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R Software Module

rwasp_multipleregression.wasp

Title produced by software

Multiple Regression

Date of computation

Wed, 19 Dec 2018 16:12:02 +0100

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Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2018/Dec/19/t15452342568zbyobvobyb9kof.htm/, Retrieved Mon, 29 Apr 2024 20:29:43 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=316105, Retrieved Mon, 29 Apr 2024 20:29:43 +0000

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

-       [Multiple Regression] [] [2018-12-19 15:12:02] [a77d3f185bf8346aeb8631871e5ed689] [Current]

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0.08 29.82 0.46 614.66 0.5
0.25 3.16 0.73 4534.37 1.18
0.17 38.48 0.73 5430.57 0.59
0.12 20.82 0.52 4665.91 2.55
NA 0.09 0.78 13205.1 0.94
0.29 41.09 0.83 13540 6.92
0.34 2.97 0.73 3426.39 0.89
NA 0.1 NA NA 0.57
0.89 23.05 0.93 66604.2 16.57
0.63 8.46 0.88 51274.1 3.07
0.11 9.31 0.75 7106.04 0.85
0.19 0.37 0.78 22647.3 9.55
0.16 1.32 0.82 24299 0.58
0.08 154.7 0.56 857.5 0.38
0.14 0.28 0.79 15722.8 0.19
0.91 9.4 0.8 6300.45 3.64
0.99 11.06 0.89 48053.3 1.19
0.26 10.05 0.48 746.83 0.88
NA 0.06 NA 70626.3 0.13
3.03 0.74 0.59 2395 5.27
0.17 10.5 0.65 2253.09 16.73
0.44 3.83 0.73 4708.85 1.63
0.24 2 0.69 7743.5 3.47
0.6 198.66 0.75 13237.6 9.08
NA 0.03 NA NA 2.05
0.26 0.41 0.85 47097.4 2.87
0.35 7.28 0.78 7615.28 2.86
0.36 16.46 0.39 671.07 0.98
0.45 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
0.21 21.7 0.5 1271.21 1.69
1.2 34.84 0.91 52145.4 16.01
1.23 0.06 NA NA 0.32
0.26 4.53 0.37 495.04 7.87
0.27 12.45 0.39 1161.22 2.03
0.99 17.46 0.83 14525.8 3.63
0.19 1408.04 0.72 5560.94 0.94
0.16 47.7 0.72 7305.22 3.6
0.18 0.72 0.5 860.24 0.32
0.38 4.34 0.57 1943.69 10.91
0.51 65.7 0.42 338.63 3.07
0.68 4.8 0.76 8979.96 1.53
0.22 19.84 NA 1016.83 1.78
0.72 4.31 0.82 14522.8 2.8
0.09 11.27 0.77 5175.94 0.76
0.23 1.13 0.85 31454.7 0.34
0.74 10.66 0.87 21676.3 2.46
0.77 5.6 0.92 61413.6 4.78
NA 0.86 0.46 1433.17 0.77
0.14 0.07 0.72 7088.01 1.03
0.12 10.28 0.71 6085.89 0.56
0.24 15.49 0.73 5192.88 2.2
0.17 80.72 0.69 2930.33 0.56
0.4 6.3 0.66 3696.33 0.61
0.26 0.74 0.58 24064 4.4
0.06 6.13 0.39 439.73 1.3
NA 1.29 0.85 17304.4 10.53
0.46 91.73 0.43 379.38 0.58
0.42 0.88 0.72 4201.37 2.37
NA 5.41 0.88 50960.2 13.44
0.53 63.98 0.89 45430.3 3.11
0.46 0.24 NA NA 111.35
0.12 0.27 NA NA 1.37
0.79 1.63 0.67 11989 26.31
0.2 1.79 0.44 505.76 0.82
0.1 4.36 0.75 3710.7 1.17
0.48 82.8 0.91 46822.4 2.27
0.65 25.37 0.57 1627.9 1.35
0.24 11.12 0.86 25987.4 1.61
NA 0.1 0.74 7410.48 1.96
0.16 0.46 NA NA 0.45
0.58 15.08 0.62 3233.8 0.99
0.45 11.45 0.41 459.09 2.09
0.67 1.66 0.42 681.25 3.03
0.77 0.8 0.63 3269.46 66.58
0.1 10.17 0.48 749.13 0.27
0.5 7.94 0.61 2269.51 1.77
0.36 9.98 0.82 13964.2 2.17
0.14 1236.69 0.6 1513.85 0.45
0.2 246.86 0.68 3688.53 1.26
0.07 76.42 0.76 7511.1 0.9
0.01 32.78 0.65 5848.54 0.29
0.46 4.58 0.91 52853.6 3.73
0.36 7.64 0.89 33718.9 0.35
0.42 60.92 0.87 38412 1.08
0.18 2.77 0.72 5226.3 0.43
0.27 127.25 0.89 46201.6 0.72
0.17 7.01 0.75 4615.17 0.21
0.12 16.27 0.78 11278 3.41
0.27 43.18 0.54 1062.11 0.51
0.14 24.76 NA NA 0.6
0.21 49 0.89 24155.8 0.68
0.21 3.25 0.82 41830.5 0.55
0.09 5.47 0.65 1116.37 1.3
0.36 6.65 0.56 1236.24 1.62
2.02 2.06 0.81 13732 9.55
0.25 4.65 0.76 9143.86 0.33
0.42 2.05 0.48 1338.42 0.78
0.75 4.19 0.42 397.38 2.57
0.14 6.16 0.74 5859.43 0.7
1.28 3.03 0.83 14373.7 5.67
1.03 0.52 0.89 114665 1.68
0.31 2.11 0.74 5174.89 1.51
0.24 22.29 0.51 456.33 2.63
0.2 15.91 0.43 493.84 0.66
0.38 29.24 0.77 10252.6 2.41
0.17 14.85 0.41 741.22 1.58
0.12 0.4 NA NA 0.39
0.21 3.8 0.5 1524.39 4.48
0.18 1.24 0.77 8811.15 0.71
0.25 120.85 0.75 10123.9 1.27
0.15 3.51 0.68 1971.03 0.8
0.17 2.8 0.71 3736.07 15.66
0.62 0.62 0.8 7251.6 3.24
NA 0 NA NA 1.36
0.14 32.52 0.62 3149.43 0.71
0.29 25.2 0.41 538.82 2.06
0.32 52.8 0.53 1117.58 1.84
0.17 2.26 0.62 5880.8 6.88
NA 0.01 NA NA 0.19
0.21 27.47 0.54 700.07 0.59
0.38 16.71 0.92 53589.9 1.17
0.18 0.25 NA NA 7.67
1.08 4.46 0.91 37488.3 10.14
0.42 5.99 0.63 1626.85 2.25
0.26 17.16 0.34 410.91 1.24
0.19 168.83 0.5 2612.12 0.7
NA 4.99 0.94 100172 8.18
0.15 3.31 0.79 22622.8 1.92
0.08 179.16 0.53 1218.6 0.35
0.19 3.8 0.77 8410.77 2.94
0.36 7.17 0.5 1871.21 3.92
0.83 6.69 0.67 3557.31 10.52
0.19 29.99 0.73 5684.73 3.97
0.09 96.71 0.66 2379.44 0.54
0.78 38.21 0.84 13769.5 2.08
0.09 10.6 0.83 23217.3 1.51
0.15 2.05 0.85 99431.5 1.24
0.15 0.86 NA NA 0.18
0.33 21.76 0.79 9213.94 2.32
0.67 143.17 0.79 13320.2 6.79
0.25 11.46 0.48 628.08 0.54
0.09 0.05 0.74 12952.5 0.62
0.17 0.18 0.73 7737.2 0.34
NA 0.11 0.72 6171.48 1.26
0.27 0.19 0.7 4067.15 1.93
0.27 0.19 0.55 1384.53 0.87
0.27 28.29 0.83 23593.8 0.5
0.21 13.73 0.46 1079.27 1.05
0.46 9.55 0.76 6426.18 1.25
0.38 5.98 0.4 499.89 1.24
0.91 5.3 0.91 53122.4 0.05
0.72 5.45 0.84 18103.1 2.71
0.65 2.07 0.88 25040.5 2.35
0.09 0.55 0.5 1647.86 4.36
0.52 10.2 NA NA 1.27
0.29 52.39 0.66 8089.87 1.15
0.17 46.76 0.87 32008.7 1.25
0.16 21.1 0.75 2880.03 0.44
0.52 0.54 0.71 8190.7 89.33
0.52 1.23 0.53 4657.48 0.88
1.3 9.51 0.9 59381.9 10.62
0.38 8 0.93 88506.2 1.3
0.04 21.89 0.62 NA 0.6
0.1 8.01 0.62 836.17 0.53
0.23 47.78 0.51 765.33 1.08
0.24 66.78 0.72 5479.29 1.24
0.04 1.11 0.6 5167.86 1.78
0.27 6.64 0.47 580.86 0.53
0.14 0.1 0.72 4330.9 1.48
0.27 1.34 0.77 18310.8 1.56
0.28 10.88 0.72 4305.07 0.93
0.34 74 0.76 10437.7 1.52
0.08 5.17 0.68 5290.14 2.79
0.54 36.35 0.48 601.35 0.59
0.16 45.53 0.74 3589.63 2.27
0.45 63.03 0.9 40980.5 1.32
0.38 9.206 0.83 40817.4 0.56
0.67 317.5 0.91 49725 3.76
0.55 3.4 0.79 14238.1 10.32
0.08 28.54 0.67 1560.85 0.92
0.12 29.96 0.763846 10237.8 2.78
0.19 90.8 0.66 1532.31 1
NA 0.01 NA NA 1.51
0.04 23.85 0.5 1302.3 0.5
0.33 14.08 0.58 1740.64 2.23
0.29 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	18 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 time18 seconds \tabularnewline
R ServerBig Analytics Cloud Computing Center \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=316105&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]

18 seconds[/C][/ROW] [ROW]

R Server[/C]

Big Analytics Cloud Computing Center[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=316105&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=316105&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	18 seconds
R Server	Big Analytics Cloud Computing Center

Multiple Linear Regression - Estimated Regression Equation
Forest_Footprint[t] = + 0.1978 -0.000144007`Population_(millions)`[t] + 0.14764HDI[t] + 4.14275e-06GDP_per_Capita[t] + 0.00826966Total_Biocapacity[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Forest_Footprint[t] =  +  0.1978 -0.000144007`Population_(millions)`[t] +  0.14764HDI[t] +  4.14275e-06GDP_per_Capita[t] +  0.00826966Total_Biocapacity[t]  + e[t] \tabularnewline
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=316105&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Forest_Footprint[t] =  +  0.1978 -0.000144007`Population_(millions)`[t] +  0.14764HDI[t] +  4.14275e-06GDP_per_Capita[t] +  0.00826966Total_Biocapacity[t]  + e[t][/C][/ROW]
[ROW][C][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=316105&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=316105&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
Forest_Footprint[t] = + 0.1978 -0.000144007`Population_(millions)`[t] + 0.14764HDI[t] + 4.14275e-06GDP_per_Capita[t] + 0.00826966Total_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)	+0.1978	0.1465	+1.3500e+00	0.1789	0.08947
`Population_(millions)`	-0.000144	0.0001788	-8.0560e-01	0.4217	0.2109
HDI	+0.1476	0.2341	+6.3050e-01	0.5293	0.2646
GDP_per_Capita	+4.143e-06	1.858e-06	+2.2300e+00	0.02719	0.0136
Total_Biocapacity	+0.00827	0.002933	+2.8190e+00	0.005434	0.002717

\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) & +0.1978 &  0.1465 & +1.3500e+00 &  0.1789 &  0.08947 \tabularnewline
`Population_(millions)` & -0.000144 &  0.0001788 & -8.0560e-01 &  0.4217 &  0.2109 \tabularnewline
HDI & +0.1476 &  0.2341 & +6.3050e-01 &  0.5293 &  0.2646 \tabularnewline
GDP_per_Capita & +4.143e-06 &  1.858e-06 & +2.2300e+00 &  0.02719 &  0.0136 \tabularnewline
Total_Biocapacity & +0.00827 &  0.002933 & +2.8190e+00 &  0.005434 &  0.002717 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=316105&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]+0.1978[/C][C] 0.1465[/C][C]+1.3500e+00[/C][C] 0.1789[/C][C] 0.08947[/C][/ROW]
[ROW][C]`Population_(millions)`[/C][C]-0.000144[/C][C] 0.0001788[/C][C]-8.0560e-01[/C][C] 0.4217[/C][C] 0.2109[/C][/ROW]
[ROW][C]HDI[/C][C]+0.1476[/C][C] 0.2341[/C][C]+6.3050e-01[/C][C] 0.5293[/C][C] 0.2646[/C][/ROW]
[ROW][C]GDP_per_Capita[/C][C]+4.143e-06[/C][C] 1.858e-06[/C][C]+2.2300e+00[/C][C] 0.02719[/C][C] 0.0136[/C][/ROW]
[ROW][C]Total_Biocapacity[/C][C]+0.00827[/C][C] 0.002933[/C][C]+2.8190e+00[/C][C] 0.005434[/C][C] 0.002717[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=316105&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=316105&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)	+0.1978	0.1465	+1.3500e+00	0.1789	0.08947
`Population_(millions)`	-0.000144	0.0001788	-8.0560e-01	0.4217	0.2109
HDI	+0.1476	0.2341	+6.3050e-01	0.5293	0.2646
GDP_per_Capita	+4.143e-06	1.858e-06	+2.2300e+00	0.02719	0.0136
Total_Biocapacity	+0.00827	0.002933	+2.8190e+00	0.005434	0.002717

Multiple Linear Regression - Regression Statistics
Multiple R	0.3595
R-squared	0.1292
Adjusted R-squared	0.107
F-TEST (value)	5.825
F-TEST (DF numerator)	4
F-TEST (DF denominator)	157
p-value	0.0002137
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	0.3412
Sum Squared Residuals	18.28

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R &  0.3595 \tabularnewline
R-squared &  0.1292 \tabularnewline
Adjusted R-squared &  0.107 \tabularnewline
F-TEST (value) &  5.825 \tabularnewline
F-TEST (DF numerator) & 4 \tabularnewline
F-TEST (DF denominator) & 157 \tabularnewline
p-value &  0.0002137 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation &  0.3412 \tabularnewline
Sum Squared Residuals &  18.28 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=316105&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C] 0.3595[/C][/ROW]
[ROW][C]R-squared[/C][C] 0.1292[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C] 0.107[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C] 5.825[/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] 0.0002137[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C] 0.3412[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C] 18.28[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=316105&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=316105&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	0.3595
R-squared	0.1292
Adjusted R-squared	0.107
F-TEST (value)	5.825
F-TEST (DF numerator)	4
F-TEST (DF denominator)	157
p-value	0.0002137
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	0.3412
Sum Squared Residuals	18.28

