Free Statistics

of Irreproducible Research!

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, 10 Nov 2012 16:11:43 -0500
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2012/Nov/10/t1352583173rpvexxa9q7xt55x.htm/, Retrieved Sat, 10 Dec 2022 05:34:16 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=187427, Retrieved Sat, 10 Dec 2022 05:34:16 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact52
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]
-  MPD    [Multiple Regression] [] [2012-11-10 21:11:43] [7338cd26db379c04f0557b08db763c32] [Current]
- RMP       [Exponential Smoothing] [] [2012-11-10 22:13:06] [391561951b5d7f721cfaa4f5575ab127]
- R P         [Exponential Smoothing] [] [2012-11-10 22:22:04] [391561951b5d7f721cfaa4f5575ab127]
- R P         [Exponential Smoothing] [] [2012-11-10 22:23:12] [391561951b5d7f721cfaa4f5575ab127]
-               [Exponential Smoothing] [] [2012-12-18 08:54:52] [391561951b5d7f721cfaa4f5575ab127]
- R         [Multiple Regression] [] [2012-12-20 16:40:22] [74be16979710d4c4e7c6647856088456]
Feedback Forum

Post a new message
Dataseries X:
617
614
647
580
614
636
388
356
639
753
611
639
630
586
695
552
619
681
421
307
754
690
644
643
608
651
691
627
634
731
475
337
803
722
590
724
627
696
825
677
656
785
412
352
839
729
696
641
695
638
762
635
721
854
418
367
824
687
601
676
740
691
683
594
729
731
386
331
706
715
657
653
642
643
718
654
632
731
392
344
792
852
649
629
685
617
715
715
629
916
531
357
917
828
708
858
775
785
1006
789
734
906
532
387
991
841
892
782
811
792
978
773
796
946
594
438
1023
868
791
760
779
852
1001
734
996
869
599
426
1138
1091
830
909




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time9 seconds
R Server'George Udny Yule' @ yule.wessa.net

\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 & 9 seconds \tabularnewline
R Server & 'George Udny Yule' @ yule.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=187427&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]9 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'George Udny Yule' @ yule.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=187427&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=187427&T=0

As an alternative you can also use a QR Code:  

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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time9 seconds
R Server'George Udny Yule' @ yule.wessa.net







Multiple Linear Regression - Estimated Regression Equation
Aantal_Faillissementen[t] = + 576.704545454545 -5.91824494949483M1[t] -11.9008838383838M2[t] + 91.2073863636363M3[t] -37.229797979798M4[t] -0.121527777777779M5[t] + 91.1685606060606M6[t] -241.541351010101M7[t] -347.705808080808M8[t] + 143.402462121212M9[t] + 82.3289141414141M10[t] -20.2900883838384M11[t] + 1.98263888888889t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Aantal_Faillissementen[t] =  +  576.704545454545 -5.91824494949483M1[t] -11.9008838383838M2[t] +  91.2073863636363M3[t] -37.229797979798M4[t] -0.121527777777779M5[t] +  91.1685606060606M6[t] -241.541351010101M7[t] -347.705808080808M8[t] +  143.402462121212M9[t] +  82.3289141414141M10[t] -20.2900883838384M11[t] +  1.98263888888889t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=187427&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Aantal_Faillissementen[t] =  +  576.704545454545 -5.91824494949483M1[t] -11.9008838383838M2[t] +  91.2073863636363M3[t] -37.229797979798M4[t] -0.121527777777779M5[t] +  91.1685606060606M6[t] -241.541351010101M7[t] -347.705808080808M8[t] +  143.402462121212M9[t] +  82.3289141414141M10[t] -20.2900883838384M11[t] +  1.98263888888889t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=187427&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=187427&T=1

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Estimated Regression Equation
Aantal_Faillissementen[t] = + 576.704545454545 -5.91824494949483M1[t] -11.9008838383838M2[t] + 91.2073863636363M3[t] -37.229797979798M4[t] -0.121527777777779M5[t] + 91.1685606060606M6[t] -241.541351010101M7[t] -347.705808080808M8[t] + 143.402462121212M9[t] + 82.3289141414141M10[t] -20.2900883838384M11[t] + 1.98263888888889t + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)576.70454545454522.39576525.750600
M1-5.9182449494948327.827114-0.21270.8319410.415971
M2-11.900883838383827.818688-0.42780.6695690.334784
M391.207386363636327.8110623.27950.0013640.000682
M4-37.22979797979827.804237-1.3390.1831240.091562
M5-0.12152777777777927.798213-0.00440.9965190.49826
M691.168560606060627.7929923.28030.0013610.00068
M7-241.54135101010127.788573-8.692100
M8-347.70580808080827.784957-12.514200
M9143.40246212121227.7821455.16171e-060
M1082.328914141414127.7801352.96360.0036730.001837
M11-20.290088383838427.77893-0.73040.4665730.233287
t1.982638888888890.14942513.268500

\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) & 576.704545454545 & 22.395765 & 25.7506 & 0 & 0 \tabularnewline
M1 & -5.91824494949483 & 27.827114 & -0.2127 & 0.831941 & 0.415971 \tabularnewline
M2 & -11.9008838383838 & 27.818688 & -0.4278 & 0.669569 & 0.334784 \tabularnewline
M3 & 91.2073863636363 & 27.811062 & 3.2795 & 0.001364 & 0.000682 \tabularnewline
M4 & -37.229797979798 & 27.804237 & -1.339 & 0.183124 & 0.091562 \tabularnewline
M5 & -0.121527777777779 & 27.798213 & -0.0044 & 0.996519 & 0.49826 \tabularnewline
M6 & 91.1685606060606 & 27.792992 & 3.2803 & 0.001361 & 0.00068 \tabularnewline
M7 & -241.541351010101 & 27.788573 & -8.6921 & 0 & 0 \tabularnewline
M8 & -347.705808080808 & 27.784957 & -12.5142 & 0 & 0 \tabularnewline
M9 & 143.402462121212 & 27.782145 & 5.1617 & 1e-06 & 0 \tabularnewline
M10 & 82.3289141414141 & 27.780135 & 2.9636 & 0.003673 & 0.001837 \tabularnewline
M11 & -20.2900883838384 & 27.77893 & -0.7304 & 0.466573 & 0.233287 \tabularnewline
t & 1.98263888888889 & 0.149425 & 13.2685 & 0 & 0 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=187427&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]576.704545454545[/C][C]22.395765[/C][C]25.7506[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]M1[/C][C]-5.91824494949483[/C][C]27.827114[/C][C]-0.2127[/C][C]0.831941[/C][C]0.415971[/C][/ROW]
[ROW][C]M2[/C][C]-11.9008838383838[/C][C]27.818688[/C][C]-0.4278[/C][C]0.669569[/C][C]0.334784[/C][/ROW]
[ROW][C]M3[/C][C]91.2073863636363[/C][C]27.811062[/C][C]3.2795[/C][C]0.001364[/C][C]0.000682[/C][/ROW]
[ROW][C]M4[/C][C]-37.229797979798[/C][C]27.804237[/C][C]-1.339[/C][C]0.183124[/C][C]0.091562[/C][/ROW]
[ROW][C]M5[/C][C]-0.121527777777779[/C][C]27.798213[/C][C]-0.0044[/C][C]0.996519[/C][C]0.49826[/C][/ROW]
[ROW][C]M6[/C][C]91.1685606060606[/C][C]27.792992[/C][C]3.2803[/C][C]0.001361[/C][C]0.00068[/C][/ROW]
[ROW][C]M7[/C][C]-241.541351010101[/C][C]27.788573[/C][C]-8.6921[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]M8[/C][C]-347.705808080808[/C][C]27.784957[/C][C]-12.5142[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]M9[/C][C]143.402462121212[/C][C]27.782145[/C][C]5.1617[/C][C]1e-06[/C][C]0[/C][/ROW]
[ROW][C]M10[/C][C]82.3289141414141[/C][C]27.780135[/C][C]2.9636[/C][C]0.003673[/C][C]0.001837[/C][/ROW]
[ROW][C]M11[/C][C]-20.2900883838384[/C][C]27.77893[/C][C]-0.7304[/C][C]0.466573[/C][C]0.233287[/C][/ROW]
[ROW][C]t[/C][C]1.98263888888889[/C][C]0.149425[/C][C]13.2685[/C][C]0[/C][C]0[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=187427&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=187427&T=2

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)576.70454545454522.39576525.750600
M1-5.9182449494948327.827114-0.21270.8319410.415971
M2-11.900883838383827.818688-0.42780.6695690.334784
M391.207386363636327.8110623.27950.0013640.000682
M4-37.22979797979827.804237-1.3390.1831240.091562
M5-0.12152777777777927.798213-0.00440.9965190.49826
M691.168560606060627.7929923.28030.0013610.00068
M7-241.54135101010127.788573-8.692100
M8-347.70580808080827.784957-12.514200
M9143.40246212121227.7821455.16171e-060
M1082.328914141414127.7801352.96360.0036730.001837
M11-20.290088383838427.77893-0.73040.4665730.233287
t1.982638888888890.14942513.268500







Multiple Linear Regression - Regression Statistics
Multiple R0.928510769134262
R-squared0.862132248398299
Adjusted R-squared0.848229617984682
F-TEST (value)62.0121676797135
F-TEST (DF numerator)12
F-TEST (DF denominator)119
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation65.1464225614041
Sum Squared Residuals505042.708333333

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.928510769134262 \tabularnewline
R-squared & 0.862132248398299 \tabularnewline
Adjusted R-squared & 0.848229617984682 \tabularnewline
F-TEST (value) & 62.0121676797135 \tabularnewline
F-TEST (DF numerator) & 12 \tabularnewline
F-TEST (DF denominator) & 119 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 65.1464225614041 \tabularnewline
Sum Squared Residuals & 505042.708333333 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=187427&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.928510769134262[/C][/ROW]
[ROW][C]R-squared[/C][C]0.862132248398299[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.848229617984682[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]62.0121676797135[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]12[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]119[/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]65.1464225614041[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]505042.708333333[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=187427&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=187427&T=3

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Regression Statistics
Multiple R0.928510769134262
R-squared0.862132248398299
Adjusted R-squared0.848229617984682
F-TEST (value)62.0121676797135
F-TEST (DF numerator)12
F-TEST (DF denominator)119
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation65.1464225614041
Sum Squared Residuals505042.708333333







