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Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationThu, 19 Nov 2009 12:04:39 -0700
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2009/Nov/19/t1258657566aw328f0ycs84z3h.htm/, Retrieved Fri, 29 Mar 2024 13:05:44 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=57901, Retrieved Fri, 29 Mar 2024 13:05:44 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact129
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [Q1 The Seatbeltlaw] [2007-11-14 19:27:43] [8cd6641b921d30ebe00b648d1481bba0]
- RM D  [Multiple Regression] [Seatbelt] [2009-11-12 14:06:21] [b98453cac15ba1066b407e146608df68]
- R  D      [Multiple Regression] [] [2009-11-19 19:04:39] [6974478841a4d28b8cb590971bfdefb0] [Current]
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Dataseries X:
611	0
594	0
595	0
591	0
589	0
584	0
573	0
567	0
569	0
621	0
629	0
628	0
612	0
595	0
597	0
593	0
590	0
580	0
574	0
573	0
573	0
620	0
626	0
620	0
588	0
566	0
557	0
561	0
549	0
532	0
526	0
511	0
499	0
555	0
565	0
542	0
527	0
510	0
514	0
517	0
508	0
493	0
490	0
469	0
478	0
528	0
534	0
518	1
506	1
502	1
516	1
528	1
533	1
536	1
537	1
524	1
536	1
587	1
597	1
581	1




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time3 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 3 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57901&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]3 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57901&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57901&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 time3 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135







Multiple Linear Regression - Estimated Regression Equation
Y[t] = + 649.098782608696 + 49.8834782608695X[t] -26.9059130434782M1[t] -39.7711304347826M2[t] -34.8363478260870M3[t] -30.1015652173913M4[t] -31.7667826086957M5[t] -38.0320000000000M6[t] -40.4972173913044M7[t] -49.1624347826087M8[t] -44.427652173913M9[t] + 9.3071304347826M10[t] + 19.8419130434782M11[t] -2.53478260869565t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Y[t] =  +  649.098782608696 +  49.8834782608695X[t] -26.9059130434782M1[t] -39.7711304347826M2[t] -34.8363478260870M3[t] -30.1015652173913M4[t] -31.7667826086957M5[t] -38.0320000000000M6[t] -40.4972173913044M7[t] -49.1624347826087M8[t] -44.427652173913M9[t] +  9.3071304347826M10[t] +  19.8419130434782M11[t] -2.53478260869565t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57901&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Y[t] =  +  649.098782608696 +  49.8834782608695X[t] -26.9059130434782M1[t] -39.7711304347826M2[t] -34.8363478260870M3[t] -30.1015652173913M4[t] -31.7667826086957M5[t] -38.0320000000000M6[t] -40.4972173913044M7[t] -49.1624347826087M8[t] -44.427652173913M9[t] +  9.3071304347826M10[t] +  19.8419130434782M11[t] -2.53478260869565t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57901&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57901&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] = + 649.098782608696 + 49.8834782608695X[t] -26.9059130434782M1[t] -39.7711304347826M2[t] -34.8363478260870M3[t] -30.1015652173913M4[t] -31.7667826086957M5[t] -38.0320000000000M6[t] -40.4972173913044M7[t] -49.1624347826087M8[t] -44.427652173913M9[t] + 9.3071304347826M10[t] + 19.8419130434782M11[t] -2.53478260869565t + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)649.09878260869611.6266955.828300
X49.88347826086959.9393995.01888e-064e-06
M1-26.905913043478213.880686-1.93840.0587290.029364
M2-39.771130434782613.862139-2.8690.0061980.003099
M3-34.836347826087013.847697-2.51570.0154350.007718
M4-30.101565217391313.837372-2.17540.0347770.017389
M5-31.766782608695713.831173-2.29680.0262360.013118
M6-38.032000000000013.829106-2.75010.0084880.004244
M7-40.497217391304413.831173-2.9280.005290.002645
M8-49.162434782608713.837372-3.55290.0008940.000447
M9-44.42765217391313.847697-3.20830.0024330.001217
M109.307130434782613.8621390.67140.5053190.25266
M1119.841913043478213.8806861.42950.1596280.079814
t-2.534782608695650.239105-10.601100

\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) & 649.098782608696 & 11.62669 & 55.8283 & 0 & 0 \tabularnewline
X & 49.8834782608695 & 9.939399 & 5.0188 & 8e-06 & 4e-06 \tabularnewline
M1 & -26.9059130434782 & 13.880686 & -1.9384 & 0.058729 & 0.029364 \tabularnewline
M2 & -39.7711304347826 & 13.862139 & -2.869 & 0.006198 & 0.003099 \tabularnewline
M3 & -34.8363478260870 & 13.847697 & -2.5157 & 0.015435 & 0.007718 \tabularnewline
M4 & -30.1015652173913 & 13.837372 & -2.1754 & 0.034777 & 0.017389 \tabularnewline
M5 & -31.7667826086957 & 13.831173 & -2.2968 & 0.026236 & 0.013118 \tabularnewline
M6 & -38.0320000000000 & 13.829106 & -2.7501 & 0.008488 & 0.004244 \tabularnewline
