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R Software Module

rwasp_multipleregression.wasp

Title produced by software

Multiple Regression

Date of computation

Thu, 29 Aug 2019 11:28:29 +0200

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Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2019/Aug/29/t1567070964vw0tcdn358ksn7m.htm/, Retrieved Mon, 20 May 2024 00:43:11 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=318889, Retrieved Mon, 20 May 2024 00:43:11 +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] [Countries 1 Multi...] [2019-08-29 09:28:29] [a402f79d74ae0cb0ee81df74c36c0208] [Current]

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0.46 29.82 614.66 0.3 0.2 0.18 0 0.02 0.04
0.73 3.16 4534.37 0.78 0.22 0.87 0.02 0.29 0.06
0.73 38.48 5430.57 0.6 0.16 1.14 0.01 0.03 0.03
0.52 20.82 4665.91 0.33 0.15 0.2 0.09 0.64 0.04
0.78 0.09 13205.1 NA NA NA NA NA NA
0.83 41.09 13540 0.78 0.79 1.08 0.1 0.66 0.1
0.73 2.97 3426.39 0.74 0.18 0.89 0.01 0.1 0.07
NA 0.1 NA NA NA NA NA NA NA
0.93 23.05 66604.2 2.68 0.63 4.85 0.11 2.01 0.14
0.88 8.46 51274.1 0.82 0.27 4.14 0.06 2.04 0.15
0.75 9.31 7106.04 0.66 0.22 1.25 0.01 0.11 0.06
0.78 0.37 22647.3 0.97 1.05 4.46 0.14 1.18 0.04
0.82 1.32 24299 0.52 0.45 6.19 0.07 0 0.1
0.56 154.7 857.5 0.29 0 0.26 0.02 0 0.07
0.79 0.28 15722.8 0.56 0.24 3.28 0.22 0.02 0.04
0.8 9.4 6300.45 1.32 0.12 2.57 0.08 1.71 0.09
0.89 11.06 48053.3 1.15 0.48 4.43 0.12 0.28 0.27
0.48 10.05 746.83 0.49 0.04 0.51 0.08 0.34 0.04
NA 0.06 70626.3 NA NA NA NA NA NA
0.59 0.74 2395 0.5 0.42 0.63 0.02 4.38 0.25
0.65 10.5 2253.09 0.37 1.69 0.67 0.01 13.86 0.06
0.73 3.83 4708.85 0.63 0.27 1.74 0.02 1.13 0.02
0.69 2 7743.5 0.3 0.89 2.36 0.01 0.63 0.02
0.75 198.66 13237.6 0.62 0.85 0.91 0.04 6.82 0.09
NA 0.03 NA NA NA NA NA NA NA
0.85 0.41 47097.4 0.31 0.13 3.24 0.09 1.57 0.03
0.78 7.28 7615.28 0.6 0.13 2.08 0.03 1.2 0.13
0.39 16.46 671.07 0.47 0.17 0.12 0.03 0.25 0.05
0.39 9.85 276.69 0.21 0.07 0.04 0 0.01 0.03
0.64 0.49 3801.45 NA NA NA NA NA NA
0.55 14.86 877.64 NA NA NA NA NA NA
0.5 21.7 1271.21 0.54 0.1 0.19 0.08 0.92 0.05
0.91 34.84 52145.4 1.46 0.33 5 0.12 9.12 0.07
NA 0.06 NA 0.36 0.45 3.56 0.05 0.19 0
0.37 4.53 495.04 0.3 0.55 0.08 0.02 6.98 0.04
0.39 12.45 1161.22 0.36 0.75 0.01 0.01 0.41 0.05
0.83 17.46 14525.8 0.61 0.33 2.04 0.25 2.19 0.15
0.72 1408.04 5560.94 0.55 0.13 2.32 0.08 0.22 0.12
0.72 47.7 7305.22 0.35 0.58 0.67 0.03 2.09 0.09
0.5 0.72 860.24 0.33 0.03 0.25 0.25 0.02 0
0.57 4.34 1943.69 0.22 0.09 0.47 0.1 7.33 0.03
0.42 65.7 338.63 0.15 0.01 0.07 0.01 2.52 0.05
0.76 4.8 8979.96 0.4 0.21 1.37 0.08 0.63 0.1
NA 19.84 1016.83 0.51 0.06 0.26 0.14 0.48 0.08
0.82 4.31 14522.8 0.74 0.13 2.21 0.06 1.55 0.06
0.77 11.27 5175.94 0.48 0.11 1.23 0.02 0.23 0.03
0.85 1.13 31454.7 0.77 0.08 2.94 0.14 0.06 0.05
0.87 10.66 21676.3 0.62 0.24 3.42 0.03 1.31 0.13
0.92 5.6 61413.6 1.18 0.47 2.6 0.24 0.34 0.25
0.46 0.86 1433.17 NA NA NA NA NA NA
0.72 0.07 7088.01 0.64 0.15 1.47 0.17 0.52 0
0.71 10.28 6085.89 0.35 0.12 0.86 0.04 0.16 0.05
0.73 15.49 5192.88 0.3 0.3 1.08 0.18 1.41 0.06
0.69 80.72 2930.33 0.68 0.08 1.02 0.04 0 0.15
0.66 6.3 3696.33 0.44 0.21 0.84 0.14 0.04 0.05
0.58 0.74 24064 0.27 0.02 3.17 0.22 3.1 0.02
0.39 6.13 439.73 0.1 0.18 0.03 0.01 0.09 0.02
0.85 1.29 17304.4 NA NA NA NA NA NA
0.43 91.73 379.38 0.31 0.12 0.07 0 0.05 0.06
0.72 0.88 4201.37 0.55 0.2 1.06 0.62 1.3 0.04
0.88 5.41 50960.2 NA NA NA NA NA NA
0.89 63.98 45430.3 1.23 0.27 2.71 0.19 0.96 0.21
NA 0.24 NA 0.07 0.06 1.58 0.17 95.16 0
NA 0.27 NA 0.75 0.65 2.39 0.82 0.73 0
0.67 1.63 11989 0.53 0.13 0.43 0.11 19.5 0.03
0.44 1.79 505.76 0.46 0.05 0.21 0.08 0.2 0.03
0.75 4.36 3710.7 0.39 0.2 0.83 0.02 0.61 0.04
0.91 82.8 46822.4 1.1 0.14 3.28 0.05 0.68 0.24
0.57 25.37 1627.9 0.56 0.07 0.43 0.19 0.29 0.07
0.86 11.12 25987.4 1.07 0.35 2.58 0.08 0.23 0.06
0.74 0.1 7410.48 NA NA NA NA NA NA
NA 0.46 NA 0.11 0.03 2.61 0.31 0.13 0
0.62 15.08 3233.8 0.37 0.16 0.7 0.01 0.34 0.07
0.41 11.45 459.09 0.39 0.32 0.16 0.05 0.64 0.04
0.42 1.66 681.25 0.35 0.36 0.09 0.01 0.34 0.06
0.63 0.8 3269.46 0.7 0.22 1.25 0.06 59.19 0.06
0.48 10.17 749.13 0.27 0.04 0.15 0.01 0.01 0.03
0.61 7.94 2269.51 0.28 0.25 0.6 0 0.89 0.06
0.82 9.98 13964.2 0.42 0.08 1.9 0.02 0.69 0.13
0.6 1236.69 1513.85 0.34 0.01 0.61 0.02 0.02 0.05
0.68 246.86 3688.53 0.44 0.03 0.64 0.21 0.3 0.06
0.76 76.42 7511.1 0.69 0.09 1.72 0.12 0.06 0.09
0.65 32.78 5848.54 0.43 0.03 1.36 0.01 0.05 0.04
0.91 4.58 52853.6 1.08 0.49 3.22 0.18 0.5 0.13
0.89 7.64 33718.9 0.89 0.22 4.59 0.07 0.03 0.08
0.87 60.92 38412 0.91 0.32 2.77 0.12 0.33 0.06
0.72 2.77 5226.3 0.41 0.09 1.09 0.07 0.1 0.05
0.89 127.25 46201.6 0.53 0.1 3.69 0.33 0.35 0.1
0.75 7.01 4615.17 0.54 0.18 1.09 0.03 0.03 0.07
0.78 16.27 11278 0.58 0.19 4.59 0.03 0.24 0.03
0.54 43.18 1062.11 0.25 0.23 0.2 0.05 0.02 0.04
NA 24.76 NA 0.28 0 0.68 0.02 0.21 0.06
0.89 49 24155.8 0.71 0.12 4.17 0.41 0.09 0.06
0.82 3.25 41830.5 0.55 0.24 6.89 0.09 0 0.15
0.65 5.47 1116.37 0.59 0.18 0.95 0.01 0.06 0.08
0.56 6.65 1236.24 0.57 0.08 0.09 0.01 0.78 0.11
0.81 2.06 13732 2.28 0.09 1.66 0.11 4.08 0.13
0.76 4.65 9143.86 0.67 0.3 2.52 0.05 0.06 0.06
0.48 2.05 1338.42 0.22 0.49 0.51 0 0 0.01
0.42 4.19 397.38 0.23 0.03 0.14 0.03 1.45 0.03
0.74 6.16 5859.43 0.79 0.32 2.33 0.1 0.02 0.02
0.83 3.03 14373.7 1.89 0.21 2.15 0.16 2.09 0.15
0.89 0.52 114665 1.1 0.76 12.65 0.13 0.89 0.14
0.74 2.11 5174.89 0.62 0.21 2.06 0.03 0.92 0.02
0.51 22.29 456.33 0.27 0.34 0.07 0.02 0.8 0.06
0.43 15.91 493.84 0.43 0.05 0.07 0.01 0.02 0.05
0.77 29.24 10252.6 0.67 0.12 2.1 0.36 0.73 0.07
0.41 14.85 741.22 0.52 0.66 0.1 0.03 0.25 0.06
NA 0.4 NA 0.13 0.02 1.73 0.04 0.1 0.04
0.5 3.8 1524.39 0.39 1.2 0.55 0.15 0.05 0.04
0.77 1.24 8811.15 0.52 0.22 1.99 0.55 0.01 0
0.75 120.85 10123.9 0.55 0.24 1.74 0.06 0.47 0.05
0.68 3.51 1971.03 0.43 0.08 1.03 0.06 0.1 0.03
0.71 2.8 3736.07 0.29 3.47 2.09 0 7.03 0.05
0.8 0.62 7251.6 0.64 0.33 2.13 0.05 2.63 0.01
NA 0 NA NA NA NA NA NA NA
0.62 32.52 3149.43 0.6 0.17 0.67 0.06 0.1 0.03
0.41 25.2 538.82 0.31 0.03 0.17 0.03 0.61 0.05
0.53 52.8 1117.58 0.8 0.01 0.09 0.1 0.56 0.11
0.62 2.26 5880.8 0.33 0.23 1.02 0.72 0.36 0.02
NA 0.01 NA NA NA NA NA NA NA
0.54 27.47 700.07 0.43 0.07 0.16 0 0.06 0.11
0.92 16.71 53589.9 0.76 0.58 3.23 0.16 0.08 0.17
NA 0.25 NA 0.68 0.62 1.78 0.32 1.94 0
0.91 4.46 37488.3 0.63 0.23 2.84 0.7 5.95 0.13
0.63 5.99 1626.85 0.34 0.12 0.45 0.03 0.74 0.05
0.34 17.16 410.91 0.67 0.48 0.1 0.02 0.05 0.03
0.5 168.83 2612.12 0.53 0.1 0.21 0.08 0.02 0.05
0.94 4.99 100172 NA NA NA NA NA NA
0.79 3.31 22622.8 0.57 0.4 5.8 0.4 0 0.19
0.53 179.16 1218.6 0.27 0.01 0.38 0.02 0.01 0.03
0.77 3.8 8410.77 0.36 0.4 1.44 0.37 1.74 0.02
0.5 7.17 1871.21 0.3 0.05 0.35 0.73 2.66 0.13
0.67 6.69 3557.31 1.11 1.1 0.97 0.01 5.54 0.14
0.73 29.99 5684.73 0.5 0.51 0.67 0.33 2.82 0.07
0.66 96.71 2379.44 0.36 0.03 0.34 0.23 0.09 0.05
0.84 38.21 13769.5 0.84 0.04 2.64 0.05 0.77 0.09
0.83 10.6 23217.3 1.03 0.25 2.15 0.31 0.85 0.05
0.85 2.05 99431.5 0.57 0.27 9.57 0.19 0 0.06
NA 0.86 NA 0.14 0.01 3.27 0.09 0.04 0
0.79 21.76 9213.94 0.72 0.05 1.46 0.03 1.14 0.12
0.79 143.17 13320.2 0.77 0.15 3.87 0.19 4.38 0.03
0.48 11.46 628.08 0.43 0.05 0.07 0.01 0.01 0.05
0.74 0.05 12952.5 0.51 0.18 3.34 0.81 0.17 0
0.73 0.18 7737.2 0.38 0.1 1.56 0.23 0.1 0
0.72 0.11 6171.48 NA NA NA NA NA NA
0.7 0.19 4067.15 0.97 0.16 0.96 0.42 1.17 0
0.55 0.19 1384.53 0.36 0.02 0.37 0.47 0.22 0
0.83 28.29 23593.8 0.74 0.27 4.21 0.08 0.07 0.04
0.46 13.73 1079.27 0.34 0.21 0.3 0.12 0.46 0.02
0.76 9.55 6426.18 0.49 0.02 1.66 0.02 0.46 0.04
0.4 5.98 499.89 0.47 0.11 0.07 0.15 0.19 0.05
0.91 5.3 53122.4 0.67 0.24 5.91 0.22 0 0.03
0.84 5.45 18103.1 0.31 0.08 2.82 0.03 1.85 0.09
0.88 2.07 25040.5 0.64 0.17 4.27 0.05 1.87 0.04
0.5 0.55 1647.86 0.47 0.03 0 0.46 2.28 0.23
NA 10.2 NA 0.16 0.43 0.07 0.01 0.22 0.06
0.66 52.39 8089.87 0.44 0.11 2.34 0.08 0.02 0.04
0.87 46.76 32008.7 0.78 0.15 2.22 0.32 0.34 0.04
0.75 21.1 2880.03 0.31 0.01 0.52 0.27 0.04 0.05
0.71 0.54 8190.7 0.43 0.06 3.01 0.14 81.52 0.09
0.53 1.23 4657.48 0.35 0.41 0.67 0.01 0.05 0.06
0.9 9.51 59381.9 1.47 0.27 3.88 0.09 6.66 0.24
0.93 8 88506.2 0.75 0.22 4.26 0.07 0.74 0.12
0.62 21.89 NA 0.52 0.09 0.81 0.01 0.04 0.05
0.62 8.01 836.17 0.46 0.14 0.13 0 0.01 0.08
0.51 47.78 765.33 0.44 0.34 0.17 0.09 0.17 0.06
0.72 66.78 5479.29 0.67 0.02 1.54 0.13 0.2 0.07
0.6 1.11 5167.86 0.25 0.07 0.06 0.02 0.52 0.04
0.47 6.64 580.86 0.34 0.1 0.31 0.08 0.03 0.02
0.72 0.1 4330.9 1.19 0.32 0.88 0.18 0.11 0
0.77 1.34 18310.8 0.46 0.19 6.89 0.11 0.15 0
0.72 10.88 4305.07 0.76 0.09 1.11 0.06 0.05 0.04
0.76 74 10437.7 0.87 0.12 1.92 0.04 0.6 0.04
0.68 5.17 5290.14 0.73 0.44 4.13 0.01 0.02 0.09
0.48 36.35 601.35 0.34 0.15 0.08 0.11 0.01 0.04
0.74 45.53 3589.63 0.62 0.01 1.92 0.06 0.43 0.07
0.9 63.03 40980.5 0.82 0.28 3.14 0.08 0.12 0.18
0.83 9.206 40817.4 0.8 0.19 6.37 0.19 0.07 0
0.91 317.5 49725 1.13 0.3 5.9 0.12 1.57 0.09
0.79 3.4 14238.1 0.19 0.98 0.98 0.05 1.22 0.17
0.67 28.54 1560.85 0.62 0.13 1.41 0 0.06 0.08
0.763846 29.96 10237.8 0.45 0.74 2.13 0.09 1.79 0.04
0.66 90.8 1532.31 0.5 0.01 0.79 0.05 0.17 0.1
NA 0.01 NA NA NA NA NA NA NA
0.5 23.85 1302.3 0.34 0.14 0.42 0.04 0.04 0.04
0.58 14.08 1740.64 0.19 0.18 0.24 0.01 0.99 0.04
0.49 13.72 865.91 0.2 0.32 0.53 0.01 0.12 0.02

