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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 computationSat, 11 Dec 2010 10:24:16 +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/2010/Dec/11/t12920629452ligowc844upbmj.htm/, Retrieved Fri, 15 Aug 2025 01:22:32 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=108046, Retrieved Fri, 15 Aug 2025 01:22:32 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact328

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [HPC Retail Sales] [2008-03-08 13:40:54] [1c0f2c85e8a48e42648374b3bcceca26]
-  M D  [Multiple Regression] [W8 Regressiemodel] [2010-11-26 11:00:47] [56d90b683fcd93137645f9226b43c62b]
-   PD    [Multiple Regression] [W8 Regressiemodel] [2010-11-26 12:03:41] [56d90b683fcd93137645f9226b43c62b]
-    D        [Multiple Regression] [Paper Multiple Re...] [2010-12-11 10:24:16] [59f7d3e7fcb6374015f4e6b9053b0f01] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
17.848
19.592
21.092
20.899
25.890
24.965
22.225
20.977
22.897
22.785
22.769
19.637
20.203
20.450
23.083
21.738
26.766
25.280
22.574
22.729
21.378
22.902
24.989
21.116
15.169
15.846
20.927
18.273
22.538
15.596
14.034
11.366
14.861
15.149
13.577
13.026
13.190
13.196
15.826
14.733
16.307
15.703
14.589
12.043
15.057
14.053
12.698
10.888
10.045
11.549
13.767
12.434
13.116
14.211
12.266
12.602
15.714
13.742
12.745
10.491
10.057
10.900
11.771
11.992
11.933
14.504
11.727
11.477
13.578
11.555
11.846
11.397
10.066
10.269
14.279
13.870
13.695
14.420
11.424
9.704
12.464
14.301
13.464
9.893
11.572
12.380
16.692
16.052
16.459
14.761
13.654
13.480
18.068
16.560
14.530
10.650
11.651
13.735
13.360
17.818
20.613
16.231
13.862
12.004
17.734
15.034
12.609
12.320
10.833
11.350
13.648
14.890
16.325
18.045
15.616
11.926
16.855
15.083
12.520
12.355

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time8 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135

\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 & 8 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=108046&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]8 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=108046&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=108046&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 time8 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135






Multiple Linear Regression - Estimated Regression Equation
Pas[t] = + 17.7650666666667 -0.878527777777776M1[t] + 0.0542838383838419M2[t] + 2.64159545454546M3[t] + 2.53650707070707M4[t] + 4.70031868686869M5[t] + 3.77723030303032M6[t] + 1.67224191919192M7[t] + 0.375453535353537M8[t] + 3.47476515151516M9[t] + 2.80007676767677M10[t] + 1.92788838383839M11[t] -0.0695116161616161t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Pas[t] =  +  17.7650666666667 -0.878527777777776M1[t] +  0.0542838383838419M2[t] +  2.64159545454546M3[t] +  2.53650707070707M4[t] +  4.70031868686869M5[t] +  3.77723030303032M6[t] +  1.67224191919192M7[t] +  0.375453535353537M8[t] +  3.47476515151516M9[t] +  2.80007676767677M10[t] +  1.92788838383839M11[t] -0.0695116161616161t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=108046&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Pas[t] =  +  17.7650666666667 -0.878527777777776M1[t] +  0.0542838383838419M2[t] +  2.64159545454546M3[t] +  2.53650707070707M4[t] +  4.70031868686869M5[t] +  3.77723030303032M6[t] +  1.67224191919192M7[t] +  0.375453535353537M8[t] +  3.47476515151516M9[t] +  2.80007676767677M10[t] +  1.92788838383839M11[t] -0.0695116161616161t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=108046&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=108046&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
Pas[t] = + 17.7650666666667 -0.878527777777776M1[t] + 0.0542838383838419M2[t] + 2.64159545454546M3[t] + 2.53650707070707M4[t] + 4.70031868686869M5[t] + 3.77723030303032M6[t] + 1.67224191919192M7[t] + 0.375453535353537M8[t] + 3.47476515151516M9[t] + 2.80007676767677M10[t] + 1.92788838383839M11[t] -0.0695116161616161t + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)17.76506666666671.14300515.542400
M1-0.8785277777777761.417703-0.61970.5367830.268392
M20.05428383838384191.4171830.03830.9695170.484758
M32.641595454545461.4167121.86460.0649790.032489
M42.536507070707071.4162911.7910.0761290.038064
M54.700318686868691.4159193.31960.0012330.000617
M63.777230303030321.4155972.66830.008810.004405
M71.672241919191921.4153241.18150.2400130.120007
M80.3754535353535371.4151010.26530.7912740.395637
M93.474765151515161.4149272.45580.0156670.007834
M102.800076767676771.4148031.97910.0503710.025185
M111.927888383838391.4147291.36270.175830.087915
t-0.06951161616161610.008378-8.296700

\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) & 17.7650666666667 & 1.143005 & 15.5424 & 0 & 0 \tabularnewline
M1 & -0.878527777777776 & 1.417703 & -0.6197 & 0.536783 & 0.268392 \tabularnewline
M2 & 0.0542838383838419 & 1.417183 & 0.0383 & 0.969517 & 0.484758 \tabularnewline
M3 & 2.64159545454546 & 1.416712 & 1.8646 & 0.064979 & 0.032489 \tabularnewline
M4 & 2.53650707070707 & 1.416291 & 1.791 & 0.076129 & 0.038064 \tabularnewline
M5 & 4.70031868686869 & 1.415919 & 3.3196 & 0.001233 & 0.000617 \tabularnewline
M6 & 3.77723030303032 & 1.415597 & 2.6683 & 0.00881 & 0.004405 \tabularnewline
M7 & 1.67224191919192 & 1.415324 & 1.1815 & 0.240013 & 0.120007 \tabularnewline
M8 & 0.375453535353537 & 1.415101 & 0.2653 & 0.791274 & 0.395637 \tabularnewline
M9 & 3.47476515151516 & 1.414927 & 2.4558 & 0.015667 & 0.007834 \tabularnewline
M10 & 2.80007676767677 & 1.414803 & 1.9791 & 0.050371 & 0.025185 \tabularnewline
M11 & 1.92788838383839 & 1.414729 & 1.3627 & 0.17583 & 0.087915 \tabularnewline
t & -0.0695116161616161 & 0.008378 & -8.2967 & 0 & 0 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=108046&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]17.7650666666667[/C][C]1.143005[/C][C]15.5424[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]M1[/C][C]-0.878527777777776[/C][C]1.417703[/C][C]-0.6197[/C][C]0.536783[/C][C]0.268392[/C][/ROW]
[ROW][C]M2[/C][C]0.0542838383838419[/C][C]1.417183[/C][C]0.0383[/C][C]0.969517[/C][C]0.484758[/C][/ROW]
[ROW][C]M3[/C][C]2.64159545454546[/C][C]1.416712[/C][C]1.8646[/C][C]0.064979[/C][C]0.032489[/C][/ROW]
[ROW][C]M4[/C][C]2.53650707070707[/C][C]1.416291[/C][C]1.791[/C][C]0.076129[/C][C]0.038064[/C][/ROW]
[ROW][C]M5[/C][C]4.70031868686869[/C][C]1.415919[/C][C]3.3196[/C][C]0.001233[/C][C]0.000617[/C][/ROW]
[ROW][C]M6[/C][C]3.77723030303032[/C][C]1.415597[/C][C]2.6683[/C][C]0.00881[/C][C]0.004405[/C][/ROW]
[ROW][C]M7[/C][C]1.67224191919192[/C][C]1.415324[/C][C]1.1815[/C][C]0.240013[/C][C]0.120007[/C][/ROW]
[ROW][C]M8[/C][C]0.375453535353537[/C][C]1.415101[/C][C]0.2653[/C][C]0.791274[/C][C]0.395637[/C][/ROW]
[ROW][C]M9[/C][C]3.47476515151516[/C][C]1.414927[/C][C]2.4558[/C][C]0.015667[/C][C]0.007834[/C][/ROW]
[ROW][C]M10[/C][C]2.80007676767677[/C][C]1.414803[/C][C]1.9791[/C][C]0.050371[/C][C]0.025185[/C][/ROW]
[ROW][C]M11[/C][C]1.92788838383839[/C][C]1.414729[/C][C]1.3627[/C][C]0.17583[/C][C]0.087915[/C][/ROW]
[ROW][C]t[/C][C]-0.0695116161616161[/C][C]0.008378[/C][C]-8.2967[/C][C]0[/C][C]0[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=108046&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=108046&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)17.76506666666671.14300515.542400
M1-0.8785277777777761.417703-0.61970.5367830.268392
M20.05428383838384191.4171830.03830.9695170.484758
M32.641595454545461.4167121.86460.0649790.032489
M42.536507070707071.4162911.7910.0761290.038064
M54.700318686868691.4159193.31960.0012330.000617
M63.777230303030321.4155972.66830.008810.004405
M71.672241919191921.4153241.18150.2400130.120007
M80.3754535353535371.4151010.26530.7912740.395637
M93.474765151515161.4149272.45580.0156670.007834
M102.800076767676771.4148031.97910.0503710.025185
M111.927888383838391.4147291.36270.175830.087915
t-0.06951161616161610.008378-8.296700






Multiple Linear Regression - Regression Statistics
Multiple R0.696874486476466
R-squared0.485634049901839
Adjusted R-squared0.42794814895625
F-TEST (value)8.41859175190675
F-TEST (DF numerator)12
F-TEST (DF denominator)107
p-value5.10929076824596e-11
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation3.16337457068026
Sum Squared Residuals1070.74243816364

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.696874486476466 \tabularnewline
R-squared & 0.485634049901839 \tabularnewline
Adjusted R-squared & 0.42794814895625 \tabularnewline
F-TEST (value) & 8.41859175190675 \tabularnewline
F-TEST (DF numerator) & 12 \tabularnewline
F-TEST (DF denominator) & 107 \tabularnewline
p-value & 5.10929076824596e-11 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 3.16337457068026 \tabularnewline
Sum Squared Residuals & 1070.74243816364 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=108046&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.696874486476466[/C][/ROW]
[ROW][C]R-squared[/C][C]0.485634049901839[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.42794814895625[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]8.41859175190675[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]12[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]107[/C][/ROW]
[ROW][C]p-value[/C][C]5.10929076824596e-11[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]3.16337457068026[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]1070.74243816364[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=108046&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=108046&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 R0.696874486476466
R-squared0.485634049901839
Adjusted R-squared0.42794814895625
F-TEST (value)8.41859175190675
F-TEST (DF numerator)12
F-TEST (DF denominator)107
p-value5.10929076824596e-11
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation3.16337457068026
Sum Squared Residuals1070.74243816364