M7 & -40.4972173913044 & 13.831173 & -2.928 & 0.00529 & 0.002645 \tabularnewline
M8 & -49.1624347826087 & 13.837372 & -3.5529 & 0.000894 & 0.000447 \tabularnewline
M9 & -44.427652173913 & 13.847697 & -3.2083 & 0.002433 & 0.001217 \tabularnewline
M10 & 9.3071304347826 & 13.862139 & 0.6714 & 0.505319 & 0.25266 \tabularnewline
M11 & 19.8419130434782 & 13.880686 & 1.4295 & 0.159628 & 0.079814 \tabularnewline
t & -2.53478260869565 & 0.239105 & -10.6011 & 0 & 0 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57901&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]649.098782608696[/C][C]11.62669[/C][C]55.8283[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]X[/C][C]49.8834782608695[/C][C]9.939399[/C][C]5.0188[/C][C]8e-06[/C][C]4e-06[/C][/ROW]
[ROW][C]M1[/C][C]-26.9059130434782[/C][C]13.880686[/C][C]-1.9384[/C][C]0.058729[/C][C]0.029364[/C][/ROW]
[ROW][C]M2[/C][C]-39.7711304347826[/C][C]13.862139[/C][C]-2.869[/C][C]0.006198[/C][C]0.003099[/C][/ROW]
[ROW][C]M3[/C][C]-34.8363478260870[/C][C]13.847697[/C][C]-2.5157[/C][C]0.015435[/C][C]0.007718[/C][/ROW]
[ROW][C]M4[/C][C]-30.1015652173913[/C][C]13.837372[/C][C]-2.1754[/C][C]0.034777[/C][C]0.017389[/C][/ROW]
[ROW][C]M5[/C][C]-31.7667826086957[/C][C]13.831173[/C][C]-2.2968[/C][C]0.026236[/C][C]0.013118[/C][/ROW]
[ROW][C]M6[/C][C]-38.0320000000000[/C][C]13.829106[/C][C]-2.7501[/C][C]0.008488[/C][C]0.004244[/C][/ROW]
[ROW][C]M7[/C][C]-40.4972173913044[/C][C]13.831173[/C][C]-2.928[/C][C]0.00529[/C][C]0.002645[/C][/ROW]
[ROW][C]M8[/C][C]-49.1624347826087[/C][C]13.837372[/C][C]-3.5529[/C][C]0.000894[/C][C]0.000447[/C][/ROW]
[ROW][C]M9[/C][C]-44.427652173913[/C][C]13.847697[/C][C]-3.2083[/C][C]0.002433[/C][C]0.001217[/C][/ROW]
[ROW][C]M10[/C][C]9.3071304347826[/C][C]13.862139[/C][C]0.6714[/C][C]0.505319[/C][C]0.25266[/C][/ROW]
[ROW][C]M11[/C][C]19.8419130434782[/C][C]13.880686[/C][C]1.4295[/C][C]0.159628[/C][C]0.079814[/C][/ROW]
[ROW][C]t[/C][C]-2.53478260869565[/C][C]0.239105[/C][C]-10.6011[/C][C]0[/C][C]0[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57901&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57901&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)649.09878260869611.6266955.828300
X49.88347826086959.9393995.01888e-064e-06
M1-26.905913043478213.880686-1.93840.0587290.029364
M2-39.771130434782613.862139-2.8690.0061980.003099
M3-34.836347826087013.847697-2.51570.0154350.007718
M4-30.101565217391313.837372-2.17540.0347770.017389
M5-31.766782608695713.831173-2.29680.0262360.013118
M6-38.032000000000013.829106-2.75010.0084880.004244
M7-40.497217391304413.831173-2.9280.005290.002645
M8-49.162434782608713.837372-3.55290.0008940.000447
M9-44.42765217391313.847697-3.20830.0024330.001217
M109.307130434782613.8621390.67140.5053190.25266
M1119.841913043478213.8806861.42950.1596280.079814
t-2.534782608695650.239105-10.601100







Multiple Linear Regression - Regression Statistics
Multiple R0.888041241395152
R-squared0.788617246418643
Adjusted R-squared0.72887864214565
F-TEST (value)13.2011327686003
F-TEST (DF numerator)13
F-TEST (DF denominator)46
p-value1.89785964721523e-11
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation21.757220994567
Sum Squared Residuals21775.3266086956

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.888041241395152 \tabularnewline
R-squared & 0.788617246418643 \tabularnewline
Adjusted R-squared & 0.72887864214565 \tabularnewline
F-TEST (value) & 13.2011327686003 \tabularnewline
F-TEST (DF numerator) & 13 \tabularnewline
F-TEST (DF denominator) & 46 \tabularnewline
p-value & 1.89785964721523e-11 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 21.757220994567 \tabularnewline
Sum Squared Residuals & 21775.3266086956 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57901&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.888041241395152[/C][/ROW]
[ROW][C]R-squared[/C][C]0.788617246418643[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.72887864214565[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]13.2011327686003[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]13[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]46[/C][/ROW]
[ROW][C]p-value[/C][C]1.89785964721523e-11[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]21.757220994567[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]21775.3266086956[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57901&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57901&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 R0.888041241395152
R-squared0.788617246418643
Adjusted R-squared0.72887864214565
F-TEST (value)13.2011327686003
F-TEST (DF numerator)13
F-TEST (DF denominator)46
p-value1.89785964721523e-11
