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Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationThu, 18 Dec 2014 15:17:28 +0000
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2014/Dec/18/t1418917232u2rul9duweoz09z.htm/, Retrieved Sun, 19 May 2024 17:44:36 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=271090, Retrieved Sun, 19 May 2024 17:44:36 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact82

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [] [2014-12-18 14:22:41] [8e3afc5508de37bed770d90d46857754]
-    D    [Multiple Regression] [] [2014-12-18 15:17:28] [ce2f801bda31f4b58163e4bbe4fada83] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
12.9 8 18 68 1.8
12.2 18 31 39 2.1
12.8 12 39 32 2.2
7.4 24 46 62 2.3
6.7 16 31 33 2.1
12.6 19 67 52 2.7
14.8 16 35 62 2.1
13.3 15 52 77 2.4
11.1 28 77 76 2.9
8.2 21 37 41 2.2
11.4 18 32 48 2.1
6.4 22 36 63 2.2
10.6 19 38 30 2.2
12 22 69 78 2.7
6.3 25 21 19 1.9
11.9 16 54 66 2.5
9.3 19 36 35 2.2
10 26 23 45 1.9
6.4 24 34 21 2.1
13.8 20 112 25 3.5
10.8 19 35 44 2.1
13.8 19 47 69 2.3
11.7 23 47 54 2.3
10.9 18 37 74 2.2
9.9 21 20 61 1.9
11.5 20 22 41 1.9
8.3 15 23 46 1.9
11.7 19 32 39 2.1
9 19 30 34 2
9.7 7 92 51 3.2
10.8 20 43 42 2.3
10.3 20 55 31 2.5
10.4 19 16 39 1.8
9.3 20 71 49 2.8
11.8 18 43 53 2.3
5.9 14 29 31 2
11.4 17 56 39 2.5
13 17 46 54 2.3
10.8 8 19 49 1.8
11.3 22 59 46 2.6
11.8 20 30 55 2
12.7 22 7 50 1.6
10.9 14 19 30 1.8
13.3 21 48 45 2.4
10.1 20 23 35 1.9
14.3 18 33 41 2.1
9.3 24 34 73 2.1
12.5 19 48 17 2.4
7.6 16 18 40 1.8
15.9 16 43 64 2.3
9.2 16 33 37 2.1
11.1 22 71 65 2.8
13 21 26 100 2
14.5 15 67 28 2.7
12.3 15 80 56 2.9
11.4 14 29 29 2
13 16 43 59 2.3
13.2 26 29 61 2
7.7 18 32 51 2.1
4.35 17 23 12 1
12.7 6 16 45 1
18.1 22 33 37 4
17.85 20 32 37 4
17.1 17 52 68 4
19.1 20 75 72 4
16.1 23 72 143 4
13.35 18 15 9 2
18.4 13 29 55 4
14.7 22 13 17 1
10.6 20 40 37 3
12.6 20 19 27 3
13.6 16 121 58 3
14.1 16 36 21 3
14.5 15 23 19 3
16.15 19 85 78 4
14.75 19 41 35 3
14.8 24 46 48 3
12.45 9 18 27 2
12.65 22 35 43 2
17.35 15 17 30 3
8.6 22 4 25 1
18.4 22 28 69 4
16.1 24 44 72 3
17.75 21 38 13 4
15.25 25 57 61 4
17.65 26 23 43 4
16.35 21 36 51 4
17.65 14 22 67 4
13.6 28 40 36 3
14.35 21 31 44 3
14.75 16 11 45 4
18.25 16 38 34 4
9.9 25 24 36 4
16 21 37 72 3
18.25 22 37 39 4
16.85 9 22 43 4
18.95 24 43 80 4
15.6 22 31 40 3
17.1 10 31 61 4
15.4 21 21 29 4
15.4 20 21 29 4
13.35 17 32 54 3
19.1 7 26 43 4
7.6 14 32 20 1
19.1 23 33 61 4
14.75 18 30 57 4
19.25 17 67 54 4
13.6 20 22 36 4
12.75 19 33 16 4
9.85 19 24 40 1
15.25 23 28 27 4
11.9 20 41 61 3
16.35 19 31 69 4
12.4 16 33 34 3
18.15 21 21 34 4
17.75 20 52 34 4
12.35 20 29 13 3
15.6 19 11 12 4
19.3 19 26 51 4
17.1 20 7 19 4
18.4 22 13 81 3
19.05 19 20 42 4
18.55 23 52 22 4
19.1 16 28 85 4
12.85 18 39 25 4
9.5 23 9 22 2
4.5 20 19 19 1
13.6 23 60 45 4
11.7 13 19 45 2
13.35 26 14 51 3
17.6 13 -2 73 4
14.05 10 51 24 3
16.1 21 2 61 4
13.35 24 24 23 4
11.85 21 40 14 4
11.95 23 20 54 2
13.2 16 20 36 4
7.7 26 25 26 2
14.6 16 38 30 3

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time7 seconds
R Server'Sir Maurice George Kendall' @ kendall.wessa.net
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 Output & view raw output of R engine  \tabularnewline
Computing time & 7 seconds \tabularnewline
R Server & 'Sir Maurice George Kendall' @ kendall.wessa.net \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=271090&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]7 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Sir Maurice George Kendall' @ kendall.wessa.net[/C][/ROW]
[ROW][C]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=271090&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=271090&T=0

As an alternative you can also use a QR Code:  
QR Code


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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time7 seconds
R Server'Sir Maurice George Kendall' @ kendall.wessa.net
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
TOT[t] = + 6.24303 -0.0985288AMS.I2[t] -0.024322PRH[t] + 0.0334243CH[t] + 2.85723PR[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
TOT[t] =  +  6.24303 -0.0985288AMS.I2[t] -0.024322PRH[t] +  0.0334243CH[t] +  2.85723PR[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=271090&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]TOT[t] =  +  6.24303 -0.0985288AMS.I2[t] -0.024322PRH[t] +  0.0334243CH[t] +  2.85723PR[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=271090&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=271090&T=1

As an alternative you can also use a QR Code:  
QR Code


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

Multiple Linear Regression - Estimated Regression Equation
TOT[t] = + 6.24303 -0.0985288AMS.I2[t] -0.024322PRH[t] + 0.0334243CH[t] + 2.85723PR[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)6.243031.035026.0321.48696e-087.4348e-09
AMS.I2-0.09852880.0435345-2.2630.02522910.0126145
PRH-0.0243220.00990569-2.4550.01535560.0076778
CH0.03342430.009707533.4430.0007676960.000383848
PR2.857230.2074213.781.91918e-279.5959e-28

\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) & 6.24303 & 1.03502 & 6.032 & 1.48696e-08 & 7.4348e-09 \tabularnewline
AMS.I2 & -0.0985288 & 0.0435345 & -2.263 & 0.0252291 & 0.0126145 \tabularnewline
PRH & -0.024322 & 0.00990569 & -2.455 & 0.0153556 & 0.0076778 \tabularnewline
CH & 0.0334243 & 0.00970753 & 3.443 & 0.000767696 & 0.000383848 \tabularnewline
PR & 2.85723 & 0.20742 & 13.78 & 1.91918e-27 & 9.5959e-28 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=271090&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]6.24303[/C][C]1.03502[/C][C]6.032[/C][C]1.48696e-08[/C][C]7.4348e-09[/C][/ROW]
[ROW][C]AMS.I2[/C][C]-0.0985288[/C][C]0.0435345[/C][C]-2.263[/C][C]0.0252291[/C][C]0.0126145[/C][/ROW]
[ROW][C]PRH[/C][C]-0.024322[/C][C]0.00990569[/C][C]-2.455[/C][C]0.0153556[/C][C]0.0076778[/C][/ROW]
[ROW][C]CH[/C][C]0.0334243[/C][C]0.00970753[/C][C]3.443[/C][C]0.000767696[/C][C]0.000383848[/C][/ROW]
[ROW][C]PR[/C][C]2.85723[/C][C]0.20742[/C][C]13.78[/C][C]1.91918e-27[/C][C]9.5959e-28[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=271090&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=271090&T=2

As an alternative you can also use a QR Code:  
QR Code


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

Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)6.243031.035026.0321.48696e-087.4348e-09
AMS.I2-0.09852880.0435345-2.2630.02522910.0126145
PRH-0.0243220.00990569-2.4550.01535560.0076778
CH0.03342430.009707533.4430.0007676960.000383848
PR2.857230.2074213.781.91918e-279.5959e-28






Multiple Linear Regression - Regression Statistics
Multiple R0.783588
R-squared0.614009
Adjusted R-squared0.602487
F-TEST (value)53.2897
F-TEST (DF numerator)4
F-TEST (DF denominator)134
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.20918
Sum Squared Residuals653.983

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.783588 \tabularnewline
R-squared & 0.614009 \tabularnewline
Adjusted R-squared & 0.602487 \tabularnewline
F-TEST (value) & 53.2897 \tabularnewline
F-TEST (DF numerator) & 4 \tabularnewline
F-TEST (DF denominator) & 134 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 2.20918 \tabularnewline
Sum Squared Residuals & 653.983 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=271090&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.783588[/C][/ROW]
[ROW][C]R-squared[/C][C]0.614009[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.602487[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]53.2897[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]4[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]134[/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]2.20918[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]653.983[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=271090&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=271090&T=3

As an alternative you can also use a QR Code:  
QR Code


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

Multiple Linear Regression - Regression Statistics
Multiple R0.783588
R-squared0.614009
Adjusted R-squared0.602487
F-TEST (value)53.2897
F-TEST (DF numerator)4
F-TEST (DF denominator)134
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.20918
Sum Squared Residuals653.983






