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 Mon, 30 Jan 2023 00:46:04 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=147397, Retrieved Mon, 30 Jan 2023 00:46:04 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact106
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 Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time5 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 Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time5 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
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)7747.17412280702330.54183123.437800
t54.40341155724365.917499.193700

\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
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)7747.17412280702330.54183123.437800
t54.40341155724365.917499.193700







Multiple Linear Regression - Regression Statistics
Multiple R0.6880832236771
R-squared0.47345852270587
Adjusted R-squared0.467857017628273
F-TEST (value)84.5234479210663
F-TEST (DF numerator)1
F-TEST (DF denominator)94
p-value9.54791801177635e-15
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1606.68298860349
Sum Squared Residuals242654441.231577

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.6880832236771 \tabularnewline
R-squared & 0.47345852270587 \tabularnewline
Adjusted R-squared & 0.467857017628273 \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]Adjusted R-squared[/C][C]0.467857017628273[/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 R0.6880832236771
R-squared0.47345852270587
Adjusted R-squared0.467857017628273
F-TEST (value)84.5234479210663
F-TEST (DF numerator)1
F-TEST (DF denominator)94
p-value9.54791801177635e-15
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1606.68298860349
Sum Squared Residuals242654441.231577







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
168277801.57753436426-974.577534364261
261787855.9809459215-1677.9809459215
370847910.38435747875-826.384357478748
481627964.78776903599197.212230964008
584628019.19118059324442.808819406764
696448073.594592150481570.40540784952
7104668127.998003707722338.00199629228
8107488182.401415264972565.59858473503
999638236.804826822211726.19517317779
1081948291.20823837945-97.2082383794536
1168488345.6116499367-1497.6116499367
1270278400.01506149394-1373.01506149394
1372698454.41847305118-1185.41847305118
1467758508.82188460843-1733.82188460843
1578198563.22529616567-744.225296165672
1683718617.62870772292-246.628707722916
1790698672.03211928016396.967880719841
18102488726.43553083741521.5644691626
19110308780.838942394652249.16105760535
20108828835.242353951892046.75764604811
21103338889.645765509131443.35423449087
2291098944.04917706638164.950822933623
2376858998.45258862362-1313.45258862362
2476029052.85600018086-1450.85600018086
2583509107.25941173811-757.259411738108
