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Author's title

multiple regression toevoeging variabele uit het verleden 2 periodes terug ...

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationFri, 20 Nov 2009 12:37:54 -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/20/t1258745941k6e4mbreexgnz7h.htm/, Retrieved Fri, 29 Mar 2024 09:45:45 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=58443, Retrieved Fri, 29 Mar 2024 09:45:45 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact128
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]
- RMPD  [Multiple Regression] [Seatbelt] [2009-11-12 14:03:14] [b98453cac15ba1066b407e146608df68]
-   PD    [Multiple Regression] [] [2009-11-19 10:39:42] [d181e5359f7da6c8509e4702d1229fb0]
-    D        [Multiple Regression] [multiple regressi...] [2009-11-20 19:37:54] [371dc2189c569d90e2c1567f632c3ec0] [Current]
-    D          [Multiple Regression] [multiple regressi...] [2009-12-14 19:59:10] [34d27ebe78dc2d31581e8710befe8733]
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Dataseries X:
461	1870	455	462
461	2263	461	455
463	1802	461	461
462	1863	463	461
456	1989	462	463
455	2197	456	462
456	2409	455	456
472	2502	456	455
472	2593	472	456
471	2598	472	472
465	2053	471	472
459	2213	465	471
465	2238	459	465
468	2359	465	459
467	2151	468	465
463	2474	467	468
460	3079	463	467
462	2312	460	463
461	2565	462	460
476	1972	461	462
476	2484	476	461
471	2202	476	476
453	2151	471	476
443	1976	453	471
442	2012	443	453
444	2114	442	443
438	1772	444	442
427	1957	438	444
424	2070	427	438
416	1990	424	427
406	2182	416	424
431	2008	406	416
434	1916	431	406
418	2397	434	431
412	2114	418	434
404	1778	412	418
409	1641	404	412
412	2186	409	404
406	1773	412	409
398	1785	406	412
397	2217	398	406
385	2153	397	398
390	1895	385	397
413	2475	390	385
413	1793	413	390
401	2308	413	413
397	2051	401	413
397	1898	397	401
409	2142	397	397
419	1874	409	397
424	1560	419	409
428	1808	424	419
430	1575	428	424
424	1525	430	428
433	1997	424	430
456	1753	433	424
459	1623	456	433
446	2251	459	456
441	1890	446	459




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=58443&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=58443&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58443&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
wkl[t] = -4.01215883998919 -0.000280109648396460bvg[t] + 1.15480150856078Y1[t] -0.157907674857242Y2[t] + 11.448070491494M1[t] + 7.6106411436112M2[t] + 2.9987316791792M3[t] + 0.957862399611235M4[t] + 3.20446919765004M5[t] + 0.0296764049751569M6[t] + 6.26354126481267M7[t] + 24.8896853724151M8[t] + 2.59965723779642M9[t] -4.93086736514187M10[t] -1.81164970260289M11[t] + 0.0415408990311207t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
wkl[t] =  -4.01215883998919 -0.000280109648396460bvg[t] +  1.15480150856078Y1[t] -0.157907674857242Y2[t] +  11.448070491494M1[t] +  7.6106411436112M2[t] +  2.9987316791792M3[t] +  0.957862399611235M4[t] +  3.20446919765004M5[t] +  0.0296764049751569M6[t] +  6.26354126481267M7[t] +  24.8896853724151M8[t] +  2.59965723779642M9[t] -4.93086736514187M10[t] -1.81164970260289M11[t] +  0.0415408990311207t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58443&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]wkl[t] =  -4.01215883998919 -0.000280109648396460bvg[t] +  1.15480150856078Y1[t] -0.157907674857242Y2[t] +  11.448070491494M1[t] +  7.6106411436112M2[t] +  2.9987316791792M3[t] +  0.957862399611235M4[t] +  3.20446919765004M5[t] +  0.0296764049751569M6[t] +  6.26354126481267M7[t] +  24.8896853724151M8[t] +  2.59965723779642M9[t] -4.93086736514187M10[t] -1.81164970260289M11[t] +  0.0415408990311207t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58443&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58443&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