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=316105&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=316105&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=316105&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	0.08	0.2681	-0.1881
2	0.25	0.3337	-0.08367
3	0.17	0.3274	-0.1574
4	0.12	0.312	-0.192
5	0.29	0.4277	-0.1377
6	0.34	0.3267	0.0133
7	0.89	0.7447	0.1453
8	0.63	0.5643	0.06569
9	0.11	0.3437	-0.2337
10	0.19	0.4857	-0.2957
11	0.16	0.4241	-0.2641
12	0.08	0.2649	-0.1849
13	0.14	0.3811	-0.2411
14	0.91	0.3708	0.5392
15	0.99	0.5365	0.4535
16	0.26	0.2776	-0.01759
17	3.03	0.3383	2.692
18	0.17	0.4399	-0.2699
19	0.44	0.338	0.102
20	0.24	0.3602	-0.1202
21	0.6	0.4098	0.1902
22	0.26	0.5421	-0.2821
23	0.35	0.3671	-0.01711
24	0.36	0.2639	0.09611
25	0.45	0.2578	0.1922
26	0.21	0.2877	-0.07774
27	1.2	0.6756	0.5244
28	0.26	0.3189	-0.05891
29	0.27	0.2752	-0.005185
30	0.99	0.408	0.582
31	0.19	0.1321	0.05786
32	0.16	0.3573	-0.1973
33	0.18	0.2777	-0.09773
34	0.38	0.3796	0.0003956
35	0.51	0.2771	0.2329
36	0.68	0.3592	0.3208
37	0.72	0.4016	0.3184
38	0.09	0.3376	-0.2476
39	0.23	0.4563	-0.2263
40	0.74	0.4349	0.3051
41	0.77	0.6268	0.1432
42	0.14	0.342	-0.202
43	0.12	0.331	-0.211
44	0.24	0.3431	-0.1031
45	0.17	0.3048	-0.1348
46	0.4	0.3147	0.08531
47	0.26	0.4194	-0.1594
48	0.06	0.2671	-0.2071
49	0.46	0.2544	0.2056
50	0.42	0.341	0.07902
51	0.53	0.5339	-0.003911
52	0.79	0.5637	0.2263
53	0.2	0.2714	-0.07138
54	0.1	0.333	-0.233
55	0.48	0.533	-0.05297
56	0.65	0.2962	0.3538
57	0.24	0.4441	-0.2041
58	0.58	0.3087	0.2713
59	0.45	0.2759	0.1741
60	0.67	0.2874	0.3826
61	0.77	0.8548	-0.08484
62	0.1	0.2725	-0.1725
63	0.5	0.3108	0.1892
64	0.36	0.3932	-0.03322
65	0.14	0.1183	0.02172
66	0.2	0.2883	-0.08835
67	0.07	0.3376	-0.2676
68	0.01	0.3157	-0.3057
69	0.46	0.5813	-0.1213
70	0.36	0.4707	-0.1107
71	0.42	0.4855	-0.06554
72	0.18	0.3289	-0.1489
73	0.27	0.5082	-0.2382
74	0.17	0.3284	-0.1584
75	0.12	0.3855	-0.2655
76	0.27	0.2799	-0.009925
77	0.21	0.4278	-0.2178
78	0.21	0.4962	-0.2862
79	0.09	0.3084	-0.2184
80	0.36	0.298	0.06196
81	2.02	0.453	1.567
82	0.25	0.3499	-0.09995
83	0.42	0.2804	0.1396
84	0.75	0.2821	0.4679
85	0.14	0.3362	-0.1962
86	1.28	0.4263	0.8537
87	1.03	0.818	0.212
88	0.31	0.3407	-0.03068
89	0.24	0.2935	-0.05353
90	0.2	0.2665	-0.0665
91	0.38	0.3697	0.01032
92	0.17	0.2723	-0.1023
93	0.21	0.3144	-0.1044
94	0.18	0.3537	-0.1737
95	0.25	0.3436	-0.09357
96	0.15	0.3125	-0.1625
97	0.17	0.4472	-0.2772
98	0.62	0.3727	0.2473
99	0.14	0.3036	-0.1636
100	0.29	0.274	0.01603
101	0.32	0.2883	0.03171
102	0.17	0.3703	-0.2003
103	0.21	0.2813	-0.07135
104	0.38	0.5629	-0.1829
105	1.08	0.5707	0.5093
106	0.42	0.3153	0.1047
107	0.26	0.2575	0.002517
108	0.19	0.2639	-0.07392
109	0.15	0.4236	-0.2736
110	0.08	0.2582	-0.1782
111	0.19	0.3701	-0.1801
112	0.36	0.3108	0.04924
113	0.83	0.3975	0.4325
114	0.19	0.3576	-0.1676
115	0.09	0.2956	-0.2056
116	0.78	0.3906	0.3894
117	0.09	0.4275	-0.3375
118	0.15	0.7452	-0.5952
119	0.33	0.3687	-0.03866
120	0.67	0.4052	0.2648
121	0.25	0.2741	-0.02408
122	0.09	0.3658	-0.2758
123	0.17	0.3404	-0.1704
124	0.27	0.3339	-0.06393
125	0.27	0.2919	-0.02191
126	0.27	0.4181	-0.1481
127	0.21	0.2769	-0.06689
128	0.46	0.3456	0.1144
129	0.38	0.2683	0.1117
130	0.91	0.5519	0.3581
131	0.72	0.4184	0.3016
132	0.65	0.4506	0.1994
133	0.09	0.3144	-0.2244
134	0.29	0.3307	-0.04072
135	0.17	0.4625	-0.2925
136	0.16	0.3211	-0.1611
137	0.52	1.075	-0.5552
138	0.52	0.3024	0.2176
139	1.3	0.6631	0.6369
140	0.38	0.7114	-0.3314
141	0.1	0.296	-0.196
142	0.23	0.2783	-0.04832
143	0.24	0.3274	-0.08744
144	0.04	0.3224	-0.2824
145	0.27	0.273	-0.003024
146	0.14	0.3343	-0.1943
147	0.27	0.4	-0.13
148	0.28	0.3281	-0.04806
149	0.34	0.3552	-0.01516
150	0.08	0.3424	-0.2624
151	0.54	0.2708	0.2692
152	0.16	0.3341	-0.1741
153	0.45	0.5023	-0.05229
154	0.38	0.4927	-0.1127
155	0.67	0.5235	0.1465
156	0.55	0.4583	0.09173
157	0.08	0.3067	-0.2267
158	0.12	0.3717	-0.2517
159	0.19	0.2968	-0.1068
160	0.04	0.2777	-0.2377
161	0.33	0.3071	0.02294
162	0.29	0.2769	0.01312

