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Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationTue, 24 Nov 2009 13:27:08 -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/24/t1259094492wgpqtfjoo482kcn.htm/, Retrieved Sat, 25 May 2024 05:35:26 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=59266, Retrieved Sat, 25 May 2024 05:35:26 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact171
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]
-    D    [Multiple Regression] [SHw WS7] [2009-11-18 18:21:38] [af2352cd9a951bedd08ebe247d0de1a2]
-    D        [Multiple Regression] [REVIEW 7] [2009-11-24 20:27:08] [6198946fb53eb5eb18db46bb758f7fde] [Current]
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Dataseries X:
825	444	696	627
677	387	825	696
656	327	677	825
785	448	656	677
412	225	785	656
352	182	412	785
839	460	352	412
729	411	839	352
696	342	729	839
641	361	696	729
695	377	641	696
638	331	695	641
762	428	638	695
635	340	762	638
721	352	635	762
854	461	721	635
418	221	854	721
367	198	418	854
824	422	367	418
687	329	824	367
601	320	687	824
676	375	601	687
740	364	676	601
691	351	740	676
683	380	691	740
594	319	683	691
729	322	594	683
731	386	729	594
386	221	731	729
331	187	386	731
707	344	331	386
715	342	707	331
657	365	715	707
653	313	657	715
642	356	653	657
643	337	642	653
718	389	643	642
654	326	718	643
632	343	654	718
731	357	632	654
392	220	731	632
344	228	392	731
792	391	344	392
852	425	792	344
649	332	852	792
629	298	649	852
685	360	629	649
617	326	685	629
715	325	617	685
715	393	715	617
629	301	715	715
916	426	629	715
531	265	916	629
357	210	531	916
917	429	357	531
828	440	917	357
708	357	828	917
858	431	708	828




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=59266&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=59266&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=59266&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] = + 104.978382790316 + 1.33316588097806X[t] -0.0404063636190839`Y-1`[t] + 0.149771378690737`Y-2`[t] + 15.9683744756981M1[t] -10.3645147838434M2[t] + 23.2324200251082M3[t] + 50.5247874887435M4[t] -76.4952318017308M5[t] -150.366438656282M6[t] + 89.9676465933025M7[t] + 92.380418573932M8[t] -18.7460270834543M9[t] -2.90954882184988M10[t] + 4.9443989866286M11[t] + 0.819374350182732t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Y[t] =  +  104.978382790316 +  1.33316588097806X[t] -0.0404063636190839`Y-1`[t] +  0.149771378690737`Y-2`[t] +  15.9683744756981M1[t] -10.3645147838434M2[t] +  23.2324200251082M3[t] +  50.5247874887435M4[t] -76.4952318017308M5[t] -150.366438656282M6[t] +  89.9676465933025M7[t] +  92.380418573932M8[t] -18.7460270834543M9[t] -2.90954882184988M10[t] +  4.9443989866286M11[t] +  0.819374350182732t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=59266&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Y[t] =  +  104.978382790316 +  1.33316588097806X[t] -0.0404063636190839`Y-1`[t] +  0.149771378690737`Y-2`[t] +  15.9683744756981M1[t] -10.3645147838434M2[t] +  23.2324200251082M3[t] +  50.5247874887435M4[t] -76.4952318017308M5[t] -150.366438656282M6[t] +  89.9676465933025M7[t] +  92.380418573932M8[t] -18.7460270834543M9[t] -2.90954882184988M10[t] +  4.9443989866286M11[t] +  0.819374350182732t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=59266&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=59266&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] = + 104.978382790316 + 1.33316588097806X[t] -0.0404063636190839`Y-1`[t] + 0.149771378690737`Y-2`[t] + 15.9683744756981M1[t] -10.3645147838434M2[t] + 23.2324200251082M3[t] + 50.5247874887435M4[t] -76.4952318017308M5[t] -150.366438656282M6[t] + 89.9676465933025M7[t] + 92.380418573932M8[t] -18.7460270834543M9[t] -2.90954882184988M10[t] + 4.9443989866286M11[t] + 0.819374350182732t + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)104.97838279031695.5822051.09830.2783280.139164