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
117.84816.81702727272731.03097272727271
219.59217.68032727272731.91167272727273
321.09220.19812727272730.893872727272736
420.89920.02352727272730.875472727272718
525.8922.11782727272733.77217272727274
624.96521.12522727272733.83977272727273
722.22518.95072727272733.27427272727271
820.97717.58442727272733.39257272727273
922.89720.61422727272732.28277272727274
1022.78519.87002727272732.91497272727271
1122.76918.92832727272733.84067272727272
1219.63716.93092727272732.70607272727273
1320.20315.98288787878794.22011212121213
1420.4516.84618787878793.60381212121212
1523.08319.36398787878793.71901212121212
1621.73819.18938787878792.54861212121212
1726.76621.28368787878795.48231212121212
1825.2820.29108787878794.98891212121212
1922.57418.11658787878794.45741212121212
2022.72916.75028787878795.97871212121212
2121.37819.78008787878791.59791212121212
2222.90219.03588787878793.86611212121212
2324.98918.09418787878796.89481212121212
2421.11616.09678787878795.01921212121213
2515.16915.14874848484850.0202515151515173
2615.84616.0120484848485-0.166048484848486
2720.92718.52984848484852.39715151515152
2818.27318.3552484848485-0.082248484848484
2922.53820.44954848484852.08845151515152
3015.59619.4569484848485-3.86094848484849
3114.03417.2824484848485-3.24844848484848
3211.36615.9161484848485-4.55014848484848
3314.86118.9459484848485-4.08494848484849
3415.14918.2017484848485-3.05274848484848
3513.57717.2600484848485-3.68304848484848
3613.02615.2626484848485-2.23664848484848
3713.1914.3146090909091-1.12460909090909
3813.19615.1779090909091-1.98190909090909
3915.82617.6957090909091-1.86970909090909
4014.73317.5211090909091-2.78810909090909
4116.30719.6154090909091-3.30840909090909
4215.70318.6228090909091-2.91980909090909
4314.58916.4483090909091-1.85930909090909
4412.04315.0820090909091-3.03900909090909
4515.05718.1118090909091-3.05480909090909
4614.05317.3676090909091-3.31460909090909
4712.69816.4259090909091-3.72790909090909
4810.88814.4285090909091-3.54050909090909
4910.04513.4804696969697-3.43546969696969
5011.54914.3437696969697-2.7947696969697
5113.76716.8615696969697-3.0945696969697
5212.43416.6869696969697-4.2529696969697
5313.11618.7812696969697-5.6652696969697
5414.21117.7886696969697-3.5776696969697
5512.26615.6141696969697-3.34816969696970
5612.60214.2478696969697-1.64586969696970
5715.71417.2776696969697-1.56366969696970
5813.74216.5334696969697-2.79146969696970
5912.74515.5917696969697-2.84676969696970
6010.49113.5943696969697-3.10336969696969
6110.05712.6463303030303-2.5893303030303
6210.913.5096303030303-2.60963030303030
6311.77116.0274303030303-4.25643030303030
6411.99215.8528303030303-3.8608303030303
6511.93317.9471303030303-6.0141303030303
6614.50416.9545303030303-2.45053030303031
6711.72714.7800303030303-3.0530303030303
6811.47713.4137303030303-1.93673030303030
6913.57816.4435303030303-2.86553030303030
7011.55515.6993303030303-4.1443303030303
7111.84614.7576303030303-2.9116303030303
7211.39712.7602303030303-1.3632303030303
7310.06611.8121909090909-1.74619090909091
7410.26912.6754909090909-2.40649090909091
7514.27915.1932909090909-0.91429090909091
7613.8715.0186909090909-1.14869090909091
7713.69517.1129909090909-3.41799090909091
7814.4216.1203909090909-1.70039090909091
7911.42413.9458909090909-2.52189090909091
809.70412.5795909090909-2.87559090909091
8112.46415.6093909090909-3.14539090909091
8214.30114.8651909090909-0.564190909090909
8313.46413.9234909090909-0.459490909090908
849.89311.9260909090909-2.03309090909090
8511.57210.97805151515150.593948484848486
8612.3811.84135151515150.538648484848484
8716.69214.35915151515152.33284848484848
8816.05214.18455151515151.86744848484848
8916.45916.27885151515150.180148484848486
9014.76115.2862515151515-0.52525151515152
9113.65413.11175151515150.542248484848486
9213.4811.74545151515151.73454848484848
9318.06814.77525151515153.29274848484848
9416.5614.03105151515152.52894848484848
9514.5313.08935151515151.44064848484849
9610.6511.0919515151515-0.441951515151513
9711.65110.14391212121211.50708787878788
9813.73511.00721212121212.72778787878788
9913.3613.5250121212121-0.165012121212123
10017.81813.35041212121214.46758787878788
10120.61315.44471212121215.16828787878788
10216.23114.45211212121211.77888787878788
10313.86212.27761212121211.58438787878788
10412.00410.91131212121211.09268787878788
10517.73413.94111212121213.79288787878788
10615.03413.19691212121211.83708787878788
10712.60912.25521212121210.353787878787879
10812.3210.25781212121212.06218787878788
10910.8339.309772727272731.52322727272727
11011.3510.17307272727271.17692727272727
11113.64812.69087272727270.95712727272727
11214.8912.51627272727272.37372727272727
11316.32514.61057272727271.71442727272727
11418.04513.61797272727274.42702727272727
11515.61611.44347272727274.17252727272727
11611.92610.07717272727271.84882727272727
11716.85513.10697272727273.74802727272727
11815.08312.36277272727272.72022727272727
11912.5211.42107272727271.09892727272727
12012.3559.423672727272732.93132727272727

