## 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, 26 Nov 2011 08:19:29 -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/2011/Nov/26/t1322313782nfsx5lztbduwtrf.htm/, Retrieved Sun, 03 Dec 2023 03:23:52 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=147397, Retrieved Sun, 03 Dec 2023 03:23:52 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact118
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Multiple Regression] [WS 8 - Multiple R...] [2011-11-26 13:19:29] [e598b5cd83fcb010b35e92a01f5e81e9] [Current]
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Dataseries X:
6827
6178
7084
8162
8462
9644
10466
10748
9963
8194
6848
7027
7269
6775
7819
8371
9069
10248
11030
10882
10333
9109
7685
7602
8350
7829
8829
9948
10638
11253
11424
11391
10665
9396
7775
7933
8186
7444
8484
9864
10252
12282
11637
11577
12417
9637
8094
9280
8334
7899
9994
10078
10801
12950
12222
12246
13281
10366
8730
9614
8639
8772
10894
10455
11179
10588
10794
12770
13812
10857
9290
10925
9491
8919
11607
8852
12537
14759
13667
13731
15110
12185
10645
12161
10840
10436
13589
13402
13103
14933
14147
14057
16234
12389
11595
12772

 Summary of computational transaction Raw Input view raw input (R code) Raw Output view raw output of R engine Computing time 5 seconds R Server 'Herman Ole Andreas Wold' @ wold.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 & 5 seconds \tabularnewline
R Server & 'Herman Ole Andreas Wold' @ wold.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147397&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]5 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Herman Ole Andreas Wold' @ wold.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147397&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147397&T=0

As an alternative you can also use a QR Code:

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

 Summary of computational transaction Raw Input view raw input (R code) Raw Output view raw output of R engine Computing time 5 seconds R Server 'Herman Ole Andreas Wold' @ wold.wessa.net

 Multiple Linear Regression - Estimated Regression Equation #Miles[t] = + 7747.17412280702 + 54.4034115572436t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
#Miles[t] =  +  7747.17412280702 +  54.4034115572436t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147397&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]#Miles[t] =  +  7747.17412280702 +  54.4034115572436t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147397&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147397&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 #Miles[t] = + 7747.17412280702 + 54.4034115572436t + e[t]

 Multiple Linear Regression - Ordinary Least Squares Variable Parameter S.D. T-STATH0: parameter = 0 2-tail p-value 1-tail p-value (Intercept) 7747.17412280702 330.541831 23.4378 0 0 t 54.4034115572436 5.91749 9.1937 0 0

\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) & 7747.17412280702 & 330.541831 & 23.4378 & 0 & 0 \tabularnewline
t & 54.4034115572436 & 5.91749 & 9.1937 & 0 & 0 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147397&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]7747.17412280702[/C][C]330.541831[/C][C]23.4378[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]t[/C][C]54.4034115572436[/C][C]5.91749[/C][C]9.1937[/C][C]0[/C][C]0[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147397&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147397&T=2

As an alternative you can also use a QR Code:

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

 Multiple Linear Regression - Ordinary Least Squares Variable Parameter S.D. T-STATH0: parameter = 0 2-tail p-value 1-tail p-value (Intercept) 7747.17412280702 330.541831 23.4378 0 0 t 54.4034115572436 5.91749 9.1937 0 0

 Multiple Linear Regression - Regression Statistics Multiple R 0.6880832236771 R-squared 0.47345852270587 Adjusted R-squared 0.467857017628273 F-TEST (value) 84.5234479210663 F-TEST (DF numerator) 1 F-TEST (DF denominator) 94 p-value 9.54791801177635e-15 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 1606.68298860349 Sum Squared Residuals 242654441.231577

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.6880832236771 \tabularnewline
R-squared & 0.47345852270587 \tabularnewline
F-TEST (value) & 84.5234479210663 \tabularnewline
F-TEST (DF numerator) & 1 \tabularnewline
F-TEST (DF denominator) & 94 \tabularnewline
p-value & 9.54791801177635e-15 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 1606.68298860349 \tabularnewline
Sum Squared Residuals & 242654441.231577 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147397&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.6880832236771[/C][/ROW]
[ROW][C]R-squared[/C][C]0.47345852270587[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]84.5234479210663[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]1[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]94[/C][/ROW]
[ROW][C]p-value[/C][C]9.54791801177635e-15[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]1606.68298860349[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]242654441.231577[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147397&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147397&T=3

As an alternative you can also use a QR Code:

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

 Multiple Linear Regression - Regression Statistics Multiple R 0.6880832236771 R-squared 0.47345852270587 Adjusted R-squared 0.467857017628273 F-TEST (value) 84.5234479210663 F-TEST (DF numerator) 1 F-TEST (DF denominator) 94 p-value 9.54791801177635e-15 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 1606.68298860349 Sum Squared Residuals 242654441.231577

 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals InterpolationForecast ResidualsPrediction Error 1 6827 7801.57753436426 -974.577534364261 2 6178 7855.9809459215 -1677.9809459215 3 7084 7910.38435747875 -826.384357478748 4 8162 7964.78776903599 197.212230964008 5 8462 8019.19118059324 442.808819406764 6 9644 8073.59459215048 1570.40540784952 7 10466 8127.99800370772 2338.00199629228 8 10748 8182.40141526497 2565.59858473503 9 9963 8236.80482682221 1726.19517317779 10 8194 8291.20823837945 -97.2082383794536 11 6848 8345.6116499367 -1497.6116499367 12 7027 8400.01506149394 -1373.01506149394 13 7269 8454.41847305118 -1185.41847305118 14 6775 8508.82188460843 -1733.82188460843 15 7819 8563.22529616567 -744.225296165672 16 8371 8617.62870772292 -246.628707722916 17 9069 8672.03211928016 396.967880719841 18 10248 8726.4355308374 1521.5644691626 19 11030 8780.83894239465 2249.16105760535 20 10882 8835.24235395189 2046.75764604811 21 10333 8889.64576550913 1443.35423449087 22 9109 8944.04917706638 164.950822933623 23 7685 8998.45258862362 -1313.45258862362 24 7602 9052.85600018086 -1450.85600018086 25 8350 9107.25941173811 -757.259411738108 26 7829 9161.66282329535 -1332.66282329535 27 8829 9216.06623485259 -387.066234852595 28 9948 9270.46964640984 677.530353590161 29 10638 9324.87305796708 1313.12694203292 30 11253 9379.27646952433 1873.72353047567 31 11424 9433.67988108157 1990.32011891843 32 11391 9488.08329263881 1902.91670736119 33 10665 9542.48670419606 1122.51329580394 34 9396 9596.8901157533 -200.890115753301 35 7775 9651.29352731054 -1876.29352731054 36 7933 9705.69693886779 -1772.69693886779 37 8186 9760.10035042503 -1574.10035042503 38 7444 9814.50376198228 -2370.50376198228 39 8484 9868.90717353952 -1384.90717353952 40 9864 9923.31058509676 -59.3105850967625 41 10252 9977.71399665401 274.286003345994 42 12282 10032.1174082113 2249.88259178875 43 11637 10086.5208197685 1550.47918023151 44 11577 10140.9242313257 1436.07576867426 45 12417 10195.327642883 2221.67235711702 46 9637 10249.7310544402 -612.731054440224 47 8094 10304.1344659975 -2210.13446599747 48 9280 10358.5378775547 -1078.53787755471 49 8334 10412.941289112 -2078.94128911196 50 7899 10467.3447006692 -2568.3447006692 51 9994 10521.7481122264 -527.748112226442 52 10078 10576.1515237837 -498.151523783686 53 10801 10630.5549353409 170.44506465907 54 12950 10684.9583468982 2265.04165310183 55 12222 10739.3617584554 1482.63824154458 56 12246 10793.7651700127 1452.23482998734 57 13281 10848.1685815699 2432.8314184301 58 10366 10902.5719931271 -536.571993127148 59 8730 10956.9754046844 -2226.97540468439 60 9614 11011.3788162416 -1397.37881624163 61 8639 11065.7822277989 -2426.78222779888 62 8772 11120.1856393561 -2348.18563935612 63 10894 11174.5890509134 -280.589050913366 64 10455 11228.9924624706 -773.99246247061 65 11179 11283.3958740279 -104.395874027853 66 10588 11337.7992855851 -749.799285585097 67 10794 11392.2026971423 -598.20269714234 68 12770 11446.6061086996 1323.39389130042 69 13812 11501.0095202568 2310.99047974317 70 10857 11555.4129318141 -698.412931814071 71 9290 11609.8163433713 -2319.81634337131 72 10925 11664.2197549286 -739.219754928558 73 9491 11718.6231664858 -2227.6231664858 74 8919 11773.026578043 -2854.02657804305 75 11607 11827.4299896003 -220.429989600289 76 8852 11881.8334011575 -3029.83340115753 77 12537 11936.2368127148 600.763187285223 78 14759 11990.640224272 2768.35977572798 79 13667 12045.0436358293 1621.95636417074 80 13731 12099.4470473865 1631.55295261349 81 15110 12153.8504589438 2956.14954105625 82 12185 12208.253870501 -23.253870500995 83 10645 12262.6572820582 -1617.65728205824 84 12161 12317.0606936155 -156.060693615482 85 10840 12371.4641051727 -1531.46410517273 86 10436 12425.86751673 -1989.86751672997 87 13589 12480.2709282872 1108.72907171279 88 13402 12534.6743398445 867.325660155543 89 13103 12589.0777514017 513.9222485983 90 14933 12643.4811629589 2289.51883704106 91 14147 12697.8845745162 1449.11542548381 92 14057 12752.2879860734 1304.71201392657 93 16234 12806.6913976307 3427.30860236933 94 12389 12861.0948091879 -472.094809187919 95 11595 12915.4982207452 -1320.49822074516 96 12772 12969.9016323024 -197.901632302406