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
112.912.43290.467128
212.211.01931.18074
312.811.46761.33239
47.411.4035-4.00347
56.711.0158-4.31577
612.612.1940.406004
714.811.88782.91221
813.312.93140.36862
911.112.4376-1.33765
108.210.9303-2.73032
1111.411.29580.104241
126.411.5914-5.19144
1310.610.7354-0.135384
141212.7188-0.718798
156.39.33285-3.03285
1611.912.7023-0.802263
179.310.9511-1.65115
181010.0547-0.0547079
196.49.75349-3.35349
2013.812.38431.41569
2110.810.9906-0.190567
2213.812.10581.69424
2311.711.21030.489723
2410.912.3289-1.4289
259.911.1551-1.25511
2611.510.53650.963495
278.311.1719-2.87195
2811.710.89640.803589
29910.4922-1.49221
309.714.1635-4.46348
3110.811.2021-0.40206
3210.311.114-0.813974
3310.410.4284-0.0283942
349.312.1836-2.88363
3511.811.76680.033215
365.910.9089-5.0089
3711.411.6526-0.252633
381311.82581.17423
3910.811.7735-0.973488
4011.311.6067-0.306717
4111.811.09560.704407
4212.710.14792.55207
4310.910.54730.352747
4413.311.36791.93208
4510.110.3116-0.211637
4614.311.03753.26253
479.311.4916-2.19155
4812.510.62911.87091
497.610.7088-3.10876
5015.912.33153.56849
519.211.1008-1.90083
5211.112.5214-1.42136
531312.59840.401553
5414.511.78592.71407
5512.312.9771-0.677069
5611.410.84210.557945
571312.16440.835612
5813.210.72932.47071
597.711.396-3.69603
604.357.26696-2.91696
6112.79.624033.07597
6218.115.93842.16161
6317.8516.15981.69023
6417.117.00510.0949288
6519.116.28382.81622
6616.118.4343-2.33428
6713.3510.123.23004
6818.417.52410.875924
6914.77.184657.51535
7010.613.108-2.50796
7112.613.2845-0.684483
7213.612.23391.36609
7314.113.06461.03542
7414.513.41241.08756
7516.1516.3396-0.189632
7614.7513.11531.63468
7714.812.93561.86441
7812.4511.53540.914608
7912.6510.37582.27417
8017.3513.9263.42396
818.67.670950.929053
8218.417.12961.27042
8316.113.78642.31359
8417.7515.11312.63687
8515.2515.8613-0.611261
8617.6515.9881.66196
8716.3516.4319-0.0818943
8817.6517.9969-0.346893
8913.612.28631.31369
9014.3513.46230.887696
9114.7517.332-2.58204
9218.2516.30771.94232
939.915.8283-5.92828
941614.25231.74775
9518.2515.9082.34205
9616.8517.6874-0.837353
9718.9516.93542.01464
9815.613.23012.36992
9917.117.9716-0.871564
10015.416.0614-0.661389
10115.416.1599-0.759918
10213.3514.1663-0.816341
10319.117.78711.31288
1047.67.61104-0.0110405
10519.116.6422.45795
10614.7517.074-2.32396
10719.2516.17233.0777
10813.616.3696-2.76957
10912.7515.5321-2.78207
1109.857.981461.86854
11115.2515.6272-0.377229
11211.913.8858-1.98583
11316.3517.3522-1.0022
11412.413.5721-1.17206
11518.1516.22851.92149
11617.7515.57312.17694
11712.3512.5733-0.223323
11815.615.9335-0.333453
11919.316.87222.42783
12017.116.16620.933818
12118.415.03833.36173
12219.0516.71732.33272
12318.5514.87643.67362
12419.118.25550.844459
12512.8515.7855-2.93548
1269.510.2078-0.707765
1274.57.30263-2.80263
12813.615.4506-1.85056
12911.711.7186-0.0185928
13013.3513.6171-0.267104
13117.618.8797-1.27969
13214.0513.39120.658805
13316.117.5931-1.49308
13413.3515.4923-2.14229
13511.8515.0979-3.24791
13611.9511.00980.940198
13713.216.8123-3.61232
1387.79.65672-1.95672
13914.613.31681.28325

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 12.9 & 12.4329 & 0.467128 \tabularnewline
2 & 12.2 & 11.0193 & 1.18074 \tabularnewline
3 & 12.8 & 11.4676 & 1.33239 \tabularnewline
4 & 7.4 & 11.4035 & -4.00347 \tabularnewline
5 & 6.7 & 11.0158 & -4.31577 \tabularnewline
6 & 12.6 & 12.194 & 0.406004 \tabularnewline
7 & 14.8 & 11.8878 & 2.91221 \tabularnewline
8 & 13.3 & 12.9314 & 0.36862 \tabularnewline
9 & 11.1 & 12.4376 & -1.33765 \tabularnewline
10 & 8.2 & 10.9303 & -2.73032 \tabularnewline
11 & 11.4 & 11.2958 & 0.104241 \tabularnewline
12 & 6.4 & 11.5914 & -5.19144 \tabularnewline
13 & 10.6 & 10.7354 & -0.135384 \tabularnewline
14 & 12 & 12.7188 & -0.718798 \tabularnewline
15 & 6.3 & 9.33285 & -3.03285 \tabularnewline
16 & 11.9 & 12.7023 & -0.802263 \tabularnewline
17 & 9.3 & 10.9511 & -1.65115 \tabularnewline
18 & 10 & 10.0547 & -0.0547079 \tabularnewline
19 & 6.4 & 9.75349 & -3.35349 \tabularnewline
20 & 13.8 & 12.3843 & 1.41569 \tabularnewline
21 & 10.8 & 10.9906 & -0.190567 \tabularnewline
22 & 13.8 & 12.1058 & 1.69424 \tabularnewline
23 & 11.7 & 11.2103 & 0.489723 \tabularnewline
24 & 10.9 & 12.3289 & -1.4289 \tabularnewline
25 & 9.9 & 11.1551 & -1.25511 \tabularnewline
26 & 11.5 & 10.5365 & 0.963495 \tabularnewline
27 & 8.3 & 11.1719 & -2.87195 \tabularnewline
28 & 11.7 & 10.8964 & 0.803589 \tabularnewline
29 & 9 & 10.4922 & -1.49221 \tabularnewline
30 & 9.7 & 14.1635 & -4.46348 \tabularnewline
31 & 10.8 & 11.2021 & -0.40206 \tabularnewline
32 & 10.3 & 11.114 & -0.813974 \tabularnewline
33 & 10.4 & 10.4284 & -0.0283942 \tabularnewline
34 & 9.3 & 12.1836 & -2.88363 \tabularnewline
35 & 11.8 & 11.7668 & 0.033215 \tabularnewline
36 & 5.9 & 10.9089 & -5.0089 \tabularnewline
37 & 11.4 & 11.6526 & -0.252633 \tabularnewline
38 & 13 & 11.8258 & 1.17423 \tabularnewline
39 & 10.8 & 11.7735 & -0.973488 \tabularnewline
40 & 11.3 & 11.6067 & -0.306717 \tabularnewline
41 & 11.8 & 11.0956 & 0.704407 \tabularnewline
42 & 12.7 & 10.1479 & 2.55207 \tabularnewline
43 & 10.9 & 10.5473 & 0.352747 \tabularnewline
44 & 13.3 & 11.3679 & 1.93208 \tabularnewline
45 & 10.1 & 10.3116 & -0.211637 \tabularnewline
46 & 14.3 & 11.0375 & 3.26253 \tabularnewline
47 & 9.3 & 11.4916 & -2.19155 \tabularnewline
48 & 12.5 & 10.6291 & 1.87091 \tabularnewline
49 & 7.6 & 10.7088 & -3.10876 \tabularnewline
50 & 15.9 & 12.3315 & 3.56849 \tabularnewline
51 & 9.2 & 11.1008 & -1.90083 \tabularnewline
52 & 11.1 & 12.5214 & -1.42136 \tabularnewline
53 & 13 & 12.5984 & 0.401553 \tabularnewline
54 & 14.5 & 11.7859 & 2.71407 \tabularnewline
55 & 12.3 & 12.9771 & -0.677069 \tabularnewline
56 & 11.4 & 10.8421 & 0.557945 \tabularnewline
57 & 13 & 12.1644 & 0.835612 \tabularnewline
58 & 13.2 & 10.7293 & 2.47071 \tabularnewline
59 & 7.7 & 11.396 & -3.69603 \tabularnewline
60 & 4.35 & 7.26696 & -2.91696 \tabularnewline
61 & 12.7 & 9.62403 & 3.07597 \tabularnewline
62 & 18.1 & 15.9384 & 2.16161 \tabularnewline
63 & 17.85 & 16.1598 & 1.69023 \tabularnewline
64 & 17.1 & 17.0051 & 0.0949288 \tabularnewline
65 & 19.1 & 16.2838 & 2.81622 \tabularnewline
66 & 16.1 & 18.4343 & -2.33428 \tabularnewline
67 & 13.35 & 10.12 & 3.23004 \tabularnewline
68 & 18.4 & 17.5241 & 0.875924 \tabularnewline
69 & 14.7 & 7.18465 & 7.51535 \tabularnewline
70 & 10.6 & 13.108 & -2.50796 \tabularnewline
71 & 12.6 & 13.2845 & -0.684483 \tabularnewline
72 & 13.6 & 12.2339 & 1.36609 \tabularnewline
73 & 14.1 & 13.0646 & 1.03542 \tabularnewline
74 & 14.5 & 13.4124 & 1.08756 \tabularnewline
75 & 16.15 & 16.3396 & -0.189632 \tabularnewline
76 & 14.75 & 13.1153 & 1.63468 \tabularnewline
77 & 14.8 & 12.9356 & 1.86441 \tabularnewline
78 & 12.45 & 11.5354 & 0.914608 \tabularnewline
79 & 12.65 & 10.3758 & 2.27417 \tabularnewline
80 & 17.35 & 13.926 & 3.42396 \tabularnewline
81 & 8.6 & 7.67095 & 0.929053 \tabularnewline
82 & 18.4 & 17.1296 & 1.27042 \tabularnewline
83 & 16.1 & 13.7864 & 2.31359 \tabularnewline
84 & 17.75 & 15.1131 & 2.63687 \tabularnewline
85 & 15.25 & 15.8613 & -0.611261 \tabularnewline
86 & 17.65 & 15.988 & 1.66196 \tabularnewline
87 & 16.35 & 16.4319 & -0.0818943 \tabularnewline
88 & 17.65 & 17.9969 & -0.346893 \tabularnewline
89 & 13.6 & 12.2863 & 1.31369 \tabularnewline
90 & 14.35 & 13.4623 & 0.887696 \tabularnewline
91 & 14.75 & 17.332 & -2.58204 \tabularnewline
92 & 18.25 & 16.3077 & 1.94232 \tabularnewline
93 & 9.9 & 15.8283 & -5.92828 \tabularnewline
94 & 16 & 14.2523 & 1.74775 \tabularnewline
95 & 18.25 & 15.908 & 2.34205 \tabularnewline
96 & 16.85 & 17.6874 & -0.837353 \tabularnewline
97 & 18.95 & 16.9354 & 2.01464 \tabularnewline
98 & 15.6 & 13.2301 & 2.36992 \tabularnewline
99 & 17.1 & 17.9716 & -0.871564 \tabularnewline
100 & 15.4 & 16.0614 & -0.661389 \tabularnewline
101 & 15.4 & 16.1599 & -0.759918 \tabularnewline
102 & 13.35 & 14.1663 & -0.816341 \tabularnewline
103 & 19.1 & 17.7871 & 1.31288 \tabularnewline
104 & 7.6 & 7.61104 & -0.0110405 \tabularnewline
105 & 19.1 & 16.642 & 2.45795 \tabularnewline
106 & 14.75 & 17.074 & -2.32396 \tabularnewline
107 & 19.25 & 16.1723 & 3.0777 \tabularnewline
108 & 13.6 & 16.3696 & -2.76957 \tabularnewline
109 & 12.75 & 15.5321 & -2.78207 \tabularnewline
110 & 9.85 & 7.98146 & 1.86854 \tabularnewline
111 & 15.25 & 15.6272 & -0.377229 \tabularnewline
112 & 11.9 & 13.8858 & -1.98583 \tabularnewline
113 & 16.35 & 17.3522 & -1.0022 \tabularnewline
114 & 12.4 & 13.5721 & -1.17206 \tabularnewline
115 & 18.15 & 16.2285 & 1.92149 \tabularnewline
116 & 17.75 & 15.5731 & 2.17694 \tabularnewline
117 & 12.35 & 12.5733 & -0.223323 \tabularnewline
118 & 15.6 & 15.9335 & -0.333453 \tabularnewline
119 & 19.3 & 16.8722 & 2.42783 \tabularnewline
120 & 17.1 & 16.1662 & 0.933818 \tabularnewline
121 & 18.4 & 15.0383 & 3.36173 \tabularnewline
122 & 19.05 & 16.7173 & 2.33272 \tabularnewline
123 & 18.55 & 14.8764 & 3.67362 \tabularnewline
124 & 19.1 & 18.2555 & 0.844459 \tabularnewline
125 & 12.85 & 15.7855 & -2.93548 \tabularnewline
126 & 9.5 & 10.2078 & -0.707765 \tabularnewline
127 & 4.5 & 7.30263 & -2.80263 \tabularnewline
128 & 13.6 & 15.4506 & -1.85056 \tabularnewline
129 & 11.7 & 11.7186 & -0.0185928 \tabularnewline
130 & 13.35 & 13.6171 & -0.267104 \tabularnewline
131 & 17.6 & 18.8797 & -1.27969 \tabularnewline
132 & 14.05 & 13.3912 & 0.658805 \tabularnewline
133 & 16.1 & 17.5931 & -1.49308 \tabularnewline
134 & 13.35 & 15.4923 & -2.14229 \tabularnewline
135 & 11.85 & 15.0979 & -3.24791 \tabularnewline
136 & 11.95 & 11.0098 & 0.940198 \tabularnewline
137 & 13.2 & 16.8123 & -3.61232 \tabularnewline
138 & 7.7 & 9.65672 & -1.95672 \tabularnewline
139 & 14.6 & 13.3168 & 1.28325 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=271090&T=4