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation21.757220994567
Sum Squared Residuals21775.3266086956







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
1611619.658086956522-8.65808695652151
2594604.258086956522-10.2580869565216
3595606.658086956522-11.6580869565218
4591608.858086956522-17.8580869565217
5589604.658086956522-15.6580869565218
6584595.858086956522-11.8580869565218
7573590.858086956522-17.8580869565218
8567579.658086956522-12.6580869565217
9569581.858086956522-12.8580869565217
10621633.058086956522-12.0580869565218
11629641.058086956522-12.0580869565218
12628618.6813913043489.31860869565215
13612589.24069565217422.759304347826
14595573.84069565217421.159304347826
15597576.24069565217420.7593043478261
16593578.44069565217414.5593043478261
17590574.24069565217415.7593043478261
18580565.44069565217414.5593043478261
19574560.44069565217413.5593043478261
20573549.24069565217423.7593043478261
21573551.44069565217421.5593043478261
22620602.64069565217417.3593043478261
23626610.64069565217415.3593043478261
24620588.26431.736
25588558.82330434782629.1766956521739
26566543.42330434782622.5766956521739
27557545.82330434782611.1766956521739
28561548.02330434782612.9766956521739
29549543.8233043478265.17669565217393
30532535.023304347826-3.02330434782607
31526530.023304347826-4.02330434782608
32511518.823304347826-7.8233043478261
33499521.023304347826-22.0233043478261
34555572.223304347826-17.2233043478261
35565580.223304347826-15.2233043478261
36542557.846608695652-15.8466086956522
37527528.405913043478-1.40591304347832
38510513.005913043478-3.00591304347824
39514515.405913043478-1.40591304347824
40517517.605913043478-0.60591304347824
41508513.405913043478-5.40591304347825
42493504.605913043478-11.6059130434782
43490499.605913043478-9.60591304347823
44469488.405913043478-19.4059130434783
45478490.605913043478-12.6059130434782
46528541.805913043478-13.8059130434783
47534549.805913043478-15.8059130434783
48518577.312695652174-59.3126956521739
49506547.872-41.8720000000001
50502532.472-30.472
51516534.872-18.872
52528537.072-9.0720
53533532.8720.128000000000009
54536524.07211.928
55537519.07217.928
56524507.87216.128
57536510.07225.928
58587561.27225.728
59597569.27227.728
60581546.89530434782634.1046956521739

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 611 & 619.658086956522 & -8.65808695652151 \tabularnewline
2 & 594 & 604.258086956522 & -10.2580869565216 \tabularnewline
3 & 595 & 606.658086956522 & -11.6580869565218 \tabularnewline
4 & 591 & 608.858086956522 & -17.8580869565217 \tabularnewline
5 & 589 & 604.658086956522 & -15.6580869565218 \tabularnewline
6 & 584 & 595.858086956522 & -11.8580869565218 \tabularnewline
7 & 573 & 590.858086956522 & -17.8580869565218 \tabularnewline
8 & 567 & 579.658086956522 & -12.6580869565217 \tabularnewline
9 & 569 & 581.858086956522 & -12.8580869565217 \tabularnewline
10 & 621 & 633.058086956522 & -12.0580869565218 \tabularnewline
11 & 629 & 641.058086956522 & -12.0580869565218 \tabularnewline
12 & 628 & 618.681391304348 & 9.31860869565215 \tabularnewline
13 & 612 & 589.240695652174 & 22.759304347826 \tabularnewline
14 & 595 & 573.840695652174 & 21.159304347826 \tabularnewline
15 & 597 & 576.240695652174 & 20.7593043478261 \tabularnewline
16 & 593 & 578.440695652174 & 14.5593043478261 \tabularnewline
17 & 590 & 574.240695652174 & 15.7593043478261 \tabularnewline
18 & 580 & 565.440695652174 & 14.5593043478261 \tabularnewline
19 & 574 & 560.440695652174 & 13.5593043478261 \tabularnewline
20 & 573 & 549.240695652174 & 23.7593043478261 \tabularnewline
21 & 573 & 551.440695652174 & 21.5593043478261 \tabularnewline
22 & 620 & 602.640695652174 & 17.3593043478261 \tabularnewline
23 & 626 & 610.640695652174 & 15.3593043478261 \tabularnewline
24 & 620 & 588.264 & 31.736 \tabularnewline
25 & 588 & 558.823304347826 & 29.1766956521739 \tabularnewline
26 & 566 & 543.423304347826 & 22.5766956521739 \tabularnewline
27 & 557 & 545.823304347826 & 11.1766956521739 \tabularnewline
28 & 561 & 548.023304347826 & 12.9766956521739 \tabularnewline
29 & 549 & 543.823304347826 & 5.17669565217393 \tabularnewline
30 & 532 & 535.023304347826 & -3.02330434782607 \tabularnewline
31 & 526 & 530.023304347826 & -4.02330434782608 \tabularnewline
32 & 511 & 518.823304347826 & -7.8233043478261 \tabularnewline
33 & 499 & 521.023304347826 & -22.0233043478261 \tabularnewline
34 & 555 & 572.223304347826 & -17.2233043478261 \tabularnewline
35 & 565 & 580.223304347826 & -15.2233043478261 \tabularnewline
36 & 542 & 557.846608695652 & -15.8466086956522 \tabularnewline
37 & 527 & 528.405913043478 & -1.40591304347832 \tabularnewline
38 & 510 & 513.005913043478 & -3.00591304347824 \tabularnewline
39 & 514 & 515.405913043478 & -1.40591304347824 \tabularnewline