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 6827 & 7801.57753436426 & -974.577534364261 \tabularnewline
2 & 6178 & 7855.9809459215 & -1677.9809459215 \tabularnewline
3 & 7084 & 7910.38435747875 & -826.384357478748 \tabularnewline
4 & 8162 & 7964.78776903599 & 197.212230964008 \tabularnewline
5 & 8462 & 8019.19118059324 & 442.808819406764 \tabularnewline
6 & 9644 & 8073.59459215048 & 1570.40540784952 \tabularnewline
7 & 10466 & 8127.99800370772 & 2338.00199629228 \tabularnewline
8 & 10748 & 8182.40141526497 & 2565.59858473503 \tabularnewline
9 & 9963 & 8236.80482682221 & 1726.19517317779 \tabularnewline
10 & 8194 & 8291.20823837945 & -97.2082383794536 \tabularnewline
11 & 6848 & 8345.6116499367 & -1497.6116499367 \tabularnewline
12 & 7027 & 8400.01506149394 & -1373.01506149394 \tabularnewline
13 & 7269 & 8454.41847305118 & -1185.41847305118 \tabularnewline
14 & 6775 & 8508.82188460843 & -1733.82188460843 \tabularnewline
15 & 7819 & 8563.22529616567 & -744.225296165672 \tabularnewline
16 & 8371 & 8617.62870772292 & -246.628707722916 \tabularnewline
17 & 9069 & 8672.03211928016 & 396.967880719841 \tabularnewline
18 & 10248 & 8726.4355308374 & 1521.5644691626 \tabularnewline
19 & 11030 & 8780.83894239465 & 2249.16105760535 \tabularnewline
20 & 10882 & 8835.24235395189 & 2046.75764604811 \tabularnewline
21 & 10333 & 8889.64576550913 & 1443.35423449087 \tabularnewline
22 & 9109 & 8944.04917706638 & 164.950822933623 \tabularnewline
23 & 7685 & 8998.45258862362 & -1313.45258862362 \tabularnewline
24 & 7602 & 9052.85600018086 & -1450.85600018086 \tabularnewline
25 & 8350 & 9107.25941173811 & -757.259411738108 \tabularnewline
26 & 7829 & 9161.66282329535 & -1332.66282329535 \tabularnewline
27 & 8829 & 9216.06623485259 & -387.066234852595 \tabularnewline
28 & 9948 & 9270.46964640984 & 677.530353590161 \tabularnewline
29 & 10638 & 9324.87305796708 & 1313.12694203292 \tabularnewline
30 & 11253 & 9379.27646952433 & 1873.72353047567 \tabularnewline
31 & 11424 & 9433.67988108157 & 1990.32011891843 \tabularnewline
32 & 11391 & 9488.08329263881 & 1902.91670736119 \tabularnewline
33 & 10665 & 9542.48670419606 & 1122.51329580394 \tabularnewline
34 & 9396 & 9596.8901157533 & -200.890115753301 \tabularnewline
35 & 7775 & 9651.29352731054 & -1876.29352731054 \tabularnewline
36 & 7933 & 9705.69693886779 & -1772.69693886779 \tabularnewline
37 & 8186 & 9760.10035042503 & -1574.10035042503 \tabularnewline
38 & 7444 & 9814.50376198228 & -2370.50376198228 \tabularnewline
39 & 8484 & 9868.90717353952 & -1384.90717353952 \tabularnewline
40 & 9864 & 9923.31058509676 & -59.3105850967625 \tabularnewline
41 & 10252 & 9977.71399665401 & 274.286003345994 \tabularnewline
42 & 12282 & 10032.1174082113 & 2249.88259178875 \tabularnewline
43 & 11637 & 10086.5208197685 & 1550.47918023151 \tabularnewline
44 & 11577 & 10140.9242313257 & 1436.07576867426 \tabularnewline
45 & 12417 & 10195.327642883 & 2221.67235711702 \tabularnewline
46 & 9637 & 10249.7310544402 & -612.731054440224 \tabularnewline
47 & 8094 & 10304.1344659975 & -2210.13446599747 \tabularnewline
48 & 9280 & 10358.5378775547 & -1078.53787755471 \tabularnewline
49 & 8334 & 10412.941289112 & -2078.94128911196 \tabularnewline
50 & 7899 & 10467.3447006692 & -2568.3447006692 \tabularnewline
51 & 9994 & 10521.7481122264 & -527.748112226442 \tabularnewline
52 & 10078 & 10576.1515237837 & -498.151523783686 \tabularnewline
53 & 10801 & 10630.5549353409 & 170.44506465907 \tabularnewline
54 & 12950 & 10684.9583468982 & 2265.04165310183 \tabularnewline
55 & 12222 & 10739.3617584554 & 1482.63824154458 \tabularnewline
56 & 12246 & 10793.7651700127 & 1452.23482998734 \tabularnewline
57 & 13281 & 10848.1685815699 & 2432.8314184301 \tabularnewline
58 & 10366 & 10902.5719931271 & -536.571993127148 \tabularnewline
59 & 8730 & 10956.9754046844 & -2226.97540468439 \tabularnewline
60 & 9614 & 11011.3788162416 & -1397.37881624163 \tabularnewline
61 & 8639 & 11065.7822277989 & -2426.78222779888 \tabularnewline
62 & 8772 & 11120.1856393561 & -2348.18563935612 \tabularnewline
63 & 10894 & 11174.5890509134 & -280.589050913366 \tabularnewline
64 & 10455 & 11228.9924624706 & -773.99246247061 \tabularnewline
65 & 11179 & 11283.3958740279 & -104.395874027853 \tabularnewline
66 & 10588 & 11337.7992855851 & -749.799285585097 \tabularnewline
67 & 10794 & 11392.2026971423 & -598.20269714234 \tabularnewline
68 & 12770 & 11446.6061086996 & 1323.39389130042 \tabularnewline
69 & 13812 & 11501.0095202568 & 2310.99047974317 \tabularnewline
70 & 10857 & 11555.4129318141 & -698.412931814071 \tabularnewline
71 & 9290 & 11609.8163433713 & -2319.81634337131 \tabularnewline
72 & 10925 & 11664.2197549286 & -739.219754928558 \tabularnewline
73 & 9491 & 11718.6231664858 & -2227.6231664858 \tabularnewline
74 & 8919 & 11773.026578043 & -2854.02657804305 \tabularnewline
75 & 11607 & 11827.4299896003 & -220.429989600289 \tabularnewline
76 & 8852 & 11881.8334011575 & -3029.83340115753 \tabularnewline
77 & 12537 & 11936.2368127148 & 600.763187285223 \tabularnewline
78 & 14759 & 11990.640224272 & 2768.35977572798 \tabularnewline
79 & 13667 & 12045.0436358293 & 1621.95636417074 \tabularnewline
80 & 13731 & 12099.4470473865 & 1631.55295261349 \tabularnewline
81 & 15110 & 12153.8504589438 & 2956.14954105625 \tabularnewline
82 & 12185 & 12208.253870501 & -23.253870500995 \tabularnewline
83 & 10645 & 12262.6572820582 & -1617.65728205824 \tabularnewline
84 & 12161 & 12317.0606936155 & -156.060693615482 \tabularnewline
85 & 10840 & 12371.4641051727 & -1531.46410517273 \tabularnewline
86 & 10436 & 12425.86751673 & -1989.86751672997 \tabularnewline
87 & 13589 & 12480.2709282872 & 1108.72907171279 \tabularnewline
88 & 13402 & 12534.6743398445 & 867.325660155543 \tabularnewline
89 & 13103 & 12589.0777514017 & 513.9222485983 \tabularnewline
90 & 14933 & 12643.4811629589 & 2289.51883704106 \tabularnewline
91 & 14147 & 12697.8845745162 & 1449.11542548381 \tabularnewline
92 & 14057 & 12752.2879860734 & 1304.71201392657 \tabularnewline
93 & 16234 & 12806.6913976307 & 3427.30860236933 \tabularnewline
94 & 12389 & 12861.0948091879 & -472.094809187919 \tabularnewline
95 & 11595 & 12915.4982207452 & -1320.49822074516 \tabularnewline
96 & 12772 & 12969.9016323024 & -197.901632302406 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147397&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]6827[/C][C]7801.57753436426[/C][C]-974.577534364261[/C][/ROW]
[ROW][C]2[/C][C]6178[/C][C]7855.9809459215[/C][C]-1677.9809459215[/C][/ROW]
[ROW][C]3[/C][C]7084[/C][C]7910.38435747875[/C][C]-826.384357478748[/C][/ROW]
[ROW][C]4[/C][C]8162[/C][C]7964.78776903599[/C][C]197.212230964008[/C][/ROW]
[ROW][C]5[/C][C]8462[/C][C]8019.19118059324[/C][C]442.808819406764[/C][/ROW]
[ROW][C]6[/C][C]9644[/C][C]8073.59459215048[/C][C]1570.40540784952[/C][/ROW]
[ROW][C]7[/C][C]10466[/C][C]8127.99800370772[/C][C]2338.00199629228[/C][/ROW]
[ROW][C]8[/C][C]10748[/C][C]8182.40141526497[/C][C]2565.59858473503[/C][/ROW]
[ROW][C]9[/C][C]9963[/C][C]8236.80482682221[/C][C]1726.19517317779[/C][/ROW]
[ROW][C]10[/C][C]8194[/C][C]8291.20823837945[/C][C]-97.2082383794536[/C][/ROW]
[ROW][C]11[/C][C]6848[/C][C]8345.6116499367[/C][C]-1497.6116499367[/C][/ROW]
[ROW][C]12[/C][C]7027[/C][C]8400.01506149394[/C][C]-1373.01506149394[/C][/ROW]
[ROW][C]13[/C][C]7269[/C][C]8454.41847305118[/C][C]-1185.41847305118[/C][/ROW]
[ROW][C]14[/C][C]6775[/C][C]8508.82188460843[/C][C]-1733.82188460843[/C][/ROW]
[ROW][C]15[/C][C]7819[/C][C]8563.22529616567[/C][C]-744.225296165672[/C][/ROW]
[ROW][C]16[/C][C]8371[/C][C]8617.62870772292[/C][C]-246.628707722916[/C][/ROW]
[ROW][C]17[/C][C]9069[/C][C]8672.03211928016[/C][C]396.967880719841[/C][/ROW]
[ROW][C]18[/C][C]10248[/C][C]8726.4355308374[/C][C]1521.5644691626[/C][/ROW]
[ROW][C]19[/C][C]11030[/C][C]8780.83894239465[/C][C]2249.16105760535[/C][/ROW]
[ROW][C]20[/C][C]10882[/C][C]8835.24235395189[/C][C]2046.75764604811[/C][/ROW]
[ROW][C]21[/C][C]10333[/C][C]8889.64576550913[/C][C]1443.35423449087[/C][/ROW]
[ROW][C]22[/C][C]9109[/C][C]8944.04917706638[/C][C]164.950822933623[/C][/ROW]
[ROW][C]23[/C][C]7685[/C][C]8998.45258862362[/C][C]-1313.45258862362[/C][/ROW]
[ROW][C]24[/C][C]7602[/C][C]9052.85600018086[/C][C]-1450.85600018086[/C][/ROW]
[ROW][C]25[/C][C]8350[/C][C]9107.25941173811[/C][C]-757.259411738108[/C][/ROW]
[ROW][C]26[/C][C]7829[/C][C]9161.66282329535[/C][C]-1332.66282329535[/C][/ROW]
[ROW][C]27[/C][C]8829[/C][C]9216.06623485259[/C][C]-387.066234852595[/C][/ROW]
[ROW][C]28[/C][C]9948[/C][C]9270.46964640984[/C][C]677.530353590161[/C][/ROW]
[ROW][C]29[/C][C]10638[/C][C]9324.87305796708[/C][C]1313.12694203292[/C][/ROW]
[ROW][C]30[/C][C]11253[/C][C]9379.27646952433[/C][C]1873.72353047567[/C][/ROW]
[ROW][C]31[/C][C]11424[/C][C]9433.67988108157[/C][C]1990.32011891843[/C][/ROW]
[ROW][C]32[/C][C]11391[/C][C]9488.08329263881[/C][C]1902.91670736119[/C][/ROW]
[ROW][C]33[/C][C]10665[/C][C]9542.48670419606[/C][C]1122.51329580394[/C][/ROW]
[ROW][C]34[/C][C]9396[/C][C]9596.8901157533[/C][C]-200.890115753301[/C][/ROW]
[ROW][C]35[/C][C]7775[/C][C]9651.29352731054[/C][C]-1876.29352731054[/C][/ROW]