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
1617572.76893939393844.2310606060617
2614568.76893939393945.2310606060606
3647673.859848484848-26.8598484848486
4580547.40530303030332.594696969697
5614586.49621212121227.5037878787878
6636679.768939393939-43.7689393939394
7388349.04166666666738.9583333333333
8356244.859848484849111.140151515151
9639737.950757575758-98.9507575757577
10753678.85984848484974.1401515151514
11611578.22348484848532.7765151515151
12639600.49621212121238.5037878787879
13630596.56060606060633.4393939393939
14586592.560606060606-6.56060606060607
15695697.651515151515-2.65151515151515
16552571.19696969697-19.1969696969697
17619610.2878787878798.7121212121212
18681703.560606060606-22.5606060606061
19421372.83333333333348.1666666666667
20307268.65151515151538.3484848484849
21754761.742424242424-7.74242424242425
22690702.651515151515-12.6515151515152
23644602.01515151515241.9848484848485
24643624.28787878787918.7121212121212
25608620.352272727273-12.3522727272729
26651616.35227272727334.6477272727273
27691721.443181818182-30.4431818181818
28627594.98863636363632.0113636363636
29634634.079545454545-0.0795454545454694
30731727.3522727272733.64772727272724
31475396.62578.375
32337292.44318181818244.5568181818182
33803785.53409090909117.4659090909091
34722726.443181818182-4.44318181818185
35590625.806818181818-35.8068181818182
36724648.07954545454575.9204545454545
37627644.143939393939-17.1439393939395
38696640.14393939393955.8560606060606
39825745.23484848484879.7651515151515
40677618.78030303030358.219696969697
41656657.871212121212-1.87121212121214
42785751.14393939393933.8560606060606
43412420.416666666667-8.41666666666667
44352316.23484848484835.7651515151515
45839809.32575757575829.6742424242424
46729750.234848484848-21.2348484848485
47696649.59848484848546.4015151515151
48641671.871212121212-30.8712121212121
49695667.93560606060627.0643939393938
50638663.935606060606-25.9356060606061
51762769.026515151515-7.02651515151516
52635642.57196969697-7.57196969696972
53721681.66287878787939.3371212121212
54854774.93560606060679.0643939393939
55418444.208333333333-26.2083333333333
56367340.02651515151526.9734848484849
57824833.117424242424-9.11742424242422
58687774.026515151515-87.0265151515152
59601673.390151515152-72.3901515151515
60676695.662878787879-19.6628787878788
61740691.72727272727348.2727272727271
62691687.7272727272733.27272727272728
63683792.818181818182-109.818181818182
64594666.363636363636-72.3636363636364
65729705.45454545454523.5454545454545
66731798.727272727273-67.7272727272727
67386468-82
68331363.818181818182-32.8181818181818
69706856.909090909091-150.909090909091
70715797.818181818182-82.8181818181819
71657697.181818181818-40.1818181818182
72653719.454545454545-66.4545454545455
73642715.518939393939-73.5189393939395
74643711.518939393939-68.5189393939394
75718816.609848484848-98.6098484848485
76654690.155303030303-36.155303030303
77632729.246212121212-97.2462121212121
78731822.518939393939-91.5189393939394
79392491.791666666667-99.7916666666667
80344387.609848484848-43.6098484848485
81792880.700757575758-88.7007575757576
82852821.60984848484830.3901515151515
83649720.973484848485-71.9734848484849
84629743.246212121212-114.246212121212
85685739.310606060606-54.3106060606062
86617735.310606060606-118.310606060606
87715840.401515151515-125.401515151515
88715713.946969696971.05303030303029
89629753.037878787879-124.037878787879
90916846.31060606060669.6893939393939
91531515.58333333333315.4166666666667
92357411.401515151515-54.4015151515151
93917904.49242424242412.5075757575758
94828845.401515151515-17.4015151515151
95708744.765151515152-36.7651515151515
96858767.03787878787990.9621212121212
97775763.10227272727311.8977272727272
98785759.10227272727325.8977272727273
991006864.193181818182141.806818181818
100789737.73863636363651.2613636363637
101734776.829545454545-42.8295454545454
102906870.10227272727335.8977272727273
103532539.375-7.37499999999999
104387435.193181818182-48.1931818181818
105991928.28409090909162.7159090909091
106841869.193181818182-28.1931818181818
107892768.556818181818123.443181818182
108782790.829545454545-8.82954545454546
109811786.89393939393924.1060606060605
110792782.8939393939399.10606060606062
111978887.98484848484890.0151515151515
112773761.53030303030311.469696969697
113796800.621212121212-4.62121212121212
114946893.89393939393952.1060606060606
115594563.16666666666730.8333333333333
116438458.984848484848-20.9848484848485
1171023952.07575757575870.9242424242424
118868892.984848484848-24.9848484848485
119791792.348484848485-1.34848484848482
120760814.621212121212-54.6212121212121
121779810.685606060606-31.6856060606062
122852806.68560606060645.314393939394
1231001911.77651515151589.2234848484849
124734785.32196969697-51.3219696969696
125996824.412878787879171.587121212121
126869917.685606060606-48.685606060606
127599586.95833333333312.0416666666666
128426482.776515151515-56.7765151515151
1291138975.867424242424162.132575757576
1301091916.776515151515174.223484848485
131830816.14015151515113.8598484848486
132909838.41287878787970.5871212121213

