	-0.2346
59	0.16	0.08845	0.07155
60	0.09	0.1374	-0.04741
61	1.25	1.24	0.009714
62	0.15	0.3236	-0.1736
63	0.6	0.8434	-0.2434
64	1.9	2.265	-0.3648
65	0.61	0.9076	-0.2976
66	0.64	1.185	-0.5454
67	1.72	1.664	0.05649
68	1.36	1.197	0.1631
69	3.22	5.041	-1.821
70	4.59	3.742	0.848
71	2.77	3.987	-1.217
72	1.09	1.379	-0.2885
73	3.69	4.555	-0.8647
74	1.09	1.435	-0.3453
75	4.59	1.971	2.619
76	0.2	0.541	-0.341
77	4.17	3.138	1.032
78	6.89	4.036	2.854
79	0.95	0.8958	0.0542
80	0.09	0.6164	-0.5264
81	1.66	2.248	-0.5877
82	2.52	1.757	0.7634
83	0.51	0.3624	0.1476
84	0.14	0.1177	0.02231
85	2.33	1.485	0.8454
86	2.15	2.337	-0.1868
87	12.65	8.912	3.738
88	2.06	1.444	0.6162
89	0.07	0.4124	-0.3424
90	0.07	0.1494	-0.07938
91	2.1	1.871	0.229
92	0.1	0.1047	-0.004748
93	0.55	0.454	0.09602
94	1.99	1.769	0.2214
95	1.74	1.805	-0.06515
96	1.03	1.044	-0.0142
97	2.09	1.315	0.7752
98	2.13	1.776	0.3543
99	0.67	0.9302	-0.2602
100	0.17	0.09509	0.07491
101	0.09	0.5192	-0.4292
102	1.02	1.127	-0.1065
103	0.16	0.5163	-0.3563
104	3.23	5.111	-1.881
105	2.84	4.087	-1.247
106	0.45	0.8683	-0.4183
107	0.1	-0.1419	0.2419
108	0.21	0.5278	-0.3178
109	5.8	2.719	3.081
110	0.38	0.5349	-0.1549
111	1.44	1.753	-0.3126
112	0.35	0.4742	-0.1242
113	0.97	1.154	-0.1842
114	0.67	1.458	-0.7879
115	0.34	1.016	-0.6764
116	2.64	2.32	0.3204
117	2.15	2.885	-0.7347
118	9.57	7.81	1.76
119	1.46	1.868	-0.4076
120	3.87	2.163	1.707
121	0.07	0.3172	-0.2472
122	3.34	1.936	1.404
123	1.56	1.57	-0.01013
124	0.96	1.246	-0.2864
125	0.37	0.5899	-0.2199
126	4.21	2.907	1.303
127	0.3	0.2843	0.01573
128	1.66	1.588	0.07242
129	0.07	0.05479	0.01521
130	5.91	5.043	0.8672
131	2.82	2.595	0.2252
132	4.27	3.164	1.106
133	0	0.461	-0.461
134	2.34	1.378	0.962
135	2.22	3.577	-1.357
136	0.52	1.327	-0.8073
137	3.01	1.905	1.105
138	0.67	0.7348	-0.06479
139	3.88	5.455	-1.575
140	4.26	7.37	-3.11
141	0.13	0.7788	-0.6488
142	0.17	0.4288	-0.2588
143	1.54	1.406	0.1341
144	0.06	0.9955	-0.9355
145	0.31	0.2815	0.0285
146	0.88	1.325	-0.4454
147	6.89	2.378	4.512
148	1.11	1.323	-0.2128
149	1.92	1.853	0.06744
150	4.13	1.264	2.866
151	0.08	0.3187	-0.2387
152	1.92	1.351	0.5689
153	3.14	4.248	-1.108
154	6.37	4.004	2.366
155	5.9	4.88	1.02
156	0.98	2.219	-1.239
157	1.41	0.9895	0.4205
158	2.13	1.852	0.278
159	0.79	0.9636	-0.1736
160	0.42	0.4257	-0.005688
161	0.24	0.7161	-0.4761
162	0.53	0.365	0.165