[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]12.9[/C][C]12.4329[/C][C]0.467128[/C][/ROW]
[ROW][C]2[/C][C]12.2[/C][C]11.0193[/C][C]1.18074[/C][/ROW]
[ROW][C]3[/C][C]12.8[/C][C]11.4676[/C][C]1.33239[/C][/ROW]
[ROW][C]4[/C][C]7.4[/C][C]11.4035[/C][C]-4.00347[/C][/ROW]
[ROW][C]5[/C][C]6.7[/C][C]11.0158[/C][C]-4.31577[/C][/ROW]
[ROW][C]6[/C][C]12.6[/C][C]12.194[/C][C]0.406004[/C][/ROW]
[ROW][C]7[/C][C]14.8[/C][C]11.8878[/C][C]2.91221[/C][/ROW]
[ROW][C]8[/C][C]13.3[/C][C]12.9314[/C][C]0.36862[/C][/ROW]
[ROW][C]9[/C][C]11.1[/C][C]12.4376[/C][C]-1.33765[/C][/ROW]
[ROW][C]10[/C][C]8.2[/C][C]10.9303[/C][C]-2.73032[/C][/ROW]
[ROW][C]11[/C][C]11.4[/C][C]11.2958[/C][C]0.104241[/C][/ROW]
[ROW][C]12[/C][C]6.4[/C][C]11.5914[/C][C]-5.19144[/C][/ROW]
[ROW][C]13[/C][C]10.6[/C][C]10.7354[/C][C]-0.135384[/C][/ROW]
[ROW][C]14[/C][C]12[/C][C]12.7188[/C][C]-0.718798[/C][/ROW]
[ROW][C]15[/C][C]6.3[/C][C]9.33285[/C][C]-3.03285[/C][/ROW]
[ROW][C]16[/C][C]11.9[/C][C]12.7023[/C][C]-0.802263[/C][/ROW]
[ROW][C]17[/C][C]9.3[/C][C]10.9511[/C][C]-1.65115[/C][/ROW]
[ROW][C]18[/C][C]10[/C][C]10.0547[/C][C]-0.0547079[/C][/ROW]
[ROW][C]19[/C][C]6.4[/C][C]9.75349[/C][C]-3.35349[/C][/ROW]
[ROW][C]20[/C][C]13.8[/C][C]12.3843[/C][C]1.41569[/C][/ROW]
[ROW][C]21[/C][C]10.8[/C][C]10.9906[/C][C]-0.190567[/C][/ROW]
[ROW][C]22[/C][C]13.8[/C][C]12.1058[/C][C]1.69424[/C][/ROW]
[ROW][C]23[/C][C]11.7[/C][C]11.2103[/C][C]0.489723[/C][/ROW]
[ROW][C]24[/C][C]10.9[/C][C]12.3289[/C][C]-1.4289[/C][/ROW]
[ROW][C]25[/C][C]9.9[/C][C]11.1551[/C][C]-1.25511[/C][/ROW]
[ROW][C]26[/C][C]11.5[/C][C]10.5365[/C][C]0.963495[/C][/ROW]
[ROW][C]27[/C][C]8.3[/C][C]11.1719[/C][C]-2.87195[/C][/ROW]
[ROW][C]28[/C][C]11.7[/C][C]10.8964[/C][C]0.803589[/C][/ROW]
[ROW][C]29[/C][C]9[/C][C]10.4922[/C][C]-1.49221[/C][/ROW]
[ROW][C]30[/C][C]9.7[/C][C]14.1635[/C][C]-4.46348[/C][/ROW]
[ROW][C]31[/C][C]10.8[/C][C]11.2021[/C][C]-0.40206[/C][/ROW]
[ROW][C]32[/C][C]10.3[/C][C]11.114[/C][C]-0.813974[/C][/ROW]
[ROW][C]33[/C][C]10.4[/C][C]10.4284[/C][C]-0.0283942[/C][/ROW]
[ROW][C]34[/C][C]9.3[/C][C]12.1836[/C][C]-2.88363[/C][/ROW]
[ROW][C]35[/C][C]11.8[/C][C]11.7668[/C][C]0.033215[/C][/ROW]
[ROW][C]36[/C][C]5.9[/C][C]10.9089[/C][C]-5.0089[/C][/ROW]
[ROW][C]37[/C][C]11.4[/C][C]11.6526[/C][C]-0.252633[/C][/ROW]
[ROW][C]38[/C][C]13[/C][C]11.8258[/C][C]1.17423[/C][/ROW]
[ROW][C]39[/C][C]10.8[/C][C]11.7735[/C][C]-0.973488[/C][/ROW]
[ROW][C]40[/C][C]11.3[/C][C]11.6067[/C][C]-0.306717[/C][/ROW]
[ROW][C]41[/C][C]11.8[/C][C]11.0956[/C][C]0.704407[/C][/ROW]
[ROW][C]42[/C][C]12.7[/C][C]10.1479[/C][C]2.55207[/C][/ROW]
[ROW][C]43[/C][C]10.9[/C][C]10.5473[/C][C]0.352747[/C][/ROW]
[ROW][C]44[/C][C]13.3[/C][C]11.3679[/C][C]1.93208[/C][/ROW]
[ROW][C]45[/C][C]10.1[/C][C]10.3116[/C][C]-0.211637[/C][/ROW]
[ROW][C]46[/C][C]14.3[/C][C]11.0375[/C][C]3.26253[/C][/ROW]
[ROW][C]47[/C][C]9.3[/C][C]11.4916[/C][C]-2.19155[/C][/ROW]
[ROW][C]48[/C][C]12.5[/C][C]10.6291[/C][C]1.87091[/C][/ROW]
[ROW][C]49[/C][C]7.6[/C][C]10.7088[/C][C]-3.10876[/C][/ROW]
[ROW][C]50[/C][C]15.9[/C][C]12.3315[/C][C]3.56849[/C][/ROW]
[ROW][C]51[/C][C]9.2[/C][C]11.1008[/C][C]-1.90083[/C][/ROW]
[ROW][C]52[/C][C]11.1[/C][C]12.5214[/C][C]-1.42136[/C][/ROW]
[ROW][C]53[/C][C]13[/C][C]12.5984[/C][C]0.401553[/C][/ROW]
[ROW][C]54[/C][C]14.5[/C][C]11.7859[/C][C]2.71407[/C][/ROW]
[ROW][C]55[/C][C]12.3[/C][C]12.9771[/C][C]-0.677069[/C][/ROW]
[ROW][C]56[/C][C]11.4[/C][C]10.8421[/C][C]0.557945[/C][/ROW]
[ROW][C]57[/C][C]13[/C][C]12.1644[/C][C]0.835612[/C][/ROW]
[ROW][C]58[/C][C]13.2[/C][C]10.7293[/C][C]2.47071[/C][/ROW]
[ROW][C]59[/C][C]7.7[/C][C]11.396[/C][C]-3.69603[/C][/ROW]
[ROW][C]60[/C][C]4.35[/C][C]7.26696[/C][C]-2.91696[/C][/ROW]
[ROW][C]61[/C][C]12.7[/C][C]9.62403[/C][C]3.07597[/C][/ROW]
[ROW][C]62[/C][C]18.1[/C][C]15.9384[/C][C]2.16161[/C][/ROW]
[ROW][C]63[/C][C]17.85[/C][C]16.1598[/C][C]1.69023[/C][/ROW]
[ROW][C]64[/C][C]17.1[/C][C]17.0051[/C][C]0.0949288[/C][/ROW]
[ROW][C]65[/C][C]19.1[/C][C]16.2838[/C][C]2.81622[/C][/ROW]
[ROW][C]66[/C][C]16.1[/C][C]18.4343[/C][C]-2.33428[/C][/ROW]
[ROW][C]67[/C][C]13.35[/C][C]10.12[/C][C]3.23004[/C][/ROW]
[ROW][C]68[/C][C]18.4[/C][C]17.5241[/C][C]0.875924[/C][/ROW]
[ROW][C]69[/C][C]14.7[/C][C]7.18465[/C][C]7.51535[/C][/ROW]
[ROW][C]70[/C][C]10.6[/C][C]13.108[/C][C]-2.50796[/C][/ROW]
[ROW][C]71[/C][C]12.6[/C][C]13.2845[/C][C]-0.684483[/C][/ROW]