40 & 517 & 517.605913043478 & -0.60591304347824 \tabularnewline
41 & 508 & 513.405913043478 & -5.40591304347825 \tabularnewline
42 & 493 & 504.605913043478 & -11.6059130434782 \tabularnewline
43 & 490 & 499.605913043478 & -9.60591304347823 \tabularnewline
44 & 469 & 488.405913043478 & -19.4059130434783 \tabularnewline
45 & 478 & 490.605913043478 & -12.6059130434782 \tabularnewline
46 & 528 & 541.805913043478 & -13.8059130434783 \tabularnewline
47 & 534 & 549.805913043478 & -15.8059130434783 \tabularnewline
48 & 518 & 577.312695652174 & -59.3126956521739 \tabularnewline
49 & 506 & 547.872 & -41.8720000000001 \tabularnewline
50 & 502 & 532.472 & -30.472 \tabularnewline
51 & 516 & 534.872 & -18.872 \tabularnewline
52 & 528 & 537.072 & -9.0720 \tabularnewline
53 & 533 & 532.872 & 0.128000000000009 \tabularnewline
54 & 536 & 524.072 & 11.928 \tabularnewline
55 & 537 & 519.072 & 17.928 \tabularnewline
56 & 524 & 507.872 & 16.128 \tabularnewline
57 & 536 & 510.072 & 25.928 \tabularnewline
58 & 587 & 561.272 & 25.728 \tabularnewline
59 & 597 & 569.272 & 27.728 \tabularnewline
60 & 581 & 546.895304347826 & 34.1046956521739 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57901&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]611[/C][C]619.658086956522[/C][C]-8.65808695652151[/C][/ROW]
[ROW][C]2[/C][C]594[/C][C]604.258086956522[/C][C]-10.2580869565216[/C][/ROW]
[ROW][C]3[/C][C]595[/C][C]606.658086956522[/C][C]-11.6580869565218[/C][/ROW]
[ROW][C]4[/C][C]591[/C][C]608.858086956522[/C][C]-17.8580869565217[/C][/ROW]
[ROW][C]5[/C][C]589[/C][C]604.658086956522[/C][C]-15.6580869565218[/C][/ROW]
[ROW][C]6[/C][C]584[/C][C]595.858086956522[/C][C]-11.8580869565218[/C][/ROW]
[ROW][C]7[/C][C]573[/C][C]590.858086956522[/C][C]-17.8580869565218[/C][/ROW]
[ROW][C]8[/C][C]567[/C][C]579.658086956522[/C][C]-12.6580869565217[/C][/ROW]
[ROW][C]9[/C][C]569[/C][C]581.858086956522[/C][C]-12.8580869565217[/C][/ROW]
[ROW][C]10[/C][C]621[/C][C]633.058086956522[/C][C]-12.0580869565218[/C][/ROW]
[ROW][C]11[/C][C]629[/C][C]641.058086956522[/C][C]-12.0580869565218[/C][/ROW]
[ROW][C]12[/C][C]628[/C][C]618.681391304348[/C][C]9.31860869565215[/C][/ROW]
[ROW][C]13[/C][C]612[/C][C]589.240695652174[/C][C]22.759304347826[/C][/ROW]
[ROW][C]14[/C][C]595[/C][C]573.840695652174[/C][C]21.159304347826[/C][/ROW]
[ROW][C]15[/C][C]597[/C][C]576.240695652174[/C][C]20.7593043478261[/C][/ROW]
[ROW][C]16[/C][C]593[/C][C]578.440695652174[/C][C]14.5593043478261[/C][/ROW]
[ROW][C]17[/C][C]590[/C][C]574.240695652174[/C][C]15.7593043478261[/C][/ROW]
[ROW][C]18[/C][C]580[/C][C]565.440695652174[/C][C]14.5593043478261[/C][/ROW]
[ROW][C]19[/C][C]574[/C][C]560.440695652174[/C][C]13.5593043478261[/C][/ROW]
[ROW][C]20[/C][C]573[/C][C]549.240695652174[/C][C]23.7593043478261[/C][/ROW]
[ROW][C]21[/C][C]573[/C][C]551.440695652174[/C][C]21.5593043478261[/C][/ROW]
[ROW][C]22[/C][C]620[/C][C]602.640695652174[/C][C]17.3593043478261[/C][/ROW]
[ROW][C]23[/C][C]626[/C][C]610.640695652174[/C][C]15.3593043478261[/C][/ROW]
[ROW][C]24[/C][C]620[/C][C]588.264[/C][C]31.736[/C][/ROW]
[ROW][C]25[/C][C]588[/C][C]558.823304347826[/C][C]29.1766956521739[/C][/ROW]
[ROW][C]26[/C][C]566[/C][C]543.423304347826[/C][C]22.5766956521739[/C][/ROW]
[ROW][C]27[/C][C]557[/C][C]545.823304347826[/C][C]11.1766956521739[/C][/ROW]
[ROW][C]28[/C][C]561[/C][C]548.023304347826[/C][C]12.9766956521739[/C][/ROW]
[ROW][C]29[/C][C]549[/C][C]543.823304347826[/C][C]5.17669565217393[/C][/ROW]
[ROW][C]30[/C][C]532[/C][C]535.023304347826[/C][C]-3.02330434782607[/C][/ROW]
[ROW][C]31[/C][C]526[/C][C]530.023304347826[/C][C]-4.02330434782608[/C][/ROW]
[ROW][C]32[/C][C]511[/C][C]518.823304347826[/C][C]-7.8233043478261[/C][/ROW]
[ROW][C]33[/C][C]499[/C][C]521.023304347826[/C][C]-22.0233043478261[/C][/ROW]
[ROW][C]34[/C][C]555[/C][C]572.223304347826[/C][C]-17.2233043478261[/C][/ROW]
[ROW][C]35[/C][C]565[/C][C]580.223304347826[/C][C]-15.2233043478261[/C][/ROW]
[ROW][C]36[/C][C]542[/C][C]557.846608695652[/C][C]-15.8466086956522[/C][/ROW]
[ROW][C]37[/C][C]527[/C][C]528.405913043478[/C][C]-1.40591304347832[/C][/ROW]
[ROW][C]38[/C][C]510[/C][C]513.005913043478[/C][C]-3.00591304347824[/C][/ROW]
[ROW][C]39[/C][C]514[/C][C]515.405913043478[/C][C]-1.40591304347824[/C][/ROW]
[ROW][C]40[/C][C]517[/C][C]517.605913043478[/C][C]-0.60591304347824[/C][/ROW]
[ROW][C]41[/C][C]508[/C][C]513.405913043478[/C][C]-5.40591304347825[/C][/ROW]
[ROW][C]42[/C][C]493[/C][C]504.605913043478[/C][C]-11.6059130434782[/C][/ROW]
[ROW][C]43[/C][C]490[/C][C]499.605913043478[/C][C]-9.60591304347823[/C][/ROW]
[ROW][C]44[/C][C]469[/C][C]488.405913043478[/C][C]-19.4059130434783[/C][/ROW]
[ROW][C]45[/C][C]478[/C][C]490.605913043478[/C][C]-12.6059130434782[/C][/ROW]
[ROW][C]46[/C][C]528[/C][C]541.805913043478[/C][C]-13.8059130434783[/C][/ROW]