2678299161.66282329535-1332.66282329535
2788299216.06623485259-387.066234852595
2899489270.46964640984677.530353590161
29106389324.873057967081313.12694203292
30112539379.276469524331873.72353047567
31114249433.679881081571990.32011891843
32113919488.083292638811902.91670736119
33106659542.486704196061122.51329580394
3493969596.8901157533-200.890115753301
3577759651.29352731054-1876.29352731054
3679339705.69693886779-1772.69693886779
3781869760.10035042503-1574.10035042503
3874449814.50376198228-2370.50376198228
3984849868.90717353952-1384.90717353952
4098649923.31058509676-59.3105850967625
41102529977.71399665401274.286003345994
421228210032.11740821132249.88259178875
431163710086.52081976851550.47918023151
441157710140.92423132571436.07576867426
451241710195.3276428832221.67235711702
46963710249.7310544402-612.731054440224
47809410304.1344659975-2210.13446599747
48928010358.5378775547-1078.53787755471
49833410412.941289112-2078.94128911196
50789910467.3447006692-2568.3447006692
51999410521.7481122264-527.748112226442
521007810576.1515237837-498.151523783686
531080110630.5549353409170.44506465907
541295010684.95834689822265.04165310183
551222210739.36175845541482.63824154458
561224610793.76517001271452.23482998734
571328110848.16858156992432.8314184301
581036610902.5719931271-536.571993127148
59873010956.9754046844-2226.97540468439
60961411011.3788162416-1397.37881624163
61863911065.7822277989-2426.78222779888
62877211120.1856393561-2348.18563935612
631089411174.5890509134-280.589050913366
641045511228.9924624706-773.99246247061
651117911283.3958740279-104.395874027853
661058811337.7992855851-749.799285585097
671079411392.2026971423-598.20269714234
681277011446.60610869961323.39389130042
691381211501.00952025682310.99047974317
701085711555.4129318141-698.412931814071
71929011609.8163433713-2319.81634337131
721092511664.2197549286-739.219754928558
73949111718.6231664858-2227.6231664858
74891911773.026578043-2854.02657804305
751160711827.4299896003-220.429989600289
76885211881.8334011575-3029.83340115753
771253711936.2368127148600.763187285223
781475911990.6402242722768.35977572798
791366712045.04363582931621.95636417074
801373112099.44704738651631.55295261349
811511012153.85045894382956.14954105625
821218512208.253870501-23.253870500995
831064512262.6572820582-1617.65728205824
841216112317.0606936155-156.060693615482
851084012371.4641051727-1531.46410517273
861043612425.86751673-1989.86751672997
871358912480.27092828721108.72907171279
881340212534.6743398445867.325660155543
891310312589.0777514017513.9222485983
901493312643.48116295892289.51883704106
911414712697.88457451621449.11542548381
921405712752.28798607341304.71201392657
931623412806.69139763073427.30860236933
941238912861.0948091879-472.094809187919
951159512915.4982207452-1320.49822074516
961277212969.9016323024-197.901632302406