[ROW][C]36[/C][C]7933[/C][C]9705.69693886779[/C][C]-1772.69693886779[/C][/ROW]
[ROW][C]37[/C][C]8186[/C][C]9760.10035042503[/C][C]-1574.10035042503[/C][/ROW]
[ROW][C]38[/C][C]7444[/C][C]9814.50376198228[/C][C]-2370.50376198228[/C][/ROW]
[ROW][C]39[/C][C]8484[/C][C]9868.90717353952[/C][C]-1384.90717353952[/C][/ROW]
[ROW][C]40[/C][C]9864[/C][C]9923.31058509676[/C][C]-59.3105850967625[/C][/ROW]
[ROW][C]41[/C][C]10252[/C][C]9977.71399665401[/C][C]274.286003345994[/C][/ROW]
[ROW][C]42[/C][C]12282[/C][C]10032.1174082113[/C][C]2249.88259178875[/C][/ROW]
[ROW][C]43[/C][C]11637[/C][C]10086.5208197685[/C][C]1550.47918023151[/C][/ROW]
[ROW][C]44[/C][C]11577[/C][C]10140.9242313257[/C][C]1436.07576867426[/C][/ROW]
[ROW][C]45[/C][C]12417[/C][C]10195.327642883[/C][C]2221.67235711702[/C][/ROW]
[ROW][C]46[/C][C]9637[/C][C]10249.7310544402[/C][C]-612.731054440224[/C][/ROW]
[ROW][C]47[/C][C]8094[/C][C]10304.1344659975[/C][C]-2210.13446599747[/C][/ROW]
[ROW][C]48[/C][C]9280[/C][C]10358.5378775547[/C][C]-1078.53787755471[/C][/ROW]
[ROW][C]49[/C][C]8334[/C][C]10412.941289112[/C][C]-2078.94128911196[/C][/ROW]
[ROW][C]50[/C][C]7899[/C][C]10467.3447006692[/C][C]-2568.3447006692[/C][/ROW]
[ROW][C]51[/C][C]9994[/C][C]10521.7481122264[/C][C]-527.748112226442[/C][/ROW]
[ROW][C]52[/C][C]10078[/C][C]10576.1515237837[/C][C]-498.151523783686[/C][/ROW]
[ROW][C]53[/C][C]10801[/C][C]10630.5549353409[/C][C]170.44506465907[/C][/ROW]
[ROW][C]54[/C][C]12950[/C][C]10684.9583468982[/C][C]2265.04165310183[/C][/ROW]
[ROW][C]55[/C][C]12222[/C][C]10739.3617584554[/C][C]1482.63824154458[/C][/ROW]
[ROW][C]56[/C][C]12246[/C][C]10793.7651700127[/C][C]1452.23482998734[/C][/ROW]
[ROW][C]57[/C][C]13281[/C][C]10848.1685815699[/C][C]2432.8314184301[/C][/ROW]
[ROW][C]58[/C][C]10366[/C][C]10902.5719931271[/C][C]-536.571993127148[/C][/ROW]
[ROW][C]59[/C][C]8730[/C][C]10956.9754046844[/C][C]-2226.97540468439[/C][/ROW]
[ROW][C]60[/C][C]9614[/C][C]11011.3788162416[/C][C]-1397.37881624163[/C][/ROW]
[ROW][C]61[/C][C]8639[/C][C]11065.7822277989[/C][C]-2426.78222779888[/C][/ROW]
[ROW][C]62[/C][C]8772[/C][C]11120.1856393561[/C][C]-2348.18563935612[/C][/ROW]
[ROW][C]63[/C][C]10894[/C][C]11174.5890509134[/C][C]-280.589050913366[/C][/ROW]
[ROW][C]64[/C][C]10455[/C][C]11228.9924624706[/C][C]-773.99246247061[/C][/ROW]
[ROW][C]65[/C][C]11179[/C][C]11283.3958740279[/C][C]-104.395874027853[/C][/ROW]
[ROW][C]66[/C][C]10588[/C][C]11337.7992855851[/C][C]-749.799285585097[/C][/ROW]
[ROW][C]67[/C][C]10794[/C][C]11392.2026971423[/C][C]-598.20269714234[/C][/ROW]
[ROW][C]68[/C][C]12770[/C][C]11446.6061086996[/C][C]1323.39389130042[/C][/ROW]
[ROW][C]69[/C][C]13812[/C][C]11501.0095202568[/C][C]2310.99047974317[/C][/ROW]
[ROW][C]70[/C][C]10857[/C][C]11555.4129318141[/C][C]-698.412931814071[/C][/ROW]
[ROW][C]71[/C][C]9290[/C][C]11609.8163433713[/C][C]-2319.81634337131[/C][/ROW]
[ROW][C]72[/C][C]10925[/C][C]11664.2197549286[/C][C]-739.219754928558[/C][/ROW]
[ROW][C]73[/C][C]9491[/C][C]11718.6231664858[/C][C]-2227.6231664858[/C][/ROW]
[ROW][C]74[/C][C]8919[/C][C]11773.026578043[/C][C]-2854.02657804305[/C][/ROW]
[ROW][C]75[/C][C]11607[/C][C]11827.4299896003[/C][C]-220.429989600289[/C][/ROW]
[ROW][C]76[/C][C]8852[/C][C]11881.8334011575[/C][C]-3029.83340115753[/C][/ROW]
[ROW][C]77[/C][C]12537[/C][C]11936.2368127148[/C][C]600.763187285223[/C][/ROW]
[ROW][C]78[/C][C]14759[/C][C]11990.640224272[/C][C]2768.35977572798[/C][/ROW]
[ROW][C]79[/C][C]13667[/C][C]12045.0436358293[/C][C]1621.95636417074[/C][/ROW]
[ROW][C]80[/C][C]13731[/C][C]12099.4470473865[/C][C]1631.55295261349[/C][/ROW]
[ROW][C]81[/C][C]15110[/C][C]12153.8504589438[/C][C]2956.14954105625[/C][/ROW]
[ROW][C]82[/C][C]12185[/C][C]12208.253870501[/C][C]-23.253870500995[/C][/ROW]
[ROW][C]83[/C][C]10645[/C][C]12262.6572820582[/C][C]-1617.65728205824[/C][/ROW]
[ROW][C]84[/C][C]12161[/C][C]12317.0606936155[/C][C]-156.060693615482[/C][/ROW]
[ROW][C]85[/C][C]10840[/C][C]12371.4641051727[/C][C]-1531.46410517273[/C][/ROW]
[ROW][C]86[/C][C]10436[/C][C]12425.86751673[/C][C]-1989.86751672997[/C][/ROW]
[ROW][C]87[/C][C]13589[/C][C]12480.2709282872[/C][C]1108.72907171279[/C][/ROW]
[ROW][C]88[/C][C]13402[/C][C]12534.6743398445[/C][C]867.325660155543[/C][/ROW]
[ROW][C]89[/C][C]13103[/C][C]12589.0777514017[/C][C]513.9222485983[/C][/ROW]
[ROW][C]90[/C][C]14933[/C][C]12643.4811629589[/C][C]2289.51883704106[/C][/ROW]
[ROW][C]91[/C][C]14147[/C][C]12697.8845745162[/C][C]1449.11542548381[/C][/ROW]
[ROW][C]92[/C][C]14057[/C][C]12752.2879860734[/C][C]1304.71201392657[/C][/ROW]
[ROW][C]93[/C][C]16234[/C][C]12806.6913976307[/C][C]3427.30860236933[/C][/ROW]
[ROW][C]94[/C][C]12389[/C][C]12861.0948091879[/C][C]-472.094809187919[/C][/ROW]
[ROW][C]95[/C][C]11595[/C][C]12915.4982207452[/C][C]-1320.49822074516[/C][/ROW]
[ROW][C]96[/C][C]12772[/C][C]12969.9016323024[/C][C]-197.901632302406[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147397&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147397&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 Index Actuals InterpolationForecast ResidualsPrediction Error 1 6827 7801.57753436426 -974.577534364261 2 6178 7855.9809459215 -1677.9809459215 3 7084 7910.38435747875 -826.384357478748 4 8162 7964.78776903599 197.212230964008 5 8462 8019.19118059324 442.808819406764 6 9644 8073.59459215048 1570.40540784952 7 10466 8127.99800370772 2338.00199629228 8 10748 8182.40141526497 2565.59858473503 9 9963 8236.80482682221 1726.19517317779 10 8194 8291.20823837945 -97.2082383794536 11 6848 8345.6116499367 -1497.6116499367 12 7027 8400.01506149394 -1373.01506149394 13 7269 8454.41847305118 -1185.41847305118 14 6775 8508.82188460843 -1733.82188460843 15 7819 8563.22529616567 -744.225296165672 16 8371 8617.62870772292 -246.628707722916 17 9069 8672.03211928016 396.967880719841 18 10248 8726.4355308374 1521.5644691626 19 11030 8780.83894239465 2249.16105760535 20 10882 8835.24235395189 2046.75764604811 21 10333 8889.64576550913 1443.35423449087 22 9109 8944.04917706638 164.950822933623 23 7685 8998.45258862362 -1313.45258862362 24 7602 9052.85600018086 -1450.85600018086 25 8350 9107.25941173811 -757.259411738108 26 7829 9161.66282329535 -1332.66282329535 27 8829 9216.06623485259 -387.066234852595 28 9948 9270.46964640984 677.530353590161 29 10638 9324.87305796708 1313.12694203292 30 11253 9379.27646952433 1873.72353047567 31 11424 9433.67988108157 1990.32011891843 32 11391 9488.08329263881 1902.91670736119 33 10665 9542.48670419606 1122.51329580394 34 9396 9596.8901157533 -200.890115753301 35 7775 9651.29352731054 -1876.29352731054 36 7933 9705.69693886779 -1772.69693886779 37 8186 9760.10035042503 -1574.10035042503 38 7444 9814.50376198228 -2370.50376198228 39 8484 9868.90717353952 -1384.90717353952 40 9864 9923.31058509676 -59.3105850967625 41 10252 9977.71399665401 274.286003345994 42 12282 10032.1174082113 2249.88259178875 43 11637 10086.5208197685 1550.47918023151 44 11577 10140.9242313257 1436.07576867426 45 12417 10195.327642883 2221.67235711702 46 9637 10249.7310544402 -612.731054440224 47 8094 10304.1344659975 -2210.13446599747 48 9280 10358.5378775547 -1078.53787755471 49 8334 10412.941289112 -2078.94128911196 50 7899 10467.3447006692 -2568.3447006692 51 9994 10521.7481122264 -527.748112226442 52 10078 10576.1515237837 -498.151523783686 53 10801 10630.5549353409 170.44506465907 54 12950 10684.9583468982 2265.04165310183 55 12222 10739.3617584554 1482.63824154458 56 12246 10793.7651700127 1452.23482998734 57 13281 10848.1685815699 2432.8314184301 58 10366 10902.5719931271 -536.571993127148 59 8730 10956.9754046844 -2226.97540468439 60 9614 11011.3788162416 -1397.37881624163 61 8639 11065.7822277989 -2426.78222779888 62 8772 11120.1856393561 -2348.18563935612 63 10894 11174.5890509134 -280.589050913366 64 10455 11228.9924624706 -773.99246247061 65 11179 11283.3958740279 -104.395874027853 66 10588 11337.7992855851 -749.799285585097 67 10794 11392.2026971423 -598.20269714234 68 12770 11446.6061086996 1323.39389130042 69 13812 11501.0095202568 2310.99047974317 70 10857 11555.4129318141 -698.412931814071 71 9290 11609.8163433713 -2319.81634337131 72 10925 11664.2197549286 -739.219754928558 73 9491 11718.6231664858 -2227.6231664858 74 8919 11773.026578043 -2854.02657804305 75 11607 11827.4299896003 -220.429989600289 76 8852 11881.8334011575 -3029.83340115753 77 12537 11936.2368127148 600.763187285223 78 14759 11990.640224272 2768.35977572798 79 13667 12045.0436358293 1621.95636417074 80 13731 12099.4470473865 1631.55295261349 81 15110 12153.8504589438 2956.14954105625 82 12185 12208.253870501 -23.253870500995 83 10645 12262.6572820582 -1617.65728205824 84 12161 12317.0606936155 -156.060693615482 85 10840 12371.4641051727 -1531.46410517273 86 10436 12425.86751673 -1989.86751672997 87 13589 12480.2709282872 1108.72907171279 88 13402 12534.6743398445 867.325660155543 89 13103 12589.0777514017 513.9222485983 90 14933 12643.4811629589 2289.51883704106 91 14147 12697.8845745162 1449.11542548381 92 14057 12752.2879860734 1304.71201392657 93 16234 12806.6913976307 3427.30860236933 94 12389 12861.0948091879 -472.094809187919 95 11595 12915.4982207452 -1320.49822074516 96 12772 12969.9016323024 -197.901632302406