X1.333165880978060.1687937.898200
`Y-1`-0.04040636361908390.112674-0.35860.7216810.36084
`Y-2`0.1497713786907370.1052951.42240.1622970.081149
M115.968374475698127.8456930.57350.5693920.284696
M2-10.364514783843425.659976-0.40390.6883230.344161
M323.232420025108227.4161240.84740.4015750.200788
M450.524787488743528.8060081.7540.0867310.043366
M5-76.495231801730835.512308-2.1540.0370210.01851
M6-150.36643865628244.611142-3.37060.0016180.000809
M789.967646593302552.1172411.72630.0916520.045826
M892.38041857393244.5927462.07160.0444770.022238
M9-18.746027083454330.517866-0.61430.5423540.271177
M10-2.9095488218498828.604565-0.10170.9194660.459733
M114.944398986628627.5155370.17970.8582560.429128
t0.8193743501827320.3009932.72220.00940.0047

\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) & 104.978382790316 & 95.582205 & 1.0983 & 0.278328 & 0.139164 \tabularnewline
X & 1.33316588097806 & 0.168793 & 7.8982 & 0 & 0 \tabularnewline
`Y-1` & -0.0404063636190839 & 0.112674 & -0.3586 & 0.721681 & 0.36084 \tabularnewline
`Y-2` & 0.149771378690737 & 0.105295 & 1.4224 & 0.162297 & 0.081149 \tabularnewline
M1 & 15.9683744756981 & 27.845693 & 0.5735 & 0.569392 & 0.284696 \tabularnewline
M2 & -10.3645147838434 & 25.659976 & -0.4039 & 0.688323 & 0.344161 \tabularnewline
M3 & 23.2324200251082 & 27.416124 & 0.8474 & 0.401575 & 0.200788 \tabularnewline
M4 & 50.5247874887435 & 28.806008 & 1.754 & 0.086731 & 0.043366 \tabularnewline
M5 & -76.4952318017308 & 35.512308 & -2.154 & 0.037021 & 0.01851 \tabularnewline
M6 & -150.366438656282 & 44.611142 & -3.3706 & 0.001618 & 0.000809 \tabularnewline
M7 & 89.9676465933025 & 52.117241 & 1.7263 & 0.091652 & 0.045826 \tabularnewline
M8 & 92.380418573932 & 44.592746 & 2.0716 & 0.044477 & 0.022238 \tabularnewline
M9 & -18.7460270834543 & 30.517866 & -0.6143 & 0.542354 & 0.271177 \tabularnewline
M10 & -2.90954882184988 & 28.604565 & -0.1017 & 0.919466 & 0.459733 \tabularnewline
M11 & 4.9443989866286 & 27.515537 & 0.1797 & 0.858256 & 0.429128 \tabularnewline
t & 0.819374350182732 & 0.300993 & 2.7222 & 0.0094 & 0.0047 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=59266&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]104.978382790316[/C][C]95.582205[/C][C]1.0983[/C][C]0.278328[/C][C]0.139164[/C][/ROW]
[ROW][C]X[/C][C]1.33316588097806[/C][C]0.168793[/C][C]7.8982[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]`Y-1`[/C][C]-0.0404063636190839[/C][C]0.112674[/C][C]-0.3586[/C][C]0.721681[/C][C]0.36084[/C][/ROW]
[ROW][C]`Y-2`[/C][C]0.149771378690737[/C][C]0.105295[/C][C]1.4224[/C][C]0.162297[/C][C]0.081149[/C][/ROW]
[ROW][C]M1[/C][C]15.9683744756981[/C][C]27.845693[/C][C]0.5735[/C][C]0.569392[/C][C]0.284696[/C][/ROW]
[ROW][C]M2[/C][C]-10.3645147838434[/C][C]25.659976[/C][C]-0.4039[/C][C]0.688323[/C][C]0.344161[/C][/ROW]
[ROW][C]M3[/C][C]23.2324200251082[/C][C]27.416124[/C][C]0.8474[/C][C]0.401575[/C][C]0.200788[/C][/ROW]
[ROW][C]M4[/C][C]50.5247874887435[/C][C]28.806008[/C][C]1.754[/C][C]0.086731[/C][C]0.043366[/C][/ROW]
[ROW][C]M5[/C][C]-76.4952318017308[/C][C]35.512308[/C][C]-2.154[/C][C]0.037021[/C][C]0.01851[/C][/ROW]
[ROW][C]M6[/C][C]-150.366438656282[/C][C]44.611142[/C][C]-3.3706[/C][C]0.001618[/C][C]0.000809[/C][/ROW]
[ROW][C]M7[/C][C]89.9676465933025[/C][C]52.117241[/C][C]1.7263[/C][C]0.091652[/C][C]0.045826[/C][/ROW]
[ROW][C]M8[/C][C]92.380418573932[/C][C]44.592746[/C][C]2.0716[/C][C]0.044477[/C][C]0.022238[/C][/ROW]
[ROW][C]M9[/C][C]-18.7460270834543[/C][C]30.517866[/C][C]-0.6143[/C][C]0.542354[/C][C]0.271177[/C][/ROW]
[ROW][C]M10[/C][C]-2.90954882184988[/C][C]28.604565[/C][C]-0.1017[/C][C]0.919466[/C][C]0.459733[/C][/ROW]
[ROW][C]M11[/C][C]4.9443989866286[/C][C]27.515537[/C][C]0.1797[/C][C]0.858256[/C][C]0.429128[/C][/ROW]