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	3 seconds
R Server	Big Analytics Cloud Computing Center
R Framework error message	Warning: there are blank lines in the 'Data X' field. Please, use NA for missing data - blank lines are simply deleted and are NOT treated as missing values.

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input view raw input (R code)  \tabularnewline
Raw Outputview raw output of R engine  \tabularnewline
Computing time3 seconds \tabularnewline
R ServerBig Analytics Cloud Computing Center \tabularnewline
R Framework error message & Warning: there are blank lines in the 'Data X' field.
Please, use NA for missing data - blank lines are simply
 deleted and are NOT treated as missing values. \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=318889&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]

3 seconds[/C][/ROW] [ROW]

R Server[/C]

Big Analytics Cloud Computing Center[/C][/ROW] [ROW]

R Framework error message[/C][C]

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

[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=318889&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=318889&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	3 seconds
R Server	Big Analytics Cloud Computing Center
R Framework error message	Warning: there are blank lines in the 'Data X' field. Please, use NA for missing data - blank lines are simply deleted and are NOT treated as missing values.

Multiple Linear Regression - Estimated Regression Equation
HDI[t] = + 0.50066 + 2.71874e-05`Population_(millions)`[t] + 8.58678e-07GDP_per_Capita[t] + 0.118058Cropland_Footprint[t] + 0.00514867Grazing_Footprint[t] + 0.0365121Carbon_Footprint[t] + 0.109187Fish_Footprint[t] + 1.99391e-05Forest_Land[t] + 0.290125Urban_Land[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
HDI[t] =  +  0.50066 +  2.71874e-05`Population_(millions)`[t] +  8.58678e-07GDP_per_Capita[t] +  0.118058Cropland_Footprint[t] +  0.00514867Grazing_Footprint[t] +  0.0365121Carbon_Footprint[t] +  0.109187Fish_Footprint[t] +  1.99391e-05Forest_Land[t] +  0.290125Urban_Land[t]  + e[t] \tabularnewline
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=318889&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]HDI[t] =  +  0.50066 +  2.71874e-05`Population_(millions)`[t] +  8.58678e-07GDP_per_Capita[t] +  0.118058Cropland_Footprint[t] +  0.00514867Grazing_Footprint[t] +  0.0365121Carbon_Footprint[t] +  0.109187Fish_Footprint[t] +  1.99391e-05Forest_Land[t] +  0.290125Urban_Land[t]  + e[t][/C][/ROW]
[ROW][C][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=318889&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=318889&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
HDI[t] = + 0.50066 + 2.71874e-05`Population_(millions)`[t] + 8.58678e-07GDP_per_Capita[t] + 0.118058Cropland_Footprint[t] + 0.00514867Grazing_Footprint[t] + 0.0365121Carbon_Footprint[t] + 0.109187Fish_Footprint[t] + 1.99391e-05Forest_Land[t] + 0.290125Urban_Land[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)	+0.5007	0.01951	+2.5660e+01	2.669e-57	1.334e-57
`Population_(millions)`	+2.719e-05	5.347e-05	+5.0840e-01	0.6119	0.3059
GDP_per_Capita	+8.587e-07	8.024e-07	+1.0700e+00	0.2862	0.1431
Cropland_Footprint	+0.1181	0.02693	+4.3830e+00	2.162e-05	1.081e-05
Grazing_Footprint	+0.005149	0.02279	+2.2600e-01	0.8215	0.4108
Carbon_Footprint	+0.03651	0.007497	+4.8700e+00	2.756e-06	1.378e-06
Fish_Footprint	+0.1092	0.05379	+2.0300e+00	0.0441	0.02205
Forest_Land	+1.994e-05	0.0009885	+2.0170e-02	0.9839	0.492
Urban_Land	+0.2901	0.1722	+1.6850e+00	0.09407	0.04704

\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) & +0.5007 &  0.01951 & +2.5660e+01 &  2.669e-57 &  1.334e-57 \tabularnewline
`Population_(millions)` & +2.719e-05 &  5.347e-05 & +5.0840e-01 &  0.6119 &  0.3059 \tabularnewline
GDP_per_Capita & +8.587e-07 &  8.024e-07 & +1.0700e+00 &  0.2862 &  0.1431 \tabularnewline
Cropland_Footprint & +0.1181 &  0.02693 & +4.3830e+00 &  2.162e-05 &  1.081e-05 \tabularnewline
Grazing_Footprint & +0.005149 &  0.02279 & +2.2600e-01 &  0.8215 &  0.4108 \tabularnewline
Carbon_Footprint & +0.03651 &  0.007497 & +4.8700e+00 &  2.756e-06 &  1.378e-06 \tabularnewline
Fish_Footprint & +0.1092 &  0.05379 & +2.0300e+00 &  0.0441 &  0.02205 \tabularnewline
Forest_Land & +1.994e-05 &  0.0009885 & +2.0170e-02 &  0.9839 &  0.492 \tabularnewline
Urban_Land & +0.2901 &  0.1722 & +1.6850e+00 &  0.09407 &  0.04704 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=318889&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]+0.5007[/C][C] 0.01951[/C][C]+2.5660e+01[/C][C] 2.669e-57[/C][C] 1.334e-57[/C][/ROW]
[ROW][C]`Population_(millions)`[/C][C]+2.719e-05[/C][C] 5.347e-05[/C][C]+5.0840e-01[/C][C] 0.6119[/C][C] 0.3059[/C][/ROW]
[ROW][C]GDP_per_Capita[/C][C]+8.587e-07[/C][C] 8.024e-07[/C][C]+1.0700e+00[/C][C] 0.2862[/C][C] 0.1431[/C][/ROW]
[ROW][C]Cropland_Footprint[/C][C]+0.1181[/C][C] 0.02693[/C][C]+4.3830e+00[/C][C] 2.162e-05[/C][C] 1.081e-05[/C][/ROW]
[ROW][C]Grazing_Footprint[/C][C]+0.005149[/C][C] 0.02279[/C][C]+2.2600e-01[/C][C] 0.8215[/C][C] 0.4108[/C][/ROW]
[ROW][C]Carbon_Footprint[/C][C]+0.03651[/C][C] 0.007497[/C][C]+4.8700e+00[/C][C] 2.756e-06[/C][C] 1.378e-06[/C][/ROW]
[ROW][C]Fish_Footprint[/C][C]+0.1092[/C][C] 0.05379[/C][C]+2.0300e+00[/C][C] 0.0441[/C][C] 0.02205[/C][/ROW]
[ROW][C]Forest_Land[/C][C]+1.994e-05[/C][C] 0.0009885[/C][C]+2.0170e-02[/C][C] 0.9839[/C][C] 0.492[/C][/ROW]
[ROW][C]Urban_Land[/C][C]+0.2901[/C][C] 0.1722[/C][C]+1.6850e+00[/C][C] 0.09407[/C][C] 0.04704[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=318889&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=318889&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)	+0.5007	0.01951	+2.5660e+01	2.669e-57	1.334e-57
`Population_(millions)`	+2.719e-05	5.347e-05	+5.0840e-01	0.6119	0.3059
GDP_per_Capita	+8.587e-07	8.024e-07	+1.0700e+00	0.2862	0.1431
Cropland_Footprint	+0.1181	0.02693	+4.3830e+00	2.162e-05	1.081e-05
Grazing_Footprint	+0.005149	0.02279	+2.2600e-01	0.8215	0.4108
Carbon_Footprint	+0.03651	0.007497	+4.8700e+00	2.756e-06	1.378e-06
Fish_Footprint	+0.1092	0.05379	+2.0300e+00	0.0441	0.02205
Forest_Land	+1.994e-05	0.0009885	+2.0170e-02	0.9839	0.492
Urban_Land	+0.2901	0.1722	+1.6850e+00	0.09407	0.04704

Multiple Linear Regression - Regression Statistics
Multiple R	0.7771
R-squared	0.6039
Adjusted R-squared	0.5832
F-TEST (value)	29.16
F-TEST (DF numerator)	8
F-TEST (DF denominator)	153
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	0.1011
Sum Squared Residuals	1.563

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R &  0.7771 \tabularnewline
R-squared &  0.6039 \tabularnewline
Adjusted R-squared &  0.5832 \tabularnewline
F-TEST (value) &  29.16 \tabularnewline
F-TEST (DF numerator) & 8 \tabularnewline
F-TEST (DF denominator) & 153 \tabularnewline
p-value &  0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation &  0.1011 \tabularnewline
Sum Squared Residuals &  1.563 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=318889&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C] 0.7771[/C][/ROW]
[ROW][C]R-squared[/C][C] 0.6039[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C] 0.5832[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C] 29.16[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]8[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]153[/C][/ROW]
[ROW][C]p-value[/C][C] 0[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C] 0.1011[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C] 1.563[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=318889&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=318889&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	0.7771
R-squared	0.6039
Adjusted R-squared	0.5832
F-TEST (value)	29.16
F-TEST (DF numerator)	8
F-TEST (DF denominator)	153
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	0.1011
Sum Squared Residuals	1.563

