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R Software Module

rwasp_multipleregression.wasp

Title produced by software

Multiple Regression

Date of computation

Wed, 19 Dec 2018 21:49:14 +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/t15452538884rj4v6bhibvq6ky.htm/, Retrieved Mon, 29 Apr 2024 18:58:12 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=316117, Retrieved Mon, 29 Apr 2024 18:58:12 +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 20:49:14] [a77d3f185bf8346aeb8631871e5ed689] [Current]

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Dataseries X:

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0.3 29.82 0.46 614.66
0.78 3.16 0.73 4534.37
0.6 38.48 0.73 5430.57
0.33 20.82 0.52 4665.91
NA 0.09 0.78 13205.1
0.78 41.09 0.83 13540
0.74 2.97 0.73 3426.39
NA 0.1 NA NA
2.68 23.05 0.93 66604.2
0.82 8.46 0.88 51274.1
0.66 9.31 0.75 7106.04
0.97 0.37 0.78 22647.3
0.52 1.32 0.82 24299
0.29 154.7 0.56 857.5
0.56 0.28 0.79 15722.8
1.32 9.4 0.8 6300.45
1.15 11.06 0.89 48053.3
0.49 10.05 0.48 746.83
NA 0.06 NA 70626.3
0.5 0.74 0.59 2395
0.37 10.5 0.65 2253.09
0.63 3.83 0.73 4708.85
0.3 2 0.69 7743.5
0.62 198.66 0.75 13237.6
NA 0.03 NA NA
0.31 0.41 0.85 47097.4
0.6 7.28 0.78 7615.28
0.47 16.46 0.39 671.07
0.21 9.85 0.39 276.69
NA 0.49 0.64 3801.45
NA 14.86 0.55 877.64
0.54 21.7 0.5 1271.21
1.46 34.84 0.91 52145.4
0.36 0.06 NA NA
0.3 4.53 0.37 495.04
0.36 12.45 0.39 1161.22
0.61 17.46 0.83 14525.8
0.55 1408.04 0.72 5560.94
0.35 47.7 0.72 7305.22
0.33 0.72 0.5 860.24
0.22 4.34 0.57 1943.69
0.15 65.7 0.42 338.63
0.4 4.8 0.76 8979.96
0.51 19.84 NA 1016.83
0.74 4.31 0.82 14522.8
0.48 11.27 0.77 5175.94
0.77 1.13 0.85 31454.7
0.62 10.66 0.87 21676.3
1.18 5.6 0.92 61413.6
NA 0.86 0.46 1433.17
0.64 0.07 0.72 7088.01
0.35 10.28 0.71 6085.89
0.3 15.49 0.73 5192.88
0.68 80.72 0.69 2930.33
0.44 6.3 0.66 3696.33
0.27 0.74 0.58 24064
0.1 6.13 0.39 439.73
NA 1.29 0.85 17304.4
0.31 91.73 0.43 379.38
0.55 0.88 0.72 4201.37
NA 5.41 0.88 50960.2
1.23 63.98 0.89 45430.3
0.07 0.24 NA NA
0.75 0.27 NA NA
0.53 1.63 0.67 11989
0.46 1.79 0.44 505.76
0.39 4.36 0.75 3710.7
1.1 82.8 0.91 46822.4
0.56 25.37 0.57 1627.9
1.07 11.12 0.86 25987.4
NA 0.1 0.74 7410.48
0.11 0.46 NA NA
0.37 15.08 0.62 3233.8
0.39 11.45 0.41 459.09
0.35 1.66 0.42 681.25
0.7 0.8 0.63 3269.46
0.27 10.17 0.48 749.13
0.28 7.94 0.61 2269.51
0.42 9.98 0.82 13964.2
0.34 1236.69 0.6 1513.85
0.44 246.86 0.68 3688.53
0.69 76.42 0.76 7511.1
0.43 32.78 0.65 5848.54
1.08 4.58 0.91 52853.6
0.89 7.64 0.89 33718.9
0.91 60.92 0.87 38412
0.41 2.77 0.72 5226.3
0.53 127.25 0.89 46201.6
0.54 7.01 0.75 4615.17
0.58 16.27 0.78 11278
0.25 43.18 0.54 1062.11
0.28 24.76 NA NA
0.71 49 0.89 24155.8
0.55 3.25 0.82 41830.5
0.59 5.47 0.65 1116.37
0.57 6.65 0.56 1236.24
2.28 2.06 0.81 13732
0.67 4.65 0.76 9143.86
0.22 2.05 0.48 1338.42
0.23 4.19 0.42 397.38
0.79 6.16 0.74 5859.43
1.89 3.03 0.83 14373.7
1.1 0.52 0.89 114665
0.62 2.11 0.74 5174.89
0.27 22.29 0.51 456.33
0.43 15.91 0.43 493.84
0.67 29.24 0.77 10252.6
0.52 14.85 0.41 741.22
0.13 0.4 NA NA
0.39 3.8 0.5 1524.39
0.52 1.24 0.77 8811.15
0.55 120.85 0.75 10123.9
0.43 3.51 0.68 1971.03
0.29 2.8 0.71 3736.07
0.64 0.62 0.8 7251.6
NA 0 NA NA
0.6 32.52 0.62 3149.43
0.31 25.2 0.41 538.82
0.8 52.8 0.53 1117.58
0.33 2.26 0.62 5880.8
NA 0.01 NA NA
0.43 27.47 0.54 700.07
0.76 16.71 0.92 53589.9
0.68 0.25 NA NA
0.63 4.46 0.91 37488.3
0.34 5.99 0.63 1626.85
0.67 17.16 0.34 410.91
0.53 168.83 0.5 2612.12
NA 4.99 0.94 100172
0.57 3.31 0.79 22622.8
0.27 179.16 0.53 1218.6
0.36 3.8 0.77 8410.77
0.3 7.17 0.5 1871.21
1.11 6.69 0.67 3557.31
0.5 29.99 0.73 5684.73
0.36 96.71 0.66 2379.44
0.84 38.21 0.84 13769.5
1.03 10.6 0.83 23217.3
0.57 2.05 0.85 99431.5
0.14 0.86 NA NA
0.72 21.76 0.79 9213.94
0.77 143.17 0.79 13320.2
0.43 11.46 0.48 628.08
0.51 0.05 0.74 12952.5
0.38 0.18 0.73 7737.2
NA 0.11 0.72 6171.48
0.97 0.19 0.7 4067.15
0.36 0.19 0.55 1384.53
0.74 28.29 0.83 23593.8
0.34 13.73 0.46 1079.27
0.49 9.55 0.76 6426.18
0.47 5.98 0.4 499.89
0.67 5.3 0.91 53122.4
0.31 5.45 0.84 18103.1
0.64 2.07 0.88 25040.5
0.47 0.55 0.5 1647.86
0.16 10.2 NA NA
0.44 52.39 0.66 8089.87
0.78 46.76 0.87 32008.7
0.31 21.1 0.75 2880.03
0.43 0.54 0.71 8190.7
0.35 1.23 0.53 4657.48
1.47 9.51 0.9 59381.9
0.75 8 0.93 88506.2
0.52 21.89 0.62 NA
0.46 8.01 0.62 836.17
0.44 47.78 0.51 765.33
0.67 66.78 0.72 5479.29
0.25 1.11 0.6 5167.86
0.34 6.64 0.47 580.86
1.19 0.1 0.72 4330.9
0.46 1.34 0.77 18310.8
0.76 10.88 0.72 4305.07
0.87 74 0.76 10437.7
0.73 5.17 0.68 5290.14
0.34 36.35 0.48 601.35
0.62 45.53 0.74 3589.63
0.82 63.03 0.9 40980.5
0.8 9.206 0.83 40817.4
1.13 317.5 0.91 49725
0.19 3.4 0.79 14238.1
0.62 28.54 0.67 1560.85
0.45 29.96 0.763846 10237.8
0.5 90.8 0.66 1532.31
NA 0.01 NA NA
0.34 23.85 0.5 1302.3
0.19 14.08 0.58 1740.64
0.2 13.72 0.49 865.91

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	16 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 time16 seconds \tabularnewline
R ServerBig Analytics Cloud Computing Center \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=316117&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]

16 seconds[/C][/ROW] [ROW]

R Server[/C]

Big Analytics Cloud Computing Center[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=316117&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=316117&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	16 seconds
R Server	Big Analytics Cloud Computing Center

Multiple Linear Regression - Estimated Regression Equation
Cropland_Footprint[t] = -0.106538 -5.18308e-05`Population_(millions)`[t] + 0.947745HDI[t] + 4.04396e-06GDP_per_Capita[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Cropland_Footprint[t] =  -0.106538 -5.18308e-05`Population_(millions)`[t] +  0.947745HDI[t] +  4.04396e-06GDP_per_Capita[t]  + e[t] \tabularnewline
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=316117&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Cropland_Footprint[t] =  -0.106538 -5.18308e-05`Population_(millions)`[t] +  0.947745HDI[t] +  4.04396e-06GDP_per_Capita[t]  + e[t][/C][/ROW]
[ROW][C][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=316117&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=316117&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
Cropland_Footprint[t] = -0.106538 -5.18308e-05`Population_(millions)`[t] + 0.947745HDI[t] + 4.04396e-06GDP_per_Capita[t] + e[t]

Multiple Linear Regression - Ordinary Least Squares
Variable	Parameter	S.D.	T-STATH0: parameter = 0	2-tail p-value	1-tail p-value
(Intercept)	-0.1065	0.1247	-8.5450e-01	0.3941	0.197
`Population_(millions)`	-5.183e-05	0.0001519	-3.4130e-01	0.7333	0.3667
HDI	+0.9477	0.1989	+4.7660e+00	4.239e-06	2.12e-06
GDP_per_Capita	+4.044e-06	1.581e-06	+2.5590e+00	0.01145	0.005726

\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) & -0.1065 &  0.1247 & -8.5450e-01 &  0.3941 &  0.197 \tabularnewline
`Population_(millions)` & -5.183e-05 &  0.0001519 & -3.4130e-01 &  0.7333 &  0.3667 \tabularnewline
HDI & +0.9477 &  0.1989 & +4.7660e+00 &  4.239e-06 &  2.12e-06 \tabularnewline
GDP_per_Capita & +4.044e-06 &  1.581e-06 & +2.5590e+00 &  0.01145 &  0.005726 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=316117&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]-0.1065[/C][C] 0.1247[/C][C]-8.5450e-01[/C][C] 0.3941[/C][C] 0.197[/C][/ROW]
[ROW][C]`Population_(millions)`[/C][C]-5.183e-05[/C][C] 0.0001519[/C][C]-3.4130e-01[/C][C] 0.7333[/C][C] 0.3667[/C][/ROW]
[ROW][C]HDI[/C][C]+0.9477[/C][C] 0.1989[/C][C]+4.7660e+00[/C][C] 4.239e-06[/C][C] 2.12e-06[/C][/ROW]
[ROW][C]GDP_per_Capita[/C][C]+4.044e-06[/C][C] 1.581e-06[/C][C]+2.5590e+00[/C][C] 0.01145[/C][C] 0.005726[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=316117&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=316117&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)	-0.1065	0.1247	-8.5450e-01	0.3941	0.197
`Population_(millions)`	-5.183e-05	0.0001519	-3.4130e-01	0.7333	0.3667
HDI	+0.9477	0.1989	+4.7660e+00	4.239e-06	2.12e-06
GDP_per_Capita	+4.044e-06	1.581e-06	+2.5590e+00	0.01145	0.005726

Multiple Linear Regression - Regression Statistics
Multiple R	0.5914
R-squared	0.3497
Adjusted R-squared	0.3374
F-TEST (value)	28.32
F-TEST (DF numerator)	3
F-TEST (DF denominator)	158
p-value	1.044e-14
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	0.2904
Sum Squared Residuals	13.32

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R &  0.5914 \tabularnewline
R-squared &  0.3497 \tabularnewline
Adjusted R-squared &  0.3374 \tabularnewline
F-TEST (value) &  28.32 \tabularnewline
F-TEST (DF numerator) & 3 \tabularnewline
F-TEST (DF denominator) & 158 \tabularnewline
p-value &  1.044e-14 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation &  0.2904 \tabularnewline
Sum Squared Residuals &  13.32 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=316117&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C] 0.5914[/C][/ROW]
[ROW][C]R-squared[/C][C] 0.3497[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C] 0.3374[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C] 28.32[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]3[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]158[/C][/ROW]
[ROW][C]p-value[/C][C] 1.044e-14[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C] 0.2904[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C] 13.32[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=316117&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=316117&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	0.5914
R-squared	0.3497
Adjusted R-squared	0.3374
F-TEST (value)	28.32
F-TEST (DF numerator)	3
F-TEST (DF denominator)	158
p-value	1.044e-14
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	0.2904
Sum Squared Residuals	13.32