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 617 & 572.768939393938 & 44.2310606060617 \tabularnewline
2 & 614 & 568.768939393939 & 45.2310606060606 \tabularnewline
3 & 647 & 673.859848484848 & -26.8598484848486 \tabularnewline
4 & 580 & 547.405303030303 & 32.594696969697 \tabularnewline
5 & 614 & 586.496212121212 & 27.5037878787878 \tabularnewline
6 & 636 & 679.768939393939 & -43.7689393939394 \tabularnewline
7 & 388 & 349.041666666667 & 38.9583333333333 \tabularnewline
8 & 356 & 244.859848484849 & 111.140151515151 \tabularnewline
9 & 639 & 737.950757575758 & -98.9507575757577 \tabularnewline
10 & 753 & 678.859848484849 & 74.1401515151514 \tabularnewline
11 & 611 & 578.223484848485 & 32.7765151515151 \tabularnewline
12 & 639 & 600.496212121212 & 38.5037878787879 \tabularnewline
13 & 630 & 596.560606060606 & 33.4393939393939 \tabularnewline
14 & 586 & 592.560606060606 & -6.56060606060607 \tabularnewline
15 & 695 & 697.651515151515 & -2.65151515151515 \tabularnewline
16 & 552 & 571.19696969697 & -19.1969696969697 \tabularnewline
17 & 619 & 610.287878787879 & 8.7121212121212 \tabularnewline
18 & 681 & 703.560606060606 & -22.5606060606061 \tabularnewline
19 & 421 & 372.833333333333 & 48.1666666666667 \tabularnewline
20 & 307 & 268.651515151515 & 38.3484848484849 \tabularnewline
21 & 754 & 761.742424242424 & -7.74242424242425 \tabularnewline
22 & 690 & 702.651515151515 & -12.6515151515152 \tabularnewline
23 & 644 & 602.015151515152 & 41.9848484848485 \tabularnewline
24 & 643 & 624.287878787879 & 18.7121212121212 \tabularnewline
25 & 608 & 620.352272727273 & -12.3522727272729 \tabularnewline
26 & 651 & 616.352272727273 & 34.6477272727273 \tabularnewline
27 & 691 & 721.443181818182 & -30.4431818181818 \tabularnewline
28 & 627 & 594.988636363636 & 32.0113636363636 \tabularnewline
29 & 634 & 634.079545454545 & -0.0795454545454694 \tabularnewline
30 & 731 & 727.352272727273 & 3.64772727272724 \tabularnewline
31 & 475 & 396.625 & 78.375 \tabularnewline
32 & 337 & 292.443181818182 & 44.5568181818182 \tabularnewline
33 & 803 & 785.534090909091 & 17.4659090909091 \tabularnewline
34 & 722 & 726.443181818182 & -4.44318181818185 \tabularnewline
35 & 590 & 625.806818181818 & -35.8068181818182 \tabularnewline
36 & 724 & 648.079545454545 & 75.9204545454545 \tabularnewline
37 & 627 & 644.143939393939 & -17.1439393939395 \tabularnewline
38 & 696 & 640.143939393939 & 55.8560606060606 \tabularnewline
39 & 825 & 745.234848484848 & 79.7651515151515 \tabularnewline
40 & 677 & 618.780303030303 & 58.219696969697 \tabularnewline
41 & 656 & 657.871212121212 & -1.87121212121214 \tabularnewline
42 & 785 & 751.143939393939 & 33.8560606060606 \tabularnewline
43 & 412 & 420.416666666667 & -8.41666666666667 \tabularnewline
44 & 352 & 316.234848484848 & 35.7651515151515 \tabularnewline
45 & 839 & 809.325757575758 & 29.6742424242424 \tabularnewline
46 & 729 & 750.234848484848 & -21.2348484848485 \tabularnewline
47 & 696 & 649.598484848485 & 46.4015151515151 \tabularnewline
48 & 641 & 671.871212121212 & -30.8712121212121 \tabularnewline
49 & 695 & 667.935606060606 & 27.0643939393938 \tabularnewline
50 & 638 & 663.935606060606 & -25.9356060606061 \tabularnewline
51 & 762 & 769.026515151515 & -7.02651515151516 \tabularnewline
52 & 635 & 642.57196969697 & -7.57196969696972 \tabularnewline
53 & 721 & 681.662878787879 & 39.3371212121212 \tabularnewline
54 & 854 & 774.935606060606 & 79.0643939393939 \tabularnewline
55 & 418 & 444.208333333333 & -26.2083333333333 \tabularnewline
56 & 367 & 340.026515151515 & 26.9734848484849 \tabularnewline
57 & 824 & 833.117424242424 & -9.11742424242422 \tabularnewline
58 & 687 & 774.026515151515 & -87.0265151515152 \tabularnewline
59 & 601 & 673.390151515152 & -72.3901515151515 \tabularnewline
60 & 676 & 695.662878787879 & -19.6628787878788 \tabularnewline
61 & 740 & 691.727272727273 & 48.2727272727271 \tabularnewline
62 & 691 & 687.727272727273 & 3.27272727272728 \tabularnewline
63 & 683 & 792.818181818182 & -109.818181818182 \tabularnewline
64 & 594 & 666.363636363636 & -72.3636363636364 \tabularnewline
65 & 729 & 705.454545454545 & 23.5454545454545 \tabularnewline
66 & 731 & 798.727272727273 & -67.7272727272727 \tabularnewline
67 & 386 & 468 & -82 \tabularnewline
68 & 331 & 363.818181818182 & -32.8181818181818 \tabularnewline
69 & 706 & 856.909090909091 & -150.909090909091 \tabularnewline
70 & 715 & 797.818181818182 & -82.8181818181819 \tabularnewline
71 & 657 & 697.181818181818 & -40.1818181818182 \tabularnewline
72 & 653 & 719.454545454545 & -66.4545454545455 \tabularnewline
73 & 642 & 715.518939393939 & -73.5189393939395 \tabularnewline
74 & 643 & 711.518939393939 & -68.5189393939394 \tabularnewline
75 & 718 & 816.609848484848 & -98.6098484848485 \tabularnewline
76 & 654 & 690.155303030303 & -36.155303030303 \tabularnewline
77 & 632 & 729.246212121212 & -97.2462121212121 \tabularnewline
78 & 731 & 822.518939393939 & -91.5189393939394 \tabularnewline
79 & 392 & 491.791666666667 & -99.7916666666667 \tabularnewline
80 & 344 & 387.609848484848 & -43.6098484848485 \tabularnewline
81 & 792 & 880.700757575758 & -88.7007575757576 \tabularnewline
82 & 852 & 821.609848484848 & 30.3901515151515 \tabularnewline
83 & 649 & 720.973484848485 & -71.9734848484849 \tabularnewline
84 & 629 & 743.246212121212 & -114.246212121212 \tabularnewline
85 & 685 & 739.310606060606 & -54.3106060606062 \tabularnewline
86 & 617 & 735.310606060606 & -118.310606060606 \tabularnewline
87 & 715 & 840.401515151515 & -125.401515151515 \tabularnewline
88 & 715 & 713.94696969697 & 1.05303030303029 \tabularnewline
89 & 629 & 753.037878787879 & -124.037878787879 \tabularnewline
90 & 916 & 846.310606060606 & 69.6893939393939 \tabularnewline
91 & 531 & 515.583333333333 & 15.4166666666667 \tabularnewline
92 & 357 & 411.401515151515 & -54.4015151515151 \tabularnewline
93 & 917 & 904.492424242424 & 12.5075757575758 \tabularnewline
94 & 828 & 845.401515151515 & -17.4015151515151 \tabularnewline
95 & 708 & 744.765151515152 & -36.7651515151515 \tabularnewline
96 & 858 & 767.037878787879 & 90.9621212121212 \tabularnewline
97 & 775 & 763.102272727273 & 11.8977272727272 \tabularnewline
98 & 785 & 759.102272727273 & 25.8977272727273 \tabularnewline
99 & 1006 & 864.193181818182 & 141.806818181818 \tabularnewline
100 & 789 & 737.738636363636 & 51.2613636363637 \tabularnewline
101 & 734 & 776.829545454545 & -42.8295454545454 \tabularnewline
102 & 906 & 870.102272727273 & 35.8977272727273 \tabularnewline
103 & 532 & 539.375 & -7.37499999999999 \tabularnewline
104 & 387 & 435.193181818182 & -48.1931818181818 \tabularnewline
105 & 991 & 928.284090909091 & 62.7159090909091 \tabularnewline
106 & 841 & 869.193181818182 & -28.1931818181818 \tabularnewline
107 & 892 & 768.556818181818 & 123.443181818182 \tabularnewline
108 & 782 & 790.829545454545 & -8.82954545454546 \tabularnewline
109 & 811 & 786.893939393939 & 24.1060606060605 \tabularnewline
110 & 792 & 782.893939393939 & 9.10606060606062 \tabularnewline
111 & 978 & 887.984848484848 & 90.0151515151515 \tabularnewline
112 & 773 & 761.530303030303 & 11.469696969697 \tabularnewline
113 & 796 & 800.621212121212 & -4.62121212121212 \tabularnewline
114 & 946 & 893.893939393939 & 52.1060606060606 \tabularnewline
115 & 594 & 563.166666666667 & 30.8333333333333 \tabularnewline
116 & 438 & 458.984848484848 & -20.9848484848485 \tabularnewline
117 & 1023 & 952.075757575758 & 70.9242424242424 \tabularnewline
118 & 868 & 892.984848484848 & -24.9848484848485 \tabularnewline
119 & 791 & 792.348484848485 & -1.34848484848482 \tabularnewline
120 & 760 & 814.621212121212 & -54.6212121212121 \tabularnewline
121 & 779 & 810.685606060606 & -31.6856060606062 \tabularnewline
122 & 852 & 806.685606060606 & 45.314393939394 \tabularnewline
123 & 1001 & 911.776515151515 & 89.2234848484849 \tabularnewline
124 & 734 & 785.32196969697 & -51.3219696969696 \tabularnewline
125 & 996 & 824.412878787879 & 171.587121212121 \tabularnewline
126 & 869 & 917.685606060606 & -48.685606060606 \tabularnewline
127 & 599 & 586.958333333333 & 12.0416666666666 \tabularnewline
128 & 426 & 482.776515151515 & -56.7765151515151 \tabularnewline
129 & 1138 & 975.867424242424 & 162.132575757576 \tabularnewline
130 & 1091 & 916.776515151515 & 174.223484848485 \tabularnewline
131 & 830 & 816.140151515151 & 13.8598484848486 \tabularnewline
132 & 909 & 838.412878787879 & 70.5871212121213 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=187427&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]617[/C][C]572.768939393938[/C][C]44.2310606060617[/C][/ROW]
[ROW][C]2[/C][C]614[/C][C]568.768939393939[/C][C]45.2310606060606[/C][/ROW]
[ROW][C]3[/C][C]647[/C][C]673.859848484848[/C][C]-26.8598484848486[/C][/ROW]
[ROW][C]4[/C][C]580[/C][C]547.405303030303[/C][C]32.594696969697[/C][/ROW]
[ROW][C]5[/C][C]614[/C][C]586.496212121212[/C][C]27.5037878787878[/C][/ROW]
[ROW][C]6[/C][C]636[/C][C]679.768939393939[/C][C]-43.7689393939394[/C][/ROW]
[ROW][C]7[/C][C]388[/C][C]349.041666666667[/C][C]38.9583333333333[/C][/ROW]
[ROW][C]8[/C][C]356[/C][C]244.859848484849[/C][C]111.140151515151[/C][/ROW]
[ROW][C]9[/C][C]639[/C][C]737.950757575758[/C][C]-98.9507575757577[/C][/ROW]
[ROW][C]10[/C][C]753[/C][C]678.859848484849[/C][C]74.1401515151514[/C][/ROW]
[ROW][C]11[/C][C]611[/C][C]578.223484848485[/C][C]32.7765151515151[/C][/ROW]
[ROW][C]12[/C][C]639[/C][C]600.496212121212[/C][C]38.5037878787879[/C][/ROW]