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 &  0.08 &  0.2681 & -0.1881 \tabularnewline
2 &  0.25 &  0.3337 & -0.08367 \tabularnewline
3 &  0.17 &  0.3274 & -0.1574 \tabularnewline
4 &  0.12 &  0.312 & -0.192 \tabularnewline
5 &  0.29 &  0.4277 & -0.1377 \tabularnewline
6 &  0.34 &  0.3267 &  0.0133 \tabularnewline
7 &  0.89 &  0.7447 &  0.1453 \tabularnewline
8 &  0.63 &  0.5643 &  0.06569 \tabularnewline
9 &  0.11 &  0.3437 & -0.2337 \tabularnewline
10 &  0.19 &  0.4857 & -0.2957 \tabularnewline
11 &  0.16 &  0.4241 & -0.2641 \tabularnewline
12 &  0.08 &  0.2649 & -0.1849 \tabularnewline
13 &  0.14 &  0.3811 & -0.2411 \tabularnewline
14 &  0.91 &  0.3708 &  0.5392 \tabularnewline
15 &  0.99 &  0.5365 &  0.4535 \tabularnewline
16 &  0.26 &  0.2776 & -0.01759 \tabularnewline
17 &  3.03 &  0.3383 &  2.692 \tabularnewline
18 &  0.17 &  0.4399 & -0.2699 \tabularnewline
19 &  0.44 &  0.338 &  0.102 \tabularnewline
20 &  0.24 &  0.3602 & -0.1202 \tabularnewline
21 &  0.6 &  0.4098 &  0.1902 \tabularnewline
22 &  0.26 &  0.5421 & -0.2821 \tabularnewline
23 &  0.35 &  0.3671 & -0.01711 \tabularnewline
24 &  0.36 &  0.2639 &  0.09611 \tabularnewline
25 &  0.45 &  0.2578 &  0.1922 \tabularnewline
26 &  0.21 &  0.2877 & -0.07774 \tabularnewline
27 &  1.2 &  0.6756 &  0.5244 \tabularnewline
28 &  0.26 &  0.3189 & -0.05891 \tabularnewline
29 &  0.27 &  0.2752 & -0.005185 \tabularnewline
30 &  0.99 &  0.408 &  0.582 \tabularnewline
31 &  0.19 &  0.1321 &  0.05786 \tabularnewline
32 &  0.16 &  0.3573 & -0.1973 \tabularnewline
33 &  0.18 &  0.2777 & -0.09773 \tabularnewline
34 &  0.38 &  0.3796 &  0.0003956 \tabularnewline
35 &  0.51 &  0.2771 &  0.2329 \tabularnewline
36 &  0.68 &  0.3592 &  0.3208 \tabularnewline
37 &  0.72 &  0.4016 &  0.3184 \tabularnewline
38 &  0.09 &  0.3376 & -0.2476 \tabularnewline
39 &  0.23 &  0.4563 & -0.2263 \tabularnewline
40 &  0.74 &  0.4349 &  0.3051 \tabularnewline
41 &  0.77 &  0.6268 &  0.1432 \tabularnewline
42 &  0.14 &  0.342 & -0.202 \tabularnewline
43 &  0.12 &  0.331 & -0.211 \tabularnewline
44 &  0.24 &  0.3431 & -0.1031 \tabularnewline
45 &  0.17 &  0.3048 & -0.1348 \tabularnewline
46 &  0.4 &  0.3147 &  0.08531 \tabularnewline
47 &  0.26 &  0.4194 & -0.1594 \tabularnewline
48 &  0.06 &  0.2671 & -0.2071 \tabularnewline
49 &  0.46 &  0.2544 &  0.2056 \tabularnewline
50 &  0.42 &  0.341 &  0.07902 \tabularnewline
51 &  0.53 &  0.5339 & -0.003911 \tabularnewline
52 &  0.79 &  0.5637 &  0.2263 \tabularnewline
53 &  0.2 &  0.2714 & -0.07138 \tabularnewline
54 &  0.1 &  0.333 & -0.233 \tabularnewline
55 &  0.48 &  0.533 & -0.05297 \tabularnewline
56 &  0.65 &  0.2962 &  0.3538 \tabularnewline
57 &  0.24 &  0.4441 & -0.2041 \tabularnewline
58 &  0.58 &  0.3087 &  0.2713 \tabularnewline
59 &  0.45 &  0.2759 &  0.1741 \tabularnewline
60 &  0.67 &  0.2874 &  0.3826 \tabularnewline
61 &  0.77 &  0.8548 & -0.08484 \tabularnewline
62 &  0.1 &  0.2725 & -0.1725 \tabularnewline
63 &  0.5 &  0.3108 &  0.1892 \tabularnewline
64 &  0.36 &  0.3932 & -0.03322 \tabularnewline
65 &  0.14 &  0.1183 &  0.02172 \tabularnewline
66 &  0.2 &  0.2883 & -0.08835 \tabularnewline
67 &  0.07 &  0.3376 & -0.2676 \tabularnewline
68 &  0.01 &  0.3157 & -0.3057 \tabularnewline
69 &  0.46 &  0.5813 & -0.1213 \tabularnewline
70 &  0.36 &  0.4707 & -0.1107 \tabularnewline
71 &  0.42 &  0.4855 & -0.06554 \tabularnewline
72 &  0.18 &  0.3289 & -0.1489 \tabularnewline
73 &  0.27 &  0.5082 & -0.2382 \tabularnewline
74 &  0.17 &  0.3284 & -0.1584 \tabularnewline
75 &  0.12 &  0.3855 & -0.2655 \tabularnewline
76 &  0.27 &  0.2799 & -0.009925 \tabularnewline
77 &  0.21 &  0.4278 & -0.2178 \tabularnewline
78 &  0.21 &  0.4962 & -0.2862 \tabularnewline
79 &  0.09 &  0.3084 & -0.2184 \tabularnewline
80 &  0.36 &  0.298 &  0.06196 \tabularnewline
81 &  2.02 &  0.453 &  1.567 \tabularnewline
82 &  0.25 &  0.3499 & -0.09995 \tabularnewline
83 &  0.42 &  0.2804 &  0.1396 \tabularnewline
84 &  0.75 &  0.2821 &  0.4679 \tabularnewline
85 &  0.14 &  0.3362 & -0.1962 \tabularnewline
86 &  1.28 &  0.4263 &  0.8537 \tabularnewline
87 &  1.03 &  0.818 &  0.212 \tabularnewline
88 &  0.31 &  0.3407 & -0.03068 \tabularnewline
89 &  0.24 &  0.2935 & -0.05353 \tabularnewline
90 &  0.2 &  0.2665 & -0.0665 \tabularnewline
91 &  0.38 &  0.3697 &  0.01032 \tabularnewline
92 &  0.17 &  0.2723 & -0.1023 \tabularnewline
93 &  0.21 &  0.3144 & -0.1044 \tabularnewline
94 &  0.18 &  0.3537 & -0.1737 \tabularnewline
95 &  0.25 &  0.3436 & -0.09357 \tabularnewline
96 &  0.15 &  0.3125 & -0.1625 \tabularnewline
97 &  0.17 &  0.4472 & -0.2772 \tabularnewline
98 &  0.62 &  0.3727 &  0.2473 \tabularnewline
99 &  0.14 &  0.3036 & -0.1636 \tabularnewline
100 &  0.29 &  0.274 &  0.01603 \tabularnewline
101 &  0.32 &  0.2883 &  0.03171 \tabularnewline
102 &  0.17 &  0.3703 & -0.2003 \tabularnewline
103 &  0.21 &  0.2813 & -0.07135 \tabularnewline
104 &  0.38 &  0.5629 & -0.1829 \tabularnewline
105 &  1.08 &  0.5707 &  0.5093 \tabularnewline
106 &  0.42 &  0.3153 &  0.1047 \tabularnewline
107 &  0.26 &  0.2575 &  0.002517 \tabularnewline
108 &  0.19 &  0.2639 & -0.07392 \tabularnewline
109 &  0.15 &  0.4236 & -0.2736 \tabularnewline
110 &  0.08 &  0.2582 & -0.1782 \tabularnewline
111 &  0.19 &  0.3701 & -0.1801 \tabularnewline
112 &  0.36 &  0.3108 &  0.04924 \tabularnewline
113 &  0.83 &  0.3975 &  0.4325 \tabularnewline
114 &  0.19 &  0.3576 & -0.1676 \tabularnewline
115 &  0.09 &  0.2956 & -0.2056 \tabularnewline
116 &  0.78 &  0.3906 &  0.3894 \tabularnewline
117 &  0.09 &  0.4275 & -0.3375 \tabularnewline
118 &  0.15 &  0.7452 & -0.5952 \tabularnewline
119 &  0.33 &  0.3687 & -0.03866 \tabularnewline
120 &  0.67 &  0.4052 &  0.2648 \tabularnewline
121 &  0.25 &  0.2741 & -0.02408 \tabularnewline
122 &  0.09 &  0.3658 & -0.2758 \tabularnewline
123 &  0.17 &  0.3404 & -0.1704 \tabularnewline
124 &  0.27 &  0.3339 & -0.06393 \tabularnewline
125 &  0.27 &  0.2919 & -0.02191 \tabularnewline
126 &  0.27 &  0.4181 & -0.1481 \tabularnewline
127 &  0.21 &  0.2769 & -0.06689 \tabularnewline
128 &  0.46 &  0.3456 &  0.1144 \tabularnewline
129 &  0.38 &  0.2683 &  0.1117 \tabularnewline
130 &  0.91 &  0.5519 &  0.3581 \tabularnewline
131 &  0.72 &  0.4184 &  0.3016 \tabularnewline
132 &  0.65 &  0.4506 &  0.1994 \tabularnewline
133 &  0.09 &  0.3144 & -0.2244 \tabularnewline
134 &  0.29 &  0.3307 & -0.04072 \tabularnewline
135 &  0.17 &  0.4625 & -0.2925 \tabularnewline
136 &  0.16 &  0.3211 & -0.1611 \tabularnewline
137 &  0.52 &  1.075 & -0.5552 \tabularnewline
138 &  0.52 &  0.3024 &  0.2176 \tabularnewline
139 &  1.3 &  0.6631 &  0.6369 \tabularnewline
140 &  0.38 &  0.7114 & -0.3314 \tabularnewline
141 &  0.1 &  0.296 & -0.196 \tabularnewline
142 &  0.23 &  0.2783 & -0.04832 \tabularnewline
143 &  0.24 &  0.3274 & -0.08744 \tabularnewline
144 &  0.04 &  0.3224 & -0.2824 \tabularnewline
145 &  0.27 &  0.273 & -0.003024 \tabularnewline
146 &  0.14 &  0.3343 & -0.1943 \tabularnewline
147 &  0.27 &  0.4 & -0.13 \tabularnewline
148 &  0.28 &  0.3281 & -0.04806 \tabularnewline
149 &  0.34 &  0.3552 & -0.01516 \tabularnewline
150 &  0.08 &  0.3424 & -0.2624 \tabularnewline
151 &  0.54 &  0.2708 &  0.2692 \tabularnewline
152 &  0.16 &  0.3341 & -0.1741 \tabularnewline
153 &  0.45 &  0.5023 & -0.05229 \tabularnewline
154 &  0.38 &  0.4927 & -0.1127 \tabularnewline
155 &  0.67 &  0.5235 &  0.1465 \tabularnewline
156 &  0.55 &  0.4583 &  0.09173 \tabularnewline
157 &  0.08 &  0.3067 & -0.2267 \tabularnewline
158 &  0.12 &  0.3717 & -0.2517 \tabularnewline
159 &  0.19 &  0.2968 & -0.1068 \tabularnewline
160 &  0.04 &  0.2777 & -0.2377 \tabularnewline
161 &  0.33 &  0.3071 &  0.02294 \tabularnewline
162 &  0.29 &  0.2769 &  0.01312 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=316105&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] 0.08[/C][C] 0.2681[/C][C]-0.1881[/C][/ROW]
[ROW][C]2[/C][C] 0.25[/C][C] 0.3337[/C][C]-0.08367[/C][/ROW]
[ROW][C]3[/C][C] 0.17[/C][C] 0.3274[/C][C]-0.1574[/C][/ROW]
[ROW][C]4[/C][C] 0.12[/C][C] 0.312[/C][C]-0.192[/C][/ROW]
[ROW][C]5[/C][C] 0.29[/C][C] 0.4277[/C][C]-0.1377[/C][/ROW]
[ROW][C]6[/C][C] 0.34[/C][C] 0.3267[/C][C] 0.0133[/C][/ROW]
[ROW][C]7[/C][C] 0.89[/C][C] 0.7447[/C][C] 0.1453[/C][/ROW]
[ROW][C]8[/C][C] 0.63[/C][C] 0.5643[/C][C] 0.06569[/C][/ROW]
[ROW][C]9[/C][C] 0.11[/C][C] 0.3437[/C][C]-0.2337[/C][/ROW]
[ROW][C]10[/C][C] 0.19[/C][C] 0.4857[/C][C]-0.2957[/C][/ROW]
[ROW][C]11[/C][C] 0.16[/C][C] 0.4241[/C][C]-0.2641[/C][/ROW]
[ROW][C]12[/C][C] 0.08[/C][C] 0.2649[/C][C]-0.1849[/C][/ROW]
[ROW][C]13[/C][C] 0.14[/C][C] 0.3811[/C][C]-0.2411[/C][/ROW]
[ROW][C]14[/C][C] 0.91[/C][C] 0.3708[/C][C] 0.5392[/C][/ROW]
[ROW][C]15[/C][C] 0.99[/C][C] 0.5365[/C][C] 0.4535[/C][/ROW]
[ROW][C]16[/C][C] 0.26[/C][C] 0.2776[/C][C]-0.01759[/C][/ROW]
[ROW][C]17[/C][C] 3.03[/C][C] 0.3383[/C][C] 2.692[/C][/ROW]
[ROW][C]18[/C][C] 0.17[/C][C] 0.4399[/C][C]-0.2699[/C][/ROW]
[ROW][C]19[/C][C] 0.44[/C][C] 0.338[/C][C] 0.102[/C][/ROW]
[ROW][C]20[/C][C] 0.24[/C][C] 0.3602[/C][C]-0.1202[/C][/ROW]
[ROW][C]21[/C][C] 0.6[/C][C] 0.4098[/C][C] 0.1902[/C][/ROW]
[ROW][C]22[/C][C] 0.26[/C][C] 0.5421[/C][C]-0.2821[/C][/ROW]
[ROW][C]23[/C][C] 0.35[/C][C] 0.3671[/C][C]-0.01711[/C][/ROW]
[ROW][C]24[/C][C] 0.36[/C][C] 0.2639[/C][C] 0.09611[/C][/ROW]
[ROW][C]25[/C][C] 0.45[/C][C] 0.2578[/C][C] 0.1922[/C][/ROW]
[ROW][C]26[/C][C] 0.21[/C][C] 0.2877[/C][C]-0.07774[/C][/ROW]
[ROW][C]27[/C][C] 1.2[/C][C] 0.6756[/C][C] 0.5244[/C][/ROW]
[ROW][C]28[/C][C] 0.26[/C][C] 0.3189[/C][C]-0.05891[/C][/ROW]
[ROW][C]29[/C][C] 0.27[/C][C] 0.2752[/C][C]-0.005185[/C][/ROW]
[ROW][C]30[/C][C] 0.99[/C][C] 0.408[/C][C] 0.582[/C][/ROW]
[ROW][C]31[/C][C] 0.19[/C][C] 0.1321[/C][C] 0.05786[/C][/ROW]
[ROW][C]32[/C][C] 0.16[/C][C] 0.3573[/C][C]-0.1973[/C][/ROW]
[ROW][C]33[/C][C] 0.18[/C][C] 0.2777[/C][C]-0.09773[/C][/ROW]
[ROW][C]34[/C][C] 0.38[/C][C] 0.3796[/C][C] 0.0003956[/C][/ROW]
[ROW][C]35[/C][C] 0.51[/C][C] 0.2771[/C][C] 0.2329[/C][/ROW]
[ROW][C]36[/C][C] 0.68[/C][C] 0.3592[/C][C] 0.3208[/C][/ROW]
[ROW][C]37[/C][C] 0.72[/C][C] 0.4016[/C][C] 0.3184[/C][/ROW]
[ROW][C]38[/C][C] 0.09[/C][C] 0.3376[/C][C]-0.2476[/C][/ROW]
[ROW][C]39[/C][C] 0.23[/C][C] 0.4563[/C][C]-0.2263[/C][/ROW]
[ROW][C]40[/C][C] 0.74[/C][C] 0.4349[/C][C] 0.3051[/C][/ROW]
[ROW][C]41[/C][C] 0.77[/C][C] 0.6268[/C][C] 0.1432[/C][/ROW]
[ROW][C]42[/C][C] 0.14[/C][C] 0.342[/C][C]-0.202[/C][/ROW]
[ROW][C]43[/C][C] 0.12[/C][C] 0.331[/C][C]-0.211[/C][/ROW]
[ROW][C]44[/C][C] 0.24[/C][C] 0.3431[/C][C]-0.1031[/C][/ROW]
[ROW][C]45[/C][C] 0.17[/C][C] 0.3048[/C][C]-0.1348[/C][/ROW]
[ROW][C]46[/C][C] 0.4[/C][C] 0.3147[/C][C] 0.08531[/C][/ROW]
[ROW][C]47[/C][C] 0.26[/C][C] 0.4194[/C][C]-0.1594[/C][/ROW]
[ROW][C]48[/C][C] 0.06[/C][C] 0.2671[/C][C]-0.2071[/C][/ROW]
[ROW][C]49[/C][C] 0.46[/C][C] 0.2544[/C][C] 0.2056[/C][/ROW]
[ROW][C]50[/C][C] 0.42[/C][C] 0.341[/C][C] 0.07902[/C][/ROW]