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=316117&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=316117&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=316117&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	0.3	0.3304	-0.03036
2	0.78	0.6035	0.1765
3	0.6	0.6053	-0.005283
4	0.33	0.4041	-0.07408
5	0.78	0.7327	0.04728
6	0.74	0.599	0.141
7	2.68	1.043	1.637
8	0.82	0.9344	-0.1144
9	0.66	0.6325	0.02748
10	0.97	0.7243	0.2457
11	0.52	0.7688	-0.2488
12	0.29	0.4196	-0.1296
13	0.56	0.7057	-0.1457
14	1.32	0.6766	0.6433
15	1.15	0.9307	0.2193
16	0.49	0.3509	0.1391
17	0.5	0.4623	0.03772
18	0.37	0.5181	-0.1481
19	0.63	0.6042	0.02584
20	0.3	0.5786	-0.2786
21	0.62	0.6475	-0.02751
22	0.31	0.8895	-0.5795
23	0.6	0.6631	-0.06312
24	0.47	0.2649	0.2051
25	0.21	0.2637	-0.05369
26	0.54	0.3714	0.1686
27	1.46	0.965	0.495
28	0.3	0.2459	0.05411
29	0.36	0.2671	0.09287
30	0.61	0.7379	-0.1279
31	0.55	0.5253	0.02465
32	0.35	0.6029	-0.2529
33	0.33	0.3708	-0.04078
34	0.22	0.4413	-0.2213
35	0.15	0.2895	-0.1395
36	0.4	0.6498	-0.2498
37	0.74	0.7291	0.01088
38	0.48	0.6436	-0.1636
39	0.77	0.8262	-0.05619
40	0.62	0.8051	-0.1851
41	1.18	1.013	0.1665
42	0.64	0.6045	0.0355
43	0.35	0.5904	-0.2404
44	0.3	0.6055	-0.3055
45	0.68	0.5551	0.1249
46	0.44	0.5336	-0.0936
47	0.27	0.5404	-0.2704
48	0.1	0.2645	-0.1645
49	0.31	0.2978	0.01223
50	0.55	0.5928	-0.04278
51	1.23	0.9174	0.3126
52	0.53	0.5768	-0.04685
53	0.46	0.3124	0.1476
54	0.39	0.6191	-0.2291
55	1.1	0.941	0.159
56	0.56	0.4389	0.1211
57	1.07	0.813	0.257
58	0.37	0.4934	-0.1234
59	0.39	0.2833	0.1067
60	0.35	0.2942	0.05582
61	0.7	0.5037	0.1963
62	0.27	0.3509	-0.08088
63	0.28	0.4804	-0.2004
64	0.42	0.7266	-0.3066
65	0.34	0.4041	-0.06413
66	0.44	0.5401	-0.1001
67	0.69	0.6402	0.04984
68	0.43	0.5314	-0.1014
69	1.08	0.9694	0.1106
70	0.89	0.8729	0.01708
71	0.91	0.8702	0.03982
72	0.41	0.5968	-0.1868
73	0.53	0.9172	-0.3872
74	0.54	0.6226	-0.08257
75	0.58	0.6775	-0.09747
76	0.25	0.4073	-0.1573
77	0.71	0.8321	-0.1221
78	0.55	0.8396	-0.2896
79	0.59	0.5137	0.07627
80	0.57	0.4289	0.1411
81	2.28	0.7166	1.563
82	0.67	0.6505	0.01952
83	0.22	0.3537	-0.1337
84	0.23	0.2929	-0.0629
85	0.79	0.6182	0.1718
86	1.89	0.7381	1.152
87	1.1	1.201	-0.1006
88	0.62	0.6156	0.004389
89	0.27	0.3775	-0.1075
90	0.43	0.3022	0.1278
91	0.67	0.6632	0.006829
92	0.52	0.2843	0.2357
93	0.39	0.3733	0.0167
94	0.52	0.6588	-0.1388
95	0.55	0.6389	-0.08895
96	0.43	0.5457	-0.1157
97	0.29	0.5813	-0.2913
98	0.64	0.681	-0.04095
99	0.6	0.4921	0.1079
100	0.31	0.2829	0.02709
101	0.8	0.3976	0.4024
102	0.33	0.5047	-0.1747
103	0.43	0.4067	0.02335
104	0.76	0.9812	-0.2212
105	0.63	0.9073	-0.2773
106	0.34	0.4968	-0.1568
107	0.67	0.2165	0.4535
108	0.53	0.3691	0.1609
109	0.57	0.7335	-0.1635
110	0.27	0.3914	-0.1214
111	0.36	0.657	-0.297
112	0.3	0.3745	-0.07453
113	1.11	0.5425	0.5675
114	0.5	0.6068	-0.1067
115	0.36	0.5236	-0.1636
116	0.84	0.7433	0.09673
117	1.03	0.7734	0.2566
118	0.57	1.101	-0.531
119	0.72	0.6783	0.04169
120	0.77	0.6886	0.08137
121	0.43	0.3503	0.07967
122	0.51	0.6472	-0.1372
123	0.38	0.6166	-0.2366
124	0.97	0.5733	0.3967
125	0.36	0.4203	-0.06031
126	0.74	0.774	-0.03404
127	0.34	0.3331	0.006922
128	0.49	0.6392	-0.1492
129	0.47	0.2743	0.1957
130	0.67	0.9705	-0.3005
131	0.31	0.7625	-0.4525
132	0.64	0.8286	-0.1886
133	0.47	0.374	0.09603
134	0.44	0.549	-0.109
135	0.78	0.845	-0.06502
136	0.31	0.6148	-0.3048
137	0.43	0.5995	-0.1695
138	0.35	0.4145	-0.06454
139	1.47	0.9861	0.4839
140	0.75	1.132	-0.3824
141	0.46	0.484	-0.02403
142	0.44	0.3774	0.06257
143	0.67	0.5945	0.07546
144	0.25	0.4829	-0.2329
145	0.34	0.3409	-0.000907
146	1.19	0.5933	0.5967
147	0.46	0.6972	-0.2372
148	0.76	0.5927	0.1673
149	0.87	0.6521	0.2179
150	0.73	0.5591	0.1709
151	0.34	0.3489	-0.008927
152	0.62	0.6069	0.01305
153	0.82	0.9089	-0.08889
154	0.8	0.8447	-0.04468
155	1.13	0.9405	0.1895
156	0.19	0.6996	-0.5096
157	0.62	0.5333	0.08672
158	0.45	0.6572	-0.2072
159	0.5	0.5205	-0.02046
160	0.34	0.3714	-0.03136
161	0.19	0.4495	-0.2595
162	0.2	0.3606	-0.1606