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 17.848 & 16.8170272727273 & 1.03097272727271 \tabularnewline
2 & 19.592 & 17.6803272727273 & 1.91167272727273 \tabularnewline
3 & 21.092 & 20.1981272727273 & 0.893872727272736 \tabularnewline
4 & 20.899 & 20.0235272727273 & 0.875472727272718 \tabularnewline
5 & 25.89 & 22.1178272727273 & 3.77217272727274 \tabularnewline
6 & 24.965 & 21.1252272727273 & 3.83977272727273 \tabularnewline
7 & 22.225 & 18.9507272727273 & 3.27427272727271 \tabularnewline
8 & 20.977 & 17.5844272727273 & 3.39257272727273 \tabularnewline
9 & 22.897 & 20.6142272727273 & 2.28277272727274 \tabularnewline
10 & 22.785 & 19.8700272727273 & 2.91497272727271 \tabularnewline
11 & 22.769 & 18.9283272727273 & 3.84067272727272 \tabularnewline
12 & 19.637 & 16.9309272727273 & 2.70607272727273 \tabularnewline
13 & 20.203 & 15.9828878787879 & 4.22011212121213 \tabularnewline
14 & 20.45 & 16.8461878787879 & 3.60381212121212 \tabularnewline
15 & 23.083 & 19.3639878787879 & 3.71901212121212 \tabularnewline
16 & 21.738 & 19.1893878787879 & 2.54861212121212 \tabularnewline
17 & 26.766 & 21.2836878787879 & 5.48231212121212 \tabularnewline
18 & 25.28 & 20.2910878787879 & 4.98891212121212 \tabularnewline
19 & 22.574 & 18.1165878787879 & 4.45741212121212 \tabularnewline
20 & 22.729 & 16.7502878787879 & 5.97871212121212 \tabularnewline
21 & 21.378 & 19.7800878787879 & 1.59791212121212 \tabularnewline
22 & 22.902 & 19.0358878787879 & 3.86611212121212 \tabularnewline
23 & 24.989 & 18.0941878787879 & 6.89481212121212 \tabularnewline
24 & 21.116 & 16.0967878787879 & 5.01921212121213 \tabularnewline
25 & 15.169 & 15.1487484848485 & 0.0202515151515173 \tabularnewline
26 & 15.846 & 16.0120484848485 & -0.166048484848486 \tabularnewline
27 & 20.927 & 18.5298484848485 & 2.39715151515152 \tabularnewline
28 & 18.273 & 18.3552484848485 & -0.082248484848484 \tabularnewline
29 & 22.538 & 20.4495484848485 & 2.08845151515152 \tabularnewline
30 & 15.596 & 19.4569484848485 & -3.86094848484849 \tabularnewline
31 & 14.034 & 17.2824484848485 & -3.24844848484848 \tabularnewline
32 & 11.366 & 15.9161484848485 & -4.55014848484848 \tabularnewline
33 & 14.861 & 18.9459484848485 & -4.08494848484849 \tabularnewline
34 & 15.149 & 18.2017484848485 & -3.05274848484848 \tabularnewline
35 & 13.577 & 17.2600484848485 & -3.68304848484848 \tabularnewline
36 & 13.026 & 15.2626484848485 & -2.23664848484848 \tabularnewline
37 & 13.19 & 14.3146090909091 & -1.12460909090909 \tabularnewline
38 & 13.196 & 15.1779090909091 & -1.98190909090909 \tabularnewline
39 & 15.826 & 17.6957090909091 & -1.86970909090909 \tabularnewline
40 & 14.733 & 17.5211090909091 & -2.78810909090909 \tabularnewline
41 & 16.307 & 19.6154090909091 & -3.30840909090909 \tabularnewline
42 & 15.703 & 18.6228090909091 & -2.91980909090909 \tabularnewline
43 & 14.589 & 16.4483090909091 & -1.85930909090909 \tabularnewline
44 & 12.043 & 15.0820090909091 & -3.03900909090909 \tabularnewline
45 & 15.057 & 18.1118090909091 & -3.05480909090909 \tabularnewline
46 & 14.053 & 17.3676090909091 & -3.31460909090909 \tabularnewline
47 & 12.698 & 16.4259090909091 & -3.72790909090909 \tabularnewline
48 & 10.888 & 14.4285090909091 & -3.54050909090909 \tabularnewline
49 & 10.045 & 13.4804696969697 & -3.43546969696969 \tabularnewline
50 & 11.549 & 14.3437696969697 & -2.7947696969697 \tabularnewline
51 & 13.767 & 16.8615696969697 & -3.0945696969697 \tabularnewline
52 & 12.434 & 16.6869696969697 & -4.2529696969697 \tabularnewline
53 & 13.116 & 18.7812696969697 & -5.6652696969697 \tabularnewline
54 & 14.211 & 17.7886696969697 & -3.5776696969697 \tabularnewline
55 & 12.266 & 15.6141696969697 & -3.34816969696970 \tabularnewline
56 & 12.602 & 14.2478696969697 & -1.64586969696970 \tabularnewline
57 & 15.714 & 17.2776696969697 & -1.56366969696970 \tabularnewline
58 & 13.742 & 16.5334696969697 & -2.79146969696970 \tabularnewline
59 & 12.745 & 15.5917696969697 & -2.84676969696970 \tabularnewline
60 & 10.491 & 13.5943696969697 & -3.10336969696969 \tabularnewline
61 & 10.057 & 12.6463303030303 & -2.5893303030303 \tabularnewline
62 & 10.9 & 13.5096303030303 & -2.60963030303030 \tabularnewline
63 & 11.771 & 16.0274303030303 & -4.25643030303030 \tabularnewline
64 & 11.992 & 15.8528303030303 & -3.8608303030303 \tabularnewline
65 & 11.933 & 17.9471303030303 & -6.0141303030303 \tabularnewline
66 & 14.504 & 16.9545303030303 & -2.45053030303031 \tabularnewline
67 & 11.727 & 14.7800303030303 & -3.0530303030303 \tabularnewline
68 & 11.477 & 13.4137303030303 & -1.93673030303030 \tabularnewline
69 & 13.578 & 16.4435303030303 & -2.86553030303030 \tabularnewline
70 & 11.555 & 15.6993303030303 & -4.1443303030303 \tabularnewline
71 & 11.846 & 14.7576303030303 & -2.9116303030303 \tabularnewline
72 & 11.397 & 12.7602303030303 & -1.3632303030303 \tabularnewline
73 & 10.066 & 11.8121909090909 & -1.74619090909091 \tabularnewline
74 & 10.269 & 12.6754909090909 & -2.40649090909091 \tabularnewline
75 & 14.279 & 15.1932909090909 & -0.91429090909091 \tabularnewline
76 & 13.87 & 15.0186909090909 & -1.14869090909091 \tabularnewline
77 & 13.695 & 17.1129909090909 & -3.41799090909091 \tabularnewline
78 & 14.42 & 16.1203909090909 & -1.70039090909091 \tabularnewline
79 & 11.424 & 13.9458909090909 & -2.52189090909091 \tabularnewline
80 & 9.704 & 12.5795909090909 & -2.87559090909091 \tabularnewline
81 & 12.464 & 15.6093909090909 & -3.14539090909091 \tabularnewline
82 & 14.301 & 14.8651909090909 & -0.564190909090909 \tabularnewline
83 & 13.464 & 13.9234909090909 & -0.459490909090908 \tabularnewline
84 & 9.893 & 11.9260909090909 & -2.03309090909090 \tabularnewline
85 & 11.572 & 10.9780515151515 & 0.593948484848486 \tabularnewline
86 & 12.38 & 11.8413515151515 & 0.538648484848484 \tabularnewline
87 & 16.692 & 14.3591515151515 & 2.33284848484848 \tabularnewline
88 & 16.052 & 14.1845515151515 & 1.86744848484848 \tabularnewline
89 & 16.459 & 16.2788515151515 & 0.180148484848486 \tabularnewline
90 & 14.761 & 15.2862515151515 & -0.52525151515152 \tabularnewline
91 & 13.654 & 13.1117515151515 & 0.542248484848486 \tabularnewline
92 & 13.48 & 11.7454515151515 & 1.73454848484848 \tabularnewline
93 & 18.068 & 14.7752515151515 & 3.29274848484848 \tabularnewline
94 & 16.56 & 14.0310515151515 & 2.52894848484848 \tabularnewline
95 & 14.53 & 13.0893515151515 & 1.44064848484849 \tabularnewline
96 & 10.65 & 11.0919515151515 & -0.441951515151513 \tabularnewline
97 & 11.651 & 10.1439121212121 & 1.50708787878788 \tabularnewline
98 & 13.735 & 11.0072121212121 & 2.72778787878788 \tabularnewline
99 & 13.36 & 13.5250121212121 & -0.165012121212123 \tabularnewline
100 & 17.818 & 13.3504121212121 & 4.46758787878788 \tabularnewline
101 & 20.613 & 15.4447121212121 & 5.16828787878788 \tabularnewline
102 & 16.231 & 14.4521121212121 & 1.77888787878788 \tabularnewline
103 & 13.862 & 12.2776121212121 & 1.58438787878788 \tabularnewline
104 & 12.004 & 10.9113121212121 & 1.09268787878788 \tabularnewline
105 & 17.734 & 13.9411121212121 & 3.79288787878788 \tabularnewline
106 & 15.034 & 13.1969121212121 & 1.83708787878788 \tabularnewline
107 & 12.609 & 12.2552121212121 & 0.353787878787879 \tabularnewline
108 & 12.32 & 10.2578121212121 & 2.06218787878788 \tabularnewline
109 & 10.833 & 9.30977272727273 & 1.52322727272727 \tabularnewline
110 & 11.35 & 10.1730727272727 & 1.17692727272727 \tabularnewline
111 & 13.648 & 12.6908727272727 & 0.95712727272727 \tabularnewline
112 & 14.89 & 12.5162727272727 & 2.37372727272727 \tabularnewline
113 & 16.325 & 14.6105727272727 & 1.71442727272727 \tabularnewline
114 & 18.045 & 13.6179727272727 & 4.42702727272727 \tabularnewline
115 & 15.616 & 11.4434727272727 & 4.17252727272727 \tabularnewline
116 & 11.926 & 10.0771727272727 & 1.84882727272727 \tabularnewline
117 & 16.855 & 13.1069727272727 & 3.74802727272727 \tabularnewline
118 & 15.083 & 12.3627727272727 & 2.72022727272727 \tabularnewline
119 & 12.52 & 11.4210727272727 & 1.09892727272727 \tabularnewline
120 & 12.355 & 9.42367272727273 & 2.93132727272727 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=108046&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]17.848[/C][C]16.8170272727273[/C][C]1.03097272727271[/C][/ROW]
[ROW][C]2[/C][C]19.592[/C][C]17.6803272727273[/C][C]1.91167272727273[/C][/ROW]
[ROW][C]3[/C][C]21.092[/C][C]20.1981272727273[/C][C]0.893872727272736[/C][/ROW]
[ROW][C]4[/C][C]20.899[/C][C]20.0235272727273[/C][C]0.875472727272718[/C][/ROW]
[ROW][C]5[/C][C]25.89[/C][C]22.1178272727273[/C][C]3.77217272727274[/C][/ROW]
[ROW][C]6[/C][C]24.965[/C][C]21.1252272727273[/C][C]3.83977272727273[/C][/ROW]
[ROW][C]7[/C][C]22.225[/C][C]18.9507272727273[/C][C]3.27427272727271[/C][/ROW]
[ROW][C]8[/C][C]20.977[/C][C]17.5844272727273[/C][C]3.39257272727273[/C][/ROW]
[ROW][C]9[/C][C]22.897[/C][C]20.6142272727273[/C][C]2.28277272727274[/C][/ROW]
[ROW][C]10[/C][C]22.785[/C][C]19.8700272727273[/C][C]2.91497272727271[/C][/ROW]
[ROW][C]11[/C][C]22.769[/C][C]18.9283272727273[/C][C]3.84067272727272[/C][/ROW]
[ROW][C]12[/C][C]19.637[/C][C]16.9309272727273[/C][C]2.70607272727273[/C][/ROW]
[ROW][C]13[/C][C]20.203[/C][C]15.9828878787879[/C][C]4.22011212121213[/C][/ROW]
[ROW][C]14[/C][C]20.45[/C][C]16.8461878787879[/C][C]3.60381212121212[/C][/ROW]
[ROW][C]15[/C][C]23.083[/C][C]19.3639878787879[/C][C]3.71901212121212[/C][/ROW]
[ROW][C]16[/C][C]21.738[/C][C]19.1893878787879[/C][C]2.54861212121212[/C][/ROW]
[ROW][C]17[/C][C]26.766[/C][C]21.2836878787879[/C][C]5.48231212121212[/C][/ROW]
[ROW][C]18[/C][C]25.28[/C][C]20.2910878787879[/C][C]4.98891212121212[/C][/ROW]
[ROW][C]19[/C][C]22.574[/C][C]18.1165878787879[/C][C]4.45741212121212[/C][/ROW]
[ROW][C]20[/C][C]22.729[/C][C]16.7502878787879[/C][C]5.97871212121212[/C][/ROW]
[ROW][C]21[/C][C]21.378[/C][C]19.7800878787879[/C][C]1.59791212121212[/C][/ROW]
[ROW][C]22[/C][C]22.902[/C][C]19.0358878787879[/C][C]3.86611212121212[/C][/ROW]
[ROW][C]23[/C][C]24.989[/C][C]18.0941878787879[/C][C]6.89481212121212[/C][/ROW]
[ROW][C]24[/C][C]21.116[/C][C]16.0967878787879[/C][C]5.01921212121213[/C][/ROW]
[ROW][C]25[/C][C]15.169[/C][C]15.1487484848485[/C][C]0.0202515151515173[/C][/ROW]
[ROW][C]26[/C][C]15.846[/C][C]16.0120484848485[/C][C]-0.166048484848486[/C][/ROW]
[ROW][C]27[/C][C]20.927[/C][C]18.5298484848485[/C][C]2.39715151515152[/C][/ROW]
[ROW][C]28[/C][C]18.273[/C][C]18.3552484848485[/C][C]-0.082248484848484[/C][/ROW]
[ROW][C]29[/C][C]22.538[/C][C]20.4495484848485[/C][C]2.08845151515152[/C][/ROW]
[ROW][C]30[/C][C]15.596[/C][C]19.4569484848485[/C][C]-3.86094848484849[/C][/ROW]
[ROW][C]31[/C][C]14.034[/C][C]17.2824484848485[/C][C]-3.24844848484848[/C][/ROW]
[ROW][C]32[/C][C]11.366[/C][C]15.9161484848485[/C][C]-4.55014848484848[/C][/ROW]
[ROW][C]33[/C][C]14.861[/C][C]18.9459484848485[/C][C]-4.08494848484849[/C][/ROW]
[ROW][C]34[/C][C]15.149[/C][C]18.2017484848485[/C][C]-3.05274848484848[/C][/ROW]
[ROW][C]35[/C][C]13.577[/C][C]17.2600484848485[/C][C]-3.68304848484848[/C][/ROW]
[ROW][C]36[/C][C]13.026[/C][C]15.2626484848485[/C][C]-2.23664848484848[/C][/ROW]
[ROW][C]37[/C][C]13.19[/C][C]14.3146090909091[/C][C]-1.12460909090909[/C][/ROW]
[ROW][C]38[/C][C]13.196[/C][C]15.1779090909091[/C][C]-1.98190909090909[/C][/ROW]
[ROW][C]39[/C][C]15.826[/C][C]17.6957090909091[/C][C]-1.86970909090909[/C][/ROW]
[ROW][C]40[/C][C]14.733[/C][C]17.5211090909091[/C][C]-2.78810909090909[/C][/ROW]
[ROW][C]41[/C][C]16.307[/C][C]19.6154090909091[/C][C]-3.30840909090909[/C][/ROW]
[ROW][C]42[/C][C]15.703[/C][C]18.6228090909091[/C][C]-2.91980909090909[/C][/ROW]