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=318889&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=318889&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=318889&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	0.46	0.5566	-0.09662
2	0.73	0.6492	0.08078
3	0.73	0.6294	0.1006
4	0.52	0.5737	-0.05371
5	0.83	0.6889	0.1411
6	0.73	0.6459	0.08413
7	0.93	1.108	-0.1779
8	0.88	0.8444	0.03561
9	0.75	0.6502	0.09979
10	0.78	0.8298	-0.0498
11	0.82	0.8479	-0.02793
12	0.56	0.5718	-0.01183
13	0.79	0.7369	0.0531
14	0.8	0.7915	0.008503
15	0.89	0.9337	-0.04365
16	0.48	0.5986	-0.1186
17	0.59	0.6617	-0.07173
18	0.65	0.5985	0.0515
19	0.73	0.6521	0.07789
20	0.69	0.6404	0.04956
21	0.75	0.6588	0.09116
22	0.85	0.7152	0.1348
23	0.78	0.6959	0.08414
24	0.39	0.5802	-0.1902
25	0.39	0.5365	-0.1465
26	0.5	0.5968	-0.09681
27	0.91	0.9366	-0.0266
28	0.37	0.5563	-0.1863
29	0.39	0.5643	-0.1743
30	0.83	0.7327	0.09733
31	0.72	0.7376	-0.01758
32	0.72	0.6064	0.1136
33	0.5	0.577	-0.07696
34	0.57	0.5658	0.004187
35	0.42	0.5387	-0.1187
36	0.76	0.6446	0.1154
37	0.82	0.706	0.114
38	0.77	0.6184	0.1516
39	0.85	0.7562	0.09384
40	0.87	0.7599	0.1101
41	0.92	0.889	0.03105
42	0.72	0.6553	0.06468
43	0.71	0.5984	0.1116
44	0.73	0.619	0.111
45	0.69	0.6712	0.01881
46	0.66	0.6175	0.0425
47	0.58	0.699	-0.119
48	0.39	0.5219	-0.1319
49	0.43	0.5607	-0.1307
50	0.72	0.6883	0.03172
51	0.89	0.8687	0.02135
52	0.67	0.611	0.05896
53	0.44	0.5808	-0.1408
54	0.75	0.5951	0.1549
55	0.91	0.8686	0.04144
56	0.57	0.626	-0.05598
57	0.86	0.7718	0.08825
58	0.62	0.5953	0.02468
59	0.41	0.572	-0.162
60	0.42	0.5663	-0.1463
61	0.63	0.658	-0.02804
62	0.48	0.5489	-0.06893
63	0.61	0.5765	0.0335
64	0.82	0.6722	0.1478
65	0.6	0.6147	-0.01474
66	0.68	0.6263	0.05365
67	0.76	0.6931	0.06687
68	0.65	0.6198	0.03015
69	0.91	0.8511	0.05886
70	0.89	0.8345	0.05553
71	0.87	0.776	0.09396
72	0.72	0.616	0.104
73	0.89	0.8067	0.08334
74	0.75	0.6329	0.1171
75	0.78	0.7598	0.02019
76	0.54	0.5578	-0.01781
77	0.89	0.8216	0.06839
78	0.82	0.9077	-0.08775
79	0.65	0.6313	0.01866
80	0.56	0.6059	-0.04592
81	0.81	0.8926	-0.08256
82	0.76	0.7042	0.05584
83	0.48	0.5519	-0.07188
84	0.42	0.5455	-0.1255
85	0.74	0.7026	0.03743
86	0.83	0.8768	-0.04683
87	0.89	1.25	-0.3596
88	0.74	0.6637	0.07625
89	0.51	0.5574	-0.04745
90	0.43	0.5707	-0.1407
91	0.77	0.7263	0.04372
92	0.41	0.5908	-0.1808
93	0.5	0.6024	-0.1024
94	0.77	0.7035	0.0665
95	0.75	0.6634	0.0866
96	0.68	0.6065	0.07351
97	0.71	0.647	0.063
98	0.8	0.6703	0.1297
99	0.62	0.6157	0.004321
100	0.41	0.5626	-0.1526
101	0.53	0.6437	-0.1137
102	0.62	0.6676	-0.04758
103	0.54	0.5909	-0.05089
104	0.92	0.8246	0.09543
105	0.91	0.8265	0.08351
106	0.63	0.5772	0.0528
107	0.34	0.5976	-0.2576
108	0.5	0.6015	-0.1015
109	0.79	0.9001	-0.1101
110	0.53	0.5633	-0.03327
111	0.77	0.6514	0.1186
112	0.5	0.6684	-0.1684
113	0.67	0.7178	-0.04784
114	0.73	0.6489	0.08113
115	0.66	0.6	0.05998
116	0.84	0.7409	0.09912
117	0.83	0.7706	0.05936
118	0.85	1.042	-0.1924
119	0.79	0.6858	0.1042
120	0.79	0.7785	0.01149
121	0.48	0.5707	-0.09069
122	0.74	0.7833	-0.04332
123	0.73	0.6348	0.09524
124	0.7	0.7004	-0.0004319
125	0.55	0.6093	-0.05929
126	0.83	0.7845	0.0455
127	0.46	0.573	-0.113
128	0.76	0.6388	0.1212
129	0.4	0.5907	-0.1908
130	0.91	0.8753	0.03473
131	0.84	0.6858	0.1542
132	0.88	0.7717	0.1083
133	0.5	0.6747	-0.1747
134	0.66	0.6673	-0.007322
135	0.87	0.7499	0.1201
136	0.75	0.6033	0.1467
137	0.71	0.7117	-0.001706
138	0.53	0.5911	-0.06109
139	0.9	0.9481	-0.0481
140	0.93	0.8646	0.06543
141	0.62	0.5846	0.03542
142	0.51	0.5898	-0.07976
143	0.72	0.6771	0.04288
144	0.6	0.551	0.04901
145	0.47	0.5679	-0.09785
146	0.72	0.6983	0.02169
147	0.77	0.8353	-0.06529
148	0.72	0.6535	0.06647
149	0.76	0.7011	0.05895
150	0.68	0.7718	-0.09179
151	0.48	0.5696	-0.08961
152	0.74	0.6752	0.0648
153	0.9	0.8114	0.08858
154	0.83	0.8847	-0.05471
155	0.91	0.9416	-0.03161
156	0.79	0.631	0.159
157	0.67	0.6513	0.01866
158	0.7638	0.6664	0.09741
159	0.66	0.6268	0.03315
160	0.5	0.5746	-0.0746
161	0.58	0.5474	0.03262
162	0.49	0.5533	-0.06328