\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 IndexActualsInterpolationForecastResidualsPrediction Error
168277801.57753436426-974.577534364261
261787855.9809459215-1677.9809459215
370847910.38435747875-826.384357478748
481627964.78776903599197.212230964008
584628019.19118059324442.808819406764
696448073.594592150481570.40540784952
7104668127.998003707722338.00199629228
8107488182.401415264972565.59858473503
999638236.804826822211726.19517317779
1081948291.20823837945-97.2082383794536
1168488345.6116499367-1497.6116499367
1270278400.01506149394-1373.01506149394
1372698454.41847305118-1185.41847305118
1467758508.82188460843-1733.82188460843
1578198563.22529616567-744.225296165672
1683718617.62870772292-246.628707722916
1790698672.03211928016396.967880719841
18102488726.43553083741521.5644691626
19110308780.838942394652249.16105760535
20108828835.242353951892046.75764604811
21103338889.645765509131443.35423449087
2291098944.04917706638164.950822933623
2376858998.45258862362-1313.45258862362
2476029052.85600018086-1450.85600018086
2583509107.25941173811-757.259411738108
2678299161.66282329535-1332.66282329535
2788299216.06623485259-387.066234852595
2899489270.46964640984677.530353590161
29106389324.873057967081313.12694203292
30112539379.276469524331873.72353047567
31114249433.679881081571990.32011891843
32113919488.083292638811902.91670736119
33106659542.486704196061122.51329580394
3493969596.8901157533-200.890115753301
3577759651.29352731054-1876.29352731054
3679339705.69693886779-1772.69693886779
3781869760.10035042503-1574.10035042503
3874449814.50376198228-2370.50376198228
3984849868.90717353952-1384.90717353952
4098649923.31058509676-59.3105850967625
41102529977.71399665401274.286003345994
421228210032.11740821132249.88259178875
431163710086.52081976851550.47918023151
441157710140.92423132571436.07576867426
451241710195.3276428832221.67235711702
46963710249.7310544402-612.731054440224
47809410304.1344659975-2210.13446599747
48928010358.5378775547-1078.53787755471
49833410412.941289112-2078.94128911196
50789910467.3447006692-2568.3447006692
51999410521.7481122264-527.748112226442
521007810576.1515237837-498.151523783686
531080110630.5549353409170.44506465907
541295010684.95834689822265.04165310183
551222210739.36175845541482.63824154458
561224610793.76517001271452.23482998734
571328110848.16858156992432.8314184301
581036610902.5719931271-536.571993127148
59873010956.9754046844-2226.97540468439
60961411011.3788162416-1397.37881624163
61863911065.7822277989-2426.78222779888
62877211120.1856393561-2348.18563935612
631089411174.5890509134-280.589050913366
641045511228.9924624706-773.99246247061
651117911283.3958740279-104.395874027853
661058811337.7992855851-749.799285585097
671079411392.2026971423-598.20269714234
681277011446.60610869961323.39389130042
691381211501.00952025682310.99047974317
701085711555.4129318141-698.412931814071
71929011609.8163433713-2319.81634337131
721092511664.2197549286-739.219754928558
73949111718.6231664858-2227.6231664858
74891911773.026578043-2854.02657804305
751160711827.4299896003-220.429989600289
76885211881.8334011575-3029.83340115753
771253711936.2368127148600.763187285223
781475911990.6402242722768.35977572798
791366712045.04363582931621.95636417074
801373112099.44704738651631.55295261349
811511012153.85045894382956.14954105625
821218512208.253870501-23.253870500995
831064512262.6572820582-1617.65728205824
841216112317.0606936155-156.060693615482
851084012371.4641051727-1531.46410517273
861043612425.86751673-1989.86751672997
871358912480.27092828721108.72907171279
881340212534.6743398445867.325660155543
891310312589.0777514017513.9222485983
901493312643.48116295892289.51883704106
911414712697.88457451621449.11542548381
921405712752.28798607341304.71201392657
931623412806.69139763073427.30860236933
941238912861.0948091879-472.094809187919
951159512915.4982207452-1320.49822074516
961277212969.9016323024-197.901632302406