[ROW][C]13[/C][C]630[/C][C]596.560606060606[/C][C]33.4393939393939[/C][/ROW]
[ROW][C]14[/C][C]586[/C][C]592.560606060606[/C][C]-6.56060606060607[/C][/ROW]
[ROW][C]15[/C][C]695[/C][C]697.651515151515[/C][C]-2.65151515151515[/C][/ROW]
[ROW][C]16[/C][C]552[/C][C]571.19696969697[/C][C]-19.1969696969697[/C][/ROW]
[ROW][C]17[/C][C]619[/C][C]610.287878787879[/C][C]8.7121212121212[/C][/ROW]
[ROW][C]18[/C][C]681[/C][C]703.560606060606[/C][C]-22.5606060606061[/C][/ROW]
[ROW][C]19[/C][C]421[/C][C]372.833333333333[/C][C]48.1666666666667[/C][/ROW]
[ROW][C]20[/C][C]307[/C][C]268.651515151515[/C][C]38.3484848484849[/C][/ROW]
[ROW][C]21[/C][C]754[/C][C]761.742424242424[/C][C]-7.74242424242425[/C][/ROW]
[ROW][C]22[/C][C]690[/C][C]702.651515151515[/C][C]-12.6515151515152[/C][/ROW]
[ROW][C]23[/C][C]644[/C][C]602.015151515152[/C][C]41.9848484848485[/C][/ROW]
[ROW][C]24[/C][C]643[/C][C]624.287878787879[/C][C]18.7121212121212[/C][/ROW]
[ROW][C]25[/C][C]608[/C][C]620.352272727273[/C][C]-12.3522727272729[/C][/ROW]
[ROW][C]26[/C][C]651[/C][C]616.352272727273[/C][C]34.6477272727273[/C][/ROW]
[ROW][C]27[/C][C]691[/C][C]721.443181818182[/C][C]-30.4431818181818[/C][/ROW]
[ROW][C]28[/C][C]627[/C][C]594.988636363636[/C][C]32.0113636363636[/C][/ROW]
[ROW][C]29[/C][C]634[/C][C]634.079545454545[/C][C]-0.0795454545454694[/C][/ROW]
[ROW][C]30[/C][C]731[/C][C]727.352272727273[/C][C]3.64772727272724[/C][/ROW]
[ROW][C]31[/C][C]475[/C][C]396.625[/C][C]78.375[/C][/ROW]
[ROW][C]32[/C][C]337[/C][C]292.443181818182[/C][C]44.5568181818182[/C][/ROW]
[ROW][C]33[/C][C]803[/C][C]785.534090909091[/C][C]17.4659090909091[/C][/ROW]
[ROW][C]34[/C][C]722[/C][C]726.443181818182[/C][C]-4.44318181818185[/C][/ROW]
[ROW][C]35[/C][C]590[/C][C]625.806818181818[/C][C]-35.8068181818182[/C][/ROW]
[ROW][C]36[/C][C]724[/C][C]648.079545454545[/C][C]75.9204545454545[/C][/ROW]
[ROW][C]37[/C][C]627[/C][C]644.143939393939[/C][C]-17.1439393939395[/C][/ROW]
[ROW][C]38[/C][C]696[/C][C]640.143939393939[/C][C]55.8560606060606[/C][/ROW]
[ROW][C]39[/C][C]825[/C][C]745.234848484848[/C][C]79.7651515151515[/C][/ROW]
[ROW][C]40[/C][C]677[/C][C]618.780303030303[/C][C]58.219696969697[/C][/ROW]
[ROW][C]41[/C][C]656[/C][C]657.871212121212[/C][C]-1.87121212121214[/C][/ROW]
[ROW][C]42[/C][C]785[/C][C]751.143939393939[/C][C]33.8560606060606[/C][/ROW]
[ROW][C]43[/C][C]412[/C][C]420.416666666667[/C][C]-8.41666666666667[/C][/ROW]
[ROW][C]44[/C][C]352[/C][C]316.234848484848[/C][C]35.7651515151515[/C][/ROW]
[ROW][C]45[/C][C]839[/C][C]809.325757575758[/C][C]29.6742424242424[/C][/ROW]
[ROW][C]46[/C][C]729[/C][C]750.234848484848[/C][C]-21.2348484848485[/C][/ROW]
[ROW][C]47[/C][C]696[/C][C]649.598484848485[/C][C]46.4015151515151[/C][/ROW]
[ROW][C]48[/C][C]641[/C][C]671.871212121212[/C][C]-30.8712121212121[/C][/ROW]
[ROW][C]49[/C][C]695[/C][C]667.935606060606[/C][C]27.0643939393938[/C][/ROW]
[ROW][C]50[/C][C]638[/C][C]663.935606060606[/C][C]-25.9356060606061[/C][/ROW]
[ROW][C]51[/C][C]762[/C][C]769.026515151515[/C][C]-7.02651515151516[/C][/ROW]
[ROW][C]52[/C][C]635[/C][C]642.57196969697[/C][C]-7.57196969696972[/C][/ROW]
[ROW][C]53[/C][C]721[/C][C]681.662878787879[/C][C]39.3371212121212[/C][/ROW]
[ROW][C]54[/C][C]854[/C][C]774.935606060606[/C][C]79.0643939393939[/C][/ROW]
[ROW][C]55[/C][C]418[/C][C]444.208333333333[/C][C]-26.2083333333333[/C][/ROW]
[ROW][C]56[/C][C]367[/C][C]340.026515151515[/C][C]26.9734848484849[/C][/ROW]
[ROW][C]57[/C][C]824[/C][C]833.117424242424[/C][C]-9.11742424242422[/C][/ROW]
[ROW][C]58[/C][C]687[/C][C]774.026515151515[/C][C]-87.0265151515152[/C][/ROW]
[ROW][C]59[/C][C]601[/C][C]673.390151515152[/C][C]-72.3901515151515[/C][/ROW]
[ROW][C]60[/C][C]676[/C][C]695.662878787879[/C][C]-19.6628787878788[/C][/ROW]
[ROW][C]61[/C][C]740[/C][C]691.727272727273[/C][C]48.2727272727271[/C][/ROW]
[ROW][C]62[/C][C]691[/C][C]687.727272727273[/C][C]3.27272727272728[/C][/ROW]
[ROW][C]63[/C][C]683[/C][C]792.818181818182[/C][C]-109.818181818182[/C][/ROW]
[ROW][C]64[/C][C]594[/C][C]666.363636363636[/C][C]-72.3636363636364[/C][/ROW]
[ROW][C]65[/C][C]729[/C][C]705.454545454545[/C][C]23.5454545454545[/C][/ROW]
[ROW][C]66[/C][C]731[/C][C]798.727272727273[/C][C]-67.7272727272727[/C][/ROW]
[ROW][C]67[/C][C]386[/C][C]468[/C][C]-82[/C][/ROW]
[ROW][C]68[/C][C]331[/C][C]363.818181818182[/C][C]-32.8181818181818[/C][/ROW]
[ROW][C]69[/C][C]706[/C][C]856.909090909091[/C][C]-150.909090909091[/C][/ROW]
[ROW][C]70[/C][C]715[/C][C]797.818181818182[/C][C]-82.8181818181819[/C][/ROW]
[ROW][C]71[/C][C]657[/C][C]697.181818181818[/C][C]-40.1818181818182[/C][/ROW]
[ROW][C]72[/C][C]653[/C][C]719.454545454545[/C][C]-66.4545454545455[/C][/ROW]
[ROW][C]73[/C][C]642[/C][C]715.518939393939[/C][C]-73.5189393939395[/C][/ROW]
[ROW][C]74[/C][C]643[/C][C]711.518939393939[/C][C]-68.5189393939394[/C][/ROW]
[ROW][C]75[/C][C]718[/C][C]816.609848484848[/C][C]-98.6098484848485[/C][/ROW]
[ROW][C]76[/C][C]654[/C][C]690.155303030303[/C][C]-36.155303030303[/C][/ROW]
[ROW][C]77[/C][C]632[/C][C]729.246212121212[/C][C]-97.2462121212121[/C][/ROW]
[ROW][C]78[/C][C]731[/C][C]822.518939393939[/C][C]-91.5189393939394[/C][/ROW]
[ROW][C]79[/C][C]392[/C][C]491.791666666667[/C][C]-99.7916666666667[/C][/ROW]
[ROW][C]80[/C][C]344[/C][C]387.609848484848[/C][C]-43.6098484848485[/C][/ROW]
[ROW][C]81[/C][C]792[/C][C]880.700757575758[/C][C]-88.7007575757576[/C][/ROW]
[ROW][C]82[/C][C]852[/C][C]821.609848484848[/C][C]30.3901515151515[/C][/ROW]
[ROW][C]83[/C][C]649[/C][C]720.973484848485[/C][C]-71.9734848484849[/C][/ROW]
[ROW][C]84[/C][C]629[/C][C]743.246212121212[/C][C]-114.246212121212[/C][/ROW]
[ROW][C]85[/C][C]685[/C][C]739.310606060606[/C][C]-54.3106060606062[/C][/ROW]
[ROW][C]86[/C][C]617[/C][C]735.310606060606[/C][C]-118.310606060606[/C][/ROW]
[ROW][C]87[/C][C]715[/C][C]840.401515151515[/C][C]-125.401515151515[/C][/ROW]
[ROW][C]88[/C][C]715[/C][C]713.94696969697[/C][C]1.05303030303029[/C][/ROW]
[ROW][C]89[/C][C]629[/C][C]753.037878787879[/C][C]-124.037878787879[/C][/ROW]
[ROW][C]90[/C][C]916[/C][C]846.310606060606[/C][C]69.6893939393939[/C][/ROW]
[ROW][C]91[/C][C]531[/C][C]515.583333333333[/C][C]15.4166666666667[/C][/ROW]
[ROW][C]92[/C][C]357[/C][C]411.401515151515[/C][C]-54.4015151515151[/C][/ROW]
[ROW][C]93[/C][C]917[/C][C]904.492424242424[/C][C]12.5075757575758[/C][/ROW]
[ROW][C]94[/C][C]828[/C][C]845.401515151515[/C][C]-17.4015151515151[/C][/ROW]
[ROW][C]95[/C][C]708[/C][C]744.765151515152[/C][C]-36.7651515151515[/C][/ROW]
[ROW][C]96[/C][C]858[/C][C]767.037878787879[/C][C]90.9621212121212[/C][/ROW]
[ROW][C]97[/C][C]775[/C][C]763.102272727273[/C][C]11.8977272727272[/C][/ROW]
[ROW][C]98[/C][C]785[/C][C]759.102272727273[/C][C]25.8977272727273[/C][/ROW]
[ROW][C]99[/C][C]1006[/C][C]864.193181818182[/C][C]141.806818181818[/C][/ROW]
[ROW][C]100[/C][C]789[/C][C]737.738636363636[/C][C]51.2613636363637[/C][/ROW]
[ROW][C]101[/C][C]734[/C][C]776.829545454545[/C][C]-42.8295454545454[/C][/ROW]
[ROW][C]102[/C][C]906[/C][C]870.102272727273[/C][C]35.8977272727273[/C][/ROW]
[ROW][C]103[/C][C]532[/C][C]539.375[/C][C]-7.37499999999999[/C][/ROW]
[ROW][C]104[/C][C]387[/C][C]435.193181818182[/C][C]-48.1931818181818[/C][/ROW]
[ROW][C]105[/C][C]991[/C][C]928.284090909091[/C][C]62.7159090909091[/C][/ROW]
[ROW][C]106[/C][C]841[/C][C]869.193181818182[/C][C]-28.1931818181818[/C][/ROW]
[ROW][C]107[/C][C]892[/C][C]768.556818181818[/C][C]123.443181818182[/C][/ROW]
[ROW][C]108[/C][C]782[/C][C]790.829545454545[/C][C]-8.82954545454546[/C][/ROW]
[ROW][C]109[/C][C]811[/C][C]786.893939393939[/C][C]24.1060606060605[/C][/ROW]
[ROW][C]110[/C][C]792[/C][C]782.893939393939[/C][C]9.10606060606062[/C][/ROW]
[ROW][C]111[/C][C]978[/C][C]887.984848484848[/C][C]90.0151515151515[/C][/ROW]
[ROW][C]112[/C][C]773[/C][C]761.530303030303[/C][C]11.469696969697[/C][/ROW]
[ROW][C]113[/C][C]796[/C][C]800.621212121212[/C][C]-4.62121212121212[/C][/ROW]
[ROW][C]114[/C][C]946[/C][C]893.893939393939[/C][C]52.1060606060606[/C][/ROW]
[ROW][C]115[/C][C]594[/C][C]563.166666666667[/C][C]30.8333333333333[/C][/ROW]
[ROW][C]116[/C][C]438[/C][C]458.984848484848[/C][C]-20.9848484848485[/C][/ROW]
[ROW][C]117[/C][C]1023[/C][C]952.075757575758[/C][C]70.9242424242424[/C][/ROW]
[ROW][C]118[/C][C]868[/C][C]892.984848484848[/C][C]-24.9848484848485[/C][/ROW]
[ROW][C]119[/C][C]791[/C][C]792.348484848485[/C][C]-1.34848484848482[/C][/ROW]
[ROW][C]120[/C][C]760[/C][C]814.621212121212[/C][C]-54.6212121212121[/C][/ROW]
[ROW][C]121[/C][C]779[/C][C]810.685606060606[/C][C]-31.6856060606062[/C][/ROW]
[ROW][C]122[/C][C]852[/C][C]806.685606060606[/C][C]45.314393939394[/C][/ROW]
[ROW][C]123[/C][C]1001[/C][C]911.776515151515[/C][C]89.2234848484849[/C][/ROW]
[ROW][C]124[/C][C]734[/C][C]785.32196969697[/C][C]-51.3219696969696[/C][/ROW]
[ROW][C]125[/C][C]996[/C][C]824.412878787879[/C][C]171.587121212121[/C][/ROW]
[ROW][C]126[/C][C]869[/C][C]917.685606060606[/C][C]-48.685606060606[/C][/ROW]
[ROW][C]127[/C][C]599[/C][C]586.958333333333[/C][C]12.0416666666666[/C][/ROW]
[ROW][C]128[/C][C]426[/C][C]482.776515151515[/C][C]-56.7765151515151[/C][/ROW]
[ROW][C]129[/C][C]1138[/C][C]975.867424242424[/C][C]162.132575757576[/C][/ROW]
[ROW][C]130[/C][C]1091[/C][C]916.776515151515[/C][C]174.223484848485[/C][/ROW]
[ROW][C]131[/C][C]830[/C][C]816.140151515151[/C][C]13.8598484848486[/C][/ROW]
[ROW][C]132[/C][C]909[/C][C]838.412878787879[/C][C]70.5871212121213[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=187427&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=187427&T=4