[ROW][C]51[/C][C] 0.53[/C][C] 0.5339[/C][C]-0.003911[/C][/ROW]
[ROW][C]52[/C][C] 0.79[/C][C] 0.5637[/C][C] 0.2263[/C][/ROW]
[ROW][C]53[/C][C] 0.2[/C][C] 0.2714[/C][C]-0.07138[/C][/ROW]
[ROW][C]54[/C][C] 0.1[/C][C] 0.333[/C][C]-0.233[/C][/ROW]
[ROW][C]55[/C][C] 0.48[/C][C] 0.533[/C][C]-0.05297[/C][/ROW]
[ROW][C]56[/C][C] 0.65[/C][C] 0.2962[/C][C] 0.3538[/C][/ROW]
[ROW][C]57[/C][C] 0.24[/C][C] 0.4441[/C][C]-0.2041[/C][/ROW]
[ROW][C]58[/C][C] 0.58[/C][C] 0.3087[/C][C] 0.2713[/C][/ROW]
[ROW][C]59[/C][C] 0.45[/C][C] 0.2759[/C][C] 0.1741[/C][/ROW]
[ROW][C]60[/C][C] 0.67[/C][C] 0.2874[/C][C] 0.3826[/C][/ROW]
[ROW][C]61[/C][C] 0.77[/C][C] 0.8548[/C][C]-0.08484[/C][/ROW]
[ROW][C]62[/C][C] 0.1[/C][C] 0.2725[/C][C]-0.1725[/C][/ROW]
[ROW][C]63[/C][C] 0.5[/C][C] 0.3108[/C][C] 0.1892[/C][/ROW]
[ROW][C]64[/C][C] 0.36[/C][C] 0.3932[/C][C]-0.03322[/C][/ROW]
[ROW][C]65[/C][C] 0.14[/C][C] 0.1183[/C][C] 0.02172[/C][/ROW]
[ROW][C]66[/C][C] 0.2[/C][C] 0.2883[/C][C]-0.08835[/C][/ROW]
[ROW][C]67[/C][C] 0.07[/C][C] 0.3376[/C][C]-0.2676[/C][/ROW]
[ROW][C]68[/C][C] 0.01[/C][C] 0.3157[/C][C]-0.3057[/C][/ROW]
[ROW][C]69[/C][C] 0.46[/C][C] 0.5813[/C][C]-0.1213[/C][/ROW]
[ROW][C]70[/C][C] 0.36[/C][C] 0.4707[/C][C]-0.1107[/C][/ROW]
[ROW][C]71[/C][C] 0.42[/C][C] 0.4855[/C][C]-0.06554[/C][/ROW]
[ROW][C]72[/C][C] 0.18[/C][C] 0.3289[/C][C]-0.1489[/C][/ROW]
[ROW][C]73[/C][C] 0.27[/C][C] 0.5082[/C][C]-0.2382[/C][/ROW]
[ROW][C]74[/C][C] 0.17[/C][C] 0.3284[/C][C]-0.1584[/C][/ROW]
[ROW][C]75[/C][C] 0.12[/C][C] 0.3855[/C][C]-0.2655[/C][/ROW]
[ROW][C]76[/C][C] 0.27[/C][C] 0.2799[/C][C]-0.009925[/C][/ROW]
[ROW][C]77[/C][C] 0.21[/C][C] 0.4278[/C][C]-0.2178[/C][/ROW]
[ROW][C]78[/C][C] 0.21[/C][C] 0.4962[/C][C]-0.2862[/C][/ROW]
[ROW][C]79[/C][C] 0.09[/C][C] 0.3084[/C][C]-0.2184[/C][/ROW]
[ROW][C]80[/C][C] 0.36[/C][C] 0.298[/C][C] 0.06196[/C][/ROW]
[ROW][C]81[/C][C] 2.02[/C][C] 0.453[/C][C] 1.567[/C][/ROW]
[ROW][C]82[/C][C] 0.25[/C][C] 0.3499[/C][C]-0.09995[/C][/ROW]
[ROW][C]83[/C][C] 0.42[/C][C] 0.2804[/C][C] 0.1396[/C][/ROW]
[ROW][C]84[/C][C] 0.75[/C][C] 0.2821[/C][C] 0.4679[/C][/ROW]
[ROW][C]85[/C][C] 0.14[/C][C] 0.3362[/C][C]-0.1962[/C][/ROW]
[ROW][C]86[/C][C] 1.28[/C][C] 0.4263[/C][C] 0.8537[/C][/ROW]
[ROW][C]87[/C][C] 1.03[/C][C] 0.818[/C][C] 0.212[/C][/ROW]
[ROW][C]88[/C][C] 0.31[/C][C] 0.3407[/C][C]-0.03068[/C][/ROW]
[ROW][C]89[/C][C] 0.24[/C][C] 0.2935[/C][C]-0.05353[/C][/ROW]
[ROW][C]90[/C][C] 0.2[/C][C] 0.2665[/C][C]-0.0665[/C][/ROW]
[ROW][C]91[/C][C] 0.38[/C][C] 0.3697[/C][C] 0.01032[/C][/ROW]
[ROW][C]92[/C][C] 0.17[/C][C] 0.2723[/C][C]-0.1023[/C][/ROW]
[ROW][C]93[/C][C] 0.21[/C][C] 0.3144[/C][C]-0.1044[/C][/ROW]
[ROW][C]94[/C][C] 0.18[/C][C] 0.3537[/C][C]-0.1737[/C][/ROW]
[ROW][C]95[/C][C] 0.25[/C][C] 0.3436[/C][C]-0.09357[/C][/ROW]
[ROW][C]96[/C][C] 0.15[/C][C] 0.3125[/C][C]-0.1625[/C][/ROW]
[ROW][C]97[/C][C] 0.17[/C][C] 0.4472[/C][C]-0.2772[/C][/ROW]
[ROW][C]98[/C][C] 0.62[/C][C] 0.3727[/C][C] 0.2473[/C][/ROW]
[ROW][C]99[/C][C] 0.14[/C][C] 0.3036[/C][C]-0.1636[/C][/ROW]
[ROW][C]100[/C][C] 0.29[/C][C] 0.274[/C][C] 0.01603[/C][/ROW]
[ROW][C]101[/C][C] 0.32[/C][C] 0.2883[/C][C] 0.03171[/C][/ROW]
[ROW][C]102[/C][C] 0.17[/C][C] 0.3703[/C][C]-0.2003[/C][/ROW]
[ROW][C]103[/C][C] 0.21[/C][C] 0.2813[/C][C]-0.07135[/C][/ROW]
[ROW][C]104[/C][C] 0.38[/C][C] 0.5629[/C][C]-0.1829[/C][/ROW]
[ROW][C]105[/C][C] 1.08[/C][C] 0.5707[/C][C] 0.5093[/C][/ROW]
[ROW][C]106[/C][C] 0.42[/C][C] 0.3153[/C][C] 0.1047[/C][/ROW]
[ROW][C]107[/C][C] 0.26[/C][C] 0.2575[/C][C] 0.002517[/C][/ROW]
[ROW][C]108[/C][C] 0.19[/C][C] 0.2639[/C][C]-0.07392[/C][/ROW]
[ROW][C]109[/C][C] 0.15[/C][C] 0.4236[/C][C]-0.2736[/C][/ROW]
[ROW][C]110[/C][C] 0.08[/C][C] 0.2582[/C][C]-0.1782[/C][/ROW]
[ROW][C]111[/C][C] 0.19[/C][C] 0.3701[/C][C]-0.1801[/C][/ROW]
[ROW][C]112[/C][C] 0.36[/C][C] 0.3108[/C][C] 0.04924[/C][/ROW]
[ROW][C]113[/C][C] 0.83[/C][C] 0.3975[/C][C] 0.4325[/C][/ROW]
[ROW][C]114[/C][C] 0.19[/C][C] 0.3576[/C][C]-0.1676[/C][/ROW]
[ROW][C]115[/C][C] 0.09[/C][C] 0.2956[/C][C]-0.2056[/C][/ROW]
[ROW][C]116[/C][C] 0.78[/C][C] 0.3906[/C][C] 0.3894[/C][/ROW]
[ROW][C]117[/C][C] 0.09[/C][C] 0.4275[/C][C]-0.3375[/C][/ROW]
[ROW][C]118[/C][C] 0.15[/C][C] 0.7452[/C][C]-0.5952[/C][/ROW]
[ROW][C]119[/C][C] 0.33[/C][C] 0.3687[/C][C]-0.03866[/C][/ROW]
[ROW][C]120[/C][C] 0.67[/C][C] 0.4052[/C][C] 0.2648[/C][/ROW]
[ROW][C]121[/C][C] 0.25[/C][C] 0.2741[/C][C]-0.02408[/C][/ROW]
[ROW][C]122[/C][C] 0.09[/C][C] 0.3658[/C][C]-0.2758[/C][/ROW]
[ROW][C]123[/C][C] 0.17[/C][C] 0.3404[/C][C]-0.1704[/C][/ROW]
[ROW][C]124[/C][C] 0.27[/C][C] 0.3339[/C][C]-0.06393[/C][/ROW]
[ROW][C]125[/C][C] 0.27[/C][C] 0.2919[/C][C]-0.02191[/C][/ROW]
[ROW][C]126[/C][C] 0.27[/C][C] 0.4181[/C][C]-0.1481[/C][/ROW]
[ROW][C]127[/C][C] 0.21[/C][C] 0.2769[/C][C]-0.06689[/C][/ROW]
[ROW][C]128[/C][C] 0.46[/C][C] 0.3456[/C][C] 0.1144[/C][/ROW]
[ROW][C]129[/C][C] 0.38[/C][C] 0.2683[/C][C] 0.1117[/C][/ROW]
[ROW][C]130[/C][C] 0.91[/C][C] 0.5519[/C][C] 0.3581[/C][/ROW]
[ROW][C]131[/C][C] 0.72[/C][C] 0.4184[/C][C] 0.3016[/C][/ROW]
[ROW][C]132[/C][C] 0.65[/C][C] 0.4506[/C][C] 0.1994[/C][/ROW]
[ROW][C]133[/C][C] 0.09[/C][C] 0.3144[/C][C]-0.2244[/C][/ROW]
[ROW][C]134[/C][C] 0.29[/C][C] 0.3307[/C][C]-0.04072[/C][/ROW]
[ROW][C]135[/C][C] 0.17[/C][C] 0.4625[/C][C]-0.2925[/C][/ROW]
[ROW][C]136[/C][C] 0.16[/C][C] 0.3211[/C][C]-0.1611[/C][/ROW]
[ROW][C]137[/C][C] 0.52[/C][C] 1.075[/C][C]-0.5552[/C][/ROW]
[ROW][C]138[/C][C] 0.52[/C][C] 0.3024[/C][C] 0.2176[/C][/ROW]
[ROW][C]139[/C][C] 1.3[/C][C] 0.6631[/C][C] 0.6369[/C][/ROW]
[ROW][C]140[/C][C] 0.38[/C][C] 0.7114[/C][C]-0.3314[/C][/ROW]
[ROW][C]141[/C][C] 0.1[/C][C] 0.296[/C][C]-0.196[/C][/ROW]
[ROW][C]142[/C][C] 0.23[/C][C] 0.2783[/C][C]-0.04832[/C][/ROW]
[ROW][C]143[/C][C] 0.24[/C][C] 0.3274[/C][C]-0.08744[/C][/ROW]
[ROW][C]144[/C][C] 0.04[/C][C] 0.3224[/C][C]-0.2824[/C][/ROW]
[ROW][C]145[/C][C] 0.27[/C][C] 0.273[/C][C]-0.003024[/C][/ROW]
[ROW][C]146[/C][C] 0.14[/C][C] 0.3343[/C][C]-0.1943[/C][/ROW]
[ROW][C]147[/C][C] 0.27[/C][C] 0.4[/C][C]-0.13[/C][/ROW]
[ROW][C]148[/C][C] 0.28[/C][C] 0.3281[/C][C]-0.04806[/C][/ROW]
[ROW][C]149[/C][C] 0.34[/C][C] 0.3552[/C][C]-0.01516[/C][/ROW]
[ROW][C]150[/C][C] 0.08[/C][C] 0.3424[/C][C]-0.2624[/C][/ROW]
[ROW][C]151[/C][C] 0.54[/C][C] 0.2708[/C][C] 0.2692[/C][/ROW]
[ROW][C]152[/C][C] 0.16[/C][C] 0.3341[/C][C]-0.1741[/C][/ROW]
[ROW][C]153[/C][C] 0.45[/C][C] 0.5023[/C][C]-0.05229[/C][/ROW]
[ROW][C]154[/C][C] 0.38[/C][C] 0.4927[/C][C]-0.1127[/C][/ROW]
[ROW][C]155[/C][C] 0.67[/C][C] 0.5235[/C][C] 0.1465[/C][/ROW]
[ROW][C]156[/C][C] 0.55[/C][C] 0.4583[/C][C] 0.09173[/C][/ROW]
[ROW][C]157[/C][C] 0.08[/C][C] 0.3067[/C][C]-0.2267[/C][/ROW]
[ROW][C]158[/C][C] 0.12[/C][C] 0.3717[/C][C]-0.2517[/C][/ROW]
[ROW][C]159[/C][C] 0.19[/C][C] 0.2968[/C][C]-0.1068[/C][/ROW]
[ROW][C]160[/C][C] 0.04[/C][C] 0.2777[/C][C]-0.2377[/C][/ROW]
[ROW][C]161[/C][C] 0.33[/C][C] 0.3071[/C][C] 0.02294[/C][/ROW]
[ROW][C]162[/C][C] 0.29[/C][C] 0.2769[/C][C] 0.01312[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=316105&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=316105&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	0.08	0.2681	-0.1881
2	0.25	0.3337	-0.08367
3	0.17	0.3274	-0.1574
4	0.12	0.312	-0.192
5	0.29	0.4277	-0.1377
6	0.34	0.3267	0.0133
7	0.89	0.7447	0.1453
8	0.63	0.5643	0.06569
9	0.11	0.3437	-0.2337
10	0.19	0.4857	-0.2957
11	0.16	0.4241	-0.2641
12	0.08	0.2649	-0.1849
13	0.14	0.3811	-0.2411
14	0.91	0.3708	0.5392
15	0.99	0.5365	0.4535
16	0.26	0.2776	-0.01759
17	3.03	0.3383	2.692
18	0.17	0.4399	-0.2699
19	0.44	0.338	0.102
20	0.24	0.3602	-0.1202
21	0.6	0.4098	0.1902
22	0.26	0.5421	-0.2821
23	0.35	0.3671	-0.01711
24	0.36	0.2639	0.09611
25	0.45	0.2578	0.1922
26	0.21	0.2877	-0.07774
27	1.2	0.6756	0.5244
28	0.26	0.3189	-0.05891
29	0.27	0.2752	-0.005185
30	0.99	0.408	0.582
31	0.19	0.1321	0.05786
32	0.16	0.3573	-0.1973
33	0.18	0.2777	-0.09773
34	0.38	0.3796	0.0003956
35	0.51	0.2771	0.2329
36	0.68	0.3592	0.3208
37	0.72	0.4016	0.3184
38	0.09	0.3376	-0.2476
39	0.23	0.4563	-0.2263
40	0.74	0.4349	0.3051
41	0.77	0.6268	0.1432
42	0.14	0.342	-0.202
43	0.12	0.331	-0.211
44	0.24	0.3431	-0.1031
45	0.17	0.3048	-0.1348
46	0.4	0.3147	0.08531
47	0.26	0.4194	-0.1594
48	0.06	0.2671	-0.2071
49	0.46	0.2544	0.2056
50	0.42	0.341	0.07902
51	0.53	0.5339	-0.003911
52	0.79	0.5637	0.2263
53	0.2	0.2714	-0.07138
54	0.1	0.333	-0.233
55	0.48	0.533	-0.05297
56	0.65	0.2962	0.3538
57	0.24	0.4441	-0.2041
58	0.58	0.3087	0.2713
59	0.45	0.2759	0.1741
60	0.67	0.2874	0.3826
61	0.77	0.8548	-0.08484
62	0.1	0.2725	-0.1725
63	0.5	0.3108	0.1892
64	0.36	0.3932	-0.03322
65	0.14	0.1183	0.02172
66	0.2	0.2883	-0.08835
67	0.07	0.3376	-0.2676
68	0.01	0.3157	-0.3057
69	0.46	0.5813	-0.1213
70	0.36	0.4707	-0.1107
71	0.42	0.4855	-0.06554
72	0.18	0.3289	-0.1489
73	0.27	0.5082	-0.2382
74	0.17	0.3284	-0.1584
75	0.12	0.3855	-0.2655
76	0.27	0.2799	-0.009925
77	0.21	0.4278	-0.2178
78	0.21	0.4962	-0.2862
79	0.09	0.3084	-0.2184
80	0.36	0.298	0.06196
81	2.02	0.453	1.567
82	0.25	0.3499	-0.09995
83	0.42	0.2804	0.1396
84	0.75	0.2821	0.4679
85	0.14	0.3362	-0.1962
86	1.28	0.4263	0.8537
87	1.03	0.818	0.212
88	0.31	0.3407	-0.03068
89	0.24	0.2935	-0.05353
90	0.2	0.2665	-0.0665
91	0.38	0.3697	0.01032
92	0.17	0.2723	-0.1023
93	0.21	0.3144	-0.1044
94	0.18	0.3537	-0.1737
95	0.25	0.3436	-0.09357
96	0.15	0.3125	-0.1625
97	0.17	0.4472	-0.2772
98	0.62	0.3727	0.2473
99	0.14	0.3036	-0.1636
100	0.29	0.274	0.01603
101	0.32	0.2883	0.03171
102	0.17	0.3703	-0.2003
103	0.21	0.2813	-0.07135
104	0.38	0.5629	-0.1829
105	1.08	0.5707	0.5093
106	0.42	0.3153	0.1047
107	0.26	0.2575	0.002517
108	0.19	0.2639	-0.07392
109	0.15	0.4236	-0.2736
110	0.08	0.2582	-0.1782
111	0.19	0.3701	-0.1801
112	0.36	0.3108	0.04924
113	0.83	0.3975	0.4325
114	0.19	0.3576	-0.1676
115	0.09	0.2956	-0.2056
116	0.78	0.3906	0.3894
117	0.09	0.4275	-0.3375
118	0.15	0.7452	-0.5952
119	0.33	0.3687	-0.03866
120	0.67	0.4052	0.2648
121	0.25	0.2741	-0.02408
122	0.09	0.3658	-0.2758
123	0.17	0.3404	-0.1704
124	0.27	0.3339	-0.06393
125	0.27	0.2919	-0.02191
126	0.27	0.4181	-0.1481
127	0.21	0.2769	-0.06689
128	0.46	0.3456	0.1144
129	0.38	0.2683	0.1117
130	0.91	0.5519	0.3581
131	0.72	0.4184	0.3016
132	0.65	0.4506	0.1994
133	0.09	0.3144	-0.2244
134	0.29	0.3307	-0.04072
135	0.17	0.4625	-0.2925
136	0.16	0.3211	-0.1611
137	0.52	1.075	-0.5552
138	0.52	0.3024	0.2176
139	1.3	0.6631	0.6369
140	0.38	0.7114	-0.3314
141	0.1	0.296	-0.196
142	0.23	0.2783	-0.04832
143	0.24	0.3274	-0.08744
144	0.04	0.3224	-0.2824
145	0.27	0.273	-0.003024
146	0.14	0.3343	-0.1943
147	0.27	0.4	-0.13
148	0.28	0.3281	-0.04806
149	0.34	0.3552	-0.01516
150	0.08	0.3424	-0.2624
151	0.54	0.2708	0.2692
152	0.16	0.3341	-0.1741
153	0.45	0.5023	-0.05229
154	0.38	0.4927	-0.1127
155	0.67	0.5235	0.1465
156	0.55	0.4583	0.09173
157	0.08	0.3067	-0.2267
158	0.12	0.3717	-0.2517
159	0.19	0.2968	-0.1068
160	0.04	0.2777	-0.2377
161	0.33	0.3071	0.02294
162	0.29	0.2769	0.01312