 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 5 0.043926742623791 0.0878534852475819 0.956073257376209 6 0.0188698199364317 0.0377396398728634 0.981130180063568 7 0.00726539048489739 0.0145307809697948 0.992734609515103 8 0.00214332466506287 0.00428664933012573 0.997856675334937 9 0.00656407383728544 0.0131281476745709 0.993435926162715 10 0.126676651212481 0.253353302424963 0.873323348787519 11 0.432658608066338 0.865317216132676 0.567341391933662 12 0.516758450710488 0.966483098579024 0.483241549289512 13 0.498472142428048 0.996944284856097 0.501527857571952 14 0.49467173883747 0.989343477674939 0.50532826116253 15 0.411481923477049 0.822963846954098 0.588518076522951 16 0.331935248994143 0.663870497988286 0.668064751005857 17 0.276263899157198 0.552527798314395 0.723736100842802 18 0.285473298166612 0.570946596333225 0.714526701833388 19 0.334896057917292 0.669792115834583 0.665103942082709 20 0.334394435952379 0.668788871904757 0.665605564047622 21 0.288071886270787 0.576143772541575 0.711928113729213 22 0.240266479393191 0.480532958786382 0.759733520606809 23 0.26855915357753 0.537118307155059 0.73144084642247 24 0.283219940796793 0.566439881593586 0.716780059203207 25 0.243460828783648 0.486921657567295 0.756539171216352 26 0.226787673239283 0.453575346478566 0.773212326760717 27 0.179394040840351 0.358788081680702 0.820605959159649 28 0.147779202009686 0.295558404019373 0.852220797990314 29 0.137354012697483 0.274708025394966 0.862645987302517 30 0.1483713774838 0.2967427549676 0.8516286225162 31 0.160660966328193 0.321321932656386 0.839339033671807 32 0.165579801187604 0.331159602375208 0.834420198812396 33 0.142451149907204 0.284902299814409 0.857548850092796 34 0.119943761550762 0.239887523101524 0.880056238449238 35 0.156093899729964 0.312187799459927 0.843906100270036 36 0.174385074579698 0.348770149159396 0.825614925420302 37 0.172821517115761 0.345643034231522 0.827178482884239 38 0.211057368346473 0.422114736692946 0.788942631653527 39 0.188885267633867 0.377770535267733 0.811114732366133 40 0.151143509354633 0.302287018709267 0.848856490645367 41 0.12202103445838 0.24404206891676 0.87797896554162 42 0.172192766261948 0.344385532523896 0.827807233738052 43 0.180595415859277 0.361190831718553 0.819404584140723 44 0.183720167973407 0.367440335946814 0.816279832026593 45 0.244545580500471 0.489091161000941 0.755454419499529 46 0.209975642337825 0.419951284675649 0.790024357662175 47 0.237707430711422 0.475414861422844 0.762292569288578 48 0.206481667157446 0.412963334314893 0.793518332842553 49 0.214739341287004 0.429478682574009 0.785260658712996 50 0.253063130579655 0.506126261159309 0.746936869420345 51 0.20821084828965 0.4164216965793 0.79178915171035 52 0.168305613352613 0.336611226705226 0.831694386647387 53 0.137353067150547 0.274706134301094 0.862646932849453 54 0.201066290565621 0.402132581131243 0.798933709434379 55 0.220031395753454 0.440062791506908 0.779968604246546 56 0.244367717197299 0.488735434394598 0.755632282802701 57 0.39258294787539 0.78516589575078 0.60741705212461 58 0.348069551638915 0.69613910327783 0.651930448361085 59 0.352173846793654 0.704347693587309 0.647826153206346 60 0.312976430103921 0.625952860207842 0.687023569896079 61 0.32691858564132 0.653837171282641 0.67308141435868 62 0.337675372819643 0.675350745639286 0.662324627180357 63 0.284424957833506 0.568849915667012 0.715575042166494 64 0.234911782937247 0.469823565874494 0.765088217062753 65 0.192263313091167 0.384526626182335 0.807736686908833 66 0.152742211121819 0.305484422243637 0.847257788878181 67 0.118305509574019 0.236611019148039 0.881694490425981 68 0.126155960321835 0.252311920643669 0.873844039678165 69 0.222503771488155 0.445007542976309 0.777496228511846 70 0.178601850703832 0.357203701407664 0.821398149296168 71 0.177427448118138 0.354854896236276 0.822572551881862 72 0.137299264964063 0.274598529928126 0.862700735035937 73 0.141342132226965 0.282684264453929 0.858657867773035 74 0.214484455336785 0.428968910673571 0.785515544663215 75 0.170497204055269 0.340994408110537 0.829502795944731 76 0.38155380274963 0.763107605499261 0.61844619725037 77 0.328040744933511 0.656081489867021 0.671959255066489 78 0.395091421291152 0.790182842582305 0.604908578708848 79 0.36789684835855 0.7357936967171 0.63210315164145 80 0.352117138964509 0.704234277929018 0.647882861035491 81 0.586832143678101 0.826335712643799 0.413167856321899 82 0.508968324532214 0.982063350935571 0.491031675467786 83 0.471277685514511 0.942555371029022 0.528722314485489 84 0.378718281528865 0.75743656305773 0.621281718471135 85 0.390200779836658 0.780401559673316 0.609799220163342 86 0.679282614716026 0.641434770567948 0.320717385283974 87 0.599489369668389 0.801021260663223 0.400510630331611 88 0.543404848134763 0.913190303730474 0.456595151865237 89 0.61125808672073 0.77748382655854 0.38874191327927 90 0.483127245637102 0.966254491274204 0.516872754362898 91 0.379648283154382 0.759296566308763 0.620351716845619