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
8	0.01925	0.03851	0.9807
9	0.004609	0.009219	0.9954
10	0.328	0.6559	0.672
11	0.9355	0.129	0.0645
12	0.9588	0.0824	0.0412
13	0.9385	0.123	0.0615
14	0.9154	0.1692	0.0846
15	0.8849	0.2303	0.1151
16	0.837	0.326	0.163
17	0.7818	0.4364	0.2182
18	0.7184	0.5632	0.2816
19	0.6478	0.7044	0.3522
20	0.6016	0.7968	0.3984
21	0.5408	0.9183	0.4592
22	0.5664	0.8672	0.4336
23	0.4951	0.9903	0.5049
24	0.4267	0.8535	0.5733
25	0.359	0.718	0.641
26	0.2996	0.5992	0.7004
27	0.2633	0.5267	0.7367
28	0.2151	0.4301	0.7849
29	0.1697	0.3394	0.8303
30	0.1406	0.2811	0.8594
31	0.2001	0.4002	0.7999
32	0.1949	0.3898	0.8051
33	0.1561	0.3121	0.8439
34	0.1224	0.2448	0.8776
35	0.09425	0.1885	0.9057
36	0.07686	0.1537	0.9231
37	0.05797	0.1159	0.942
38	0.04556	0.09112	0.9544
39	0.03643	0.07287	0.9636
40	0.02854	0.05707	0.9715
41	0.1373	0.2746	0.8627
42	0.1093	0.2185	0.8907
43	0.09423	0.1885	0.9058
44	0.07652	0.153	0.9235
45	0.05958	0.1192	0.9404
46	0.04624	0.09248	0.9538
47	0.0585	0.117	0.9415
48	0.04451	0.08902	0.9555
49	0.03347	0.06694	0.9665
50	0.02547	0.05095	0.9745
51	0.03537	0.07075	0.9646
52	0.03548	0.07096	0.9645
53	0.02652	0.05305	0.9735
54	0.02204	0.04408	0.978
55	0.02293	0.04586	0.9771
56	0.01725	0.0345	0.9828
57	0.01339	0.02678	0.9866
58	0.009845	0.01969	0.9902
59	0.007024	0.01405	0.993
60	0.00493	0.00986	0.9951
61	0.003976	0.007952	0.996
62	0.002765	0.00553	0.9972
63	0.001927	0.003854	0.9981
64	0.001347	0.002693	0.9987
65	0.001006	0.002012	0.999
66	0.000744	0.001488	0.9993
67	0.0004884	0.0009768	0.9995
68	0.0003217	0.0006434	0.9997
69	0.0005293	0.001059	0.9995
70	0.0006924	0.001385	0.9993
71	0.000693	0.001386	0.9993
72	0.0004768	0.0009536	0.9995
73	0.0003836	0.0007673	0.9996
74	0.0002667	0.0005334	0.9997
75	0.003672	0.007345	0.9963
76	0.002685	0.00537	0.9973
77	0.003057	0.006113	0.9969
78	0.0436	0.08719	0.9564
79	0.0339	0.06779	0.9661
80	0.02806	0.05612	0.9719
81	0.02364	0.04729	0.9764
82	0.02059	0.04118	0.9794
83	0.01561	0.03122	0.9844
84	0.01163	0.02325	0.9884
85	0.01025	0.02051	0.9897
86	0.007627	0.01525	0.9924
87	0.1813	0.3626	0.8187
88	0.1623	0.3246	0.8377
89	0.1382	0.2763	0.8618
90	0.1145	0.2289	0.8855
91	0.09478	0.1896	0.9052
92	0.0768	0.1536	0.9232
93	0.0616	0.1232	0.9384
94	0.04932	0.09864	0.9507
95	0.0389	0.07779	0.9611
96	0.03014	0.06027	0.9699
97	0.02715	0.0543	0.9729
98	0.02131	0.04262	0.9787
99	0.01634	0.03268	0.9837
100	0.01218	0.02435	0.9878
101	0.009417	0.01883	0.9906
102	0.006858	0.01372	0.9931
103	0.005107	0.01021	0.9949
104	0.01026	0.02052	0.9897
105	0.01207	0.02415	0.9879
106	0.009337	0.01867	0.9907
107	0.006995	0.01399	0.993
108	0.005145	0.01029	0.9949
109	0.04056	0.08113	0.9594
110	0.03173	0.06346	0.9683
111	0.02488	0.04976	0.9751
112	0.0186	0.03721	0.9814
113	0.01397	0.02794	0.986
114	0.01259	0.02519	0.9874
115	0.01109	0.02219	0.9889
116	0.008119	0.01624	0.9919
117	0.007068	0.01414	0.9929
118	0.02358	0.04716	0.9764
119	0.02041	0.04083	0.9796
120	0.02424	0.04849	0.9758
121	0.01788	0.03576	0.9821
122	0.02016	0.04032	0.9798
123	0.01483	0.02966	0.9852
124	0.01146	0.02291	0.9885
125	0.008133	0.01627	0.9919
126	0.00845	0.0169	0.9916
127	0.005838	0.01168	0.9942
128	0.004069	0.008137	0.9959
129	0.002801	0.005602	0.9972
130	0.003126	0.006252	0.9969
131	0.002038	0.004075	0.998
132	0.001875	0.003749	0.9981
133	0.001233	0.002465	0.9988
134	0.0009777	0.001955	0.999
135	0.001155	0.002311	0.9988
136	0.001289	0.002577	0.9987
137	0.002021	0.004042	0.998
138	0.001229	0.002459	0.9988
139	0.0009996	0.001999	0.999
140	0.01694	0.03388	0.9831
141	0.01433	0.02867	0.9857
142	0.009146	0.01829	0.9909
143	0.00567	0.01134	0.9943
144	0.005541	0.01108	0.9945
145	0.003237	0.006474	0.9968
146	0.002724	0.005449	0.9973
147	0.1771	0.3541	0.8229
148	0.1391	0.2781	0.8609
149	0.09864	0.1973	0.9014
150	0.5239	0.9523	0.4761
151	0.4168	0.8336	0.5832
152	0.3457	0.6914	0.6543
153	0.9975	0.005039	0.00252
154	0.9886	0.02275	0.01137