[ROW][C]72[/C][C]13.6[/C][C]12.2339[/C][C]1.36609[/C][/ROW]
[ROW][C]73[/C][C]14.1[/C][C]13.0646[/C][C]1.03542[/C][/ROW]
[ROW][C]74[/C][C]14.5[/C][C]13.4124[/C][C]1.08756[/C][/ROW]
[ROW][C]75[/C][C]16.15[/C][C]16.3396[/C][C]-0.189632[/C][/ROW]
[ROW][C]76[/C][C]14.75[/C][C]13.1153[/C][C]1.63468[/C][/ROW]
[ROW][C]77[/C][C]14.8[/C][C]12.9356[/C][C]1.86441[/C][/ROW]
[ROW][C]78[/C][C]12.45[/C][C]11.5354[/C][C]0.914608[/C][/ROW]
[ROW][C]79[/C][C]12.65[/C][C]10.3758[/C][C]2.27417[/C][/ROW]
[ROW][C]80[/C][C]17.35[/C][C]13.926[/C][C]3.42396[/C][/ROW]
[ROW][C]81[/C][C]8.6[/C][C]7.67095[/C][C]0.929053[/C][/ROW]
[ROW][C]82[/C][C]18.4[/C][C]17.1296[/C][C]1.27042[/C][/ROW]
[ROW][C]83[/C][C]16.1[/C][C]13.7864[/C][C]2.31359[/C][/ROW]
[ROW][C]84[/C][C]17.75[/C][C]15.1131[/C][C]2.63687[/C][/ROW]
[ROW][C]85[/C][C]15.25[/C][C]15.8613[/C][C]-0.611261[/C][/ROW]
[ROW][C]86[/C][C]17.65[/C][C]15.988[/C][C]1.66196[/C][/ROW]
[ROW][C]87[/C][C]16.35[/C][C]16.4319[/C][C]-0.0818943[/C][/ROW]
[ROW][C]88[/C][C]17.65[/C][C]17.9969[/C][C]-0.346893[/C][/ROW]
[ROW][C]89[/C][C]13.6[/C][C]12.2863[/C][C]1.31369[/C][/ROW]
[ROW][C]90[/C][C]14.35[/C][C]13.4623[/C][C]0.887696[/C][/ROW]
[ROW][C]91[/C][C]14.75[/C][C]17.332[/C][C]-2.58204[/C][/ROW]
[ROW][C]92[/C][C]18.25[/C][C]16.3077[/C][C]1.94232[/C][/ROW]
[ROW][C]93[/C][C]9.9[/C][C]15.8283[/C][C]-5.92828[/C][/ROW]
[ROW][C]94[/C][C]16[/C][C]14.2523[/C][C]1.74775[/C][/ROW]
[ROW][C]95[/C][C]18.25[/C][C]15.908[/C][C]2.34205[/C][/ROW]
[ROW][C]96[/C][C]16.85[/C][C]17.6874[/C][C]-0.837353[/C][/ROW]
[ROW][C]97[/C][C]18.95[/C][C]16.9354[/C][C]2.01464[/C][/ROW]
[ROW][C]98[/C][C]15.6[/C][C]13.2301[/C][C]2.36992[/C][/ROW]
[ROW][C]99[/C][C]17.1[/C][C]17.9716[/C][C]-0.871564[/C][/ROW]
[ROW][C]100[/C][C]15.4[/C][C]16.0614[/C][C]-0.661389[/C][/ROW]
[ROW][C]101[/C][C]15.4[/C][C]16.1599[/C][C]-0.759918[/C][/ROW]
[ROW][C]102[/C][C]13.35[/C][C]14.1663[/C][C]-0.816341[/C][/ROW]
[ROW][C]103[/C][C]19.1[/C][C]17.7871[/C][C]1.31288[/C][/ROW]
[ROW][C]104[/C][C]7.6[/C][C]7.61104[/C][C]-0.0110405[/C][/ROW]
[ROW][C]105[/C][C]19.1[/C][C]16.642[/C][C]2.45795[/C][/ROW]
[ROW][C]106[/C][C]14.75[/C][C]17.074[/C][C]-2.32396[/C][/ROW]
[ROW][C]107[/C][C]19.25[/C][C]16.1723[/C][C]3.0777[/C][/ROW]
[ROW][C]108[/C][C]13.6[/C][C]16.3696[/C][C]-2.76957[/C][/ROW]
[ROW][C]109[/C][C]12.75[/C][C]15.5321[/C][C]-2.78207[/C][/ROW]
[ROW][C]110[/C][C]9.85[/C][C]7.98146[/C][C]1.86854[/C][/ROW]
[ROW][C]111[/C][C]15.25[/C][C]15.6272[/C][C]-0.377229[/C][/ROW]
[ROW][C]112[/C][C]11.9[/C][C]13.8858[/C][C]-1.98583[/C][/ROW]
[ROW][C]113[/C][C]16.35[/C][C]17.3522[/C][C]-1.0022[/C][/ROW]
[ROW][C]114[/C][C]12.4[/C][C]13.5721[/C][C]-1.17206[/C][/ROW]
[ROW][C]115[/C][C]18.15[/C][C]16.2285[/C][C]1.92149[/C][/ROW]
[ROW][C]116[/C][C]17.75[/C][C]15.5731[/C][C]2.17694[/C][/ROW]
[ROW][C]117[/C][C]12.35[/C][C]12.5733[/C][C]-0.223323[/C][/ROW]
[ROW][C]118[/C][C]15.6[/C][C]15.9335[/C][C]-0.333453[/C][/ROW]
[ROW][C]119[/C][C]19.3[/C][C]16.8722[/C][C]2.42783[/C][/ROW]
[ROW][C]120[/C][C]17.1[/C][C]16.1662[/C][C]0.933818[/C][/ROW]
[ROW][C]121[/C][C]18.4[/C][C]15.0383[/C][C]3.36173[/C][/ROW]
[ROW][C]122[/C][C]19.05[/C][C]16.7173[/C][C]2.33272[/C][/ROW]
[ROW][C]123[/C][C]18.55[/C][C]14.8764[/C][C]3.67362[/C][/ROW]
[ROW][C]124[/C][C]19.1[/C][C]18.2555[/C][C]0.844459[/C][/ROW]
[ROW][C]125[/C][C]12.85[/C][C]15.7855[/C][C]-2.93548[/C][/ROW]
[ROW][C]126[/C][C]9.5[/C][C]10.2078[/C][C]-0.707765[/C][/ROW]
[ROW][C]127[/C][C]4.5[/C][C]7.30263[/C][C]-2.80263[/C][/ROW]
[ROW][C]128[/C][C]13.6[/C][C]15.4506[/C][C]-1.85056[/C][/ROW]
[ROW][C]129[/C][C]11.7[/C][C]11.7186[/C][C]-0.0185928[/C][/ROW]
[ROW][C]130[/C][C]13.35[/C][C]13.6171[/C][C]-0.267104[/C][/ROW]
[ROW][C]131[/C][C]17.6[/C][C]18.8797[/C][C]-1.27969[/C][/ROW]
[ROW][C]132[/C][C]14.05[/C][C]13.3912[/C][C]0.658805[/C][/ROW]
[ROW][C]133[/C][C]16.1[/C][C]17.5931[/C][C]-1.49308[/C][/ROW]
[ROW][C]134[/C][C]13.35[/C][C]15.4923[/C][C]-2.14229[/C][/ROW]
[ROW][C]135[/C][C]11.85[/C][C]15.0979[/C][C]-3.24791[/C][/ROW]
[ROW][C]136[/C][C]11.95[/C][C]11.0098[/C][C]0.940198[/C][/ROW]
[ROW][C]137[/C][C]13.2[/C][C]16.8123[/C][C]-3.61232[/C][/ROW]
[ROW][C]138[/C][C]7.7[/C][C]9.65672[/C][C]-1.95672[/C][/ROW]
[ROW][C]139[/C][C]14.6[/C][C]13.3168[/C][C]1.28325[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=271090&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=271090&T=4