wkl[t] = -4.01215883998919 -0.000280109648396460bvg[t] + 1.15480150856078Y1[t] -0.157907674857242Y2[t] + 11.448070491494M1[t] + 7.6106411436112M2[t] + 2.9987316791792M3[t] + 0.957862399611235M4[t] + 3.20446919765004M5[t] + 0.0296764049751569M6[t] + 6.26354126481267M7[t] + 24.8896853724151M8[t] + 2.59965723779642M9[t] -4.93086736514187M10[t] -1.81164970260289M11[t] + 0.0415408990311207t + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)-4.0121588399891920.454621-0.19610.8454170.422709
bvg-0.0002801096483964600.003006-0.09320.9261990.463099
Y11.154801508560780.1509847.648500
Y2-0.1579076748572420.156917-1.00630.3198960.159948
M111.4480704914943.4317283.33590.001760.00088
M27.61064114361124.0994681.85650.0702430.035121
M32.99873167917923.8983250.76920.4459580.222979
M40.9578623996112353.5679710.26850.7896280.394814
M53.204469197650043.5201170.91030.3677230.183862
M60.02967640497515693.532160.00840.9933350.496668
M76.263541264812673.5229851.77790.0824910.041246
M824.88968537241513.7395346.655800
M92.599657237796425.550970.46830.6419180.320959
M10-4.930867365141873.949867-1.24840.2186530.109326
M11-1.811649702602893.481531-0.52040.6054820.302741
t0.04154089903112070.0673180.61710.5404330.270216

\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) & -4.01215883998919 & 20.454621 & -0.1961 & 0.845417 & 0.422709 \tabularnewline
bvg & -0.000280109648396460 & 0.003006 & -0.0932 & 0.926199 & 0.463099 \tabularnewline
Y1 & 1.15480150856078 & 0.150984 & 7.6485 & 0 & 0 \tabularnewline
Y2 & -0.157907674857242 & 0.156917 & -1.0063 & 0.319896 & 0.159948 \tabularnewline
M1 & 11.448070491494 & 3.431728 & 3.3359 & 0.00176 & 0.00088 \tabularnewline
M2 & 7.6106411436112 & 4.099468 & 1.8565 & 0.070243 & 0.035121 \tabularnewline
M3 & 2.9987316791792 & 3.898325 & 0.7692 & 0.445958 & 0.222979 \tabularnewline
M4 & 0.957862399611235 & 3.567971 & 0.2685 & 0.789628 & 0.394814 \tabularnewline
M5 & 3.20446919765004 & 3.520117 & 0.9103 & 0.367723 & 0.183862 \tabularnewline
M6 & 0.0296764049751569 & 3.53216 & 0.0084 & 0.993335 & 0.496668 \tabularnewline
M7 & 6.26354126481267 & 3.522985 & 1.7779 & 0.082491 & 0.041246 \tabularnewline
M8 & 24.8896853724151 & 3.739534 & 6.6558 & 0 & 0 \tabularnewline
M9 & 2.59965723779642 & 5.55097 & 0.4683 & 0.641918 & 0.320959 \tabularnewline
M10 & -4.93086736514187 & 3.949867 & -1.2484 & 0.218653 & 0.109326 \tabularnewline
M11 & -1.81164970260289 & 3.481531 & -0.5204 & 0.605482 & 0.302741 \tabularnewline
t & 0.0415408990311207 & 0.067318 & 0.6171 & 0.540433 & 0.270216 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58443&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]-4.01215883998919[/C][C]20.454621[/C][C]-0.1961[/C][C]0.845417[/C][C]0.422709[/C][/ROW]
[ROW][C]bvg[/C][C]-0.000280109648396460[/C][C]0.003006[/C][C]-0.0932[/C][C]0.926199[/C][C]0.463099[/C][/ROW]
[ROW][C]Y1[/C][C]1.15480150856078[/C][C]0.150984[/C][C]7.6485[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Y2[/C][C]-0.157907674857242[/C][C]0.156917[/C][C]-1.0063[/C][C]0.319896[/C][C]0.159948[/C][/ROW]
[ROW][C]M1[/C][C]11.448070491494[/C][C]3.431728[/C][C]3.3359[/C][C]0.00176[/C][C]0.00088[/C][/ROW]
[ROW][C]M2[/C][C]7.6106411436112[/C][C]4.099468[/C][C]1.8565[/C][C]0.070243[/C][C]0.035121[/C][/ROW]
[ROW][C]M3[/C][C]2.9987316791792[/C][C]3.898325[/C][C]0.7692[/C][C]0.445958[/C][C]0.222979[/C][/ROW]
[ROW][C]M4[/C][C]0.957862399611235[/C][C]3.567971[/C][C]0.2685[/C][C]0.789628[/C][C]0.394814[/C][/ROW]
[ROW][C]M5[/C][C]3.20446919765004[/C][C]3.520117[/C][C]0.9103[/C][C]0.367723[/C][C]0.183862[/C][/ROW]
[ROW][C]M6[/C][C]0.0296764049751569[/C][C]3.53216[/C][C]0.0084[/C][C]0.993335[/C][C]0.496668[/C][/ROW]
