Free Statistics

of Irreproducible Research!

Author's title

Author*Unverified author*
R Software Module--
Title produced by softwareMultiple Regression
Date of computationTue, 29 Nov 2011 14:48:54 -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/29/t1322596164hinpp5wci5xftrv.htm/, Retrieved Thu, 28 Mar 2024 16:26:36 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=148706, Retrieved Thu, 28 Mar 2024 16:26:36 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact104
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Univariate Data Series] [data set] [2008-12-01 19:54:57] [b98453cac15ba1066b407e146608df68]
- RMPD  [Classical Decomposition] [compendium 8] [2011-11-24 14:23:11] [380049693c521f4999989215fb37aeca]
- RMPD    [Multiple Regression] [WS 8 Q2] [2011-11-24 15:19:54] [380049693c521f4999989215fb37aeca]
- RM          [Multiple Regression] [WS8-4] [2011-11-29 19:48:54] [d41d8cd98f00b204e9800998ecf8427e] [Current]
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Dataseries X:
135094
135411
135698
135880
135891
135971
136173
136358
136514
136506
136711
136891
137094
137182
137400
137479
137620
137687
137638
137612
137681
137772
137899
137983
137996
137913
137841
137656
137423
137245
137014
136747
136313
135804
135002
134383
133563
132837
132041
131381
130995
130493
130193
129962
129726
129505
129450
129320
129281
129246
129438
129715
130173
129981
129932
129873
129844
130015
130108
130260




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time4 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 & 4 seconds \tabularnewline
R Server & 'Herman Ole Andreas Wold' @ wold.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=148706&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]4 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=148706&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=148706&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 time4 seconds
R Server'Herman Ole Andreas Wold' @ wold.wessa.net







Multiple Linear Regression - Estimated Regression Equation
y[t] = + 139568.525 -934.365972222166M1[t] -861.023611111117M2[t] -734.081250000006M3[t] -634.338888888894M4[t] -474.996527777783M5[t] -458.854166666671M6[t] -383.11180555556M7[t] -301.569444444448M8[t] -235.227083333337M9[t] -169.284722222226M10[t] -94.5423611111136M11[t] -161.142361111112t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
y[t] =  +  139568.525 -934.365972222166M1[t] -861.023611111117M2[t] -734.081250000006M3[t] -634.338888888894M4[t] -474.996527777783M5[t] -458.854166666671M6[t] -383.11180555556M7[t] -301.569444444448M8[t] -235.227083333337M9[t] -169.284722222226M10[t] -94.5423611111136M11[t] -161.142361111112t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=148706&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]y[t] =  +  139568.525 -934.365972222166M1[t] -861.023611111117M2[t] -734.081250000006M3[t] -634.338888888894M4[t] -474.996527777783M5[t] -458.854166666671M6[t] -383.11180555556M7[t] -301.569444444448M8[t] -235.227083333337M9[t] -169.284722222226M10[t] -94.5423611111136M11[t] -161.142361111112t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=148706&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=148706&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
y[t] = + 139568.525 -934.365972222166M1[t] -861.023611111117M2[t] -734.081250000006M3[t] -634.338888888894M4[t] -474.996527777783M5[t] -458.854166666671M6[t] -383.11180555556M7[t] -301.569444444448M8[t] -235.227083333337M9[t] -169.284722222226M10[t] -94.5423611111136M11[t] -161.142361111112t + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)139568.5251132.34252123.256500
M1-934.3659722221661377.558048-0.67830.5009220.250461
M2-861.0236111111171375.499869-0.6260.5343620.267181
M3-734.0812500000061373.635049-0.53440.5955790.297789
M4-634.3388888888941371.964378-0.46240.6459580.322979
M5-474.9965277777831370.488566-0.34660.7304470.365223
M6-458.8541666666711369.208241-0.33510.7390230.369511
M7-383.111805555561368.123954-0.280.7806860.390343
M8-301.5694444444481367.23617-0.22060.8263840.413192
M9-235.2270833333371366.545273-0.17210.8640720.432036
M10-169.2847222222261366.051561-0.12390.9019050.450952
M11-94.54236111111361365.755248-0.06920.9451050.472553
t-161.14236111111216.426294-9.8100