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
5 & 0.043926742623791 & 0.0878534852475819 & 0.956073257376209 \tabularnewline
6 & 0.0188698199364317 & 0.0377396398728634 & 0.981130180063568 \tabularnewline
7 & 0.00726539048489739 & 0.0145307809697948 & 0.992734609515103 \tabularnewline
8 & 0.00214332466506287 & 0.00428664933012573 & 0.997856675334937 \tabularnewline
9 & 0.00656407383728544 & 0.0131281476745709 & 0.993435926162715 \tabularnewline
10 & 0.126676651212481 & 0.253353302424963 & 0.873323348787519 \tabularnewline
11 & 0.432658608066338 & 0.865317216132676 & 0.567341391933662 \tabularnewline
12 & 0.516758450710488 & 0.966483098579024 & 0.483241549289512 \tabularnewline
13 & 0.498472142428048 & 0.996944284856097 & 0.501527857571952 \tabularnewline
14 & 0.49467173883747 & 0.989343477674939 & 0.50532826116253 \tabularnewline
15 & 0.411481923477049 & 0.822963846954098 & 0.588518076522951 \tabularnewline
16 & 0.331935248994143 & 0.663870497988286 & 0.668064751005857 \tabularnewline
17 & 0.276263899157198 & 0.552527798314395 & 0.723736100842802 \tabularnewline
18 & 0.285473298166612 & 0.570946596333225 & 0.714526701833388 \tabularnewline
19 & 0.334896057917292 & 0.669792115834583 & 0.665103942082709 \tabularnewline
20 & 0.334394435952379 & 0.668788871904757 & 0.665605564047622 \tabularnewline
21 & 0.288071886270787 & 0.576143772541575 & 0.711928113729213 \tabularnewline
22 & 0.240266479393191 & 0.480532958786382 & 0.759733520606809 \tabularnewline
23 & 0.26855915357753 & 0.537118307155059 & 0.73144084642247 \tabularnewline
24 & 0.283219940796793 & 0.566439881593586 & 0.716780059203207 \tabularnewline
25 & 0.243460828783648 & 0.486921657567295 & 0.756539171216352 \tabularnewline
26 & 0.226787673239283 & 0.453575346478566 & 0.773212326760717 \tabularnewline
27 & 0.179394040840351 & 0.358788081680702 & 0.820605959159649 \tabularnewline
28 & 0.147779202009686 & 0.295558404019373 & 0.852220797990314 \tabularnewline
29 & 0.137354012697483 & 0.274708025394966 & 0.862645987302517 \tabularnewline
30 & 0.1483713774838 & 0.2967427549676 & 0.8516286225162 \tabularnewline
31 & 0.160660966328193 & 0.321321932656386 & 0.839339033671807 \tabularnewline
32 & 0.165579801187604 & 0.331159602375208 & 0.834420198812396 \tabularnewline
33 & 0.142451149907204 & 0.284902299814409 & 0.857548850092796 \tabularnewline
34 & 0.119943761550762 & 0.239887523101524 & 0.880056238449238 \tabularnewline
35 & 0.156093899729964 & 0.312187799459927 & 0.843906100270036 \tabularnewline
36 & 0.174385074579698 & 0.348770149159396 & 0.825614925420302 \tabularnewline
37 & 0.172821517115761 & 0.345643034231522 & 0.827178482884239 \tabularnewline
38 & 0.211057368346473 & 0.422114736692946 & 0.788942631653527 \tabularnewline
39 & 0.188885267633867 & 0.377770535267733 & 0.811114732366133 \tabularnewline
40 & 0.151143509354633 & 0.302287018709267 & 0.848856490645367 \tabularnewline
41 & 0.12202103445838 & 0.24404206891676 & 0.87797896554162 \tabularnewline
42 & 0.172192766261948 & 0.344385532523896 & 0.827807233738052 \tabularnewline
43 & 0.180595415859277 & 0.361190831718553 & 0.819404584140723 \tabularnewline
44 & 0.183720167973407 & 0.367440335946814 & 0.816279832026593 \tabularnewline
45 & 0.244545580500471 & 0.489091161000941 & 0.755454419499529 \tabularnewline
46 & 0.209975642337825 & 0.419951284675649 & 0.790024357662175 \tabularnewline
47 & 0.237707430711422 & 0.475414861422844 & 0.762292569288578 \tabularnewline
48 & 0.206481667157446 & 0.412963334314893 & 0.793518332842553 \tabularnewline
49 & 0.214739341287004 & 0.429478682574009 & 0.785260658712996 \tabularnewline
50 & 0.253063130579655 & 0.506126261159309 & 0.746936869420345 \tabularnewline
51 & 0.20821084828965 & 0.4164216965793 & 0.79178915171035 \tabularnewline
52 & 0.168305613352613 & 0.336611226705226 & 0.831694386647387 \tabularnewline
53 & 0.137353067150547 & 0.274706134301094 & 0.862646932849453 \tabularnewline
54 & 0.201066290565621 & 0.402132581131243 & 0.798933709434379 \tabularnewline
55 & 0.220031395753454 & 0.440062791506908 & 0.779968604246546 \tabularnewline
56 & 0.244367717197299 & 0.488735434394598 & 0.755632282802701 \tabularnewline
57 & 0.39258294787539 & 0.78516589575078 & 0.60741705212461 \tabularnewline
58 & 0.348069551638915 & 0.69613910327783 & 0.651930448361085 \tabularnewline
59 & 0.352173846793654 & 0.704347693587309 & 0.647826153206346 \tabularnewline
60 & 0.312976430103921 & 0.625952860207842 & 0.687023569896079 \tabularnewline
61 & 0.32691858564132 & 0.653837171282641 & 0.67308141435868 \tabularnewline
62 & 0.337675372819643 & 0.675350745639286 & 0.662324627180357 \tabularnewline
63 & 0.284424957833506 & 0.568849915667012 & 0.715575042166494 \tabularnewline
64 & 0.234911782937247 & 0.469823565874494 & 0.765088217062753 \tabularnewline
65 & 0.192263313091167 & 0.384526626182335 & 0.807736686908833 \tabularnewline
66 & 0.152742211121819 & 0.305484422243637 & 0.847257788878181 \tabularnewline
67 & 0.118305509574019 & 0.236611019148039 & 0.881694490425981 \tabularnewline
68 & 0.126155960321835 & 0.252311920643669 & 0.873844039678165 \tabularnewline
69 & 0.222503771488155 & 0.445007542976309 & 0.777496228511846 \tabularnewline
70 & 0.178601850703832 & 0.357203701407664 & 0.821398149296168 \tabularnewline
71 & 0.177427448118138 & 0.354854896236276 & 0.822572551881862 \tabularnewline
72 & 0.137299264964063 & 0.274598529928126 & 0.862700735035937 \tabularnewline
73 & 0.141342132226965 & 0.282684264453929 & 0.858657867773035 \tabularnewline
74 & 0.214484455336785 & 0.428968910673571 & 0.785515544663215 \tabularnewline
75 & 0.170497204055269 & 0.340994408110537 & 0.829502795944731 \tabularnewline
76 & 0.38155380274963 & 0.763107605499261 & 0.61844619725037 \tabularnewline
77 & 0.328040744933511 & 0.656081489867021 & 0.671959255066489 \tabularnewline
78 & 0.395091421291152 & 0.790182842582305 & 0.604908578708848 \tabularnewline
79 & 0.36789684835855 & 0.7357936967171 & 0.63210315164145 \tabularnewline
80 & 0.352117138964509 & 0.704234277929018 & 0.647882861035491 \tabularnewline
81 & 0.586832143678101 & 0.826335712643799 & 0.413167856321899 \tabularnewline
82 & 0.508968324532214 & 0.982063350935571 & 0.491031675467786 \tabularnewline
83 & 0.471277685514511 & 0.942555371029022 & 0.528722314485489 \tabularnewline
84 & 0.378718281528865 & 0.75743656305773 & 0.621281718471135 \tabularnewline
85 & 0.390200779836658 & 0.780401559673316 & 0.609799220163342 \tabularnewline
86 & 0.679282614716026 & 0.641434770567948 & 0.320717385283974 \tabularnewline
87 & 0.599489369668389 & 0.801021260663223 & 0.400510630331611 \tabularnewline
88 & 0.543404848134763 & 0.913190303730474 & 0.456595151865237 \tabularnewline
89 & 0.61125808672073 & 0.77748382655854 & 0.38874191327927 \tabularnewline