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 &  0.46 &  0.5566 & -0.09662 \tabularnewline
2 &  0.73 &  0.6492 &  0.08078 \tabularnewline
3 &  0.73 &  0.6294 &  0.1006 \tabularnewline
4 &  0.52 &  0.5737 & -0.05371 \tabularnewline
5 &  0.83 &  0.6889 &  0.1411 \tabularnewline
6 &  0.73 &  0.6459 &  0.08413 \tabularnewline
7 &  0.93 &  1.108 & -0.1779 \tabularnewline
8 &  0.88 &  0.8444 &  0.03561 \tabularnewline
9 &  0.75 &  0.6502 &  0.09979 \tabularnewline
10 &  0.78 &  0.8298 & -0.0498 \tabularnewline
11 &  0.82 &  0.8479 & -0.02793 \tabularnewline
12 &  0.56 &  0.5718 & -0.01183 \tabularnewline
13 &  0.79 &  0.7369 &  0.0531 \tabularnewline
14 &  0.8 &  0.7915 &  0.008503 \tabularnewline
15 &  0.89 &  0.9337 & -0.04365 \tabularnewline
16 &  0.48 &  0.5986 & -0.1186 \tabularnewline
17 &  0.59 &  0.6617 & -0.07173 \tabularnewline
18 &  0.65 &  0.5985 &  0.0515 \tabularnewline
19 &  0.73 &  0.6521 &  0.07789 \tabularnewline
20 &  0.69 &  0.6404 &  0.04956 \tabularnewline
21 &  0.75 &  0.6588 &  0.09116 \tabularnewline
22 &  0.85 &  0.7152 &  0.1348 \tabularnewline
23 &  0.78 &  0.6959 &  0.08414 \tabularnewline
24 &  0.39 &  0.5802 & -0.1902 \tabularnewline
25 &  0.39 &  0.5365 & -0.1465 \tabularnewline
26 &  0.5 &  0.5968 & -0.09681 \tabularnewline
27 &  0.91 &  0.9366 & -0.0266 \tabularnewline
28 &  0.37 &  0.5563 & -0.1863 \tabularnewline
29 &  0.39 &  0.5643 & -0.1743 \tabularnewline
30 &  0.83 &  0.7327 &  0.09733 \tabularnewline
31 &  0.72 &  0.7376 & -0.01758 \tabularnewline
32 &  0.72 &  0.6064 &  0.1136 \tabularnewline
33 &  0.5 &  0.577 & -0.07696 \tabularnewline
34 &  0.57 &  0.5658 &  0.004187 \tabularnewline
35 &  0.42 &  0.5387 & -0.1187 \tabularnewline
36 &  0.76 &  0.6446 &  0.1154 \tabularnewline
37 &  0.82 &  0.706 &  0.114 \tabularnewline
38 &  0.77 &  0.6184 &  0.1516 \tabularnewline
39 &  0.85 &  0.7562 &  0.09384 \tabularnewline
40 &  0.87 &  0.7599 &  0.1101 \tabularnewline
41 &  0.92 &  0.889 &  0.03105 \tabularnewline
42 &  0.72 &  0.6553 &  0.06468 \tabularnewline
43 &  0.71 &  0.5984 &  0.1116 \tabularnewline
44 &  0.73 &  0.619 &  0.111 \tabularnewline
45 &  0.69 &  0.6712 &  0.01881 \tabularnewline
46 &  0.66 &  0.6175 &  0.0425 \tabularnewline
47 &  0.58 &  0.699 & -0.119 \tabularnewline
48 &  0.39 &  0.5219 & -0.1319 \tabularnewline
49 &  0.43 &  0.5607 & -0.1307 \tabularnewline
50 &  0.72 &  0.6883 &  0.03172 \tabularnewline
51 &  0.89 &  0.8687 &  0.02135 \tabularnewline
52 &  0.67 &  0.611 &  0.05896 \tabularnewline
53 &  0.44 &  0.5808 & -0.1408 \tabularnewline
54 &  0.75 &  0.5951 &  0.1549 \tabularnewline
55 &  0.91 &  0.8686 &  0.04144 \tabularnewline
56 &  0.57 &  0.626 & -0.05598 \tabularnewline
57 &  0.86 &  0.7718 &  0.08825 \tabularnewline
58 &  0.62 &  0.5953 &  0.02468 \tabularnewline
59 &  0.41 &  0.572 & -0.162 \tabularnewline
60 &  0.42 &  0.5663 & -0.1463 \tabularnewline
61 &  0.63 &  0.658 & -0.02804 \tabularnewline
62 &  0.48 &  0.5489 & -0.06893 \tabularnewline
63 &  0.61 &  0.5765 &  0.0335 \tabularnewline
64 &  0.82 &  0.6722 &  0.1478 \tabularnewline
65 &  0.6 &  0.6147 & -0.01474 \tabularnewline
66 &  0.68 &  0.6263 &  0.05365 \tabularnewline
67 &  0.76 &  0.6931 &  0.06687 \tabularnewline
68 &  0.65 &  0.6198 &  0.03015 \tabularnewline
69 &  0.91 &  0.8511 &  0.05886 \tabularnewline
70 &  0.89 &  0.8345 &  0.05553 \tabularnewline
71 &  0.87 &  0.776 &  0.09396 \tabularnewline
72 &  0.72 &  0.616 &  0.104 \tabularnewline
73 &  0.89 &  0.8067 &  0.08334 \tabularnewline
74 &  0.75 &  0.6329 &  0.1171 \tabularnewline
75 &  0.78 &  0.7598 &  0.02019 \tabularnewline
76 &  0.54 &  0.5578 & -0.01781 \tabularnewline
77 &  0.89 &  0.8216 &  0.06839 \tabularnewline
78 &  0.82 &  0.9077 & -0.08775 \tabularnewline
79 &  0.65 &  0.6313 &  0.01866 \tabularnewline
80 &  0.56 &  0.6059 & -0.04592 \tabularnewline
81 &  0.81 &  0.8926 & -0.08256 \tabularnewline
82 &  0.76 &  0.7042 &  0.05584 \tabularnewline
83 &  0.48 &  0.5519 & -0.07188 \tabularnewline
84 &  0.42 &  0.5455 & -0.1255 \tabularnewline
85 &  0.74 &  0.7026 &  0.03743 \tabularnewline
86 &  0.83 &  0.8768 & -0.04683 \tabularnewline
87 &  0.89 &  1.25 & -0.3596 \tabularnewline
88 &  0.74 &  0.6637 &  0.07625 \tabularnewline
89 &  0.51 &  0.5574 & -0.04745 \tabularnewline
90 &  0.43 &  0.5707 & -0.1407 \tabularnewline
91 &  0.77 &  0.7263 &  0.04372 \tabularnewline
92 &  0.41 &  0.5908 & -0.1808 \tabularnewline
93 &  0.5 &  0.6024 & -0.1024 \tabularnewline
94 &  0.77 &  0.7035 &  0.0665 \tabularnewline
95 &  0.75 &  0.6634 &  0.0866 \tabularnewline
96 &  0.68 &  0.6065 &  0.07351 \tabularnewline
97 &  0.71 &  0.647 &  0.063 \tabularnewline
98 &  0.8 &  0.6703 &  0.1297 \tabularnewline
99 &  0.62 &  0.6157 &  0.004321 \tabularnewline
100 &  0.41 &  0.5626 & -0.1526 \tabularnewline
101 &  0.53 &  0.6437 & -0.1137 \tabularnewline
102 &  0.62 &  0.6676 & -0.04758 \tabularnewline
103 &  0.54 &  0.5909 & -0.05089 \tabularnewline
104 &  0.92 &  0.8246 &  0.09543 \tabularnewline
105 &  0.91 &  0.8265 &  0.08351 \tabularnewline
106 &  0.63 &  0.5772 &  0.0528 \tabularnewline
107 &  0.34 &  0.5976 & -0.2576 \tabularnewline
108 &  0.5 &  0.6015 & -0.1015 \tabularnewline
109 &  0.79 &  0.9001 & -0.1101 \tabularnewline
110 &  0.53 &  0.5633 & -0.03327 \tabularnewline
111 &  0.77 &  0.6514 &  0.1186 \tabularnewline
112 &  0.5 &  0.6684 & -0.1684 \tabularnewline
113 &  0.67 &  0.7178 & -0.04784 \tabularnewline
114 &  0.73 &  0.6489 &  0.08113 \tabularnewline
115 &  0.66 &  0.6 &  0.05998 \tabularnewline
116 &  0.84 &  0.7409 &  0.09912 \tabularnewline
117 &  0.83 &  0.7706 &  0.05936 \tabularnewline
118 &  0.85 &  1.042 & -0.1924 \tabularnewline
119 &  0.79 &  0.6858 &  0.1042 \tabularnewline
120 &  0.79 &  0.7785 &  0.01149 \tabularnewline
121 &  0.48 &  0.5707 & -0.09069 \tabularnewline
122 &  0.74 &  0.7833 & -0.04332 \tabularnewline
123 &  0.73 &  0.6348 &  0.09524 \tabularnewline
124 &  0.7 &  0.7004 & -0.0004319 \tabularnewline
125 &  0.55 &  0.6093 & -0.05929 \tabularnewline
126 &  0.83 &  0.7845 &  0.0455 \tabularnewline
127 &  0.46 &  0.573 & -0.113 \tabularnewline
128 &  0.76 &  0.6388 &  0.1212 \tabularnewline
129 &  0.4 &  0.5907 & -0.1908 \tabularnewline
130 &  0.91 &  0.8753 &  0.03473 \tabularnewline
131 &  0.84 &  0.6858 &  0.1542 \tabularnewline
132 &  0.88 &  0.7717 &  0.1083 \tabularnewline
133 &  0.5 &  0.6747 & -0.1747 \tabularnewline
134 &  0.66 &  0.6673 & -0.007322 \tabularnewline
135 &  0.87 &  0.7499 &  0.1201 \tabularnewline
136 &  0.75 &  0.6033 &  0.1467 \tabularnewline
137 &  0.71 &  0.7117 & -0.001706 \tabularnewline
138 &  0.53 &  0.5911 & -0.06109 \tabularnewline
139 &  0.9 &  0.9481 & -0.0481 \tabularnewline
140 &  0.93 &  0.8646 &  0.06543 \tabularnewline
141 &  0.62 &  0.5846 &  0.03542 \tabularnewline
142 &  0.51 &  0.5898 & -0.07976 \tabularnewline
143 &  0.72 &  0.6771 &  0.04288 \tabularnewline
144 &  0.6 &  0.551 &  0.04901 \tabularnewline
145 &  0.47 &  0.5679 & -0.09785 \tabularnewline
146 &  0.72 &  0.6983 &  0.02169 \tabularnewline
147 &  0.77 &  0.8353 & -0.06529 \tabularnewline
148 &  0.72 &  0.6535 &  0.06647 \tabularnewline
149 &  0.76 &  0.7011 &  0.05895 \tabularnewline
150 &  0.68 &  0.7718 & -0.09179 \tabularnewline
151 &  0.48 &  0.5696 & -0.08961 \tabularnewline
152 &  0.74 &  0.6752 &  0.0648 \tabularnewline
153 &  0.9 &  0.8114 &  0.08858 \tabularnewline
154 &  0.83 &  0.8847 & -0.05471 \tabularnewline
155 &  0.91 &  0.9416 & -0.03161 \tabularnewline
156 &  0.79 &  0.631 &  0.159 \tabularnewline
157 &  0.67 &  0.6513 &  0.01866 \tabularnewline
158 &  0.7638 &  0.6664 &  0.09741 \tabularnewline
159 &  0.66 &  0.6268 &  0.03315 \tabularnewline
160 &  0.5 &  0.5746 & -0.0746 \tabularnewline
161 &  0.58 &  0.5474 &  0.03262 \tabularnewline
162 &  0.49 &  0.5533 & -0.06328 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=318889&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] 0.46[/C][C] 0.5566[/C][C]-0.09662[/C][/ROW]
[ROW][C]2[/C][C] 0.73[/C][C] 0.6492[/C][C] 0.08078[/C][/ROW]
[ROW][C]3[/C][C] 0.73[/C][C] 0.6294[/C][C] 0.1006[/C][/ROW]
[ROW][C]4[/C][C] 0.52[/C][C] 0.5737[/C][C]-0.05371[/C][/ROW]
[ROW][C]5[/C][C] 0.83[/C][C] 0.6889[/C][C] 0.1411[/C][/ROW]
[ROW][C]6[/C][C] 0.73[/C][C] 0.6459[/C][C] 0.08413[/C][/ROW]
[ROW][C]7[/C][C] 0.93[/C][C] 1.108[/C][C]-0.1779[/C][/ROW]
[ROW][C]8[/C][C] 0.88[/C][C] 0.8444[/C][C] 0.03561[/C][/ROW]
[ROW][C]9[/C][C] 0.75[/C][C] 0.6502[/C][C] 0.09979[/C][/ROW]
[ROW][C]10[/C][C] 0.78[/C][C] 0.8298[/C][C]-0.0498[/C][/ROW]
[ROW][C]11[/C][C] 0.82[/C][C] 0.8479[/C][C]-0.02793[/C][/ROW]
[ROW][C]12[/C][C] 0.56[/C][C] 0.5718[/C][C]-0.01183[/C][/ROW]
[ROW][C]13[/C][C] 0.79[/C][C] 0.7369[/C][C] 0.0531[/C][/ROW]
[ROW][C]14[/C][C] 0.8[/C][C] 0.7915[/C][C] 0.008503[/C][/ROW]
[ROW][C]15[/C][C] 0.89[/C][C] 0.9337[/C][C]-0.04365[/C][/ROW]
[ROW][C]16[/C][C] 0.48[/C][C] 0.5986[/C][C]-0.1186[/C][/ROW]
[ROW][C]17[/C][C] 0.59[/C][C] 0.6617[/C][C]-0.07173[/C][/ROW]
[ROW][C]18[/C][C] 0.65[/C][C] 0.5985[/C][C] 0.0515[/C][/ROW]
[ROW][C]19[/C][C] 0.73[/C][C] 0.6521[/C][C] 0.07789[/C][/ROW]
[ROW][C]20[/C][C] 0.69[/C][C] 0.6404[/C][C] 0.04956[/C][/ROW]
[ROW][C]21[/C][C] 0.75[/C][C] 0.6588[/C][C] 0.09116[/C][/ROW]
[ROW][C]22[/C][C] 0.85[/C][C] 0.7152[/C][C] 0.1348[/C][/ROW]
[ROW][C]23[/C][C] 0.78[/C][C] 0.6959[/C][C] 0.08414[/C][/ROW]
[ROW][C]24[/C][C] 0.39[/C][C] 0.5802[/C][C]-0.1902[/C][/ROW]
[ROW][C]25[/C][C] 0.39[/C][C] 0.5365[/C][C]-0.1465[/C][/ROW]
[ROW][C]26[/C][C] 0.5[/C][C] 0.5968[/C][C]-0.09681[/C][/ROW]
[ROW][C]27[/C][C] 0.91[/C][C] 0.9366[/C][C]-0.0266[/C][/ROW]
[ROW][C]28[/C][C] 0.37[/C][C] 0.5563[/C][C]-0.1863[/C][/ROW]
[ROW][C]29[/C][C] 0.39[/C][C] 0.5643[/C][C]-0.1743[/C][/ROW]
[ROW][C]30[/C][C] 0.83[/C][C] 0.7327[/C][C] 0.09733[/C][/ROW]
[ROW][C]31[/C][C] 0.72[/C][C] 0.7376[/C][C]-0.01758[/C][/ROW]
[ROW][C]32[/C][C] 0.72[/C][C] 0.6064[/C][C] 0.1136[/C][/ROW]
[ROW][C]33[/C][C] 0.5[/C][C] 0.577[/C][C]-0.07696[/C][/ROW]
[ROW][C]34[/C][C] 0.57[/C][C] 0.5658[/C][C] 0.004187[/C][/ROW]
[ROW][C]35[/C][C] 0.42[/C][C] 0.5387[/C][C]-0.1187[/C][/ROW]
[ROW][C]36[/C][C] 0.76[/C][C] 0.6446[/C][C] 0.1154[/C][/ROW]
[ROW][C]37[/C][C] 0.82[/C][C] 0.706[/C][C] 0.114[/C][/ROW]
[ROW][C]38[/C][C] 0.77[/C][C] 0.6184[/C][C] 0.1516[/C][/ROW]
[ROW][C]39[/C][C] 0.85[/C][C] 0.7562[/C][C] 0.09384[/C][/ROW]
[ROW][C]40[/C][C] 0.87[/C][C] 0.7599[/C][C] 0.1101[/C][/ROW]
[ROW][C]41[/C][C] 0.92[/C][C] 0.889[/C][C] 0.03105[/C][/ROW]
[ROW][C]42[/C][C] 0.72[/C][C] 0.6553[/C][C] 0.06468[/C][/ROW]
[ROW][C]43[/C][C] 0.71[/C][C] 0.5984[/C][C] 0.1116[/C][/ROW]
[ROW][C]44[/C][C] 0.73[/C][C] 0.619[/C][C] 0.111[/C][/ROW]
[ROW][C]45[/C][C] 0.69[/C][C] 0.6712[/C][C] 0.01881[/C][/ROW]
[ROW][C]46[/C][C] 0.66[/C][C] 0.6175[/C][C] 0.0425[/C][/ROW]
[ROW][C]47[/C][C] 0.58[/C][C] 0.699[/C][C]-0.119[/C][/ROW]
[ROW][C]48[/C][C] 0.39[/C][C] 0.5219[/C][C]-0.1319[/C][/ROW]
[ROW][C]49[/C][C] 0.43[/C][C] 0.5607[/C][C]-0.1307[/C][/ROW]