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
8	0.005119	0.01024	0.9949
9	0.008285	0.01657	0.9917
10	0.01452	0.02904	0.9855
11	0.01361	0.02721	0.9864
12	0.005336	0.01067	0.9947
13	0.002438	0.004876	0.9976
14	0.1244	0.2489	0.8756
15	0.1753	0.3507	0.8247
16	0.1385	0.2769	0.8615
17	1	3.182e-11	1.591e-11
18	1	8.944e-12	4.472e-12
19	1	2.557e-11	1.279e-11
20	1	5.368e-11	2.684e-11
21	1	5.953e-11	2.977e-11
22	1	9.448e-11	4.724e-11
23	1	2.455e-10	1.227e-10
24	1	5.686e-10	2.843e-10
25	1	1.241e-09	6.205e-10
26	1	2.556e-09	1.278e-09
27	1	1.962e-09	9.811e-10
28	1	3.294e-09	1.647e-09
29	1	7.234e-09	3.617e-09
30	1	2.974e-09	1.487e-09
31	1	5.791e-09	2.895e-09
32	1	9.313e-09	4.657e-09
33	1	1.88e-08	9.401e-09
34	1	3.655e-08	1.828e-08
35	1	5.991e-08	2.996e-08
36	1	7.561e-08	3.781e-08
37	1	9.765e-08	4.883e-08
38	1	1.347e-07	6.736e-08
39	1	2.06e-07	1.03e-07
40	1	2.601e-07	1.3e-07
41	1	4.553e-07	2.276e-07
42	1	6.908e-07	3.454e-07
43	1	1.037e-06	5.184e-07
44	1	1.802e-06	9.008e-07
45	1	3.027e-06	1.514e-06
46	1	5.262e-06	2.631e-06
47	1	7.968e-06	3.984e-06
48	1	1.153e-05	5.767e-06
49	1	1.646e-05	8.23e-06
50	1	2.75e-05	1.375e-05
51	1	4.611e-05	2.306e-05
52	1	6.251e-05	3.126e-05
53	1	0.0001008	5.041e-05
54	0.9999	0.0001319	6.597e-05
55	0.9999	0.0002094	0.0001047
56	0.9999	0.000206	0.000103
57	0.9999	0.0002808	0.0001404
58	0.9998	0.0003328	0.0001664
59	0.9998	0.0004627	0.0002314
60	0.9998	0.0004166	0.0002083
61	0.9998	0.0004562	0.0002281
62	0.9997	0.0006247	0.0003124
63	0.9996	0.0008348	0.0004174
64	0.9994	0.001253	0.0006266
65	0.9991	0.001803	0.0009017
66	0.9987	0.002603	0.001302
67	0.9985	0.00304	0.00152
68	0.9984	0.003288	0.001644
69	0.9978	0.004477	0.002238
70	0.9969	0.006135	0.003067
71	0.9957	0.008554	0.004277
72	0.9944	0.01121	0.005604
73	0.9933	0.01335	0.006673
74	0.9914	0.01711	0.008556
75	0.9903	0.0193	0.009651
76	0.987	0.02605	0.01303
77	0.9846	0.03075	0.01538
78	0.9834	0.03314	0.01657
79	0.9804	0.03914	0.01957
80	0.9746	0.0509	0.02545
81	1	7.336e-06	3.668e-06
82	1	1.206e-05	6.031e-06
83	1	1.765e-05	8.826e-06
84	1	6.799e-06	3.399e-06
85	1	9.603e-06	4.802e-06
86	1	1.214e-07	6.07e-08
87	1	1.327e-07	6.637e-08
88	1	2.544e-07	1.272e-07
89	1	4.738e-07	2.369e-07
90	1	8.685e-07	4.342e-07
91	1	1.598e-06	7.988e-07
92	1	2.826e-06	1.413e-06
93	1	4.937e-06	2.468e-06
94	1	7.494e-06	3.747e-06
95	1	1.249e-05	6.246e-06
96	1	1.928e-05	9.638e-06
97	1	2.354e-05	1.177e-05
98	1	2.715e-05	1.358e-05
99	1	4.178e-05	2.089e-05
100	1	6.757e-05	3.378e-05
101	0.9999	0.00011	5.498e-05
102	0.9999	0.0001604	8.021e-05
103	0.9999	0.0002632	0.0001316
104	0.9998	0.0003786	0.0001893
105	0.9999	0.0001052	5.26e-05
106	0.9999	0.0001565	7.827e-05
107	0.9999	0.0002482	0.0001241
108	0.9998	0.0004028	0.0002014
109	0.9998	0.0004874	0.0002437
110	0.9997	0.0006469	0.0003234
111	0.9995	0.0009277	0.0004639
112	0.9993	0.001373	0.0006865
113	0.9997	0.0005408	0.0002704
114	0.9996	0.0008019	0.000401
115	0.9995	0.0009952	0.0004976
116	0.9997	0.0006136	0.0003068
117	0.9997	0.0006065	0.0003033
118	1	9.257e-05	4.628e-05
119	0.9999	0.000167	8.352e-05
120	0.9999	0.0001484	7.421e-05
121	0.9999	0.0002683	0.0001342
122	0.9998	0.0003232	0.0001616
123	0.9997	0.0005209	0.0002605
124	0.9995	0.000914	0.000457
125	0.9992	0.00157	0.0007849
126	0.9988	0.002381	0.00119
127	0.998	0.003979	0.00199
128	0.9973	0.005384	0.002692
129	0.9962	0.007544	0.003772
130	0.9967	0.006518	0.003259
131	0.998	0.00406	0.00203
132	0.9983	0.003332	0.001666
133	0.9977	0.0046	0.0023
134	0.996	0.007941	0.003971
135	0.9951	0.009782	0.004891
136	0.9918	0.01631	0.008155
137	0.9999	0.0002265	0.0001132
138	0.9999	0.0001735	8.674e-05
139	1	1.038e-05	5.192e-06
140	1	9.492e-06	4.746e-06
141	1	2.59e-05	1.295e-05
142	1	7.216e-05	3.608e-05
143	0.9999	0.0001908	9.54e-05
144	0.9999	0.0001598	7.989e-05
145	0.9998	0.0004639	0.0002319
146	0.9994	0.001286	0.0006428
147	0.9983	0.00343	0.001715
148	0.9974	0.005195	0.002598
149	0.9963	0.007411	0.003705
150	0.9933	0.01335	0.006677
151	0.9979	0.004234	0.002117
152	0.9923	0.0153	0.007651
153	0.9775	0.04495	0.02247
154	0.9438	0.1123	0.05616