[ROW][C]47[/C][C]534[/C][C]549.805913043478[/C][C]-15.8059130434783[/C][/ROW]
[ROW][C]48[/C][C]518[/C][C]577.312695652174[/C][C]-59.3126956521739[/C][/ROW]
[ROW][C]49[/C][C]506[/C][C]547.872[/C][C]-41.8720000000001[/C][/ROW]
[ROW][C]50[/C][C]502[/C][C]532.472[/C][C]-30.472[/C][/ROW]
[ROW][C]51[/C][C]516[/C][C]534.872[/C][C]-18.872[/C][/ROW]
[ROW][C]52[/C][C]528[/C][C]537.072[/C][C]-9.0720[/C][/ROW]
[ROW][C]53[/C][C]533[/C][C]532.872[/C][C]0.128000000000009[/C][/ROW]
[ROW][C]54[/C][C]536[/C][C]524.072[/C][C]11.928[/C][/ROW]
[ROW][C]55[/C][C]537[/C][C]519.072[/C][C]17.928[/C][/ROW]
[ROW][C]56[/C][C]524[/C][C]507.872[/C][C]16.128[/C][/ROW]
[ROW][C]57[/C][C]536[/C][C]510.072[/C][C]25.928[/C][/ROW]
[ROW][C]58[/C][C]587[/C][C]561.272[/C][C]25.728[/C][/ROW]
[ROW][C]59[/C][C]597[/C][C]569.272[/C][C]27.728[/C][/ROW]
[ROW][C]60[/C][C]581[/C][C]546.895304347826[/C][C]34.1046956521739[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57901&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57901&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
1611619.658086956522-8.65808695652151
2594604.258086956522-10.2580869565216
3595606.658086956522-11.6580869565218
4591608.858086956522-17.8580869565217
5589604.658086956522-15.6580869565218
6584595.858086956522-11.8580869565218
7573590.858086956522-17.8580869565218
8567579.658086956522-12.6580869565217
9569581.858086956522-12.8580869565217
10621633.058086956522-12.0580869565218
11629641.058086956522-12.0580869565218
12628618.6813913043489.31860869565215
13612589.24069565217422.759304347826
14595573.84069565217421.159304347826
15597576.24069565217420.7593043478261
16593578.44069565217414.5593043478261
17590574.24069565217415.7593043478261
18580565.44069565217414.5593043478261
19574560.44069565217413.5593043478261
20573549.24069565217423.7593043478261
21573551.44069565217421.5593043478261
22620602.64069565217417.3593043478261
23626610.64069565217415.3593043478261
24620588.26431.736
25588558.82330434782629.1766956521739
26566543.42330434782622.5766956521739
27557545.82330434782611.1766956521739
28561548.02330434782612.9766956521739
29549543.8233043478265.17669565217393
30532535.023304347826-3.02330434782607
31526530.023304347826-4.02330434782608
32511518.823304347826-7.8233043478261
33499521.023304347826-22.0233043478261
34555572.223304347826-17.2233043478261
35565580.223304347826-15.2233043478261
36542557.846608695652-15.8466086956522
37527528.405913043478-1.40591304347832
38510513.005913043478-3.00591304347824
39514515.405913043478-1.40591304347824
40517517.605913043478-0.60591304347824
41508513.405913043478-5.40591304347825
42493504.605913043478-11.6059130434782
43490499.605913043478-9.60591304347823
44469488.405913043478-19.4059130434783
45478490.605913043478-12.6059130434782
46528541.805913043478-13.8059130434783
47534549.805913043478-15.8059130434783
48518577.312695652174-59.3126956521739
49506547.872-41.8720000000001
50502532.472-30.472
51516534.872-18.872
52528537.072-9.0720
53533532.8720.128000000000009
54536524.07211.928
55537519.07217.928
56524507.87216.128
57536510.07225.928
58587561.27225.728
59597569.27227.728
60581546.89530434782634.1046956521739







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
171.00872762296752e-052.01745524593504e-050.99998991272377
186.88981666961962e-050.0001377963333923920.999931101833304
194.39749428598553e-068.79498857197107e-060.999995602505714
202.03112426922738e-064.06224853845476e-060.99999796887573
212.34205806664728e-074.68411613329457e-070.999999765794193
222.50951335990665e-085.01902671981329e-080.999999974904866
235.04519717219935e-091.00903943443987e-080.999999994954803
241.47067919612813e-082.94135839225625e-080.999999985293208
251.5714384797056e-053.1428769594112e-050.999984285615203
260.0002040718968456980.0004081437936913960.999795928103154
270.001918543583177810.003837087166355610.998081456416822
280.002565728996874260.005131457993748510.997434271003126
290.005243368151662660.01048673630332530.994756631848337
300.01277978940952580.02555957881905170.987220210590474
310.01647253909389510.03294507818779030.983527460906105
320.03514178813908670.07028357627817340.964858211860913
330.07135702832504450.1427140566500890.928642971674956
340.08607310126579830.1721462025315970.913926898734202
350.1192587616327730.2385175232655450.880741238367228
360.2903034983042510.5806069966085010.709696501695749
370.4936996155854360.9873992311708720.506300384414564
380.6781300729003030.6437398541993940.321869927099697
390.8322074160720440.3355851678559120.167792583927956
400.9517592266761790.09648154664764260.0482407733238213
410.9929888722064930.01402225558701330.00701112779350663
420.9936698286871480.01266034262570480.00633017131285241
430.9966899181959680.006620163608064740.00331008180403237