[ROW][C]50[/C][C] 0.72[/C][C] 0.6883[/C][C] 0.03172[/C][/ROW]
[ROW][C]51[/C][C] 0.89[/C][C] 0.8687[/C][C] 0.02135[/C][/ROW]
[ROW][C]52[/C][C] 0.67[/C][C] 0.611[/C][C] 0.05896[/C][/ROW]
[ROW][C]53[/C][C] 0.44[/C][C] 0.5808[/C][C]-0.1408[/C][/ROW]
[ROW][C]54[/C][C] 0.75[/C][C] 0.5951[/C][C] 0.1549[/C][/ROW]
[ROW][C]55[/C][C] 0.91[/C][C] 0.8686[/C][C] 0.04144[/C][/ROW]
[ROW][C]56[/C][C] 0.57[/C][C] 0.626[/C][C]-0.05598[/C][/ROW]
[ROW][C]57[/C][C] 0.86[/C][C] 0.7718[/C][C] 0.08825[/C][/ROW]
[ROW][C]58[/C][C] 0.62[/C][C] 0.5953[/C][C] 0.02468[/C][/ROW]
[ROW][C]59[/C][C] 0.41[/C][C] 0.572[/C][C]-0.162[/C][/ROW]
[ROW][C]60[/C][C] 0.42[/C][C] 0.5663[/C][C]-0.1463[/C][/ROW]
[ROW][C]61[/C][C] 0.63[/C][C] 0.658[/C][C]-0.02804[/C][/ROW]
[ROW][C]62[/C][C] 0.48[/C][C] 0.5489[/C][C]-0.06893[/C][/ROW]
[ROW][C]63[/C][C] 0.61[/C][C] 0.5765[/C][C] 0.0335[/C][/ROW]
[ROW][C]64[/C][C] 0.82[/C][C] 0.6722[/C][C] 0.1478[/C][/ROW]
[ROW][C]65[/C][C] 0.6[/C][C] 0.6147[/C][C]-0.01474[/C][/ROW]
[ROW][C]66[/C][C] 0.68[/C][C] 0.6263[/C][C] 0.05365[/C][/ROW]
[ROW][C]67[/C][C] 0.76[/C][C] 0.6931[/C][C] 0.06687[/C][/ROW]
[ROW][C]68[/C][C] 0.65[/C][C] 0.6198[/C][C] 0.03015[/C][/ROW]
[ROW][C]69[/C][C] 0.91[/C][C] 0.8511[/C][C] 0.05886[/C][/ROW]
[ROW][C]70[/C][C] 0.89[/C][C] 0.8345[/C][C] 0.05553[/C][/ROW]
[ROW][C]71[/C][C] 0.87[/C][C] 0.776[/C][C] 0.09396[/C][/ROW]
[ROW][C]72[/C][C] 0.72[/C][C] 0.616[/C][C] 0.104[/C][/ROW]
[ROW][C]73[/C][C] 0.89[/C][C] 0.8067[/C][C] 0.08334[/C][/ROW]
[ROW][C]74[/C][C] 0.75[/C][C] 0.6329[/C][C] 0.1171[/C][/ROW]
[ROW][C]75[/C][C] 0.78[/C][C] 0.7598[/C][C] 0.02019[/C][/ROW]
[ROW][C]76[/C][C] 0.54[/C][C] 0.5578[/C][C]-0.01781[/C][/ROW]
[ROW][C]77[/C][C] 0.89[/C][C] 0.8216[/C][C] 0.06839[/C][/ROW]
[ROW][C]78[/C][C] 0.82[/C][C] 0.9077[/C][C]-0.08775[/C][/ROW]
[ROW][C]79[/C][C] 0.65[/C][C] 0.6313[/C][C] 0.01866[/C][/ROW]
[ROW][C]80[/C][C] 0.56[/C][C] 0.6059[/C][C]-0.04592[/C][/ROW]
[ROW][C]81[/C][C] 0.81[/C][C] 0.8926[/C][C]-0.08256[/C][/ROW]
[ROW][C]82[/C][C] 0.76[/C][C] 0.7042[/C][C] 0.05584[/C][/ROW]
[ROW][C]83[/C][C] 0.48[/C][C] 0.5519[/C][C]-0.07188[/C][/ROW]
[ROW][C]84[/C][C] 0.42[/C][C] 0.5455[/C][C]-0.1255[/C][/ROW]
[ROW][C]85[/C][C] 0.74[/C][C] 0.7026[/C][C] 0.03743[/C][/ROW]
[ROW][C]86[/C][C] 0.83[/C][C] 0.8768[/C][C]-0.04683[/C][/ROW]
[ROW][C]87[/C][C] 0.89[/C][C] 1.25[/C][C]-0.3596[/C][/ROW]
[ROW][C]88[/C][C] 0.74[/C][C] 0.6637[/C][C] 0.07625[/C][/ROW]
[ROW][C]89[/C][C] 0.51[/C][C] 0.5574[/C][C]-0.04745[/C][/ROW]
[ROW][C]90[/C][C] 0.43[/C][C] 0.5707[/C][C]-0.1407[/C][/ROW]
[ROW][C]91[/C][C] 0.77[/C][C] 0.7263[/C][C] 0.04372[/C][/ROW]
[ROW][C]92[/C][C] 0.41[/C][C] 0.5908[/C][C]-0.1808[/C][/ROW]
[ROW][C]93[/C][C] 0.5[/C][C] 0.6024[/C][C]-0.1024[/C][/ROW]
[ROW][C]94[/C][C] 0.77[/C][C] 0.7035[/C][C] 0.0665[/C][/ROW]
[ROW][C]95[/C][C] 0.75[/C][C] 0.6634[/C][C] 0.0866[/C][/ROW]
[ROW][C]96[/C][C] 0.68[/C][C] 0.6065[/C][C] 0.07351[/C][/ROW]
[ROW][C]97[/C][C] 0.71[/C][C] 0.647[/C][C] 0.063[/C][/ROW]
[ROW][C]98[/C][C] 0.8[/C][C] 0.6703[/C][C] 0.1297[/C][/ROW]
[ROW][C]99[/C][C] 0.62[/C][C] 0.6157[/C][C] 0.004321[/C][/ROW]
[ROW][C]100[/C][C] 0.41[/C][C] 0.5626[/C][C]-0.1526[/C][/ROW]
[ROW][C]101[/C][C] 0.53[/C][C] 0.6437[/C][C]-0.1137[/C][/ROW]
[ROW][C]102[/C][C] 0.62[/C][C] 0.6676[/C][C]-0.04758[/C][/ROW]
[ROW][C]103[/C][C] 0.54[/C][C] 0.5909[/C][C]-0.05089[/C][/ROW]
[ROW][C]104[/C][C] 0.92[/C][C] 0.8246[/C][C] 0.09543[/C][/ROW]
[ROW][C]105[/C][C] 0.91[/C][C] 0.8265[/C][C] 0.08351[/C][/ROW]
[ROW][C]106[/C][C] 0.63[/C][C] 0.5772[/C][C] 0.0528[/C][/ROW]
[ROW][C]107[/C][C] 0.34[/C][C] 0.5976[/C][C]-0.2576[/C][/ROW]
[ROW][C]108[/C][C] 0.5[/C][C] 0.6015[/C][C]-0.1015[/C][/ROW]
[ROW][C]109[/C][C] 0.79[/C][C] 0.9001[/C][C]-0.1101[/C][/ROW]
[ROW][C]110[/C][C] 0.53[/C][C] 0.5633[/C][C]-0.03327[/C][/ROW]
[ROW][C]111[/C][C] 0.77[/C][C] 0.6514[/C][C] 0.1186[/C][/ROW]
[ROW][C]112[/C][C] 0.5[/C][C] 0.6684[/C][C]-0.1684[/C][/ROW]
[ROW][C]113[/C][C] 0.67[/C][C] 0.7178[/C][C]-0.04784[/C][/ROW]
[ROW][C]114[/C][C] 0.73[/C][C] 0.6489[/C][C] 0.08113[/C][/ROW]
[ROW][C]115[/C][C] 0.66[/C][C] 0.6[/C][C] 0.05998[/C][/ROW]
[ROW][C]116[/C][C] 0.84[/C][C] 0.7409[/C][C] 0.09912[/C][/ROW]
[ROW][C]117[/C][C] 0.83[/C][C] 0.7706[/C][C] 0.05936[/C][/ROW]
[ROW][C]118[/C][C] 0.85[/C][C] 1.042[/C][C]-0.1924[/C][/ROW]
[ROW][C]119[/C][C] 0.79[/C][C] 0.6858[/C][C] 0.1042[/C][/ROW]
[ROW][C]120[/C][C] 0.79[/C][C] 0.7785[/C][C] 0.01149[/C][/ROW]
[ROW][C]121[/C][C] 0.48[/C][C] 0.5707[/C][C]-0.09069[/C][/ROW]
[ROW][C]122[/C][C] 0.74[/C][C] 0.7833[/C][C]-0.04332[/C][/ROW]
[ROW][C]123[/C][C] 0.73[/C][C] 0.6348[/C][C] 0.09524[/C][/ROW]
[ROW][C]124[/C][C] 0.7[/C][C] 0.7004[/C][C]-0.0004319[/C][/ROW]
[ROW][C]125[/C][C] 0.55[/C][C] 0.6093[/C][C]-0.05929[/C][/ROW]
[ROW][C]126[/C][C] 0.83[/C][C] 0.7845[/C][C] 0.0455[/C][/ROW]
[ROW][C]127[/C][C] 0.46[/C][C] 0.573[/C][C]-0.113[/C][/ROW]
[ROW][C]128[/C][C] 0.76[/C][C] 0.6388[/C][C] 0.1212[/C][/ROW]
[ROW][C]129[/C][C] 0.4[/C][C] 0.5907[/C][C]-0.1908[/C][/ROW]
[ROW][C]130[/C][C] 0.91[/C][C] 0.8753[/C][C] 0.03473[/C][/ROW]
[ROW][C]131[/C][C] 0.84[/C][C] 0.6858[/C][C] 0.1542[/C][/ROW]
[ROW][C]132[/C][C] 0.88[/C][C] 0.7717[/C][C] 0.1083[/C][/ROW]
[ROW][C]133[/C][C] 0.5[/C][C] 0.6747[/C][C]-0.1747[/C][/ROW]
[ROW][C]134[/C][C] 0.66[/C][C] 0.6673[/C][C]-0.007322[/C][/ROW]
[ROW][C]135[/C][C] 0.87[/C][C] 0.7499[/C][C] 0.1201[/C][/ROW]
[ROW][C]136[/C][C] 0.75[/C][C] 0.6033[/C][C] 0.1467[/C][/ROW]
[ROW][C]137[/C][C] 0.71[/C][C] 0.7117[/C][C]-0.001706[/C][/ROW]
[ROW][C]138[/C][C] 0.53[/C][C] 0.5911[/C][C]-0.06109[/C][/ROW]
[ROW][C]139[/C][C] 0.9[/C][C] 0.9481[/C][C]-0.0481[/C][/ROW]
[ROW][C]140[/C][C] 0.93[/C][C] 0.8646[/C][C] 0.06543[/C][/ROW]
[ROW][C]141[/C][C] 0.62[/C][C] 0.5846[/C][C] 0.03542[/C][/ROW]
[ROW][C]142[/C][C] 0.51[/C][C] 0.5898[/C][C]-0.07976[/C][/ROW]
[ROW][C]143[/C][C] 0.72[/C][C] 0.6771[/C][C] 0.04288[/C][/ROW]
[ROW][C]144[/C][C] 0.6[/C][C] 0.551[/C][C] 0.04901[/C][/ROW]
[ROW][C]145[/C][C] 0.47[/C][C] 0.5679[/C][C]-0.09785[/C][/ROW]
[ROW][C]146[/C][C] 0.72[/C][C] 0.6983[/C][C] 0.02169[/C][/ROW]
[ROW][C]147[/C][C] 0.77[/C][C] 0.8353[/C][C]-0.06529[/C][/ROW]
[ROW][C]148[/C][C] 0.72[/C][C] 0.6535[/C][C] 0.06647[/C][/ROW]
[ROW][C]149[/C][C] 0.76[/C][C] 0.7011[/C][C] 0.05895[/C][/ROW]
[ROW][C]150[/C][C] 0.68[/C][C] 0.7718[/C][C]-0.09179[/C][/ROW]
[ROW][C]151[/C][C] 0.48[/C][C] 0.5696[/C][C]-0.08961[/C][/ROW]
[ROW][C]152[/C][C] 0.74[/C][C] 0.6752[/C][C] 0.0648[/C][/ROW]
[ROW][C]153[/C][C] 0.9[/C][C] 0.8114[/C][C] 0.08858[/C][/ROW]
[ROW][C]154[/C][C] 0.83[/C][C] 0.8847[/C][C]-0.05471[/C][/ROW]
[ROW][C]155[/C][C] 0.91[/C][C] 0.9416[/C][C]-0.03161[/C][/ROW]
[ROW][C]156[/C][C] 0.79[/C][C] 0.631[/C][C] 0.159[/C][/ROW]
[ROW][C]157[/C][C] 0.67[/C][C] 0.6513[/C][C] 0.01866[/C][/ROW]
[ROW][C]158[/C][C] 0.7638[/C][C] 0.6664[/C][C] 0.09741[/C][/ROW]
[ROW][C]159[/C][C] 0.66[/C][C] 0.6268[/C][C] 0.03315[/C][/ROW]
[ROW][C]160[/C][C] 0.5[/C][C] 0.5746[/C][C]-0.0746[/C][/ROW]
[ROW][C]161[/C][C] 0.58[/C][C] 0.5474[/C][C] 0.03262[/C][/ROW]
[ROW][C]162[/C][C] 0.49[/C][C] 0.5533[/C][C]-0.06328[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=318889&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=318889&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	0.46	0.5566	-0.09662
2	0.73	0.6492	0.08078
3	0.73	0.6294	0.1006
4	0.52	0.5737	-0.05371
5	0.83	0.6889	0.1411
6	0.73	0.6459	0.08413
7	0.93	1.108	-0.1779
8	0.88	0.8444	0.03561
9	0.75	0.6502	0.09979
10	0.78	0.8298	-0.0498
11	0.82	0.8479	-0.02793
12	0.56	0.5718	-0.01183
13	0.79	0.7369	0.0531
14	0.8	0.7915	0.008503
15	0.89	0.9337	-0.04365
16	0.48	0.5986	-0.1186
17	0.59	0.6617	-0.07173
18	0.65	0.5985	0.0515
19	0.73	0.6521	0.07789
20	0.69	0.6404	0.04956
21	0.75	0.6588	0.09116
22	0.85	0.7152	0.1348
23	0.78	0.6959	0.08414
24	0.39	0.5802	-0.1902
25	0.39	0.5365	-0.1465
26	0.5	0.5968	-0.09681
27	0.91	0.9366	-0.0266
28	0.37	0.5563	-0.1863
29	0.39	0.5643	-0.1743
30	0.83	0.7327	0.09733
31	0.72	0.7376	-0.01758
32	0.72	0.6064	0.1136
33	0.5	0.577	-0.07696
34	0.57	0.5658	0.004187
35	0.42	0.5387	-0.1187
36	0.76	0.6446	0.1154
37	0.82	0.706	0.114
38	0.77	0.6184	0.1516
39	0.85	0.7562	0.09384
40	0.87	0.7599	0.1101
41	0.92	0.889	0.03105
42	0.72	0.6553	0.06468
43	0.71	0.5984	0.1116
44	0.73	0.619	0.111
45	0.69	0.6712	0.01881
46	0.66	0.6175	0.0425
47	0.58	0.699	-0.119
48	0.39	0.5219	-0.1319
49	0.43	0.5607	-0.1307
50	0.72	0.6883	0.03172
51	0.89	0.8687	0.02135
52	0.67	0.611	0.05896
53	0.44	0.5808	-0.1408
54	0.75	0.5951	0.1549
55	0.91	0.8686	0.04144
56	0.57	0.626	-0.05598
57	0.86	0.7718	0.08825
58	0.62	0.5953	0.02468
59	0.41	0.572	-0.162
60	0.42	0.5663	-0.1463
61	0.63	0.658	-0.02804
62	0.48	0.5489	-0.06893
63	0.61	0.5765	0.0335
64	0.82	0.6722	0.1478
65	0.6	0.6147	-0.01474
66	0.68	0.6263	0.05365
67	0.76	0.6931	0.06687
68	0.65	0.6198	0.03015
69	0.91	0.8511	0.05886
70	0.89	0.8345	0.05553
71	0.87	0.776	0.09396
72	0.72	0.616	0.104
73	0.89	0.8067	0.08334
74	0.75	0.6329	0.1171
75	0.78	0.7598	0.02019
76	0.54	0.5578	-0.01781
77	0.89	0.8216	0.06839
78	0.82	0.9077	-0.08775
79	0.65	0.6313	0.01866
80	0.56	0.6059	-0.04592
81	0.81	0.8926	-0.08256
82	0.76	0.7042	0.05584
83	0.48	0.5519	-0.07188
84	0.42	0.5455	-0.1255
85	0.74	0.7026	0.03743
86	0.83	0.8768	-0.04683
87	0.89	1.25	-0.3596
88	0.74	0.6637	0.07625
89	0.51	0.5574	-0.04745
90	0.43	0.5707	-0.1407
91	0.77	0.7263	0.04372
92	0.41	0.5908	-0.1808
93	0.5	0.6024	-0.1024
94	0.77	0.7035	0.0665
95	0.75	0.6634	0.0866
96	0.68	0.6065	0.07351
97	0.71	0.647	0.063
98	0.8	0.6703	0.1297
99	0.62	0.6157	0.004321
100	0.41	0.5626	-0.1526
101	0.53	0.6437	-0.1137
102	0.62	0.6676	-0.04758
103	0.54	0.5909	-0.05089
104	0.92	0.8246	0.09543
105	0.91	0.8265	0.08351
106	0.63	0.5772	0.0528
107	0.34	0.5976	-0.2576
108	0.5	0.6015	-0.1015
109	0.79	0.9001	-0.1101
110	0.53	0.5633	-0.03327
111	0.77	0.6514	0.1186
112	0.5	0.6684	-0.1684
113	0.67	0.7178	-0.04784
114	0.73	0.6489	0.08113
115	0.66	0.6	0.05998
116	0.84	0.7409	0.09912
117	0.83	0.7706	0.05936
118	0.85	1.042	-0.1924
119	0.79	0.6858	0.1042
120	0.79	0.7785	0.01149
121	0.48	0.5707	-0.09069
122	0.74	0.7833	-0.04332
123	0.73	0.6348	0.09524
124	0.7	0.7004	-0.0004319
125	0.55	0.6093	-0.05929
126	0.83	0.7845	0.0455
127	0.46	0.573	-0.113
128	0.76	0.6388	0.1212
129	0.4	0.5907	-0.1908
130	0.91	0.8753	0.03473
131	0.84	0.6858	0.1542
132	0.88	0.7717	0.1083
133	0.5	0.6747	-0.1747
134	0.66	0.6673	-0.007322
135	0.87	0.7499	0.1201
136	0.75	0.6033	0.1467
137	0.71	0.7117	-0.001706
138	0.53	0.5911	-0.06109
139	0.9	0.9481	-0.0481
140	0.93	0.8646	0.06543
141	0.62	0.5846	0.03542
142	0.51	0.5898	-0.07976
143	0.72	0.6771	0.04288
144	0.6	0.551	0.04901
145	0.47	0.5679	-0.09785
146	0.72	0.6983	0.02169
147	0.77	0.8353	-0.06529
148	0.72	0.6535	0.06647
149	0.76	0.7011	0.05895
150	0.68	0.7718	-0.09179
151	0.48	0.5696	-0.08961
152	0.74	0.6752	0.0648
153	0.9	0.8114	0.08858
154	0.83	0.8847	-0.05471
155	0.91	0.9416	-0.03161
156	0.79	0.631	0.159
157	0.67	0.6513	0.01866
158	0.7638	0.6664	0.09741
159	0.66	0.6268	0.03315
160	0.5	0.5746	-0.0746
161	0.58	0.5474	0.03262
162	0.49	0.5533	-0.06328