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
8 &  0.01925 &  0.03851 &  0.9807 \tabularnewline
9 &  0.004609 &  0.009219 &  0.9954 \tabularnewline
10 &  0.328 &  0.6559 &  0.672 \tabularnewline
11 &  0.9355 &  0.129 &  0.0645 \tabularnewline
12 &  0.9588 &  0.0824 &  0.0412 \tabularnewline
13 &  0.9385 &  0.123 &  0.0615 \tabularnewline
14 &  0.9154 &  0.1692 &  0.0846 \tabularnewline
15 &  0.8849 &  0.2303 &  0.1151 \tabularnewline
16 &  0.837 &  0.326 &  0.163 \tabularnewline
17 &  0.7818 &  0.4364 &  0.2182 \tabularnewline
18 &  0.7184 &  0.5632 &  0.2816 \tabularnewline
19 &  0.6478 &  0.7044 &  0.3522 \tabularnewline
20 &  0.6016 &  0.7968 &  0.3984 \tabularnewline
21 &  0.5408 &  0.9183 &  0.4592 \tabularnewline
22 &  0.5664 &  0.8672 &  0.4336 \tabularnewline
23 &  0.4951 &  0.9903 &  0.5049 \tabularnewline
24 &  0.4267 &  0.8535 &  0.5733 \tabularnewline
25 &  0.359 &  0.718 &  0.641 \tabularnewline
26 &  0.2996 &  0.5992 &  0.7004 \tabularnewline
27 &  0.2633 &  0.5267 &  0.7367 \tabularnewline
28 &  0.2151 &  0.4301 &  0.7849 \tabularnewline
29 &  0.1697 &  0.3394 &  0.8303 \tabularnewline
30 &  0.1406 &  0.2811 &  0.8594 \tabularnewline
31 &  0.2001 &  0.4002 &  0.7999 \tabularnewline
32 &  0.1949 &  0.3898 &  0.8051 \tabularnewline
33 &  0.1561 &  0.3121 &  0.8439 \tabularnewline
34 &  0.1224 &  0.2448 &  0.8776 \tabularnewline
35 &  0.09425 &  0.1885 &  0.9057 \tabularnewline
36 &  0.07686 &  0.1537 &  0.9231 \tabularnewline
37 &  0.05797 &  0.1159 &  0.942 \tabularnewline
38 &  0.04556 &  0.09112 &  0.9544 \tabularnewline
39 &  0.03643 &  0.07287 &  0.9636 \tabularnewline
40 &  0.02854 &  0.05707 &  0.9715 \tabularnewline
41 &  0.1373 &  0.2746 &  0.8627 \tabularnewline
42 &  0.1093 &  0.2185 &  0.8907 \tabularnewline
43 &  0.09423 &  0.1885 &  0.9058 \tabularnewline
44 &  0.07652 &  0.153 &  0.9235 \tabularnewline
45 &  0.05958 &  0.1192 &  0.9404 \tabularnewline
46 &  0.04624 &  0.09248 &  0.9538 \tabularnewline
47 &  0.0585 &  0.117 &  0.9415 \tabularnewline
48 &  0.04451 &  0.08902 &  0.9555 \tabularnewline
49 &  0.03347 &  0.06694 &  0.9665 \tabularnewline
50 &  0.02547 &  0.05095 &  0.9745 \tabularnewline
51 &  0.03537 &  0.07075 &  0.9646 \tabularnewline
52 &  0.03548 &  0.07096 &  0.9645 \tabularnewline
53 &  0.02652 &  0.05305 &  0.9735 \tabularnewline
54 &  0.02204 &  0.04408 &  0.978 \tabularnewline
55 &  0.02293 &  0.04586 &  0.9771 \tabularnewline
56 &  0.01725 &  0.0345 &  0.9828 \tabularnewline
57 &  0.01339 &  0.02678 &  0.9866 \tabularnewline
58 &  0.009845 &  0.01969 &  0.9902 \tabularnewline
59 &  0.007024 &  0.01405 &  0.993 \tabularnewline
60 &  0.00493 &  0.00986 &  0.9951 \tabularnewline
61 &  0.003976 &  0.007952 &  0.996 \tabularnewline
62 &  0.002765 &  0.00553 &  0.9972 \tabularnewline
63 &  0.001927 &  0.003854 &  0.9981 \tabularnewline
64 &  0.001347 &  0.002693 &  0.9987 \tabularnewline
65 &  0.001006 &  0.002012 &  0.999 \tabularnewline
66 &  0.000744 &  0.001488 &  0.9993 \tabularnewline
67 &  0.0004884 &  0.0009768 &  0.9995 \tabularnewline
68 &  0.0003217 &  0.0006434 &  0.9997 \tabularnewline
69 &  0.0005293 &  0.001059 &  0.9995 \tabularnewline
70 &  0.0006924 &  0.001385 &  0.9993 \tabularnewline
71 &  0.000693 &  0.001386 &  0.9993 \tabularnewline
72 &  0.0004768 &  0.0009536 &  0.9995 \tabularnewline
73 &  0.0003836 &  0.0007673 &  0.9996 \tabularnewline
74 &  0.0002667 &  0.0005334 &  0.9997 \tabularnewline
75 &  0.003672 &  0.007345 &  0.9963 \tabularnewline
76 &  0.002685 &  0.00537 &  0.9973 \tabularnewline
77 &  0.003057 &  0.006113 &  0.9969 \tabularnewline
78 &  0.0436 &  0.08719 &  0.9564 \tabularnewline
79 &  0.0339 &  0.06779 &  0.9661 \tabularnewline
80 &  0.02806 &  0.05612 &  0.9719 \tabularnewline
81 &  0.02364 &  0.04729 &  0.9764 \tabularnewline
82 &  0.02059 &  0.04118 &  0.9794 \tabularnewline
83 &  0.01561 &  0.03122 &  0.9844 \tabularnewline
84 &  0.01163 &  0.02325 &  0.9884 \tabularnewline
85 &  0.01025 &  0.02051 &  0.9897 \tabularnewline
86 &  0.007627 &  0.01525 &  0.9924 \tabularnewline
87 &  0.1813 &  0.3626 &  0.8187 \tabularnewline
88 &  0.1623 &  0.3246 &  0.8377 \tabularnewline
89 &  0.1382 &  0.2763 &  0.8618 \tabularnewline
90 &  0.1145 &  0.2289 &  0.8855 \tabularnewline
91 &  0.09478 &  0.1896 &  0.9052 \tabularnewline
92 &  0.0768 &  0.1536 &  0.9232 \tabularnewline
93 &  0.0616 &  0.1232 &  0.9384 \tabularnewline
94 &  0.04932 &  0.09864 &  0.9507 \tabularnewline
95 &  0.0389 &  0.07779 &  0.9611 \tabularnewline
96 &  0.03014 &  0.06027 &  0.9699 \tabularnewline
97 &  0.02715 &  0.0543 &  0.9729 \tabularnewline
98 &  0.02131 &  0.04262 &  0.9787 \tabularnewline
99 &  0.01634 &  0.03268 &  0.9837 \tabularnewline
100 &  0.01218 &  0.02435 &  0.9878 \tabularnewline
101 &  0.009417 &  0.01883 &  0.9906 \tabularnewline
102 &  0.006858 &  0.01372 &  0.9931 \tabularnewline
103 &  0.005107 &  0.01021 &  0.9949 \tabularnewline
104 &  0.01026 &  0.02052 &  0.9897 \tabularnewline
105 &  0.01207 &  0.02415 &  0.9879 \tabularnewline
106 &  0.009337 &  0.01867 &  0.9907 \tabularnewline
107 &  0.006995 &  0.01399 &  0.993 \tabularnewline
108 &  0.005145 &  0.01029 &  0.9949 \tabularnewline
109 &  0.04056 &  0.08113 &  0.9594 \tabularnewline
110 &  0.03173 &  0.06346 &  0.9683 \tabularnewline
111 &  0.02488 &  0.04976 &  0.9751 \tabularnewline