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
17 & 1.00872762296752e-05 & 2.01745524593504e-05 & 0.99998991272377 \tabularnewline
18 & 6.88981666961962e-05 & 0.000137796333392392 & 0.999931101833304 \tabularnewline
19 & 4.39749428598553e-06 & 8.79498857197107e-06 & 0.999995602505714 \tabularnewline
20 & 2.03112426922738e-06 & 4.06224853845476e-06 & 0.99999796887573 \tabularnewline
21 & 2.34205806664728e-07 & 4.68411613329457e-07 & 0.999999765794193 \tabularnewline
22 & 2.50951335990665e-08 & 5.01902671981329e-08 & 0.999999974904866 \tabularnewline
23 & 5.04519717219935e-09 & 1.00903943443987e-08 & 0.999999994954803 \tabularnewline
24 & 1.47067919612813e-08 & 2.94135839225625e-08 & 0.999999985293208 \tabularnewline
25 & 1.5714384797056e-05 & 3.1428769594112e-05 & 0.999984285615203 \tabularnewline
26 & 0.000204071896845698 & 0.000408143793691396 & 0.999795928103154 \tabularnewline
27 & 0.00191854358317781 & 0.00383708716635561 & 0.998081456416822 \tabularnewline
28 & 0.00256572899687426 & 0.00513145799374851 & 0.997434271003126 \tabularnewline
29 & 0.00524336815166266 & 0.0104867363033253 & 0.994756631848337 \tabularnewline
30 & 0.0127797894095258 & 0.0255595788190517 & 0.987220210590474 \tabularnewline
31 & 0.0164725390938951 & 0.0329450781877903 & 0.983527460906105 \tabularnewline
32 & 0.0351417881390867 & 0.0702835762781734 & 0.964858211860913 \tabularnewline
33 & 0.0713570283250445 & 0.142714056650089 & 0.928642971674956 \tabularnewline
34 & 0.0860731012657983 & 0.172146202531597 & 0.913926898734202 \tabularnewline
35 & 0.119258761632773 & 0.238517523265545 & 0.880741238367228 \tabularnewline
36 & 0.290303498304251 & 0.580606996608501 & 0.709696501695749 \tabularnewline
37 & 0.493699615585436 & 0.987399231170872 & 0.506300384414564 \tabularnewline
38 & 0.678130072900303 & 0.643739854199394 & 0.321869927099697 \tabularnewline
39 & 0.832207416072044 & 0.335585167855912 & 0.167792583927956 \tabularnewline
40 & 0.951759226676179 & 0.0964815466476426 & 0.0482407733238213 \tabularnewline
41 & 0.992988872206493 & 0.0140222555870133 & 0.00701112779350663 \tabularnewline
42 & 0.993669828687148 & 0.0126603426257048 & 0.00633017131285241 \tabularnewline
43 & 0.996689918195968 & 0.00662016360806474 & 0.00331008180403237 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57901&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]17[/C][C]1.00872762296752e-05[/C][C]2.01745524593504e-05[/C][C]0.99998991272377[/C][/ROW]
[ROW][C]18[/C][C]6.88981666961962e-05[/C][C]0.000137796333392392[/C][C]0.999931101833304[/C][/ROW]
[ROW][C]19[/C][C]4.39749428598553e-06[/C][C]8.79498857197107e-06[/C][C]0.999995602505714[/C][/ROW]
[ROW][C]20[/C][C]2.03112426922738e-06[/C][C]4.06224853845476e-06[/C][C]0.99999796887573[/C][/ROW]
[ROW][C]21[/C][C]2.34205806664728e-07[/C][C]4.68411613329457e-07[/C][C]0.999999765794193[/C][/ROW]
[ROW][C]22[/C][C]2.50951335990665e-08[/C][C]5.01902671981329e-08[/C][C]0.999999974904866[/C][/ROW]
[ROW][C]23[/C][C]5.04519717219935e-09[/C][C]1.00903943443987e-08[/C][C]0.999999994954803[/C][/ROW]
[ROW][C]24[/C][C]1.47067919612813e-08[/C][C]2.94135839225625e-08[/C][C]0.999999985293208[/C][/ROW]
[ROW][C]25[/C][C]1.5714384797056e-05[/C][C]3.1428769594112e-05[/C][C]0.999984285615203[/C][/ROW]
[ROW][C]26[/C][C]0.000204071896845698[/C][C]0.000408143793691396[/C][C]0.999795928103154[/C][/ROW]
[ROW][C]27[/C][C]0.00191854358317781[/C][C]0.00383708716635561[/C][C]0.998081456416822[/C][/ROW]
[ROW][C]28[/C][C]0.00256572899687426[/C][C]0.00513145799374851[/C][C]0.997434271003126[/C][/ROW]
[ROW][C]29[/C][C]0.00524336815166266[/C][C]0.0104867363033253[/C][C]0.994756631848337[/C][/ROW]
[ROW][C]30[/C][C]0.0127797894095258[/C][C]0.0255595788190517[/C][C]0.987220210590474[/C][/ROW]
[ROW][C]31[/C][C]0.0164725390938951[/C][C]0.0329450781877903[/C][C]0.983527460906105[/C][/ROW]
[ROW][C]32[/C][C]0.0351417881390867[/C][C]0.0702835762781734[/C][C]0.964858211860913[/C][/ROW]
[ROW][C]33[/C][C]0.0713570283250445[/C][C]0.142714056650089[/C][C]0.928642971674956[/C][/ROW]
[ROW][C]34[/C][C]0.0860731012657983[/C][C]0.172146202531597[/C][C]0.913926898734202[/C][/ROW]
[ROW][C]35[/C][C]0.119258761632773[/C][C]0.238517523265545[/C][C]0.880741238367228[/C][/ROW]
[ROW][C]36[/C][C]0.290303498304251[/C][C]0.580606996608501[/C][C]0.709696501695749[/C][/ROW]
[ROW][C]37[/C][C]0.493699615585436[/C][C]0.987399231170872[/C][C]0.506300384414564[/C][/ROW]
[ROW][C]38[/C][C]0.678130072900303[/C][C]0.643739854199394[/C][C]0.321869927099697[/C][/ROW]
[ROW][C]39[/C][C]0.832207416072044[/C][C]0.335585167855912[/C][C]0.167792583927956[/C][/ROW]
[ROW][C]40[/C][C]0.951759226676179[/C][C]0.0964815466476426[/C][C]0.0482407733238213[/C][/ROW]
[ROW][C]41[/C][C]0.992988872206493[/C][C]0.0140222555870133[/C][C]0.00701112779350663[/C][/ROW]