[ROW][C]M7[/C][C]6.26354126481267[/C][C]3.522985[/C][C]1.7779[/C][C]0.082491[/C][C]0.041246[/C][/ROW]
[ROW][C]M8[/C][C]24.8896853724151[/C][C]3.739534[/C][C]6.6558[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]M9[/C][C]2.59965723779642[/C][C]5.55097[/C][C]0.4683[/C][C]0.641918[/C][C]0.320959[/C][/ROW]
[ROW][C]M10[/C][C]-4.93086736514187[/C][C]3.949867[/C][C]-1.2484[/C][C]0.218653[/C][C]0.109326[/C][/ROW]
[ROW][C]M11[/C][C]-1.81164970260289[/C][C]3.481531[/C][C]-0.5204[/C][C]0.605482[/C][C]0.302741[/C][/ROW]
[ROW][C]t[/C][C]0.0415408990311207[/C][C]0.067318[/C][C]0.6171[/C][C]0.540433[/C][C]0.270216[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58443&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58443&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)-4.0121588399891920.454621-0.19610.8454170.422709
bvg-0.0002801096483964600.003006-0.09320.9261990.463099
Y11.154801508560780.1509847.648500
Y2-0.1579076748572420.156917-1.00630.3198960.159948
M111.4480704914943.4317283.33590.001760.00088
M27.61064114361124.0994681.85650.0702430.035121
M32.99873167917923.8983250.76920.4459580.222979
M40.9578623996112353.5679710.26850.7896280.394814
M53.204469197650043.5201170.91030.3677230.183862
M60.02967640497515693.532160.00840.9933350.496668
M76.263541264812673.5229851.77790.0824910.041246
M824.88968537241513.7395346.655800
M92.599657237796425.550970.46830.6419180.320959
M10-4.930867365141873.949867-1.24840.2186530.109326
M11-1.811649702602893.481531-0.52040.6054820.302741
t0.04154089903112070.0673180.61710.5404330.270216







Multiple Linear Regression - Regression Statistics
Multiple R0.986330732011576
R-squared0.972848312910491
Adjusted R-squared0.963376794158337
F-TEST (value)102.713021888831
F-TEST (DF numerator)15
F-TEST (DF denominator)43
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation5.04826909520104
Sum Squared Residuals1095.85589687516

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.986330732011576 \tabularnewline
R-squared & 0.972848312910491 \tabularnewline
Adjusted R-squared & 0.963376794158337 \tabularnewline
F-TEST (value) & 102.713021888831 \tabularnewline
F-TEST (DF numerator) & 15 \tabularnewline
F-TEST (DF denominator) & 43 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 5.04826909520104 \tabularnewline
Sum Squared Residuals & 1095.85589687516 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58443&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.986330732011576[/C][/ROW]
[ROW][C]R-squared[/C][C]0.972848312910491[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.963376794158337[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]102.713021888831[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]15[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]43[/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]5.04826909520104[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]1095.85589687516[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58443&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58443&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 R0.986330732011576
R-squared0.972848312910491
Adjusted R-squared0.963376794158337
F-TEST (value)102.713021888831
F-TEST (DF numerator)15
F-TEST (DF denominator)43
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation5.04826909520104
Sum Squared Residuals1095.85589687516







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
1461459.4349881191431.56501188085739
2461463.563179353837-2.56317935383657
3463458.1744952872034.82550471279691
4462458.4676832352363.53231676476441
5456459.249920258332-3.24992025833231
6455449.2875041813155.71249581868537
7456455.2961712353060.703828764694123
8472475.250515228057-3.25051522805659
9472471.295454476580.704545523419844
10471461.2785474267159.72145257328487