112 &  0.0186 &  0.03721 &  0.9814 \tabularnewline
113 &  0.01397 &  0.02794 &  0.986 \tabularnewline
114 &  0.01259 &  0.02519 &  0.9874 \tabularnewline
115 &  0.01109 &  0.02219 &  0.9889 \tabularnewline
116 &  0.008119 &  0.01624 &  0.9919 \tabularnewline
117 &  0.007068 &  0.01414 &  0.9929 \tabularnewline
118 &  0.02358 &  0.04716 &  0.9764 \tabularnewline
119 &  0.02041 &  0.04083 &  0.9796 \tabularnewline
120 &  0.02424 &  0.04849 &  0.9758 \tabularnewline
121 &  0.01788 &  0.03576 &  0.9821 \tabularnewline
122 &  0.02016 &  0.04032 &  0.9798 \tabularnewline
123 &  0.01483 &  0.02966 &  0.9852 \tabularnewline
124 &  0.01146 &  0.02291 &  0.9885 \tabularnewline
125 &  0.008133 &  0.01627 &  0.9919 \tabularnewline
126 &  0.00845 &  0.0169 &  0.9916 \tabularnewline
127 &  0.005838 &  0.01168 &  0.9942 \tabularnewline
128 &  0.004069 &  0.008137 &  0.9959 \tabularnewline
129 &  0.002801 &  0.005602 &  0.9972 \tabularnewline
130 &  0.003126 &  0.006252 &  0.9969 \tabularnewline
131 &  0.002038 &  0.004075 &  0.998 \tabularnewline
132 &  0.001875 &  0.003749 &  0.9981 \tabularnewline
133 &  0.001233 &  0.002465 &  0.9988 \tabularnewline
134 &  0.0009777 &  0.001955 &  0.999 \tabularnewline
135 &  0.001155 &  0.002311 &  0.9988 \tabularnewline
136 &  0.001289 &  0.002577 &  0.9987 \tabularnewline
137 &  0.002021 &  0.004042 &  0.998 \tabularnewline
138 &  0.001229 &  0.002459 &  0.9988 \tabularnewline
139 &  0.0009996 &  0.001999 &  0.999 \tabularnewline
140 &  0.01694 &  0.03388 &  0.9831 \tabularnewline
141 &  0.01433 &  0.02867 &  0.9857 \tabularnewline
142 &  0.009146 &  0.01829 &  0.9909 \tabularnewline
143 &  0.00567 &  0.01134 &  0.9943 \tabularnewline
144 &  0.005541 &  0.01108 &  0.9945 \tabularnewline
145 &  0.003237 &  0.006474 &  0.9968 \tabularnewline
146 &  0.002724 &  0.005449 &  0.9973 \tabularnewline
147 &  0.1771 &  0.3541 &  0.8229 \tabularnewline
148 &  0.1391 &  0.2781 &  0.8609 \tabularnewline
149 &  0.09864 &  0.1973 &  0.9014 \tabularnewline
150 &  0.5239 &  0.9523 &  0.4761 \tabularnewline
151 &  0.4168 &  0.8336 &  0.5832 \tabularnewline
152 &  0.3457 &  0.6914 &  0.6543 \tabularnewline
153 &  0.9975 &  0.005039 &  0.00252 \tabularnewline
154 &  0.9886 &  0.02275 &  0.01137 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=316485&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]8[/C][C] 0.01925[/C][C] 0.03851[/C][C] 0.9807[/C][/ROW]
[ROW][C]9[/C][C] 0.004609[/C][C] 0.009219[/C][C] 0.9954[/C][/ROW]
[ROW][C]10[/C][C] 0.328[/C][C] 0.6559[/C][C] 0.672[/C][/ROW]
[ROW][C]11[/C][C] 0.9355[/C][C] 0.129[/C][C] 0.0645[/C][/ROW]
[ROW][C]12[/C][C] 0.9588[/C][C] 0.0824[/C][C] 0.0412[/C][/ROW]
[ROW][C]13[/C][C] 0.9385[/C][C] 0.123[/C][C] 0.0615[/C][/ROW]
[ROW][C]14[/C][C] 0.9154[/C][C] 0.1692[/C][C] 0.0846[/C][/ROW]
[ROW][C]15[/C][C] 0.8849[/C][C] 0.2303[/C][C] 0.1151[/C][/ROW]
[ROW][C]16[/C][C] 0.837[/C][C] 0.326[/C][C] 0.163[/C][/ROW]
[ROW][C]17[/C][C] 0.7818[/C][C] 0.4364[/C][C] 0.2182[/C][/ROW]
[ROW][C]18[/C][C] 0.7184[/C][C] 0.5632[/C][C] 0.2816[/C][/ROW]
[ROW][C]19[/C][C] 0.6478[/C][C] 0.7044[/C][C] 0.3522[/C][/ROW]
[ROW][C]20[/C][C] 0.6016[/C][C] 0.7968[/C][C] 0.3984[/C][/ROW]
[ROW][C]21[/C][C] 0.5408[/C][C] 0.9183[/C][C] 0.4592[/C][/ROW]
[ROW][C]22[/C][C] 0.5664[/C][C] 0.8672[/C][C] 0.4336[/C][/ROW]
[ROW][C]23[/C][C] 0.4951[/C][C] 0.9903[/C][C] 0.5049[/C][/ROW]
[ROW][C]24[/C][C] 0.4267[/C][C] 0.8535[/C][C] 0.5733[/C][/ROW]
[ROW][C]25[/C][C] 0.359[/C][C] 0.718[/C][C] 0.641[/C][/ROW]
[ROW][C]26[/C][C] 0.2996[/C][C] 0.5992[/C][C] 0.7004[/C][/ROW]
[ROW][C]27[/C][C] 0.2633[/C][C] 0.5267[/C][C] 0.7367[/C][/ROW]
[ROW][C]28[/C][C] 0.2151[/C][C] 0.4301[/C][C] 0.7849[/C][/ROW]
[ROW][C]29[/C][C] 0.1697[/C][C] 0.3394[/C][C] 0.8303[/C][/ROW]
[ROW][C]30[/C][C] 0.1406[/C][C] 0.2811[/C][C] 0.8594[/C][/ROW]
[ROW][C]31[/C][C] 0.2001[/C][C] 0.4002[/C][C] 0.7999[/C][/ROW]
[ROW][C]32[/C][C] 0.1949[/C][C] 0.3898[/C][C] 0.8051[/C][/ROW]
[ROW][C]33[/C][C] 0.1561[/C][C] 0.3121[/C][C] 0.8439[/C][/ROW]
[ROW][C]34[/C][C] 0.1224[/C][C] 0.2448[/C][C] 0.8776[/C][/ROW]
[ROW][C]35[/C][C] 0.09425[/C][C] 0.1885[/C][C] 0.9057[/C][/ROW]
[ROW][C]36[/C][C] 0.07686[/C][C] 0.1537[/C][C] 0.9231[/C][/ROW]
[ROW][C]37[/C][C] 0.05797[/C][C] 0.1159[/C][C] 0.942[/C][/ROW]
[ROW][C]38[/C][C] 0.04556[/C][C] 0.09112[/C][C] 0.9544[/C][/ROW]
[ROW][C]39[/C][C] 0.03643[/C][C] 0.07287[/C][C] 0.9636[/C][/ROW]
[ROW][C]40[/C][C] 0.02854[/C][C] 0.05707[/C][C] 0.9715[/C][/ROW]
[ROW][C]41[/C][C] 0.1373[/C][C] 0.2746[/C][C] 0.8627[/C][/ROW]
[ROW][C]42[/C][C] 0.1093[/C][C] 0.2185[/C][C] 0.8907[/C][/ROW]
[ROW][C]43[/C][C] 0.09423[/C][C] 0.1885[/C][C] 0.9058[/C][/ROW]
[ROW][C]44[/C][C] 0.07652[/C][C] 0.153[/C][C] 0.9235[/C][/ROW]
[ROW][C]45[/C][C] 0.05958[/C][C] 0.1192[/C][C] 0.9404[/C][/ROW]
[ROW][C]46[/C][C] 0.04624[/C][C] 0.09248[/C][C] 0.9538[/C][/ROW]
[ROW][C]47[/C][C] 0.0585[/C][C] 0.117[/C][C] 0.9415[/C][/ROW]
[ROW][C]48[/C][C] 0.04451[/C][C] 0.08902[/C][C] 0.9555[/C][/ROW]
[ROW][C]49[/C][C] 0.03347[/C][C] 0.06694[/C][C] 0.9665[/C][/ROW]
[ROW][C]50[/C][C] 0.02547[/C][C] 0.05095[/C][C] 0.9745[/C][/ROW]
[ROW][C]51[/C][C] 0.03537[/C][C] 0.07075[/C][C] 0.9646[/C][/ROW]
[ROW][C]52[/C][C] 0.03548[/C][C] 0.07096[/C][C] 0.9645[/C][/ROW]
[ROW][C]53[/C][C] 0.02652[/C][C] 0.05305[/C][C] 0.9735[/C][/ROW]
[ROW][C]54[/C][C] 0.02204[/C][C] 0.04408[/C][C] 0.978[/C][/ROW]