\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) & 139568.525 & 1132.34252 & 123.2565 & 0 & 0 \tabularnewline
M1 & -934.365972222166 & 1377.558048 & -0.6783 & 0.500922 & 0.250461 \tabularnewline
M2 & -861.023611111117 & 1375.499869 & -0.626 & 0.534362 & 0.267181 \tabularnewline
M3 & -734.081250000006 & 1373.635049 & -0.5344 & 0.595579 & 0.297789 \tabularnewline
M4 & -634.338888888894 & 1371.964378 & -0.4624 & 0.645958 & 0.322979 \tabularnewline
M5 & -474.996527777783 & 1370.488566 & -0.3466 & 0.730447 & 0.365223 \tabularnewline
M6 & -458.854166666671 & 1369.208241 & -0.3351 & 0.739023 & 0.369511 \tabularnewline
M7 & -383.11180555556 & 1368.123954 & -0.28 & 0.780686 & 0.390343 \tabularnewline
M8 & -301.569444444448 & 1367.23617 & -0.2206 & 0.826384 & 0.413192 \tabularnewline
M9 & -235.227083333337 & 1366.545273 & -0.1721 & 0.864072 & 0.432036 \tabularnewline
M10 & -169.284722222226 & 1366.051561 & -0.1239 & 0.901905 & 0.450952 \tabularnewline
M11 & -94.5423611111136 & 1365.755248 & -0.0692 & 0.945105 & 0.472553 \tabularnewline
t & -161.142361111112 & 16.426294 & -9.81 & 0 & 0 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=148706&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]139568.525[/C][C]1132.34252[/C][C]123.2565[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]M1[/C][C]-934.365972222166[/C][C]1377.558048[/C][C]-0.6783[/C][C]0.500922[/C][C]0.250461[/C][/ROW]
[ROW][C]M2[/C][C]-861.023611111117[/C][C]1375.499869[/C][C]-0.626[/C][C]0.534362[/C][C]0.267181[/C][/ROW]
[ROW][C]M3[/C][C]-734.081250000006[/C][C]1373.635049[/C][C]-0.5344[/C][C]0.595579[/C][C]0.297789[/C][/ROW]
[ROW][C]M4[/C][C]-634.338888888894[/C][C]1371.964378[/C][C]-0.4624[/C][C]0.645958[/C][C]0.322979[/C][/ROW]
[ROW][C]M5[/C][C]-474.996527777783[/C][C]1370.488566[/C][C]-0.3466[/C][C]0.730447[/C][C]0.365223[/C][/ROW]
[ROW][C]M6[/C][C]-458.854166666671[/C][C]1369.208241[/C][C]-0.3351[/C][C]0.739023[/C][C]0.369511[/C][/ROW]
[ROW][C]M7[/C][C]-383.11180555556[/C][C]1368.123954[/C][C]-0.28[/C][C]0.780686[/C][C]0.390343[/C][/ROW]
[ROW][C]M8[/C][C]-301.569444444448[/C][C]1367.23617[/C][C]-0.2206[/C][C]0.826384[/C][C]0.413192[/C][/ROW]
[ROW][C]M9[/C][C]-235.227083333337[/C][C]1366.545273[/C][C]-0.1721[/C][C]0.864072[/C][C]0.432036[/C][/ROW]
[ROW][C]M10[/C][C]-169.284722222226[/C][C]1366.051561[/C][C]-0.1239[/C][C]0.901905[/C][C]0.450952[/C][/ROW]
[ROW][C]M11[/C][C]-94.5423611111136[/C][C]1365.755248[/C][C]-0.0692[/C][C]0.945105[/C][C]0.472553[/C][/ROW]
[ROW][C]t[/C][C]-161.142361111112[/C][C]16.426294[/C][C]-9.81[/C][C]0[/C][C]0[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=148706&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=148706&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)139568.5251132.34252123.256500
M1-934.3659722221661377.558048-0.67830.5009220.250461
M2-861.0236111111171375.499869-0.6260.5343620.267181
M3-734.0812500000061373.635049-0.53440.5955790.297789
M4-634.3388888888941371.964378-0.46240.6459580.322979
M5-474.9965277777831370.488566-0.34660.7304470.365223
M6-458.8541666666711369.208241-0.33510.7390230.369511
M7-383.111805555561368.123954-0.280.7806860.390343
M8-301.5694444444481367.23617-0.22060.8263840.413192
M9-235.2270833333371366.545273-0.17210.8640720.432036
M10-169.2847222222261366.051561-0.12390.9019050.450952
M11-94.54236111111361365.755248-0.06920.9451050.472553
t-161.14236111111216.426294-9.8100







Multiple Linear Regression - Regression Statistics
Multiple R0.820999691173647
R-squared0.674040492907223
Adjusted R-squared0.590816788968642
F-TEST (value)8.09914076168326
F-TEST (DF numerator)12
F-TEST (DF denominator)47
p-value6.03553863554041e-08
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2159.29246189414
Sum Squared Residuals219139564.991665

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.820999691173647 \tabularnewline
R-squared & 0.674040492907223 \tabularnewline
Adjusted R-squared & 0.590816788968642 \tabularnewline
F-TEST (value) & 8.09914076168326 \tabularnewline
F-TEST (DF numerator) & 12 \tabularnewline
F-TEST (DF denominator) & 47 \tabularnewline
p-value & 6.03553863554041e-08 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 2159.29246189414 \tabularnewline
Sum Squared Residuals & 219139564.991665 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=148706&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.820999691173647[/C][/ROW]
[ROW][C]R-squared[/C][C]0.674040492907223[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.590816788968642[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]8.09914076168326[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]12[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]47[/C][/ROW]
[ROW][C]p-value[/C][C]6.03553863554041e-08[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]2159.29246189414[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]219139564.991665[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=148706&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=148706&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 R0.820999691173647
R-squared0.674040492907223
Adjusted R-squared0.590816788968642
F-TEST (value)8.09914076168326
F-TEST (DF numerator)12
F-TEST (DF denominator)47
p-value6.03553863554041e-08
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2159.29246189414
Sum Squared Residuals219139564.991665