11465463.4371642381011.56283576189946
12459458.4746359194840.525364080516317
13465463.9758815665781.02411843342234
14468468.022354950778-0.0223549507781517
15467466.0272076687830.972792331217376
16463462.3088793386810.691120661318806
17460459.9662623390850.0337376609146295
18462454.2150807195087.78491928049164
19461463.202944779026-2.20294477902595
20476480.566117948883-4.56611794888338
21476475.6541448765860.345855123414272
22471465.8755369706685.12446302933226
23453463.276573581502-10.2765735815022
24443445.181894591798-2.18189459179776
25442447.955745096803-5.95574509680317
26444444.555560703827-0.555560703826707
27438442.548500330156-4.54850033015622
28427433.252727263587-6.25272726358687
29424423.7538520253630.246147974637118
30416418.915588801338-2.91558880133817
31406416.3725244638-10.3725244638002
32431424.8041948625056.19580513749513
33434433.0305921771620.969407822838323
34418424.923588386627-6.92358838662707
35412409.2130708171492.78692918285082
36404406.758092006996-2.75809200699563
37409409.995112400008-0.995112400008286
38412413.083833134442-1.08383313444235
39406411.304016005225-5.30401600522536
40398401.898794232971-3.89879423297135
41397395.7749685425911.22503145740878
42385392.768103556742-7.76810355674199
43390385.4160671770254.5839328229752
44413411.5901882286791.40981177132079
45413415.30363209591-2.30363209590975
46401404.038515401362-3.03851540136181
47397393.413644039843.58635596015953
48397392.5853774817234.41462251827706
49409404.6382728174684.36172718253173
50419414.7750718571164.22492814288378
51424419.9457807086334.05421929136729
52428422.0719159295255.928084070475
53430428.2549968346281.74500316537178
54424426.813722741097-2.81372274109687
55433425.7122923448437.2877076551568
56456455.7889837318760.211016268124042
57459458.7161763737630.283823626237321
58446450.883811814628-4.88381181462826
59441438.6595473234082.34045267659235

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 461 & 459.434988119143 & 1.56501188085739 \tabularnewline
2 & 461 & 463.563179353837 & -2.56317935383657 \tabularnewline
3 & 463 & 458.174495287203 & 4.82550471279691 \tabularnewline
4 & 462 & 458.467683235236 & 3.53231676476441 \tabularnewline
5 & 456 & 459.249920258332 & -3.24992025833231 \tabularnewline
6 & 455 & 449.287504181315 & 5.71249581868537 \tabularnewline
7 & 456 & 455.296171235306 & 0.703828764694123 \tabularnewline
8 & 472 & 475.250515228057 & -3.25051522805659 \tabularnewline
9 & 472 & 471.29545447658 & 0.704545523419844 \tabularnewline
10 & 471 & 461.278547426715 & 9.72145257328487 \tabularnewline
11 & 465 & 463.437164238101 & 1.56283576189946 \tabularnewline
12 & 459 & 458.474635919484 & 0.525364080516317 \tabularnewline
13 & 465 & 463.975881566578 & 1.02411843342234 \tabularnewline
14 & 468 & 468.022354950778 & -0.0223549507781517 \tabularnewline
15 & 467 & 466.027207668783 & 0.972792331217376 \tabularnewline
16 & 463 & 462.308879338681 & 0.691120661318806 \tabularnewline
17 & 460 & 459.966262339085 & 0.0337376609146295 \tabularnewline
18 & 462 & 454.215080719508 & 7.78491928049164 \tabularnewline
19 & 461 & 463.202944779026 & -2.20294477902595 \tabularnewline
20 & 476 & 480.566117948883 & -4.56611794888338 \tabularnewline
21 & 476 & 475.654144876586 & 0.345855123414272 \tabularnewline
22 & 471 & 465.875536970668 & 5.12446302933226 \tabularnewline
23 & 453 & 463.276573581502 & -10.2765735815022 \tabularnewline
24 & 443 & 445.181894591798 & -2.18189459179776 \tabularnewline
25 & 442 & 447.955745096803 & -5.95574509680317 \tabularnewline
26 & 444 & 444.555560703827 & -0.555560703826707 \tabularnewline
27 & 438 & 442.548500330156 & -4.54850033015622 \tabularnewline
28 & 427 & 433.252727263587 & -6.25272726358687 \tabularnewline
29 & 424 & 423.753852025363 & 0.246147974637118 \tabularnewline