[ROW][C]55[/C][C] 0.02293[/C][C] 0.04586[/C][C] 0.9771[/C][/ROW]
[ROW][C]56[/C][C] 0.01725[/C][C] 0.0345[/C][C] 0.9828[/C][/ROW]
[ROW][C]57[/C][C] 0.01339[/C][C] 0.02678[/C][C] 0.9866[/C][/ROW]
[ROW][C]58[/C][C] 0.009845[/C][C] 0.01969[/C][C] 0.9902[/C][/ROW]
[ROW][C]59[/C][C] 0.007024[/C][C] 0.01405[/C][C] 0.993[/C][/ROW]
[ROW][C]60[/C][C] 0.00493[/C][C] 0.00986[/C][C] 0.9951[/C][/ROW]
[ROW][C]61[/C][C] 0.003976[/C][C] 0.007952[/C][C] 0.996[/C][/ROW]
[ROW][C]62[/C][C] 0.002765[/C][C] 0.00553[/C][C] 0.9972[/C][/ROW]
[ROW][C]63[/C][C] 0.001927[/C][C] 0.003854[/C][C] 0.9981[/C][/ROW]
[ROW][C]64[/C][C] 0.001347[/C][C] 0.002693[/C][C] 0.9987[/C][/ROW]
[ROW][C]65[/C][C] 0.001006[/C][C] 0.002012[/C][C] 0.999[/C][/ROW]
[ROW][C]66[/C][C] 0.000744[/C][C] 0.001488[/C][C] 0.9993[/C][/ROW]
[ROW][C]67[/C][C] 0.0004884[/C][C] 0.0009768[/C][C] 0.9995[/C][/ROW]
[ROW][C]68[/C][C] 0.0003217[/C][C] 0.0006434[/C][C] 0.9997[/C][/ROW]
[ROW][C]69[/C][C] 0.0005293[/C][C] 0.001059[/C][C] 0.9995[/C][/ROW]
[ROW][C]70[/C][C] 0.0006924[/C][C] 0.001385[/C][C] 0.9993[/C][/ROW]
[ROW][C]71[/C][C] 0.000693[/C][C] 0.001386[/C][C] 0.9993[/C][/ROW]
[ROW][C]72[/C][C] 0.0004768[/C][C] 0.0009536[/C][C] 0.9995[/C][/ROW]
[ROW][C]73[/C][C] 0.0003836[/C][C] 0.0007673[/C][C] 0.9996[/C][/ROW]
[ROW][C]74[/C][C] 0.0002667[/C][C] 0.0005334[/C][C] 0.9997[/C][/ROW]
[ROW][C]75[/C][C] 0.003672[/C][C] 0.007345[/C][C] 0.9963[/C][/ROW]
[ROW][C]76[/C][C] 0.002685[/C][C] 0.00537[/C][C] 0.9973[/C][/ROW]
[ROW][C]77[/C][C] 0.003057[/C][C] 0.006113[/C][C] 0.9969[/C][/ROW]
[ROW][C]78[/C][C] 0.0436[/C][C] 0.08719[/C][C] 0.9564[/C][/ROW]
[ROW][C]79[/C][C] 0.0339[/C][C] 0.06779[/C][C] 0.9661[/C][/ROW]
[ROW][C]80[/C][C] 0.02806[/C][C] 0.05612[/C][C] 0.9719[/C][/ROW]
[ROW][C]81[/C][C] 0.02364[/C][C] 0.04729[/C][C] 0.9764[/C][/ROW]
[ROW][C]82[/C][C] 0.02059[/C][C] 0.04118[/C][C] 0.9794[/C][/ROW]
[ROW][C]83[/C][C] 0.01561[/C][C] 0.03122[/C][C] 0.9844[/C][/ROW]
[ROW][C]84[/C][C] 0.01163[/C][C] 0.02325[/C][C] 0.9884[/C][/ROW]
[ROW][C]85[/C][C] 0.01025[/C][C] 0.02051[/C][C] 0.9897[/C][/ROW]
[ROW][C]86[/C][C] 0.007627[/C][C] 0.01525[/C][C] 0.9924[/C][/ROW]
[ROW][C]87[/C][C] 0.1813[/C][C] 0.3626[/C][C] 0.8187[/C][/ROW]
[ROW][C]88[/C][C] 0.1623[/C][C] 0.3246[/C][C] 0.8377[/C][/ROW]
[ROW][C]89[/C][C] 0.1382[/C][C] 0.2763[/C][C] 0.8618[/C][/ROW]
[ROW][C]90[/C][C] 0.1145[/C][C] 0.2289[/C][C] 0.8855[/C][/ROW]
[ROW][C]91[/C][C] 0.09478[/C][C] 0.1896[/C][C] 0.9052[/C][/ROW]
[ROW][C]92[/C][C] 0.0768[/C][C] 0.1536[/C][C] 0.9232[/C][/ROW]
[ROW][C]93[/C][C] 0.0616[/C][C] 0.1232[/C][C] 0.9384[/C][/ROW]
[ROW][C]94[/C][C] 0.04932[/C][C] 0.09864[/C][C] 0.9507[/C][/ROW]
[ROW][C]95[/C][C] 0.0389[/C][C] 0.07779[/C][C] 0.9611[/C][/ROW]
[ROW][C]96[/C][C] 0.03014[/C][C] 0.06027[/C][C] 0.9699[/C][/ROW]
[ROW][C]97[/C][C] 0.02715[/C][C] 0.0543[/C][C] 0.9729[/C][/ROW]
[ROW][C]98[/C][C] 0.02131[/C][C] 0.04262[/C][C] 0.9787[/C][/ROW]
[ROW][C]99[/C][C] 0.01634[/C][C] 0.03268[/C][C] 0.9837[/C][/ROW]
[ROW][C]100[/C][C] 0.01218[/C][C] 0.02435[/C][C] 0.9878[/C][/ROW]
[ROW][C]101[/C][C] 0.009417[/C][C] 0.01883[/C][C] 0.9906[/C][/ROW]
[ROW][C]102[/C][C] 0.006858[/C][C] 0.01372[/C][C] 0.9931[/C][/ROW]
[ROW][C]103[/C][C] 0.005107[/C][C] 0.01021[/C][C] 0.9949[/C][/ROW]
[ROW][C]104[/C][C] 0.01026[/C][C] 0.02052[/C][C] 0.9897[/C][/ROW]
[ROW][C]105[/C][C] 0.01207[/C][C] 0.02415[/C][C] 0.9879[/C][/ROW]
[ROW][C]106[/C][C] 0.009337[/C][C] 0.01867[/C][C] 0.9907[/C][/ROW]
[ROW][C]107[/C][C] 0.006995[/C][C] 0.01399[/C][C] 0.993[/C][/ROW]
[ROW][C]108[/C][C] 0.005145[/C][C] 0.01029[/C][C] 0.9949[/C][/ROW]
[ROW][C]109[/C][C] 0.04056[/C][C] 0.08113[/C][C] 0.9594[/C][/ROW]
[ROW][C]110[/C][C] 0.03173[/C][C] 0.06346[/C][C] 0.9683[/C][/ROW]
[ROW][C]111[/C][C] 0.02488[/C][C] 0.04976[/C][C] 0.9751[/C][/ROW]
[ROW][C]112[/C][C] 0.0186[/C][C] 0.03721[/C][C] 0.9814[/C][/ROW]
[ROW][C]113[/C][C] 0.01397[/C][C] 0.02794[/C][C] 0.986[/C][/ROW]
[ROW][C]114[/C][C] 0.01259[/C][C] 0.02519[/C][C] 0.9874[/C][/ROW]
[ROW][C]115[/C][C] 0.01109[/C][C] 0.02219[/C][C] 0.9889[/C][/ROW]
[ROW][C]116[/C][C] 0.008119[/C][C] 0.01624[/C][C] 0.9919[/C][/ROW]
[ROW][C]117[/C][C] 0.007068[/C][C] 0.01414[/C][C] 0.9929[/C][/ROW]
[ROW][C]118[/C][C] 0.02358[/C][C] 0.04716[/C][C] 0.9764[/C][/ROW]
[ROW][C]119[/C][C] 0.02041[/C][C] 0.04083[/C][C] 0.9796[/C][/ROW]
[ROW][C]120[/C][C] 0.02424[/C][C] 0.04849[/C][C] 0.9758[/C][/ROW]
[ROW][C]121[/C][C] 0.01788[/C][C] 0.03576[/C][C] 0.9821[/C][/ROW]
[ROW][C]122[/C][C] 0.02016[/C][C] 0.04032[/C][C] 0.9798[/C][/ROW]
[ROW][C]123[/C][C] 0.01483[/C][C] 0.02966[/C][C] 0.9852[/C][/ROW]
[ROW][C]124[/C][C] 0.01146[/C][C] 0.02291[/C][C] 0.9885[/C][/ROW]
[ROW][C]125[/C][C] 0.008133[/C][C] 0.01627[/C][C] 0.9919[/C][/ROW]
[ROW][C]126[/C][C] 0.00845[/C][C] 0.0169[/C][C] 0.9916[/C][/ROW]
[ROW][C]127[/C][C] 0.005838[/C][C] 0.01168[/C][C] 0.9942[/C][/ROW]
[ROW][C]128[/C][C] 0.004069[/C][C] 0.008137[/C][C] 0.9959[/C][/ROW]
[ROW][C]129[/C][C] 0.002801[/C][C] 0.005602[/C][C] 0.9972[/C][/ROW]
[ROW][C]130[/C][C] 0.003126[/C][C] 0.006252[/C][C] 0.9969[/C][/ROW]
[ROW][C]131[/C][C] 0.002038[/C][C] 0.004075[/C][C] 0.998[/C][/ROW]
[ROW][C]132[/C][C] 0.001875[/C][C] 0.003749[/C][C] 0.9981[/C][/ROW]
[ROW][C]133[/C][C] 0.001233[/C][C] 0.002465[/C][C] 0.9988[/C][/ROW]
[ROW][C]134[/C][C] 0.0009777[/C][C] 0.001955[/C][C] 0.999[/C][/ROW]