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
1135094138473.016666666-3379.01666666643
2135411138385.216666667-2974.21666666668
3135698138351.016666667-2653.01666666668
4135880138289.616666667-2409.61666666668
5135891138287.816666667-2396.81666666668
6135971138142.816666667-2171.81666666668
7136173138057.416666667-1884.41666666668
8136358137977.816666667-1619.81666666668
9136514137883.016666667-1369.01666666668
10136506137787.816666667-1281.81666666668
11136711137701.416666667-990.416666666678
12136891137634.816666667-743.816666666678
13137094136539.308333333554.691666666599
14137182136451.508333333730.491666666662
15137400136417.308333333982.691666666661
16137479136355.9083333331123.09166666666
17137620136354.1083333331265.89166666666
18137687136209.1083333331477.89166666666
19137638136123.7083333331514.29166666666
20137612136044.1083333331567.89166666666
21137681135949.3083333331731.69166666666
22137772135854.1083333331917.89166666666
23137899135767.7083333332131.29166666666
24137983135701.1083333332281.89166666666
25137996134605.63390.39999999994
26137913134517.83395.2
27137841134483.63357.4
28137656134422.23233.8
29137423134420.43002.6
30137245134275.42969.6
311370141341902824
32136747134110.42636.6
33136313134015.62297.4
34135804133920.41883.6
351350021338341168
36134383133767.4615.599999999998
37133563132671.891666667891.108333333276
38132837132584.091666667252.908333333338
39132041132549.891666667-508.891666666662
40131381132488.491666667-1107.49166666666
41130995132486.691666667-1491.69166666666
42130493132341.691666667-1848.69166666666
43130193132256.291666667-2063.29166666666
44129962132176.691666667-2214.69166666666
45129726132081.891666667-2355.89166666666
46129505131986.691666667-2481.69166666666
47129450131900.291666667-2450.29166666666
48129320131833.691666667-2513.69166666666
49129281130738.183333333-1457.18333333339
50129246130650.383333333-1404.38333333332
51129438130616.183333333-1178.18333333332
52129715130554.783333333-839.783333333322
53130173130552.983333333-379.983333333322
54129981130407.983333333-426.983333333323
55129932130322.583333333-390.583333333323
56129873130242.983333333-369.983333333322
57129844130148.183333333-304.183333333323
58130015130052.983333333-37.9833333333228
59130108129966.583333333141.416666666678
60130260129899.983333333360.016666666677