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 &  0.3 &  0.3304 & -0.03036 \tabularnewline
2 &  0.78 &  0.6035 &  0.1765 \tabularnewline
3 &  0.6 &  0.6053 & -0.005283 \tabularnewline
4 &  0.33 &  0.4041 & -0.07408 \tabularnewline
5 &  0.78 &  0.7327 &  0.04728 \tabularnewline
6 &  0.74 &  0.599 &  0.141 \tabularnewline
7 &  2.68 &  1.043 &  1.637 \tabularnewline
8 &  0.82 &  0.9344 & -0.1144 \tabularnewline
9 &  0.66 &  0.6325 &  0.02748 \tabularnewline
10 &  0.97 &  0.7243 &  0.2457 \tabularnewline
11 &  0.52 &  0.7688 & -0.2488 \tabularnewline
12 &  0.29 &  0.4196 & -0.1296 \tabularnewline
13 &  0.56 &  0.7057 & -0.1457 \tabularnewline
14 &  1.32 &  0.6766 &  0.6433 \tabularnewline
15 &  1.15 &  0.9307 &  0.2193 \tabularnewline
16 &  0.49 &  0.3509 &  0.1391 \tabularnewline
17 &  0.5 &  0.4623 &  0.03772 \tabularnewline
18 &  0.37 &  0.5181 & -0.1481 \tabularnewline
19 &  0.63 &  0.6042 &  0.02584 \tabularnewline
20 &  0.3 &  0.5786 & -0.2786 \tabularnewline
21 &  0.62 &  0.6475 & -0.02751 \tabularnewline
22 &  0.31 &  0.8895 & -0.5795 \tabularnewline
23 &  0.6 &  0.6631 & -0.06312 \tabularnewline
24 &  0.47 &  0.2649 &  0.2051 \tabularnewline
25 &  0.21 &  0.2637 & -0.05369 \tabularnewline
26 &  0.54 &  0.3714 &  0.1686 \tabularnewline
27 &  1.46 &  0.965 &  0.495 \tabularnewline
28 &  0.3 &  0.2459 &  0.05411 \tabularnewline
29 &  0.36 &  0.2671 &  0.09287 \tabularnewline
30 &  0.61 &  0.7379 & -0.1279 \tabularnewline
31 &  0.55 &  0.5253 &  0.02465 \tabularnewline
32 &  0.35 &  0.6029 & -0.2529 \tabularnewline
33 &  0.33 &  0.3708 & -0.04078 \tabularnewline
34 &  0.22 &  0.4413 & -0.2213 \tabularnewline
35 &  0.15 &  0.2895 & -0.1395 \tabularnewline
36 &  0.4 &  0.6498 & -0.2498 \tabularnewline
37 &  0.74 &  0.7291 &  0.01088 \tabularnewline
38 &  0.48 &  0.6436 & -0.1636 \tabularnewline
39 &  0.77 &  0.8262 & -0.05619 \tabularnewline
40 &  0.62 &  0.8051 & -0.1851 \tabularnewline
41 &  1.18 &  1.013 &  0.1665 \tabularnewline
42 &  0.64 &  0.6045 &  0.0355 \tabularnewline
43 &  0.35 &  0.5904 & -0.2404 \tabularnewline
44 &  0.3 &  0.6055 & -0.3055 \tabularnewline
45 &  0.68 &  0.5551 &  0.1249 \tabularnewline
46 &  0.44 &  0.5336 & -0.0936 \tabularnewline
47 &  0.27 &  0.5404 & -0.2704 \tabularnewline
48 &  0.1 &  0.2645 & -0.1645 \tabularnewline
49 &  0.31 &  0.2978 &  0.01223 \tabularnewline
50 &  0.55 &  0.5928 & -0.04278 \tabularnewline
51 &  1.23 &  0.9174 &  0.3126 \tabularnewline
52 &  0.53 &  0.5768 & -0.04685 \tabularnewline
53 &  0.46 &  0.3124 &  0.1476 \tabularnewline
54 &  0.39 &  0.6191 & -0.2291 \tabularnewline
55 &  1.1 &  0.941 &  0.159 \tabularnewline
56 &  0.56 &  0.4389 &  0.1211 \tabularnewline
57 &  1.07 &  0.813 &  0.257 \tabularnewline
58 &  0.37 &  0.4934 & -0.1234 \tabularnewline
59 &  0.39 &  0.2833 &  0.1067 \tabularnewline
60 &  0.35 &  0.2942 &  0.05582 \tabularnewline
61 &  0.7 &  0.5037 &  0.1963 \tabularnewline
62 &  0.27 &  0.3509 & -0.08088 \tabularnewline
63 &  0.28 &  0.4804 & -0.2004 \tabularnewline
64 &  0.42 &  0.7266 & -0.3066 \tabularnewline
65 &  0.34 &  0.4041 & -0.06413 \tabularnewline
66 &  0.44 &  0.5401 & -0.1001 \tabularnewline
67 &  0.69 &  0.6402 &  0.04984 \tabularnewline
68 &  0.43 &  0.5314 & -0.1014 \tabularnewline
69 &  1.08 &  0.9694 &  0.1106 \tabularnewline
70 &  0.89 &  0.8729 &  0.01708 \tabularnewline
71 &  0.91 &  0.8702 &  0.03982 \tabularnewline
72 &  0.41 &  0.5968 & -0.1868 \tabularnewline
73 &  0.53 &  0.9172 & -0.3872 \tabularnewline
74 &  0.54 &  0.6226 & -0.08257 \tabularnewline
75 &  0.58 &  0.6775 & -0.09747 \tabularnewline
76 &  0.25 &  0.4073 & -0.1573 \tabularnewline
77 &  0.71 &  0.8321 & -0.1221 \tabularnewline
78 &  0.55 &  0.8396 & -0.2896 \tabularnewline
79 &  0.59 &  0.5137 &  0.07627 \tabularnewline
80 &  0.57 &  0.4289 &  0.1411 \tabularnewline
81 &  2.28 &  0.7166 &  1.563 \tabularnewline
82 &  0.67 &  0.6505 &  0.01952 \tabularnewline
83 &  0.22 &  0.3537 & -0.1337 \tabularnewline
84 &  0.23 &  0.2929 & -0.0629 \tabularnewline
85 &  0.79 &  0.6182 &  0.1718 \tabularnewline
86 &  1.89 &  0.7381 &  1.152 \tabularnewline
87 &  1.1 &  1.201 & -0.1006 \tabularnewline
88 &  0.62 &  0.6156 &  0.004389 \tabularnewline
89 &  0.27 &  0.3775 & -0.1075 \tabularnewline
90 &  0.43 &  0.3022 &  0.1278 \tabularnewline
91 &  0.67 &  0.6632 &  0.006829 \tabularnewline
92 &  0.52 &  0.2843 &  0.2357 \tabularnewline
93 &  0.39 &  0.3733 &  0.0167 \tabularnewline
94 &  0.52 &  0.6588 & -0.1388 \tabularnewline
95 &  0.55 &  0.6389 & -0.08895 \tabularnewline
96 &  0.43 &  0.5457 & -0.1157 \tabularnewline
97 &  0.29 &  0.5813 & -0.2913 \tabularnewline
98 &  0.64 &  0.681 & -0.04095 \tabularnewline
99 &  0.6 &  0.4921 &  0.1079 \tabularnewline
100 &  0.31 &  0.2829 &  0.02709 \tabularnewline
101 &  0.8 &  0.3976 &  0.4024 \tabularnewline
102 &  0.33 &  0.5047 & -0.1747 \tabularnewline
103 &  0.43 &  0.4067 &  0.02335 \tabularnewline
104 &  0.76 &  0.9812 & -0.2212 \tabularnewline
105 &  0.63 &  0.9073 & -0.2773 \tabularnewline
106 &  0.34 &  0.4968 & -0.1568 \tabularnewline
107 &  0.67 &  0.2165 &  0.4535 \tabularnewline
108 &  0.53 &  0.3691 &  0.1609 \tabularnewline
109 &  0.57 &  0.7335 & -0.1635 \tabularnewline
110 &  0.27 &  0.3914 & -0.1214 \tabularnewline
111 &  0.36 &  0.657 & -0.297 \tabularnewline
112 &  0.3 &  0.3745 & -0.07453 \tabularnewline
113 &  1.11 &  0.5425 &  0.5675 \tabularnewline
114 &  0.5 &  0.6068 & -0.1067 \tabularnewline
115 &  0.36 &  0.5236 & -0.1636 \tabularnewline
116 &  0.84 &  0.7433 &  0.09673 \tabularnewline
117 &  1.03 &  0.7734 &  0.2566 \tabularnewline
118 &  0.57 &  1.101 & -0.531 \tabularnewline
119 &  0.72 &  0.6783 &  0.04169 \tabularnewline
120 &  0.77 &  0.6886 &  0.08137 \tabularnewline
121 &  0.43 &  0.3503 &  0.07967 \tabularnewline
122 &  0.51 &  0.6472 & -0.1372 \tabularnewline
123 &  0.38 &  0.6166 & -0.2366 \tabularnewline
124 &  0.97 &  0.5733 &  0.3967 \tabularnewline
125 &  0.36 &  0.4203 & -0.06031 \tabularnewline
126 &  0.74 &  0.774 & -0.03404 \tabularnewline
127 &  0.34 &  0.3331 &  0.006922 \tabularnewline
128 &  0.49 &  0.6392 & -0.1492 \tabularnewline
129 &  0.47 &  0.2743 &  0.1957 \tabularnewline
130 &  0.67 &  0.9705 & -0.3005 \tabularnewline
131 &  0.31 &  0.7625 & -0.4525 \tabularnewline
132 &  0.64 &  0.8286 & -0.1886 \tabularnewline
133 &  0.47 &  0.374 &  0.09603 \tabularnewline
134 &  0.44 &  0.549 & -0.109 \tabularnewline
135 &  0.78 &  0.845 & -0.06502 \tabularnewline
136 &  0.31 &  0.6148 & -0.3048 \tabularnewline
137 &  0.43 &  0.5995 & -0.1695 \tabularnewline
138 &  0.35 &  0.4145 & -0.06454 \tabularnewline
139 &  1.47 &  0.9861 &  0.4839 \tabularnewline
140 &  0.75 &  1.132 & -0.3824 \tabularnewline
141 &  0.46 &  0.484 & -0.02403 \tabularnewline
142 &  0.44 &  0.3774 &  0.06257 \tabularnewline
143 &  0.67 &  0.5945 &  0.07546 \tabularnewline
144 &  0.25 &  0.4829 & -0.2329 \tabularnewline
145 &  0.34 &  0.3409 & -0.000907 \tabularnewline
146 &  1.19 &  0.5933 &  0.5967 \tabularnewline
147 &  0.46 &  0.6972 & -0.2372 \tabularnewline
148 &  0.76 &  0.5927 &  0.1673 \tabularnewline
149 &  0.87 &  0.6521 &  0.2179 \tabularnewline
150 &  0.73 &  0.5591 &  0.1709 \tabularnewline
151 &  0.34 &  0.3489 & -0.008927 \tabularnewline
152 &  0.62 &  0.6069 &  0.01305 \tabularnewline
153 &  0.82 &  0.9089 & -0.08889 \tabularnewline
154 &  0.8 &  0.8447 & -0.04468 \tabularnewline
155 &  1.13 &  0.9405 &  0.1895 \tabularnewline
156 &  0.19 &  0.6996 & -0.5096 \tabularnewline
157 &  0.62 &  0.5333 &  0.08672 \tabularnewline
158 &  0.45 &  0.6572 & -0.2072 \tabularnewline
159 &  0.5 &  0.5205 & -0.02046 \tabularnewline
160 &  0.34 &  0.3714 & -0.03136 \tabularnewline
161 &  0.19 &  0.4495 & -0.2595 \tabularnewline
162 &  0.2 &  0.3606 & -0.1606 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=316117&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] 0.3[/C][C] 0.3304[/C][C]-0.03036[/C][/ROW]
[ROW][C]2[/C][C] 0.78[/C][C] 0.6035[/C][C] 0.1765[/C][/ROW]
[ROW][C]3[/C][C] 0.6[/C][C] 0.6053[/C][C]-0.005283[/C][/ROW]
[ROW][C]4[/C][C] 0.33[/C][C] 0.4041[/C][C]-0.07408[/C][/ROW]
[ROW][C]5[/C][C] 0.78[/C][C] 0.7327[/C][C] 0.04728[/C][/ROW]
[ROW][C]6[/C][C] 0.74[/C][C] 0.599[/C][C] 0.141[/C][/ROW]
[ROW][C]7[/C][C] 2.68[/C][C] 1.043[/C][C] 1.637[/C][/ROW]
[ROW][C]8[/C][C] 0.82[/C][C] 0.9344[/C][C]-0.1144[/C][/ROW]
[ROW][C]9[/C][C] 0.66[/C][C] 0.6325[/C][C] 0.02748[/C][/ROW]
[ROW][C]10[/C][C] 0.97[/C][C] 0.7243[/C][C] 0.2457[/C][/ROW]
[ROW][C]11[/C][C] 0.52[/C][C] 0.7688[/C][C]-0.2488[/C][/ROW]
[ROW][C]12[/C][C] 0.29[/C][C] 0.4196[/C][C]-0.1296[/C][/ROW]
[ROW][C]13[/C][C] 0.56[/C][C] 0.7057[/C][C]-0.1457[/C][/ROW]
[ROW][C]14[/C][C] 1.32[/C][C] 0.6766[/C][C] 0.6433[/C][/ROW]
[ROW][C]15[/C][C] 1.15[/C][C] 0.9307[/C][C] 0.2193[/C][/ROW]
[ROW][C]16[/C][C] 0.49[/C][C] 0.3509[/C][C] 0.1391[/C][/ROW]
[ROW][C]17[/C][C] 0.5[/C][C] 0.4623[/C][C] 0.03772[/C][/ROW]
[ROW][C]18[/C][C] 0.37[/C][C] 0.5181[/C][C]-0.1481[/C][/ROW]
[ROW][C]19[/C][C] 0.63[/C][C] 0.6042[/C][C] 0.02584[/C][/ROW]
[ROW][C]20[/C][C] 0.3[/C][C] 0.5786[/C][C]-0.2786[/C][/ROW]
[ROW][C]21[/C][C] 0.62[/C][C] 0.6475[/C][C]-0.02751[/C][/ROW]
[ROW][C]22[/C][C] 0.31[/C][C] 0.8895[/C][C]-0.5795[/C][/ROW]
[ROW][C]23[/C][C] 0.6[/C][C] 0.6631[/C][C]-0.06312[/C][/ROW]
[ROW][C]24[/C][C] 0.47[/C][C] 0.2649[/C][C] 0.2051[/C][/ROW]
[ROW][C]25[/C][C] 0.21[/C][C] 0.2637[/C][C]-0.05369[/C][/ROW]
[ROW][C]26[/C][C] 0.54[/C][C] 0.3714[/C][C] 0.1686[/C][/ROW]
[ROW][C]27[/C][C] 1.46[/C][C] 0.965[/C][C] 0.495[/C][/ROW]
[ROW][C]28[/C][C] 0.3[/C][C] 0.2459[/C][C] 0.05411[/C][/ROW]
[ROW][C]29[/C][C] 0.36[/C][C] 0.2671[/C][C] 0.09287[/C][/ROW]
[ROW][C]30[/C][C] 0.61[/C][C] 0.7379[/C][C]-0.1279[/C][/ROW]
[ROW][C]31[/C][C] 0.55[/C][C] 0.5253[/C][C] 0.02465[/C][/ROW]
[ROW][C]32[/C][C] 0.35[/C][C] 0.6029[/C][C]-0.2529[/C][/ROW]
[ROW][C]33[/C][C] 0.33[/C][C] 0.3708[/C][C]-0.04078[/C][/ROW]
[ROW][C]34[/C][C] 0.22[/C][C] 0.4413[/C][C]-0.2213[/C][/ROW]
[ROW][C]35[/C][C] 0.15[/C][C] 0.2895[/C][C]-0.1395[/C][/ROW]
[ROW][C]36[/C][C] 0.4[/C][C] 0.6498[/C][C]-0.2498[/C][/ROW]
[ROW][C]37[/C][C] 0.74[/C][C] 0.7291[/C][C] 0.01088[/C][/ROW]
[ROW][C]38[/C][C] 0.48[/C][C] 0.6436[/C][C]-0.1636[/C][/ROW]
[ROW][C]39[/C][C] 0.77[/C][C] 0.8262[/C][C]-0.05619[/C][/ROW]
[ROW][C]40[/C][C] 0.62[/C][C] 0.8051[/C][C]-0.1851[/C][/ROW]
[ROW][C]41[/C][C] 1.18[/C][C] 1.013[/C][C] 0.1665[/C][/ROW]
[ROW][C]42[/C][C] 0.64[/C][C] 0.6045[/C][C] 0.0355[/C][/ROW]
[ROW][C]43[/C][C] 0.35[/C][C] 0.5904[/C][C]-0.2404[/C][/ROW]
[ROW][C]44[/C][C] 0.3[/C][C] 0.6055[/C][C]-0.3055[/C][/ROW]
[ROW][C]45[/C][C] 0.68[/C][C] 0.5551[/C][C] 0.1249[/C][/ROW]
[ROW][C]46[/C][C] 0.44[/C][C] 0.5336[/C][C]-0.0936[/C][/ROW]
[ROW][C]47[/C][C] 0.27[/C][C] 0.5404[/C][C]-0.2704[/C][/ROW]
[ROW][C]48[/C][C] 0.1[/C][C] 0.2645[/C][C]-0.1645[/C][/ROW]
[ROW][C]49[/C][C] 0.31[/C][C] 0.2978[/C][C] 0.01223[/C][/ROW]