[ROW][C]43[/C][C]14.589[/C][C]16.4483090909091[/C][C]-1.85930909090909[/C][/ROW]
[ROW][C]44[/C][C]12.043[/C][C]15.0820090909091[/C][C]-3.03900909090909[/C][/ROW]
[ROW][C]45[/C][C]15.057[/C][C]18.1118090909091[/C][C]-3.05480909090909[/C][/ROW]
[ROW][C]46[/C][C]14.053[/C][C]17.3676090909091[/C][C]-3.31460909090909[/C][/ROW]
[ROW][C]47[/C][C]12.698[/C][C]16.4259090909091[/C][C]-3.72790909090909[/C][/ROW]
[ROW][C]48[/C][C]10.888[/C][C]14.4285090909091[/C][C]-3.54050909090909[/C][/ROW]
[ROW][C]49[/C][C]10.045[/C][C]13.4804696969697[/C][C]-3.43546969696969[/C][/ROW]
[ROW][C]50[/C][C]11.549[/C][C]14.3437696969697[/C][C]-2.7947696969697[/C][/ROW]
[ROW][C]51[/C][C]13.767[/C][C]16.8615696969697[/C][C]-3.0945696969697[/C][/ROW]
[ROW][C]52[/C][C]12.434[/C][C]16.6869696969697[/C][C]-4.2529696969697[/C][/ROW]
[ROW][C]53[/C][C]13.116[/C][C]18.7812696969697[/C][C]-5.6652696969697[/C][/ROW]
[ROW][C]54[/C][C]14.211[/C][C]17.7886696969697[/C][C]-3.5776696969697[/C][/ROW]
[ROW][C]55[/C][C]12.266[/C][C]15.6141696969697[/C][C]-3.34816969696970[/C][/ROW]
[ROW][C]56[/C][C]12.602[/C][C]14.2478696969697[/C][C]-1.64586969696970[/C][/ROW]
[ROW][C]57[/C][C]15.714[/C][C]17.2776696969697[/C][C]-1.56366969696970[/C][/ROW]
[ROW][C]58[/C][C]13.742[/C][C]16.5334696969697[/C][C]-2.79146969696970[/C][/ROW]
[ROW][C]59[/C][C]12.745[/C][C]15.5917696969697[/C][C]-2.84676969696970[/C][/ROW]
[ROW][C]60[/C][C]10.491[/C][C]13.5943696969697[/C][C]-3.10336969696969[/C][/ROW]
[ROW][C]61[/C][C]10.057[/C][C]12.6463303030303[/C][C]-2.5893303030303[/C][/ROW]
[ROW][C]62[/C][C]10.9[/C][C]13.5096303030303[/C][C]-2.60963030303030[/C][/ROW]
[ROW][C]63[/C][C]11.771[/C][C]16.0274303030303[/C][C]-4.25643030303030[/C][/ROW]
[ROW][C]64[/C][C]11.992[/C][C]15.8528303030303[/C][C]-3.8608303030303[/C][/ROW]
[ROW][C]65[/C][C]11.933[/C][C]17.9471303030303[/C][C]-6.0141303030303[/C][/ROW]
[ROW][C]66[/C][C]14.504[/C][C]16.9545303030303[/C][C]-2.45053030303031[/C][/ROW]
[ROW][C]67[/C][C]11.727[/C][C]14.7800303030303[/C][C]-3.0530303030303[/C][/ROW]
[ROW][C]68[/C][C]11.477[/C][C]13.4137303030303[/C][C]-1.93673030303030[/C][/ROW]
[ROW][C]69[/C][C]13.578[/C][C]16.4435303030303[/C][C]-2.86553030303030[/C][/ROW]
[ROW][C]70[/C][C]11.555[/C][C]15.6993303030303[/C][C]-4.1443303030303[/C][/ROW]
[ROW][C]71[/C][C]11.846[/C][C]14.7576303030303[/C][C]-2.9116303030303[/C][/ROW]
[ROW][C]72[/C][C]11.397[/C][C]12.7602303030303[/C][C]-1.3632303030303[/C][/ROW]
[ROW][C]73[/C][C]10.066[/C][C]11.8121909090909[/C][C]-1.74619090909091[/C][/ROW]
[ROW][C]74[/C][C]10.269[/C][C]12.6754909090909[/C][C]-2.40649090909091[/C][/ROW]
[ROW][C]75[/C][C]14.279[/C][C]15.1932909090909[/C][C]-0.91429090909091[/C][/ROW]
[ROW][C]76[/C][C]13.87[/C][C]15.0186909090909[/C][C]-1.14869090909091[/C][/ROW]
[ROW][C]77[/C][C]13.695[/C][C]17.1129909090909[/C][C]-3.41799090909091[/C][/ROW]
[ROW][C]78[/C][C]14.42[/C][C]16.1203909090909[/C][C]-1.70039090909091[/C][/ROW]
[ROW][C]79[/C][C]11.424[/C][C]13.9458909090909[/C][C]-2.52189090909091[/C][/ROW]
[ROW][C]80[/C][C]9.704[/C][C]12.5795909090909[/C][C]-2.87559090909091[/C][/ROW]
[ROW][C]81[/C][C]12.464[/C][C]15.6093909090909[/C][C]-3.14539090909091[/C][/ROW]
[ROW][C]82[/C][C]14.301[/C][C]14.8651909090909[/C][C]-0.564190909090909[/C][/ROW]
[ROW][C]83[/C][C]13.464[/C][C]13.9234909090909[/C][C]-0.459490909090908[/C][/ROW]
[ROW][C]84[/C][C]9.893[/C][C]11.9260909090909[/C][C]-2.03309090909090[/C][/ROW]
[ROW][C]85[/C][C]11.572[/C][C]10.9780515151515[/C][C]0.593948484848486[/C][/ROW]
[ROW][C]86[/C][C]12.38[/C][C]11.8413515151515[/C][C]0.538648484848484[/C][/ROW]
[ROW][C]87[/C][C]16.692[/C][C]14.3591515151515[/C][C]2.33284848484848[/C][/ROW]
[ROW][C]88[/C][C]16.052[/C][C]14.1845515151515[/C][C]1.86744848484848[/C][/ROW]
[ROW][C]89[/C][C]16.459[/C][C]16.2788515151515[/C][C]0.180148484848486[/C][/ROW]
[ROW][C]90[/C][C]14.761[/C][C]15.2862515151515[/C][C]-0.52525151515152[/C][/ROW]
[ROW][C]91[/C][C]13.654[/C][C]13.1117515151515[/C][C]0.542248484848486[/C][/ROW]
[ROW][C]92[/C][C]13.48[/C][C]11.7454515151515[/C][C]1.73454848484848[/C][/ROW]
[ROW][C]93[/C][C]18.068[/C][C]14.7752515151515[/C][C]3.29274848484848[/C][/ROW]
[ROW][C]94[/C][C]16.56[/C][C]14.0310515151515[/C][C]2.52894848484848[/C][/ROW]
[ROW][C]95[/C][C]14.53[/C][C]13.0893515151515[/C][C]1.44064848484849[/C][/ROW]
[ROW][C]96[/C][C]10.65[/C][C]11.0919515151515[/C][C]-0.441951515151513[/C][/ROW]
[ROW][C]97[/C][C]11.651[/C][C]10.1439121212121[/C][C]1.50708787878788[/C][/ROW]
[ROW][C]98[/C][C]13.735[/C][C]11.0072121212121[/C][C]2.72778787878788[/C][/ROW]
[ROW][C]99[/C][C]13.36[/C][C]13.5250121212121[/C][C]-0.165012121212123[/C][/ROW]
[ROW][C]100[/C][C]17.818[/C][C]13.3504121212121[/C][C]4.46758787878788[/C][/ROW]
[ROW][C]101[/C][C]20.613[/C][C]15.4447121212121[/C][C]5.16828787878788[/C][/ROW]
[ROW][C]102[/C][C]16.231[/C][C]14.4521121212121[/C][C]1.77888787878788[/C][/ROW]
[ROW][C]103[/C][C]13.862[/C][C]12.2776121212121[/C][C]1.58438787878788[/C][/ROW]
[ROW][C]104[/C][C]12.004[/C][C]10.9113121212121[/C][C]1.09268787878788[/C][/ROW]
[ROW][C]105[/C][C]17.734[/C][C]13.9411121212121[/C][C]3.79288787878788[/C][/ROW]
[ROW][C]106[/C][C]15.034[/C][C]13.1969121212121[/C][C]1.83708787878788[/C][/ROW]
[ROW][C]107[/C][C]12.609[/C][C]12.2552121212121[/C][C]0.353787878787879[/C][/ROW]
[ROW][C]108[/C][C]12.32[/C][C]10.2578121212121[/C][C]2.06218787878788[/C][/ROW]
[ROW][C]109[/C][C]10.833[/C][C]9.30977272727273[/C][C]1.52322727272727[/C][/ROW]
[ROW][C]110[/C][C]11.35[/C][C]10.1730727272727[/C][C]1.17692727272727[/C][/ROW]
[ROW][C]111[/C][C]13.648[/C][C]12.6908727272727[/C][C]0.95712727272727[/C][/ROW]
[ROW][C]112[/C][C]14.89[/C][C]12.5162727272727[/C][C]2.37372727272727[/C][/ROW]
[ROW][C]113[/C][C]16.325[/C][C]14.6105727272727[/C][C]1.71442727272727[/C][/ROW]
[ROW][C]114[/C][C]18.045[/C][C]13.6179727272727[/C][C]4.42702727272727[/C][/ROW]
[ROW][C]115[/C][C]15.616[/C][C]11.4434727272727[/C][C]4.17252727272727[/C][/ROW]
[ROW][C]116[/C][C]11.926[/C][C]10.0771727272727[/C][C]1.84882727272727[/C][/ROW]
[ROW][C]117[/C][C]16.855[/C][C]13.1069727272727[/C][C]3.74802727272727[/C][/ROW]
[ROW][C]118[/C][C]15.083[/C][C]12.3627727272727[/C][C]2.72022727272727[/C][/ROW]
[ROW][C]119[/C][C]12.52[/C][C]11.4210727272727[/C][C]1.09892727272727[/C][/ROW]
[ROW][C]120[/C][C]12.355[/C][C]9.42367272727273[/C][C]2.93132727272727[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=108046&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=108046&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
117.84816.81702727272731.03097272727271
219.59217.68032727272731.91167272727273
321.09220.19812727272730.893872727272736
420.89920.02352727272730.875472727272718
525.8922.11782727272733.77217272727274
624.96521.12522727272733.83977272727273
722.22518.95072727272733.27427272727271
820.97717.58442727272733.39257272727273
922.89720.61422727272732.28277272727274
1022.78519.87002727272732.91497272727271
1122.76918.92832727272733.84067272727272
1219.63716.93092727272732.70607272727273
1320.20315.98288787878794.22011212121213
1420.4516.84618787878793.60381212121212
1523.08319.36398787878793.71901212121212
1621.73819.18938787878792.54861212121212
1726.76621.28368787878795.48231212121212
1825.2820.29108787878794.98891212121212
1922.57418.11658787878794.45741212121212
2022.72916.75028787878795.97871212121212
2121.37819.78008787878791.59791212121212
2222.90219.03588787878793.86611212121212
2324.98918.09418787878796.89481212121212
2421.11616.09678787878795.01921212121213
2515.16915.14874848484850.0202515151515173
2615.84616.0120484848485-0.166048484848486
2720.92718.52984848484852.39715151515152
2818.27318.3552484848485-0.082248484848484
2922.53820.44954848484852.08845151515152
3015.59619.4569484848485-3.86094848484849
3114.03417.2824484848485-3.24844848484848
3211.36615.9161484848485-4.55014848484848
3314.86118.9459484848485-4.08494848484849
3415.14918.2017484848485-3.05274848484848
3513.57717.2600484848485-3.68304848484848
3613.02615.2626484848485-2.23664848484848
3713.1914.3146090909091-1.12460909090909
3813.19615.1779090909091-1.98190909090909
3915.82617.6957090909091-1.86970909090909
4014.73317.5211090909091-2.78810909090909
4116.30719.6154090909091-3.30840909090909
4215.70318.6228090909091-2.91980909090909
4314.58916.4483090909091-1.85930909090909
4412.04315.0820090909091-3.03900909090909
4515.05718.1118090909091-3.05480909090909
4614.05317.3676090909091-3.31460909090909
4712.69816.4259090909091-3.72790909090909
4810.88814.4285090909091-3.54050909090909
4910.04513.4804696969697-3.43546969696969
5011.54914.3437696969697-2.7947696969697
5113.76716.8615696969697-3.0945696969697
5212.43416.6869696969697-4.2529696969697
5313.11618.7812696969697-5.6652696969697
5414.21117.7886696969697-3.5776696969697
5512.26615.6141696969697-3.34816969696970
5612.60214.2478696969697-1.64586969696970
5715.71417.2776696969697-1.56366969696970
5813.74216.5334696969697-2.79146969696970
5912.74515.5917696969697-2.84676969696970
6010.49113.5943696969697-3.10336969696969
6110.05712.6463303030303-2.5893303030303
6210.913.5096303030303-2.60963030303030
6311.77116.0274303030303-4.25643030303030
6411.99215.8528303030303-3.8608303030303
6511.93317.9471303030303-6.0141303030303
6614.50416.9545303030303-2.45053030303031
6711.72714.7800303030303-3.0530303030303
6811.47713.4137303030303-1.93673030303030
6913.57816.4435303030303-2.86553030303030
7011.55515.6993303030303-4.1443303030303
7111.84614.7576303030303-2.9116303030303
7211.39712.7602303030303-1.3632303030303
7310.06611.8121909090909-1.74619090909091
7410.26912.6754909090909-2.40649090909091
7514.27915.1932909090909-0.91429090909091
7613.8715.0186909090909-1.14869090909091
7713.69517.1129909090909-3.41799090909091
7814.4216.1203909090909-1.70039090909091
7911.42413.9458909090909-2.52189090909091
809.70412.5795909090909-2.87559090909091
8112.46415.6093909090909-3.14539090909091
8214.30114.8651909090909-0.564190909090909
8313.46413.9234909090909-0.459490909090908
849.89311.9260909090909-2.03309090909090
8511.57210.97805151515150.593948484848486
8612.3811.84135151515150.538648484848484
8716.69214.35915151515152.33284848484848
8816.05214.18455151515151.86744848484848
8916.45916.27885151515150.180148484848486
9014.76115.2862515151515-0.52525151515152
9113.65413.11175151515150.542248484848486
9213.4811.74545151515151.73454848484848
9318.06814.77525151515153.29274848484848
9416.5614.03105151515152.52894848484848
9514.5313.08935151515151.44064848484849
9610.6511.0919515151515-0.441951515151513
9711.65110.14391212121211.50708787878788
9813.73511.00721212121212.72778787878788
9913.3613.5250121212121-0.165012121212123
10017.81813.35041212121214.46758787878788
10120.61315.44471212121215.16828787878788
10216.23114.45211212121211.77888787878788
10313.86212.27761212121211.58438787878788
10412.00410.91131212121211.09268787878788
10517.73413.94111212121213.79288787878788
10615.03413.19691212121211.83708787878788
10712.60912.25521212121210.353787878787879
10812.3210.25781212121212.06218787878788
10910.8339.309772727272731.52322727272727
11011.3510.17307272727271.17692727272727
11113.64812.69087272727270.95712727272727
11214.8912.51627272727272.37372727272727
11316.32514.61057272727271.71442727272727
11418.04513.61797272727274.42702727272727
11515.61611.44347272727274.17252727272727
11611.92610.07717272727271.84882727272727
11716.85513.10697272727273.74802727272727
11815.08312.36277272727272.72022727272727
11912.5211.42107272727271.09892727272727
12012.3559.423672727272732.93132727272727