[ROW][C]t[/C][C]0.819374350182732[/C][C]0.300993[/C][C]2.7222[/C][C]0.0094[/C][C]0.0047[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=59266&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=59266&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)104.97838279031695.5822051.09830.2783280.139164
X1.333165880978060.1687937.898200
`Y-1`-0.04040636361908390.112674-0.35860.7216810.36084
`Y-2`0.1497713786907370.1052951.42240.1622970.081149
M115.968374475698127.8456930.57350.5693920.284696
M2-10.364514783843425.659976-0.40390.6883230.344161
M323.232420025108227.4161240.84740.4015750.200788
M450.524787488743528.8060081.7540.0867310.043366
M5-76.495231801730835.512308-2.1540.0370210.01851
M6-150.36643865628244.611142-3.37060.0016180.000809
M789.967646593302552.1172411.72630.0916520.045826
M892.38041857393244.5927462.07160.0444770.022238
M9-18.746027083454330.517866-0.61430.5423540.271177
M10-2.9095488218498828.604565-0.10170.9194660.459733
M114.944398986628627.5155370.17970.8582560.429128
t0.8193743501827320.3009932.72220.00940.0047







Multiple Linear Regression - Regression Statistics
Multiple R0.975945174039772
R-squared0.95246898273152
Adjusted R-squared0.935493619421349
F-TEST (value)56.1089011957001
F-TEST (DF numerator)15
F-TEST (DF denominator)42
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation37.3795930765591
Sum Squared Residuals58683.827099904

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.975945174039772 \tabularnewline
R-squared & 0.95246898273152 \tabularnewline
Adjusted R-squared & 0.935493619421349 \tabularnewline
F-TEST (value) & 56.1089011957001 \tabularnewline
F-TEST (DF numerator) & 15 \tabularnewline
F-TEST (DF denominator) & 42 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 37.3795930765591 \tabularnewline
Sum Squared Residuals & 58683.827099904 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=59266&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.975945174039772[/C][/ROW]
[ROW][C]R-squared[/C][C]0.95246898273152[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.935493619421349[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]56.1089011957001[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]15[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]42[/C][/ROW]
[ROW][C]p-value[/C][C]0[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]37.3795930765591[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]58683.827099904[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=59266&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=59266&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 R0.975945174039772
R-squared0.95246898273152
Adjusted R-squared0.935493619421349
F-TEST (value)56.1089011957001
F-TEST (DF numerator)15
F-TEST (DF denominator)42
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation37.3795930765591
Sum Squared Residuals58683.827099904







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
1825779.47560813066845.5243918693321
2677683.093442228357-6.0934422283567
3656662.820448195537-6.82044819553671
4785830.927631197472-45.9276311974719
5412399.07337493970512.9266250602948
6352303.08749103430348.9125089656967
7839861.420723111472-22.4207231114720
8729770.663559470421-41.6635594704212
9696645.7514037962250.2485962037806
10641672.595966490038-31.5959664900384
11695699.879837246604-4.87983724660396
12638624.00981262174613.9901873782538
13762780.505469078087-18.5054690780867
14635624.1259989685210.8740010314800
15721698.24355783666622.7564421633339
16854849.1744683121284.82553168787193
17418410.5203041431677.47969585683312
18367344.34242428009122.6575757199086
19824820.8854446543573.11455534564345
20687674.0291155670612.9708844329398
21601625.704743208535-24.7047432085354
22676698.640987664726-22.6409876647261
23740676.73866929379463.261330706206
24691663.92933433481727.0706656651828