[ROW][C]50[/C][C] 0.55[/C][C] 0.5928[/C][C]-0.04278[/C][/ROW]
[ROW][C]51[/C][C] 1.23[/C][C] 0.9174[/C][C] 0.3126[/C][/ROW]
[ROW][C]52[/C][C] 0.53[/C][C] 0.5768[/C][C]-0.04685[/C][/ROW]
[ROW][C]53[/C][C] 0.46[/C][C] 0.3124[/C][C] 0.1476[/C][/ROW]
[ROW][C]54[/C][C] 0.39[/C][C] 0.6191[/C][C]-0.2291[/C][/ROW]
[ROW][C]55[/C][C] 1.1[/C][C] 0.941[/C][C] 0.159[/C][/ROW]
[ROW][C]56[/C][C] 0.56[/C][C] 0.4389[/C][C] 0.1211[/C][/ROW]
[ROW][C]57[/C][C] 1.07[/C][C] 0.813[/C][C] 0.257[/C][/ROW]
[ROW][C]58[/C][C] 0.37[/C][C] 0.4934[/C][C]-0.1234[/C][/ROW]
[ROW][C]59[/C][C] 0.39[/C][C] 0.2833[/C][C] 0.1067[/C][/ROW]
[ROW][C]60[/C][C] 0.35[/C][C] 0.2942[/C][C] 0.05582[/C][/ROW]
[ROW][C]61[/C][C] 0.7[/C][C] 0.5037[/C][C] 0.1963[/C][/ROW]
[ROW][C]62[/C][C] 0.27[/C][C] 0.3509[/C][C]-0.08088[/C][/ROW]
[ROW][C]63[/C][C] 0.28[/C][C] 0.4804[/C][C]-0.2004[/C][/ROW]
[ROW][C]64[/C][C] 0.42[/C][C] 0.7266[/C][C]-0.3066[/C][/ROW]
[ROW][C]65[/C][C] 0.34[/C][C] 0.4041[/C][C]-0.06413[/C][/ROW]
[ROW][C]66[/C][C] 0.44[/C][C] 0.5401[/C][C]-0.1001[/C][/ROW]
[ROW][C]67[/C][C] 0.69[/C][C] 0.6402[/C][C] 0.04984[/C][/ROW]
[ROW][C]68[/C][C] 0.43[/C][C] 0.5314[/C][C]-0.1014[/C][/ROW]
[ROW][C]69[/C][C] 1.08[/C][C] 0.9694[/C][C] 0.1106[/C][/ROW]
[ROW][C]70[/C][C] 0.89[/C][C] 0.8729[/C][C] 0.01708[/C][/ROW]
[ROW][C]71[/C][C] 0.91[/C][C] 0.8702[/C][C] 0.03982[/C][/ROW]
[ROW][C]72[/C][C] 0.41[/C][C] 0.5968[/C][C]-0.1868[/C][/ROW]
[ROW][C]73[/C][C] 0.53[/C][C] 0.9172[/C][C]-0.3872[/C][/ROW]
[ROW][C]74[/C][C] 0.54[/C][C] 0.6226[/C][C]-0.08257[/C][/ROW]
[ROW][C]75[/C][C] 0.58[/C][C] 0.6775[/C][C]-0.09747[/C][/ROW]
[ROW][C]76[/C][C] 0.25[/C][C] 0.4073[/C][C]-0.1573[/C][/ROW]
[ROW][C]77[/C][C] 0.71[/C][C] 0.8321[/C][C]-0.1221[/C][/ROW]
[ROW][C]78[/C][C] 0.55[/C][C] 0.8396[/C][C]-0.2896[/C][/ROW]
[ROW][C]79[/C][C] 0.59[/C][C] 0.5137[/C][C] 0.07627[/C][/ROW]
[ROW][C]80[/C][C] 0.57[/C][C] 0.4289[/C][C] 0.1411[/C][/ROW]
[ROW][C]81[/C][C] 2.28[/C][C] 0.7166[/C][C] 1.563[/C][/ROW]
[ROW][C]82[/C][C] 0.67[/C][C] 0.6505[/C][C] 0.01952[/C][/ROW]
[ROW][C]83[/C][C] 0.22[/C][C] 0.3537[/C][C]-0.1337[/C][/ROW]
[ROW][C]84[/C][C] 0.23[/C][C] 0.2929[/C][C]-0.0629[/C][/ROW]
[ROW][C]85[/C][C] 0.79[/C][C] 0.6182[/C][C] 0.1718[/C][/ROW]
[ROW][C]86[/C][C] 1.89[/C][C] 0.7381[/C][C] 1.152[/C][/ROW]
[ROW][C]87[/C][C] 1.1[/C][C] 1.201[/C][C]-0.1006[/C][/ROW]
[ROW][C]88[/C][C] 0.62[/C][C] 0.6156[/C][C] 0.004389[/C][/ROW]
[ROW][C]89[/C][C] 0.27[/C][C] 0.3775[/C][C]-0.1075[/C][/ROW]
[ROW][C]90[/C][C] 0.43[/C][C] 0.3022[/C][C] 0.1278[/C][/ROW]
[ROW][C]91[/C][C] 0.67[/C][C] 0.6632[/C][C] 0.006829[/C][/ROW]
[ROW][C]92[/C][C] 0.52[/C][C] 0.2843[/C][C] 0.2357[/C][/ROW]
[ROW][C]93[/C][C] 0.39[/C][C] 0.3733[/C][C] 0.0167[/C][/ROW]
[ROW][C]94[/C][C] 0.52[/C][C] 0.6588[/C][C]-0.1388[/C][/ROW]
[ROW][C]95[/C][C] 0.55[/C][C] 0.6389[/C][C]-0.08895[/C][/ROW]
[ROW][C]96[/C][C] 0.43[/C][C] 0.5457[/C][C]-0.1157[/C][/ROW]
[ROW][C]97[/C][C] 0.29[/C][C] 0.5813[/C][C]-0.2913[/C][/ROW]
[ROW][C]98[/C][C] 0.64[/C][C] 0.681[/C][C]-0.04095[/C][/ROW]
[ROW][C]99[/C][C] 0.6[/C][C] 0.4921[/C][C] 0.1079[/C][/ROW]
[ROW][C]100[/C][C] 0.31[/C][C] 0.2829[/C][C] 0.02709[/C][/ROW]
[ROW][C]101[/C][C] 0.8[/C][C] 0.3976[/C][C] 0.4024[/C][/ROW]
[ROW][C]102[/C][C] 0.33[/C][C] 0.5047[/C][C]-0.1747[/C][/ROW]
[ROW][C]103[/C][C] 0.43[/C][C] 0.4067[/C][C] 0.02335[/C][/ROW]
[ROW][C]104[/C][C] 0.76[/C][C] 0.9812[/C][C]-0.2212[/C][/ROW]
[ROW][C]105[/C][C] 0.63[/C][C] 0.9073[/C][C]-0.2773[/C][/ROW]
[ROW][C]106[/C][C] 0.34[/C][C] 0.4968[/C][C]-0.1568[/C][/ROW]
[ROW][C]107[/C][C] 0.67[/C][C] 0.2165[/C][C] 0.4535[/C][/ROW]
[ROW][C]108[/C][C] 0.53[/C][C] 0.3691[/C][C] 0.1609[/C][/ROW]
[ROW][C]109[/C][C] 0.57[/C][C] 0.7335[/C][C]-0.1635[/C][/ROW]
[ROW][C]110[/C][C] 0.27[/C][C] 0.3914[/C][C]-0.1214[/C][/ROW]
[ROW][C]111[/C][C] 0.36[/C][C] 0.657[/C][C]-0.297[/C][/ROW]
[ROW][C]112[/C][C] 0.3[/C][C] 0.3745[/C][C]-0.07453[/C][/ROW]
[ROW][C]113[/C][C] 1.11[/C][C] 0.5425[/C][C] 0.5675[/C][/ROW]
[ROW][C]114[/C][C] 0.5[/C][C] 0.6068[/C][C]-0.1067[/C][/ROW]
[ROW][C]115[/C][C] 0.36[/C][C] 0.5236[/C][C]-0.1636[/C][/ROW]
[ROW][C]116[/C][C] 0.84[/C][C] 0.7433[/C][C] 0.09673[/C][/ROW]
[ROW][C]117[/C][C] 1.03[/C][C] 0.7734[/C][C] 0.2566[/C][/ROW]
[ROW][C]118[/C][C] 0.57[/C][C] 1.101[/C][C]-0.531[/C][/ROW]
[ROW][C]119[/C][C] 0.72[/C][C] 0.6783[/C][C] 0.04169[/C][/ROW]
[ROW][C]120[/C][C] 0.77[/C][C] 0.6886[/C][C] 0.08137[/C][/ROW]
[ROW][C]121[/C][C] 0.43[/C][C] 0.3503[/C][C] 0.07967[/C][/ROW]
[ROW][C]122[/C][C] 0.51[/C][C] 0.6472[/C][C]-0.1372[/C][/ROW]
[ROW][C]123[/C][C] 0.38[/C][C] 0.6166[/C][C]-0.2366[/C][/ROW]
[ROW][C]124[/C][C] 0.97[/C][C] 0.5733[/C][C] 0.3967[/C][/ROW]
[ROW][C]125[/C][C] 0.36[/C][C] 0.4203[/C][C]-0.06031[/C][/ROW]
[ROW][C]126[/C][C] 0.74[/C][C] 0.774[/C][C]-0.03404[/C][/ROW]
[ROW][C]127[/C][C] 0.34[/C][C] 0.3331[/C][C] 0.006922[/C][/ROW]
[ROW][C]128[/C][C] 0.49[/C][C] 0.6392[/C][C]-0.1492[/C][/ROW]
[ROW][C]129[/C][C] 0.47[/C][C] 0.2743[/C][C] 0.1957[/C][/ROW]
[ROW][C]130[/C][C] 0.67[/C][C] 0.9705[/C][C]-0.3005[/C][/ROW]
[ROW][C]131[/C][C] 0.31[/C][C] 0.7625[/C][C]-0.4525[/C][/ROW]
[ROW][C]132[/C][C] 0.64[/C][C] 0.8286[/C][C]-0.1886[/C][/ROW]
[ROW][C]133[/C][C] 0.47[/C][C] 0.374[/C][C] 0.09603[/C][/ROW]
[ROW][C]134[/C][C] 0.44[/C][C] 0.549[/C][C]-0.109[/C][/ROW]
[ROW][C]135[/C][C] 0.78[/C][C] 0.845[/C][C]-0.06502[/C][/ROW]
[ROW][C]136[/C][C] 0.31[/C][C] 0.6148[/C][C]-0.3048[/C][/ROW]
[ROW][C]137[/C][C] 0.43[/C][C] 0.5995[/C][C]-0.1695[/C][/ROW]
[ROW][C]138[/C][C] 0.35[/C][C] 0.4145[/C][C]-0.06454[/C][/ROW]
[ROW][C]139[/C][C] 1.47[/C][C] 0.9861[/C][C] 0.4839[/C][/ROW]
[ROW][C]140[/C][C] 0.75[/C][C] 1.132[/C][C]-0.3824[/C][/ROW]
[ROW][C]141[/C][C] 0.46[/C][C] 0.484[/C][C]-0.02403[/C][/ROW]
[ROW][C]142[/C][C] 0.44[/C][C] 0.3774[/C][C] 0.06257[/C][/ROW]
[ROW][C]143[/C][C] 0.67[/C][C] 0.5945[/C][C] 0.07546[/C][/ROW]
[ROW][C]144[/C][C] 0.25[/C][C] 0.4829[/C][C]-0.2329[/C][/ROW]
[ROW][C]145[/C][C] 0.34[/C][C] 0.3409[/C][C]-0.000907[/C][/ROW]
[ROW][C]146[/C][C] 1.19[/C][C] 0.5933[/C][C] 0.5967[/C][/ROW]
[ROW][C]147[/C][C] 0.46[/C][C] 0.6972[/C][C]-0.2372[/C][/ROW]
[ROW][C]148[/C][C] 0.76[/C][C] 0.5927[/C][C] 0.1673[/C][/ROW]
[ROW][C]149[/C][C] 0.87[/C][C] 0.6521[/C][C] 0.2179[/C][/ROW]
[ROW][C]150[/C][C] 0.73[/C][C] 0.5591[/C][C] 0.1709[/C][/ROW]
[ROW][C]151[/C][C] 0.34[/C][C] 0.3489[/C][C]-0.008927[/C][/ROW]
[ROW][C]152[/C][C] 0.62[/C][C] 0.6069[/C][C] 0.01305[/C][/ROW]
[ROW][C]153[/C][C] 0.82[/C][C] 0.9089[/C][C]-0.08889[/C][/ROW]
[ROW][C]154[/C][C] 0.8[/C][C] 0.8447[/C][C]-0.04468[/C][/ROW]
[ROW][C]155[/C][C] 1.13[/C][C] 0.9405[/C][C] 0.1895[/C][/ROW]
[ROW][C]156[/C][C] 0.19[/C][C] 0.6996[/C][C]-0.5096[/C][/ROW]
[ROW][C]157[/C][C] 0.62[/C][C] 0.5333[/C][C] 0.08672[/C][/ROW]
[ROW][C]158[/C][C] 0.45[/C][C] 0.6572[/C][C]-0.2072[/C][/ROW]
[ROW][C]159[/C][C] 0.5[/C][C] 0.5205[/C][C]-0.02046[/C][/ROW]
[ROW][C]160[/C][C] 0.34[/C][C] 0.3714[/C][C]-0.03136[/C][/ROW]
[ROW][C]161[/C][C] 0.19[/C][C] 0.4495[/C][C]-0.2595[/C][/ROW]
[ROW][C]162[/C][C] 0.2[/C][C] 0.3606[/C][C]-0.1606[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=316117&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=316117&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	0.3	0.3304	-0.03036
2	0.78	0.6035	0.1765
3	0.6	0.6053	-0.005283
4	0.33	0.4041	-0.07408
5	0.78	0.7327	0.04728
6	0.74	0.599	0.141
7	2.68	1.043	1.637
8	0.82	0.9344	-0.1144
9	0.66	0.6325	0.02748
10	0.97	0.7243	0.2457
11	0.52	0.7688	-0.2488
12	0.29	0.4196	-0.1296
13	0.56	0.7057	-0.1457
14	1.32	0.6766	0.6433
15	1.15	0.9307	0.2193
16	0.49	0.3509	0.1391
17	0.5	0.4623	0.03772
18	0.37	0.5181	-0.1481
19	0.63	0.6042	0.02584
20	0.3	0.5786	-0.2786
21	0.62	0.6475	-0.02751
22	0.31	0.8895	-0.5795
23	0.6	0.6631	-0.06312
24	0.47	0.2649	0.2051
25	0.21	0.2637	-0.05369
26	0.54	0.3714	0.1686
27	1.46	0.965	0.495
28	0.3	0.2459	0.05411
29	0.36	0.2671	0.09287
30	0.61	0.7379	-0.1279
31	0.55	0.5253	0.02465
32	0.35	0.6029	-0.2529
33	0.33	0.3708	-0.04078
34	0.22	0.4413	-0.2213
35	0.15	0.2895	-0.1395
36	0.4	0.6498	-0.2498
37	0.74	0.7291	0.01088
38	0.48	0.6436	-0.1636
39	0.77	0.8262	-0.05619
40	0.62	0.8051	-0.1851
41	1.18	1.013	0.1665
42	0.64	0.6045	0.0355
43	0.35	0.5904	-0.2404
44	0.3	0.6055	-0.3055
45	0.68	0.5551	0.1249
46	0.44	0.5336	-0.0936
47	0.27	0.5404	-0.2704
48	0.1	0.2645	-0.1645
49	0.31	0.2978	0.01223
50	0.55	0.5928	-0.04278
51	1.23	0.9174	0.3126
52	0.53	0.5768	-0.04685
53	0.46	0.3124	0.1476
54	0.39	0.6191	-0.2291
55	1.1	0.941	0.159
56	0.56	0.4389	0.1211
57	1.07	0.813	0.257
58	0.37	0.4934	-0.1234
59	0.39	0.2833	0.1067
60	0.35	0.2942	0.05582
61	0.7	0.5037	0.1963
62	0.27	0.3509	-0.08088
63	0.28	0.4804	-0.2004
64	0.42	0.7266	-0.3066
65	0.34	0.4041	-0.06413
66	0.44	0.5401	-0.1001
67	0.69	0.6402	0.04984
68	0.43	0.5314	-0.1014
69	1.08	0.9694	0.1106
70	0.89	0.8729	0.01708
71	0.91	0.8702	0.03982
72	0.41	0.5968	-0.1868
73	0.53	0.9172	-0.3872
74	0.54	0.6226	-0.08257
75	0.58	0.6775	-0.09747
76	0.25	0.4073	-0.1573
77	0.71	0.8321	-0.1221
78	0.55	0.8396	-0.2896
79	0.59	0.5137	0.07627
80	0.57	0.4289	0.1411
81	2.28	0.7166	1.563
82	0.67	0.6505	0.01952
83	0.22	0.3537	-0.1337
84	0.23	0.2929	-0.0629
85	0.79	0.6182	0.1718
86	1.89	0.7381	1.152
87	1.1	1.201	-0.1006
88	0.62	0.6156	0.004389
89	0.27	0.3775	-0.1075
90	0.43	0.3022	0.1278
91	0.67	0.6632	0.006829
92	0.52	0.2843	0.2357
93	0.39	0.3733	0.0167
94	0.52	0.6588	-0.1388
95	0.55	0.6389	-0.08895
96	0.43	0.5457	-0.1157
97	0.29	0.5813	-0.2913
98	0.64	0.681	-0.04095
99	0.6	0.4921	0.1079
100	0.31	0.2829	0.02709
101	0.8	0.3976	0.4024
102	0.33	0.5047	-0.1747
103	0.43	0.4067	0.02335
104	0.76	0.9812	-0.2212
105	0.63	0.9073	-0.2773
106	0.34	0.4968	-0.1568
107	0.67	0.2165	0.4535
108	0.53	0.3691	0.1609
109	0.57	0.7335	-0.1635
110	0.27	0.3914	-0.1214
111	0.36	0.657	-0.297
112	0.3	0.3745	-0.07453
113	1.11	0.5425	0.5675
114	0.5	0.6068	-0.1067
115	0.36	0.5236	-0.1636
116	0.84	0.7433	0.09673
117	1.03	0.7734	0.2566
118	0.57	1.101	-0.531
119	0.72	0.6783	0.04169
120	0.77	0.6886	0.08137
121	0.43	0.3503	0.07967
122	0.51	0.6472	-0.1372
123	0.38	0.6166	-0.2366
124	0.97	0.5733	0.3967
125	0.36	0.4203	-0.06031
126	0.74	0.774	-0.03404
127	0.34	0.3331	0.006922
128	0.49	0.6392	-0.1492
129	0.47	0.2743	0.1957
130	0.67	0.9705	-0.3005
131	0.31	0.7625	-0.4525
132	0.64	0.8286	-0.1886
133	0.47	0.374	0.09603
134	0.44	0.549	-0.109
135	0.78	0.845	-0.06502
136	0.31	0.6148	-0.3048
137	0.43	0.5995	-0.1695
138	0.35	0.4145	-0.06454
139	1.47	0.9861	0.4839
140	0.75	1.132	-0.3824
141	0.46	0.484	-0.02403
142	0.44	0.3774	0.06257
143	0.67	0.5945	0.07546
144	0.25	0.4829	-0.2329
145	0.34	0.3409	-0.000907
146	1.19	0.5933	0.5967
147	0.46	0.6972	-0.2372
148	0.76	0.5927	0.1673
149	0.87	0.6521	0.2179
150	0.73	0.5591	0.1709
151	0.34	0.3489	-0.008927
152	0.62	0.6069	0.01305
153	0.82	0.9089	-0.08889
154	0.8	0.8447	-0.04468
155	1.13	0.9405	0.1895
156	0.19	0.6996	-0.5096
157	0.62	0.5333	0.08672
158	0.45	0.6572	-0.2072
159	0.5	0.5205	-0.02046
160	0.34	0.3714	-0.03136
161	0.19	0.4495	-0.2595
162	0.2	0.3606	-0.1606