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
160.008184384545522430.01636876909104490.991815615454477
170.001918711341441540.003837422682883080.998081288658558
180.0008436644125731470.001687328825146290.999156335587427
190.0003041989900978780.0006083979801957570.999695801009902
200.0001182822516846640.0002365645033693280.999881717748315
210.0008768266113424320.001753653222684860.999123173388658
220.000445569174432130.000891138348864260.999554430825568
230.0008004582911997740.001600916582399550.9991995417088
240.0008605387184112780.001721077436822560.999139461281589
250.0802174725886550.160434945177310.919782527411345
260.2490160517614700.4980321035229410.75098394823853
270.298282228599880.596564457199760.70171777140012
280.3643403813013630.7286807626027260.635659618698637
290.6516880232629520.6966239534740960.348311976737048
300.9844014267486680.03119714650266360.0155985732513318
310.9974140815629380.005171836874124470.00258591843706223
320.9998326638739470.0003346722521062680.000167336126053134
330.9998620999301160.0002758001397676890.000137900069883844
340.999904516315220.0001909673695596449.5483684779822e-05
350.999977781961264.44360774804321e-052.22180387402161e-05
360.99998400388223.19922355998212e-051.59961177999106e-05
370.9999880323340652.39353318695694e-051.19676659347847e-05
380.9999857992642552.84014714892668e-051.42007357446334e-05
390.9999869638407852.60723184297817e-051.30361592148909e-05
400.9999785660334364.28679331270897e-052.14339665635448e-05
410.9999826252314773.47495370451112e-051.73747685225556e-05
420.9999739625725185.20748549638262e-052.60374274819131e-05
430.9999781218361164.37563277688306e-052.18781638844153e-05
440.9999684171349566.31657300871102e-053.15828650435551e-05
450.9999492722639520.0001014554720960705.07277360480349e-05
460.9999203156313970.0001593687372063357.96843686031677e-05
470.9998969142304150.0002061715391702350.000103085769585118
480.9998543585193460.000291282961307980.00014564148065399
490.9997659908365910.000468018326817550.000234009163408775
500.9996987109242310.0006025781515369840.000301289075768492
510.9995809205006270.000838158998746180.00041907949937309
520.9993502704725050.001299459054990290.000649729527495143
530.9992324016641420.001535196671715260.00076759833585763
540.9988078758217340.002384248356531760.00119212417826588
550.9982256406700930.003548718659813470.00177435932990673
560.9987795197548620.002440960490275290.00122048024513765
570.9991860461440650.001627907711870680.00081395385593534
580.9989287419509170.002142516098166250.00107125804908312
590.9986689915526640.002662016894672940.00133100844733647
600.9981550970692810.003689805861437710.00184490293071886
610.9978067674231860.004386465153627940.00219323257681397
620.9973019618159470.005396076368105150.00269803818405257
630.996071139824750.007857720350501280.00392886017525064
640.9959401897328120.008119620534376920.00405981026718846
650.9974150240886870.005169951822626050.00258497591131303
660.9968221660679570.006355667864085830.00317783393204291
670.9956265227735850.00874695445283030.00437347722641515
680.9951250466553140.009749906689371540.00487495334468577
690.9944979270684640.01100414586307290.00550207293153646
700.9945417771687530.01091644566249370.00545822283124684
710.9920586634133260.01588267317334730.00794133658667363
720.99199151653290.01601696693419850.00800848346709924
730.9909208618918850.01815827621622950.00907913810811477
740.9893643814020270.02127123719594580.0106356185979729
750.989110878710450.02177824257910110.0108891212895506
760.9902291066687960.01954178666240780.0097708933312039
770.9932006726679420.01359865466411650.00679932733205827
780.9920278490609260.01594430187814820.00797215093907411
790.992467990778020.01506401844396080.00753200922198038
800.9925456281503440.01490874369931190.00745437184965594
810.9991514936224380.001697012755124170.000848506377562085
820.999161001506040.001677996987921720.00083899849396086
830.9987725340268970.002454931946205560.00122746597310278
840.9988912213523520.002217557295296620.00110877864764831
850.998629472671470.002741054657060780.00137052732853039
860.998257671315960.003484657368080130.00174232868404007
870.9993243430432450.001351313913508960.000675656956754481
880.9991967211082080.001606557783584340.000803278891792172
890.9993450737599490.001309852480102280.000654926240051142
900.9996587670154840.0006824659690320880.000341232984516044
910.9996432207204530.0007135585590948670.000356779279547434
920.9993976180022570.001204763995486920.000602381997743459
930.999099252716420.001801494567160820.00090074728358041
940.9985062258812760.002987548237447580.00149377411872379
950.997690854657540.004618290684918720.00230914534245936
960.9977823788251440.004435242349712430.00221762117485622
970.9951798440318340.009640311936331920.00482015596816596
980.9932743879278070.01345122414438560.00672561207219278
990.985398522615490.02920295476901910.0146014773845096
1000.9858839798807370.02823204023852530.0141160201192626
1010.9993727053804940.001254589239012740.000627294619506369
1020.999168438845050.001663122309897650.000831561154948823
1030.9998803026603460.0002393946793081960.000119697339654098
1040.9986169468901750.002766106219649770.00138305310982488