As an alternative you can also use a QR Code:  
QR Code


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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
112.912.43290.467128
212.211.01931.18074
312.811.46761.33239
47.411.4035-4.00347
56.711.0158-4.31577
612.612.1940.406004
714.811.88782.91221
813.312.93140.36862
911.112.4376-1.33765
108.210.9303-2.73032
1111.411.29580.104241
126.411.5914-5.19144
1310.610.7354-0.135384
141212.7188-0.718798
156.39.33285-3.03285
1611.912.7023-0.802263
179.310.9511-1.65115
181010.0547-0.0547079
196.49.75349-3.35349
2013.812.38431.41569
2110.810.9906-0.190567
2213.812.10581.69424
2311.711.21030.489723
2410.912.3289-1.4289
259.911.1551-1.25511
2611.510.53650.963495
278.311.1719-2.87195
2811.710.89640.803589
29910.4922-1.49221
309.714.1635-4.46348
3110.811.2021-0.40206
3210.311.114-0.813974
3310.410.4284-0.0283942
349.312.1836-2.88363
3511.811.76680.033215
365.910.9089-5.0089
3711.411.6526-0.252633
381311.82581.17423
3910.811.7735-0.973488
4011.311.6067-0.306717
4111.811.09560.704407
4212.710.14792.55207
4310.910.54730.352747
4413.311.36791.93208
4510.110.3116-0.211637
4614.311.03753.26253
479.311.4916-2.19155
4812.510.62911.87091
497.610.7088-3.10876
5015.912.33153.56849
519.211.1008-1.90083
5211.112.5214-1.42136
531312.59840.401553
5414.511.78592.71407
5512.312.9771-0.677069
5611.410.84210.557945
571312.16440.835612
5813.210.72932.47071
597.711.396-3.69603
604.357.26696-2.91696
6112.79.624033.07597
6218.115.93842.16161
6317.8516.15981.69023
6417.117.00510.0949288
6519.116.28382.81622
6616.118.4343-2.33428
6713.3510.123.23004
6818.417.52410.875924
6914.77.184657.51535
7010.613.108-2.50796
7112.613.2845-0.684483
7213.612.23391.36609
7314.113.06461.03542
7414.513.41241.08756
7516.1516.3396-0.189632
7614.7513.11531.63468
7714.812.93561.86441
7812.4511.53540.914608
7912.6510.37582.27417
8017.3513.9263.42396
818.67.670950.929053
8218.417.12961.27042
8316.113.78642.31359
8417.7515.11312.63687
8515.2515.8613-0.611261
8617.6515.9881.66196
8716.3516.4319-0.0818943
8817.6517.9969-0.346893
8913.612.28631.31369
9014.3513.46230.887696
9114.7517.332-2.58204
9218.2516.30771.94232
939.915.8283-5.92828
941614.25231.74775
9518.2515.9082.34205
9616.8517.6874-0.837353
9718.9516.93542.01464
9815.613.23012.36992
9917.117.9716-0.871564
10015.416.0614-0.661389
10115.416.1599-0.759918
10213.3514.1663-0.816341
10319.117.78711.31288
1047.67.61104-0.0110405
10519.116.6422.45795
10614.7517.074-2.32396
10719.2516.17233.0777
10813.616.3696-2.76957
10912.7515.5321-2.78207
1109.857.981461.86854
11115.2515.6272-0.377229
11211.913.8858-1.98583
11316.3517.3522-1.0022
11412.413.5721-1.17206
11518.1516.22851.92149
11617.7515.57312.17694
11712.3512.5733-0.223323
11815.615.9335-0.333453
11919.316.87222.42783
12017.116.16620.933818
12118.415.03833.36173
12219.0516.71732.33272
12318.5514.87643.67362
12419.118.25550.844459
12512.8515.7855-2.93548
1269.510.2078-0.707765
1274.57.30263-2.80263
12813.615.4506-1.85056
12911.711.7186-0.0185928
13013.3513.6171-0.267104
13117.618.8797-1.27969
13214.0513.39120.658805
13316.117.5931-1.49308
13413.3515.4923-2.14229
13511.8515.0979-3.24791
13611.9511.00980.940198
13713.216.8123-3.61232
1387.79.65672-1.95672
13914.613.31681.28325






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
80.9186070.1627850.0813925
90.8556320.2887350.144368
100.7756680.4486630.224332
110.7105110.5789780.289489
120.7210810.5578390.278919
130.6351610.7296790.364839
140.5472690.9054630.452731
150.4656430.9312860.534357
160.3742010.7484020.625799
170.2964610.5929220.703539
180.314730.629460.68527
190.3543340.7086670.645666
200.2824630.5649260.717537
210.2185530.4371060.781447
220.1876070.3752130.812393
230.1441190.2882380.855881
240.1088960.2177920.891104
250.09504220.1900840.904958
260.09743050.1948610.902569
270.1474710.2949420.852529
280.1473870.2947750.852613
290.1339130.2678250.866087
300.3014240.6028480.698576
310.2633470.5266940.736653
320.2181390.4362780.781861
330.1828570.3657140.817143
340.1780010.3560020.821999
350.156220.312440.84378
360.3971610.7943210.602839
370.3470670.6941330.652933
380.3106990.6213970.689301
390.2815940.5631870.718406
400.2538330.5076660.746167
410.2138430.4276850.786157
420.2367010.4734020.763299
430.1962110.3924220.803789
440.2427220.4854450.757278
450.2035480.4070960.796452
460.2931390.5862780.706861
470.3003320.6006630.699668
480.3277110.6554210.672289
490.3901710.7803430.609829
500.5164080.9671850.483592
510.5014730.9970530.498527
520.4750670.9501350.524933
530.4319210.8638430.568079
540.4651030.9302060.534897
550.4306630.8613270.569337
560.384720.7694410.61528
570.3511670.7023330.648833
580.3579050.7158110.642095
590.4500210.9000410.549979
600.5708720.8582570.429128
610.5964660.8070680.403534
620.5976990.8046010.402301
630.564190.871620.43581
640.5218020.9563960.478198
650.5230680.9538640.476932
660.5849140.8301720.415086
670.6277220.7445550.372278
680.5874060.8251880.412594
690.93570.1285990.0642996
700.9461730.1076540.0538271
710.9336460.1327090.0663543
720.9277430.1445140.0722569
730.911830.176340.0881701
740.8963260.2073490.103674
750.8924940.2150120.107506
760.8757980.2484030.124202
770.8593280.2813440.140672
780.8343140.3313730.165686
790.8260350.3479290.173965
800.8776570.2446850.122343
810.8614120.2771760.138588
820.8345030.3309940.165497
830.8193560.3612880.180644
840.8397440.3205120.160256
850.831640.336720.16836
860.8180830.3638340.181917
870.7850650.4298710.214935
880.750470.4990610.24953
890.7148490.5703010.285151
900.672350.6552990.32765
910.6874170.6251660.312583
920.6755750.648850.324425
930.9090590.1818830.0909415
940.890670.2186590.10933
950.8915150.2169690.108485
960.8660570.2678850.133943
970.842410.3151810.15759
980.8475010.3049970.152499
990.8228210.3543570.177179
1000.7862220.4275560.213778
1010.7450870.5098270.254913
1020.7103680.5792630.289632
1030.6859420.6281160.314058
1040.6330770.7338450.366923
1050.6234850.7530290.376515
1060.6433010.7133990.356699
1070.6516930.6966130.348307
1080.6677610.6644770.332239
1090.6736520.6526960.326348
1100.6746490.6507010.325351
1110.6138640.7722710.386136
1120.6265320.7469350.373468
1130.6073610.7852780.392639
1140.553590.892820.44641
1150.5502480.8995040.449752
1160.5307860.9384280.469214
1170.4669230.9338460.533077
1180.4175130.8350270.582487
1190.4264110.8528220.573589
1200.5072740.9854520.492726
1210.5282170.9435660.471783
1220.678660.6426810.32134
1230.9673020.06539620.0326981
1240.9427430.1145130.0572567
1250.918020.163960.0819798
1260.8984120.2031760.101588
1270.9342180.1315650.0657824
1280.9760590.04788150.0239407
1290.9504850.09902980.0495149
1300.8872620.2254760.112738
1310.7664420.4671160.233558