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
7	0.07408	0.1482	0.9259
8	0.9978	0.004436	0.002218
9	0.9948	0.01047	0.005233
10	0.9895	0.02092	0.01046
11	0.9931	0.01377	0.006886
12	0.9906	0.0188	0.009399
13	0.9867	0.02669	0.01335
14	0.9976	0.004797	0.002398
15	0.9975	0.004958	0.002479
16	0.9964	0.007208	0.003604
17	0.9938	0.01231	0.006156
18	0.9907	0.01869	0.009343
19	0.9851	0.02976	0.01488
20	0.9846	0.03085	0.01543
21	0.9773	0.04538	0.02269
22	0.9991	0.001854	0.0009269
23	0.9985	0.003056	0.001528
24	0.998	0.003935	0.001968
25	0.9969	0.006269	0.003135
26	0.9957	0.008658	0.004329
27	0.9956	0.008761	0.004381
28	0.9933	0.01331	0.006656
29	0.9903	0.01947	0.009737
30	0.987	0.02594	0.01297
31	0.9814	0.03717	0.01858
32	0.979	0.04203	0.02101
33	0.971	0.05807	0.02903
34	0.9657	0.06863	0.03431
35	0.9568	0.08641	0.04321
36	0.9514	0.09721	0.04861
37	0.9361	0.1277	0.06386
38	0.9206	0.1588	0.07941
39	0.9077	0.1845	0.09226
40	0.8962	0.2075	0.1038
41	0.8888	0.2224	0.1112
42	0.8639	0.2722	0.1361
43	0.8488	0.3025	0.1512
44	0.8416	0.3167	0.1584
45	0.8223	0.3554	0.1777
46	0.7891	0.4219	0.2109
47	0.811	0.378	0.189
48	0.7858	0.4284	0.2142
49	0.7482	0.5037	0.2518
50	0.708	0.5839	0.292
51	0.6927	0.6147	0.3073
52	0.6492	0.7016	0.3508
53	0.6185	0.7631	0.3815
54	0.5902	0.8196	0.4098
55	0.5565	0.887	0.4435
56	0.5228	0.9544	0.4772
57	0.5069	0.9862	0.4931
58	0.4648	0.9297	0.5352
59	0.4244	0.8487	0.5756
60	0.3799	0.7597	0.6201
61	0.3645	0.729	0.6355
62	0.3239	0.6478	0.6761
63	0.2984	0.5969	0.7016
64	0.3003	0.6006	0.6997
65	0.2632	0.5264	0.7368
66	0.2305	0.4611	0.7695
67	0.2002	0.4003	0.7998
68	0.1718	0.3437	0.8282
69	0.1563	0.3126	0.8437
70	0.1324	0.2648	0.8676
71	0.1126	0.2252	0.8874
72	0.09767	0.1953	0.9023
73	0.1489	0.2978	0.8511
74	0.125	0.2499	0.875
75	0.1042	0.2085	0.8958
76	0.0902	0.1804	0.9098
77	0.07582	0.1516	0.9242
78	0.08771	0.1754	0.9123
79	0.07393	0.1479	0.9261
80	0.06376	0.1275	0.9362
81	0.973	0.05407	0.02703
82	0.9652	0.06967	0.03484
83	0.958	0.08408	0.04204
84	0.9477	0.1047	0.05234
85	0.941	0.118	0.05901
86	0.9999	0.000223	0.0001115
87	0.9999	0.0002238	0.0001119
88	0.9998	0.0003472	0.0001736
89	0.9998	0.0004839	0.0002419
90	0.9997	0.000699	0.0003495
91	0.9995	0.001053	0.0005263
92	0.9994	0.001242	0.0006209
93	0.9991	0.001869	0.0009344
94	0.9987	0.002625	0.001312
95	0.9982	0.003573	0.001787
96	0.9975	0.004974	0.002487
97	0.9975	0.004978	0.002489
98	0.9964	0.007158	0.003579
99	0.9951	0.009737	0.004869
100	0.9932	0.01365	0.006827
101	0.995	0.009965	0.004982
102	0.9937	0.01252	0.006259
103	0.9912	0.01759	0.008793
104	0.9892	0.02155	0.01078
105	0.9876	0.02483	0.01241
106	0.9845	0.03098	0.01549
107	0.9909	0.01827	0.009137
108	0.9877	0.02453	0.01226
109	0.9841	0.03189	0.01595
110	0.9824	0.03519	0.01759
111	0.9824	0.03514	0.01757
112	0.9764	0.04714	0.02357
113	0.9936	0.01286	0.00643
114	0.9912	0.01766	0.008832
115	0.9903	0.01942	0.009708
116	0.9871	0.02583	0.01291
117	0.9897	0.02064	0.01032
118	0.9925	0.01494	0.007472
119	0.9897	0.02059	0.01029
120	0.9853	0.02949	0.01474
121	0.9796	0.04086	0.02043
122	0.9722	0.05552	0.02776
123	0.9669	0.06627	0.03314
124	0.9831	0.03376	0.01688
125	0.976	0.04795	0.02398
126	0.9668	0.06633	0.03316
127	0.9541	0.09184	0.04592
128	0.9391	0.1217	0.06087
129	0.9288	0.1424	0.0712
130	0.9177	0.1646	0.08231
131	0.9399	0.1202	0.06008
132	0.9243	0.1514	0.07572
133	0.907	0.1859	0.09297
134	0.881	0.2379	0.119
135	0.8458	0.3084	0.1542
136	0.8582	0.2837	0.1418
137	0.8307	0.3387	0.1693
138	0.7825	0.4349	0.2175
139	0.9472	0.1056	0.05282
140	0.9291	0.1418	0.07091
141	0.8985	0.2029	0.1015
142	0.8623	0.2753	0.1377
143	0.8126	0.3748	0.1874
144	0.778	0.4439	0.222
145	0.7157	0.5687	0.2843
146	0.9532	0.09351	0.04676
147	0.9328	0.1345	0.06723
148	0.9301	0.1398	0.06989
149	0.934	0.1319	0.06595
150	0.9612	0.07763	0.03882
151	0.9271	0.1457	0.07286
152	0.9051	0.1898	0.09489
153	0.8304	0.3392	0.1696
154	0.9084	0.1832	0.09162
155	0.8747	0.2505	0.1253