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
16 & 0.00818438454552243 & 0.0163687690910449 & 0.991815615454477 \tabularnewline
17 & 0.00191871134144154 & 0.00383742268288308 & 0.998081288658558 \tabularnewline
18 & 0.000843664412573147 & 0.00168732882514629 & 0.999156335587427 \tabularnewline
19 & 0.000304198990097878 & 0.000608397980195757 & 0.999695801009902 \tabularnewline
20 & 0.000118282251684664 & 0.000236564503369328 & 0.999881717748315 \tabularnewline
21 & 0.000876826611342432 & 0.00175365322268486 & 0.999123173388658 \tabularnewline
22 & 0.00044556917443213 & 0.00089113834886426 & 0.999554430825568 \tabularnewline
23 & 0.000800458291199774 & 0.00160091658239955 & 0.9991995417088 \tabularnewline
24 & 0.000860538718411278 & 0.00172107743682256 & 0.999139461281589 \tabularnewline
25 & 0.080217472588655 & 0.16043494517731 & 0.919782527411345 \tabularnewline
26 & 0.249016051761470 & 0.498032103522941 & 0.75098394823853 \tabularnewline
27 & 0.29828222859988 & 0.59656445719976 & 0.70171777140012 \tabularnewline
28 & 0.364340381301363 & 0.728680762602726 & 0.635659618698637 \tabularnewline
29 & 0.651688023262952 & 0.696623953474096 & 0.348311976737048 \tabularnewline
30 & 0.984401426748668 & 0.0311971465026636 & 0.0155985732513318 \tabularnewline
31 & 0.997414081562938 & 0.00517183687412447 & 0.00258591843706223 \tabularnewline
32 & 0.999832663873947 & 0.000334672252106268 & 0.000167336126053134 \tabularnewline
33 & 0.999862099930116 & 0.000275800139767689 & 0.000137900069883844 \tabularnewline
34 & 0.99990451631522 & 0.000190967369559644 & 9.5483684779822e-05 \tabularnewline
35 & 0.99997778196126 & 4.44360774804321e-05 & 2.22180387402161e-05 \tabularnewline
36 & 0.9999840038822 & 3.19922355998212e-05 & 1.59961177999106e-05 \tabularnewline
37 & 0.999988032334065 & 2.39353318695694e-05 & 1.19676659347847e-05 \tabularnewline
38 & 0.999985799264255 & 2.84014714892668e-05 & 1.42007357446334e-05 \tabularnewline
39 & 0.999986963840785 & 2.60723184297817e-05 & 1.30361592148909e-05 \tabularnewline
40 & 0.999978566033436 & 4.28679331270897e-05 & 2.14339665635448e-05 \tabularnewline
41 & 0.999982625231477 & 3.47495370451112e-05 & 1.73747685225556e-05 \tabularnewline
42 & 0.999973962572518 & 5.20748549638262e-05 & 2.60374274819131e-05 \tabularnewline
43 & 0.999978121836116 & 4.37563277688306e-05 & 2.18781638844153e-05 \tabularnewline
44 & 0.999968417134956 & 6.31657300871102e-05 & 3.15828650435551e-05 \tabularnewline
45 & 0.999949272263952 & 0.000101455472096070 & 5.07277360480349e-05 \tabularnewline
46 & 0.999920315631397 & 0.000159368737206335 & 7.96843686031677e-05 \tabularnewline
47 & 0.999896914230415 & 0.000206171539170235 & 0.000103085769585118 \tabularnewline
48 & 0.999854358519346 & 0.00029128296130798 & 0.00014564148065399 \tabularnewline
49 & 0.999765990836591 & 0.00046801832681755 & 0.000234009163408775 \tabularnewline
50 & 0.999698710924231 & 0.000602578151536984 & 0.000301289075768492 \tabularnewline
51 & 0.999580920500627 & 0.00083815899874618 & 0.00041907949937309 \tabularnewline
52 & 0.999350270472505 & 0.00129945905499029 & 0.000649729527495143 \tabularnewline
53 & 0.999232401664142 & 0.00153519667171526 & 0.00076759833585763 \tabularnewline
54 & 0.998807875821734 & 0.00238424835653176 & 0.00119212417826588 \tabularnewline
55 & 0.998225640670093 & 0.00354871865981347 & 0.00177435932990673 \tabularnewline
56 & 0.998779519754862 & 0.00244096049027529 & 0.00122048024513765 \tabularnewline
57 & 0.999186046144065 & 0.00162790771187068 & 0.00081395385593534 \tabularnewline
58 & 0.998928741950917 & 0.00214251609816625 & 0.00107125804908312 \tabularnewline
59 & 0.998668991552664 & 0.00266201689467294 & 0.00133100844733647 \tabularnewline
60 & 0.998155097069281 & 0.00368980586143771 & 0.00184490293071886 \tabularnewline
61 & 0.997806767423186 & 0.00438646515362794 & 0.00219323257681397 \tabularnewline
62 & 0.997301961815947 & 0.00539607636810515 & 0.00269803818405257 \tabularnewline
63 & 0.99607113982475 & 0.00785772035050128 & 0.00392886017525064 \tabularnewline
64 & 0.995940189732812 & 0.00811962053437692 & 0.00405981026718846 \tabularnewline
65 & 0.997415024088687 & 0.00516995182262605 & 0.00258497591131303 \tabularnewline
66 & 0.996822166067957 & 0.00635566786408583 & 0.00317783393204291 \tabularnewline
67 & 0.995626522773585 & 0.0087469544528303 & 0.00437347722641515 \tabularnewline
68 & 0.995125046655314 & 0.00974990668937154 & 0.00487495334468577 \tabularnewline
69 & 0.994497927068464 & 0.0110041458630729 & 0.00550207293153646 \tabularnewline
70 & 0.994541777168753 & 0.0109164456624937 & 0.00545822283124684 \tabularnewline
71 & 0.992058663413326 & 0.0158826731733473 & 0.00794133658667363 \tabularnewline
72 & 0.9919915165329 & 0.0160169669341985 & 0.00800848346709924 \tabularnewline
73 & 0.990920861891885 & 0.0181582762162295 & 0.00907913810811477 \tabularnewline
74 & 0.989364381402027 & 0.0212712371959458 & 0.0106356185979729 \tabularnewline
75 & 0.98911087871045 & 0.0217782425791011 & 0.0108891212895506 \tabularnewline
76 & 0.990229106668796 & 0.0195417866624078 & 0.0097708933312039 \tabularnewline
77 & 0.993200672667942 & 0.0135986546641165 & 0.00679932733205827 \tabularnewline
78 & 0.992027849060926 & 0.0159443018781482 & 0.00797215093907411 \tabularnewline
79 & 0.99246799077802 & 0.0150640184439608 & 0.00753200922198038 \tabularnewline
80 & 0.992545628150344 & 0.0149087436993119 & 0.00745437184965594 \tabularnewline
81 & 0.999151493622438 & 0.00169701275512417 & 0.000848506377562085 \tabularnewline
82 & 0.99916100150604 & 0.00167799698792172 & 0.00083899849396086 \tabularnewline
83 & 0.998772534026897 & 0.00245493194620556 & 0.00122746597310278 \tabularnewline
84 & 0.998891221352352 & 0.00221755729529662 & 0.00110877864764831 \tabularnewline
85 & 0.99862947267147 & 0.00274105465706078 & 0.00137052732853039 \tabularnewline
86 & 0.99825767131596 & 0.00348465736808013 & 0.00174232868404007 \tabularnewline
87 & 0.999324343043245 & 0.00135131391350896 & 0.000675656956754481 \tabularnewline
88 & 0.999196721108208 & 0.00160655778358434 & 0.000803278891792172 \tabularnewline
89 & 0.999345073759949 & 0.00130985248010228 & 0.000654926240051142 \tabularnewline
90 & 0.999658767015484 & 0.000682465969032088 & 0.000341232984516044 \tabularnewline
91 & 0.999643220720453 & 0.000713558559094867 & 0.000356779279547434 \tabularnewline
92 & 0.999397618002257 & 0.00120476399548692 & 0.000602381997743459 \tabularnewline
93 & 0.99909925271642 & 0.00180149456716082 & 0.00090074728358041 \tabularnewline
94 & 0.998506225881276 & 0.00298754823744758 & 0.00149377411872379 \tabularnewline
95 & 0.99769085465754 & 0.00461829068491872 & 0.00230914534245936 \tabularnewline
96 & 0.997782378825144 & 0.00443524234971243 & 0.00221762117485622 \tabularnewline
97 & 0.995179844031834 & 0.00964031193633192 & 0.00482015596816596 \tabularnewline
98 & 0.993274387927807 & 0.0134512241443856 & 0.00672561207219278 \tabularnewline
99 & 0.98539852261549 & 0.0292029547690191 & 0.0146014773845096 \tabularnewline
100 & 0.985883979880737 & 0.0282320402385253 & 0.0141160201192626 \tabularnewline
101 & 0.999372705380494 & 0.00125458923901274 & 0.000627294619506369 \tabularnewline
102 & 0.99916843884505 & 0.00166312230989765 & 0.000831561154948823 \tabularnewline
103 & 0.999880302660346 & 0.000239394679308196 & 0.000119697339654098 \tabularnewline
104 & 0.998616946890175 & 0.00276610621964977 & 0.00138305310982488 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=108046&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]16[/C][C]0.00818438454552243[/C][C]0.0163687690910449[/C][C]0.991815615454477[/C][/ROW]
[ROW][C]17[/C][C]0.00191871134144154[/C][C]0.00383742268288308[/C][C]0.998081288658558[/C][/ROW]
[ROW][C]18[/C][C]0.000843664412573147[/C][C]0.00168732882514629[/C][C]0.999156335587427[/C][/ROW]
[ROW][C]19[/C][C]0.000304198990097878[/C][C]0.000608397980195757[/C][C]0.999695801009902[/C][/ROW]
[ROW][C]20[/C][C]0.000118282251684664[/C][C]0.000236564503369328[/C][C]0.999881717748315[/C][/ROW]
[ROW][C]21[/C][C]0.000876826611342432[/C][C]0.00175365322268486[/C][C]0.999123173388658[/C][/ROW]
[ROW][C]22[/C][C]0.00044556917443213[/C][C]0.00089113834886426[/C][C]0.999554430825568[/C][/ROW]
[ROW][C]23[/C][C]0.000800458291199774[/C][C]0.00160091658239955[/C][C]0.9991995417088[/C][/ROW]
[ROW][C]24[/C][C]0.000860538718411278[/C][C]0.00172107743682256[/C][C]0.999139461281589[/C][/ROW]
[ROW][C]25[/C][C]0.080217472588655[/C][C]0.16043494517731[/C][C]0.919782527411345[/C][/ROW]
[ROW][C]26[/C][C]0.249016051761470[/C][C]0.498032103522941[/C][C]0.75098394823853[/C][/ROW]
[ROW][C]27[/C][C]0.29828222859988[/C][C]0.59656445719976[/C][C]0.70171777140012[/C][/ROW]
[ROW][C]28[/C][C]0.364340381301363[/C][C]0.728680762602726[/C][C]0.635659618698637[/C][/ROW]
[ROW][C]29[/C][C]0.651688023262952[/C][C]0.696623953474096[/C][C]0.348311976737048[/C][/ROW]
[ROW][C]30[/C][C]0.984401426748668[/C][C]0.0311971465026636[/C][C]0.0155985732513318[/C][/ROW]
[ROW][C]31[/C][C]0.997414081562938[/C][C]0.00517183687412447[/C][C]0.00258591843706223[/C][/ROW]
[ROW][C]32[/C][C]0.999832663873947[/C][C]0.000334672252106268[/C][C]0.000167336126053134[/C][/ROW]
[ROW][C]33[/C][C]0.999862099930116[/C][C]0.000275800139767689[/C][C]0.000137900069883844[/C][/ROW]
[ROW][C]34[/C][C]0.99990451631522[/C][C]0.000190967369559644[/C][C]9.5483684779822e-05[/C][/ROW]
[ROW][C]35[/C][C]0.99997778196126[/C][C]4.44360774804321e-05[/C][C]2.22180387402161e-05[/C][/ROW]
[ROW][C]36[/C][C]0.9999840038822[/C][C]3.19922355998212e-05[/C][C]1.59961177999106e-05[/C][/ROW]
[ROW][C]37[/C][C]0.999988032334065[/C][C]2.39353318695694e-05[/C][C]1.19676659347847e-05[/C][/ROW]
[ROW][C]38[/C][C]0.999985799264255[/C][C]2.84014714892668e-05[/C][C]1.42007357446334e-05[/C][/ROW]
[ROW][C]39[/C][C]0.999986963840785[/C][C]2.60723184297817e-05[/C][C]1.30361592148909e-05[/C][/ROW]
[ROW][C]40[/C][C]0.999978566033436[/C][C]4.28679331270897e-05[/C][C]2.14339665635448e-05[/C][/ROW]
[ROW][C]41[/C][C]0.999982625231477[/C][C]3.47495370451112e-05[/C][C]1.73747685225556e-05[/C][/ROW]
[ROW][C]42[/C][C]0.999973962572518[/C][C]5.20748549638262e-05[/C][C]2.60374274819131e-05[/C][/ROW]
[ROW][C]43[/C][C]0.999978121836116[/C][C]4.37563277688306e-05[/C][C]2.18781638844153e-05[/C][/ROW]
[ROW][C]44[/C][C]0.999968417134956[/C][C]6.31657300871102e-05[/C][C]3.15828650435551e-05[/C][/ROW]
[ROW][C]45[/C][C]0.999949272263952[/C][C]0.000101455472096070[/C][C]5.07277360480349e-05[/C][/ROW]
[ROW][C]46[/C][C]0.999920315631397[/C][C]0.000159368737206335[/C][C]7.96843686031677e-05[/C][/ROW]
[ROW][C]47[/C][C]0.999896914230415[/C][C]0.000206171539170235[/C][C]0.000103085769585118[/C][/ROW]
[ROW][C]48[/C][C]0.999854358519346[/C][C]0.00029128296130798[/C][C]0.00014564148065399[/C][/ROW]
[ROW][C]49[/C][C]0.999765990836591[/C][C]0.00046801832681755[/C][C]0.000234009163408775[/C][/ROW]
[ROW][C]50[/C][C]0.999698710924231[/C][C]0.000602578151536984[/C][C]0.000301289075768492[/C][/ROW]
[ROW][C]51[/C][C]0.999580920500627[/C][C]0.00083815899874618[/C][C]0.00041907949937309[/C][/ROW]
[ROW][C]52[/C][C]0.999350270472505[/C][C]0.00129945905499029[/C][C]0.000649729527495143[/C][/ROW]
[ROW][C]53[/C][C]0.999232401664142[/C][C]0.00153519667171526[/C][C]0.00076759833585763[/C][/ROW]
[ROW][C]54[/C][C]0.998807875821734[/C][C]0.00238424835653176[/C][C]0.00119212417826588[/C][/ROW]
[ROW][C]55[/C][C]0.998225640670093[/C][C]0.00354871865981347[/C][C]0.00177435932990673[/C][/ROW]
[ROW][C]56[/C][C]0.998779519754862[/C][C]0.00244096049027529[/C][C]0.00122048024513765[/C][/ROW]
[ROW][C]57[/C][C]0.999186046144065[/C][C]0.00162790771187068[/C][C]0.00081395385593534[/C][/ROW]
[ROW][C]58[/C][C]0.998928741950917[/C][C]0.00214251609816625[/C][C]0.00107125804908312[/C][/ROW]
[ROW][C]59[/C][C]0.998668991552664[/C][C]0.00266201689467294[/C][C]0.00133100844733647[/C][/ROW]
[ROW][C]60[/C][C]0.998155097069281[/C][C]0.00368980586143771[/C][C]0.00184490293071886[/C][/ROW]
[ROW][C]61[/C][C]0.997806767423186[/C][C]0.00438646515362794[/C][C]0.00219323257681397[/C][/ROW]
[ROW][C]62[/C][C]0.997301961815947[/C][C]0.00539607636810515[/C][C]0.00269803818405257[/C][/ROW]
[ROW][C]63[/C][C]0.99607113982475[/C][C]0.00785772035050128[/C][C]0.00392886017525064[/C][/ROW]
[ROW][C]64[/C][C]0.995940189732812[/C][C]0.00811962053437692[/C][C]0.00405981026718846[/C][/ROW]
[ROW][C]65[/C][C]0.997415024088687[/C][C]0.00516995182262605[/C][C]0.00258497591131303[/C][/ROW]
[ROW][C]66[/C][C]0.996822166067957[/C][C]0.00635566786408583[/C][C]0.00317783393204291[/C][/ROW]
[ROW][C]67[/C][C]0.995626522773585[/C][C]0.0087469544528303[/C][C]0.00437347722641515[/C][/ROW]
[ROW][C]68[/C][C]0.995125046655314[/C][C]0.00974990668937154[/C][C]0.00487495334468577[/C][/ROW]
[ROW][C]69[/C][C]0.994497927068464[/C][C]0.0110041458630729[/C][C]0.00550207293153646[/C][/ROW]
[ROW][C]70[/C][C]0.994541777168753[/C][C]0.0109164456624937[/C][C]0.00545822283124684[/C][/ROW]
[ROW][C]71[/C][C]0.992058663413326[/C][C]0.0158826731733473[/C][C]0.00794133658667363[/C][/ROW]
[ROW][C]72[/C][C]0.9919915165329[/C][C]0.0160169669341985[/C][C]0.00800848346709924[/C][/ROW]
[ROW][C]73[/C][C]0.990920861891885[/C][C]0.0181582762162295[/C][C]0.00907913810811477[/C][/ROW]
[ROW][C]74[/C][C]0.989364381402027[/C][C]0.0212712371959458[/C][C]0.0106356185979729[/C][/ROW]
[ROW][C]75[/C][C]0.98911087871045[/C][C]0.0217782425791011[/C][C]0.0108891212895506[/C][/ROW]
[ROW][C]76[/C][C]0.990229106668796[/C][C]0.0195417866624078[/C][C]0.0097708933312039[/C][/ROW]
[ROW][C]77[/C][C]0.993200672667942[/C][C]0.0135986546641165[/C][C]0.00679932733205827[/C][/ROW]
[ROW][C]78[/C][C]0.992027849060926[/C][C]0.0159443018781482[/C][C]0.00797215093907411[/C][/ROW]
[ROW][C]79[/C][C]0.99246799077802[/C][C]0.0150640184439608[/C][C]0.00753200922198038[/C][/ROW]
[ROW][C]80[/C][C]0.992545628150344[/C][C]0.0149087436993119[/C][C]0.00745437184965594[/C][/ROW]
[ROW][C]81[/C][C]0.999151493622438[/C][C]0.00169701275512417[/C][C]0.000848506377562085[/C][/ROW]
[ROW][C]82[/C][C]0.99916100150604[/C][C]0.00167799698792172[/C][C]0.00083899849396086[/C][/ROW]
[ROW][C]83[/C][C]0.998772534026897[/C][C]0.00245493194620556[/C][C]0.00122746597310278[/C][/ROW]
[ROW][C]84[/C][C]0.998891221352352[/C][C]0.00221755729529662[/C][C]0.00110877864764831[/C][/ROW]
[ROW][C]85[/C][C]0.99862947267147[/C][C]0.00274105465706078[/C][C]0.00137052732853039[/C][/ROW]
[ROW][C]86[/C][C]0.99825767131596[/C][C]0.00348465736808013[/C][C]0.00174232868404007[/C][/ROW]
[ROW][C]87[/C][C]0.999324343043245[/C][C]0.00135131391350896[/C][C]0.000675656956754481[/C][/ROW]
[ROW][C]88[/C][C]0.999196721108208[/C][C]0.00160655778358434[/C][C]0.000803278891792172[/C][/ROW]
[ROW][C]89[/C][C]0.999345073759949[/C][C]0.00130985248010228[/C][C]0.000654926240051142[/C][/ROW]
[ROW][C]90[/C][C]0.999658767015484[/C][C]0.000682465969032088[/C][C]0.000341232984516044[/C][/ROW]
[ROW][C]91[/C][C]0.999643220720453[/C][C]0.000713558559094867[/C][C]0.000356779279547434[/C][/ROW]
[ROW][C]92[/C][C]0.999397618002257[/C][C]0.00120476399548692[/C][C]0.000602381997743459[/C][/ROW]
[ROW][C]93[/C][C]0.99909925271642[/C][C]0.00180149456716082[/C][C]0.00090074728358041[/C][/ROW]
[ROW][C]94[/C][C]0.998506225881276[/C][C]0.00298754823744758[/C][C]0.00149377411872379[/C][/ROW]
[ROW][C]95[/C][C]0.99769085465754[/C][C]0.00461829068491872[/C][C]0.00230914534245936[/C][/ROW]
[ROW][C]96[/C][C]0.997782378825144[/C][C]0.00443524234971243[/C][C]0.00221762117485622[/C][/ROW]
[ROW][C]97[/C][C]0.995179844031834[/C][C]0.00964031193633192[/C][C]0.00482015596816596[/C][/ROW]
[ROW][C]98[/C][C]0.993274387927807[/C][C]0.0134512241443856[/C][C]0.00672561207219278[/C][/ROW]
[ROW][C]99[/C][C]0.98539852261549[/C][C]0.0292029547690191[/C][C]0.0146014773845096[/C][/ROW]
[ROW][C]100[/C][C]0.985883979880737[/C][C]0.0282320402385253[/C][C]0.0141160201192626[/C][/ROW]
[ROW][C]101[/C][C]0.999372705380494[/C][C]0.00125458923901274[/C][C]0.000627294619506369[/C][/ROW]
[ROW][C]102[/C][C]0.99916843884505[/C][C]0.00166312230989765[/C][C]0.000831561154948823[/C][/ROW]
[ROW][C]103[/C][C]0.999880302660346[/C][C]0.000239394679308196[/C][C]0.000119697339654098[/C][/ROW]
[ROW][C]104[/C][C]0.998616946890175[/C][C]0.00276610621964977[/C][C]0.00138305310982488[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=108046&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=108046&T=5