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
50.0439267426237910.08785348524758190.956073257376209
60.01886981993643170.03773963987286340.981130180063568
70.007265390484897390.01453078096979480.992734609515103
80.002143324665062870.004286649330125730.997856675334937
90.006564073837285440.01312814767457090.993435926162715
100.1266766512124810.2533533024249630.873323348787519
110.4326586080663380.8653172161326760.567341391933662
120.5167584507104880.9664830985790240.483241549289512
130.4984721424280480.9969442848560970.501527857571952
140.494671738837470.9893434776749390.50532826116253
150.4114819234770490.8229638469540980.588518076522951
160.3319352489941430.6638704979882860.668064751005857
170.2762638991571980.5525277983143950.723736100842802
180.2854732981666120.5709465963332250.714526701833388
190.3348960579172920.6697921158345830.665103942082709
200.3343944359523790.6687888719047570.665605564047622
210.2880718862707870.5761437725415750.711928113729213
220.2402664793931910.4805329587863820.759733520606809
230.268559153577530.5371183071550590.73144084642247
240.2832199407967930.5664398815935860.716780059203207
250.2434608287836480.4869216575672950.756539171216352
260.2267876732392830.4535753464785660.773212326760717
270.1793940408403510.3587880816807020.820605959159649
280.1477792020096860.2955584040193730.852220797990314
290.1373540126974830.2747080253949660.862645987302517
300.14837137748380.29674275496760.8516286225162
310.1606609663281930.3213219326563860.839339033671807
320.1655798011876040.3311596023752080.834420198812396
330.1424511499072040.2849022998144090.857548850092796
340.1199437615507620.2398875231015240.880056238449238
350.1560938997299640.3121877994599270.843906100270036
360.1743850745796980.3487701491593960.825614925420302
370.1728215171157610.3456430342315220.827178482884239
380.2110573683464730.4221147366929460.788942631653527
390.1888852676338670.3777705352677330.811114732366133
400.1511435093546330.3022870187092670.848856490645367
410.122021034458380.244042068916760.87797896554162
420.1721927662619480.3443855325238960.827807233738052
430.1805954158592770.3611908317185530.819404584140723
440.1837201679734070.3674403359468140.816279832026593
450.2445455805004710.4890911610009410.755454419499529
460.2099756423378250.4199512846756490.790024357662175
470.2377074307114220.4754148614228440.762292569288578
480.2064816671574460.4129633343148930.793518332842553
490.2147393412870040.4294786825740090.785260658712996
500.2530631305796550.5061262611593090.746936869420345
510.208210848289650.41642169657930.79178915171035
520.1683056133526130.3366112267052260.831694386647387
530.1373530671505470.2747061343010940.862646932849453
540.2010662905656210.4021325811312430.798933709434379
550.2200313957534540.4400627915069080.779968604246546
560.2443677171972990.4887354343945980.755632282802701
570.392582947875390.785165895750780.60741705212461
580.3480695516389150.696139103277830.651930448361085
590.3521738467936540.7043476935873090.647826153206346
600.3129764301039210.6259528602078420.687023569896079
610.326918585641320.6538371712826410.67308141435868
620.3376753728196430.6753507456392860.662324627180357
630.2844249578335060.5688499156670120.715575042166494
640.2349117829372470.4698235658744940.765088217062753
650.1922633130911670.3845266261823350.807736686908833
660.1527422111218190.3054844222436370.847257788878181
670.1183055095740190.2366110191480390.881694490425981
680.1261559603218350.2523119206436690.873844039678165
690.2225037714881550.4450075429763090.777496228511846
700.1786018507038320.3572037014076640.821398149296168
710.1774274481181380.3548548962362760.822572551881862
720.1372992649640630.2745985299281260.862700735035937
730.1413421322269650.2826842644539290.858657867773035
740.2144844553367850.4289689106735710.785515544663215
750.1704972040552690.3409944081105370.829502795944731
760.381553802749630.7631076054992610.61844619725037
770.3280407449335110.6560814898670210.671959255066489
780.3950914212911520.7901828425823050.604908578708848
790.367896848358550.73579369671710.63210315164145
800.3521171389645090.7042342779290180.647882861035491
810.5868321436781010.8263357126437990.413167856321899
820.5089683245322140.9820633509355710.491031675467786
830.4712776855145110.9425553710290220.528722314485489
840.3787182815288650.757436563057730.621281718471135
850.3902007798366580.7804015596733160.609799220163342
860.6792826147160260.6414347705679480.320717385283974
870.5994893696683890.8010212606632230.400510630331611
880.5434048481347630.9131903037304740.456595151865237
890.611258086720730.777483826558540.38874191327927
900.4831272456371020.9662544912742040.516872754362898
910.3796482831543820.7592965663087630.620351716845619