25683730.944173762604-47.9441737626042
26594617.09199346669-23.0919934666902
27729657.90579560133171.0942043986688
28731752.555642005693-21.5556420056934
29386426.520950100033-40.5209501000329
30331322.3812158483768.6187841516245
31707723.392943112444-16.3929431124440
32715700.52853913253414.4714608674659
33657676.87507056659-19.8750705665903
34653627.74803748695125.2519625130490
35642685.222378018083-43.2223780180825
36643655.6125861281-12.6125861281004
37718740.037069235623-22.0370692356233
38654627.65339793190626.3466020680939
39632698.552387741094-66.5523877410941
40731736.632023652018-5.63202365201774
41392420.492452688246-28.4924526882461
42344386.631070988954-42.6310709889543
43792796.257577265702-4.25757726570202
44852819.52628647126332.4737135287366
45649649.907984069405-0.907984069405233
46629638.424971264057-9.42497126405656
47685700.15911544152-15.1591154415196
48617645.448266915336-28.4482669153362
49715672.03767979301842.962320206982
50715723.035167404527-8.03516740452706
51629649.477810625372-20.4778106253720
52916847.71023483268968.289765167311
53531482.39291812884948.6070818711511
54357394.557797848276-37.5577978482756
55917877.04331185602639.9566881439745
56828846.252499358721-18.2524993587212
57708712.76079835925-4.76079835924968
58858819.59003709422838.4099629057722

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 825 & 779.475608130668 & 45.5243918693321 \tabularnewline
2 & 677 & 683.093442228357 & -6.0934422283567 \tabularnewline
3 & 656 & 662.820448195537 & -6.82044819553671 \tabularnewline
4 & 785 & 830.927631197472 & -45.9276311974719 \tabularnewline
5 & 412 & 399.073374939705 & 12.9266250602948 \tabularnewline
6 & 352 & 303.087491034303 & 48.9125089656967 \tabularnewline
7 & 839 & 861.420723111472 & -22.4207231114720 \tabularnewline
8 & 729 & 770.663559470421 & -41.6635594704212 \tabularnewline
9 & 696 & 645.75140379622 & 50.2485962037806 \tabularnewline
10 & 641 & 672.595966490038 & -31.5959664900384 \tabularnewline
11 & 695 & 699.879837246604 & -4.87983724660396 \tabularnewline
12 & 638 & 624.009812621746 & 13.9901873782538 \tabularnewline
13 & 762 & 780.505469078087 & -18.5054690780867 \tabularnewline
14 & 635 & 624.12599896852 & 10.8740010314800 \tabularnewline
15 & 721 & 698.243557836666 & 22.7564421633339 \tabularnewline
16 & 854 & 849.174468312128 & 4.82553168787193 \tabularnewline
17 & 418 & 410.520304143167 & 7.47969585683312 \tabularnewline
18 & 367 & 344.342424280091 & 22.6575757199086 \tabularnewline
19 & 824 & 820.885444654357 & 3.11455534564345 \tabularnewline
20 & 687 & 674.02911556706 & 12.9708844329398 \tabularnewline
21 & 601 & 625.704743208535 & -24.7047432085354 \tabularnewline
22 & 676 & 698.640987664726 & -22.6409876647261 \tabularnewline
23 & 740 & 676.738669293794 & 63.261330706206 \tabularnewline
24 & 691 & 663.929334334817 & 27.0706656651828 \tabularnewline
25 & 683 & 730.944173762604 & -47.9441737626042 \tabularnewline
26 & 594 & 617.09199346669 & -23.0919934666902 \tabularnewline
27 & 729 & 657.905795601331 & 71.0942043986688 \tabularnewline
28 & 731 & 752.555642005693 & -21.5556420056934 \tabularnewline
29 & 386 & 426.520950100033 & -40.5209501000329 \tabularnewline
30 & 331 & 322.381215848376 & 8.6187841516245 \tabularnewline
31 & 707 & 723.392943112444 & -16.3929431124440 \tabularnewline
32 & 715 & 700.528539132534 & 14.4714608674659 \tabularnewline
33 & 657 & 676.87507056659 & -19.8750705665903 \tabularnewline
34 & 653 & 627.748037486951 & 25.2519625130490 \tabularnewline
35 & 642 & 685.222378018083 & -43.2223780180825 \tabularnewline
36 & 643 & 655.6125861281 & -12.6125861281004 \tabularnewline
37 & 718 & 740.037069235623 & -22.0370692356233 \tabularnewline
38 & 654 & 627.653397931906 & 26.3466020680939 \tabularnewline
39 & 632 & 698.552387741094 & -66.5523877410941 \tabularnewline