30 & 416 & 418.915588801338 & -2.91558880133817 \tabularnewline
31 & 406 & 416.3725244638 & -10.3725244638002 \tabularnewline
32 & 431 & 424.804194862505 & 6.19580513749513 \tabularnewline
33 & 434 & 433.030592177162 & 0.969407822838323 \tabularnewline
34 & 418 & 424.923588386627 & -6.92358838662707 \tabularnewline
35 & 412 & 409.213070817149 & 2.78692918285082 \tabularnewline
36 & 404 & 406.758092006996 & -2.75809200699563 \tabularnewline
37 & 409 & 409.995112400008 & -0.995112400008286 \tabularnewline
38 & 412 & 413.083833134442 & -1.08383313444235 \tabularnewline
39 & 406 & 411.304016005225 & -5.30401600522536 \tabularnewline
40 & 398 & 401.898794232971 & -3.89879423297135 \tabularnewline
41 & 397 & 395.774968542591 & 1.22503145740878 \tabularnewline
42 & 385 & 392.768103556742 & -7.76810355674199 \tabularnewline
43 & 390 & 385.416067177025 & 4.5839328229752 \tabularnewline
44 & 413 & 411.590188228679 & 1.40981177132079 \tabularnewline
45 & 413 & 415.30363209591 & -2.30363209590975 \tabularnewline
46 & 401 & 404.038515401362 & -3.03851540136181 \tabularnewline
47 & 397 & 393.41364403984 & 3.58635596015953 \tabularnewline
48 & 397 & 392.585377481723 & 4.41462251827706 \tabularnewline
49 & 409 & 404.638272817468 & 4.36172718253173 \tabularnewline
50 & 419 & 414.775071857116 & 4.22492814288378 \tabularnewline
51 & 424 & 419.945780708633 & 4.05421929136729 \tabularnewline
52 & 428 & 422.071915929525 & 5.928084070475 \tabularnewline
53 & 430 & 428.254996834628 & 1.74500316537178 \tabularnewline
54 & 424 & 426.813722741097 & -2.81372274109687 \tabularnewline
55 & 433 & 425.712292344843 & 7.2877076551568 \tabularnewline
56 & 456 & 455.788983731876 & 0.211016268124042 \tabularnewline
57 & 459 & 458.716176373763 & 0.283823626237321 \tabularnewline
58 & 446 & 450.883811814628 & -4.88381181462826 \tabularnewline
59 & 441 & 438.659547323408 & 2.34045267659235 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58443&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]461[/C][C]459.434988119143[/C][C]1.56501188085739[/C][/ROW]
[ROW][C]2[/C][C]461[/C][C]463.563179353837[/C][C]-2.56317935383657[/C][/ROW]
[ROW][C]3[/C][C]463[/C][C]458.174495287203[/C][C]4.82550471279691[/C][/ROW]
[ROW][C]4[/C][C]462[/C][C]458.467683235236[/C][C]3.53231676476441[/C][/ROW]
[ROW][C]5[/C][C]456[/C][C]459.249920258332[/C][C]-3.24992025833231[/C][/ROW]
[ROW][C]6[/C][C]455[/C][C]449.287504181315[/C][C]5.71249581868537[/C][/ROW]
[ROW][C]7[/C][C]456[/C][C]455.296171235306[/C][C]0.703828764694123[/C][/ROW]
[ROW][C]8[/C][C]472[/C][C]475.250515228057[/C][C]-3.25051522805659[/C][/ROW]
[ROW][C]9[/C][C]472[/C][C]471.29545447658[/C][C]0.704545523419844[/C][/ROW]
[ROW][C]10[/C][C]471[/C][C]461.278547426715[/C][C]9.72145257328487[/C][/ROW]
[ROW][C]11[/C][C]465[/C][C]463.437164238101[/C][C]1.56283576189946[/C][/ROW]
[ROW][C]12[/C][C]459[/C][C]458.474635919484[/C][C]0.525364080516317[/C][/ROW]
[ROW][C]13[/C][C]465[/C][C]463.975881566578[/C][C]1.02411843342234[/C][/ROW]
[ROW][C]14[/C][C]468[/C][C]468.022354950778[/C][C]-0.0223549507781517[/C][/ROW]
[ROW][C]15[/C][C]467[/C][C]466.027207668783[/C][C]0.972792331217376[/C][/ROW]
[ROW][C]16[/C][C]463[/C][C]462.308879338681[/C][C]0.691120661318806[/C][/ROW]
[ROW][C]17[/C][C]460[/C][C]459.966262339085[/C][C]0.0337376609146295[/C][/ROW]
[ROW][C]18[/C][C]462[/C][C]454.215080719508[/C][C]7.78491928049164[/C][/ROW]
[ROW][C]19[/C][C]461[/C][C]463.202944779026[/C][C]-2.20294477902595[/C][/ROW]
[ROW][C]20[/C][C]476[/C][C]480.566117948883[/C][C]-4.56611794888338[/C][/ROW]
[ROW][C]21[/C][C]476[/C][C]475.654144876586[/C][C]0.345855123414272[/C][/ROW]
[ROW][C]22[/C][C]471[/C][C]465.875536970668[/C][C]5.12446302933226[/C][/ROW]
[ROW][C]23[/C][C]453[/C][C]463.276573581502[/C][C]-10.2765735815022[/C][/ROW]