[ROW][C]135[/C][C] 0.001155[/C][C] 0.002311[/C][C] 0.9988[/C][/ROW]
[ROW][C]136[/C][C] 0.001289[/C][C] 0.002577[/C][C] 0.9987[/C][/ROW]
[ROW][C]137[/C][C] 0.002021[/C][C] 0.004042[/C][C] 0.998[/C][/ROW]
[ROW][C]138[/C][C] 0.001229[/C][C] 0.002459[/C][C] 0.9988[/C][/ROW]
[ROW][C]139[/C][C] 0.0009996[/C][C] 0.001999[/C][C] 0.999[/C][/ROW]
[ROW][C]140[/C][C] 0.01694[/C][C] 0.03388[/C][C] 0.9831[/C][/ROW]
[ROW][C]141[/C][C] 0.01433[/C][C] 0.02867[/C][C] 0.9857[/C][/ROW]
[ROW][C]142[/C][C] 0.009146[/C][C] 0.01829[/C][C] 0.9909[/C][/ROW]
[ROW][C]143[/C][C] 0.00567[/C][C] 0.01134[/C][C] 0.9943[/C][/ROW]
[ROW][C]144[/C][C] 0.005541[/C][C] 0.01108[/C][C] 0.9945[/C][/ROW]
[ROW][C]145[/C][C] 0.003237[/C][C] 0.006474[/C][C] 0.9968[/C][/ROW]
[ROW][C]146[/C][C] 0.002724[/C][C] 0.005449[/C][C] 0.9973[/C][/ROW]
[ROW][C]147[/C][C] 0.1771[/C][C] 0.3541[/C][C] 0.8229[/C][/ROW]
[ROW][C]148[/C][C] 0.1391[/C][C] 0.2781[/C][C] 0.8609[/C][/ROW]
[ROW][C]149[/C][C] 0.09864[/C][C] 0.1973[/C][C] 0.9014[/C][/ROW]
[ROW][C]150[/C][C] 0.5239[/C][C] 0.9523[/C][C] 0.4761[/C][/ROW]
[ROW][C]151[/C][C] 0.4168[/C][C] 0.8336[/C][C] 0.5832[/C][/ROW]
[ROW][C]152[/C][C] 0.3457[/C][C] 0.6914[/C][C] 0.6543[/C][/ROW]
[ROW][C]153[/C][C] 0.9975[/C][C] 0.005039[/C][C] 0.00252[/C][/ROW]
[ROW][C]154[/C][C] 0.9886[/C][C] 0.02275[/C][C] 0.01137[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=316485&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=316485&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
8	0.01925	0.03851	0.9807
9	0.004609	0.009219	0.9954
10	0.328	0.6559	0.672
11	0.9355	0.129	0.0645
12	0.9588	0.0824	0.0412
13	0.9385	0.123	0.0615
14	0.9154	0.1692	0.0846
15	0.8849	0.2303	0.1151
16	0.837	0.326	0.163
17	0.7818	0.4364	0.2182
18	0.7184	0.5632	0.2816
19	0.6478	0.7044	0.3522
20	0.6016	0.7968	0.3984
21	0.5408	0.9183	0.4592
22	0.5664	0.8672	0.4336
23	0.4951	0.9903	0.5049
24	0.4267	0.8535	0.5733
25	0.359	0.718	0.641
26	0.2996	0.5992	0.7004
27	0.2633	0.5267	0.7367
28	0.2151	0.4301	0.7849
29	0.1697	0.3394	0.8303
30	0.1406	0.2811	0.8594
31	0.2001	0.4002	0.7999
32	0.1949	0.3898	0.8051
33	0.1561	0.3121	0.8439
34	0.1224	0.2448	0.8776
35	0.09425	0.1885	0.9057
36	0.07686	0.1537	0.9231
37	0.05797	0.1159	0.942
38	0.04556	0.09112	0.9544
39	0.03643	0.07287	0.9636
40	0.02854	0.05707	0.9715
41	0.1373	0.2746	0.8627
42	0.1093	0.2185	0.8907
43	0.09423	0.1885	0.9058
44	0.07652	0.153	0.9235
45	0.05958	0.1192	0.9404
46	0.04624	0.09248	0.9538
47	0.0585	0.117	0.9415
48	0.04451	0.08902	0.9555
49	0.03347	0.06694	0.9665
50	0.02547	0.05095	0.9745
51	0.03537	0.07075	0.9646
52	0.03548	0.07096	0.9645
53	0.02652	0.05305	0.9735
54	0.02204	0.04408	0.978
55	0.02293	0.04586	0.9771
56	0.01725	0.0345	0.9828
57	0.01339	0.02678	0.9866
58	0.009845	0.01969	0.9902
59	0.007024	0.01405	0.993
60	0.00493	0.00986	0.9951
61	0.003976	0.007952	0.996
62	0.002765	0.00553	0.9972
63	0.001927	0.003854	0.9981
64	0.001347	0.002693	0.9987
65	0.001006	0.002012	0.999
66	0.000744	0.001488	0.9993
67	0.0004884	0.0009768	0.9995
68	0.0003217	0.0006434	0.9997
69	0.0005293	0.001059	0.9995
70	0.0006924	0.001385	0.9993
71	0.000693	0.001386	0.9993
72	0.0004768	0.0009536	0.9995
73	0.0003836	0.0007673	0.9996
74	0.0002667	0.0005334	0.9997
75	0.003672	0.007345	0.9963
76	0.002685	0.00537	0.9973
77	0.003057	0.006113	0.9969
78	0.0436	0.08719	0.9564
79	0.0339	0.06779	0.9661
80	0.02806	0.05612	0.9719
81	0.02364	0.04729	0.9764
82	0.02059	0.04118	0.9794
83	0.01561	0.03122	0.9844
84	0.01163	0.02325	0.9884
85	0.01025	0.02051	0.9897
86	0.007627	0.01525	0.9924
87	0.1813	0.3626	0.8187
88	0.1623	0.3246	0.8377
89	0.1382	0.2763	0.8618
90	0.1145	0.2289	0.8855
91	0.09478	0.1896	0.9052
92	0.0768	0.1536	0.9232
93	0.0616	0.1232	0.9384
94	0.04932	0.09864	0.9507
95	0.0389	0.07779	0.9611
96	0.03014	0.06027	0.9699
97	0.02715	0.0543	0.9729
98	0.02131	0.04262	0.9787
99	0.01634	0.03268	0.9837
100	0.01218	0.02435	0.9878
101	0.009417	0.01883	0.9906
102	0.006858	0.01372	0.9931
103	0.005107	0.01021	0.9949
104	0.01026	0.02052	0.9897
105	0.01207	0.02415	0.9879
106	0.009337	0.01867	0.9907
107	0.006995	0.01399	0.993
108	0.005145	0.01029	0.9949
109	0.04056	0.08113	0.9594
110	0.03173	0.06346	0.9683
111	0.02488	0.04976	0.9751
112	0.0186	0.03721	0.9814
113	0.01397	0.02794	0.986
114	0.01259	0.02519	0.9874
115	0.01109	0.02219	0.9889
116	0.008119	0.01624	0.9919
117	0.007068	0.01414	0.9929
118	0.02358	0.04716	0.9764
119	0.02041	0.04083	0.9796
120	0.02424	0.04849	0.9758
121	0.01788	0.03576	0.9821
122	0.02016	0.04032	0.9798
123	0.01483	0.02966	0.9852
124	0.01146	0.02291	0.9885
125	0.008133	0.01627	0.9919
126	0.00845	0.0169	0.9916
127	0.005838	0.01168	0.9942
128	0.004069	0.008137	0.9959
129	0.002801	0.005602	0.9972
130	0.003126	0.006252	0.9969
131	0.002038	0.004075	0.998
132	0.001875	0.003749	0.9981
133	0.001233	0.002465	0.9988
134	0.0009777	0.001955	0.999
135	0.001155	0.002311	0.9988
136	0.001289	0.002577	0.9987
137	0.002021	0.004042	0.998
138	0.001229	0.002459	0.9988
139	0.0009996	0.001999	0.999
140	0.01694	0.03388	0.9831
141	0.01433	0.02867	0.9857
142	0.009146	0.01829	0.9909
143	0.00567	0.01134	0.9943
144	0.005541	0.01108	0.9945
145	0.003237	0.006474	0.9968
146	0.002724	0.005449	0.9973
147	0.1771	0.3541	0.8229
148	0.1391	0.2781	0.8609
149	0.09864	0.1973	0.9014
150	0.5239	0.9523	0.4761
151	0.4168	0.8336	0.5832
152	0.3457	0.6914	0.6543
153	0.9975	0.005039	0.00252
154	0.9886	0.02275	0.01137