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
12	0.7626	0.4748	0.2374
13	0.7456	0.5088	0.2544
14	0.6224	0.7552	0.3776
15	0.6413	0.7175	0.3587
16	0.7292	0.5416	0.2708
17	0.6768	0.6464	0.3232
18	0.6259	0.7482	0.3741
19	0.552	0.8961	0.4481
20	0.4726	0.9451	0.5274
21	0.4311	0.8622	0.5689
22	0.4195	0.8391	0.5805
23	0.3924	0.7847	0.6076
24	0.6345	0.7311	0.3655
25	0.723	0.554	0.277
26	0.6928	0.6145	0.3072
27	0.6352	0.7296	0.3648
28	0.7414	0.5172	0.2586
29	0.8069	0.3862	0.1931
30	0.8004	0.3992	0.1996
31	0.7945	0.411	0.2055
32	0.8049	0.3902	0.1951
33	0.7792	0.4416	0.2208
34	0.7308	0.5383	0.2692
35	0.7296	0.5407	0.2704
36	0.7397	0.5205	0.2603
37	0.7536	0.4927	0.2464
38	0.8021	0.3958	0.1979
39	0.7863	0.4274	0.2137
40	0.7704	0.4591	0.2296
41	0.7305	0.5389	0.2695
42	0.7004	0.5992	0.2996
43	0.7013	0.5975	0.2987
44	0.6917	0.6166	0.3083
45	0.6451	0.7099	0.3549
46	0.5986	0.8027	0.4014
47	0.6644	0.6712	0.3356
48	0.6957	0.6085	0.3043
49	0.7123	0.5754	0.2877
50	0.6676	0.6648	0.3324
51	0.6208	0.7583	0.3792
52	0.5943	0.8115	0.4057
53	0.6216	0.7569	0.3784
54	0.6846	0.6307	0.3154
55	0.6444	0.7111	0.3556
56	0.6079	0.7843	0.3921
57	0.5961	0.8077	0.4039
58	0.5504	0.8993	0.4496
59	0.6083	0.7834	0.3917
60	0.6414	0.7171	0.3586
61	0.6045	0.7909	0.3955
62	0.574	0.8521	0.426
63	0.5319	0.9363	0.4681
64	0.5723	0.8554	0.4277
65	0.5372	0.9256	0.4628
66	0.4997	0.9994	0.5003
67	0.469	0.938	0.531
68	0.4247	0.8493	0.5753
69	0.3909	0.7818	0.6091
70	0.3555	0.7111	0.6445
71	0.3429	0.6858	0.6571
72	0.3444	0.6888	0.6556
73	0.3166	0.6332	0.6834
74	0.3348	0.6696	0.6652
75	0.3013	0.6025	0.6987
76	0.262	0.5241	0.738
77	0.2368	0.4736	0.7632
78	0.2771	0.5541	0.7229
79	0.2412	0.4825	0.7588
80	0.2097	0.4194	0.7903
81	0.1904	0.3809	0.8096
82	0.169	0.338	0.831
83	0.1539	0.3078	0.8461
84	0.1667	0.3334	0.8333
85	0.1419	0.2838	0.8581
86	0.1202	0.2405	0.8798
87	0.5861	0.8278	0.4139
88	0.5657	0.8687	0.4343
89	0.5269	0.9462	0.4731
90	0.5636	0.8727	0.4364
91	0.5244	0.9511	0.4756
92	0.6237	0.7526	0.3763
93	0.6282	0.7436	0.3718
94	0.5987	0.8025	0.4013
95	0.5874	0.8253	0.4126
96	0.5635	0.8729	0.4365
97	0.5368	0.9264	0.4632
98	0.5612	0.8776	0.4388
99	0.5127	0.9746	0.4873
100	0.5675	0.8651	0.4325
101	0.581	0.838	0.419
102	0.5533	0.8934	0.4467
103	0.5169	0.9661	0.4831
104	0.505	0.9899	0.495
105	0.5016	0.9969	0.4984
106	0.4624	0.9249	0.5376
107	0.7562	0.4877	0.2438
108	0.7657	0.4687	0.2343
109	0.7582	0.4837	0.2418
110	0.7285	0.543	0.2715
111	0.7534	0.4932	0.2466
112	0.7853	0.4294	0.2147
113	0.7761	0.4479	0.2239
114	0.7575	0.485	0.2425
115	0.7311	0.5378	0.2689
116	0.7202	0.5596	0.2798
117	0.6848	0.6303	0.3152
118	0.8299	0.3401	0.1701
119	0.8362	0.3276	0.1638
120	0.7994	0.4012	0.2006
121	0.7957	0.4086	0.2043
122	0.7574	0.4853	0.2426
123	0.7569	0.4862	0.2431
124	0.7199	0.5603	0.2801
125	0.6709	0.6583	0.3291
126	0.6204	0.7591	0.3796
127	0.639	0.722	0.361
128	0.6551	0.6898	0.3449
129	0.7941	0.4118	0.2059
130	0.7453	0.5094	0.2547
131	0.8154	0.3691	0.1846
132	0.8323	0.3354	0.1677
133	0.964	0.07209	0.03605
134	0.9462	0.1076	0.05378
135	0.9283	0.1433	0.07166
136	0.933	0.1341	0.06703
137	0.9235	0.1529	0.07647
138	0.923	0.154	0.07698
139	0.9999	0.0002703	0.0001351
140	0.9999	0.0002838	0.0001419
141	0.9997	0.0006951	0.0003475
142	0.9992	0.001581	0.0007904
143	0.9981	0.003795	0.001897
144	0.9952	0.009578	0.004789
145	0.9937	0.01253	0.006266
146	0.9845	0.03099	0.01549
147	0.984	0.0321	0.01605
148	0.9662	0.06762	0.03381
149	0.932	0.136	0.06801
150	0.9866	0.02688	0.01344