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 135094 & 138473.016666666 & -3379.01666666643 \tabularnewline
2 & 135411 & 138385.216666667 & -2974.21666666668 \tabularnewline
3 & 135698 & 138351.016666667 & -2653.01666666668 \tabularnewline
4 & 135880 & 138289.616666667 & -2409.61666666668 \tabularnewline
5 & 135891 & 138287.816666667 & -2396.81666666668 \tabularnewline
6 & 135971 & 138142.816666667 & -2171.81666666668 \tabularnewline
7 & 136173 & 138057.416666667 & -1884.41666666668 \tabularnewline
8 & 136358 & 137977.816666667 & -1619.81666666668 \tabularnewline
9 & 136514 & 137883.016666667 & -1369.01666666668 \tabularnewline
10 & 136506 & 137787.816666667 & -1281.81666666668 \tabularnewline
11 & 136711 & 137701.416666667 & -990.416666666678 \tabularnewline
12 & 136891 & 137634.816666667 & -743.816666666678 \tabularnewline
13 & 137094 & 136539.308333333 & 554.691666666599 \tabularnewline
14 & 137182 & 136451.508333333 & 730.491666666662 \tabularnewline
15 & 137400 & 136417.308333333 & 982.691666666661 \tabularnewline
16 & 137479 & 136355.908333333 & 1123.09166666666 \tabularnewline
17 & 137620 & 136354.108333333 & 1265.89166666666 \tabularnewline
18 & 137687 & 136209.108333333 & 1477.89166666666 \tabularnewline
19 & 137638 & 136123.708333333 & 1514.29166666666 \tabularnewline
20 & 137612 & 136044.108333333 & 1567.89166666666 \tabularnewline
21 & 137681 & 135949.308333333 & 1731.69166666666 \tabularnewline
22 & 137772 & 135854.108333333 & 1917.89166666666 \tabularnewline
23 & 137899 & 135767.708333333 & 2131.29166666666 \tabularnewline
24 & 137983 & 135701.108333333 & 2281.89166666666 \tabularnewline
25 & 137996 & 134605.6 & 3390.39999999994 \tabularnewline
26 & 137913 & 134517.8 & 3395.2 \tabularnewline
27 & 137841 & 134483.6 & 3357.4 \tabularnewline
28 & 137656 & 134422.2 & 3233.8 \tabularnewline
29 & 137423 & 134420.4 & 3002.6 \tabularnewline
30 & 137245 & 134275.4 & 2969.6 \tabularnewline
31 & 137014 & 134190 & 2824 \tabularnewline
32 & 136747 & 134110.4 & 2636.6 \tabularnewline
33 & 136313 & 134015.6 & 2297.4 \tabularnewline
34 & 135804 & 133920.4 & 1883.6 \tabularnewline
35 & 135002 & 133834 & 1168 \tabularnewline
36 & 134383 & 133767.4 & 615.599999999998 \tabularnewline
37 & 133563 & 132671.891666667 & 891.108333333276 \tabularnewline
38 & 132837 & 132584.091666667 & 252.908333333338 \tabularnewline
39 & 132041 & 132549.891666667 & -508.891666666662 \tabularnewline
40 & 131381 & 132488.491666667 & -1107.49166666666 \tabularnewline
41 & 130995 & 132486.691666667 & -1491.69166666666 \tabularnewline
42 & 130493 & 132341.691666667 & -1848.69166666666 \tabularnewline
43 & 130193 & 132256.291666667 & -2063.29166666666 \tabularnewline
44 & 129962 & 132176.691666667 & -2214.69166666666 \tabularnewline
45 & 129726 & 132081.891666667 & -2355.89166666666 \tabularnewline
46 & 129505 & 131986.691666667 & -2481.69166666666 \tabularnewline
47 & 129450 & 131900.291666667 & -2450.29166666666 \tabularnewline
48 & 129320 & 131833.691666667 & -2513.69166666666 \tabularnewline
49 & 129281 & 130738.183333333 & -1457.18333333339 \tabularnewline
50 & 129246 & 130650.383333333 & -1404.38333333332 \tabularnewline
51 & 129438 & 130616.183333333 & -1178.18333333332 \tabularnewline
52 & 129715 & 130554.783333333 & -839.783333333322 \tabularnewline
53 & 130173 & 130552.983333333 & -379.983333333322 \tabularnewline
54 & 129981 & 130407.983333333 & -426.983333333323 \tabularnewline
55 & 129932 & 130322.583333333 & -390.583333333323 \tabularnewline
56 & 129873 & 130242.983333333 & -369.983333333322 \tabularnewline
57 & 129844 & 130148.183333333 & -304.183333333323 \tabularnewline
58 & 130015 & 130052.983333333 & -37.9833333333228 \tabularnewline
59 & 130108 & 129966.583333333 & 141.416666666678 \tabularnewline
60 & 130260 & 129899.983333333 & 360.016666666677 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=148706&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]135094[/C][C]138473.016666666[/C][C]-3379.01666666643[/C][/ROW]
[ROW][C]2[/C][C]135411[/C][C]138385.216666667[/C][C]-2974.21666666668[/C][/ROW]
[ROW][C]3[/C][C]135698[/C][C]138351.016666667[/C][C]-2653.01666666668[/C][/ROW]
[ROW][C]4[/C][C]135880[/C][C]138289.616666667[/C][C]-2409.61666666668[/C][/ROW]
[ROW][C]5[/C][C]135891[/C][C]138287.816666667[/C][C]-2396.81666666668[/C][/ROW]
[ROW][C]6[/C][C]135971[/C][C]138142.816666667[/C][C]-2171.81666666668[/C][/ROW]
[ROW][C]7[/C][C]136173[/C][C]138057.416666667[/C][C]-1884.41666666668[/C][/ROW]
[ROW][C]8[/C][C]136358[/C][C]137977.816666667[/C][C]-1619.81666666668[/C][/ROW]
[ROW][C]9[/C][C]136514[/C][C]137883.016666667[/C][C]-1369.01666666668[/C][/ROW]
[ROW][C]10[/C][C]136506[/C][C]137787.816666667[/C][C]-1281.81666666668[/C][/ROW]
[ROW][C]11[/C][C]136711[/C][C]137701.416666667[/C][C]-990.416666666678[/C][/ROW]
[ROW][C]12[/C][C]136891[/C][C]137634.816666667[/C][C]-743.816666666678[/C][/ROW]
[ROW][C]13[/C][C]137094[/C][C]136539.308333333[/C][C]554.691666666599[/C][/ROW]
[ROW][C]14[/C][C]137182[/C][C]136451.508333333[/C][C]730.491666666662[/C][/ROW]
[ROW][C]15[/C][C]137400[/C][C]136417.308333333[/C][C]982.691666666661[/C][/ROW]
[ROW][C]16[/C][C]137479[/C][C]136355.908333333[/C][C]1123.09166666666[/C][/ROW]
[ROW][C]17[/C][C]137620[/C][C]136354.108333333[/C][C]1265.89166666666[/C][/ROW]
[ROW][C]18[/C][C]137687[/C][C]136209.108333333[/C][C]1477.89166666666[/C][/ROW]
[ROW][C]19[/C][C]137638[/C][C]136123.708333333[/C][C]1514.29166666666[/C][/ROW]
[ROW][C]20[/C][C]137612[/C][C]136044.108333333[/C][C]1567.89166666666[/C][/ROW]
[ROW][C]21[/C][C]137681[/C][C]135949.308333333[/C][C]1731.69166666666[/C][/ROW]
[ROW][C]22[/C][C]137772[/C][C]135854.108333333[/C][C]1917.89166666666[/C][/ROW]
[ROW][C]23[/C][C]137899[/C][C]135767.708333333[/C][C]2131.29166666666[/C][/ROW]
[ROW][C]24[/C][C]137983[/C][C]135701.108333333[/C][C]2281.89166666666[/C][/ROW]
[ROW][C]25[/C][C]137996[/C][C]134605.6[/C][C]3390.39999999994[/C][/ROW]
[ROW][C]26[/C][C]137913[/C][C]134517.8[/C][C]3395.2[/C][/ROW]
[ROW][C]27[/C][C]137841[/C][C]134483.6[/C][C]3357.4[/C][/ROW]
[ROW][C]28[/C][C]137656[/C][C]134422.2[/C][C]3233.8[/C][/ROW]
[ROW][C]29[/C][C]137423[/C][C]134420.4[/C][C]3002.6[/C][/ROW]
[ROW][C]30[/C][C]137245[/C][C]134275.4[/C][C]2969.6[/C][/ROW]
[ROW][C]31[/C][C]137014[/C][C]134190[/C][C]2824[/C][/ROW]
[ROW][C]32[/C][C]136747[/C][C]134110.4[/C][C]2636.6[/C][/ROW]
[ROW][C]33[/C][C]136313[/C][C]134015.6[/C][C]2297.4[/C][/ROW]
[ROW][C]34[/C][C]135804[/C][C]133920.4[/C][C]1883.6[/C][/ROW]
[ROW][C]35[/C][C]135002[/C][C]133834[/C][C]1168[/C][/ROW]
[ROW][C]36[/C][C]134383[/C][C]133767.4[/C][C]615.599999999998[/C][/ROW]
[ROW][C]37[/C][C]133563[/C][C]132671.891666667[/C][C]891.108333333276[/C][/ROW]
[ROW][C]38[/C][C]132837[/C][C]132584.091666667[/C][C]252.908333333338[/C][/ROW]
[ROW][C]39[/C][C]132041[/C][C]132549.891666667[/C][C]-508.891666666662[/C][/ROW]
[ROW][C]40[/C][C]131381[/C][C]132488.491666667[/C][C]-1107.49166666666[/C][/ROW]
[ROW][C]41[/C][C]130995[/C][C]132486.691666667[/C][C]-1491.69166666666[/C][/ROW]
[ROW][C]42[/C][C]130493[/C][C]132341.691666667[/C][C]-1848.69166666666[/C][/ROW]
[ROW][C]43[/C][C]130193[/C][C]132256.291666667[/C][C]-2063.29166666666[/C][/ROW]
[ROW][C]44[/C][C]129962[/C][C]132176.691666667[/C][C]-2214.69166666666[/C][/ROW]
[ROW][C]45[/C][C]129726[/C][C]132081.891666667[/C][C]-2355.89166666666[/C][/ROW]
[ROW][C]46[/C][C]129505[/C][C]131986.691666667[/C][C]-2481.69166666666[/C][/ROW]
[ROW][C]47[/C][C]129450[/C][C]131900.291666667[/C][C]-2450.29166666666[/C][/ROW]
[ROW][C]48[/C][C]129320[/C][C]131833.691666667[/C][C]-2513.69166666666[/C][/ROW]
[ROW][C]49[/C][C]129281[/C][C]130738.183333333[/C][C]-1457.18333333339[/C][/ROW]
[ROW][C]50[/C][C]129246[/C][C]130650.383333333[/C][C]-1404.38333333332[/C][/ROW]
[ROW][C]51[/C][C]129438[/C][C]130616.183333333[/C][C]-1178.18333333332[/C][/ROW]
[ROW][C]52[/C][C]129715[/C][C]130554.783333333[/C][C]-839.783333333322[/C][/ROW]
[ROW][C]53[/C][C]130173[/C][C]130552.983333333[/C][C]-379.983333333322[/C][/ROW]
[ROW][C]54[/C][C]129981[/C][C]130407.983333333[/C][C]-426.983333333323[/C][/ROW]
[ROW][C]55[/C][C]129932[/C][C]130322.583333333[/C][C]-390.583333333323[/C][/ROW]
[ROW][C]56[/C][C]129873[/C][C]130242.983333333[/C][C]-369.983333333322[/C][/ROW]
[ROW][C]57[/C][C]129844[/C][C]130148.183333333[/C][C]-304.183333333323[/C][/ROW]
[ROW][C]58[/C][C]130015[/C][C]130052.983333333[/C][C]-37.9833333333228[/C][/ROW]
[ROW][C]59[/C][C]130108[/C][C]129966.583333333[/C][C]141.416666666678[/C][/ROW]
[ROW][C]60[/C][C]130260[/C][C]129899.983333333[/C][C]360.016666666677[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=148706&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=148706&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
1135094138473.016666666-3379.01666666643
2135411138385.216666667-2974.21666666668
3135698138351.016666667-2653.01666666668
4135880138289.616666667-2409.61666666668
5135891138287.816666667-2396.81666666668
6135971138142.816666667-2171.81666666668
7136173138057.416666667-1884.41666666668
8136358137977.816666667-1619.81666666668
9136514137883.016666667-1369.01666666668
10136506137787.816666667-1281.81666666668
11136711137701.416666667-990.416666666678
12136891137634.816666667-743.816666666678
13137094136539.308333333554.691666666599
14137182136451.508333333730.491666666662
15137400136417.308333333982.691666666661
16137479136355.9083333331123.09166666666
17137620136354.1083333331265.89166666666
18137687136209.1083333331477.89166666666
19137638136123.7083333331514.29166666666
20137612136044.1083333331567.89166666666
21137681135949.3083333331731.69166666666
22137772135854.1083333331917.89166666666
23137899135767.7083333332131.29166666666
24137983135701.1083333332281.89166666666
25137996134605.63390.39999999994
26137913134517.83395.2
27137841134483.63357.4
28137656134422.23233.8
29137423134420.43002.6
30137245134275.42969.6
311370141341902824
32136747134110.42636.6
33136313134015.62297.4
34135804133920.41883.6
351350021338341168
36134383133767.4615.599999999998
37133563132671.891666667891.108333333276
38132837132584.091666667252.908333333338
39132041132549.891666667-508.891666666662
40131381132488.491666667-1107.49166666666
41130995132486.691666667-1491.69166666666
42130493132341.691666667-1848.69166666666
43130193132256.291666667-2063.29166666666
44129962132176.691666667-2214.69166666666
45129726132081.891666667-2355.89166666666
46129505131986.691666667-2481.69166666666
47129450131900.291666667-2450.29166666666
48129320131833.691666667-2513.69166666666
49129281130738.183333333-1457.18333333339
50129246130650.383333333-1404.38333333332
51129438130616.183333333-1178.18333333332
52129715130554.783333333-839.783333333322
53130173130552.983333333-379.983333333322
54129981130407.983333333-426.983333333323
55129932130322.583333333-390.583333333323
56129873130242.983333333-369.983333333322
57129844130148.183333333-304.183333333323
58130015130052.983333333-37.9833333333228
59130108129966.583333333141.416666666678
60130260129899.983333333360.016666666677