90 & 0.483127245637102 & 0.966254491274204 & 0.516872754362898 \tabularnewline
91 & 0.379648283154382 & 0.759296566308763 & 0.620351716845619 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147397&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]5[/C][C]0.043926742623791[/C][C]0.0878534852475819[/C][C]0.956073257376209[/C][/ROW]
[ROW][C]6[/C][C]0.0188698199364317[/C][C]0.0377396398728634[/C][C]0.981130180063568[/C][/ROW]
[ROW][C]7[/C][C]0.00726539048489739[/C][C]0.0145307809697948[/C][C]0.992734609515103[/C][/ROW]
[ROW][C]8[/C][C]0.00214332466506287[/C][C]0.00428664933012573[/C][C]0.997856675334937[/C][/ROW]
[ROW][C]9[/C][C]0.00656407383728544[/C][C]0.0131281476745709[/C][C]0.993435926162715[/C][/ROW]
[ROW][C]10[/C][C]0.126676651212481[/C][C]0.253353302424963[/C][C]0.873323348787519[/C][/ROW]
[ROW][C]11[/C][C]0.432658608066338[/C][C]0.865317216132676[/C][C]0.567341391933662[/C][/ROW]
[ROW][C]12[/C][C]0.516758450710488[/C][C]0.966483098579024[/C][C]0.483241549289512[/C][/ROW]
[ROW][C]13[/C][C]0.498472142428048[/C][C]0.996944284856097[/C][C]0.501527857571952[/C][/ROW]
[ROW][C]14[/C][C]0.49467173883747[/C][C]0.989343477674939[/C][C]0.50532826116253[/C][/ROW]
[ROW][C]15[/C][C]0.411481923477049[/C][C]0.822963846954098[/C][C]0.588518076522951[/C][/ROW]
[ROW][C]16[/C][C]0.331935248994143[/C][C]0.663870497988286[/C][C]0.668064751005857[/C][/ROW]
[ROW][C]17[/C][C]0.276263899157198[/C][C]0.552527798314395[/C][C]0.723736100842802[/C][/ROW]
[ROW][C]18[/C][C]0.285473298166612[/C][C]0.570946596333225[/C][C]0.714526701833388[/C][/ROW]
[ROW][C]19[/C][C]0.334896057917292[/C][C]0.669792115834583[/C][C]0.665103942082709[/C][/ROW]
[ROW][C]20[/C][C]0.334394435952379[/C][C]0.668788871904757[/C][C]0.665605564047622[/C][/ROW]
[ROW][C]21[/C][C]0.288071886270787[/C][C]0.576143772541575[/C][C]0.711928113729213[/C][/ROW]
[ROW][C]22[/C][C]0.240266479393191[/C][C]0.480532958786382[/C][C]0.759733520606809[/C][/ROW]
[ROW][C]23[/C][C]0.26855915357753[/C][C]0.537118307155059[/C][C]0.73144084642247[/C][/ROW]
[ROW][C]24[/C][C]0.283219940796793[/C][C]0.566439881593586[/C][C]0.716780059203207[/C][/ROW]
[ROW][C]25[/C][C]0.243460828783648[/C][C]0.486921657567295[/C][C]0.756539171216352[/C][/ROW]
[ROW][C]26[/C][C]0.226787673239283[/C][C]0.453575346478566[/C][C]0.773212326760717[/C][/ROW]
[ROW][C]27[/C][C]0.179394040840351[/C][C]0.358788081680702[/C][C]0.820605959159649[/C][/ROW]
[ROW][C]28[/C][C]0.147779202009686[/C][C]0.295558404019373[/C][C]0.852220797990314[/C][/ROW]
[ROW][C]29[/C][C]0.137354012697483[/C][C]0.274708025394966[/C][C]0.862645987302517[/C][/ROW]
[ROW][C]30[/C][C]0.1483713774838[/C][C]0.2967427549676[/C][C]0.8516286225162[/C][/ROW]
[ROW][C]31[/C][C]0.160660966328193[/C][C]0.321321932656386[/C][C]0.839339033671807[/C][/ROW]
[ROW][C]32[/C][C]0.165579801187604[/C][C]0.331159602375208[/C][C]0.834420198812396[/C][/ROW]
[ROW][C]33[/C][C]0.142451149907204[/C][C]0.284902299814409[/C][C]0.857548850092796[/C][/ROW]
[ROW][C]34[/C][C]0.119943761550762[/C][C]0.239887523101524[/C][C]0.880056238449238[/C][/ROW]
[ROW][C]35[/C][C]0.156093899729964[/C][C]0.312187799459927[/C][C]0.843906100270036[/C][/ROW]
[ROW][C]36[/C][C]0.174385074579698[/C][C]0.348770149159396[/C][C]0.825614925420302[/C][/ROW]
[ROW][C]37[/C][C]0.172821517115761[/C][C]0.345643034231522[/C][C]0.827178482884239[/C][/ROW]
[ROW][C]38[/C][C]0.211057368346473[/C][C]0.422114736692946[/C][C]0.788942631653527[/C][/ROW]
[ROW][C]39[/C][C]0.188885267633867[/C][C]0.377770535267733[/C][C]0.811114732366133[/C][/ROW]
[ROW][C]40[/C][C]0.151143509354633[/C][C]0.302287018709267[/C][C]0.848856490645367[/C][/ROW]
[ROW][C]41[/C][C]0.12202103445838[/C][C]0.24404206891676[/C][C]0.87797896554162[/C][/ROW]
[ROW][C]42[/C][C]0.172192766261948[/C][C]0.344385532523896[/C][C]0.827807233738052[/C][/ROW]
[ROW][C]43[/C][C]0.180595415859277[/C][C]0.361190831718553[/C][C]0.819404584140723[/C][/ROW]
[ROW][C]44[/C][C]0.183720167973407[/C][C]0.367440335946814[/C][C]0.816279832026593[/C][/ROW]
[ROW][C]45[/C][C]0.244545580500471[/C][C]0.489091161000941[/C][C]0.755454419499529[/C][/ROW]
[ROW][C]46[/C][C]0.209975642337825[/C][C]0.419951284675649[/C][C]0.790024357662175[/C][/ROW]
[ROW][C]47[/C][C]0.237707430711422[/C][C]0.475414861422844[/C][C]0.762292569288578[/C][/ROW]
[ROW][C]48[/C][C]0.206481667157446[/C][C]0.412963334314893[/C][C]0.793518332842553[/C][/ROW]
[ROW][C]49[/C][C]0.214739341287004[/C][C]0.429478682574009[/C][C]0.785260658712996[/C][/ROW]
[ROW][C]50[/C][C]0.253063130579655[/C][C]0.506126261159309[/C][C]0.746936869420345[/C][/ROW]
[ROW][C]51[/C][C]0.20821084828965[/C][C]0.4164216965793[/C][C]0.79178915171035[/C][/ROW]
[ROW][C]52[/C][C]0.168305613352613[/C][C]0.336611226705226[/C][C]0.831694386647387[/C][/ROW]
[ROW][C]53[/C][C]0.137353067150547[/C][C]0.274706134301094[/C][C]0.862646932849453[/C][/ROW]
[ROW][C]54[/C][C]0.201066290565621[/C][C]0.402132581131243[/C][C]0.798933709434379[/C][/ROW]
[ROW][C]55[/C][C]0.220031395753454[/C][C]0.440062791506908[/C][C]0.779968604246546[/C][/ROW]
[ROW][C]56[/C][C]0.244367717197299[/C][C]0.488735434394598[/C][C]0.755632282802701[/C][/ROW]
[ROW][C]57[/C][C]0.39258294787539[/C][C]0.78516589575078[/C][C]0.60741705212461[/C][/ROW]
[ROW][C]58[/C][C]0.348069551638915[/C][C]0.69613910327783[/C][C]0.651930448361085[/C][/ROW]
[ROW][C]59[/C][C]0.352173846793654[/C][C]0.704347693587309[/C][C]0.647826153206346[/C][/ROW]
[ROW][C]60[/C][C]0.312976430103921[/C][C]0.625952860207842[/C][C]0.687023569896079[/C][/ROW]
[ROW][C]61[/C][C]0.32691858564132[/C][C]0.653837171282641[/C][C]0.67308141435868[/C][/ROW]
[ROW][C]62[/C][C]0.337675372819643[/C][C]0.675350745639286[/C][C]0.662324627180357[/C][/ROW]
[ROW][C]63[/C][C]0.284424957833506[/C][C]0.568849915667012[/C][C]0.715575042166494[/C][/ROW]
[ROW][C]64[/C][C]0.234911782937247[/C][C]0.469823565874494[/C][C]0.765088217062753[/C][/ROW]
[ROW][C]65[/C][C]0.192263313091167[/C][C]0.384526626182335[/C][C]0.807736686908833[/C][/ROW]
[ROW][C]66[/C][C]0.152742211121819[/C][C]0.305484422243637[/C][C]0.847257788878181[/C][/ROW]
[ROW][C]67[/C][C]0.118305509574019[/C][C]0.236611019148039[/C][C]0.881694490425981[/C][/ROW]
[ROW][C]68[/C][C]0.126155960321835[/C][C]0.252311920643669[/C][C]0.873844039678165[/C][/ROW]
[ROW][C]69[/C][C]0.222503771488155[/C][C]0.445007542976309[/C][C]0.777496228511846[/C][/ROW]
[ROW][C]70[/C][C]0.178601850703832[/C][C]0.357203701407664[/C][C]0.821398149296168[/C][/ROW]
[ROW][C]71[/C][C]0.177427448118138[/C][C]0.354854896236276[/C][C]0.822572551881862[/C][/ROW]
[ROW][C]72[/C][C]0.137299264964063[/C][C]0.274598529928126[/C][C]0.862700735035937[/C][/ROW]
[ROW][C]73[/C][C]0.141342132226965[/C][C]0.282684264453929[/C][C]0.858657867773035[/C][/ROW]
[ROW][C]74[/C][C]0.214484455336785[/C][C]0.428968910673571[/C][C]0.785515544663215[/C][/ROW]