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
12 &  0.7626 &  0.4748 &  0.2374 \tabularnewline
13 &  0.7456 &  0.5088 &  0.2544 \tabularnewline
14 &  0.6224 &  0.7552 &  0.3776 \tabularnewline
15 &  0.6413 &  0.7175 &  0.3587 \tabularnewline
16 &  0.7292 &  0.5416 &  0.2708 \tabularnewline
17 &  0.6768 &  0.6464 &  0.3232 \tabularnewline
18 &  0.6259 &  0.7482 &  0.3741 \tabularnewline
19 &  0.552 &  0.8961 &  0.4481 \tabularnewline
20 &  0.4726 &  0.9451 &  0.5274 \tabularnewline
21 &  0.4311 &  0.8622 &  0.5689 \tabularnewline
22 &  0.4195 &  0.8391 &  0.5805 \tabularnewline
23 &  0.3924 &  0.7847 &  0.6076 \tabularnewline
24 &  0.6345 &  0.7311 &  0.3655 \tabularnewline
25 &  0.723 &  0.554 &  0.277 \tabularnewline
26 &  0.6928 &  0.6145 &  0.3072 \tabularnewline
27 &  0.6352 &  0.7296 &  0.3648 \tabularnewline
28 &  0.7414 &  0.5172 &  0.2586 \tabularnewline
29 &  0.8069 &  0.3862 &  0.1931 \tabularnewline
30 &  0.8004 &  0.3992 &  0.1996 \tabularnewline
31 &  0.7945 &  0.411 &  0.2055 \tabularnewline
32 &  0.8049 &  0.3902 &  0.1951 \tabularnewline
33 &  0.7792 &  0.4416 &  0.2208 \tabularnewline
34 &  0.7308 &  0.5383 &  0.2692 \tabularnewline
35 &  0.7296 &  0.5407 &  0.2704 \tabularnewline
36 &  0.7397 &  0.5205 &  0.2603 \tabularnewline
37 &  0.7536 &  0.4927 &  0.2464 \tabularnewline
38 &  0.8021 &  0.3958 &  0.1979 \tabularnewline
39 &  0.7863 &  0.4274 &  0.2137 \tabularnewline
40 &  0.7704 &  0.4591 &  0.2296 \tabularnewline
41 &  0.7305 &  0.5389 &  0.2695 \tabularnewline
42 &  0.7004 &  0.5992 &  0.2996 \tabularnewline
43 &  0.7013 &  0.5975 &  0.2987 \tabularnewline
44 &  0.6917 &  0.6166 &  0.3083 \tabularnewline
45 &  0.6451 &  0.7099 &  0.3549 \tabularnewline
46 &  0.5986 &  0.8027 &  0.4014 \tabularnewline
47 &  0.6644 &  0.6712 &  0.3356 \tabularnewline
48 &  0.6957 &  0.6085 &  0.3043 \tabularnewline
49 &  0.7123 &  0.5754 &  0.2877 \tabularnewline
50 &  0.6676 &  0.6648 &  0.3324 \tabularnewline
51 &  0.6208 &  0.7583 &  0.3792 \tabularnewline
52 &  0.5943 &  0.8115 &  0.4057 \tabularnewline
53 &  0.6216 &  0.7569 &  0.3784 \tabularnewline
54 &  0.6846 &  0.6307 &  0.3154 \tabularnewline
55 &  0.6444 &  0.7111 &  0.3556 \tabularnewline
56 &  0.6079 &  0.7843 &  0.3921 \tabularnewline
57 &  0.5961 &  0.8077 &  0.4039 \tabularnewline
58 &  0.5504 &  0.8993 &  0.4496 \tabularnewline
59 &  0.6083 &  0.7834 &  0.3917 \tabularnewline
60 &  0.6414 &  0.7171 &  0.3586 \tabularnewline
61 &  0.6045 &  0.7909 &  0.3955 \tabularnewline
62 &  0.574 &  0.8521 &  0.426 \tabularnewline
63 &  0.5319 &  0.9363 &  0.4681 \tabularnewline
64 &  0.5723 &  0.8554 &  0.4277 \tabularnewline
65 &  0.5372 &  0.9256 &  0.4628 \tabularnewline
66 &  0.4997 &  0.9994 &  0.5003 \tabularnewline
67 &  0.469 &  0.938 &  0.531 \tabularnewline
68 &  0.4247 &  0.8493 &  0.5753 \tabularnewline
69 &  0.3909 &  0.7818 &  0.6091 \tabularnewline
70 &  0.3555 &  0.7111 &  0.6445 \tabularnewline
71 &  0.3429 &  0.6858 &  0.6571 \tabularnewline
72 &  0.3444 &  0.6888 &  0.6556 \tabularnewline
73 &  0.3166 &  0.6332 &  0.6834 \tabularnewline
74 &  0.3348 &  0.6696 &  0.6652 \tabularnewline
75 &  0.3013 &  0.6025 &  0.6987 \tabularnewline
76 &  0.262 &  0.5241 &  0.738 \tabularnewline
77 &  0.2368 &  0.4736 &  0.7632 \tabularnewline
78 &  0.2771 &  0.5541 &  0.7229 \tabularnewline
79 &  0.2412 &  0.4825 &  0.7588 \tabularnewline
80 &  0.2097 &  0.4194 &  0.7903 \tabularnewline
81 &  0.1904 &  0.3809 &  0.8096 \tabularnewline
82 &  0.169 &  0.338 &  0.831 \tabularnewline
83 &  0.1539 &  0.3078 &  0.8461 \tabularnewline
84 &  0.1667 &  0.3334 &  0.8333 \tabularnewline
85 &  0.1419 &  0.2838 &  0.8581 \tabularnewline
86 &  0.1202 &  0.2405 &  0.8798 \tabularnewline
87 &  0.5861 &  0.8278 &  0.4139 \tabularnewline
88 &  0.5657 &  0.8687 &  0.4343 \tabularnewline
89 &  0.5269 &  0.9462 &  0.4731 \tabularnewline
90 &  0.5636 &  0.8727 &  0.4364 \tabularnewline
91 &  0.5244 &  0.9511 &  0.4756 \tabularnewline
92 &  0.6237 &  0.7526 &  0.3763 \tabularnewline
93 &  0.6282 &  0.7436 &  0.3718 \tabularnewline
94 &  0.5987 &  0.8025 &  0.4013 \tabularnewline
95 &  0.5874 &  0.8253 &  0.4126 \tabularnewline
96 &  0.5635 &  0.8729 &  0.4365 \tabularnewline
97 &  0.5368 &  0.9264 &  0.4632 \tabularnewline
98 &  0.5612 &  0.8776 &  0.4388 \tabularnewline
99 &  0.5127 &  0.9746 &  0.4873 \tabularnewline
100 &  0.5675 &  0.8651 &  0.4325 \tabularnewline
101 &  0.581 &  0.838 &  0.419 \tabularnewline
102 &  0.5533 &  0.8934 &  0.4467 \tabularnewline
103 &  0.5169 &  0.9661 &  0.4831 \tabularnewline
104 &  0.505 &  0.9899 &  0.495 \tabularnewline
105 &  0.5016 &  0.9969 &  0.4984 \tabularnewline
106 &  0.4624 &  0.9249 &  0.5376 \tabularnewline
107 &  0.7562 &  0.4877 &  0.2438 \tabularnewline
108 &  0.7657 &  0.4687 &  0.2343 \tabularnewline
109 &  0.7582 &  0.4837 &  0.2418 \tabularnewline
110 &  0.7285 &  0.543 &  0.2715 \tabularnewline
111 &  0.7534 &  0.4932 &  0.2466 \tabularnewline
112 &  0.7853 &  0.4294 &  0.2147 \tabularnewline
113 &  0.7761 &  0.4479 &  0.2239 \tabularnewline
114 &  0.7575 &  0.485 &  0.2425 \tabularnewline
115 &  0.7311 &  0.5378 &  0.2689 \tabularnewline
116 &  0.7202 &  0.5596 &  0.2798 \tabularnewline
117 &  0.6848 &  0.6303 &  0.3152 \tabularnewline
118 &  0.8299 &  0.3401 &  0.1701 \tabularnewline
119 &  0.8362 &  0.3276 &  0.1638 \tabularnewline
120 &  0.7994 &  0.4012 &  0.2006 \tabularnewline
121 &  0.7957 &  0.4086 &  0.2043 \tabularnewline
122 &  0.7574 &  0.4853 &  0.2426 \tabularnewline
123 &  0.7569 &  0.4862 &  0.2431 \tabularnewline
124 &  0.7199 &  0.5603 &  0.2801 \tabularnewline
125 &  0.6709 &  0.6583 &  0.3291 \tabularnewline
126 &  0.6204 &  0.7591 &  0.3796 \tabularnewline
127 &  0.639 &  0.722 &  0.361 \tabularnewline
128 &  0.6551 &  0.6898 &  0.3449 \tabularnewline
129 &  0.7941 &  0.4118 &  0.2059 \tabularnewline
130 &  0.7453 &  0.5094 &  0.2547 \tabularnewline
131 &  0.8154 &  0.3691 &  0.1846 \tabularnewline
132 &  0.8323 &  0.3354 &  0.1677 \tabularnewline
133 &  0.964 &  0.07209 &  0.03605 \tabularnewline
134 &  0.9462 &  0.1076 &  0.05378 \tabularnewline
135 &  0.9283 &  0.1433 &  0.07166 \tabularnewline
136 &  0.933 &  0.1341 &  0.06703 \tabularnewline
137 &  0.9235 &  0.1529 &  0.07647 \tabularnewline
138 &  0.923 &  0.154 &  0.07698 \tabularnewline
139 &  0.9999 &  0.0002703 &  0.0001351 \tabularnewline
140 &  0.9999 &  0.0002838 &  0.0001419 \tabularnewline
141 &  0.9997 &  0.0006951 &  0.0003475 \tabularnewline
142 &  0.9992 &  0.001581 &  0.0007904 \tabularnewline
143 &  0.9981 &  0.003795 &  0.001897 \tabularnewline
144 &  0.9952 &  0.009578 &  0.004789 \tabularnewline
145 &  0.9937 &  0.01253 &  0.006266 \tabularnewline
146 &  0.9845 &  0.03099 &  0.01549 \tabularnewline
147 &  0.984 &  0.0321 &  0.01605 \tabularnewline
148 &  0.9662 &  0.06762 &  0.03381 \tabularnewline
149 &  0.932 &  0.136 &  0.06801 \tabularnewline
150 &  0.9866 &  0.02688 &  0.01344 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=318889&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]12[/C][C] 0.7626[/C][C] 0.4748[/C][C] 0.2374[/C][/ROW]
[ROW][C]13[/C][C] 0.7456[/C][C] 0.5088[/C][C] 0.2544[/C][/ROW]
[ROW][C]14[/C][C] 0.6224[/C][C] 0.7552[/C][C] 0.3776[/C][/ROW]
[ROW][C]15[/C][C] 0.6413[/C][C] 0.7175[/C][C] 0.3587[/C][/ROW]
[ROW][C]16[/C][C] 0.7292[/C][C] 0.5416[/C][C] 0.2708[/C][/ROW]
[ROW][C]17[/C][C] 0.6768[/C][C] 0.6464[/C][C] 0.3232[/C][/ROW]
[ROW][C]18[/C][C] 0.6259[/C][C] 0.7482[/C][C] 0.3741[/C][/ROW]
[ROW][C]19[/C][C] 0.552[/C][C] 0.8961[/C][C] 0.4481[/C][/ROW]
[ROW][C]20[/C][C] 0.4726[/C][C] 0.9451[/C][C] 0.5274[/C][/ROW]
[ROW][C]21[/C][C] 0.4311[/C][C] 0.8622[/C][C] 0.5689[/C][/ROW]
[ROW][C]22[/C][C] 0.4195[/C][C] 0.8391[/C][C] 0.5805[/C][/ROW]
[ROW][C]23[/C][C] 0.3924[/C][C] 0.7847[/C][C] 0.6076[/C][/ROW]
[ROW][C]24[/C][C] 0.6345[/C][C] 0.7311[/C][C] 0.3655[/C][/ROW]
[ROW][C]25[/C][C] 0.723[/C][C] 0.554[/C][C] 0.277[/C][/ROW]
[ROW][C]26[/C][C] 0.6928[/C][C] 0.6145[/C][C] 0.3072[/C][/ROW]
[ROW][C]27[/C][C] 0.6352[/C][C] 0.7296[/C][C] 0.3648[/C][/ROW]
[ROW][C]28[/C][C] 0.7414[/C][C] 0.5172[/C][C] 0.2586[/C][/ROW]
[ROW][C]29[/C][C] 0.8069[/C][C] 0.3862[/C][C] 0.1931[/C][/ROW]
[ROW][C]30[/C][C] 0.8004[/C][C] 0.3992[/C][C] 0.1996[/C][/ROW]
[ROW][C]31[/C][C] 0.7945[/C][C] 0.411[/C][C] 0.2055[/C][/ROW]
[ROW][C]32[/C][C] 0.8049[/C][C] 0.3902[/C][C] 0.1951[/C][/ROW]
[ROW][C]33[/C][C] 0.7792[/C][C] 0.4416[/C][C] 0.2208[/C][/ROW]
[ROW][C]34[/C][C] 0.7308[/C][C] 0.5383[/C][C] 0.2692[/C][/ROW]
[ROW][C]35[/C][C] 0.7296[/C][C] 0.5407[/C][C] 0.2704[/C][/ROW]
[ROW][C]36[/C][C] 0.7397[/C][C] 0.5205[/C][C] 0.2603[/C][/ROW]
[ROW][C]37[/C][C] 0.7536[/C][C] 0.4927[/C][C] 0.2464[/C][/ROW]
[ROW][C]38[/C][C] 0.8021[/C][C] 0.3958[/C][C] 0.1979[/C][/ROW]
[ROW][C]39[/C][C] 0.7863[/C][C] 0.4274[/C][C] 0.2137[/C][/ROW]
[ROW][C]40[/C][C] 0.7704[/C][C] 0.4591[/C][C] 0.2296[/C][/ROW]
[ROW][C]41[/C][C] 0.7305[/C][C] 0.5389[/C][C] 0.2695[/C][/ROW]
[ROW][C]42[/C][C] 0.7004[/C][C] 0.5992[/C][C] 0.2996[/C][/ROW]
[ROW][C]43[/C][C] 0.7013[/C][C] 0.5975[/C][C] 0.2987[/C][/ROW]
[ROW][C]44[/C][C] 0.6917[/C][C] 0.6166[/C][C] 0.3083[/C][/ROW]
[ROW][C]45[/C][C] 0.6451[/C][C] 0.7099[/C][C] 0.3549[/C][/ROW]
[ROW][C]46[/C][C] 0.5986[/C][C] 0.8027[/C][C] 0.4014[/C][/ROW]
[ROW][C]47[/C][C] 0.6644[/C][C] 0.6712[/C][C] 0.3356[/C][/ROW]
[ROW][C]48[/C][C] 0.6957[/C][C] 0.6085[/C][C] 0.3043[/C][/ROW]
[ROW][C]49[/C][C] 0.7123[/C][C] 0.5754[/C][C] 0.2877[/C][/ROW]
[ROW][C]50[/C][C] 0.6676[/C][C] 0.6648[/C][C] 0.3324[/C][/ROW]
[ROW][C]51[/C][C] 0.6208[/C][C] 0.7583[/C][C] 0.3792[/C][/ROW]
[ROW][C]52[/C][C] 0.5943[/C][C] 0.8115[/C][C] 0.4057[/C][/ROW]
[ROW][C]53[/C][C] 0.6216[/C][C] 0.7569[/C][C] 0.3784[/C][/ROW]
[ROW][C]54[/C][C] 0.6846[/C][C] 0.6307[/C][C] 0.3154[/C][/ROW]
[ROW][C]55[/C][C] 0.6444[/C][C] 0.7111[/C][C] 0.3556[/C][/ROW]
[ROW][C]56[/C][C] 0.6079[/C][C] 0.7843[/C][C] 0.3921[/C][/ROW]
[ROW][C]57[/C][C] 0.5961[/C][C] 0.8077[/C][C] 0.4039[/C][/ROW]
[ROW][C]58[/C][C] 0.5504[/C][C] 0.8993[/C][C] 0.4496[/C][/ROW]
[ROW][C]59[/C][C] 0.6083[/C][C] 0.7834[/C][C] 0.3917[/C][/ROW]
[ROW][C]60[/C][C] 0.6414[/C][C] 0.7171[/C][C] 0.3586[/C][/ROW]
[ROW][C]61[/C][C] 0.6045[/C][C] 0.7909[/C][C] 0.3955[/C][/ROW]
[ROW][C]62[/C][C] 0.574[/C][C] 0.8521[/C][C] 0.426[/C][/ROW]
[ROW][C]63[/C][C] 0.5319[/C][C] 0.9363[/C][C] 0.4681[/C][/ROW]
[ROW][C]64[/C][C] 0.5723[/C][C] 0.8554[/C][C] 0.4277[/C][/ROW]
[ROW][C]65[/C][C] 0.5372[/C][C] 0.9256[/C][C] 0.4628[/C][/ROW]
[ROW][C]66[/C][C] 0.4997[/C][C] 0.9994[/C][C] 0.5003[/C][/ROW]
[ROW][C]67[/C][C] 0.469[/C][C] 0.938[/C][C] 0.531[/C][/ROW]
[ROW][C]68[/C][C] 0.4247[/C][C] 0.8493[/C][C] 0.5753[/C][/ROW]
[ROW][C]69[/C][C] 0.3909[/C][C] 0.7818[/C][C] 0.6091[/C][/ROW]
[ROW][C]70[/C][C] 0.3555[/C][C] 0.7111[/C][C] 0.6445[/C][/ROW]
[ROW][C]71[/C][C] 0.3429[/C][C] 0.6858[/C][C] 0.6571[/C][/ROW]
[ROW][C]72[/C][C] 0.3444[/C][C] 0.6888[/C][C] 0.6556[/C][/ROW]
[ROW][C]73[/C][C] 0.3166[/C][C] 0.6332[/C][C] 0.6834[/C][/ROW]
[ROW][C]74[/C][C] 0.3348[/C][C] 0.6696[/C][C] 0.6652[/C][/ROW]
[ROW][C]75[/C][C] 0.3013[/C][C] 0.6025[/C][C] 0.6987[/C][/ROW]
[ROW][C]76[/C][C] 0.262[/C][C] 0.5241[/C][C] 0.738[/C][/ROW]
[ROW][C]77[/C][C] 0.2368[/C][C] 0.4736[/C][C] 0.7632[/C][/ROW]
[ROW][C]78[/C][C] 0.2771[/C][C] 0.5541[/C][C] 0.7229[/C][/ROW]
[ROW][C]79[/C][C] 0.2412[/C][C] 0.4825[/C][C] 0.7588[/C][/ROW]
[ROW][C]80[/C][C] 0.2097[/C][C] 0.4194[/C][C] 0.7903[/C][/ROW]
[ROW][C]81[/C][C] 0.1904[/C][C] 0.3809[/C][C] 0.8096[/C][/ROW]
[ROW][C]82[/C][C] 0.169[/C][C] 0.338[/C][C] 0.831[/C][/ROW]
[ROW][C]83[/C][C] 0.1539[/C][C] 0.3078[/C][C] 0.8461[/C][/ROW]
[ROW][C]84[/C][C] 0.1667[/C][C] 0.3334[/C][C] 0.8333[/C][/ROW]
[ROW][C]85[/C][C] 0.1419[/C][C] 0.2838[/C][C] 0.8581[/C][/ROW]
[ROW][C]86[/C][C] 0.1202[/C][C] 0.2405[/C][C] 0.8798[/C][/ROW]
[ROW][C]87[/C][C] 0.5861[/C][C] 0.8278[/C][C] 0.4139[/C][/ROW]
[ROW][C]88[/C][C] 0.5657[/C][C] 0.8687[/C][C] 0.4343[/C][/ROW]
[ROW][C]89[/C][C] 0.5269[/C][C] 0.9462[/C][C] 0.4731[/C][/ROW]
[ROW][C]90[/C][C] 0.5636[/C][C] 0.8727[/C][C] 0.4364[/C][/ROW]
[ROW][C]91[/C][C] 0.5244[/C][C] 0.9511[/C][C] 0.4756[/C][/ROW]
[ROW][C]92[/C][C] 0.6237[/C][C] 0.7526[/C][C] 0.3763[/C][/ROW]
[ROW][C]93[/C][C] 0.6282[/C][C] 0.7436[/C][C] 0.3718[/C][/ROW]
[ROW][C]94[/C][C] 0.5987[/C][C] 0.8025[/C][C] 0.4013[/C][/ROW]
[ROW][C]95[/C][C] 0.5874[/C][C] 0.8253[/C][C] 0.4126[/C][/ROW]
[ROW][C]96[/C][C] 0.5635[/C][C] 0.8729[/C][C] 0.4365[/C][/ROW]
[ROW][C]97[/C][C] 0.5368[/C][C] 0.9264[/C][C] 0.4632[/C][/ROW]
[ROW][C]98[/C][C] 0.5612[/C][C] 0.8776[/C][C] 0.4388[/C][/ROW]
[ROW][C]99[/C][C] 0.5127[/C][C] 0.9746[/C][C] 0.4873[/C][/ROW]
[ROW][C]100[/C][C] 0.5675[/C][C] 0.8651[/C][C] 0.4325[/C][/ROW]
[ROW][C]101[/C][C] 0.581[/C][C] 0.838[/C][C] 0.419[/C][/ROW]
[ROW][C]102[/C][C] 0.5533[/C][C] 0.8934[/C][C] 0.4467[/C][/ROW]
[ROW][C]103[/C][C] 0.5169[/C][C] 0.9661[/C][C] 0.4831[/C][/ROW]
[ROW][C]104[/C][C] 0.505[/C][C] 0.9899[/C][C] 0.495[/C][/ROW]
[ROW][C]105[/C][C] 0.5016[/C][C] 0.9969[/C][C] 0.4984[/C][/ROW]
[ROW][C]106[/C][C] 0.4624[/C][C] 0.9249[/C][C] 0.5376[/C][/ROW]
[ROW][C]107[/C][C] 0.7562[/C][C] 0.4877[/C][C] 0.2438[/C][/ROW]
[ROW][C]108[/C][C] 0.7657[/C][C] 0.4687[/C][C] 0.2343[/C][/ROW]
[ROW][C]109[/C][C] 0.7582[/C][C] 0.4837[/C][C] 0.2418[/C][/ROW]
[ROW][C]110[/C][C] 0.7285[/C][C] 0.543[/C][C] 0.2715[/C][/ROW]
[ROW][C]111[/C][C] 0.7534[/C][C] 0.4932[/C][C] 0.2466[/C][/ROW]
[ROW][C]112[/C][C] 0.7853[/C][C] 0.4294[/C][C] 0.2147[/C][/ROW]
[ROW][C]113[/C][C] 0.7761[/C][C] 0.4479[/C][C] 0.2239[/C][/ROW]
[ROW][C]114[/C][C] 0.7575[/C][C] 0.485[/C][C] 0.2425[/C][/ROW]
[ROW][C]115[/C][C] 0.7311[/C][C] 0.5378[/C][C] 0.2689[/C][/ROW]
[ROW][C]116[/C][C] 0.7202[/C][C] 0.5596[/C][C] 0.2798[/C][/ROW]
[ROW][C]117[/C][C] 0.6848[/C][C] 0.6303[/C][C] 0.3152[/C][/ROW]
[ROW][C]118[/C][C] 0.8299[/C][C] 0.3401[/C][C] 0.1701[/C][/ROW]
[ROW][C]119[/C][C] 0.8362[/C][C] 0.3276[/C][C] 0.1638[/C][/ROW]
[ROW][C]120[/C][C] 0.7994[/C][C] 0.4012[/C][C] 0.2006[/C][/ROW]
[ROW][C]121[/C][C] 0.7957[/C][C] 0.4086[/C][C] 0.2043[/C][/ROW]
[ROW][C]122[/C][C] 0.7574[/C][C] 0.4853[/C][C] 0.2426[/C][/ROW]
[ROW][C]123[/C][C] 0.7569[/C][C] 0.4862[/C][C] 0.2431[/C][/ROW]
[ROW][C]124[/C][C] 0.7199[/C][C] 0.5603[/C][C] 0.2801[/C][/ROW]
[ROW][C]125[/C][C] 0.6709[/C][C] 0.6583[/C][C] 0.3291[/C][/ROW]
[ROW][C]126[/C][C] 0.6204[/C][C] 0.7591[/C][C] 0.3796[/C][/ROW]
[ROW][C]127[/C][C] 0.639[/C][C] 0.722[/C][C] 0.361[/C][/ROW]
[ROW][C]128[/C][C] 0.6551[/C][C] 0.6898[/C][C] 0.3449[/C][/ROW]
[ROW][C]129[/C][C] 0.7941[/C][C] 0.4118[/C][C] 0.2059[/C][/ROW]
[ROW][C]130[/C][C] 0.7453[/C][C] 0.5094[/C][C] 0.2547[/C][/ROW]
[ROW][C]131[/C][C] 0.8154[/C][C] 0.3691[/C][C] 0.1846[/C][/ROW]
[ROW][C]132[/C][C] 0.8323[/C][C] 0.3354[/C][C] 0.1677[/C][/ROW]
[ROW][C]133[/C][C] 0.964[/C][C] 0.07209[/C][C] 0.03605[/C][/ROW]
[ROW][C]134[/C][C] 0.9462[/C][C] 0.1076[/C][C] 0.05378[/C][/ROW]
[ROW][C]135[/C][C] 0.9283[/C][C] 0.1433[/C][C] 0.07166[/C][/ROW]
[ROW][C]136[/C][C] 0.933[/C][C] 0.1341[/C][C] 0.06703[/C][/ROW]
[ROW][C]137[/C][C] 0.9235[/C][C] 0.1529[/C][C] 0.07647[/C][/ROW]
[ROW][C]138[/C][C] 0.923[/C][C] 0.154[/C][C] 0.07698[/C][/ROW]
[ROW][C]139[/C][C] 0.9999[/C][C] 0.0002703[/C][C] 0.0001351[/C][/ROW]
[ROW][C]140[/C][C] 0.9999[/C][C] 0.0002838[/C][C] 0.0001419[/C][/ROW]
[ROW][C]141[/C][C] 0.9997[/C][C] 0.0006951[/C][C] 0.0003475[/C][/ROW]
[ROW][C]142[/C][C] 0.9992[/C][C] 0.001581[/C][C] 0.0007904[/C][/ROW]
[ROW][C]143[/C][C] 0.9981[/C][C] 0.003795[/C][C] 0.001897[/C][/ROW]
[ROW][C]144[/C][C] 0.9952[/C][C] 0.009578[/C][C] 0.004789[/C][/ROW]
[ROW][C]145[/C][C] 0.9937[/C][C] 0.01253[/C][C] 0.006266[/C][/ROW]
[ROW][C]146[/C][C] 0.9845[/C][C] 0.03099[/C][C] 0.01549[/C][/ROW]
[ROW][C]147[/C][C] 0.984[/C][C] 0.0321[/C][C] 0.01605[/C][/ROW]
[ROW][C]148[/C][C] 0.9662[/C][C] 0.06762[/C][C] 0.03381[/C][/ROW]
[ROW][C]149[/C][C] 0.932[/C][C] 0.136[/C][C] 0.06801[/C][/ROW]
[ROW][C]150[/C][C] 0.9866[/C][C] 0.02688[/C][C] 0.01344[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=318889&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=318889&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
12	0.7626	0.4748	0.2374
13	0.7456	0.5088	0.2544
14	0.6224	0.7552	0.3776
15	0.6413	0.7175	0.3587
16	0.7292	0.5416	0.2708
17	0.6768	0.6464	0.3232
18	0.6259	0.7482	0.3741
19	0.552	0.8961	0.4481
20	0.4726	0.9451	0.5274
21	0.4311	0.8622	0.5689
22	0.4195	0.8391	0.5805
23	0.3924	0.7847	0.6076
24	0.6345	0.7311	0.3655
25	0.723	0.554	0.277
26	0.6928	0.6145	0.3072
27	0.6352	0.7296	0.3648
28	0.7414	0.5172	0.2586
29	0.8069	0.3862	0.1931
30	0.8004	0.3992	0.1996
31	0.7945	0.411	0.2055
32	0.8049	0.3902	0.1951
33	0.7792	0.4416	0.2208
34	0.7308	0.5383	0.2692
35	0.7296	0.5407	0.2704
36	0.7397	0.5205	0.2603
37	0.7536	0.4927	0.2464
38	0.8021	0.3958	0.1979
39	0.7863	0.4274	0.2137
40	0.7704	0.4591	0.2296
41	0.7305	0.5389	0.2695
42	0.7004	0.5992	0.2996
43	0.7013	0.5975	0.2987
44	0.6917	0.6166	0.3083
45	0.6451	0.7099	0.3549
46	0.5986	0.8027	0.4014
47	0.6644	0.6712	0.3356
48	0.6957	0.6085	0.3043
49	0.7123	0.5754	0.2877
50	0.6676	0.6648	0.3324
51	0.6208	0.7583	0.3792
52	0.5943	0.8115	0.4057
53	0.6216	0.7569	0.3784
54	0.6846	0.6307	0.3154
55	0.6444	0.7111	0.3556
56	0.6079	0.7843	0.3921
57	0.5961	0.8077	0.4039
58	0.5504	0.8993	0.4496
59	0.6083	0.7834	0.3917
60	0.6414	0.7171	0.3586
61	0.6045	0.7909	0.3955
62	0.574	0.8521	0.426
63	0.5319	0.9363	0.4681
64	0.5723	0.8554	0.4277
65	0.5372	0.9256	0.4628
66	0.4997	0.9994	0.5003
67	0.469	0.938	0.531
68	0.4247	0.8493	0.5753
69	0.3909	0.7818	0.6091
70	0.3555	0.7111	0.6445
71	0.3429	0.6858	0.6571
72	0.3444	0.6888	0.6556
73	0.3166	0.6332	0.6834
74	0.3348	0.6696	0.6652
75	0.3013	0.6025	0.6987
76	0.262	0.5241	0.738
77	0.2368	0.4736	0.7632
78	0.2771	0.5541	0.7229
79	0.2412	0.4825	0.7588
80	0.2097	0.4194	0.7903
81	0.1904	0.3809	0.8096
82	0.169	0.338	0.831
83	0.1539	0.3078	0.8461
84	0.1667	0.3334	0.8333
85	0.1419	0.2838	0.8581
86	0.1202	0.2405	0.8798
87	0.5861	0.8278	0.4139
88	0.5657	0.8687	0.4343
89	0.5269	0.9462	0.4731
90	0.5636	0.8727	0.4364
91	0.5244	0.9511	0.4756
92	0.6237	0.7526	0.3763
93	0.6282	0.7436	0.3718
94	0.5987	0.8025	0.4013
95	0.5874	0.8253	0.4126
96	0.5635	0.8729	0.4365
97	0.5368	0.9264	0.4632
98	0.5612	0.8776	0.4388
99	0.5127	0.9746	0.4873
100	0.5675	0.8651	0.4325
101	0.581	0.838	0.419
102	0.5533	0.8934	0.4467
103	0.5169	0.9661	0.4831
104	0.505	0.9899	0.495
105	0.5016	0.9969	0.4984
106	0.4624	0.9249	0.5376
107	0.7562	0.4877	0.2438
108	0.7657	0.4687	0.2343
109	0.7582	0.4837	0.2418
110	0.7285	0.543	0.2715
111	0.7534	0.4932	0.2466
112	0.7853	0.4294	0.2147
113	0.7761	0.4479	0.2239
114	0.7575	0.485	0.2425
115	0.7311	0.5378	0.2689
116	0.7202	0.5596	0.2798
117	0.6848	0.6303	0.3152
118	0.8299	0.3401	0.1701
119	0.8362	0.3276	0.1638
120	0.7994	0.4012	0.2006
121	0.7957	0.4086	0.2043
122	0.7574	0.4853	0.2426
123	0.7569	0.4862	0.2431
124	0.7199	0.5603	0.2801
125	0.6709	0.6583	0.3291
126	0.6204	0.7591	0.3796
127	0.639	0.722	0.361
128	0.6551	0.6898	0.3449
129	0.7941	0.4118	0.2059
130	0.7453	0.5094	0.2547
131	0.8154	0.3691	0.1846
132	0.8323	0.3354	0.1677
133	0.964	0.07209	0.03605
134	0.9462	0.1076	0.05378
135	0.9283	0.1433	0.07166
136	0.933	0.1341	0.06703
137	0.9235	0.1529	0.07647
138	0.923	0.154	0.07698
139	0.9999	0.0002703	0.0001351
140	0.9999	0.0002838	0.0001419
141	0.9997	0.0006951	0.0003475
142	0.9992	0.001581	0.0007904
143	0.9981	0.003795	0.001897
144	0.9952	0.009578	0.004789
145	0.9937	0.01253	0.006266
146	0.9845	0.03099	0.01549
147	0.984	0.0321	0.01605
148	0.9662	0.06762	0.03381
149	0.932	0.136	0.06801
150	0.9866	0.02688	0.01344