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	34	0.2313	NOK
5% type I error level	81	0.55102	NOK
10% type I error level	101	0.687075	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 & 34 &  0.2313 & NOK \tabularnewline
5% type I error level & 81 & 0.55102 & NOK \tabularnewline
10% type I error level & 101 & 0.687075 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=316485&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]34[/C][C] 0.2313[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]81[/C][C]0.55102[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]101[/C][C]0.687075[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=316485&T=7

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=316485&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	34	0.2313	NOK
5% type I error level	81	0.55102	NOK
10% type I error level	101	0.687075	NOK

Ramsey RESET F-Test for powers (2 and 3) of fitted values
> reset_test_fitted RESET test data: mylm RESET = 27.945, df1 = 2, df2 = 155, p-value = 4.328e-11
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors RESET test data: mylm RESET = 11.408, df1 = 8, df2 = 149, p-value = 1.519e-12
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components RESET test data: mylm RESET = 32.554, df1 = 2, df2 = 155, p-value = 1.572e-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 = 27.945, df1 = 2, df2 = 155, p-value = 4.328e-11
 \tabularnewline
Ramsey RESET F-Test for powers (2 and 3) of regressors \tabularnewline
> reset_test_regressors
	RESET test
data:  mylm
RESET = 11.408, df1 = 8, df2 = 149, p-value = 1.519e-12
 \tabularnewline
Ramsey RESET F-Test for powers (2 and 3) of principal components \tabularnewline
> reset_test_principal_components
	RESET test
data:  mylm
RESET = 32.554, df1 = 2, df2 = 155, p-value = 1.572e-12
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=316485&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 = 27.945, df1 = 2, df2 = 155, p-value = 4.328e-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 = 11.408, df1 = 8, df2 = 149, p-value = 1.519e-12
[/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 = 32.554, df1 = 2, df2 = 155, p-value = 1.572e-12
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=316485&T=8