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
160.000607467340764190.001214934681528380.999392532659236
173.9371240151208e-057.87424803024159e-050.999960628759849
182.34577214062529e-064.69154428125058e-060.999997654227859
197.0797034117476e-071.41594068234952e-060.999999292029659
206.53332033791137e-071.30666406758227e-060.999999346667966
213.81620417175415e-077.63240834350831e-070.999999618379583
228.21849428986936e-081.64369885797387e-070.999999917815057
232.06219191783918e-084.12438383567836e-080.999999979378081
246.47694987615527e-091.29538997523105e-080.99999999352305
251.59266509227882e-093.18533018455764e-090.999999998407335
261.23092773391168e-092.46185546782337e-090.999999998769072
273.16312249726122e-096.32624499452243e-090.999999996836877
281.48080822606901e-082.96161645213802e-080.999999985191918
298.06953007466768e-081.61390601493354e-070.999999919304699
304.49742878195913e-078.99485756391826e-070.999999550257122
313.16925719007608e-066.33851438015216e-060.99999683074281
323.01133612531012e-056.02267225062024e-050.999969886638747
330.0004745294205446040.0009490588410892080.999525470579455
340.007121477066770370.01424295413354070.99287852293323
350.07887751732978980.157755034659580.92112248267021
360.3596043082500280.7192086165000550.640395691749972
370.7770672936405580.4458654127188840.222932706359442
380.9699357194005670.0601285611988650.0300642805994325
390.9973882018131030.005223596373795080.00261179818689754
400.9996166132441270.0007667735117462540.000383386755873127
410.9997171660146480.0005656679707036450.000282833985351823
420.9996402246506860.0007195506986284820.000359775349314241
430.9992616706752020.001476658649596890.000738329324798444
440.998183238877330.003633522245340030.00181676112267001