40 & 731 & 736.632023652018 & -5.63202365201774 \tabularnewline
41 & 392 & 420.492452688246 & -28.4924526882461 \tabularnewline
42 & 344 & 386.631070988954 & -42.6310709889543 \tabularnewline
43 & 792 & 796.257577265702 & -4.25757726570202 \tabularnewline
44 & 852 & 819.526286471263 & 32.4737135287366 \tabularnewline
45 & 649 & 649.907984069405 & -0.907984069405233 \tabularnewline
46 & 629 & 638.424971264057 & -9.42497126405656 \tabularnewline
47 & 685 & 700.15911544152 & -15.1591154415196 \tabularnewline
48 & 617 & 645.448266915336 & -28.4482669153362 \tabularnewline
49 & 715 & 672.037679793018 & 42.962320206982 \tabularnewline
50 & 715 & 723.035167404527 & -8.03516740452706 \tabularnewline
51 & 629 & 649.477810625372 & -20.4778106253720 \tabularnewline
52 & 916 & 847.710234832689 & 68.289765167311 \tabularnewline
53 & 531 & 482.392918128849 & 48.6070818711511 \tabularnewline
54 & 357 & 394.557797848276 & -37.5577978482756 \tabularnewline
55 & 917 & 877.043311856026 & 39.9566881439745 \tabularnewline
56 & 828 & 846.252499358721 & -18.2524993587212 \tabularnewline
57 & 708 & 712.76079835925 & -4.76079835924968 \tabularnewline
58 & 858 & 819.590037094228 & 38.4099629057722 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=59266&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]825[/C][C]779.475608130668[/C][C]45.5243918693321[/C][/ROW]
[ROW][C]2[/C][C]677[/C][C]683.093442228357[/C][C]-6.0934422283567[/C][/ROW]
[ROW][C]3[/C][C]656[/C][C]662.820448195537[/C][C]-6.82044819553671[/C][/ROW]
[ROW][C]4[/C][C]785[/C][C]830.927631197472[/C][C]-45.9276311974719[/C][/ROW]
[ROW][C]5[/C][C]412[/C][C]399.073374939705[/C][C]12.9266250602948[/C][/ROW]
[ROW][C]6[/C][C]352[/C][C]303.087491034303[/C][C]48.9125089656967[/C][/ROW]
[ROW][C]7[/C][C]839[/C][C]861.420723111472[/C][C]-22.4207231114720[/C][/ROW]
[ROW][C]8[/C][C]729[/C][C]770.663559470421[/C][C]-41.6635594704212[/C][/ROW]
[ROW][C]9[/C][C]696[/C][C]645.75140379622[/C][C]50.2485962037806[/C][/ROW]
[ROW][C]10[/C][C]641[/C][C]672.595966490038[/C][C]-31.5959664900384[/C][/ROW]
[ROW][C]11[/C][C]695[/C][C]699.879837246604[/C][C]-4.87983724660396[/C][/ROW]
[ROW][C]12[/C][C]638[/C][C]624.009812621746[/C][C]13.9901873782538[/C][/ROW]
[ROW][C]13[/C][C]762[/C][C]780.505469078087[/C][C]-18.5054690780867[/C][/ROW]
[ROW][C]14[/C][C]635[/C][C]624.12599896852[/C][C]10.8740010314800[/C][/ROW]
[ROW][C]15[/C][C]721[/C][C]698.243557836666[/C][C]22.7564421633339[/C][/ROW]
[ROW][C]16[/C][C]854[/C][C]849.174468312128[/C][C]4.82553168787193[/C][/ROW]
[ROW][C]17[/C][C]418[/C][C]410.520304143167[/C][C]7.47969585683312[/C][/ROW]
[ROW][C]18[/C][C]367[/C][C]344.342424280091[/C][C]22.6575757199086[/C][/ROW]
[ROW][C]19[/C][C]824[/C][C]820.885444654357[/C][C]3.11455534564345[/C][/ROW]
[ROW][C]20[/C][C]687[/C][C]674.02911556706[/C][C]12.9708844329398[/C][/ROW]
[ROW][C]21[/C][C]601[/C][C]625.704743208535[/C][C]-24.7047432085354[/C][/ROW]
[ROW][C]22[/C][C]676[/C][C]698.640987664726[/C][C]-22.6409876647261[/C][/ROW]
[ROW][C]23[/C][C]740[/C][C]676.738669293794[/C][C]63.261330706206[/C][/ROW]
[ROW][C]24[/C][C]691[/C][C]663.929334334817[/C][C]27.0706656651828[/C][/ROW]
[ROW][C]25[/C][C]683[/C][C]730.944173762604[/C][C]-47.9441737626042[/C][/ROW]
[ROW][C]26[/C][C]594[/C][C]617.09199346669[/C][C]-23.0919934666902[/C][/ROW]
[ROW][C]27[/C][C]729[/C][C]657.905795601331[/C][C]71.0942043986688[/C][/ROW]
[ROW][C]28[/C][C]731[/C][C]752.555642005693[/C][C]-21.5556420056934[/C][/ROW]
[ROW][C]29[/C][C]386[/C][C]426.520950100033[/C][C]-40.5209501000329[/C][/ROW]
[ROW][C]30[/C][C]331[/C][C]322.381215848376[/C][C]8.6187841516245[/C][/ROW]
[ROW][C]31[/C][C]707[/C][C]723.392943112444[/C][C]-16.3929431124440[/C][/ROW]
[ROW][C]32[/C][C]715[/C][C]700.528539132534[/C][C]14.4714608674659[/C][/ROW]
[ROW][C]33[/C][C]657[/C][C]676.87507056659[/C][C]-19.8750705665903[/C][/ROW]