\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-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
50.0439267426237910.08785348524758190.956073257376209
60.01886981993643170.03773963987286340.981130180063568
70.007265390484897390.01453078096979480.992734609515103
80.002143324665062870.004286649330125730.997856675334937
90.006564073837285440.01312814767457090.993435926162715
100.1266766512124810.2533533024249630.873323348787519
110.4326586080663380.8653172161326760.567341391933662
120.5167584507104880.9664830985790240.483241549289512
130.4984721424280480.9969442848560970.501527857571952
140.494671738837470.9893434776749390.50532826116253
150.4114819234770490.8229638469540980.588518076522951
160.3319352489941430.6638704979882860.668064751005857
170.2762638991571980.5525277983143950.723736100842802
180.2854732981666120.5709465963332250.714526701833388
190.3348960579172920.6697921158345830.665103942082709
200.3343944359523790.6687888719047570.665605564047622
210.2880718862707870.5761437725415750.711928113729213
220.2402664793931910.4805329587863820.759733520606809
230.268559153577530.5371183071550590.73144084642247
240.2832199407967930.5664398815935860.716780059203207
250.2434608287836480.4869216575672950.756539171216352
260.2267876732392830.4535753464785660.773212326760717
270.1793940408403510.3587880816807020.820605959159649
280.1477792020096860.2955584040193730.852220797990314
290.1373540126974830.2747080253949660.862645987302517
300.14837137748380.29674275496760.8516286225162
310.1606609663281930.3213219326563860.839339033671807
320.1655798011876040.3311596023752080.834420198812396
330.1424511499072040.2849022998144090.857548850092796
340.1199437615507620.2398875231015240.880056238449238
350.1560938997299640.3121877994599270.843906100270036
360.1743850745796980.3487701491593960.825614925420302
370.1728215171157610.3456430342315220.827178482884239
380.2110573683464730.4221147366929460.788942631653527
390.1888852676338670.3777705352677330.811114732366133
400.1511435093546330.3022870187092670.848856490645367
410.122021034458380.244042068916760.87797896554162
420.1721927662619480.3443855325238960.827807233738052
430.1805954158592770.3611908317185530.819404584140723
440.1837201679734070.3674403359468140.816279832026593
450.2445455805004710.4890911610009410.755454419499529
460.2099756423378250.4199512846756490.790024357662175
470.2377074307114220.4754148614228440.762292569288578
480.2064816671574460.4129633343148930.793518332842553
490.2147393412870040.4294786825740090.785260658712996
500.2530631305796550.5061262611593090.746936869420345
510.208210848289650.41642169657930.79178915171035
520.1683056133526130.3366112267052260.831694386647387
530.1373530671505470.2747061343010940.862646932849453
540.2010662905656210.4021325811312430.798933709434379
550.2200313957534540.4400627915069080.779968604246546
560.2443677171972990.4887354343945980.755632282802701
570.392582947875390.785165895750780.60741705212461
580.3480695516389150.696139103277830.651930448361085
590.3521738467936540.7043476935873090.647826153206346
600.3129764301039210.6259528602078420.687023569896079
610.326918585641320.6538371712826410.67308141435868
620.3376753728196430.6753507456392860.662324627180357
630.2844249578335060.5688499156670120.715575042166494
640.2349117829372470.4698235658744940.765088217062753
650.1922633130911670.3845266261823350.807736686908833
660.1527422111218190.3054844222436370.847257788878181
670.1183055095740190.2366110191480390.881694490425981
680.1261559603218350.2523119206436690.873844039678165
690.2225037714881550.4450075429763090.777496228511846
700.1786018507038320.3572037014076640.821398149296168
710.1774274481181380.3548548962362760.822572551881862
720.1372992649640630.2745985299281260.862700735035937
730.1413421322269650.2826842644539290.858657867773035
740.2144844553367850.4289689106735710.785515544663215
750.1704972040552690.3409944081105370.829502795944731
760.381553802749630.7631076054992610.61844619725037
770.3280407449335110.6560814898670210.671959255066489
780.3950914212911520.7901828425823050.604908578708848
790.367896848358550.73579369671710.63210315164145
800.3521171389645090.7042342779290180.647882861035491
810.5868321436781010.8263357126437990.413167856321899
820.5089683245322140.9820633509355710.491031675467786
830.4712776855145110.9425553710290220.528722314485489
840.3787182815288650.757436563057730.621281718471135
850.3902007798366580.7804015596733160.609799220163342
860.6792826147160260.6414347705679480.320717385283974
870.5994893696683890.8010212606632230.400510630331611
880.5434048481347630.9131903037304740.456595151865237
890.611258086720730.777483826558540.38874191327927
900.4831272456371020.9662544912742040.516872754362898
910.3796482831543820.7592965663087630.620351716845619







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level10.0114942528735632NOK
5% type I error level40.0459770114942529OK
10% type I error level50.0574712643678161OK

\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 testsOK/NOK
1% type I error level10.0114942528735632NOK
5% type I error level40.0459770114942529OK
10% type I error level50.0574712643678161OK



Parameters (Session):
par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = Linear Trend ;
Parameters (R input):
par1 = 1 ; par2 = Do not include Seasonal 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')
}