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	6	0.04317	NOK
5% type I error level	10	0.0719424	NOK
10% type I error level	12	0.0863309	OK

\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 & 6 &  0.04317 & NOK \tabularnewline
5% type I error level & 10 & 0.0719424 & NOK \tabularnewline
10% type I error level & 12 & 0.0863309 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=318889&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]6[/C][C] 0.04317[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]10[/C][C]0.0719424[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]12[/C][C]0.0863309[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=318889&T=7

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=318889&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	6	0.04317	NOK
5% type I error level	10	0.0719424	NOK
10% type I error level	12	0.0863309	OK

Ramsey RESET F-Test for powers (2 and 3) of fitted values
> reset_test_fitted RESET test data: mylm RESET = 77.28, df1 = 2, df2 = 151, p-value < 2.2e-16
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors RESET test data: mylm RESET = 17.006, df1 = 16, df2 = 137, p-value < 2.2e-16
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components RESET test data: mylm RESET = 51.395, df1 = 2, df2 = 151, p-value < 2.2e-16

\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 = 77.28, df1 = 2, df2 = 151, p-value < 2.2e-16
 \tabularnewline
Ramsey RESET F-Test for powers (2 and 3) of regressors \tabularnewline
> reset_test_regressors
	RESET test
data:  mylm
RESET = 17.006, df1 = 16, df2 = 137, p-value < 2.2e-16
 \tabularnewline
Ramsey RESET F-Test for powers (2 and 3) of principal components \tabularnewline
> reset_test_principal_components
	RESET test
data:  mylm
RESET = 51.395, df1 = 2, df2 = 151, p-value < 2.2e-16
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=318889&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 = 77.28, df1 = 2, df2 = 151, p-value < 2.2e-16
[/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 = 17.006, df1 = 16, df2 = 137, p-value < 2.2e-16
[/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 = 51.395, df1 = 2, df2 = 151, p-value < 2.2e-16
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=318889&T=8

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=318889&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 = 77.28, df1 = 2, df2 = 151, p-value < 2.2e-16
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors RESET test data: mylm RESET = 17.006, df1 = 16, df2 = 137, p-value < 2.2e-16
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components RESET test data: mylm RESET = 51.395, df1 = 2, df2 = 151, p-value < 2.2e-16

Variance Inflation Factors (Multicollinearity)

> vif
`Population_(millions)`          GDP_per_Capita      Cropland_Footprint 
               1.027382                3.944740                1.455004 
      Grazing_Footprint        Carbon_Footprint          Fish_Footprint 
               1.048083                3.333956                1.048722 
            Forest_Land              Urban_Land 
               1.017120                1.416198

\begin{tabular}{lllllllll}
\hline
Variance Inflation Factors (Multicollinearity) \tabularnewline
> vif
`Population_(millions)`          GDP_per_Capita      Cropland_Footprint 
               1.027382                3.944740                1.455004 
      Grazing_Footprint        Carbon_Footprint          Fish_Footprint 
               1.048083                3.333956                1.048722 
            Forest_Land              Urban_Land 
               1.017120                1.416198 
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=318889&T=9

[TABLE]
[ROW][C]Variance Inflation Factors (Multicollinearity)[/C][/ROW]
[ROW][C]> vif
`Population_(millions)`          GDP_per_Capita      Cropland_Footprint 
               1.027382                3.944740                1.455004 
      Grazing_Footprint        Carbon_Footprint          Fish_Footprint 
               1.048083                3.333956                1.048722 
            Forest_Land              Urban_Land 
               1.017120                1.416198 
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=318889&T=9

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=318889&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)`          GDP_per_Capita      Cropland_Footprint 
               1.027382                3.944740                1.455004 
      Grazing_Footprint        Carbon_Footprint          Fish_Footprint 
               1.048083                3.333956                1.048722 
            Forest_Land              Urban_Land 
               1.017120                1.416198

Figure 1

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Figure 2

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Figure 3

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Figure 4

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Figure 5

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Figure 6

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Figure 7

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Figure 8

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Figure 9

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Figure 10

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Parameters (Session):

par1 = 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