[ROW][C]34[/C][C]653[/C][C]627.748037486951[/C][C]25.2519625130490[/C][/ROW]
[ROW][C]35[/C][C]642[/C][C]685.222378018083[/C][C]-43.2223780180825[/C][/ROW]
[ROW][C]36[/C][C]643[/C][C]655.6125861281[/C][C]-12.6125861281004[/C][/ROW]
[ROW][C]37[/C][C]718[/C][C]740.037069235623[/C][C]-22.0370692356233[/C][/ROW]
[ROW][C]38[/C][C]654[/C][C]627.653397931906[/C][C]26.3466020680939[/C][/ROW]
[ROW][C]39[/C][C]632[/C][C]698.552387741094[/C][C]-66.5523877410941[/C][/ROW]
[ROW][C]40[/C][C]731[/C][C]736.632023652018[/C][C]-5.63202365201774[/C][/ROW]
[ROW][C]41[/C][C]392[/C][C]420.492452688246[/C][C]-28.4924526882461[/C][/ROW]
[ROW][C]42[/C][C]344[/C][C]386.631070988954[/C][C]-42.6310709889543[/C][/ROW]
[ROW][C]43[/C][C]792[/C][C]796.257577265702[/C][C]-4.25757726570202[/C][/ROW]
[ROW][C]44[/C][C]852[/C][C]819.526286471263[/C][C]32.4737135287366[/C][/ROW]
[ROW][C]45[/C][C]649[/C][C]649.907984069405[/C][C]-0.907984069405233[/C][/ROW]
[ROW][C]46[/C][C]629[/C][C]638.424971264057[/C][C]-9.42497126405656[/C][/ROW]
[ROW][C]47[/C][C]685[/C][C]700.15911544152[/C][C]-15.1591154415196[/C][/ROW]
[ROW][C]48[/C][C]617[/C][C]645.448266915336[/C][C]-28.4482669153362[/C][/ROW]
[ROW][C]49[/C][C]715[/C][C]672.037679793018[/C][C]42.962320206982[/C][/ROW]
[ROW][C]50[/C][C]715[/C][C]723.035167404527[/C][C]-8.03516740452706[/C][/ROW]
[ROW][C]51[/C][C]629[/C][C]649.477810625372[/C][C]-20.4778106253720[/C][/ROW]
[ROW][C]52[/C][C]916[/C][C]847.710234832689[/C][C]68.289765167311[/C][/ROW]
[ROW][C]53[/C][C]531[/C][C]482.392918128849[/C][C]48.6070818711511[/C][/ROW]
[ROW][C]54[/C][C]357[/C][C]394.557797848276[/C][C]-37.5577978482756[/C][/ROW]
[ROW][C]55[/C][C]917[/C][C]877.043311856026[/C][C]39.9566881439745[/C][/ROW]
[ROW][C]56[/C][C]828[/C][C]846.252499358721[/C][C]-18.2524993587212[/C][/ROW]
[ROW][C]57[/C][C]708[/C][C]712.76079835925[/C][C]-4.76079835924968[/C][/ROW]
[ROW][C]58[/C][C]858[/C][C]819.590037094228[/C][C]38.4099629057722[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=59266&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=59266&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
1825779.47560813066845.5243918693321
2677683.093442228357-6.0934422283567
3656662.820448195537-6.82044819553671
4785830.927631197472-45.9276311974719
5412399.07337493970512.9266250602948
6352303.08749103430348.9125089656967
7839861.420723111472-22.4207231114720
8729770.663559470421-41.6635594704212
9696645.7514037962250.2485962037806
10641672.595966490038-31.5959664900384
11695699.879837246604-4.87983724660396
12638624.00981262174613.9901873782538
13762780.505469078087-18.5054690780867
14635624.1259989685210.8740010314800
15721698.24355783666622.7564421633339
16854849.1744683121284.82553168787193
17418410.5203041431677.47969585683312
18367344.34242428009122.6575757199086
19824820.8854446543573.11455534564345
20687674.0291155670612.9708844329398
21601625.704743208535-24.7047432085354
22676698.640987664726-22.6409876647261
23740676.73866929379463.261330706206
24691663.92933433481727.0706656651828
25683730.944173762604-47.9441737626042
26594617.09199346669-23.0919934666902
27729657.90579560133171.0942043986688
28731752.555642005693-21.5556420056934
29386426.520950100033-40.5209501000329
30331322.3812158483768.6187841516245
31707723.392943112444-16.3929431124440
32715700.52853913253414.4714608674659
33657676.87507056659-19.8750705665903
34653627.74803748695125.2519625130490
35642685.222378018083-43.2223780180825
36643655.6125861281-12.6125861281004
37718740.037069235623-22.0370692356233
38654627.65339793190626.3466020680939
39632698.552387741094-66.5523877410941
40731736.632023652018-5.63202365201774
41392420.492452688246-28.4924526882461
42344386.631070988954-42.6310709889543
43792796.257577265702-4.25757726570202
44852819.52628647126332.4737135287366
45649649.907984069405-0.907984069405233
46629638.424971264057-9.42497126405656