[ROW][C]75[/C][C]0.170497204055269[/C][C]0.340994408110537[/C][C]0.829502795944731[/C][/ROW]
[ROW][C]76[/C][C]0.38155380274963[/C][C]0.763107605499261[/C][C]0.61844619725037[/C][/ROW]
[ROW][C]77[/C][C]0.328040744933511[/C][C]0.656081489867021[/C][C]0.671959255066489[/C][/ROW]
[ROW][C]78[/C][C]0.395091421291152[/C][C]0.790182842582305[/C][C]0.604908578708848[/C][/ROW]
[ROW][C]79[/C][C]0.36789684835855[/C][C]0.7357936967171[/C][C]0.63210315164145[/C][/ROW]
[ROW][C]80[/C][C]0.352117138964509[/C][C]0.704234277929018[/C][C]0.647882861035491[/C][/ROW]
[ROW][C]81[/C][C]0.586832143678101[/C][C]0.826335712643799[/C][C]0.413167856321899[/C][/ROW]
[ROW][C]82[/C][C]0.508968324532214[/C][C]0.982063350935571[/C][C]0.491031675467786[/C][/ROW]
[ROW][C]83[/C][C]0.471277685514511[/C][C]0.942555371029022[/C][C]0.528722314485489[/C][/ROW]
[ROW][C]84[/C][C]0.378718281528865[/C][C]0.75743656305773[/C][C]0.621281718471135[/C][/ROW]
[ROW][C]85[/C][C]0.390200779836658[/C][C]0.780401559673316[/C][C]0.609799220163342[/C][/ROW]
[ROW][C]86[/C][C]0.679282614716026[/C][C]0.641434770567948[/C][C]0.320717385283974[/C][/ROW]
[ROW][C]87[/C][C]0.599489369668389[/C][C]0.801021260663223[/C][C]0.400510630331611[/C][/ROW]
[ROW][C]88[/C][C]0.543404848134763[/C][C]0.913190303730474[/C][C]0.456595151865237[/C][/ROW]
[ROW][C]89[/C][C]0.61125808672073[/C][C]0.77748382655854[/C][C]0.38874191327927[/C][/ROW]
[ROW][C]90[/C][C]0.483127245637102[/C][C]0.966254491274204[/C][C]0.516872754362898[/C][/ROW]
[ROW][C]91[/C][C]0.379648283154382[/C][C]0.759296566308763[/C][C]0.620351716845619[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147397&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147397&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-values Alternative Hypothesis breakpoint index greater 2-sided less 5 0.043926742623791 0.0878534852475819 0.956073257376209 6 0.0188698199364317 0.0377396398728634 0.981130180063568 7 0.00726539048489739 0.0145307809697948 0.992734609515103 8 0.00214332466506287 0.00428664933012573 0.997856675334937 9 0.00656407383728544 0.0131281476745709 0.993435926162715 10 0.126676651212481 0.253353302424963 0.873323348787519 11 0.432658608066338 0.865317216132676 0.567341391933662 12 0.516758450710488 0.966483098579024 0.483241549289512 13 0.498472142428048 0.996944284856097 0.501527857571952 14 0.49467173883747 0.989343477674939 0.50532826116253 15 0.411481923477049 0.822963846954098 0.588518076522951 16 0.331935248994143 0.663870497988286 0.668064751005857 17 0.276263899157198 0.552527798314395 0.723736100842802 18 0.285473298166612 0.570946596333225 0.714526701833388 19 0.334896057917292 0.669792115834583 0.665103942082709 20 0.334394435952379 0.668788871904757 0.665605564047622 21 0.288071886270787 0.576143772541575 0.711928113729213 22 0.240266479393191 0.480532958786382 0.759733520606809 23 0.26855915357753 0.537118307155059 0.73144084642247 24 0.283219940796793 0.566439881593586 0.716780059203207 25 0.243460828783648 0.486921657567295 0.756539171216352 26 0.226787673239283 0.453575346478566 0.773212326760717 27 0.179394040840351 0.358788081680702 0.820605959159649 28 0.147779202009686 0.295558404019373 0.852220797990314 29 0.137354012697483 0.274708025394966 0.862645987302517 30 0.1483713774838 0.2967427549676 0.8516286225162 31 0.160660966328193 0.321321932656386 0.839339033671807 32 0.165579801187604 0.331159602375208 0.834420198812396 33 0.142451149907204 0.284902299814409 0.857548850092796 34 0.119943761550762 0.239887523101524 0.880056238449238 35 0.156093899729964 0.312187799459927 0.843906100270036 36 0.174385074579698 0.348770149159396 0.825614925420302 37 0.172821517115761 0.345643034231522 0.827178482884239 38 0.211057368346473 0.422114736692946 0.788942631653527 39 0.188885267633867 0.377770535267733 0.811114732366133 40 0.151143509354633 0.302287018709267 0.848856490645367 41 0.12202103445838 0.24404206891676 0.87797896554162 42 0.172192766261948 0.344385532523896 0.827807233738052 43 0.180595415859277 0.361190831718553 0.819404584140723 44 0.183720167973407 0.367440335946814 0.816279832026593 45 0.244545580500471 0.489091161000941 0.755454419499529 46 0.209975642337825 0.419951284675649 0.790024357662175 47 0.237707430711422 0.475414861422844 0.762292569288578 48 0.206481667157446 0.412963334314893 0.793518332842553 49 0.214739341287004 0.429478682574009 0.785260658712996 50 0.253063130579655 0.506126261159309 0.746936869420345 51 0.20821084828965 0.4164216965793 0.79178915171035 52 0.168305613352613 0.336611226705226 0.831694386647387 53 0.137353067150547 0.274706134301094 0.862646932849453 54 0.201066290565621 0.402132581131243 0.798933709434379 55 0.220031395753454 0.440062791506908 0.779968604246546 56 0.244367717197299 0.488735434394598 0.755632282802701 57 0.39258294787539 0.78516589575078 0.60741705212461 58 0.348069551638915 0.69613910327783 0.651930448361085 59 0.352173846793654 0.704347693587309 0.647826153206346 60 0.312976430103921 0.625952860207842 0.687023569896079 61 0.32691858564132 0.653837171282641 0.67308141435868 62 0.337675372819643 0.675350745639286 0.662324627180357 63 0.284424957833506 0.568849915667012 0.715575042166494 64 0.234911782937247 0.469823565874494 0.765088217062753 65 0.192263313091167 0.384526626182335 0.807736686908833 66 0.152742211121819 0.305484422243637 0.847257788878181 67 0.118305509574019 0.236611019148039 0.881694490425981 68 0.126155960321835 0.252311920643669 0.873844039678165 69 0.222503771488155 0.445007542976309 0.777496228511846 70 0.178601850703832 0.357203701407664 0.821398149296168 71 0.177427448118138 0.354854896236276 0.822572551881862 72 0.137299264964063 0.274598529928126 0.862700735035937 73 0.141342132226965 0.282684264453929 0.858657867773035 74 0.214484455336785 0.428968910673571 0.785515544663215 75 0.170497204055269 0.340994408110537 0.829502795944731 76 0.38155380274963 0.763107605499261 0.61844619725037 77 0.328040744933511 0.656081489867021 0.671959255066489 78 0.395091421291152 0.790182842582305 0.604908578708848 79 0.36789684835855 0.7357936967171 0.63210315164145 80 0.352117138964509 0.704234277929018 0.647882861035491 81 0.586832143678101 0.826335712643799 0.413167856321899 82 0.508968324532214 0.982063350935571 0.491031675467786 83 0.471277685514511 0.942555371029022 0.528722314485489 84 0.378718281528865 0.75743656305773 0.621281718471135 85 0.390200779836658 0.780401559673316 0.609799220163342 86 0.679282614716026 0.641434770567948 0.320717385283974 87 0.599489369668389 0.801021260663223 0.400510630331611 88 0.543404848134763 0.913190303730474 0.456595151865237 89 0.61125808672073 0.77748382655854 0.38874191327927 90 0.483127245637102 0.966254491274204 0.516872754362898 91 0.379648283154382 0.759296566308763 0.620351716845619