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
1617572.76893939393844.2310606060617
2614568.76893939393945.2310606060606
3647673.859848484848-26.8598484848486
4580547.40530303030332.594696969697
5614586.49621212121227.5037878787878
6636679.768939393939-43.7689393939394
7388349.04166666666738.9583333333333
8356244.859848484849111.140151515151
9639737.950757575758-98.9507575757577
10753678.85984848484974.1401515151514
11611578.22348484848532.7765151515151
12639600.49621212121238.5037878787879
13630596.56060606060633.4393939393939
14586592.560606060606-6.56060606060607
15695697.651515151515-2.65151515151515
16552571.19696969697-19.1969696969697
17619610.2878787878798.7121212121212
18681703.560606060606-22.5606060606061
19421372.83333333333348.1666666666667
20307268.65151515151538.3484848484849
21754761.742424242424-7.74242424242425
22690702.651515151515-12.6515151515152
23644602.01515151515241.9848484848485
24643624.28787878787918.7121212121212
25608620.352272727273-12.3522727272729
26651616.35227272727334.6477272727273
27691721.443181818182-30.4431818181818
28627594.98863636363632.0113636363636
29634634.079545454545-0.0795454545454694
30731727.3522727272733.64772727272724
31475396.62578.375
32337292.44318181818244.5568181818182
33803785.53409090909117.4659090909091
34722726.443181818182-4.44318181818185
35590625.806818181818-35.8068181818182
36724648.07954545454575.9204545454545
37627644.143939393939-17.1439393939395
38696640.14393939393955.8560606060606
39825745.23484848484879.7651515151515
40677618.78030303030358.219696969697
41656657.871212121212-1.87121212121214
42785751.14393939393933.8560606060606
43412420.416666666667-8.41666666666667
44352316.23484848484835.7651515151515
45839809.32575757575829.6742424242424
46729750.234848484848-21.2348484848485
47696649.59848484848546.4015151515151
48641671.871212121212-30.8712121212121
49695667.93560606060627.0643939393938
50638663.935606060606-25.9356060606061
51762769.026515151515-7.02651515151516
52635642.57196969697-7.57196969696972
53721681.66287878787939.3371212121212
54854774.93560606060679.0643939393939
55418444.208333333333-26.2083333333333
56367340.02651515151526.9734848484849
57824833.117424242424-9.11742424242422
58687774.026515151515-87.0265151515152
59601673.390151515152-72.3901515151515
60676695.662878787879-19.6628787878788
61740691.72727272727348.2727272727271
62691687.7272727272733.27272727272728
63683792.818181818182-109.818181818182
64594666.363636363636-72.3636363636364
65729705.45454545454523.5454545454545
66731798.727272727273-67.7272727272727
67386468-82
68331363.818181818182-32.8181818181818
69706856.909090909091-150.909090909091
70715797.818181818182-82.8181818181819
71657697.181818181818-40.1818181818182
72653719.454545454545-66.4545454545455
73642715.518939393939-73.5189393939395
74643711.518939393939-68.5189393939394
75718816.609848484848-98.6098484848485
76654690.155303030303-36.155303030303
77632729.246212121212-97.2462121212121
78731822.518939393939-91.5189393939394
79392491.791666666667-99.7916666666667
80344387.609848484848-43.6098484848485
81792880.700757575758-88.7007575757576
82852821.60984848484830.3901515151515
83649720.973484848485-71.9734848484849
84629743.246212121212-114.246212121212
85685739.310606060606-54.3106060606062
86617735.310606060606-118.310606060606
87715840.401515151515-125.401515151515
88715713.946969696971.05303030303029
89629753.037878787879-124.037878787879
90916846.31060606060669.6893939393939
91531515.58333333333315.4166666666667
92357411.401515151515-54.4015151515151
93917904.49242424242412.5075757575758
94828845.401515151515-17.4015151515151
95708744.765151515152-36.7651515151515
96858767.03787878787990.9621212121212
97775763.10227272727311.8977272727272
98785759.10227272727325.8977272727273
991006864.193181818182141.806818181818
100789737.73863636363651.2613636363637
101734776.829545454545-42.8295454545454
102906870.10227272727335.8977272727273
103532539.375-7.37499999999999
104387435.193181818182-48.1931818181818
105991928.28409090909162.7159090909091
106841869.193181818182-28.1931818181818
107892768.556818181818123.443181818182
108782790.829545454545-8.82954545454546
109811786.89393939393924.1060606060605
110792782.8939393939399.10606060606062
111978887.98484848484890.0151515151515
112773761.53030303030311.469696969697
113796800.621212121212-4.62121212121212
114946893.89393939393952.1060606060606
115594563.16666666666730.8333333333333
116438458.984848484848-20.9848484848485
1171023952.07575757575870.9242424242424
118868892.984848484848-24.9848484848485
119791792.348484848485-1.34848484848482
120760814.621212121212-54.6212121212121
121779810.685606060606-31.6856060606062
122852806.68560606060645.314393939394
1231001911.77651515151589.2234848484849
124734785.32196969697-51.3219696969696
125996824.412878787879171.587121212121
126869917.685606060606-48.685606060606
127599586.95833333333312.0416666666666
128426482.776515151515-56.7765151515151
1291138975.867424242424162.132575757576
1301091916.776515151515174.223484848485
131830816.14015151515113.8598484848486
132909838.41287878787970.5871212121213