47685700.15911544152-15.1591154415196
48617645.448266915336-28.4482669153362
49715672.03767979301842.962320206982
50715723.035167404527-8.03516740452706
51629649.477810625372-20.4778106253720
52916847.71023483268968.289765167311
53531482.39291812884948.6070818711511
54357394.557797848276-37.5577978482756
55917877.04331185602639.9566881439745
56828846.252499358721-18.2524993587212
57708712.76079835925-4.76079835924968
58858819.59003709422838.4099629057722







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
190.008675816114451930.01735163222890390.991324183885548
200.02224019351983850.0444803870396770.977759806480161
210.1506273062272190.3012546124544380.849372693772781
220.1210003866175160.2420007732350320.878999613382484
230.1004465550468710.2008931100937420.899553444953129
240.07982095230852420.1596419046170480.920179047691476
250.0829341591972780.1658683183945560.917065840802722
260.05175589662619640.1035117932523930.948244103373804
270.1400352093418510.2800704186837020.85996479065815
280.1879957602673490.3759915205346990.81200423973265
290.1335333592375550.2670667184751100.866466640762445
300.2964899361398780.5929798722797550.703510063860122
310.2124962448862230.4249924897724460.787503755113777
320.2144044267381840.4288088534763670.785595573261817
330.2460059728749330.4920119457498660.753994027125067
340.2830631153930040.5661262307860080.716936884606996
350.2478469520119140.4956939040238290.752153047988086
360.1922815236971000.3845630473941990.8077184763029
370.1699094819748960.3398189639497910.830090518025104
380.2308858963871560.4617717927743120.769114103612844
390.3570622692761440.7141245385522880.642937730723856

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
19 & 0.00867581611445193 & 0.0173516322289039 & 0.991324183885548 \tabularnewline
20 & 0.0222401935198385 & 0.044480387039677 & 0.977759806480161 \tabularnewline
21 & 0.150627306227219 & 0.301254612454438 & 0.849372693772781 \tabularnewline
22 & 0.121000386617516 & 0.242000773235032 & 0.878999613382484 \tabularnewline
23 & 0.100446555046871 & 0.200893110093742 & 0.899553444953129 \tabularnewline
24 & 0.0798209523085242 & 0.159641904617048 & 0.920179047691476 \tabularnewline
25 & 0.082934159197278 & 0.165868318394556 & 0.917065840802722 \tabularnewline
26 & 0.0517558966261964 & 0.103511793252393 & 0.948244103373804 \tabularnewline
27 & 0.140035209341851 & 0.280070418683702 & 0.85996479065815 \tabularnewline
28 & 0.187995760267349 & 0.375991520534699 & 0.81200423973265 \tabularnewline
29 & 0.133533359237555 & 0.267066718475110 & 0.866466640762445 \tabularnewline
30 & 0.296489936139878 & 0.592979872279755 & 0.703510063860122 \tabularnewline
31 & 0.212496244886223 & 0.424992489772446 & 0.787503755113777 \tabularnewline
32 & 0.214404426738184 & 0.428808853476367 & 0.785595573261817 \tabularnewline
33 & 0.246005972874933 & 0.492011945749866 & 0.753994027125067 \tabularnewline
34 & 0.283063115393004 & 0.566126230786008 & 0.716936884606996 \tabularnewline
35 & 0.247846952011914 & 0.495693904023829 & 0.752153047988086 \tabularnewline
36 & 0.192281523697100 & 0.384563047394199 & 0.8077184763029 \tabularnewline
37 & 0.169909481974896 & 0.339818963949791 & 0.830090518025104 \tabularnewline
38 & 0.230885896387156 & 0.461771792774312 & 0.769114103612844 \tabularnewline
39 & 0.357062269276144 & 0.714124538552288 & 0.642937730723856 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=59266&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]19[/C][C]0.00867581611445193[/C][C]0.0173516322289039[/C][C]0.991324183885548[/C][/ROW]
[ROW][C]20[/C][C]0.0222401935198385[/C][C]0.044480387039677[/C][C]0.977759806480161[/C][/ROW]
[ROW][C]21[/C][C]0.150627306227219[/C][C]0.301254612454438[/C][C]0.849372693772781[/C][/ROW]
[ROW][C]22[/C][C]0.121000386617516[/C][C]0.242000773235032[/C][C]0.878999613382484[/C][/ROW]