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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
160.008184384545522430.01636876909104490.991815615454477
170.001918711341441540.003837422682883080.998081288658558
180.0008436644125731470.001687328825146290.999156335587427
190.0003041989900978780.0006083979801957570.999695801009902
200.0001182822516846640.0002365645033693280.999881717748315
210.0008768266113424320.001753653222684860.999123173388658
220.000445569174432130.000891138348864260.999554430825568
230.0008004582911997740.001600916582399550.9991995417088
240.0008605387184112780.001721077436822560.999139461281589
250.0802174725886550.160434945177310.919782527411345
260.2490160517614700.4980321035229410.75098394823853
270.298282228599880.596564457199760.70171777140012
280.3643403813013630.7286807626027260.635659618698637
290.6516880232629520.6966239534740960.348311976737048
300.9844014267486680.03119714650266360.0155985732513318
310.9974140815629380.005171836874124470.00258591843706223
320.9998326638739470.0003346722521062680.000167336126053134
330.9998620999301160.0002758001397676890.000137900069883844
340.999904516315220.0001909673695596449.5483684779822e-05
350.999977781961264.44360774804321e-052.22180387402161e-05
360.99998400388223.19922355998212e-051.59961177999106e-05
370.9999880323340652.39353318695694e-051.19676659347847e-05
380.9999857992642552.84014714892668e-051.42007357446334e-05
390.9999869638407852.60723184297817e-051.30361592148909e-05
400.9999785660334364.28679331270897e-052.14339665635448e-05
410.9999826252314773.47495370451112e-051.73747685225556e-05
420.9999739625725185.20748549638262e-052.60374274819131e-05
430.9999781218361164.37563277688306e-052.18781638844153e-05
440.9999684171349566.31657300871102e-053.15828650435551e-05
450.9999492722639520.0001014554720960705.07277360480349e-05
460.9999203156313970.0001593687372063357.96843686031677e-05
470.9998969142304150.0002061715391702350.000103085769585118
480.9998543585193460.000291282961307980.00014564148065399
490.9997659908365910.000468018326817550.000234009163408775
500.9996987109242310.0006025781515369840.000301289075768492
510.9995809205006270.000838158998746180.00041907949937309
520.9993502704725050.001299459054990290.000649729527495143
530.9992324016641420.001535196671715260.00076759833585763
540.9988078758217340.002384248356531760.00119212417826588
550.9982256406700930.003548718659813470.00177435932990673
560.9987795197548620.002440960490275290.00122048024513765
570.9991860461440650.001627907711870680.00081395385593534
580.9989287419509170.002142516098166250.00107125804908312
590.9986689915526640.002662016894672940.00133100844733647
600.9981550970692810.003689805861437710.00184490293071886
610.9978067674231860.004386465153627940.00219323257681397
620.9973019618159470.005396076368105150.00269803818405257
630.996071139824750.007857720350501280.00392886017525064
640.9959401897328120.008119620534376920.00405981026718846
650.9974150240886870.005169951822626050.00258497591131303
660.9968221660679570.006355667864085830.00317783393204291
670.9956265227735850.00874695445283030.00437347722641515
680.9951250466553140.009749906689371540.00487495334468577
690.9944979270684640.01100414586307290.00550207293153646
700.9945417771687530.01091644566249370.00545822283124684
710.9920586634133260.01588267317334730.00794133658667363
720.99199151653290.01601696693419850.00800848346709924
730.9909208618918850.01815827621622950.00907913810811477
740.9893643814020270.02127123719594580.0106356185979729
750.989110878710450.02177824257910110.0108891212895506
760.9902291066687960.01954178666240780.0097708933312039
770.9932006726679420.01359865466411650.00679932733205827
780.9920278490609260.01594430187814820.00797215093907411
790.992467990778020.01506401844396080.00753200922198038
800.9925456281503440.01490874369931190.00745437184965594
810.9991514936224380.001697012755124170.000848506377562085
820.999161001506040.001677996987921720.00083899849396086
830.9987725340268970.002454931946205560.00122746597310278
840.9988912213523520.002217557295296620.00110877864764831
850.998629472671470.002741054657060780.00137052732853039
860.998257671315960.003484657368080130.00174232868404007
870.9993243430432450.001351313913508960.000675656956754481
880.9991967211082080.001606557783584340.000803278891792172
890.9993450737599490.001309852480102280.000654926240051142
900.9996587670154840.0006824659690320880.000341232984516044
910.9996432207204530.0007135585590948670.000356779279547434
920.9993976180022570.001204763995486920.000602381997743459
930.999099252716420.001801494567160820.00090074728358041
940.9985062258812760.002987548237447580.00149377411872379
950.997690854657540.004618290684918720.00230914534245936
960.9977823788251440.004435242349712430.00221762117485622
970.9951798440318340.009640311936331920.00482015596816596
980.9932743879278070.01345122414438560.00672561207219278
990.985398522615490.02920295476901910.0146014773845096
1000.9858839798807370.02823204023852530.0141160201192626
1010.9993727053804940.001254589239012740.000627294619506369
1020.999168438845050.001663122309897650.000831561154948823
1030.9998803026603460.0002393946793081960.000119697339654098
1040.9986169468901750.002766106219649770.00138305310982488