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=316485&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 = 27.945, df1 = 2, df2 = 155, p-value = 4.328e-11
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors RESET test data: mylm RESET = 11.408, df1 = 8, df2 = 149, p-value = 1.519e-12
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components RESET test data: mylm RESET = 32.554, df1 = 2, df2 = 155, p-value = 1.572e-12

Variance Inflation Factors (Multicollinearity)
> vif `Population_(millions)` HDI GDP_per_Capita 1.007334 1.857608 1.855642 Total_Biocapacity 1.007501

\begin{tabular}{lllllllll}
\hline
Variance Inflation Factors (Multicollinearity) \tabularnewline
> vif
`Population_(millions)`                     HDI          GDP_per_Capita 
               1.007334                1.857608                1.855642 
      Total_Biocapacity 
               1.007501 
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=316485&T=9

[TABLE]
[ROW][C]Variance Inflation Factors (Multicollinearity)[/C][/ROW]
[ROW][C]> vif
`Population_(millions)`                     HDI          GDP_per_Capita 
               1.007334                1.857608                1.855642 
      Total_Biocapacity 
               1.007501 
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=316485&T=9

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=316485&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 1.007334 1.857608 1.855642 Total_Biocapacity 1.007501

Figure 1

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Figure 2

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Figure 3

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Figure 4

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Figure 5

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Figure 6

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Figure 7

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Figure 8

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Figure 9

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Figure 10

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Parameters (Session):

par1 = grey ; par2 = no ;

Parameters (R input):

par1 = 1 ; par2 = no ; 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