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
8 &  0.005119 &  0.01024 &  0.9949 \tabularnewline
9 &  0.008285 &  0.01657 &  0.9917 \tabularnewline
10 &  0.01452 &  0.02904 &  0.9855 \tabularnewline
11 &  0.01361 &  0.02721 &  0.9864 \tabularnewline
12 &  0.005336 &  0.01067 &  0.9947 \tabularnewline
13 &  0.002438 &  0.004876 &  0.9976 \tabularnewline
14 &  0.1244 &  0.2489 &  0.8756 \tabularnewline
15 &  0.1753 &  0.3507 &  0.8247 \tabularnewline
16 &  0.1385 &  0.2769 &  0.8615 \tabularnewline
17 &  1 &  3.182e-11 &  1.591e-11 \tabularnewline
18 &  1 &  8.944e-12 &  4.472e-12 \tabularnewline
19 &  1 &  2.557e-11 &  1.279e-11 \tabularnewline
20 &  1 &  5.368e-11 &  2.684e-11 \tabularnewline
21 &  1 &  5.953e-11 &  2.977e-11 \tabularnewline
22 &  1 &  9.448e-11 &  4.724e-11 \tabularnewline
23 &  1 &  2.455e-10 &  1.227e-10 \tabularnewline
24 &  1 &  5.686e-10 &  2.843e-10 \tabularnewline
25 &  1 &  1.241e-09 &  6.205e-10 \tabularnewline
26 &  1 &  2.556e-09 &  1.278e-09 \tabularnewline
27 &  1 &  1.962e-09 &  9.811e-10 \tabularnewline
28 &  1 &  3.294e-09 &  1.647e-09 \tabularnewline
29 &  1 &  7.234e-09 &  3.617e-09 \tabularnewline
30 &  1 &  2.974e-09 &  1.487e-09 \tabularnewline
31 &  1 &  5.791e-09 &  2.895e-09 \tabularnewline
32 &  1 &  9.313e-09 &  4.657e-09 \tabularnewline
33 &  1 &  1.88e-08 &  9.401e-09 \tabularnewline
34 &  1 &  3.655e-08 &  1.828e-08 \tabularnewline
35 &  1 &  5.991e-08 &  2.996e-08 \tabularnewline
36 &  1 &  7.561e-08 &  3.781e-08 \tabularnewline
37 &  1 &  9.765e-08 &  4.883e-08 \tabularnewline
38 &  1 &  1.347e-07 &  6.736e-08 \tabularnewline
39 &  1 &  2.06e-07 &  1.03e-07 \tabularnewline
40 &  1 &  2.601e-07 &  1.3e-07 \tabularnewline
41 &  1 &  4.553e-07 &  2.276e-07 \tabularnewline
42 &  1 &  6.908e-07 &  3.454e-07 \tabularnewline
43 &  1 &  1.037e-06 &  5.184e-07 \tabularnewline
44 &  1 &  1.802e-06 &  9.008e-07 \tabularnewline
45 &  1 &  3.027e-06 &  1.514e-06 \tabularnewline
46 &  1 &  5.262e-06 &  2.631e-06 \tabularnewline
47 &  1 &  7.968e-06 &  3.984e-06 \tabularnewline
48 &  1 &  1.153e-05 &  5.767e-06 \tabularnewline
49 &  1 &  1.646e-05 &  8.23e-06 \tabularnewline
50 &  1 &  2.75e-05 &  1.375e-05 \tabularnewline
51 &  1 &  4.611e-05 &  2.306e-05 \tabularnewline
52 &  1 &  6.251e-05 &  3.126e-05 \tabularnewline
53 &  1 &  0.0001008 &  5.041e-05 \tabularnewline
54 &  0.9999 &  0.0001319 &  6.597e-05 \tabularnewline
55 &  0.9999 &  0.0002094 &  0.0001047 \tabularnewline
56 &  0.9999 &  0.000206 &  0.000103 \tabularnewline
57 &  0.9999 &  0.0002808 &  0.0001404 \tabularnewline
58 &  0.9998 &  0.0003328 &  0.0001664 \tabularnewline
59 &  0.9998 &  0.0004627 &  0.0002314 \tabularnewline
60 &  0.9998 &  0.0004166 &  0.0002083 \tabularnewline
61 &  0.9998 &  0.0004562 &  0.0002281 \tabularnewline
62 &  0.9997 &  0.0006247 &  0.0003124 \tabularnewline
63 &  0.9996 &  0.0008348 &  0.0004174 \tabularnewline
64 &  0.9994 &  0.001253 &  0.0006266 \tabularnewline
65 &  0.9991 &  0.001803 &  0.0009017 \tabularnewline
66 &  0.9987 &  0.002603 &  0.001302 \tabularnewline
67 &  0.9985 &  0.00304 &  0.00152 \tabularnewline
68 &  0.9984 &  0.003288 &  0.001644 \tabularnewline
69 &  0.9978 &  0.004477 &  0.002238 \tabularnewline
70 &  0.9969 &  0.006135 &  0.003067 \tabularnewline
71 &  0.9957 &  0.008554 &  0.004277 \tabularnewline
72 &  0.9944 &  0.01121 &  0.005604 \tabularnewline
73 &  0.9933 &  0.01335 &  0.006673 \tabularnewline
74 &  0.9914 &  0.01711 &  0.008556 \tabularnewline
75 &  0.9903 &  0.0193 &  0.009651 \tabularnewline
76 &  0.987 &  0.02605 &  0.01303 \tabularnewline
77 &  0.9846 &  0.03075 &  0.01538 \tabularnewline
78 &  0.9834 &  0.03314 &  0.01657 \tabularnewline
79 &  0.9804 &  0.03914 &  0.01957 \tabularnewline
80 &  0.9746 &  0.0509 &  0.02545 \tabularnewline
81 &  1 &  7.336e-06 &  3.668e-06 \tabularnewline
82 &  1 &  1.206e-05 &  6.031e-06 \tabularnewline
83 &  1 &  1.765e-05 &  8.826e-06 \tabularnewline
84 &  1 &  6.799e-06 &  3.399e-06 \tabularnewline
85 &  1 &  9.603e-06 &  4.802e-06 \tabularnewline
86 &  1 &  1.214e-07 &  6.07e-08 \tabularnewline
87 &  1 &  1.327e-07 &  6.637e-08 \tabularnewline
88 &  1 &  2.544e-07 &  1.272e-07 \tabularnewline
89 &  1 &  4.738e-07 &  2.369e-07 \tabularnewline
90 &  1 &  8.685e-07 &  4.342e-07 \tabularnewline
91 &  1 &  1.598e-06 &  7.988e-07 \tabularnewline
92 &  1 &  2.826e-06 &  1.413e-06 \tabularnewline
93 &  1 &  4.937e-06 &  2.468e-06 \tabularnewline
94 &  1 &  7.494e-06 &  3.747e-06 \tabularnewline
95 &  1 &  1.249e-05 &  6.246e-06 \tabularnewline
96 &  1 &  1.928e-05 &  9.638e-06 \tabularnewline
97 &  1 &  2.354e-05 &  1.177e-05 \tabularnewline
98 &  1 &  2.715e-05 &  1.358e-05 \tabularnewline
99 &  1 &  4.178e-05 &  2.089e-05 \tabularnewline
100 &  1 &  6.757e-05 &  3.378e-05 \tabularnewline
101 &  0.9999 &  0.00011 &  5.498e-05 \tabularnewline
102 &  0.9999 &  0.0001604 &  8.021e-05 \tabularnewline
103 &  0.9999 &  0.0002632 &  0.0001316 \tabularnewline
104 &  0.9998 &  0.0003786 &  0.0001893 \tabularnewline
105 &  0.9999 &  0.0001052 &  5.26e-05 \tabularnewline
106 &  0.9999 &  0.0001565 &  7.827e-05 \tabularnewline
107 &  0.9999 &  0.0002482 &  0.0001241 \tabularnewline
108 &  0.9998 &  0.0004028 &  0.0002014 \tabularnewline
109 &  0.9998 &  0.0004874 &  0.0002437 \tabularnewline
110 &  0.9997 &  0.0006469 &  0.0003234 \tabularnewline
111 &  0.9995 &  0.0009277 &  0.0004639 \tabularnewline
112 &  0.9993 &  0.001373 &  0.0006865 \tabularnewline
113 &  0.9997 &  0.0005408 &  0.0002704 \tabularnewline
114 &  0.9996 &  0.0008019 &  0.000401 \tabularnewline
115 &  0.9995 &  0.0009952 &  0.0004976 \tabularnewline
116 &  0.9997 &  0.0006136 &  0.0003068 \tabularnewline
117 &  0.9997 &  0.0006065 &  0.0003033 \tabularnewline
118 &  1 &  9.257e-05 &  4.628e-05 \tabularnewline
119 &  0.9999 &  0.000167 &  8.352e-05 \tabularnewline
120 &  0.9999 &  0.0001484 &  7.421e-05 \tabularnewline
121 &  0.9999 &  0.0002683 &  0.0001342 \tabularnewline
122 &  0.9998 &  0.0003232 &  0.0001616 \tabularnewline
123 &  0.9997 &  0.0005209 &  0.0002605 \tabularnewline
124 &  0.9995 &  0.000914 &  0.000457 \tabularnewline
125 &  0.9992 &  0.00157 &  0.0007849 \tabularnewline
126 &  0.9988 &  0.002381 &  0.00119 \tabularnewline
127 &  0.998 &  0.003979 &  0.00199 \tabularnewline
128 &  0.9973 &  0.005384 &  0.002692 \tabularnewline
129 &  0.9962 &  0.007544 &  0.003772 \tabularnewline
130 &  0.9967 &  0.006518 &  0.003259 \tabularnewline
131 &  0.998 &  0.00406 &  0.00203 \tabularnewline
132 &  0.9983 &  0.003332 &  0.001666 \tabularnewline
133 &  0.9977 &  0.0046 &  0.0023 \tabularnewline
134 &  0.996 &  0.007941 &  0.003971 \tabularnewline
135 &  0.9951 &  0.009782 &  0.004891 \tabularnewline
136 &  0.9918 &  0.01631 &  0.008155 \tabularnewline
137 &  0.9999 &  0.0002265 &  0.0001132 \tabularnewline
138 &  0.9999 &  0.0001735 &  8.674e-05 \tabularnewline
139 &  1 &  1.038e-05 &  5.192e-06 \tabularnewline
140 &  1 &  9.492e-06 &  4.746e-06 \tabularnewline
141 &  1 &  2.59e-05 &  1.295e-05 \tabularnewline
142 &  1 &  7.216e-05 &  3.608e-05 \tabularnewline
143 &  0.9999 &  0.0001908 &  9.54e-05 \tabularnewline
144 &  0.9999 &  0.0001598 &  7.989e-05 \tabularnewline
145 &  0.9998 &  0.0004639 &  0.0002319 \tabularnewline
146 &  0.9994 &  0.001286 &  0.0006428 \tabularnewline
147 &  0.9983 &  0.00343 &  0.001715 \tabularnewline
148 &  0.9974 &  0.005195 &  0.002598 \tabularnewline
149 &  0.9963 &  0.007411 &  0.003705 \tabularnewline
150 &  0.9933 &  0.01335 &  0.006677 \tabularnewline
151 &  0.9979 &  0.004234 &  0.002117 \tabularnewline
152 &  0.9923 &  0.0153 &  0.007651 \tabularnewline
153 &  0.9775 &  0.04495 &  0.02247 \tabularnewline
154 &  0.9438 &  0.1123 &  0.05616 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=316105&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] 0.005119[/C][C] 0.01024[/C][C] 0.9949[/C][/ROW]
[ROW][C]9[/C][C] 0.008285[/C][C] 0.01657[/C][C] 0.9917[/C][/ROW]
[ROW][C]10[/C][C] 0.01452[/C][C] 0.02904[/C][C] 0.9855[/C][/ROW]
[ROW][C]11[/C][C] 0.01361[/C][C] 0.02721[/C][C] 0.9864[/C][/ROW]
[ROW][C]12[/C][C] 0.005336[/C][C] 0.01067[/C][C] 0.9947[/C][/ROW]
[ROW][C]13[/C][C] 0.002438[/C][C] 0.004876[/C][C] 0.9976[/C][/ROW]
[ROW][C]14[/C][C] 0.1244[/C][C] 0.2489[/C][C] 0.8756[/C][/ROW]
[ROW][C]15[/C][C] 0.1753[/C][C] 0.3507[/C][C] 0.8247[/C][/ROW]
[ROW][C]16[/C][C] 0.1385[/C][C] 0.2769[/C][C] 0.8615[/C][/ROW]
[ROW][C]17[/C][C] 1[/C][C] 3.182e-11[/C][C] 1.591e-11[/C][/ROW]
[ROW][C]18[/C][C] 1[/C][C] 8.944e-12[/C][C] 4.472e-12[/C][/ROW]
[ROW][C]19[/C][C] 1[/C][C] 2.557e-11[/C][C] 1.279e-11[/C][/ROW]
[ROW][C]20[/C][C] 1[/C][C] 5.368e-11[/C][C] 2.684e-11[/C][/ROW]
[ROW][C]21[/C][C] 1[/C][C] 5.953e-11[/C][C] 2.977e-11[/C][/ROW]
[ROW][C]22[/C][C] 1[/C][C] 9.448e-11[/C][C] 4.724e-11[/C][/ROW]
[ROW][C]23[/C][C] 1[/C][C] 2.455e-10[/C][C] 1.227e-10[/C][/ROW]
[ROW][C]24[/C][C] 1[/C][C] 5.686e-10[/C][C] 2.843e-10[/C][/ROW]
[ROW][C]25[/C][C] 1[/C][C] 1.241e-09[/C][C] 6.205e-10[/C][/ROW]
[ROW][C]26[/C][C] 1[/C][C] 2.556e-09[/C][C] 1.278e-09[/C][/ROW]
[ROW][C]27[/C][C] 1[/C][C] 1.962e-09[/C][C] 9.811e-10[/C][/ROW]
[ROW][C]28[/C][C] 1[/C][C] 3.294e-09[/C][C] 1.647e-09[/C][/ROW]
[ROW][C]29[/C][C] 1[/C][C] 7.234e-09[/C][C] 3.617e-09[/C][/ROW]
[ROW][C]30[/C][C] 1[/C][C] 2.974e-09[/C][C] 1.487e-09[/C][/ROW]
[ROW][C]31[/C][C] 1[/C][C] 5.791e-09[/C][C] 2.895e-09[/C][/ROW]
[ROW][C]32[/C][C] 1[/C][C] 9.313e-09[/C][C] 4.657e-09[/C][/ROW]
[ROW][C]33[/C][C] 1[/C][C] 1.88e-08[/C][C] 9.401e-09[/C][/ROW]
[ROW][C]34[/C][C] 1[/C][C] 3.655e-08[/C][C] 1.828e-08[/C][/ROW]
[ROW][C]35[/C][C] 1[/C][C] 5.991e-08[/C][C] 2.996e-08[/C][/ROW]
[ROW][C]36[/C][C] 1[/C][C] 7.561e-08[/C][C] 3.781e-08[/C][/ROW]
[ROW][C]37[/C][C] 1[/C][C] 9.765e-08[/C][C] 4.883e-08[/C][/ROW]
[ROW][C]38[/C][C] 1[/C][C] 1.347e-07[/C][C] 6.736e-08[/C][/ROW]
[ROW][C]39[/C][C] 1[/C][C] 2.06e-07[/C][C] 1.03e-07[/C][/ROW]
[ROW][C]40[/C][C] 1[/C][C] 2.601e-07[/C][C] 1.3e-07[/C][/ROW]
[ROW][C]41[/C][C] 1[/C][C] 4.553e-07[/C][C] 2.276e-07[/C][/ROW]
[ROW][C]42[/C][C] 1[/C][C] 6.908e-07[/C][C] 3.454e-07[/C][/ROW]
[ROW][C]43[/C][C] 1[/C][C] 1.037e-06[/C][C] 5.184e-07[/C][/ROW]
[ROW][C]44[/C][C] 1[/C][C] 1.802e-06[/C][C] 9.008e-07[/C][/ROW]
[ROW][C]45[/C][C] 1[/C][C] 3.027e-06[/C][C] 1.514e-06[/C][/ROW]
[ROW][C]46[/C][C] 1[/C][C] 5.262e-06[/C][C] 2.631e-06[/C][/ROW]
[ROW][C]47[/C][C] 1[/C][C] 7.968e-06[/C][C] 3.984e-06[/C][/ROW]
[ROW][C]48[/C][C] 1[/C][C] 1.153e-05[/C][C] 5.767e-06[/C][/ROW]
[ROW][C]49[/C][C] 1[/C][C] 1.646e-05[/C][C] 8.23e-06[/C][/ROW]
[ROW][C]50[/C][C] 1[/C][C] 2.75e-05[/C][C] 1.375e-05[/C][/ROW]
[ROW][C]51[/C][C] 1[/C][C] 4.611e-05[/C][C] 2.306e-05[/C][/ROW]
[ROW][C]52[/C][C] 1[/C][C] 6.251e-05[/C][C] 3.126e-05[/C][/ROW]
[ROW][C]53[/C][C] 1[/C][C] 0.0001008[/C][C] 5.041e-05[/C][/ROW]
[ROW][C]54[/C][C] 0.9999[/C][C] 0.0001319[/C][C] 6.597e-05[/C][/ROW]