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
160.0717110752035670.1434221504071340.928288924796433
170.02237803762731610.04475607525463230.977621962372684
180.01325985487492770.02651970974985550.986740145125072
190.005172480460144430.01034496092028890.994827519539856
200.006195934772163860.01239186954432770.993804065227836
210.02770102698803610.05540205397607220.972298973011964
220.03089151270454790.06178302540909570.969108487295452
230.01717009060567750.0343401812113550.982829909394322
240.008484779984233940.01696955996846790.991515220015766
250.004736067650236910.009472135300473820.995263932349763
260.00312655634170520.00625311268341040.996873443658295
270.001426116199252170.002852232398504340.998573883800748
280.0009986882725632350.001997376545126470.999001311727437
290.0004426583611100940.0008853167222201880.99955734163889
300.0003511987450267090.0007023974900534190.999648801254973
310.0002917316242877990.0005834632485755990.999708268375712
320.0001700137876473760.0003400275752947530.999829986212353
330.0002971382731209550.000594276546241910.999702861726879
340.0001775090675450230.0003550181350900460.999822490932455
350.0002212288897825460.0004424577795650930.999778771110217
360.00024430828756490.00048861657512980.999755691712435
370.0001465383077933990.0002930766155867980.999853461692207
380.0001217984617659630.0002435969235319260.999878201538234
390.0005797427385142570.001159485477028510.999420257261486
400.0004977244843841170.0009954489687682350.999502275515616
410.0002924285332827660.0005848570665655320.999707571466717
420.000253964129796470.000507928259592940.999746035870203
430.0003343504257679240.0006687008515358480.999665649574232
440.000289121555756270.0005782431115125410.999710878444244
450.0002891618713778080.0005783237427556160.999710838128622
460.0002208191930884190.0004416383861768390.999779180806912
470.0001911955038180530.0003823910076361060.999808804496182
480.0002766021381471240.0005532042762942480.999723397861853
490.0001969979370030930.0003939958740061860.999803002062997
500.0001951295286737180.0003902590573474360.999804870471326
510.0001164867190845250.000232973438169050.999883513280916
528.27206675721817e-050.0001654413351443630.999917279332428
538.12612739009483e-050.0001625225478018970.999918738726099
540.0003070127208168810.0006140254416337610.999692987279183
550.000369242186967010.0007384843739340190.999630757813033
560.0004902128315445250.0009804256630890510.999509787168456
570.0003259814020779480.0006519628041558960.999674018597922
580.0005694325636534840.001138865127306970.999430567436347
590.0007970560019696690.001594112003939340.99920294399803
600.0006709895382105210.001341979076421040.999329010461789
610.001151975853372990.002303951706745980.998848024146627
620.001069682042657610.002139364085315230.998930317957342
630.002046343739541590.004092687479083180.997953656260458
640.002175770172596070.004351540345192140.997824229827404
650.0027407082730710.005481416546142010.997259291726929
660.002396716663308860.004793433326617730.997603283336691
670.002904920449985030.005809840899970060.997095079550015
680.003668970100564290.007337940201128580.996331029899436
690.009417471168735250.01883494233747050.990582528831265
700.007402247712584730.01480449542516950.992597752287415
710.005299353586781520.0105987071735630.994700646413218
720.004233989687407720.008467979374815440.995766010312592
730.00339963415906310.006799268318126210.996600365840937
740.002531101777342020.005062203554684030.997468898222658
750.002394662464056240.004789324928112490.997605337535944
760.001604347664475690.003208695328951380.998395652335524
770.001473216390257940.002946432780515890.998526783609742
780.001197461650571260.002394923301142520.998802538349429
790.001081387684311280.002162775368622560.998918612315689
800.0008802777763594110.001760555552718820.999119722223641
810.0009869970101590080.001973994020318020.999013002989841
820.00176394091741840.003527881834836790.998236059082582
830.001213961522204310.002427923044408620.998786038477796
840.001480571720315070.002961143440630140.998519428279685
850.0009311720011275540.001862344002255110.999068827998872
860.001397909412542170.002795818825084350.998602090587458
870.009527320211014610.01905464042202920.990472679788985
880.008417085574992790.01683417114998560.991582914425007
890.02234973371725710.04469946743451420.977650266282743
900.05159988502986510.103199770059730.948400114970135
910.04892120845263030.09784241690526060.95107879154737
920.03619990089676570.07239980179353150.963800099103234
930.04915891616647360.09831783233294720.950841083833526
940.04183933304292660.08367866608585320.958160666957073
950.04001356235362290.08002712470724590.959986437646377
960.09385601393712470.1877120278742490.906143986062875
970.0801379802174830.1602759604349660.919862019782517
980.07041995982558320.1408399196511660.929580040174417
990.1680406629720890.3360813259441770.831959337027911
1000.2038482508041510.4076965016083020.796151749195849
1010.2246498648172570.4492997296345150.775350135182743
1020.2132286715034860.4264573430069720.786771328496514
1030.165319886098030.3306397721960590.83468011390197
1040.1259996654625180.2519993309250370.874000334537482
1050.1193688926935040.2387377853870090.880631107306496
1060.1152693106687520.2305386213375050.884730689331248
1070.2690698828173780.5381397656347560.730930117182622
1080.2086071312279440.4172142624558880.791392868772056
1090.203473295571960.4069465911439190.79652670442804
1100.1460475406419950.292095081283990.853952459358005
1110.1245779814541430.2491559629082850.875422018545857
1120.1356970535909720.2713941071819450.864302946409028
1130.1764901396000680.3529802792001360.823509860399932
1140.323044966649980.646089933299960.67695503335002
1150.315246888003450.63049377600690.68475311199655
1160.4790094634377320.9580189268754650.520990536562268