[ROW][C]24[/C][C]443[/C][C]445.181894591798[/C][C]-2.18189459179776[/C][/ROW]
[ROW][C]25[/C][C]442[/C][C]447.955745096803[/C][C]-5.95574509680317[/C][/ROW]
[ROW][C]26[/C][C]444[/C][C]444.555560703827[/C][C]-0.555560703826707[/C][/ROW]
[ROW][C]27[/C][C]438[/C][C]442.548500330156[/C][C]-4.54850033015622[/C][/ROW]
[ROW][C]28[/C][C]427[/C][C]433.252727263587[/C][C]-6.25272726358687[/C][/ROW]
[ROW][C]29[/C][C]424[/C][C]423.753852025363[/C][C]0.246147974637118[/C][/ROW]
[ROW][C]30[/C][C]416[/C][C]418.915588801338[/C][C]-2.91558880133817[/C][/ROW]
[ROW][C]31[/C][C]406[/C][C]416.3725244638[/C][C]-10.3725244638002[/C][/ROW]
[ROW][C]32[/C][C]431[/C][C]424.804194862505[/C][C]6.19580513749513[/C][/ROW]
[ROW][C]33[/C][C]434[/C][C]433.030592177162[/C][C]0.969407822838323[/C][/ROW]
[ROW][C]34[/C][C]418[/C][C]424.923588386627[/C][C]-6.92358838662707[/C][/ROW]
[ROW][C]35[/C][C]412[/C][C]409.213070817149[/C][C]2.78692918285082[/C][/ROW]
[ROW][C]36[/C][C]404[/C][C]406.758092006996[/C][C]-2.75809200699563[/C][/ROW]
[ROW][C]37[/C][C]409[/C][C]409.995112400008[/C][C]-0.995112400008286[/C][/ROW]
[ROW][C]38[/C][C]412[/C][C]413.083833134442[/C][C]-1.08383313444235[/C][/ROW]
[ROW][C]39[/C][C]406[/C][C]411.304016005225[/C][C]-5.30401600522536[/C][/ROW]
[ROW][C]40[/C][C]398[/C][C]401.898794232971[/C][C]-3.89879423297135[/C][/ROW]
[ROW][C]41[/C][C]397[/C][C]395.774968542591[/C][C]1.22503145740878[/C][/ROW]
[ROW][C]42[/C][C]385[/C][C]392.768103556742[/C][C]-7.76810355674199[/C][/ROW]
[ROW][C]43[/C][C]390[/C][C]385.416067177025[/C][C]4.5839328229752[/C][/ROW]
[ROW][C]44[/C][C]413[/C][C]411.590188228679[/C][C]1.40981177132079[/C][/ROW]
[ROW][C]45[/C][C]413[/C][C]415.30363209591[/C][C]-2.30363209590975[/C][/ROW]
[ROW][C]46[/C][C]401[/C][C]404.038515401362[/C][C]-3.03851540136181[/C][/ROW]
[ROW][C]47[/C][C]397[/C][C]393.41364403984[/C][C]3.58635596015953[/C][/ROW]
[ROW][C]48[/C][C]397[/C][C]392.585377481723[/C][C]4.41462251827706[/C][/ROW]
[ROW][C]49[/C][C]409[/C][C]404.638272817468[/C][C]4.36172718253173[/C][/ROW]
[ROW][C]50[/C][C]419[/C][C]414.775071857116[/C][C]4.22492814288378[/C][/ROW]
[ROW][C]51[/C][C]424[/C][C]419.945780708633[/C][C]4.05421929136729[/C][/ROW]
[ROW][C]52[/C][C]428[/C][C]422.071915929525[/C][C]5.928084070475[/C][/ROW]
[ROW][C]53[/C][C]430[/C][C]428.254996834628[/C][C]1.74500316537178[/C][/ROW]
[ROW][C]54[/C][C]424[/C][C]426.813722741097[/C][C]-2.81372274109687[/C][/ROW]
[ROW][C]55[/C][C]433[/C][C]425.712292344843[/C][C]7.2877076551568[/C][/ROW]
[ROW][C]56[/C][C]456[/C][C]455.788983731876[/C][C]0.211016268124042[/C][/ROW]
[ROW][C]57[/C][C]459[/C][C]458.716176373763[/C][C]0.283823626237321[/C][/ROW]
[ROW][C]58[/C][C]446[/C][C]450.883811814628[/C][C]-4.88381181462826[/C][/ROW]
[ROW][C]59[/C][C]441[/C][C]438.659547323408[/C][C]2.34045267659235[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58443&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58443&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
1461459.4349881191431.56501188085739
2461463.563179353837-2.56317935383657
3463458.1744952872034.82550471279691
4462458.4676832352363.53231676476441
5456459.249920258332-3.24992025833231
6455449.2875041813155.71249581868537
7456455.2961712353060.703828764694123
8472475.250515228057-3.25051522805659
9472471.295454476580.704545523419844
10471461.2785474267159.72145257328487
11465463.4371642381011.56283576189946
12459458.4746359194840.525364080516317
13465463.9758815665781.02411843342234
14468468.022354950778-0.0223549507781517
15467466.0272076687830.972792331217376
16463462.3088793386810.691120661318806
17460459.9662623390850.0337376609146295
18462454.2150807195087.78491928049164
19461463.202944779026-2.20294477902595
20476480.566117948883-4.56611794888338
21476475.6541448765860.345855123414272
22471465.8755369706685.12446302933226