[ROW][C]55[/C][C] 0.9999[/C][C] 0.0002094[/C][C] 0.0001047[/C][/ROW]
[ROW][C]56[/C][C] 0.9999[/C][C] 0.000206[/C][C] 0.000103[/C][/ROW]
[ROW][C]57[/C][C] 0.9999[/C][C] 0.0002808[/C][C] 0.0001404[/C][/ROW]
[ROW][C]58[/C][C] 0.9998[/C][C] 0.0003328[/C][C] 0.0001664[/C][/ROW]
[ROW][C]59[/C][C] 0.9998[/C][C] 0.0004627[/C][C] 0.0002314[/C][/ROW]
[ROW][C]60[/C][C] 0.9998[/C][C] 0.0004166[/C][C] 0.0002083[/C][/ROW]
[ROW][C]61[/C][C] 0.9998[/C][C] 0.0004562[/C][C] 0.0002281[/C][/ROW]
[ROW][C]62[/C][C] 0.9997[/C][C] 0.0006247[/C][C] 0.0003124[/C][/ROW]
[ROW][C]63[/C][C] 0.9996[/C][C] 0.0008348[/C][C] 0.0004174[/C][/ROW]
[ROW][C]64[/C][C] 0.9994[/C][C] 0.001253[/C][C] 0.0006266[/C][/ROW]
[ROW][C]65[/C][C] 0.9991[/C][C] 0.001803[/C][C] 0.0009017[/C][/ROW]
[ROW][C]66[/C][C] 0.9987[/C][C] 0.002603[/C][C] 0.001302[/C][/ROW]
[ROW][C]67[/C][C] 0.9985[/C][C] 0.00304[/C][C] 0.00152[/C][/ROW]
[ROW][C]68[/C][C] 0.9984[/C][C] 0.003288[/C][C] 0.001644[/C][/ROW]
[ROW][C]69[/C][C] 0.9978[/C][C] 0.004477[/C][C] 0.002238[/C][/ROW]
[ROW][C]70[/C][C] 0.9969[/C][C] 0.006135[/C][C] 0.003067[/C][/ROW]
[ROW][C]71[/C][C] 0.9957[/C][C] 0.008554[/C][C] 0.004277[/C][/ROW]
[ROW][C]72[/C][C] 0.9944[/C][C] 0.01121[/C][C] 0.005604[/C][/ROW]
[ROW][C]73[/C][C] 0.9933[/C][C] 0.01335[/C][C] 0.006673[/C][/ROW]
[ROW][C]74[/C][C] 0.9914[/C][C] 0.01711[/C][C] 0.008556[/C][/ROW]
[ROW][C]75[/C][C] 0.9903[/C][C] 0.0193[/C][C] 0.009651[/C][/ROW]
[ROW][C]76[/C][C] 0.987[/C][C] 0.02605[/C][C] 0.01303[/C][/ROW]
[ROW][C]77[/C][C] 0.9846[/C][C] 0.03075[/C][C] 0.01538[/C][/ROW]
[ROW][C]78[/C][C] 0.9834[/C][C] 0.03314[/C][C] 0.01657[/C][/ROW]
[ROW][C]79[/C][C] 0.9804[/C][C] 0.03914[/C][C] 0.01957[/C][/ROW]
[ROW][C]80[/C][C] 0.9746[/C][C] 0.0509[/C][C] 0.02545[/C][/ROW]
[ROW][C]81[/C][C] 1[/C][C] 7.336e-06[/C][C] 3.668e-06[/C][/ROW]
[ROW][C]82[/C][C] 1[/C][C] 1.206e-05[/C][C] 6.031e-06[/C][/ROW]
[ROW][C]83[/C][C] 1[/C][C] 1.765e-05[/C][C] 8.826e-06[/C][/ROW]
[ROW][C]84[/C][C] 1[/C][C] 6.799e-06[/C][C] 3.399e-06[/C][/ROW]
[ROW][C]85[/C][C] 1[/C][C] 9.603e-06[/C][C] 4.802e-06[/C][/ROW]
[ROW][C]86[/C][C] 1[/C][C] 1.214e-07[/C][C] 6.07e-08[/C][/ROW]
[ROW][C]87[/C][C] 1[/C][C] 1.327e-07[/C][C] 6.637e-08[/C][/ROW]
[ROW][C]88[/C][C] 1[/C][C] 2.544e-07[/C][C] 1.272e-07[/C][/ROW]
[ROW][C]89[/C][C] 1[/C][C] 4.738e-07[/C][C] 2.369e-07[/C][/ROW]
[ROW][C]90[/C][C] 1[/C][C] 8.685e-07[/C][C] 4.342e-07[/C][/ROW]
[ROW][C]91[/C][C] 1[/C][C] 1.598e-06[/C][C] 7.988e-07[/C][/ROW]
[ROW][C]92[/C][C] 1[/C][C] 2.826e-06[/C][C] 1.413e-06[/C][/ROW]
[ROW][C]93[/C][C] 1[/C][C] 4.937e-06[/C][C] 2.468e-06[/C][/ROW]
[ROW][C]94[/C][C] 1[/C][C] 7.494e-06[/C][C] 3.747e-06[/C][/ROW]
[ROW][C]95[/C][C] 1[/C][C] 1.249e-05[/C][C] 6.246e-06[/C][/ROW]
[ROW][C]96[/C][C] 1[/C][C] 1.928e-05[/C][C] 9.638e-06[/C][/ROW]
[ROW][C]97[/C][C] 1[/C][C] 2.354e-05[/C][C] 1.177e-05[/C][/ROW]
[ROW][C]98[/C][C] 1[/C][C] 2.715e-05[/C][C] 1.358e-05[/C][/ROW]
[ROW][C]99[/C][C] 1[/C][C] 4.178e-05[/C][C] 2.089e-05[/C][/ROW]
[ROW][C]100[/C][C] 1[/C][C] 6.757e-05[/C][C] 3.378e-05[/C][/ROW]
[ROW][C]101[/C][C] 0.9999[/C][C] 0.00011[/C][C] 5.498e-05[/C][/ROW]
[ROW][C]102[/C][C] 0.9999[/C][C] 0.0001604[/C][C] 8.021e-05[/C][/ROW]
[ROW][C]103[/C][C] 0.9999[/C][C] 0.0002632[/C][C] 0.0001316[/C][/ROW]
[ROW][C]104[/C][C] 0.9998[/C][C] 0.0003786[/C][C] 0.0001893[/C][/ROW]
[ROW][C]105[/C][C] 0.9999[/C][C] 0.0001052[/C][C] 5.26e-05[/C][/ROW]
[ROW][C]106[/C][C] 0.9999[/C][C] 0.0001565[/C][C] 7.827e-05[/C][/ROW]
[ROW][C]107[/C][C] 0.9999[/C][C] 0.0002482[/C][C] 0.0001241[/C][/ROW]
[ROW][C]108[/C][C] 0.9998[/C][C] 0.0004028[/C][C] 0.0002014[/C][/ROW]
[ROW][C]109[/C][C] 0.9998[/C][C] 0.0004874[/C][C] 0.0002437[/C][/ROW]
[ROW][C]110[/C][C] 0.9997[/C][C] 0.0006469[/C][C] 0.0003234[/C][/ROW]
[ROW][C]111[/C][C] 0.9995[/C][C] 0.0009277[/C][C] 0.0004639[/C][/ROW]
[ROW][C]112[/C][C] 0.9993[/C][C] 0.001373[/C][C] 0.0006865[/C][/ROW]
[ROW][C]113[/C][C] 0.9997[/C][C] 0.0005408[/C][C] 0.0002704[/C][/ROW]
[ROW][C]114[/C][C] 0.9996[/C][C] 0.0008019[/C][C] 0.000401[/C][/ROW]
[ROW][C]115[/C][C] 0.9995[/C][C] 0.0009952[/C][C] 0.0004976[/C][/ROW]
[ROW][C]116[/C][C] 0.9997[/C][C] 0.0006136[/C][C] 0.0003068[/C][/ROW]
[ROW][C]117[/C][C] 0.9997[/C][C] 0.0006065[/C][C] 0.0003033[/C][/ROW]
[ROW][C]118[/C][C] 1[/C][C] 9.257e-05[/C][C] 4.628e-05[/C][/ROW]
[ROW][C]119[/C][C] 0.9999[/C][C] 0.000167[/C][C] 8.352e-05[/C][/ROW]
[ROW][C]120[/C][C] 0.9999[/C][C] 0.0001484[/C][C] 7.421e-05[/C][/ROW]
[ROW][C]121[/C][C] 0.9999[/C][C] 0.0002683[/C][C] 0.0001342[/C][/ROW]
[ROW][C]122[/C][C] 0.9998[/C][C] 0.0003232[/C][C] 0.0001616[/C][/ROW]
[ROW][C]123[/C][C] 0.9997[/C][C] 0.0005209[/C][C] 0.0002605[/C][/ROW]
[ROW][C]124[/C][C] 0.9995[/C][C] 0.000914[/C][C] 0.000457[/C][/ROW]
[ROW][C]125[/C][C] 0.9992[/C][C] 0.00157[/C][C] 0.0007849[/C][/ROW]
[ROW][C]126[/C][C] 0.9988[/C][C] 0.002381[/C][C] 0.00119[/C][/ROW]
[ROW][C]127[/C][C] 0.998[/C][C] 0.003979[/C][C] 0.00199[/C][/ROW]
[ROW][C]128[/C][C] 0.9973[/C][C] 0.005384[/C][C] 0.002692[/C][/ROW]
[ROW][C]129[/C][C] 0.9962[/C][C] 0.007544[/C][C] 0.003772[/C][/ROW]
[ROW][C]130[/C][C] 0.9967[/C][C] 0.006518[/C][C] 0.003259[/C][/ROW]
[ROW][C]131[/C][C] 0.998[/C][C] 0.00406[/C][C] 0.00203[/C][/ROW]
[ROW][C]132[/C][C] 0.9983[/C][C] 0.003332[/C][C] 0.001666[/C][/ROW]
[ROW][C]133[/C][C] 0.9977[/C][C] 0.0046[/C][C] 0.0023[/C][/ROW]
[ROW][C]134[/C][C] 0.996[/C][C] 0.007941[/C][C] 0.003971[/C][/ROW]
[ROW][C]135[/C][C] 0.9951[/C][C] 0.009782[/C][C] 0.004891[/C][/ROW]
[ROW][C]136[/C][C] 0.9918[/C][C] 0.01631[/C][C] 0.008155[/C][/ROW]
[ROW][C]137[/C][C] 0.9999[/C][C] 0.0002265[/C][C] 0.0001132[/C][/ROW]
[ROW][C]138[/C][C] 0.9999[/C][C] 0.0001735[/C][C] 8.674e-05[/C][/ROW]
[ROW][C]139[/C][C] 1[/C][C] 1.038e-05[/C][C] 5.192e-06[/C][/ROW]
[ROW][C]140[/C][C] 1[/C][C] 9.492e-06[/C][C] 4.746e-06[/C][/ROW]
[ROW][C]141[/C][C] 1[/C][C] 2.59e-05[/C][C] 1.295e-05[/C][/ROW]
[ROW][C]142[/C][C] 1[/C][C] 7.216e-05[/C][C] 3.608e-05[/C][/ROW]
[ROW][C]143[/C][C] 0.9999[/C][C] 0.0001908[/C][C] 9.54e-05[/C][/ROW]
[ROW][C]144[/C][C] 0.9999[/C][C] 0.0001598[/C][C] 7.989e-05[/C][/ROW]
[ROW][C]145[/C][C] 0.9998[/C][C] 0.0004639[/C][C] 0.0002319[/C][/ROW]
[ROW][C]146[/C][C] 0.9994[/C][C] 0.001286[/C][C] 0.0006428[/C][/ROW]
[ROW][C]147[/C][C] 0.9983[/C][C] 0.00343[/C][C] 0.001715[/C][/ROW]
[ROW][C]148[/C][C] 0.9974[/C][C] 0.005195[/C][C] 0.002598[/C][/ROW]
[ROW][C]149[/C][C] 0.9963[/C][C] 0.007411[/C][C] 0.003705[/C][/ROW]
[ROW][C]150[/C][C] 0.9933[/C][C] 0.01335[/C][C] 0.006677[/C][/ROW]
[ROW][C]151[/C][C] 0.9979[/C][C] 0.004234[/C][C] 0.002117[/C][/ROW]
[ROW][C]152[/C][C] 0.9923[/C][C] 0.0153[/C][C] 0.007651[/C][/ROW]
[ROW][C]153[/C][C] 0.9775[/C][C] 0.04495[/C][C] 0.02247[/C][/ROW]
[ROW][C]154[/C][C] 0.9438[/C][C] 0.1123[/C][C] 0.05616[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=316105&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=316105&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	0.005119	0.01024	0.9949
9	0.008285	0.01657	0.9917
10	0.01452	0.02904	0.9855
11	0.01361	0.02721	0.9864
12	0.005336	0.01067	0.9947
13	0.002438	0.004876	0.9976
14	0.1244	0.2489	0.8756
15	0.1753	0.3507	0.8247
16	0.1385	0.2769	0.8615
17	1	3.182e-11	1.591e-11
18	1	8.944e-12	4.472e-12
19	1	2.557e-11	1.279e-11
20	1	5.368e-11	2.684e-11
21	1	5.953e-11	2.977e-11
22	1	9.448e-11	4.724e-11
23	1	2.455e-10	1.227e-10
24	1	5.686e-10	2.843e-10
25	1	1.241e-09	6.205e-10
26	1	2.556e-09	1.278e-09
27	1	1.962e-09	9.811e-10
28	1	3.294e-09	1.647e-09
29	1	7.234e-09	3.617e-09
30	1	2.974e-09	1.487e-09
31	1	5.791e-09	2.895e-09
32	1	9.313e-09	4.657e-09
33	1	1.88e-08	9.401e-09
34	1	3.655e-08	1.828e-08
35	1	5.991e-08	2.996e-08
36	1	7.561e-08	3.781e-08
37	1	9.765e-08	4.883e-08
38	1	1.347e-07	6.736e-08
39	1	2.06e-07	1.03e-07
40	1	2.601e-07	1.3e-07
41	1	4.553e-07	2.276e-07
42	1	6.908e-07	3.454e-07
43	1	1.037e-06	5.184e-07
44	1	1.802e-06	9.008e-07
45	1	3.027e-06	1.514e-06
46	1	5.262e-06	2.631e-06
47	1	7.968e-06	3.984e-06
48	1	1.153e-05	5.767e-06
49	1	1.646e-05	8.23e-06
50	1	2.75e-05	1.375e-05
51	1	4.611e-05	2.306e-05
52	1	6.251e-05	3.126e-05
53	1	0.0001008	5.041e-05
54	0.9999	0.0001319	6.597e-05
55	0.9999	0.0002094	0.0001047
56	0.9999	0.000206	0.000103
57	0.9999	0.0002808	0.0001404
58	0.9998	0.0003328	0.0001664
59	0.9998	0.0004627	0.0002314
60	0.9998	0.0004166	0.0002083
61	0.9998	0.0004562	0.0002281
62	0.9997	0.0006247	0.0003124
63	0.9996	0.0008348	0.0004174
64	0.9994	0.001253	0.0006266
65	0.9991	0.001803	0.0009017
66	0.9987	0.002603	0.001302
67	0.9985	0.00304	0.00152
68	0.9984	0.003288	0.001644
69	0.9978	0.004477	0.002238
70	0.9969	0.006135	0.003067
71	0.9957	0.008554	0.004277
72	0.9944	0.01121	0.005604
73	0.9933	0.01335	0.006673
74	0.9914	0.01711	0.008556
75	0.9903	0.0193	0.009651
76	0.987	0.02605	0.01303
77	0.9846	0.03075	0.01538
78	0.9834	0.03314	0.01657
79	0.9804	0.03914	0.01957
80	0.9746	0.0509	0.02545
81	1	7.336e-06	3.668e-06
82	1	1.206e-05	6.031e-06
83	1	1.765e-05	8.826e-06
84	1	6.799e-06	3.399e-06
85	1	9.603e-06	4.802e-06
86	1	1.214e-07	6.07e-08
87	1	1.327e-07	6.637e-08
88	1	2.544e-07	1.272e-07
89	1	4.738e-07	2.369e-07
90	1	8.685e-07	4.342e-07
91	1	1.598e-06	7.988e-07
92	1	2.826e-06	1.413e-06
93	1	4.937e-06	2.468e-06
94	1	7.494e-06	3.747e-06
95	1	1.249e-05	6.246e-06
96	1	1.928e-05	9.638e-06
97	1	2.354e-05	1.177e-05
98	1	2.715e-05	1.358e-05
99	1	4.178e-05	2.089e-05
100	1	6.757e-05	3.378e-05
101	0.9999	0.00011	5.498e-05
102	0.9999	0.0001604	8.021e-05
103	0.9999	0.0002632	0.0001316
104	0.9998	0.0003786	0.0001893
105	0.9999	0.0001052	5.26e-05
106	0.9999	0.0001565	7.827e-05
107	0.9999	0.0002482	0.0001241
108	0.9998	0.0004028	0.0002014
109	0.9998	0.0004874	0.0002437
110	0.9997	0.0006469	0.0003234
111	0.9995	0.0009277	0.0004639
112	0.9993	0.001373	0.0006865
113	0.9997	0.0005408	0.0002704
114	0.9996	0.0008019	0.000401
115	0.9995	0.0009952	0.0004976
116	0.9997	0.0006136	0.0003068
117	0.9997	0.0006065	0.0003033
118	1	9.257e-05	4.628e-05
119	0.9999	0.000167	8.352e-05
120	0.9999	0.0001484	7.421e-05
121	0.9999	0.0002683	0.0001342
122	0.9998	0.0003232	0.0001616
123	0.9997	0.0005209	0.0002605
124	0.9995	0.000914	0.000457
125	0.9992	0.00157	0.0007849
126	0.9988	0.002381	0.00119
127	0.998	0.003979	0.00199
128	0.9973	0.005384	0.002692
129	0.9962	0.007544	0.003772
130	0.9967	0.006518	0.003259
131	0.998	0.00406	0.00203
132	0.9983	0.003332	0.001666
133	0.9977	0.0046	0.0023
134	0.996	0.007941	0.003971
135	0.9951	0.009782	0.004891
136	0.9918	0.01631	0.008155
137	0.9999	0.0002265	0.0001132
138	0.9999	0.0001735	8.674e-05
139	1	1.038e-05	5.192e-06
140	1	9.492e-06	4.746e-06
141	1	2.59e-05	1.295e-05
142	1	7.216e-05	3.608e-05
143	0.9999	0.0001908	9.54e-05
144	0.9999	0.0001598	7.989e-05
145	0.9998	0.0004639	0.0002319
146	0.9994	0.001286	0.0006428
147	0.9983	0.00343	0.001715
148	0.9974	0.005195	0.002598
149	0.9963	0.007411	0.003705
150	0.9933	0.01335	0.006677
151	0.9979	0.004234	0.002117
152	0.9923	0.0153	0.007651
153	0.9775	0.04495	0.02247
154	0.9438	0.1123	0.05616