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
16 & 0.00060746734076419 & 0.00121493468152838 & 0.999392532659236 \tabularnewline
17 & 3.9371240151208e-05 & 7.87424803024159e-05 & 0.999960628759849 \tabularnewline
18 & 2.34577214062529e-06 & 4.69154428125058e-06 & 0.999997654227859 \tabularnewline
19 & 7.0797034117476e-07 & 1.41594068234952e-06 & 0.999999292029659 \tabularnewline
20 & 6.53332033791137e-07 & 1.30666406758227e-06 & 0.999999346667966 \tabularnewline
21 & 3.81620417175415e-07 & 7.63240834350831e-07 & 0.999999618379583 \tabularnewline
22 & 8.21849428986936e-08 & 1.64369885797387e-07 & 0.999999917815057 \tabularnewline
23 & 2.06219191783918e-08 & 4.12438383567836e-08 & 0.999999979378081 \tabularnewline
24 & 6.47694987615527e-09 & 1.29538997523105e-08 & 0.99999999352305 \tabularnewline
25 & 1.59266509227882e-09 & 3.18533018455764e-09 & 0.999999998407335 \tabularnewline
26 & 1.23092773391168e-09 & 2.46185546782337e-09 & 0.999999998769072 \tabularnewline
27 & 3.16312249726122e-09 & 6.32624499452243e-09 & 0.999999996836877 \tabularnewline
28 & 1.48080822606901e-08 & 2.96161645213802e-08 & 0.999999985191918 \tabularnewline
29 & 8.06953007466768e-08 & 1.61390601493354e-07 & 0.999999919304699 \tabularnewline
30 & 4.49742878195913e-07 & 8.99485756391826e-07 & 0.999999550257122 \tabularnewline
31 & 3.16925719007608e-06 & 6.33851438015216e-06 & 0.99999683074281 \tabularnewline
32 & 3.01133612531012e-05 & 6.02267225062024e-05 & 0.999969886638747 \tabularnewline
33 & 0.000474529420544604 & 0.000949058841089208 & 0.999525470579455 \tabularnewline
34 & 0.00712147706677037 & 0.0142429541335407 & 0.99287852293323 \tabularnewline
35 & 0.0788775173297898 & 0.15775503465958 & 0.92112248267021 \tabularnewline
36 & 0.359604308250028 & 0.719208616500055 & 0.640395691749972 \tabularnewline
37 & 0.777067293640558 & 0.445865412718884 & 0.222932706359442 \tabularnewline
38 & 0.969935719400567 & 0.060128561198865 & 0.0300642805994325 \tabularnewline
39 & 0.997388201813103 & 0.00522359637379508 & 0.00261179818689754 \tabularnewline
40 & 0.999616613244127 & 0.000766773511746254 & 0.000383386755873127 \tabularnewline
41 & 0.999717166014648 & 0.000565667970703645 & 0.000282833985351823 \tabularnewline
42 & 0.999640224650686 & 0.000719550698628482 & 0.000359775349314241 \tabularnewline
43 & 0.999261670675202 & 0.00147665864959689 & 0.000738329324798444 \tabularnewline
44 & 0.99818323887733 & 0.00363352224534003 & 0.00181676112267001 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=148706&T=5