23453463.276573581502-10.2765735815022
24443445.181894591798-2.18189459179776
25442447.955745096803-5.95574509680317
26444444.555560703827-0.555560703826707
27438442.548500330156-4.54850033015622
28427433.252727263587-6.25272726358687
29424423.7538520253630.246147974637118
30416418.915588801338-2.91558880133817
31406416.3725244638-10.3725244638002
32431424.8041948625056.19580513749513
33434433.0305921771620.969407822838323
34418424.923588386627-6.92358838662707
35412409.2130708171492.78692918285082
36404406.758092006996-2.75809200699563
37409409.995112400008-0.995112400008286
38412413.083833134442-1.08383313444235
39406411.304016005225-5.30401600522536
40398401.898794232971-3.89879423297135
41397395.7749685425911.22503145740878
42385392.768103556742-7.76810355674199
43390385.4160671770254.5839328229752
44413411.5901882286791.40981177132079
45413415.30363209591-2.30363209590975
46401404.038515401362-3.03851540136181
47397393.413644039843.58635596015953
48397392.5853774817234.41462251827706
49409404.6382728174684.36172718253173
50419414.7750718571164.22492814288378
51424419.9457807086334.05421929136729
52428422.0719159295255.928084070475
53430428.2549968346281.74500316537178
54424426.813722741097-2.81372274109687
55433425.7122923448437.2877076551568
56456455.7889837318760.211016268124042
57459458.7161763737630.283823626237321
58446450.883811814628-4.88381181462826
59441438.6595473234082.34045267659235







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
190.01177068223554850.02354136447109690.988229317764452
200.002261351385334480.004522702770668960.997738648614666
210.0005171232449525530.001034246489905110.999482876755047
220.01903816922608830.03807633845217670.980961830773912
230.3100823309155870.6201646618311740.689917669084413
240.2294768874391230.4589537748782450.770523112560877
250.175089366420150.35017873284030.82491063357985
260.1920543711723270.3841087423446540.807945628827673
270.1542290450652390.3084580901304780.845770954934761
280.1128400686478420.2256801372956840.887159931352158
290.1524158795413530.3048317590827060.847584120458647
300.1736818657696020.3473637315392040.826318134230398
310.356164762217010.712329524434020.64383523778299
320.9564077370921840.08718452581563310.0435922629078166
330.9562470989436060.08750580211278810.0437529010563940
340.9583909648117240.08321807037655180.0416090351882759
350.9838071588703450.03238568225931080.0161928411296554
360.9650010548225880.06999789035482390.0349989451774120
370.9704613332197010.05907733356059730.0295386667802987
380.986247421759230.02750515648153990.0137525782407699
390.9927653287460940.01446934250781210.00723467125390604
400.9696938941405390.0606122117189220.030306105859461

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
19 & 0.0117706822355485 & 0.0235413644710969 & 0.988229317764452 \tabularnewline
20 & 0.00226135138533448 & 0.00452270277066896 & 0.997738648614666 \tabularnewline
21 & 0.000517123244952553 & 0.00103424648990511 & 0.999482876755047 \tabularnewline
22 & 0.0190381692260883 & 0.0380763384521767 & 0.980961830773912 \tabularnewline
23 & 0.310082330915587 & 0.620164661831174 & 0.689917669084413 \tabularnewline
24 & 0.229476887439123 & 0.458953774878245 & 0.770523112560877 \tabularnewline
25 & 0.17508936642015 & 0.3501787328403 & 0.82491063357985 \tabularnewline
26 & 0.192054371172327 & 0.384108742344654 & 0.807945628827673 \tabularnewline
27 & 0.154229045065239 & 0.308458090130478 & 0.845770954934761 \tabularnewline
28 & 0.112840068647842 & 0.225680137295684 & 0.887159931352158 \tabularnewline
29 & 0.152415879541353 & 0.304831759082706 & 0.847584120458647 \tabularnewline
30 & 0.173681865769602 & 0.347363731539204 & 0.826318134230398 \tabularnewline
31 & 0.35616476221701 & 0.71232952443402 & 0.64383523778299 \tabularnewline