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level670.752808988764045NOK
5% type I error level840.943820224719101NOK
10% type I error level840.943820224719101NOK

\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 & 67 & 0.752808988764045 & NOK \tabularnewline
5% type I error level & 84 & 0.943820224719101 & NOK \tabularnewline
10% type I error level & 84 & 0.943820224719101 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=108046&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]67[/C][C]0.752808988764045[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]84[/C][C]0.943820224719101[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]84[/C][C]0.943820224719101[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=108046&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=108046&T=6

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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 level670.752808988764045NOK
5% type I error level840.943820224719101NOK
10% type I error level840.943820224719101NOK


Figures (Output of Computation)

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Figure 10
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Input Parameters & R Code

Parameters (Session):
par1 = 1 ; par2 = Include Monthly Dummies ; par3 = Linear Trend ;
Parameters (R input):
par1 = 1 ; par2 = Include Monthly Dummies ; par3 = 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$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
myeq <- paste(myeq, mysum$coefficients[i,1], 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,mysum$coefficients[i,1])
a<-table.element(a, round(mysum$coefficients[i,2],6))
a<-table.element(a, round(mysum$coefficients[i,3],4))
a<-table.element(a, round(mysum$coefficients[i,4],6))
a<-table.element(a, round(mysum$coefficients[i,4]/2,6))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable2.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple R',1,TRUE)
a<-table.element(a, sqrt(mysum$r.squared))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'R-squared',1,TRUE)
a<-table.element(a, mysum$r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Adjusted R-squared',1,TRUE)
a<-table.element(a, mysum$adj.r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (value)',1,TRUE)
a<-table.element(a, mysum$fstatistic[1])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[2])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[3])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'p-value',1,TRUE)
a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]))
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, mysum$sigma)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
a<-table.element(a, sum(myerror*myerror))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,x[i])
a<-table.element(a,x[i]-mysum$resid[i])
a<-table.element(a,mysum$resid[i])
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,gqarr[mypoint-kp3+1,1])
a<-table.element(a,gqarr[mypoint-kp3+1,2])
a<-table.element(a,gqarr[mypoint-kp3+1,3])
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,numsignificant1)
a<-table.element(a,numsignificant1/numgqtests)
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,numsignificant5)
a<-table.element(a,numsignificant5/numgqtests)
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,numsignificant10)
a<-table.element(a,numsignificant10/numgqtests)
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')
}