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
7 &  0.07408 &  0.1482 &  0.9259 \tabularnewline
8 &  0.9978 &  0.004436 &  0.002218 \tabularnewline
9 &  0.9948 &  0.01047 &  0.005233 \tabularnewline
10 &  0.9895 &  0.02092 &  0.01046 \tabularnewline
11 &  0.9931 &  0.01377 &  0.006886 \tabularnewline
12 &  0.9906 &  0.0188 &  0.009399 \tabularnewline
13 &  0.9867 &  0.02669 &  0.01335 \tabularnewline
14 &  0.9976 &  0.004797 &  0.002398 \tabularnewline
15 &  0.9975 &  0.004958 &  0.002479 \tabularnewline
16 &  0.9964 &  0.007208 &  0.003604 \tabularnewline
17 &  0.9938 &  0.01231 &  0.006156 \tabularnewline
18 &  0.9907 &  0.01869 &  0.009343 \tabularnewline
19 &  0.9851 &  0.02976 &  0.01488 \tabularnewline
20 &  0.9846 &  0.03085 &  0.01543 \tabularnewline
21 &  0.9773 &  0.04538 &  0.02269 \tabularnewline
22 &  0.9991 &  0.001854 &  0.0009269 \tabularnewline
23 &  0.9985 &  0.003056 &  0.001528 \tabularnewline
24 &  0.998 &  0.003935 &  0.001968 \tabularnewline
25 &  0.9969 &  0.006269 &  0.003135 \tabularnewline
26 &  0.9957 &  0.008658 &  0.004329 \tabularnewline
27 &  0.9956 &  0.008761 &  0.004381 \tabularnewline
28 &  0.9933 &  0.01331 &  0.006656 \tabularnewline
29 &  0.9903 &  0.01947 &  0.009737 \tabularnewline
30 &  0.987 &  0.02594 &  0.01297 \tabularnewline
31 &  0.9814 &  0.03717 &  0.01858 \tabularnewline
32 &  0.979 &  0.04203 &  0.02101 \tabularnewline
33 &  0.971 &  0.05807 &  0.02903 \tabularnewline
34 &  0.9657 &  0.06863 &  0.03431 \tabularnewline
35 &  0.9568 &  0.08641 &  0.04321 \tabularnewline
36 &  0.9514 &  0.09721 &  0.04861 \tabularnewline
37 &  0.9361 &  0.1277 &  0.06386 \tabularnewline
38 &  0.9206 &  0.1588 &  0.07941 \tabularnewline
39 &  0.9077 &  0.1845 &  0.09226 \tabularnewline
40 &  0.8962 &  0.2075 &  0.1038 \tabularnewline
41 &  0.8888 &  0.2224 &  0.1112 \tabularnewline
42 &  0.8639 &  0.2722 &  0.1361 \tabularnewline
43 &  0.8488 &  0.3025 &  0.1512 \tabularnewline
44 &  0.8416 &  0.3167 &  0.1584 \tabularnewline
45 &  0.8223 &  0.3554 &  0.1777 \tabularnewline
46 &  0.7891 &  0.4219 &  0.2109 \tabularnewline
47 &  0.811 &  0.378 &  0.189 \tabularnewline
48 &  0.7858 &  0.4284 &  0.2142 \tabularnewline
49 &  0.7482 &  0.5037 &  0.2518 \tabularnewline
50 &  0.708 &  0.5839 &  0.292 \tabularnewline
51 &  0.6927 &  0.6147 &  0.3073 \tabularnewline
52 &  0.6492 &  0.7016 &  0.3508 \tabularnewline
53 &  0.6185 &  0.7631 &  0.3815 \tabularnewline
54 &  0.5902 &  0.8196 &  0.4098 \tabularnewline
55 &  0.5565 &  0.887 &  0.4435 \tabularnewline
56 &  0.5228 &  0.9544 &  0.4772 \tabularnewline
57 &  0.5069 &  0.9862 &  0.4931 \tabularnewline
58 &  0.4648 &  0.9297 &  0.5352 \tabularnewline
59 &  0.4244 &  0.8487 &  0.5756 \tabularnewline
60 &  0.3799 &  0.7597 &  0.6201 \tabularnewline
61 &  0.3645 &  0.729 &  0.6355 \tabularnewline
62 &  0.3239 &  0.6478 &  0.6761 \tabularnewline
63 &  0.2984 &  0.5969 &  0.7016 \tabularnewline
64 &  0.3003 &  0.6006 &  0.6997 \tabularnewline
65 &  0.2632 &  0.5264 &  0.7368 \tabularnewline
66 &  0.2305 &  0.4611 &  0.7695 \tabularnewline
67 &  0.2002 &  0.4003 &  0.7998 \tabularnewline
68 &  0.1718 &  0.3437 &  0.8282 \tabularnewline
69 &  0.1563 &  0.3126 &  0.8437 \tabularnewline
70 &  0.1324 &  0.2648 &  0.8676 \tabularnewline
71 &  0.1126 &  0.2252 &  0.8874 \tabularnewline
72 &  0.09767 &  0.1953 &  0.9023 \tabularnewline
73 &  0.1489 &  0.2978 &  0.8511 \tabularnewline
74 &  0.125 &  0.2499 &  0.875 \tabularnewline
75 &  0.1042 &  0.2085 &  0.8958 \tabularnewline
76 &  0.0902 &  0.1804 &  0.9098 \tabularnewline
77 &  0.07582 &  0.1516 &  0.9242 \tabularnewline
78 &  0.08771 &  0.1754 &  0.9123 \tabularnewline
79 &  0.07393 &  0.1479 &  0.9261 \tabularnewline
80 &  0.06376 &  0.1275 &  0.9362 \tabularnewline
81 &  0.973 &  0.05407 &  0.02703 \tabularnewline
82 &  0.9652 &  0.06967 &  0.03484 \tabularnewline
83 &  0.958 &  0.08408 &  0.04204 \tabularnewline
84 &  0.9477 &  0.1047 &  0.05234 \tabularnewline
85 &  0.941 &  0.118 &  0.05901 \tabularnewline
86 &  0.9999 &  0.000223 &  0.0001115 \tabularnewline
87 &  0.9999 &  0.0002238 &  0.0001119 \tabularnewline
88 &  0.9998 &  0.0003472 &  0.0001736 \tabularnewline
89 &  0.9998 &  0.0004839 &  0.0002419 \tabularnewline
90 &  0.9997 &  0.000699 &  0.0003495 \tabularnewline
91 &  0.9995 &  0.001053 &  0.0005263 \tabularnewline
92 &  0.9994 &  0.001242 &  0.0006209 \tabularnewline
93 &  0.9991 &  0.001869 &  0.0009344 \tabularnewline
94 &  0.9987 &  0.002625 &  0.001312 \tabularnewline
95 &  0.9982 &  0.003573 &  0.001787 \tabularnewline
96 &  0.9975 &  0.004974 &  0.002487 \tabularnewline
97 &  0.9975 &  0.004978 &  0.002489 \tabularnewline
98 &  0.9964 &  0.007158 &  0.003579 \tabularnewline
99 &  0.9951 &  0.009737 &  0.004869 \tabularnewline
100 &  0.9932 &  0.01365 &  0.006827 \tabularnewline
101 &  0.995 &  0.009965 &  0.004982 \tabularnewline
102 &  0.9937 &  0.01252 &  0.006259 \tabularnewline
103 &  0.9912 &  0.01759 &  0.008793 \tabularnewline
104 &  0.9892 &  0.02155 &  0.01078 \tabularnewline
105 &  0.9876 &  0.02483 &  0.01241 \tabularnewline
106 &  0.9845 &  0.03098 &  0.01549 \tabularnewline
107 &  0.9909 &  0.01827 &  0.009137 \tabularnewline
108 &  0.9877 &  0.02453 &  0.01226 \tabularnewline
109 &  0.9841 &  0.03189 &  0.01595 \tabularnewline
110 &  0.9824 &  0.03519 &  0.01759 \tabularnewline
111 &  0.9824 &  0.03514 &  0.01757 \tabularnewline
112 &  0.9764 &  0.04714 &  0.02357 \tabularnewline
113 &  0.9936 &  0.01286 &  0.00643 \tabularnewline
114 &  0.9912 &  0.01766 &  0.008832 \tabularnewline
115 &  0.9903 &  0.01942 &  0.009708 \tabularnewline
116 &  0.9871 &  0.02583 &  0.01291 \tabularnewline
117 &  0.9897 &  0.02064 &  0.01032 \tabularnewline
118 &  0.9925 &  0.01494 &  0.007472 \tabularnewline
119 &  0.9897 &  0.02059 &  0.01029 \tabularnewline
120 &  0.9853 &  0.02949 &  0.01474 \tabularnewline
121 &  0.9796 &  0.04086 &  0.02043 \tabularnewline
122 &  0.9722 &  0.05552 &  0.02776 \tabularnewline
123 &  0.9669 &  0.06627 &  0.03314 \tabularnewline
124 &  0.9831 &  0.03376 &  0.01688 \tabularnewline
125 &  0.976 &  0.04795 &  0.02398 \tabularnewline
126 &  0.9668 &  0.06633 &  0.03316 \tabularnewline
127 &  0.9541 &  0.09184 &  0.04592 \tabularnewline
128 &  0.9391 &  0.1217 &  0.06087 \tabularnewline
129 &  0.9288 &  0.1424 &  0.0712 \tabularnewline
130 &  0.9177 &  0.1646 &  0.08231 \tabularnewline
131 &  0.9399 &  0.1202 &  0.06008 \tabularnewline
132 &  0.9243 &  0.1514 &  0.07572 \tabularnewline
133 &  0.907 &  0.1859 &  0.09297 \tabularnewline
134 &  0.881 &  0.2379 &  0.119 \tabularnewline
135 &  0.8458 &  0.3084 &  0.1542 \tabularnewline
136 &  0.8582 &  0.2837 &  0.1418 \tabularnewline
137 &  0.8307 &  0.3387 &  0.1693 \tabularnewline
138 &  0.7825 &  0.4349 &  0.2175 \tabularnewline
139 &  0.9472 &  0.1056 &  0.05282 \tabularnewline
140 &  0.9291 &  0.1418 &  0.07091 \tabularnewline
141 &  0.8985 &  0.2029 &  0.1015 \tabularnewline
142 &  0.8623 &  0.2753 &  0.1377 \tabularnewline
143 &  0.8126 &  0.3748 &  0.1874 \tabularnewline
144 &  0.778 &  0.4439 &  0.222 \tabularnewline
145 &  0.7157 &  0.5687 &  0.2843 \tabularnewline
146 &  0.9532 &  0.09351 &  0.04676 \tabularnewline
147 &  0.9328 &  0.1345 &  0.06723 \tabularnewline
148 &  0.9301 &  0.1398 &  0.06989 \tabularnewline
149 &  0.934 &  0.1319 &  0.06595 \tabularnewline
150 &  0.9612 &  0.07763 &  0.03882 \tabularnewline
151 &  0.9271 &  0.1457 &  0.07286 \tabularnewline
152 &  0.9051 &  0.1898 &  0.09489 \tabularnewline
153 &  0.8304 &  0.3392 &  0.1696 \tabularnewline
154 &  0.9084 &  0.1832 &  0.09162 \tabularnewline
155 &  0.8747 &  0.2505 &  0.1253 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=316117&T=6