[ROW][C]23[/C][C]0.100446555046871[/C][C]0.200893110093742[/C][C]0.899553444953129[/C][/ROW]
[ROW][C]24[/C][C]0.0798209523085242[/C][C]0.159641904617048[/C][C]0.920179047691476[/C][/ROW]
[ROW][C]25[/C][C]0.082934159197278[/C][C]0.165868318394556[/C][C]0.917065840802722[/C][/ROW]
[ROW][C]26[/C][C]0.0517558966261964[/C][C]0.103511793252393[/C][C]0.948244103373804[/C][/ROW]
[ROW][C]27[/C][C]0.140035209341851[/C][C]0.280070418683702[/C][C]0.85996479065815[/C][/ROW]
[ROW][C]28[/C][C]0.187995760267349[/C][C]0.375991520534699[/C][C]0.81200423973265[/C][/ROW]
[ROW][C]29[/C][C]0.133533359237555[/C][C]0.267066718475110[/C][C]0.866466640762445[/C][/ROW]
[ROW][C]30[/C][C]0.296489936139878[/C][C]0.592979872279755[/C][C]0.703510063860122[/C][/ROW]
[ROW][C]31[/C][C]0.212496244886223[/C][C]0.424992489772446[/C][C]0.787503755113777[/C][/ROW]
[ROW][C]32[/C][C]0.214404426738184[/C][C]0.428808853476367[/C][C]0.785595573261817[/C][/ROW]
[ROW][C]33[/C][C]0.246005972874933[/C][C]0.492011945749866[/C][C]0.753994027125067[/C][/ROW]
[ROW][C]34[/C][C]0.283063115393004[/C][C]0.566126230786008[/C][C]0.716936884606996[/C][/ROW]
[ROW][C]35[/C][C]0.247846952011914[/C][C]0.495693904023829[/C][C]0.752153047988086[/C][/ROW]
[ROW][C]36[/C][C]0.192281523697100[/C][C]0.384563047394199[/C][C]0.8077184763029[/C][/ROW]
[ROW][C]37[/C][C]0.169909481974896[/C][C]0.339818963949791[/C][C]0.830090518025104[/C][/ROW]
[ROW][C]38[/C][C]0.230885896387156[/C][C]0.461771792774312[/C][C]0.769114103612844[/C][/ROW]
[ROW][C]39[/C][C]0.357062269276144[/C][C]0.714124538552288[/C][C]0.642937730723856[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=59266&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=59266&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
190.008675816114451930.01735163222890390.991324183885548
200.02224019351983850.0444803870396770.977759806480161
210.1506273062272190.3012546124544380.849372693772781
220.1210003866175160.2420007732350320.878999613382484
230.1004465550468710.2008931100937420.899553444953129
240.07982095230852420.1596419046170480.920179047691476
250.0829341591972780.1658683183945560.917065840802722
260.05175589662619640.1035117932523930.948244103373804
270.1400352093418510.2800704186837020.85996479065815
280.1879957602673490.3759915205346990.81200423973265
290.1335333592375550.2670667184751100.866466640762445
300.2964899361398780.5929798722797550.703510063860122
310.2124962448862230.4249924897724460.787503755113777
320.2144044267381840.4288088534763670.785595573261817
330.2460059728749330.4920119457498660.753994027125067
340.2830631153930040.5661262307860080.716936884606996
350.2478469520119140.4956939040238290.752153047988086
360.1922815236971000.3845630473941990.8077184763029
370.1699094819748960.3398189639497910.830090518025104
380.2308858963871560.4617717927743120.769114103612844
390.3570622692761440.7141245385522880.642937730723856







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level20.0952380952380952NOK
10% type I error level20.0952380952380952OK

\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 & 0 & 0 & OK \tabularnewline
5% type I error level & 2 & 0.0952380952380952 & NOK \tabularnewline
10% type I error level & 2 & 0.0952380952380952 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=59266&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]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]2[/C][C]0.0952380952380952[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]2[/C][C]0.0952380952380952[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=59266&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=59266&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 level00OK
5% type I error level20.0952380952380952NOK
10% type I error level20.0952380952380952OK



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')
}