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
8 & 0.918607 & 0.162785 & 0.0813925 \tabularnewline
9 & 0.855632 & 0.288735 & 0.144368 \tabularnewline
10 & 0.775668 & 0.448663 & 0.224332 \tabularnewline
11 & 0.710511 & 0.578978 & 0.289489 \tabularnewline
12 & 0.721081 & 0.557839 & 0.278919 \tabularnewline
13 & 0.635161 & 0.729679 & 0.364839 \tabularnewline
14 & 0.547269 & 0.905463 & 0.452731 \tabularnewline
15 & 0.465643 & 0.931286 & 0.534357 \tabularnewline
16 & 0.374201 & 0.748402 & 0.625799 \tabularnewline
17 & 0.296461 & 0.592922 & 0.703539 \tabularnewline
18 & 0.31473 & 0.62946 & 0.68527 \tabularnewline
19 & 0.354334 & 0.708667 & 0.645666 \tabularnewline
20 & 0.282463 & 0.564926 & 0.717537 \tabularnewline
21 & 0.218553 & 0.437106 & 0.781447 \tabularnewline
22 & 0.187607 & 0.375213 & 0.812393 \tabularnewline
23 & 0.144119 & 0.288238 & 0.855881 \tabularnewline
24 & 0.108896 & 0.217792 & 0.891104 \tabularnewline
25 & 0.0950422 & 0.190084 & 0.904958 \tabularnewline
26 & 0.0974305 & 0.194861 & 0.902569 \tabularnewline
27 & 0.147471 & 0.294942 & 0.852529 \tabularnewline
28 & 0.147387 & 0.294775 & 0.852613 \tabularnewline
29 & 0.133913 & 0.267825 & 0.866087 \tabularnewline
30 & 0.301424 & 0.602848 & 0.698576 \tabularnewline
31 & 0.263347 & 0.526694 & 0.736653 \tabularnewline
32 & 0.218139 & 0.436278 & 0.781861 \tabularnewline
33 & 0.182857 & 0.365714 & 0.817143 \tabularnewline
34 & 0.178001 & 0.356002 & 0.821999 \tabularnewline
35 & 0.15622 & 0.31244 & 0.84378 \tabularnewline
36 & 0.397161 & 0.794321 & 0.602839 \tabularnewline
37 & 0.347067 & 0.694133 & 0.652933 \tabularnewline
38 & 0.310699 & 0.621397 & 0.689301 \tabularnewline
39 & 0.281594 & 0.563187 & 0.718406 \tabularnewline
40 & 0.253833 & 0.507666 & 0.746167 \tabularnewline
41 & 0.213843 & 0.427685 & 0.786157 \tabularnewline
42 & 0.236701 & 0.473402 & 0.763299 \tabularnewline
43 & 0.196211 & 0.392422 & 0.803789 \tabularnewline
44 & 0.242722 & 0.485445 & 0.757278 \tabularnewline
45 & 0.203548 & 0.407096 & 0.796452 \tabularnewline
46 & 0.293139 & 0.586278 & 0.706861 \tabularnewline
47 & 0.300332 & 0.600663 & 0.699668 \tabularnewline
48 & 0.327711 & 0.655421 & 0.672289 \tabularnewline
49 & 0.390171 & 0.780343 & 0.609829 \tabularnewline
50 & 0.516408 & 0.967185 & 0.483592 \tabularnewline
51 & 0.501473 & 0.997053 & 0.498527 \tabularnewline
52 & 0.475067 & 0.950135 & 0.524933 \tabularnewline
53 & 0.431921 & 0.863843 & 0.568079 \tabularnewline
54 & 0.465103 & 0.930206 & 0.534897 \tabularnewline
55 & 0.430663 & 0.861327 & 0.569337 \tabularnewline
56 & 0.38472 & 0.769441 & 0.61528 \tabularnewline
57 & 0.351167 & 0.702333 & 0.648833 \tabularnewline
58 & 0.357905 & 0.715811 & 0.642095 \tabularnewline
59 & 0.450021 & 0.900041 & 0.549979 \tabularnewline
60 & 0.570872 & 0.858257 & 0.429128 \tabularnewline
61 & 0.596466 & 0.807068 & 0.403534 \tabularnewline
62 & 0.597699 & 0.804601 & 0.402301 \tabularnewline
63 & 0.56419 & 0.87162 & 0.43581 \tabularnewline
64 & 0.521802 & 0.956396 & 0.478198 \tabularnewline
65 & 0.523068 & 0.953864 & 0.476932 \tabularnewline
66 & 0.584914 & 0.830172 & 0.415086 \tabularnewline
67 & 0.627722 & 0.744555 & 0.372278 \tabularnewline
68 & 0.587406 & 0.825188 & 0.412594 \tabularnewline
69 & 0.9357 & 0.128599 & 0.0642996 \tabularnewline
70 & 0.946173 & 0.107654 & 0.0538271 \tabularnewline
71 & 0.933646 & 0.132709 & 0.0663543 \tabularnewline
72 & 0.927743 & 0.144514 & 0.0722569 \tabularnewline
73 & 0.91183 & 0.17634 & 0.0881701 \tabularnewline
74 & 0.896326 & 0.207349 & 0.103674 \tabularnewline
75 & 0.892494 & 0.215012 & 0.107506 \tabularnewline
76 & 0.875798 & 0.248403 & 0.124202 \tabularnewline
77 & 0.859328 & 0.281344 & 0.140672 \tabularnewline
78 & 0.834314 & 0.331373 & 0.165686 \tabularnewline
79 & 0.826035 & 0.347929 & 0.173965 \tabularnewline
80 & 0.877657 & 0.244685 & 0.122343 \tabularnewline
81 & 0.861412 & 0.277176 & 0.138588 \tabularnewline
82 & 0.834503 & 0.330994 & 0.165497 \tabularnewline
83 & 0.819356 & 0.361288 & 0.180644 \tabularnewline
84 & 0.839744 & 0.320512 & 0.160256 \tabularnewline
85 & 0.83164 & 0.33672 & 0.16836 \tabularnewline
86 & 0.818083 & 0.363834 & 0.181917 \tabularnewline
87 & 0.785065 & 0.429871 & 0.214935 \tabularnewline
88 & 0.75047 & 0.499061 & 0.24953 \tabularnewline
89 & 0.714849 & 0.570301 & 0.285151 \tabularnewline
90 & 0.67235 & 0.655299 & 0.32765 \tabularnewline
91 & 0.687417 & 0.625166 & 0.312583 \tabularnewline
92 & 0.675575 & 0.64885 & 0.324425 \tabularnewline
93 & 0.909059 & 0.181883 & 0.0909415 \tabularnewline
94 & 0.89067 & 0.218659 & 0.10933 \tabularnewline
95 & 0.891515 & 0.216969 & 0.108485 \tabularnewline
96 & 0.866057 & 0.267885 & 0.133943 \tabularnewline
97 & 0.84241 & 0.315181 & 0.15759 \tabularnewline
98 & 0.847501 & 0.304997 & 0.152499 \tabularnewline
99 & 0.822821 & 0.354357 & 0.177179 \tabularnewline
100 & 0.786222 & 0.427556 & 0.213778 \tabularnewline
101 & 0.745087 & 0.509827 & 0.254913 \tabularnewline
102 & 0.710368 & 0.579263 & 0.289632 \tabularnewline
103 & 0.685942 & 0.628116 & 0.314058 \tabularnewline
104 & 0.633077 & 0.733845 & 0.366923 \tabularnewline
105 & 0.623485 & 0.753029 & 0.376515 \tabularnewline
106 & 0.643301 & 0.713399 & 0.356699 \tabularnewline
107 & 0.651693 & 0.696613 & 0.348307 \tabularnewline
108 & 0.667761 & 0.664477 & 0.332239 \tabularnewline
109 & 0.673652 & 0.652696 & 0.326348 \tabularnewline
110 & 0.674649 & 0.650701 & 0.325351 \tabularnewline
111 & 0.613864 & 0.772271 & 0.386136 \tabularnewline
112 & 0.626532 & 0.746935 & 0.373468 \tabularnewline
113 & 0.607361 & 0.785278 & 0.392639 \tabularnewline
114 & 0.55359 & 0.89282 & 0.44641 \tabularnewline
115 & 0.550248 & 0.899504 & 0.449752 \tabularnewline
116 & 0.530786 & 0.938428 & 0.469214 \tabularnewline
117 & 0.466923 & 0.933846 & 0.533077 \tabularnewline
118 & 0.417513 & 0.835027 & 0.582487 \tabularnewline
119 & 0.426411 & 0.852822 & 0.573589 \tabularnewline
120 & 0.507274 & 0.985452 & 0.492726 \tabularnewline
121 & 0.528217 & 0.943566 & 0.471783 \tabularnewline
122 & 0.67866 & 0.642681 & 0.32134 \tabularnewline
123 & 0.967302 & 0.0653962 & 0.0326981 \tabularnewline
124 & 0.942743 & 0.114513 & 0.0572567 \tabularnewline
125 & 0.91802 & 0.16396 & 0.0819798 \tabularnewline
126 & 0.898412 & 0.203176 & 0.101588 \tabularnewline
127 & 0.934218 & 0.131565 & 0.0657824 \tabularnewline
128 & 0.976059 & 0.0478815 & 0.0239407 \tabularnewline
129 & 0.950485 & 0.0990298 & 0.0495149 \tabularnewline
130 & 0.887262 & 0.225476 & 0.112738 \tabularnewline
131 & 0.766442 & 0.467116 & 0.233558 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=271090&T=5