[TABLE]
[ROW][C]Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]p-values[/C][C]Alternative Hypothesis[/C][/ROW]
[ROW][C]breakpoint index[/C][C]greater[/C][C]2-sided[/C][C]less[/C][/ROW]
[ROW][C]7[/C][C] 0.07408[/C][C] 0.1482[/C][C] 0.9259[/C][/ROW]
[ROW][C]8[/C][C] 0.9978[/C][C] 0.004436[/C][C] 0.002218[/C][/ROW]
[ROW][C]9[/C][C] 0.9948[/C][C] 0.01047[/C][C] 0.005233[/C][/ROW]
[ROW][C]10[/C][C] 0.9895[/C][C] 0.02092[/C][C] 0.01046[/C][/ROW]
[ROW][C]11[/C][C] 0.9931[/C][C] 0.01377[/C][C] 0.006886[/C][/ROW]
[ROW][C]12[/C][C] 0.9906[/C][C] 0.0188[/C][C] 0.009399[/C][/ROW]
[ROW][C]13[/C][C] 0.9867[/C][C] 0.02669[/C][C] 0.01335[/C][/ROW]
[ROW][C]14[/C][C] 0.9976[/C][C] 0.004797[/C][C] 0.002398[/C][/ROW]
[ROW][C]15[/C][C] 0.9975[/C][C] 0.004958[/C][C] 0.002479[/C][/ROW]
[ROW][C]16[/C][C] 0.9964[/C][C] 0.007208[/C][C] 0.003604[/C][/ROW]
[ROW][C]17[/C][C] 0.9938[/C][C] 0.01231[/C][C] 0.006156[/C][/ROW]
[ROW][C]18[/C][C] 0.9907[/C][C] 0.01869[/C][C] 0.009343[/C][/ROW]
[ROW][C]19[/C][C] 0.9851[/C][C] 0.02976[/C][C] 0.01488[/C][/ROW]
[ROW][C]20[/C][C] 0.9846[/C][C] 0.03085[/C][C] 0.01543[/C][/ROW]
[ROW][C]21[/C][C] 0.9773[/C][C] 0.04538[/C][C] 0.02269[/C][/ROW]
[ROW][C]22[/C][C] 0.9991[/C][C] 0.001854[/C][C] 0.0009269[/C][/ROW]
[ROW][C]23[/C][C] 0.9985[/C][C] 0.003056[/C][C] 0.001528[/C][/ROW]
[ROW][C]24[/C][C] 0.998[/C][C] 0.003935[/C][C] 0.001968[/C][/ROW]
[ROW][C]25[/C][C] 0.9969[/C][C] 0.006269[/C][C] 0.003135[/C][/ROW]
[ROW][C]26[/C][C] 0.9957[/C][C] 0.008658[/C][C] 0.004329[/C][/ROW]
[ROW][C]27[/C][C] 0.9956[/C][C] 0.008761[/C][C] 0.004381[/C][/ROW]
[ROW][C]28[/C][C] 0.9933[/C][C] 0.01331[/C][C] 0.006656[/C][/ROW]
[ROW][C]29[/C][C] 0.9903[/C][C] 0.01947[/C][C] 0.009737[/C][/ROW]
[ROW][C]30[/C][C] 0.987[/C][C] 0.02594[/C][C] 0.01297[/C][/ROW]
[ROW][C]31[/C][C] 0.9814[/C][C] 0.03717[/C][C] 0.01858[/C][/ROW]
[ROW][C]32[/C][C] 0.979[/C][C] 0.04203[/C][C] 0.02101[/C][/ROW]
[ROW][C]33[/C][C] 0.971[/C][C] 0.05807[/C][C] 0.02903[/C][/ROW]
[ROW][C]34[/C][C] 0.9657[/C][C] 0.06863[/C][C] 0.03431[/C][/ROW]
[ROW][C]35[/C][C] 0.9568[/C][C] 0.08641[/C][C] 0.04321[/C][/ROW]
[ROW][C]36[/C][C] 0.9514[/C][C] 0.09721[/C][C] 0.04861[/C][/ROW]
[ROW][C]37[/C][C] 0.9361[/C][C] 0.1277[/C][C] 0.06386[/C][/ROW]
[ROW][C]38[/C][C] 0.9206[/C][C] 0.1588[/C][C] 0.07941[/C][/ROW]
[ROW][C]39[/C][C] 0.9077[/C][C] 0.1845[/C][C] 0.09226[/C][/ROW]
[ROW][C]40[/C][C] 0.8962[/C][C] 0.2075[/C][C] 0.1038[/C][/ROW]
[ROW][C]41[/C][C] 0.8888[/C][C] 0.2224[/C][C] 0.1112[/C][/ROW]
[ROW][C]42[/C][C] 0.8639[/C][C] 0.2722[/C][C] 0.1361[/C][/ROW]
[ROW][C]43[/C][C] 0.8488[/C][C] 0.3025[/C][C] 0.1512[/C][/ROW]
[ROW][C]44[/C][C] 0.8416[/C][C] 0.3167[/C][C] 0.1584[/C][/ROW]
[ROW][C]45[/C][C] 0.8223[/C][C] 0.3554[/C][C] 0.1777[/C][/ROW]
[ROW][C]46[/C][C] 0.7891[/C][C] 0.4219[/C][C] 0.2109[/C][/ROW]
[ROW][C]47[/C][C] 0.811[/C][C] 0.378[/C][C] 0.189[/C][/ROW]
[ROW][C]48[/C][C] 0.7858[/C][C] 0.4284[/C][C] 0.2142[/C][/ROW]
[ROW][C]49[/C][C] 0.7482[/C][C] 0.5037[/C][C] 0.2518[/C][/ROW]
[ROW][C]50[/C][C] 0.708[/C][C] 0.5839[/C][C] 0.292[/C][/ROW]
[ROW][C]51[/C][C] 0.6927[/C][C] 0.6147[/C][C] 0.3073[/C][/ROW]
[ROW][C]52[/C][C] 0.6492[/C][C] 0.7016[/C][C] 0.3508[/C][/ROW]
[ROW][C]53[/C][C] 0.6185[/C][C] 0.7631[/C][C] 0.3815[/C][/ROW]
[ROW][C]54[/C][C] 0.5902[/C][C] 0.8196[/C][C] 0.4098[/C][/ROW]
[ROW][C]55[/C][C] 0.5565[/C][C] 0.887[/C][C] 0.4435[/C][/ROW]
[ROW][C]56[/C][C] 0.5228[/C][C] 0.9544[/C][C] 0.4772[/C][/ROW]
[ROW][C]57[/C][C] 0.5069[/C][C] 0.9862[/C][C] 0.4931[/C][/ROW]
[ROW][C]58[/C][C] 0.4648[/C][C] 0.9297[/C][C] 0.5352[/C][/ROW]
[ROW][C]59[/C][C] 0.4244[/C][C] 0.8487[/C][C] 0.5756[/C][/ROW]
[ROW][C]60[/C][C] 0.3799[/C][C] 0.7597[/C][C] 0.6201[/C][/ROW]
[ROW][C]61[/C][C] 0.3645[/C][C] 0.729[/C][C] 0.6355[/C][/ROW]
[ROW][C]62[/C][C] 0.3239[/C][C] 0.6478[/C][C] 0.6761[/C][/ROW]
[ROW][C]63[/C][C] 0.2984[/C][C] 0.5969[/C][C] 0.7016[/C][/ROW]
[ROW][C]64[/C][C] 0.3003[/C][C] 0.6006[/C][C] 0.6997[/C][/ROW]
[ROW][C]65[/C][C] 0.2632[/C][C] 0.5264[/C][C] 0.7368[/C][/ROW]
[ROW][C]66[/C][C] 0.2305[/C][C] 0.4611[/C][C] 0.7695[/C][/ROW]
[ROW][C]67[/C][C] 0.2002[/C][C] 0.4003[/C][C] 0.7998[/C][/ROW]
[ROW][C]68[/C][C] 0.1718[/C][C] 0.3437[/C][C] 0.8282[/C][/ROW]
[ROW][C]69[/C][C] 0.1563[/C][C] 0.3126[/C][C] 0.8437[/C][/ROW]
[ROW][C]70[/C][C] 0.1324[/C][C] 0.2648[/C][C] 0.8676[/C][/ROW]
[ROW][C]71[/C][C] 0.1126[/C][C] 0.2252[/C][C] 0.8874[/C][/ROW]
[ROW][C]72[/C][C] 0.09767[/C][C] 0.1953[/C][C] 0.9023[/C][/ROW]
[ROW][C]73[/C][C] 0.1489[/C][C] 0.2978[/C][C] 0.8511[/C][/ROW]
[ROW][C]74[/C][C] 0.125[/C][C] 0.2499[/C][C] 0.875[/C][/ROW]
[ROW][C]75[/C][C] 0.1042[/C][C] 0.2085[/C][C] 0.8958[/C][/ROW]
[ROW][C]76[/C][C] 0.0902[/C][C] 0.1804[/C][C] 0.9098[/C][/ROW]
[ROW][C]77[/C][C] 0.07582[/C][C] 0.1516[/C][C] 0.9242[/C][/ROW]
[ROW][C]78[/C][C] 0.08771[/C][C] 0.1754[/C][C] 0.9123[/C][/ROW]
[ROW][C]79[/C][C] 0.07393[/C][C] 0.1479[/C][C] 0.9261[/C][/ROW]
[ROW][C]80[/C][C] 0.06376[/C][C] 0.1275[/C][C] 0.9362[/C][/ROW]
[ROW][C]81[/C][C] 0.973[/C][C] 0.05407[/C][C] 0.02703[/C][/ROW]
[ROW][C]82[/C][C] 0.9652[/C][C] 0.06967[/C][C] 0.03484[/C][/ROW]
[ROW][C]83[/C][C] 0.958[/C][C] 0.08408[/C][C] 0.04204[/C][/ROW]
[ROW][C]84[/C][C] 0.9477[/C][C] 0.1047[/C][C] 0.05234[/C][/ROW]
[ROW][C]85[/C][C] 0.941[/C][C] 0.118[/C][C] 0.05901[/C][/ROW]
[ROW][C]86[/C][C] 0.9999[/C][C] 0.000223[/C][C] 0.0001115[/C][/ROW]
[ROW][C]87[/C][C] 0.9999[/C][C] 0.0002238[/C][C] 0.0001119[/C][/ROW]
[ROW][C]88[/C][C] 0.9998[/C][C] 0.0003472[/C][C] 0.0001736[/C][/ROW]
[ROW][C]89[/C][C] 0.9998[/C][C] 0.0004839[/C][C] 0.0002419[/C][/ROW]
[ROW][C]90[/C][C] 0.9997[/C][C] 0.000699[/C][C] 0.0003495[/C][/ROW]
[ROW][C]91[/C][C] 0.9995[/C][C] 0.001053[/C][C] 0.0005263[/C][/ROW]
[ROW][C]92[/C][C] 0.9994[/C][C] 0.001242[/C][C] 0.0006209[/C][/ROW]
[ROW][C]93[/C][C] 0.9991[/C][C] 0.001869[/C][C] 0.0009344[/C][/ROW]
[ROW][C]94[/C][C] 0.9987[/C][C] 0.002625[/C][C] 0.001312[/C][/ROW]
[ROW][C]95[/C][C] 0.9982[/C][C] 0.003573[/C][C] 0.001787[/C][/ROW]
[ROW][C]96[/C][C] 0.9975[/C][C] 0.004974[/C][C] 0.002487[/C][/ROW]
[ROW][C]97[/C][C] 0.9975[/C][C] 0.004978[/C][C] 0.002489[/C][/ROW]
[ROW][C]98[/C][C] 0.9964[/C][C] 0.007158[/C][C] 0.003579[/C][/ROW]
[ROW][C]99[/C][C] 0.9951[/C][C] 0.009737[/C][C] 0.004869[/C][/ROW]
[ROW][C]100[/C][C] 0.9932[/C][C] 0.01365[/C][C] 0.006827[/C][/ROW]
[ROW][C]101[/C][C] 0.995[/C][C] 0.009965[/C][C] 0.004982[/C][/ROW]
[ROW][C]102[/C][C] 0.9937[/C][C] 0.01252[/C][C] 0.006259[/C][/ROW]
[ROW][C]103[/C][C] 0.9912[/C][C] 0.01759[/C][C] 0.008793[/C][/ROW]
[ROW][C]104[/C][C] 0.9892[/C][C] 0.02155[/C][C] 0.01078[/C][/ROW]
[ROW][C]105[/C][C] 0.9876[/C][C] 0.02483[/C][C] 0.01241[/C][/ROW]
[ROW][C]106[/C][C] 0.9845[/C][C] 0.03098[/C][C] 0.01549[/C][/ROW]
[ROW][C]107[/C][C] 0.9909[/C][C] 0.01827[/C][C] 0.009137[/C][/ROW]
[ROW][C]108[/C][C] 0.9877[/C][C] 0.02453[/C][C] 0.01226[/C][/ROW]
[ROW][C]109[/C][C] 0.9841[/C][C] 0.03189[/C][C] 0.01595[/C][/ROW]
[ROW][C]110[/C][C] 0.9824[/C][C] 0.03519[/C][C] 0.01759[/C][/ROW]
[ROW][C]111[/C][C] 0.9824[/C][C] 0.03514[/C][C] 0.01757[/C][/ROW]
[ROW][C]112[/C][C] 0.9764[/C][C] 0.04714[/C][C] 0.02357[/C][/ROW]
[ROW][C]113[/C][C] 0.9936[/C][C] 0.01286[/C][C] 0.00643[/C][/ROW]
[ROW][C]114[/C][C] 0.9912[/C][C] 0.01766[/C][C] 0.008832[/C][/ROW]
[ROW][C]115[/C][C] 0.9903[/C][C] 0.01942[/C][C] 0.009708[/C][/ROW]
[ROW][C]116[/C][C] 0.9871[/C][C] 0.02583[/C][C] 0.01291[/C][/ROW]
[ROW][C]117[/C][C] 0.9897[/C][C] 0.02064[/C][C] 0.01032[/C][/ROW]
[ROW][C]118[/C][C] 0.9925[/C][C] 0.01494[/C][C] 0.007472[/C][/ROW]
[ROW][C]119[/C][C] 0.9897[/C][C] 0.02059[/C][C] 0.01029[/C][/ROW]
[ROW][C]120[/C][C] 0.9853[/C][C] 0.02949[/C][C] 0.01474[/C][/ROW]
[ROW][C]121[/C][C] 0.9796[/C][C] 0.04086[/C][C] 0.02043[/C][/ROW]
[ROW][C]122[/C][C] 0.9722[/C][C] 0.05552[/C][C] 0.02776[/C][/ROW]
[ROW][C]123[/C][C] 0.9669[/C][C] 0.06627[/C][C] 0.03314[/C][/ROW]
[ROW][C]124[/C][C] 0.9831[/C][C] 0.03376[/C][C] 0.01688[/C][/ROW]
[ROW][C]125[/C][C] 0.976[/C][C] 0.04795[/C][C] 0.02398[/C][/ROW]
[ROW][C]126[/C][C] 0.9668[/C][C] 0.06633[/C][C] 0.03316[/C][/ROW]
[ROW][C]127[/C][C] 0.9541[/C][C] 0.09184[/C][C] 0.04592[/C][/ROW]
[ROW][C]128[/C][C] 0.9391[/C][C] 0.1217[/C][C] 0.06087[/C][/ROW]
[ROW][C]129[/C][C] 0.9288[/C][C] 0.1424[/C][C] 0.0712[/C][/ROW]
[ROW][C]130[/C][C] 0.9177[/C][C] 0.1646[/C][C] 0.08231[/C][/ROW]
[ROW][C]131[/C][C] 0.9399[/C][C] 0.1202[/C][C] 0.06008[/C][/ROW]
[ROW][C]132[/C][C] 0.9243[/C][C] 0.1514[/C][C] 0.07572[/C][/ROW]
[ROW][C]133[/C][C] 0.907[/C][C] 0.1859[/C][C] 0.09297[/C][/ROW]
[ROW][C]134[/C][C] 0.881[/C][C] 0.2379[/C][C] 0.119[/C][/ROW]
[ROW][C]135[/C][C] 0.8458[/C][C] 0.3084[/C][C] 0.1542[/C][/ROW]
[ROW][C]136[/C][C] 0.8582[/C][C] 0.2837[/C][C] 0.1418[/C][/ROW]
[ROW][C]137[/C][C] 0.8307[/C][C] 0.3387[/C][C] 0.1693[/C][/ROW]
[ROW][C]138[/C][C] 0.7825[/C][C] 0.4349[/C][C] 0.2175[/C][/ROW]
[ROW][C]139[/C][C] 0.9472[/C][C] 0.1056[/C][C] 0.05282[/C][/ROW]
[ROW][C]140[/C][C] 0.9291[/C][C] 0.1418[/C][C] 0.07091[/C][/ROW]
[ROW][C]141[/C][C] 0.8985[/C][C] 0.2029[/C][C] 0.1015[/C][/ROW]
[ROW][C]142[/C][C] 0.8623[/C][C] 0.2753[/C][C] 0.1377[/C][/ROW]
[ROW][C]143[/C][C] 0.8126[/C][C] 0.3748[/C][C] 0.1874[/C][/ROW]
[ROW][C]144[/C][C] 0.778[/C][C] 0.4439[/C][C] 0.222[/C][/ROW]
[ROW][C]145[/C][C] 0.7157[/C][C] 0.5687[/C][C] 0.2843[/C][/ROW]
[ROW][C]146[/C][C] 0.9532[/C][C] 0.09351[/C][C] 0.04676[/C][/ROW]
[ROW][C]147[/C][C] 0.9328[/C][C] 0.1345[/C][C] 0.06723[/C][/ROW]
[ROW][C]148[/C][C] 0.9301[/C][C] 0.1398[/C][C] 0.06989[/C][/ROW]
[ROW][C]149[/C][C] 0.934[/C][C] 0.1319[/C][C] 0.06595[/C][/ROW]
[ROW][C]150[/C][C] 0.9612[/C][C] 0.07763[/C][C] 0.03882[/C][/ROW]
[ROW][C]151[/C][C] 0.9271[/C][C] 0.1457[/C][C] 0.07286[/C][/ROW]
[ROW][C]152[/C][C] 0.9051[/C][C] 0.1898[/C][C] 0.09489[/C][/ROW]
[ROW][C]153[/C][C] 0.8304[/C][C] 0.3392[/C][C] 0.1696[/C][/ROW]
[ROW][C]154[/C][C] 0.9084[/C][C] 0.1832[/C][C] 0.09162[/C][/ROW]
[ROW][C]155[/C][C] 0.8747[/C][C] 0.2505[/C][C] 0.1253[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=316117&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=316117&T=6