 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 1 0.0114942528735632 NOK 5% type I error level 4 0.0459770114942529 OK 10% type I error level 5 0.0574712643678161 OK

\begin{tabular}{lllllllll}
\hline
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
Description & # significant tests & % significant tests & OK/NOK \tabularnewline
1% type I error level & 1 & 0.0114942528735632 & NOK \tabularnewline
5% type I error level & 4 & 0.0459770114942529 & OK \tabularnewline
10% type I error level & 5 & 0.0574712643678161 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147397&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]1[/C][C]0.0114942528735632[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]4[/C][C]0.0459770114942529[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]5[/C][C]0.0574712643678161[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147397&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147397&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 tests OK/NOK 1% type I error level 1 0.0114942528735632 NOK 5% type I error level 4 0.0459770114942529 OK 10% type I error level 5 0.0574712643678161 OK

library(lattice)library(lmtest)n25 <- 25 #minimum number of obs. for Goldfeld-Quandt testpar1 <- 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 <- x1if (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'}xk <- length(x[1,])df <- as.data.frame(x)(mylm <- lm(df))(mysum <- summary(mylm))if (n > n25) {kp3 <- k + 3nmkm3 <- n - k - 3gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))numgqtests <- 0numsignificant1 <- 0numsignificant5 <- 0numsignificant10 <- 0for (mypoint in kp3:nmkm3) {j <- 0numgqtests <- numgqtests + 1for (myalt in c('greater', 'two.sided', 'less')) {j <- j + 1gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value}if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1}gqarr}bitmap(file='test0.png')plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')points(x[,1]-mysum$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)dumdum1 <- dum[2:length(myerror),]dum1z <- as.data.frame(dum1)zplot(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$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')}}myeq <- paste(myeq, ' + e[t]')a<-table.row.start(a)a<-table.element(a, myeq)a<-table.row.end(a)a<-table.end(a)table.save(a,file='mytable1.tab')a<-table.start()a<-table.row.start(a)a<-table.element(a,hyperlink('ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)a<-table.row.end(a)a<-table.row.start(a)a<-table.element(a,'Variable',header=TRUE)a<-table.element(a,'Parameter',header=TRUE)a<-table.element(a,'S.D.',header=TRUE)a<-table.element(a,'T-STATH0: parameter = 0',header=TRUE)a<-table.element(a,'2-tail p-value',header=TRUE)a<-table.element(a,'1-tail p-value',header=TRUE)a<-table.row.end(a)for (i in 1:k){a<-table.row.start(a)a<-table.element(a,rownames(mysum$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$sigma)a<-table.row.end(a)a<-table.row.start(a)a<-table.element(a, 'Sum Squared Residuals',1,TRUE)a<-table.element(a, sum(myerror*myerror))a<-table.row.end(a)a<-table.end(a)table.save(a,file='mytable3.tab')a<-table.start()a<-table.row.start(a)a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)a<-table.row.end(a)a<-table.row.start(a)a<-table.element(a, 'Time or Index', 1, TRUE)a<-table.element(a, 'Actuals', 1, TRUE)a<-table.element(a, 'InterpolationForecast', 1, TRUE)a<-table.element(a, 'ResidualsPrediction Error', 1, TRUE)a<-table.row.end(a)for (i in 1:n) {a<-table.row.start(a)a<-table.element(a,i, 1, TRUE)a<-table.element(a,x[i])a<-table.element(a,x[i]-mysum$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')}