[TABLE]
[ROW][C]Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]p-values[/C][C]Alternative Hypothesis[/C][/ROW]
[ROW][C]breakpoint index[/C][C]greater[/C][C]2-sided[/C][C]less[/C][/ROW]
[ROW][C]8[/C][C]0.918607[/C][C]0.162785[/C][C]0.0813925[/C][/ROW]
[ROW][C]9[/C][C]0.855632[/C][C]0.288735[/C][C]0.144368[/C][/ROW]
[ROW][C]10[/C][C]0.775668[/C][C]0.448663[/C][C]0.224332[/C][/ROW]
[ROW][C]11[/C][C]0.710511[/C][C]0.578978[/C][C]0.289489[/C][/ROW]
[ROW][C]12[/C][C]0.721081[/C][C]0.557839[/C][C]0.278919[/C][/ROW]
[ROW][C]13[/C][C]0.635161[/C][C]0.729679[/C][C]0.364839[/C][/ROW]
[ROW][C]14[/C][C]0.547269[/C][C]0.905463[/C][C]0.452731[/C][/ROW]
[ROW][C]15[/C][C]0.465643[/C][C]0.931286[/C][C]0.534357[/C][/ROW]
[ROW][C]16[/C][C]0.374201[/C][C]0.748402[/C][C]0.625799[/C][/ROW]
[ROW][C]17[/C][C]0.296461[/C][C]0.592922[/C][C]0.703539[/C][/ROW]
[ROW][C]18[/C][C]0.31473[/C][C]0.62946[/C][C]0.68527[/C][/ROW]
[ROW][C]19[/C][C]0.354334[/C][C]0.708667[/C][C]0.645666[/C][/ROW]
[ROW][C]20[/C][C]0.282463[/C][C]0.564926[/C][C]0.717537[/C][/ROW]
[ROW][C]21[/C][C]0.218553[/C][C]0.437106[/C][C]0.781447[/C][/ROW]
[ROW][C]22[/C][C]0.187607[/C][C]0.375213[/C][C]0.812393[/C][/ROW]
[ROW][C]23[/C][C]0.144119[/C][C]0.288238[/C][C]0.855881[/C][/ROW]
[ROW][C]24[/C][C]0.108896[/C][C]0.217792[/C][C]0.891104[/C][/ROW]
[ROW][C]25[/C][C]0.0950422[/C][C]0.190084[/C][C]0.904958[/C][/ROW]
[ROW][C]26[/C][C]0.0974305[/C][C]0.194861[/C][C]0.902569[/C][/ROW]
[ROW][C]27[/C][C]0.147471[/C][C]0.294942[/C][C]0.852529[/C][/ROW]
[ROW][C]28[/C][C]0.147387[/C][C]0.294775[/C][C]0.852613[/C][/ROW]
[ROW][C]29[/C][C]0.133913[/C][C]0.267825[/C][C]0.866087[/C][/ROW]
[ROW][C]30[/C][C]0.301424[/C][C]0.602848[/C][C]0.698576[/C][/ROW]
[ROW][C]31[/C][C]0.263347[/C][C]0.526694[/C][C]0.736653[/C][/ROW]
[ROW][C]32[/C][C]0.218139[/C][C]0.436278[/C][C]0.781861[/C][/ROW]
[ROW][C]33[/C][C]0.182857[/C][C]0.365714[/C][C]0.817143[/C][/ROW]
[ROW][C]34[/C][C]0.178001[/C][C]0.356002[/C][C]0.821999[/C][/ROW]
[ROW][C]35[/C][C]0.15622[/C][C]0.31244[/C][C]0.84378[/C][/ROW]
[ROW][C]36[/C][C]0.397161[/C][C]0.794321[/C][C]0.602839[/C][/ROW]
[ROW][C]37[/C][C]0.347067[/C][C]0.694133[/C][C]0.652933[/C][/ROW]
[ROW][C]38[/C][C]0.310699[/C][C]0.621397[/C][C]0.689301[/C][/ROW]
[ROW][C]39[/C][C]0.281594[/C][C]0.563187[/C][C]0.718406[/C][/ROW]
[ROW][C]40[/C][C]0.253833[/C][C]0.507666[/C][C]0.746167[/C][/ROW]
[ROW][C]41[/C][C]0.213843[/C][C]0.427685[/C][C]0.786157[/C][/ROW]
[ROW][C]42[/C][C]0.236701[/C][C]0.473402[/C][C]0.763299[/C][/ROW]
[ROW][C]43[/C][C]0.196211[/C][C]0.392422[/C][C]0.803789[/C][/ROW]
[ROW][C]44[/C][C]0.242722[/C][C]0.485445[/C][C]0.757278[/C][/ROW]
[ROW][C]45[/C][C]0.203548[/C][C]0.407096[/C][C]0.796452[/C][/ROW]
[ROW][C]46[/C][C]0.293139[/C][C]0.586278[/C][C]0.706861[/C][/ROW]
[ROW][C]47[/C][C]0.300332[/C][C]0.600663[/C][C]0.699668[/C][/ROW]
[ROW][C]48[/C][C]0.327711[/C][C]0.655421[/C][C]0.672289[/C][/ROW]
[ROW][C]49[/C][C]0.390171[/C][C]0.780343[/C][C]0.609829[/C][/ROW]
[ROW][C]50[/C][C]0.516408[/C][C]0.967185[/C][C]0.483592[/C][/ROW]
[ROW][C]51[/C][C]0.501473[/C][C]0.997053[/C][C]0.498527[/C][/ROW]
[ROW][C]52[/C][C]0.475067[/C][C]0.950135[/C][C]0.524933[/C][/ROW]
[ROW][C]53[/C][C]0.431921[/C][C]0.863843[/C][C]0.568079[/C][/ROW]
[ROW][C]54[/C][C]0.465103[/C][C]0.930206[/C][C]0.534897[/C][/ROW]
[ROW][C]55[/C][C]0.430663[/C][C]0.861327[/C][C]0.569337[/C][/ROW]
[ROW][C]56[/C][C]0.38472[/C][C]0.769441[/C][C]0.61528[/C][/ROW]
[ROW][C]57[/C][C]0.351167[/C][C]0.702333[/C][C]0.648833[/C][/ROW]
[ROW][C]58[/C][C]0.357905[/C][C]0.715811[/C][C]0.642095[/C][/ROW]
[ROW][C]59[/C][C]0.450021[/C][C]0.900041[/C][C]0.549979[/C][/ROW]
[ROW][C]60[/C][C]0.570872[/C][C]0.858257[/C][C]0.429128[/C][/ROW]
[ROW][C]61[/C][C]0.596466[/C][C]0.807068[/C][C]0.403534[/C][/ROW]
[ROW][C]62[/C][C]0.597699[/C][C]0.804601[/C][C]0.402301[/C][/ROW]
[ROW][C]63[/C][C]0.56419[/C][C]0.87162[/C][C]0.43581[/C][/ROW]
[ROW][C]64[/C][C]0.521802[/C][C]0.956396[/C][C]0.478198[/C][/ROW]
[ROW][C]65[/C][C]0.523068[/C][C]0.953864[/C][C]0.476932[/C][/ROW]
[ROW][C]66[/C][C]0.584914[/C][C]0.830172[/C][C]0.415086[/C][/ROW]
[ROW][C]67[/C][C]0.627722[/C][C]0.744555[/C][C]0.372278[/C][/ROW]
[ROW][C]68[/C][C]0.587406[/C][C]0.825188[/C][C]0.412594[/C][/ROW]
[ROW][C]69[/C][C]0.9357[/C][C]0.128599[/C][C]0.0642996[/C][/ROW]
[ROW][C]70[/C][C]0.946173[/C][C]0.107654[/C][C]0.0538271[/C][/ROW]
[ROW][C]71[/C][C]0.933646[/C][C]0.132709[/C][C]0.0663543[/C][/ROW]
[ROW][C]72[/C][C]0.927743[/C][C]0.144514[/C][C]0.0722569[/C][/ROW]
[ROW][C]73[/C][C]0.91183[/C][C]0.17634[/C][C]0.0881701[/C][/ROW]
[ROW][C]74[/C][C]0.896326[/C][C]0.207349[/C][C]0.103674[/C][/ROW]
[ROW][C]75[/C][C]0.892494[/C][C]0.215012[/C][C]0.107506[/C][/ROW]
[ROW][C]76[/C][C]0.875798[/C][C]0.248403[/C][C]0.124202[/C][/ROW]
[ROW][C]77[/C][C]0.859328[/C][C]0.281344[/C][C]0.140672[/C][/ROW]
[ROW][C]78[/C][C]0.834314[/C][C]0.331373[/C][C]0.165686[/C][/ROW]
[ROW][C]79[/C][C]0.826035[/C][C]0.347929[/C][C]0.173965[/C][/ROW]
[ROW][C]80[/C][C]0.877657[/C][C]0.244685[/C][C]0.122343[/C][/ROW]
[ROW][C]81[/C][C]0.861412[/C][C]0.277176[/C][C]0.138588[/C][/ROW]
[ROW][C]82[/C][C]0.834503[/C][C]0.330994[/C][C]0.165497[/C][/ROW]
[ROW][C]83[/C][C]0.819356[/C][C]0.361288[/C][C]0.180644[/C][/ROW]
[ROW][C]84[/C][C]0.839744[/C][C]0.320512[/C][C]0.160256[/C][/ROW]
[ROW][C]85[/C][C]0.83164[/C][C]0.33672[/C][C]0.16836[/C][/ROW]
[ROW][C]86[/C][C]0.818083[/C][C]0.363834[/C][C]0.181917[/C][/ROW]
[ROW][C]87[/C][C]0.785065[/C][C]0.429871[/C][C]0.214935[/C][/ROW]
[ROW][C]88[/C][C]0.75047[/C][C]0.499061[/C][C]0.24953[/C][/ROW]
[ROW][C]89[/C][C]0.714849[/C][C]0.570301[/C][C]0.285151[/C][/ROW]
[ROW][C]90[/C][C]0.67235[/C][C]0.655299[/C][C]0.32765[/C][/ROW]
[ROW][C]91[/C][C]0.687417[/C][C]0.625166[/C][C]0.312583[/C][/ROW]
[ROW][C]92[/C][C]0.675575[/C][C]0.64885[/C][C]0.324425[/C][/ROW]
[ROW][C]93[/C][C]0.909059[/C][C]0.181883[/C][C]0.0909415[/C][/ROW]
[ROW][C]94[/C][C]0.89067[/C][C]0.218659[/C][C]0.10933[/C][/ROW]
[ROW][C]95[/C][C]0.891515[/C][C]0.216969[/C][C]0.108485[/C][/ROW]
[ROW][C]96[/C][C]0.866057[/C][C]0.267885[/C][C]0.133943[/C][/ROW]
[ROW][C]97[/C][C]0.84241[/C][C]0.315181[/C][C]0.15759[/C][/ROW]
[ROW][C]98[/C][C]0.847501[/C][C]0.304997[/C][C]0.152499[/C][/ROW]
[ROW][C]99[/C][C]0.822821[/C][C]0.354357[/C][C]0.177179[/C][/ROW]
[ROW][C]100[/C][C]0.786222[/C][C]0.427556[/C][C]0.213778[/C][/ROW]
[ROW][C]101[/C][C]0.745087[/C][C]0.509827[/C][C]0.254913[/C][/ROW]
[ROW][C]102[/C][C]0.710368[/C][C]0.579263[/C][C]0.289632[/C][/ROW]
[ROW][C]103[/C][C]0.685942[/C][C]0.628116[/C][C]0.314058[/C][/ROW]
[ROW][C]104[/C][C]0.633077[/C][C]0.733845[/C][C]0.366923[/C][/ROW]
[ROW][C]105[/C][C]0.623485[/C][C]0.753029[/C][C]0.376515[/C][/ROW]
[ROW][C]106[/C][C]0.643301[/C][C]0.713399[/C][C]0.356699[/C][/ROW]
[ROW][C]107[/C][C]0.651693[/C][C]0.696613[/C][C]0.348307[/C][/ROW]
[ROW][C]108[/C][C]0.667761[/C][C]0.664477[/C][C]0.332239[/C][/ROW]
[ROW][C]109[/C][C]0.673652[/C][C]0.652696[/C][C]0.326348[/C][/ROW]
[ROW][C]110[/C][C]0.674649[/C][C]0.650701[/C][C]0.325351[/C][/ROW]
[ROW][C]111[/C][C]0.613864[/C][C]0.772271[/C][C]0.386136[/C][/ROW]
[ROW][C]112[/C][C]0.626532[/C][C]0.746935[/C][C]0.373468[/C][/ROW]
[ROW][C]113[/C][C]0.607361[/C][C]0.785278[/C][C]0.392639[/C][/ROW]
[ROW][C]114[/C][C]0.55359[/C][C]0.89282[/C][C]0.44641[/C][/ROW]
[ROW][C]115[/C][C]0.550248[/C][C]0.899504[/C][C]0.449752[/C][/ROW]
[ROW][C]116[/C][C]0.530786[/C][C]0.938428[/C][C]0.469214[/C][/ROW]
[ROW][C]117[/C][C]0.466923[/C][C]0.933846[/C][C]0.533077[/C][/ROW]
[ROW][C]118[/C][C]0.417513[/C][C]0.835027[/C][C]0.582487[/C][/ROW]
[ROW][C]119[/C][C]0.426411[/C][C]0.852822[/C][C]0.573589[/C][/ROW]
[ROW][C]120[/C][C]0.507274[/C][C]0.985452[/C][C]0.492726[/C][/ROW]
[ROW][C]121[/C][C]0.528217[/C][C]0.943566[/C][C]0.471783[/C][/ROW]
[ROW][C]122[/C][C]0.67866[/C][C]0.642681[/C][C]0.32134[/C][/ROW]
[ROW][C]123[/C][C]0.967302[/C][C]0.0653962[/C][C]0.0326981[/C][/ROW]
[ROW][C]124[/C][C]0.942743[/C][C]0.114513[/C][C]0.0572567[/C][/ROW]
[ROW][C]125[/C][C]0.91802[/C][C]0.16396[/C][C]0.0819798[/C][/ROW]
[ROW][C]126[/C][C]0.898412[/C][C]0.203176[/C][C]0.101588[/C][/ROW]
[ROW][C]127[/C][C]0.934218[/C][C]0.131565[/C][C]0.0657824[/C][/ROW]
[ROW][C]128[/C][C]0.976059[/C][C]0.0478815[/C][C]0.0239407[/C][/ROW]
[ROW][C]129[/C][C]0.950485[/C][C]0.0990298[/C][C]0.0495149[/C][/ROW]
[ROW][C]130[/C][C]0.887262[/C][C]0.225476[/C][C]0.112738[/C][/ROW]
[ROW][C]131[/C][C]0.766442[/C][C]0.467116[/C][C]0.233558[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=271090&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=271090&T=5