[TABLE]
[ROW][C]Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]p-values[/C][C]Alternative Hypothesis[/C][/ROW]
[ROW][C]breakpoint index[/C][C]greater[/C][C]2-sided[/C][C]less[/C][/ROW]
[ROW][C]16[/C][C]0.00060746734076419[/C][C]0.00121493468152838[/C][C]0.999392532659236[/C][/ROW]
[ROW][C]17[/C][C]3.9371240151208e-05[/C][C]7.87424803024159e-05[/C][C]0.999960628759849[/C][/ROW]
[ROW][C]18[/C][C]2.34577214062529e-06[/C][C]4.69154428125058e-06[/C][C]0.999997654227859[/C][/ROW]
[ROW][C]19[/C][C]7.0797034117476e-07[/C][C]1.41594068234952e-06[/C][C]0.999999292029659[/C][/ROW]
[ROW][C]20[/C][C]6.53332033791137e-07[/C][C]1.30666406758227e-06[/C][C]0.999999346667966[/C][/ROW]
[ROW][C]21[/C][C]3.81620417175415e-07[/C][C]7.63240834350831e-07[/C][C]0.999999618379583[/C][/ROW]
[ROW][C]22[/C][C]8.21849428986936e-08[/C][C]1.64369885797387e-07[/C][C]0.999999917815057[/C][/ROW]
[ROW][C]23[/C][C]2.06219191783918e-08[/C][C]4.12438383567836e-08[/C][C]0.999999979378081[/C][/ROW]
[ROW][C]24[/C][C]6.47694987615527e-09[/C][C]1.29538997523105e-08[/C][C]0.99999999352305[/C][/ROW]
[ROW][C]25[/C][C]1.59266509227882e-09[/C][C]3.18533018455764e-09[/C][C]0.999999998407335[/C][/ROW]
[ROW][C]26[/C][C]1.23092773391168e-09[/C][C]2.46185546782337e-09[/C][C]0.999999998769072[/C][/ROW]
[ROW][C]27[/C][C]3.16312249726122e-09[/C][C]6.32624499452243e-09[/C][C]0.999999996836877[/C][/ROW]
[ROW][C]28[/C][C]1.48080822606901e-08[/C][C]2.96161645213802e-08[/C][C]0.999999985191918[/C][/ROW]
[ROW][C]29[/C][C]8.06953007466768e-08[/C][C]1.61390601493354e-07[/C][C]0.999999919304699[/C][/ROW]
[ROW][C]30[/C][C]4.49742878195913e-07[/C][C]8.99485756391826e-07[/C][C]0.999999550257122[/C][/ROW]
[ROW][C]31[/C][C]3.16925719007608e-06[/C][C]6.33851438015216e-06[/C][C]0.99999683074281[/C][/ROW]
[ROW][C]32[/C][C]3.01133612531012e-05[/C][C]6.02267225062024e-05[/C][C]0.999969886638747[/C][/ROW]
[ROW][C]33[/C][C]0.000474529420544604[/C][C]0.000949058841089208[/C][C]0.999525470579455[/C][/ROW]
[ROW][C]34[/C][C]0.00712147706677037[/C][C]0.0142429541335407[/C][C]0.99287852293323[/C][/ROW]
[ROW][C]35[/C][C]0.0788775173297898[/C][C]0.15775503465958[/C][C]0.92112248267021[/C][/ROW]
[ROW][C]36[/C][C]0.359604308250028[/C][C]0.719208616500055[/C][C]0.640395691749972[/C][/ROW]
[ROW][C]37[/C][C]0.777067293640558[/C][C]0.445865412718884[/C][C]0.222932706359442[/C][/ROW]
[ROW][C]38[/C][C]0.969935719400567[/C][C]0.060128561198865[/C][C]0.0300642805994325[/C][/ROW]
[ROW][C]39[/C][C]0.997388201813103[/C][C]0.00522359637379508[/C][C]0.00261179818689754[/C][/ROW]
[ROW][C]40[/C][C]0.999616613244127[/C][C]0.000766773511746254[/C][C]0.000383386755873127[/C][/ROW]
[ROW][C]41[/C][C]0.999717166014648[/C][C]0.000565667970703645[/C][C]0.000282833985351823[/C][/ROW]
[ROW][C]42[/C][C]0.999640224650686[/C][C]0.000719550698628482[/C][C]0.000359775349314241[/C][/ROW]
[ROW][C]43[/C][C]0.999261670675202[/C][C]0.00147665864959689[/C][C]0.000738329324798444[/C][/ROW]
[ROW][C]44[/C][C]0.99818323887733[/C][C]0.00363352224534003[/C][C]0.00181676112267001[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=148706&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=148706&T=5