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
16 & 0.071711075203567 & 0.143422150407134 & 0.928288924796433 \tabularnewline
17 & 0.0223780376273161 & 0.0447560752546323 & 0.977621962372684 \tabularnewline
18 & 0.0132598548749277 & 0.0265197097498555 & 0.986740145125072 \tabularnewline
19 & 0.00517248046014443 & 0.0103449609202889 & 0.994827519539856 \tabularnewline
20 & 0.00619593477216386 & 0.0123918695443277 & 0.993804065227836 \tabularnewline
21 & 0.0277010269880361 & 0.0554020539760722 & 0.972298973011964 \tabularnewline
22 & 0.0308915127045479 & 0.0617830254090957 & 0.969108487295452 \tabularnewline
23 & 0.0171700906056775 & 0.034340181211355 & 0.982829909394322 \tabularnewline
24 & 0.00848477998423394 & 0.0169695599684679 & 0.991515220015766 \tabularnewline
25 & 0.00473606765023691 & 0.00947213530047382 & 0.995263932349763 \tabularnewline
26 & 0.0031265563417052 & 0.0062531126834104 & 0.996873443658295 \tabularnewline
27 & 0.00142611619925217 & 0.00285223239850434 & 0.998573883800748 \tabularnewline
28 & 0.000998688272563235 & 0.00199737654512647 & 0.999001311727437 \tabularnewline
29 & 0.000442658361110094 & 0.000885316722220188 & 0.99955734163889 \tabularnewline
30 & 0.000351198745026709 & 0.000702397490053419 & 0.999648801254973 \tabularnewline
31 & 0.000291731624287799 & 0.000583463248575599 & 0.999708268375712 \tabularnewline
32 & 0.000170013787647376 & 0.000340027575294753 & 0.999829986212353 \tabularnewline
33 & 0.000297138273120955 & 0.00059427654624191 & 0.999702861726879 \tabularnewline
34 & 0.000177509067545023 & 0.000355018135090046 & 0.999822490932455 \tabularnewline
35 & 0.000221228889782546 & 0.000442457779565093 & 0.999778771110217 \tabularnewline
36 & 0.0002443082875649 & 0.0004886165751298 & 0.999755691712435 \tabularnewline
37 & 0.000146538307793399 & 0.000293076615586798 & 0.999853461692207 \tabularnewline
38 & 0.000121798461765963 & 0.000243596923531926 & 0.999878201538234 \tabularnewline
39 & 0.000579742738514257 & 0.00115948547702851 & 0.999420257261486 \tabularnewline
40 & 0.000497724484384117 & 0.000995448968768235 & 0.999502275515616 \tabularnewline
41 & 0.000292428533282766 & 0.000584857066565532 & 0.999707571466717 \tabularnewline
42 & 0.00025396412979647 & 0.00050792825959294 & 0.999746035870203 \tabularnewline
43 & 0.000334350425767924 & 0.000668700851535848 & 0.999665649574232 \tabularnewline
44 & 0.00028912155575627 & 0.000578243111512541 & 0.999710878444244 \tabularnewline
45 & 0.000289161871377808 & 0.000578323742755616 & 0.999710838128622 \tabularnewline
46 & 0.000220819193088419 & 0.000441638386176839 & 0.999779180806912 \tabularnewline
47 & 0.000191195503818053 & 0.000382391007636106 & 0.999808804496182 \tabularnewline
48 & 0.000276602138147124 & 0.000553204276294248 & 0.999723397861853 \tabularnewline
49 & 0.000196997937003093 & 0.000393995874006186 & 0.999803002062997 \tabularnewline
50 & 0.000195129528673718 & 0.000390259057347436 & 0.999804870471326 \tabularnewline
51 & 0.000116486719084525 & 0.00023297343816905 & 0.999883513280916 \tabularnewline
52 & 8.27206675721817e-05 & 0.000165441335144363 & 0.999917279332428 \tabularnewline
53 & 8.12612739009483e-05 & 0.000162522547801897 & 0.999918738726099 \tabularnewline
54 & 0.000307012720816881 & 0.000614025441633761 & 0.999692987279183 \tabularnewline
55 & 0.00036924218696701 & 0.000738484373934019 & 0.999630757813033 \tabularnewline
56 & 0.000490212831544525 & 0.000980425663089051 & 0.999509787168456 \tabularnewline
57 & 0.000325981402077948 & 0.000651962804155896 & 0.999674018597922 \tabularnewline
58 & 0.000569432563653484 & 0.00113886512730697 & 0.999430567436347 \tabularnewline
59 & 0.000797056001969669 & 0.00159411200393934 & 0.99920294399803 \tabularnewline
60 & 0.000670989538210521 & 0.00134197907642104 & 0.999329010461789 \tabularnewline
61 & 0.00115197585337299 & 0.00230395170674598 & 0.998848024146627 \tabularnewline
62 & 0.00106968204265761 & 0.00213936408531523 & 0.998930317957342 \tabularnewline
63 & 0.00204634373954159 & 0.00409268747908318 & 0.997953656260458 \tabularnewline
64 & 0.00217577017259607 & 0.00435154034519214 & 0.997824229827404 \tabularnewline
65 & 0.002740708273071 & 0.00548141654614201 & 0.997259291726929 \tabularnewline
66 & 0.00239671666330886 & 0.00479343332661773 & 0.997603283336691 \tabularnewline
67 & 0.00290492044998503 & 0.00580984089997006 & 0.997095079550015 \tabularnewline
68 & 0.00366897010056429 & 0.00733794020112858 & 0.996331029899436 \tabularnewline
69 & 0.00941747116873525 & 0.0188349423374705 & 0.990582528831265 \tabularnewline
70 & 0.00740224771258473 & 0.0148044954251695 & 0.992597752287415 \tabularnewline
71 & 0.00529935358678152 & 0.010598707173563 & 0.994700646413218 \tabularnewline
72 & 0.00423398968740772 & 0.00846797937481544 & 0.995766010312592 \tabularnewline
73 & 0.0033996341590631 & 0.00679926831812621 & 0.996600365840937 \tabularnewline
74 & 0.00253110177734202 & 0.00506220355468403 & 0.997468898222658 \tabularnewline
75 & 0.00239466246405624 & 0.00478932492811249 & 0.997605337535944 \tabularnewline
76 & 0.00160434766447569 & 0.00320869532895138 & 0.998395652335524 \tabularnewline
77 & 0.00147321639025794 & 0.00294643278051589 & 0.998526783609742 \tabularnewline
78 & 0.00119746165057126 & 0.00239492330114252 & 0.998802538349429 \tabularnewline
79 & 0.00108138768431128 & 0.00216277536862256 & 0.998918612315689 \tabularnewline
80 & 0.000880277776359411 & 0.00176055555271882 & 0.999119722223641 \tabularnewline
81 & 0.000986997010159008 & 0.00197399402031802 & 0.999013002989841 \tabularnewline
82 & 0.0017639409174184 & 0.00352788183483679 & 0.998236059082582 \tabularnewline
83 & 0.00121396152220431 & 0.00242792304440862 & 0.998786038477796 \tabularnewline
84 & 0.00148057172031507 & 0.00296114344063014 & 0.998519428279685 \tabularnewline
85 & 0.000931172001127554 & 0.00186234400225511 & 0.999068827998872 \tabularnewline
86 & 0.00139790941254217 & 0.00279581882508435 & 0.998602090587458 \tabularnewline
87 & 0.00952732021101461 & 0.0190546404220292 & 0.990472679788985 \tabularnewline
88 & 0.00841708557499279 & 0.0168341711499856 & 0.991582914425007 \tabularnewline
89 & 0.0223497337172571 & 0.0446994674345142 & 0.977650266282743 \tabularnewline
90 & 0.0515998850298651 & 0.10319977005973 & 0.948400114970135 \tabularnewline
91 & 0.0489212084526303 & 0.0978424169052606 & 0.95107879154737 \tabularnewline
92 & 0.0361999008967657 & 0.0723998017935315 & 0.963800099103234 \tabularnewline
93 & 0.0491589161664736 & 0.0983178323329472 & 0.950841083833526 \tabularnewline
94 & 0.0418393330429266 & 0.0836786660858532 & 0.958160666957073 \tabularnewline
95 & 0.0400135623536229 & 0.0800271247072459 & 0.959986437646377 \tabularnewline
96 & 0.0938560139371247 & 0.187712027874249 & 0.906143986062875 \tabularnewline
97 & 0.080137980217483 & 0.160275960434966 & 0.919862019782517 \tabularnewline
98 & 0.0704199598255832 & 0.140839919651166 & 0.929580040174417 \tabularnewline
99 & 0.168040662972089 & 0.336081325944177 & 0.831959337027911 \tabularnewline
100 & 0.203848250804151 & 0.407696501608302 & 0.796151749195849 \tabularnewline
101 & 0.224649864817257 & 0.449299729634515 & 0.775350135182743 \tabularnewline
102 & 0.213228671503486 & 0.426457343006972 & 0.786771328496514 \tabularnewline
103 & 0.16531988609803 & 0.330639772196059 & 0.83468011390197 \tabularnewline
104 & 0.125999665462518 & 0.251999330925037 & 0.874000334537482 \tabularnewline
105 & 0.119368892693504 & 0.238737785387009 & 0.880631107306496 \tabularnewline
106 & 0.115269310668752 & 0.230538621337505 & 0.884730689331248 \tabularnewline
107 & 0.269069882817378 & 0.538139765634756 & 0.730930117182622 \tabularnewline
108 & 0.208607131227944 & 0.417214262455888 & 0.791392868772056 \tabularnewline
109 & 0.20347329557196 & 0.406946591143919 & 0.79652670442804 \tabularnewline
110 & 0.146047540641995 & 0.29209508128399 & 0.853952459358005 \tabularnewline
111 & 0.124577981454143 & 0.249155962908285 & 0.875422018545857 \tabularnewline
112 & 0.135697053590972 & 0.271394107181945 & 0.864302946409028 \tabularnewline
113 & 0.176490139600068 & 0.352980279200136 & 0.823509860399932 \tabularnewline
114 & 0.32304496664998 & 0.64608993329996 & 0.67695503335002 \tabularnewline
115 & 0.31524688800345 & 0.6304937760069 & 0.68475311199655 \tabularnewline
116 & 0.479009463437732 & 0.958018926875465 & 0.520990536562268 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=187427&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]0.071711075203567[/C][C]0.143422150407134[/C][C]0.928288924796433[/C][/ROW]
[ROW][C]17[/C][C]0.0223780376273161[/C][C]0.0447560752546323[/C][C]0.977621962372684[/C][/ROW]
[ROW][C]18[/C][C]0.0132598548749277[/C][C]0.0265197097498555[/C][C]0.986740145125072[/C][/ROW]
[ROW][C]19[/C][C]0.00517248046014443[/C][C]0.0103449609202889[/C][C]0.994827519539856[/C][/ROW]
[ROW][C]20[/C][C]0.00619593477216386[/C][C]0.0123918695443277[/C][C]0.993804065227836[/C][/ROW]
[ROW][C]21[/C][C]0.0277010269880361[/C][C]0.0554020539760722[/C][C]0.972298973011964[/C][/ROW]
[ROW][C]22[/C][C]0.0308915127045479[/C][C]0.0617830254090957[/C][C]0.969108487295452[/C][/ROW]
[ROW][C]23[/C][C]0.0171700906056775[/C][C]0.034340181211355[/C][C]0.982829909394322[/C][/ROW]
[ROW][C]24[/C][C]0.00848477998423394[/C][C]0.0169695599684679[/C][C]0.991515220015766[/C][/ROW]
[ROW][C]25[/C][C]0.00473606765023691[/C][C]0.00947213530047382[/C][C]0.995263932349763[/C][/ROW]
[ROW][C]26[/C][C]0.0031265563417052[/C][C]0.0062531126834104[/C][C]0.996873443658295[/C][/ROW]
[ROW][C]27[/C][C]0.00142611619925217[/C][C]0.00285223239850434[/C][C]0.998573883800748[/C][/ROW]
[ROW][C]28[/C][C]0.000998688272563235[/C][C]0.00199737654512647[/C][C]0.999001311727437[/C][/ROW]
[ROW][C]29[/C][C]0.000442658361110094[/C][C]0.000885316722220188[/C][C]0.99955734163889[/C][/ROW]
[ROW][C]30[/C][C]0.000351198745026709[/C][C]0.000702397490053419[/C][C]0.999648801254973[/C][/ROW]
[ROW][C]31[/C][C]0.000291731624287799[/C][C]0.000583463248575599[/C][C]0.999708268375712[/C][/ROW]
[ROW][C]32[/C][C]0.000170013787647376[/C][C]0.000340027575294753[/C][C]0.999829986212353[/C][/ROW]
[ROW][C]33[/C][C]0.000297138273120955[/C][C]0.00059427654624191[/C][C]0.999702861726879[/C][/ROW]
[ROW][C]34[/C][C]0.000177509067545023[/C][C]0.000355018135090046[/C][C]0.999822490932455[/C][/ROW]
[ROW][C]35[/C][C]0.000221228889782546[/C][C]0.000442457779565093[/C][C]0.999778771110217[/C][/ROW]
[ROW][C]36[/C][C]0.0002443082875649[/C][C]0.0004886165751298[/C][C]0.999755691712435[/C][/ROW]
[ROW][C]37[/C][C]0.000146538307793399[/C][C]0.000293076615586798[/C][C]0.999853461692207[/C][/ROW]
[ROW][C]38[/C][C]0.000121798461765963[/C][C]0.000243596923531926[/C][C]0.999878201538234[/C][/ROW]
[ROW][C]39[/C][C]0.000579742738514257[/C][C]0.00115948547702851[/C][C]0.999420257261486[/C][/ROW]
[ROW][C]40[/C][C]0.000497724484384117[/C][C]0.000995448968768235[/C][C]0.999502275515616[/C][/ROW]
[ROW][C]41[/C][C]0.000292428533282766[/C][C]0.000584857066565532[/C][C]0.999707571466717[/C][/ROW]
[ROW][C]42[/C][C]0.00025396412979647[/C][C]0.00050792825959294[/C][C]0.999746035870203[/C][/ROW]
[ROW][C]43[/C][C]0.000334350425767924[/C][C]0.000668700851535848[/C][C]0.999665649574232[/C][/ROW]
[ROW][C]44[/C][C]0.00028912155575627[/C][C]0.000578243111512541[/C][C]0.999710878444244[/C][/ROW]
[ROW][C]45[/C][C]0.000289161871377808[/C][C]0.000578323742755616[/C][C]0.999710838128622[/C][/ROW]
[ROW][C]46[/C][C]0.000220819193088419[/C][C]0.000441638386176839[/C][C]0.999779180806912[/C][/ROW]
[ROW][C]47[/C][C]0.000191195503818053[/C][C]0.000382391007636106[/C][C]0.999808804496182[/C][/ROW]
[ROW][C]48[/C][C]0.000276602138147124[/C][C]0.000553204276294248[/C][C]0.999723397861853[/C][/ROW]
[ROW][C]49[/C][C]0.000196997937003093[/C][C]0.000393995874006186[/C][C]0.999803002062997[/C][/ROW]
[ROW][C]50[/C][C]0.000195129528673718[/C][C]0.000390259057347436[/C][C]0.999804870471326[/C][/ROW]
[ROW][C]51[/C][C]0.000116486719084525[/C][C]0.00023297343816905[/C][C]0.999883513280916[/C][/ROW]
[ROW][C]52[/C][C]8.27206675721817e-05[/C][C]0.000165441335144363[/C][C]0.999917279332428[/C][/ROW]
[ROW][C]53[/C][C]8.12612739009483e-05[/C][C]0.000162522547801897[/C][C]0.999918738726099[/C][/ROW]
[ROW][C]54[/C][C]0.000307012720816881[/C][C]0.000614025441633761[/C][C]0.999692987279183[/C][/ROW]
[ROW][C]55[/C][C]0.00036924218696701[/C][C]0.000738484373934019[/C][C]0.999630757813033[/C][/ROW]
[ROW][C]56[/C][C]0.000490212831544525[/C][C]0.000980425663089051[/C][C]0.999509787168456[/C][/ROW]
[ROW][C]57[/C][C]0.000325981402077948[/C][C]0.000651962804155896[/C][C]0.999674018597922[/C][/ROW]
[ROW][C]58[/C][C]0.000569432563653484[/C][C]0.00113886512730697[/C][C]0.999430567436347[/C][/ROW]
[ROW][C]59[/C][C]0.000797056001969669[/C][C]0.00159411200393934[/C][C]0.99920294399803[/C][/ROW]
[ROW][C]60[/C][C]0.000670989538210521[/C][C]0.00134197907642104[/C][C]0.999329010461789[/C][/ROW]
[ROW][C]61[/C][C]0.00115197585337299[/C][C]0.00230395170674598[/C][C]0.998848024146627[/C][/ROW]
[ROW][C]62[/C][C]0.00106968204265761[/C][C]0.00213936408531523[/C][C]0.998930317957342[/C][/ROW]
[ROW][C]63[/C][C]0.00204634373954159[/C][C]0.00409268747908318[/C][C]0.997953656260458[/C][/ROW]
[ROW][C]64[/C][C]0.00217577017259607[/C][C]0.00435154034519214[/C][C]0.997824229827404[/C][/ROW]
[ROW][C]65[/C][C]0.002740708273071[/C][C]0.00548141654614201[/C][C]0.997259291726929[/C][/ROW]
[ROW][C]66[/C][C]0.00239671666330886[/C][C]0.00479343332661773[/C][C]0.997603283336691[/C][/ROW]
[ROW][C]67[/C][C]0.00290492044998503[/C][C]0.00580984089997006[/C][C]0.997095079550015[/C][/ROW]
[ROW][C]68[/C][C]0.00366897010056429[/C][C]0.00733794020112858[/C][C]0.996331029899436[/C][/ROW]
[ROW][C]69[/C][C]0.00941747116873525[/C][C]0.0188349423374705[/C][C]0.990582528831265[/C][/ROW]
[ROW][C]70[/C][C]0.00740224771258473[/C][C]0.0148044954251695[/C][C]0.992597752287415[/C][/ROW]
[ROW][C]71[/C][C]0.00529935358678152[/C][C]0.010598707173563[/C][C]0.994700646413218[/C][/ROW]
[ROW][C]72[/C][C]0.00423398968740772[/C][C]0.00846797937481544[/C][C]0.995766010312592[/C][/ROW]
[ROW][C]73[/C][C]0.0033996341590631[/C][C]0.00679926831812621[/C][C]0.996600365840937[/C][/ROW]
[ROW][C]74[/C][C]0.00253110177734202[/C][C]0.00506220355468403[/C][C]0.997468898222658[/C][/ROW]
[ROW][C]75[/C][C]0.00239466246405624[/C][C]0.00478932492811249[/C][C]0.997605337535944[/C][/ROW]
[ROW][C]76[/C][C]0.00160434766447569[/C][C]0.00320869532895138[/C][C]0.998395652335524[/C][/ROW]
[ROW][C]77[/C][C]0.00147321639025794[/C][C]0.00294643278051589[/C][C]0.998526783609742[/C][/ROW]
[ROW][C]78[/C][C]0.00119746165057126[/C][C]0.00239492330114252[/C][C]0.998802538349429[/C][/ROW]
[ROW][C]79[/C][C]0.00108138768431128[/C][C]0.00216277536862256[/C][C]0.998918612315689[/C][/ROW]
[ROW][C]80[/C][C]0.000880277776359411[/C][C]0.00176055555271882[/C][C]0.999119722223641[/C][/ROW]
[ROW][C]81[/C][C]0.000986997010159008[/C][C]0.00197399402031802[/C][C]0.999013002989841[/C][/ROW]
[ROW][C]82[/C][C]0.0017639409174184[/C][C]0.00352788183483679[/C][C]0.998236059082582[/C][/ROW]
[ROW][C]83[/C][C]0.00121396152220431[/C][C]0.00242792304440862[/C][C]0.998786038477796[/C][/ROW]
[ROW][C]84[/C][C]0.00148057172031507[/C][C]0.00296114344063014[/C][C]0.998519428279685[/C][/ROW]
[ROW][C]85[/C][C]0.000931172001127554[/C][C]0.00186234400225511[/C][C]0.999068827998872[/C][/ROW]
[ROW][C]86[/C][C]0.00139790941254217[/C][C]0.00279581882508435[/C][C]0.998602090587458[/C][/ROW]
[ROW][C]87[/C][C]0.00952732021101461[/C][C]0.0190546404220292[/C][C]0.990472679788985[/C][/ROW]
[ROW][C]88[/C][C]0.00841708557499279[/C][C]0.0168341711499856[/C][C]0.991582914425007[/C][/ROW]
[ROW][C]89[/C][C]0.0223497337172571[/C][C]0.0446994674345142[/C][C]0.977650266282743[/C][/ROW]
[ROW][C]90[/C][C]0.0515998850298651[/C][C]0.10319977005973[/C][C]0.948400114970135[/C][/ROW]
[ROW][C]91[/C][C]0.0489212084526303[/C][C]0.0978424169052606[/C][C]0.95107879154737[/C][/ROW]
[ROW][C]92[/C][C]0.0361999008967657[/C][C]0.0723998017935315[/C][C]0.963800099103234[/C][/ROW]
[ROW][C]93[/C][C]0.0491589161664736[/C][C]0.0983178323329472[/C][C]0.950841083833526[/C][/ROW]
[ROW][C]94[/C][C]0.0418393330429266[/C][C]0.0836786660858532[/C][C]0.958160666957073[/C][/ROW]
[ROW][C]95[/C][C]0.0400135623536229[/C][C]0.0800271247072459[/C][C]0.959986437646377[/C][/ROW]
[ROW][C]96[/C][C]0.0938560139371247[/C][C]0.187712027874249[/C][C]0.906143986062875[/C][/ROW]
[ROW][C]97[/C][C]0.080137980217483[/C][C]0.160275960434966[/C][C]0.919862019782517[/C][/ROW]
[ROW][C]98[/C][C]0.0704199598255832[/C][C]0.140839919651166[/C][C]0.929580040174417[/C][/ROW]
[ROW][C]99[/C][C]0.168040662972089[/C][C]0.336081325944177[/C][C]0.831959337027911[/C][/ROW]
[ROW][C]100[/C][C]0.203848250804151[/C][C]0.407696501608302[/C][C]0.796151749195849[/C][/ROW]
[ROW][C]101[/C][C]0.224649864817257[/C][C]0.449299729634515[/C][C]0.775350135182743[/C][/ROW]
[ROW][C]102[/C][C]0.213228671503486[/C][C]0.426457343006972[/C][C]0.786771328496514[/C][/ROW]
[ROW][C]103[/C][C]0.16531988609803[/C][C]0.330639772196059[/C][C]0.83468011390197[/C][/ROW]
[ROW][C]104[/C][C]0.125999665462518[/C][C]0.251999330925037[/C][C]0.874000334537482[/C][/ROW]
[ROW][C]105[/C][C]0.119368892693504[/C][C]0.238737785387009[/C][C]0.880631107306496[/C][/ROW]
[ROW][C]106[/C][C]0.115269310668752[/C][C]0.230538621337505[/C][C]0.884730689331248[/C][/ROW]
[ROW][C]107[/C][C]0.269069882817378[/C][C]0.538139765634756[/C][C]0.730930117182622[/C][/ROW]
[ROW][C]108[/C][C]0.208607131227944[/C][C]0.417214262455888[/C][C]0.791392868772056[/C][/ROW]
[ROW][C]109[/C][C]0.20347329557196[/C][C]0.406946591143919[/C][C]0.79652670442804[/C][/ROW]
[ROW][C]110[/C][C]0.146047540641995[/C][C]0.29209508128399[/C][C]0.853952459358005[/C][/ROW]
[ROW][C]111[/C][C]0.124577981454143[/C][C]0.249155962908285[/C][C]0.875422018545857[/C][/ROW]
[ROW][C]112[/C][C]0.135697053590972[/C][C]0.271394107181945[/C][C]0.864302946409028[/C][/ROW]
[ROW][C]113[/C][C]0.176490139600068[/C][C]0.352980279200136[/C][C]0.823509860399932[/C][/ROW]
[ROW][C]114[/C][C]0.32304496664998[/C][C]0.64608993329996[/C][C]0.67695503335002[/C][/ROW]
[ROW][C]115[/C][C]0.31524688800345[/C][C]0.6304937760069[/C][C]0.68475311199655[/C][/ROW]
[ROW][C]116[/C][C]0.479009463437732[/C][C]0.958018926875465[/C][C]0.520990536562268[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=187427&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=187427&T=5