As an alternative you can also use a QR Code:  
QR Code


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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
80.9186070.1627850.0813925
90.8556320.2887350.144368
100.7756680.4486630.224332
110.7105110.5789780.289489
120.7210810.5578390.278919
130.6351610.7296790.364839
140.5472690.9054630.452731
150.4656430.9312860.534357
160.3742010.7484020.625799
170.2964610.5929220.703539
180.314730.629460.68527
190.3543340.7086670.645666
200.2824630.5649260.717537
210.2185530.4371060.781447
220.1876070.3752130.812393
230.1441190.2882380.855881
240.1088960.2177920.891104
250.09504220.1900840.904958
260.09743050.1948610.902569
270.1474710.2949420.852529
280.1473870.2947750.852613
290.1339130.2678250.866087
300.3014240.6028480.698576
310.2633470.5266940.736653
320.2181390.4362780.781861
330.1828570.3657140.817143
340.1780010.3560020.821999
350.156220.312440.84378
360.3971610.7943210.602839
370.3470670.6941330.652933
380.3106990.6213970.689301
390.2815940.5631870.718406
400.2538330.5076660.746167
410.2138430.4276850.786157
420.2367010.4734020.763299
430.1962110.3924220.803789
440.2427220.4854450.757278
450.2035480.4070960.796452
460.2931390.5862780.706861
470.3003320.6006630.699668
480.3277110.6554210.672289
490.3901710.7803430.609829
500.5164080.9671850.483592
510.5014730.9970530.498527
520.4750670.9501350.524933
530.4319210.8638430.568079
540.4651030.9302060.534897
550.4306630.8613270.569337
560.384720.7694410.61528
570.3511670.7023330.648833
580.3579050.7158110.642095
590.4500210.9000410.549979
600.5708720.8582570.429128
610.5964660.8070680.403534
620.5976990.8046010.402301
630.564190.871620.43581
640.5218020.9563960.478198
650.5230680.9538640.476932
660.5849140.8301720.415086
670.6277220.7445550.372278
680.5874060.8251880.412594
690.93570.1285990.0642996
700.9461730.1076540.0538271
710.9336460.1327090.0663543
720.9277430.1445140.0722569
730.911830.176340.0881701
740.8963260.2073490.103674
750.8924940.2150120.107506
760.8757980.2484030.124202
770.8593280.2813440.140672
780.8343140.3313730.165686
790.8260350.3479290.173965
800.8776570.2446850.122343
810.8614120.2771760.138588
820.8345030.3309940.165497
830.8193560.3612880.180644
840.8397440.3205120.160256
850.831640.336720.16836
860.8180830.3638340.181917
870.7850650.4298710.214935
880.750470.4990610.24953
890.7148490.5703010.285151
900.672350.6552990.32765
910.6874170.6251660.312583
920.6755750.648850.324425
930.9090590.1818830.0909415
940.890670.2186590.10933
950.8915150.2169690.108485
960.8660570.2678850.133943
970.842410.3151810.15759
980.8475010.3049970.152499
990.8228210.3543570.177179
1000.7862220.4275560.213778
1010.7450870.5098270.254913
1020.7103680.5792630.289632
1030.6859420.6281160.314058
1040.6330770.7338450.366923
1050.6234850.7530290.376515
1060.6433010.7133990.356699
1070.6516930.6966130.348307
1080.6677610.6644770.332239
1090.6736520.6526960.326348
1100.6746490.6507010.325351
1110.6138640.7722710.386136
1120.6265320.7469350.373468
1130.6073610.7852780.392639
1140.553590.892820.44641
1150.5502480.8995040.449752
1160.5307860.9384280.469214
1170.4669230.9338460.533077
1180.4175130.8350270.582487
1190.4264110.8528220.573589
1200.5072740.9854520.492726
1210.5282170.9435660.471783
1220.678660.6426810.32134
1230.9673020.06539620.0326981
1240.9427430.1145130.0572567
1250.918020.163960.0819798
1260.8984120.2031760.101588
1270.9342180.1315650.0657824
1280.9760590.04788150.0239407
1290.9504850.09902980.0495149
1300.8872620.2254760.112738
1310.7664420.4671160.233558






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level10.00806452OK
10% type I error level30.0241935OK

\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 & 0 & 0 & OK \tabularnewline
5% type I error level & 1 & 0.00806452 & OK \tabularnewline
10% type I error level & 3 & 0.0241935 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=271090&T=6

[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]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]1[/C][C]0.00806452[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]3[/C][C]0.0241935[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=271090&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=271090&T=6

As an alternative you can also use a QR Code:  
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 testsOK/NOK
1% type I error level00OK
5% type I error level10.00806452OK
10% type I error level30.0241935OK


Figures (Output of Computation)

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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Input Parameters & R Code

Parameters (Session):
par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
Parameters (R input):
par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
R code (references can be found in the software module):
library(lattice)
library(lmtest)
n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
par1 <- as.numeric(par1)
x <- 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'){
x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
for (i in 1:n-1) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
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[1,])
if (par3 == 'Linear Trend'){
x <- cbind(x, c(1:n))
colnames(x)[k+1] <- 't'
}
x
k <- length(x[1,])
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$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
grid()
dev.off()
bitmap(file='test2.png')
hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
grid()
dev.off()
bitmap(file='test3.png')
densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
dev.off()
bitmap(file='test4.png')
qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
qqline(mysum$resid)
grid()
dev.off()
(myerror <- as.ts(mysum$resid))
bitmap(file='test5.png')
dum <- cbind(lag(myerror,k=1),myerror)
dum
dum1 <- dum[2:length(myerror),]
dum1
z <- as.data.frame(dum1)
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$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
}
}
myeq <- paste(myeq, ' + e[t]')
a<-table.row.start(a)
a<-table.element(a, myeq)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable1.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,hyperlink('ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Variable',header=TRUE)
a<-table.element(a,'Parameter',header=TRUE)
a<-table.element(a,'S.D.',header=TRUE)
a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
a<-table.element(a,'2-tail p-value',header=TRUE)
a<-table.element(a,'1-tail p-value',header=TRUE)
a<-table.row.end(a)
for (i in 1:k){
a<-table.row.start(a)
a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
a<-table.element(a,signif(mysum$coefficients[i,1],6))
a<-table.element(a, signif(mysum$coefficients[i,2],6))
a<-table.element(a, signif(mysum$coefficients[i,3],4))
a<-table.element(a, signif(mysum$coefficients[i,4],6))
a<-table.element(a, signif(mysum$coefficients[i,4]/2,6))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable2.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple R',1,TRUE)
a<-table.element(a, signif(sqrt(mysum$r.squared),6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'R-squared',1,TRUE)
a<-table.element(a, signif(mysum$r.squared,6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Adjusted R-squared',1,TRUE)
a<-table.element(a, signif(mysum$adj.r.squared,6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (value)',1,TRUE)
a<-table.element(a, signif(mysum$fstatistic[1],6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
a<-table.element(a, signif(mysum$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$fstatistic[3],6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'p-value',1,TRUE)
a<-table.element(a, signif(1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]),6))
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, signif(mysum$sigma,6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
a<-table.element(a, signif(sum(myerror*myerror),6))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,signif(x[i],6))
a<-table.element(a,signif(x[i]-mysum$resid[i],6))
a<-table.element(a,signif(mysum$resid[i],6))
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,signif(gqarr[mypoint-kp3+1,1],6))
a<-table.element(a,signif(gqarr[mypoint-kp3+1,2],6))
a<-table.element(a,signif(gqarr[mypoint-kp3+1,3],6))
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,signif(numsignificant1/numgqtests,6))
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')
}