As an alternative you can also use a QR Code:

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

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
7	0.07408	0.1482	0.9259
8	0.9978	0.004436	0.002218
9	0.9948	0.01047	0.005233
10	0.9895	0.02092	0.01046
11	0.9931	0.01377	0.006886
12	0.9906	0.0188	0.009399
13	0.9867	0.02669	0.01335
14	0.9976	0.004797	0.002398
15	0.9975	0.004958	0.002479
16	0.9964	0.007208	0.003604
17	0.9938	0.01231	0.006156
18	0.9907	0.01869	0.009343
19	0.9851	0.02976	0.01488
20	0.9846	0.03085	0.01543
21	0.9773	0.04538	0.02269
22	0.9991	0.001854	0.0009269
23	0.9985	0.003056	0.001528
24	0.998	0.003935	0.001968
25	0.9969	0.006269	0.003135
26	0.9957	0.008658	0.004329
27	0.9956	0.008761	0.004381
28	0.9933	0.01331	0.006656
29	0.9903	0.01947	0.009737
30	0.987	0.02594	0.01297
31	0.9814	0.03717	0.01858
32	0.979	0.04203	0.02101
33	0.971	0.05807	0.02903
34	0.9657	0.06863	0.03431
35	0.9568	0.08641	0.04321
36	0.9514	0.09721	0.04861
37	0.9361	0.1277	0.06386
38	0.9206	0.1588	0.07941
39	0.9077	0.1845	0.09226
40	0.8962	0.2075	0.1038
41	0.8888	0.2224	0.1112
42	0.8639	0.2722	0.1361
43	0.8488	0.3025	0.1512
44	0.8416	0.3167	0.1584
45	0.8223	0.3554	0.1777
46	0.7891	0.4219	0.2109
47	0.811	0.378	0.189
48	0.7858	0.4284	0.2142
49	0.7482	0.5037	0.2518
50	0.708	0.5839	0.292
51	0.6927	0.6147	0.3073
52	0.6492	0.7016	0.3508
53	0.6185	0.7631	0.3815
54	0.5902	0.8196	0.4098
55	0.5565	0.887	0.4435
56	0.5228	0.9544	0.4772
57	0.5069	0.9862	0.4931
58	0.4648	0.9297	0.5352
59	0.4244	0.8487	0.5756
60	0.3799	0.7597	0.6201
61	0.3645	0.729	0.6355
62	0.3239	0.6478	0.6761
63	0.2984	0.5969	0.7016
64	0.3003	0.6006	0.6997
65	0.2632	0.5264	0.7368
66	0.2305	0.4611	0.7695
67	0.2002	0.4003	0.7998
68	0.1718	0.3437	0.8282
69	0.1563	0.3126	0.8437
70	0.1324	0.2648	0.8676
71	0.1126	0.2252	0.8874
72	0.09767	0.1953	0.9023
73	0.1489	0.2978	0.8511
74	0.125	0.2499	0.875
75	0.1042	0.2085	0.8958
76	0.0902	0.1804	0.9098
77	0.07582	0.1516	0.9242
78	0.08771	0.1754	0.9123
79	0.07393	0.1479	0.9261
80	0.06376	0.1275	0.9362
81	0.973	0.05407	0.02703
82	0.9652	0.06967	0.03484
83	0.958	0.08408	0.04204
84	0.9477	0.1047	0.05234
85	0.941	0.118	0.05901
86	0.9999	0.000223	0.0001115
87	0.9999	0.0002238	0.0001119
88	0.9998	0.0003472	0.0001736
89	0.9998	0.0004839	0.0002419
90	0.9997	0.000699	0.0003495
91	0.9995	0.001053	0.0005263
92	0.9994	0.001242	0.0006209
93	0.9991	0.001869	0.0009344
94	0.9987	0.002625	0.001312
95	0.9982	0.003573	0.001787
96	0.9975	0.004974	0.002487
97	0.9975	0.004978	0.002489
98	0.9964	0.007158	0.003579
99	0.9951	0.009737	0.004869
100	0.9932	0.01365	0.006827
101	0.995	0.009965	0.004982
102	0.9937	0.01252	0.006259
103	0.9912	0.01759	0.008793
104	0.9892	0.02155	0.01078
105	0.9876	0.02483	0.01241
106	0.9845	0.03098	0.01549
107	0.9909	0.01827	0.009137
108	0.9877	0.02453	0.01226
109	0.9841	0.03189	0.01595
110	0.9824	0.03519	0.01759
111	0.9824	0.03514	0.01757
112	0.9764	0.04714	0.02357
113	0.9936	0.01286	0.00643
114	0.9912	0.01766	0.008832
115	0.9903	0.01942	0.009708
116	0.9871	0.02583	0.01291
117	0.9897	0.02064	0.01032
118	0.9925	0.01494	0.007472
119	0.9897	0.02059	0.01029
120	0.9853	0.02949	0.01474
121	0.9796	0.04086	0.02043
122	0.9722	0.05552	0.02776
123	0.9669	0.06627	0.03314
124	0.9831	0.03376	0.01688
125	0.976	0.04795	0.02398
126	0.9668	0.06633	0.03316
127	0.9541	0.09184	0.04592
128	0.9391	0.1217	0.06087
129	0.9288	0.1424	0.0712
130	0.9177	0.1646	0.08231
131	0.9399	0.1202	0.06008
132	0.9243	0.1514	0.07572
133	0.907	0.1859	0.09297
134	0.881	0.2379	0.119
135	0.8458	0.3084	0.1542
136	0.8582	0.2837	0.1418
137	0.8307	0.3387	0.1693
138	0.7825	0.4349	0.2175
139	0.9472	0.1056	0.05282
140	0.9291	0.1418	0.07091
141	0.8985	0.2029	0.1015
142	0.8623	0.2753	0.1377
143	0.8126	0.3748	0.1874
144	0.778	0.4439	0.222
145	0.7157	0.5687	0.2843
146	0.9532	0.09351	0.04676
147	0.9328	0.1345	0.06723
148	0.9301	0.1398	0.06989
149	0.934	0.1319	0.06595
150	0.9612	0.07763	0.03882
151	0.9271	0.1457	0.07286
152	0.9051	0.1898	0.09489
153	0.8304	0.3392	0.1696
154	0.9084	0.1832	0.09162
155	0.8747	0.2505	0.1253

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	25	0.1678	NOK
5% type I error level	63	0.422819	NOK
10% type I error level	76	0.510067	NOK

\begin{tabular}{lllllllll}
\hline
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
Description & # significant tests & % significant tests & OK/NOK \tabularnewline
1% type I error level & 25 &  0.1678 & NOK \tabularnewline
5% type I error level & 63 & 0.422819 & NOK \tabularnewline
10% type I error level & 76 & 0.510067 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=316117&T=7

[TABLE]
[ROW][C]Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]Description[/C][C]# significant tests[/C][C]% significant tests[/C][C]OK/NOK[/C][/ROW]
[ROW][C]1% type I error level[/C][C]25[/C][C] 0.1678[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]63[/C][C]0.422819[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]76[/C][C]0.510067[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=316117&T=7

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=316117&T=7

As an alternative you can also use a QR Code:

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

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	25	0.1678	NOK
5% type I error level	63	0.422819	NOK
10% type I error level	76	0.510067	NOK

Ramsey RESET F-Test for powers (2 and 3) of fitted values
> reset_test_fitted RESET test data: mylm RESET = 5.9551, df1 = 2, df2 = 156, p-value = 0.003219
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors RESET test data: mylm RESET = 2.2789, df1 = 6, df2 = 152, p-value = 0.03904
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components RESET test data: mylm RESET = 2.4239, df1 = 2, df2 = 156, p-value = 0.0919

\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 = 5.9551, df1 = 2, df2 = 156, p-value = 0.003219
 \tabularnewline
Ramsey RESET F-Test for powers (2 and 3) of regressors \tabularnewline
> reset_test_regressors
	RESET test
data:  mylm
RESET = 2.2789, df1 = 6, df2 = 152, p-value = 0.03904
 \tabularnewline
Ramsey RESET F-Test for powers (2 and 3) of principal components \tabularnewline
> reset_test_principal_components
	RESET test
data:  mylm
RESET = 2.4239, df1 = 2, df2 = 156, p-value = 0.0919
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=316117&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 = 5.9551, df1 = 2, df2 = 156, p-value = 0.003219
[/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 = 2.2789, df1 = 6, df2 = 152, p-value = 0.03904
[/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 = 2.4239, df1 = 2, df2 = 156, p-value = 0.0919
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=316117&T=8

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=316117&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 = 5.9551, df1 = 2, df2 = 156, p-value = 0.003219
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors RESET test data: mylm RESET = 2.2789, df1 = 6, df2 = 152, p-value = 0.03904
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components RESET test data: mylm RESET = 2.4239, df1 = 2, df2 = 156, p-value = 0.0919

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

\begin{tabular}{lllllllll}
\hline
Variance Inflation Factors (Multicollinearity) \tabularnewline
> vif
`Population_(millions)`                     HDI          GDP_per_Capita 
               1.003850                1.850481                1.854204 
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=316117&T=9

[TABLE]
[ROW][C]Variance Inflation Factors (Multicollinearity)[/C][/ROW]
[ROW][C]> vif
`Population_(millions)`                     HDI          GDP_per_Capita 
               1.003850                1.850481                1.854204 
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=316117&T=9

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=316117&T=9

As an alternative you can also use a QR Code:

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

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

Figure 1

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Figure 2

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Figure 3

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Figure 4

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Figure 5

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Figure 6

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Figure 7

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Figure 8

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Figure 9

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Figure 10

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PDF link

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