[ROW][C]42[/C][C]0.993669828687148[/C][C]0.0126603426257048[/C][C]0.00633017131285241[/C][/ROW]
[ROW][C]43[/C][C]0.996689918195968[/C][C]0.00662016360806474[/C][C]0.00331008180403237[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57901&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57901&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
171.00872762296752e-052.01745524593504e-050.99998991272377
186.88981666961962e-050.0001377963333923920.999931101833304
194.39749428598553e-068.79498857197107e-060.999995602505714
202.03112426922738e-064.06224853845476e-060.99999796887573
212.34205806664728e-074.68411613329457e-070.999999765794193
222.50951335990665e-085.01902671981329e-080.999999974904866
235.04519717219935e-091.00903943443987e-080.999999994954803
241.47067919612813e-082.94135839225625e-080.999999985293208
251.5714384797056e-053.1428769594112e-050.999984285615203
260.0002040718968456980.0004081437936913960.999795928103154
270.001918543583177810.003837087166355610.998081456416822
280.002565728996874260.005131457993748510.997434271003126
290.005243368151662660.01048673630332530.994756631848337
300.01277978940952580.02555957881905170.987220210590474
310.01647253909389510.03294507818779030.983527460906105
320.03514178813908670.07028357627817340.964858211860913
330.07135702832504450.1427140566500890.928642971674956
340.08607310126579830.1721462025315970.913926898734202
350.1192587616327730.2385175232655450.880741238367228
360.2903034983042510.5806069966085010.709696501695749
370.4936996155854360.9873992311708720.506300384414564
380.6781300729003030.6437398541993940.321869927099697
390.8322074160720440.3355851678559120.167792583927956
400.9517592266761790.09648154664764260.0482407733238213
410.9929888722064930.01402225558701330.00701112779350663
420.9936698286871480.01266034262570480.00633017131285241
430.9966899181959680.006620163608064740.00331008180403237







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level130.481481481481481NOK
5% type I error level180.666666666666667NOK
10% type I error level200.740740740740741NOK

\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 & 13 & 0.481481481481481 & NOK \tabularnewline
5% type I error level & 18 & 0.666666666666667 & NOK \tabularnewline
10% type I error level & 20 & 0.740740740740741 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57901&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]13[/C][C]0.481481481481481[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]18[/C][C]0.666666666666667[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]20[/C][C]0.740740740740741[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57901&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57901&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 level130.481481481481481NOK
5% type I error level180.666666666666667NOK
10% type I error level200.740740740740741NOK



Parameters (Session):
par1 = 1 ; par2 = Include Monthly Dummies ; par3 = Linear Trend ;
Parameters (R input):
par1 = 1 ; par2 = Include Monthly Dummies ; par3 = Linear Trend ;
R code (references can be found in the software module):
library(lattice)
library(lmtest)
n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
par1 <- as.numeric(par1)
x <- t(y)
k <- length(x[1,])
n <- length(x[,1])
x1 <- cbind(x[,par1], x[,1:k!=par1])
mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
colnames(x1) <- mycolnames #colnames(x)[par1]
x <- x1
if (par3 == 'First Differences'){
x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
for (i in 1:n-1) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
}
if (par2 == 'Include Monthly Dummies'){
x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
for (i in 1:11){
x2[seq(i,n,12),i] <- 1
}
x <- cbind(x, x2)
}
if (par2 == 'Include Quarterly Dummies'){
x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
for (i in 1:3){
x2[seq(i,n,4),i] <- 1
}
x <- cbind(x, x2)
}
k <- length(x[1,])
if (par3 == 'Linear Trend'){
x <- cbind(x, c(1:n))
colnames(x)[k+1] <- 't'
}
x
k <- length(x[1,])
df <- as.data.frame(x)
(mylm <- lm(df))
(mysum <- summary(mylm))
if (n > n25) {
kp3 <- k + 3
nmkm3 <- n - k - 3
gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
numgqtests <- 0
numsignificant1 <- 0
numsignificant5 <- 0
numsignificant10 <- 0
for (mypoint in kp3:nmkm3) {
j <- 0
numgqtests <- numgqtests + 1
for (myalt in c('greater', 'two.sided', 'less')) {
j <- j + 1
gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
}
if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
}
gqarr
}
bitmap(file='test0.png')
plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
points(x[,1]-mysum$resid)
grid()
dev.off()
bitmap(file='test1.png')
plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