As an alternative you can also use a QR Code:  

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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
160.0717110752035670.1434221504071340.928288924796433
170.02237803762731610.04475607525463230.977621962372684
180.01325985487492770.02651970974985550.986740145125072
190.005172480460144430.01034496092028890.994827519539856
200.006195934772163860.01239186954432770.993804065227836
210.02770102698803610.05540205397607220.972298973011964
220.03089151270454790.06178302540909570.969108487295452
230.01717009060567750.0343401812113550.982829909394322
240.008484779984233940.01696955996846790.991515220015766
250.004736067650236910.009472135300473820.995263932349763
260.00312655634170520.00625311268341040.996873443658295
270.001426116199252170.002852232398504340.998573883800748
280.0009986882725632350.001997376545126470.999001311727437
290.0004426583611100940.0008853167222201880.99955734163889
300.0003511987450267090.0007023974900534190.999648801254973
310.0002917316242877990.0005834632485755990.999708268375712
320.0001700137876473760.0003400275752947530.999829986212353
330.0002971382731209550.000594276546241910.999702861726879
340.0001775090675450230.0003550181350900460.999822490932455
350.0002212288897825460.0004424577795650930.999778771110217
360.00024430828756490.00048861657512980.999755691712435
370.0001465383077933990.0002930766155867980.999853461692207
380.0001217984617659630.0002435969235319260.999878201538234
390.0005797427385142570.001159485477028510.999420257261486
400.0004977244843841170.0009954489687682350.999502275515616
410.0002924285332827660.0005848570665655320.999707571466717
420.000253964129796470.000507928259592940.999746035870203
430.0003343504257679240.0006687008515358480.999665649574232
440.000289121555756270.0005782431115125410.999710878444244
450.0002891618713778080.0005783237427556160.999710838128622
460.0002208191930884190.0004416383861768390.999779180806912
470.0001911955038180530.0003823910076361060.999808804496182
480.0002766021381471240.0005532042762942480.999723397861853
490.0001969979370030930.0003939958740061860.999803002062997
500.0001951295286737180.0003902590573474360.999804870471326
510.0001164867190845250.000232973438169050.999883513280916
528.27206675721817e-050.0001654413351443630.999917279332428
538.12612739009483e-050.0001625225478018970.999918738726099
540.0003070127208168810.0006140254416337610.999692987279183
550.000369242186967010.0007384843739340190.999630757813033
560.0004902128315445250.0009804256630890510.999509787168456
570.0003259814020779480.0006519628041558960.999674018597922
580.0005694325636534840.001138865127306970.999430567436347
590.0007970560019696690.001594112003939340.99920294399803
600.0006709895382105210.001341979076421040.999329010461789
610.001151975853372990.002303951706745980.998848024146627
620.001069682042657610.002139364085315230.998930317957342
630.002046343739541590.004092687479083180.997953656260458
640.002175770172596070.004351540345192140.997824229827404
650.0027407082730710.005481416546142010.997259291726929
660.002396716663308860.004793433326617730.997603283336691
670.002904920449985030.005809840899970060.997095079550015
680.003668970100564290.007337940201128580.996331029899436
690.009417471168735250.01883494233747050.990582528831265
700.007402247712584730.01480449542516950.992597752287415
710.005299353586781520.0105987071735630.994700646413218
720.004233989687407720.008467979374815440.995766010312592
730.00339963415906310.006799268318126210.996600365840937
740.002531101777342020.005062203554684030.997468898222658
750.002394662464056240.004789324928112490.997605337535944
760.001604347664475690.003208695328951380.998395652335524
770.001473216390257940.002946432780515890.998526783609742
780.001197461650571260.002394923301142520.998802538349429
790.001081387684311280.002162775368622560.998918612315689
800.0008802777763594110.001760555552718820.999119722223641
810.0009869970101590080.001973994020318020.999013002989841
820.00176394091741840.003527881834836790.998236059082582
830.001213961522204310.002427923044408620.998786038477796
840.001480571720315070.002961143440630140.998519428279685
850.0009311720011275540.001862344002255110.999068827998872
860.001397909412542170.002795818825084350.998602090587458
870.009527320211014610.01905464042202920.990472679788985
880.008417085574992790.01683417114998560.991582914425007
890.02234973371725710.04469946743451420.977650266282743
900.05159988502986510.103199770059730.948400114970135
910.04892120845263030.09784241690526060.95107879154737
920.03619990089676570.07239980179353150.963800099103234
930.04915891616647360.09831783233294720.950841083833526
940.04183933304292660.08367866608585320.958160666957073
950.04001356235362290.08002712470724590.959986437646377
960.09385601393712470.1877120278742490.906143986062875
970.0801379802174830.1602759604349660.919862019782517
980.07041995982558320.1408399196511660.929580040174417
990.1680406629720890.3360813259441770.831959337027911
1000.2038482508041510.4076965016083020.796151749195849
1010.2246498648172570.4492997296345150.775350135182743
1020.2132286715034860.4264573430069720.786771328496514
1030.165319886098030.3306397721960590.83468011390197
1040.1259996654625180.2519993309250370.874000334537482
1050.1193688926935040.2387377853870090.880631107306496
1060.1152693106687520.2305386213375050.884730689331248
1070.2690698828173780.5381397656347560.730930117182622
1080.2086071312279440.4172142624558880.791392868772056
1090.203473295571960.4069465911439190.79652670442804
1100.1460475406419950.292095081283990.853952459358005
1110.1245779814541430.2491559629082850.875422018545857
1120.1356970535909720.2713941071819450.864302946409028
1130.1764901396000680.3529802792001360.823509860399932
1140.323044966649980.646089933299960.67695503335002
1150.315246888003450.63049377600690.68475311199655
1160.4790094634377320.9580189268754650.520990536562268







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level590.584158415841584NOK
5% type I error level710.702970297029703NOK
10% type I error level780.772277227722772NOK

\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 & 59 & 0.584158415841584 & NOK \tabularnewline
5% type I error level & 71 & 0.702970297029703 & NOK \tabularnewline
10% type I error level & 78 & 0.772277227722772 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=187427&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]59[/C][C]0.584158415841584[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]71[/C][C]0.702970297029703[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]78[/C][C]0.772277227722772[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=187427&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=187427&T=6

As an alternative you can also use a QR Code:  

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

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level590.584158415841584NOK
5% type I error level710.702970297029703NOK
10% type I error level780.772277227722772NOK



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')
}