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	125	0.8503	NOK
5% type I error level	142	0.965986	NOK
10% type I error level	143	0.972789	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 & 125 &  0.8503 & NOK \tabularnewline
5% type I error level & 142 & 0.965986 & NOK \tabularnewline
10% type I error level & 143 & 0.972789 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=316105&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]125[/C][C] 0.8503[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]142[/C][C]0.965986[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]143[/C][C]0.972789[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=316105&T=7

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=316105&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	125	0.8503	NOK
5% type I error level	142	0.965986	NOK
10% type I error level	143	0.972789	NOK

Ramsey RESET F-Test for powers (2 and 3) of fitted values
> reset_test_fitted RESET test data: mylm RESET = 4.7239, df1 = 2, df2 = 155, p-value = 0.0102
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors RESET test data: mylm RESET = 4.5707, df1 = 8, df2 = 149, p-value = 5.342e-05
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components RESET test data: mylm RESET = 0.19068, df1 = 2, df2 = 155, p-value = 0.8266

\begin{tabular}{lllllllll}
\hline
Ramsey RESET F-Test for powers (2 and 3) of fitted values \tabularnewline
> reset_test_fitted
	RESET test
data:  mylm
RESET = 4.7239, df1 = 2, df2 = 155, p-value = 0.0102
 \tabularnewline
Ramsey RESET F-Test for powers (2 and 3) of regressors \tabularnewline
> reset_test_regressors
	RESET test
data:  mylm
RESET = 4.5707, df1 = 8, df2 = 149, p-value = 5.342e-05
 \tabularnewline
Ramsey RESET F-Test for powers (2 and 3) of principal components \tabularnewline
> reset_test_principal_components
	RESET test
data:  mylm
RESET = 0.19068, df1 = 2, df2 = 155, p-value = 0.8266
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=316105&T=8

[TABLE]
[ROW][C]Ramsey RESET F-Test for powers (2 and 3) of fitted values[/C][/ROW]
[ROW][C]> reset_test_fitted
	RESET test
data:  mylm
RESET = 4.7239, df1 = 2, df2 = 155, p-value = 0.0102
[/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 = 4.5707, df1 = 8, df2 = 149, p-value = 5.342e-05
[/C][/ROW]
[ROW][C]Ramsey RESET F-Test for powers (2 and 3) of principal components[/C][/ROW]
[ROW][C]> reset_test_principal_components
	RESET test
data:  mylm
RESET = 0.19068, df1 = 2, df2 = 155, p-value = 0.8266
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=316105&T=8

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=316105&T=8

As an alternative you can also use a QR Code:

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

Ramsey RESET F-Test for powers (2 and 3) of fitted values
> reset_test_fitted RESET test data: mylm RESET = 4.7239, df1 = 2, df2 = 155, p-value = 0.0102
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors RESET test data: mylm RESET = 4.5707, df1 = 8, df2 = 149, p-value = 5.342e-05
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components RESET test data: mylm RESET = 0.19068, df1 = 2, df2 = 155, p-value = 0.8266

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=316105&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=316105&T=9

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=316105&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 8

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Figure 9

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Figure 10

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Parameters (Session):

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

Parameters (R input):

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

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

library(lattice)
library(lmtest)
library(car)
library(MASS)
n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
mywarning <- ''
par6 <- as.numeric(par6)
if(is.na(par6)) {
par6 <- 12
mywarning = 'Warning: you did not specify the seasonality. The seasonal period was set to s = 12.'
}
par1 <- as.numeric(par1)
if(is.na(par1)) {
par1 <- 1
mywarning = 'Warning: you did not specify the column number of the endogenous series! The first column was selected by default.'
}
if (par4=='') par4 <- 0
par4 <- as.numeric(par4)
if (!is.numeric(par4)) par4 <- 0
if (par5=='') par5 <- 0
par5 <- as.numeric(par5)
if (!is.numeric(par5)) par5 <- 0
x <- na.omit(t(y))
k <- length(x[1,])
n <- length(x[,1])
x1 <- cbind(x[,par1], x[,1:k!=par1])
mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
colnames(x1) <- mycolnames #colnames(x)[par1]
x <- x1
if (par3 == 'First Differences'){
(n <- n -1)
x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-B)',colnames(x),sep='')))
for (i in 1:n) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
}
if (par3 == 'Seasonal Differences (s)'){
(n <- n - par6)
x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-Bs)',colnames(x),sep='')))
for (i in 1:n) {
for (j in 1:k) {
x2[i,j] <- x[i+par6,j] - x[i,j]
}
}
x <- x2
}
if (par3 == 'First and Seasonal Differences (s)'){
(n <- n -1)
x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-B)',colnames(x),sep='')))
for (i in 1:n) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
(n <- n - par6)
x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-Bs)',colnames(x),sep='')))
for (i in 1:n) {
for (j in 1:k) {
x2[i,j] <- x[i+par6,j] - x[i,j]
}
}
x <- x2
}
if(par4 > 0) {
x2 <- array(0, dim=c(n-par4,par4), dimnames=list(1:(n-par4), paste(colnames(x)[par1],'(t-',1:par4,')',sep='')))
for (i in 1:(n-par4)) {
for (j in 1:par4) {
x2[i,j] <- x[i+par4-j,par1]
}
}
x <- cbind(x[(par4+1):n,], x2)
n <- n - par4
}
if(par5 > 0) {
x2 <- array(0, dim=c(n-par5*par6,par5), dimnames=list(1:(n-par5*par6), paste(colnames(x)[par1],'(t-',1:par5,'s)',sep='')))
for (i in 1:(n-par5*par6)) {
for (j in 1:par5) {
x2[i,j] <- x[i+par5*par6-j*par6,par1]
}
}
x <- cbind(x[(par5*par6+1):n,], x2)
n <- n - par5*par6
}
if (par2 == 'Include Seasonal Dummies'){
x2 <- array(0, dim=c(n,par6-1), dimnames=list(1:n, paste('M', seq(1:(par6-1)), sep ='')))
for (i in 1:(par6-1)){
x2[seq(i,n,par6),i] <- 1
}
x <- cbind(x, x2)
}
if (par2 == 'Include Monthly Dummies'){
x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
for (i in 1:11){
x2[seq(i,n,12),i] <- 1
}
x <- cbind(x, x2)
}
if (par2 == 'Include Quarterly Dummies'){
x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
for (i in 1:3){
x2[seq(i,n,4),i] <- 1
}
x <- cbind(x, x2)
}
(k <- length(x[n,]))
if (par3 == 'Linear Trend'){
x <- cbind(x, c(1:n))
colnames(x)[k+1] <- 't'
}
print(x)
(k <- length(x[n,]))
head(x)
df <- as.data.frame(x)
(mylm <- lm(df))
(mysum <- summary(mylm))
if (n > n25) {
kp3 <- k + 3
nmkm3 <- n - k - 3
gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
numgqtests <- 0
numsignificant1 <- 0
numsignificant5 <- 0
numsignificant10 <- 0
for (mypoint in kp3:nmkm3) {
j <- 0
numgqtests <- numgqtests + 1
for (myalt in c('greater', 'two.sided', 'less')) {
j <- j + 1
gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
}
if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
}
gqarr
}
bitmap(file='test0.png')
plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
points(x[,1]-mysum$resid)
grid()
dev.off()
bitmap(file='test1.png')
plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
grid()
dev.off()
bitmap(file='test2.png')
sresid <- studres(mylm)
hist(sresid, freq=FALSE, main='Distribution of Studentized Residuals')
xfit<-seq(min(sresid),max(sresid),length=40)
yfit<-dnorm(xfit)
lines(xfit, yfit)
grid()
dev.off()
bitmap(file='test3.png')
densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
dev.off()
bitmap(file='test4.png')
qqPlot(mylm, main='QQ Plot')
grid()
dev.off()
(myerror <- as.ts(mysum$resid))
bitmap(file='test5.png')
dum <- cbind(lag(myerror,k=1),myerror)
dum
dum1 <- dum[2:length(myerror),]
dum1
z <- as.data.frame(dum1)
print(z)
plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
lines(lowess(z))
abline(lm(z))
grid()
dev.off()
bitmap(file='test6.png')
acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
grid()
dev.off()
bitmap(file='test7.png')
pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
grid()
dev.off()
bitmap(file='test8.png')
opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
plot(mylm, las = 1, sub='Residual Diagnostics')
par(opar)
dev.off()
if (n > n25) {
bitmap(file='test9.png')
plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
grid()
dev.off()
}
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
a<-table.row.end(a)
myeq <- colnames(x)[1]
myeq <- paste(myeq, '[t] = ', sep='')
for (i in 1:k){
if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
myeq <- paste(myeq, signif(mysum$coefficients[i,1],6), sep=' ')
if (rownames(mysum$coefficients)[i] != '(Intercept)') {
myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
}
}
myeq <- paste(myeq, ' + e[t]')
a<-table.row.start(a)
a<-table.element(a, myeq)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, mywarning)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable1.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Multiple Linear Regression - Ordinary Least Squares', 6, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Variable',header=TRUE)
a<-table.element(a,'Parameter',header=TRUE)
a<-table.element(a,'S.D.',header=TRUE)
a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
a<-table.element(a,'2-tail p-value',header=TRUE)
a<-table.element(a,'1-tail p-value',header=TRUE)
a<-table.row.end(a)
for (i in 1:k){
a<-table.row.start(a)
a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
a<-table.element(a,formatC(signif(mysum$coefficients[i,1],5),format='g',flag='+'))
a<-table.element(a,formatC(signif(mysum$coefficients[i,2],5),format='g',flag=' '))
a<-table.element(a,formatC(signif(mysum$coefficients[i,3],4),format='e',flag='+'))
a<-table.element(a,formatC(signif(mysum$coefficients[i,4],4),format='g',flag=' '))
a<-table.element(a,formatC(signif(mysum$coefficients[i,4]/2,4),format='g',flag=' '))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable2.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple R',1,TRUE)
a<-table.element(a,formatC(signif(sqrt(mysum$r.squared),6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'R-squared',1,TRUE)
a<-table.element(a,formatC(signif(mysum$r.squared,6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Adjusted R-squared',1,TRUE)
a<-table.element(a,formatC(signif(mysum$adj.r.squared,6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (value)',1,TRUE)
a<-table.element(a,formatC(signif(mysum$fstatistic[1],6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
a<-table.element(a, signif(mysum$fstatistic[2],6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
a<-table.element(a, signif(mysum$fstatistic[3],6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'p-value',1,TRUE)
a<-table.element(a,formatC(signif(1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]),6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
a<-table.element(a,formatC(signif(mysum$sigma,6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
a<-table.element(a,formatC(signif(sum(myerror*myerror),6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
myr <- as.numeric(mysum$resid)
myr
a <-table.start()
a <- table.row.start(a)
a <- table.element(a,'Menu of Residual Diagnostics',2,TRUE)
a <- table.row.end(a)
a <- table.row.start(a)
a <- table.element(a,'Description',1,TRUE)
a <- table.element(a,'Link',1,TRUE)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Histogram',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_histogram.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Central Tendency',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_centraltendency.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'QQ Plot',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_fitdistrnorm.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Kernel Density Plot',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_density.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Skewness/Kurtosis Test',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_skewness_kurtosis.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Skewness-Kurtosis Plot',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_skewness_kurtosis_plot.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Harrell-Davis Plot',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_harrell_davis.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Bootstrap Plot -- Central Tendency',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_bootstrapplot1.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Blocked Bootstrap Plot -- Central Tendency',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_bootstrapplot.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'(Partial) Autocorrelation Plot',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_autocorrelation.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Spectral Analysis',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_spectrum.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Tukey lambda PPCC Plot',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_tukeylambda.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Box-Cox Normality Plot',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_boxcoxnorm.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <- table.element(a,'Summary Statistics',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_summary1.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable7.tab')
if(n < 200) {
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,formatC(signif(x[i],6),format='g',flag=' '))
a<-table.element(a,formatC(signif(x[i]-mysum$resid[i],6),format='g',flag=' '))
a<-table.element(a,formatC(signif(mysum$resid[i],6),format='g',flag=' '))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable4.tab')
if (n > n25) {
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'p-values',header=TRUE)
a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'breakpoint index',header=TRUE)
a<-table.element(a,'greater',header=TRUE)
a<-table.element(a,'2-sided',header=TRUE)
a<-table.element(a,'less',header=TRUE)
a<-table.row.end(a)
for (mypoint in kp3:nmkm3) {
a<-table.row.start(a)
a<-table.element(a,mypoint,header=TRUE)
a<-table.element(a,formatC(signif(gqarr[mypoint-kp3+1,1],6),format='g',flag=' '))
a<-table.element(a,formatC(signif(gqarr[mypoint-kp3+1,2],6),format='g',flag=' '))
a<-table.element(a,formatC(signif(gqarr[mypoint-kp3+1,3],6),format='g',flag=' '))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable5.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Description',header=TRUE)
a<-table.element(a,'# significant tests',header=TRUE)
a<-table.element(a,'% significant tests',header=TRUE)
a<-table.element(a,'OK/NOK',header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'1% type I error level',header=TRUE)
a<-table.element(a,signif(numsignificant1,6))
a<-table.element(a,formatC(signif(numsignificant1/numgqtests,6),format='g',flag=' '))
if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'5% type I error level',header=TRUE)
a<-table.element(a,signif(numsignificant5,6))
a<-table.element(a,signif(numsignificant5/numgqtests,6))
if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'10% type I error level',header=TRUE)
a<-table.element(a,signif(numsignificant10,6))
a<-table.element(a,signif(numsignificant10/numgqtests,6))
if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable6.tab')
}
}
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Ramsey RESET F-Test for powers (2 and 3) of fitted values',1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
reset_test_fitted <- resettest(mylm,power=2:3,type='fitted')
a<-table.element(a,paste('',RC.texteval('reset_test_fitted'),'
',sep=''))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Ramsey RESET F-Test for powers (2 and 3) of regressors',1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
reset_test_regressors <- resettest(mylm,power=2:3,type='regressor')
a<-table.element(a,paste('',RC.texteval('reset_test_regressors'),'
',sep=''))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Ramsey RESET F-Test for powers (2 and 3) of principal components',1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
reset_test_principal_components <- resettest(mylm,power=2:3,type='princomp')
a<-table.element(a,paste('',RC.texteval('reset_test_principal_components'),'
',sep=''))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable8.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Variance Inflation Factors (Multicollinearity)',1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
vif <- vif(mylm)
a<-table.element(a,paste('',RC.texteval('vif'),'
',sep=''))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable9.tab')

Free Statistics

Description of Statistical Computation

Tree of Dependent Computations

Dataset

Tables (Output of Computation)

Figures (Output of Computation)

Input Parameters & R Code