grid()
dev.off()
bitmap(file='test2.png')
hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
grid()
dev.off()
bitmap(file='test3.png')
densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
dev.off()
bitmap(file='test4.png')
qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
qqline(mysum$resid)
grid()
dev.off()
(myerror <- as.ts(mysum$resid))
bitmap(file='test5.png')
dum <- cbind(lag(myerror,k=1),myerror)
dum
dum1 <- dum[2:length(myerror),]
dum1
z <- as.data.frame(dum1)
z
plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
lines(lowess(z))
abline(lm(z))
grid()
dev.off()
bitmap(file='test6.png')
acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
grid()
dev.off()
bitmap(file='test7.png')
pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
grid()
dev.off()
bitmap(file='test8.png')
opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
plot(mylm, las = 1, sub='Residual Diagnostics')
par(opar)
dev.off()
if (n > n25) {
bitmap(file='test9.png')
plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
grid()
dev.off()
}
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
a<-table.row.end(a)
myeq <- colnames(x)[1]
myeq <- paste(myeq, '[t] = ', sep='')
for (i in 1:k){
if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ')
if (rownames(mysum$coefficients)[i] != '(Intercept)') {
myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
}
}
myeq <- paste(myeq, ' + e[t]')
a<-table.row.start(a)
a<-table.element(a, myeq)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable1.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,hyperlink('ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Variable',header=TRUE)
a<-table.element(a,'Parameter',header=TRUE)
a<-table.element(a,'S.D.',header=TRUE)
a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
a<-table.element(a,'2-tail p-value',header=TRUE)
a<-table.element(a,'1-tail p-value',header=TRUE)
a<-table.row.end(a)
for (i in 1:k){
a<-table.row.start(a)
a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
a<-table.element(a,mysum$coefficients[i,1])
a<-table.element(a, round(mysum$coefficients[i,2],6))
a<-table.element(a, round(mysum$coefficients[i,3],4))
a<-table.element(a, round(mysum$coefficients[i,4],6))
a<-table.element(a, round(mysum$coefficients[i,4]/2,6))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable2.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple R',1,TRUE)
a<-table.element(a, sqrt(mysum$r.squared))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'R-squared',1,TRUE)
a<-table.element(a, mysum$r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Adjusted R-squared',1,TRUE)
a<-table.element(a, mysum$adj.r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (value)',1,TRUE)
a<-table.element(a, mysum$fstatistic[1])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[2])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[3])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'p-value',1,TRUE)
a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
a<-table.element(a, mysum$sigma)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
a<-table.element(a, sum(myerror*myerror))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,x[i])
a<-table.element(a,x[i]-mysum$resid[i])
a<-table.element(a,mysum$resid[i])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable4.tab')
if (n > n25) {
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'p-values',header=TRUE)
a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'breakpoint index',header=TRUE)
a<-table.element(a,'greater',header=TRUE)
a<-table.element(a,'2-sided',header=TRUE)
a<-table.element(a,'less',header=TRUE)
a<-table.row.end(a)
for (mypoint in kp3:nmkm3) {
a<-table.row.start(a)
a<-table.element(a,mypoint,header=TRUE)
a<-table.element(a,gqarr[mypoint-kp3+1,1])
a<-table.element(a,gqarr[mypoint-kp3+1,2])
a<-table.element(a,gqarr[mypoint-kp3+1,3])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable5.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Description',header=TRUE)
a<-table.element(a,'# significant tests',header=TRUE)
a<-table.element(a,'% significant tests',header=TRUE)
a<-table.element(a,'OK/NOK',header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'1% type I error level',header=TRUE)
a<-table.element(a,numsignificant1)
a<-table.element(a,numsignificant1/numgqtests)
if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'5% type I error level',header=TRUE)
a<-table.element(a,numsignificant5)
a<-table.element(a,numsignificant5/numgqtests)
if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'10% type I error level',header=TRUE)
a<-table.element(a,numsignificant10)
a<-table.element(a,numsignificant10/numgqtests)
if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable6.tab')
}