As an alternative you can also use a QR Code:  

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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
160.000607467340764190.001214934681528380.999392532659236
173.9371240151208e-057.87424803024159e-050.999960628759849
182.34577214062529e-064.69154428125058e-060.999997654227859
197.0797034117476e-071.41594068234952e-060.999999292029659
206.53332033791137e-071.30666406758227e-060.999999346667966
213.81620417175415e-077.63240834350831e-070.999999618379583
228.21849428986936e-081.64369885797387e-070.999999917815057
232.06219191783918e-084.12438383567836e-080.999999979378081
246.47694987615527e-091.29538997523105e-080.99999999352305
251.59266509227882e-093.18533018455764e-090.999999998407335
261.23092773391168e-092.46185546782337e-090.999999998769072
273.16312249726122e-096.32624499452243e-090.999999996836877
281.48080822606901e-082.96161645213802e-080.999999985191918
298.06953007466768e-081.61390601493354e-070.999999919304699
304.49742878195913e-078.99485756391826e-070.999999550257122
313.16925719007608e-066.33851438015216e-060.99999683074281
323.01133612531012e-056.02267225062024e-050.999969886638747
330.0004745294205446040.0009490588410892080.999525470579455
340.007121477066770370.01424295413354070.99287852293323
350.07887751732978980.157755034659580.92112248267021
360.3596043082500280.7192086165000550.640395691749972
370.7770672936405580.4458654127188840.222932706359442
380.9699357194005670.0601285611988650.0300642805994325
390.9973882018131030.005223596373795080.00261179818689754
400.9996166132441270.0007667735117462540.000383386755873127
410.9997171660146480.0005656679707036450.000282833985351823
420.9996402246506860.0007195506986284820.000359775349314241
430.9992616706752020.001476658649596890.000738329324798444
440.998183238877330.003633522245340030.00181676112267001







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level240.827586206896552NOK
5% type I error level250.862068965517241NOK
10% type I error level260.896551724137931NOK

\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 & 24 & 0.827586206896552 & NOK \tabularnewline
5% type I error level & 25 & 0.862068965517241 & NOK \tabularnewline
10% type I error level & 26 & 0.896551724137931 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=148706&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]24[/C][C]0.827586206896552[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]25[/C][C]0.862068965517241[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]26[/C][C]0.896551724137931[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=148706&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=148706&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 level240.827586206896552NOK
5% type I error level250.862068965517241NOK
10% type I error level260.896551724137931NOK



Parameters (Session):
par1 = 1 ; par2 = Include Monthly Dummies ; par3 = Linear Trend ;
Parameters (R input):
par1 = 1 ; par2 = Include Monthly Dummies ; par3 = Linear Trend ; par4 = ; par5 = ; par6 = ; par7 = ; par8 = ; par9 = ; par10 = ; par11 = ; par12 = ; par13 = ; par14 = ; par15 = ; par16 = ; par17 = ; par18 = ; par19 = ; par20 = ;
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')
}