32 & 0.956407737092184 & 0.0871845258156331 & 0.0435922629078166 \tabularnewline
33 & 0.956247098943606 & 0.0875058021127881 & 0.0437529010563940 \tabularnewline
34 & 0.958390964811724 & 0.0832180703765518 & 0.0416090351882759 \tabularnewline
35 & 0.983807158870345 & 0.0323856822593108 & 0.0161928411296554 \tabularnewline
36 & 0.965001054822588 & 0.0699978903548239 & 0.0349989451774120 \tabularnewline
37 & 0.970461333219701 & 0.0590773335605973 & 0.0295386667802987 \tabularnewline
38 & 0.98624742175923 & 0.0275051564815399 & 0.0137525782407699 \tabularnewline
39 & 0.992765328746094 & 0.0144693425078121 & 0.00723467125390604 \tabularnewline
40 & 0.969693894140539 & 0.060612211718922 & 0.030306105859461 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58443&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]0.0117706822355485[/C][C]0.0235413644710969[/C][C]0.988229317764452[/C][/ROW]
[ROW][C]20[/C][C]0.00226135138533448[/C][C]0.00452270277066896[/C][C]0.997738648614666[/C][/ROW]
[ROW][C]21[/C][C]0.000517123244952553[/C][C]0.00103424648990511[/C][C]0.999482876755047[/C][/ROW]
[ROW][C]22[/C][C]0.0190381692260883[/C][C]0.0380763384521767[/C][C]0.980961830773912[/C][/ROW]
[ROW][C]23[/C][C]0.310082330915587[/C][C]0.620164661831174[/C][C]0.689917669084413[/C][/ROW]
[ROW][C]24[/C][C]0.229476887439123[/C][C]0.458953774878245[/C][C]0.770523112560877[/C][/ROW]
[ROW][C]25[/C][C]0.17508936642015[/C][C]0.3501787328403[/C][C]0.82491063357985[/C][/ROW]
[ROW][C]26[/C][C]0.192054371172327[/C][C]0.384108742344654[/C][C]0.807945628827673[/C][/ROW]
[ROW][C]27[/C][C]0.154229045065239[/C][C]0.308458090130478[/C][C]0.845770954934761[/C][/ROW]
[ROW][C]28[/C][C]0.112840068647842[/C][C]0.225680137295684[/C][C]0.887159931352158[/C][/ROW]
[ROW][C]29[/C][C]0.152415879541353[/C][C]0.304831759082706[/C][C]0.847584120458647[/C][/ROW]
[ROW][C]30[/C][C]0.173681865769602[/C][C]0.347363731539204[/C][C]0.826318134230398[/C][/ROW]
[ROW][C]31[/C][C]0.35616476221701[/C][C]0.71232952443402[/C][C]0.64383523778299[/C][/ROW]
[ROW][C]32[/C][C]0.956407737092184[/C][C]0.0871845258156331[/C][C]0.0435922629078166[/C][/ROW]
[ROW][C]33[/C][C]0.956247098943606[/C][C]0.0875058021127881[/C][C]0.0437529010563940[/C][/ROW]
[ROW][C]34[/C][C]0.958390964811724[/C][C]0.0832180703765518[/C][C]0.0416090351882759[/C][/ROW]
[ROW][C]35[/C][C]0.983807158870345[/C][C]0.0323856822593108[/C][C]0.0161928411296554[/C][/ROW]
[ROW][C]36[/C][C]0.965001054822588[/C][C]0.0699978903548239[/C][C]0.0349989451774120[/C][/ROW]
[ROW][C]37[/C][C]0.970461333219701[/C][C]0.0590773335605973[/C][C]0.0295386667802987[/C][/ROW]
[ROW][C]38[/C][C]0.98624742175923[/C][C]0.0275051564815399[/C][C]0.0137525782407699[/C][/ROW]
[ROW][C]39[/C][C]0.992765328746094[/C][C]0.0144693425078121[/C][C]0.00723467125390604[/C][/ROW]
[ROW][C]40[/C][C]0.969693894140539[/C][C]0.060612211718922[/C][C]0.030306105859461[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58443&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58443&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
190.01177068223554850.02354136447109690.988229317764452
200.002261351385334480.004522702770668960.997738648614666
210.0005171232449525530.001034246489905110.999482876755047
220.01903816922608830.03807633845217670.980961830773912
230.3100823309155870.6201646618311740.689917669084413
240.2294768874391230.4589537748782450.770523112560877
250.175089366420150.35017873284030.82491063357985
260.1920543711723270.3841087423446540.807945628827673
270.1542290450652390.3084580901304780.845770954934761
280.1128400686478420.2256801372956840.887159931352158
290.1524158795413530.3048317590827060.847584120458647
300.1736818657696020.3473637315392040.826318134230398
310.356164762217010.712329524434020.64383523778299
320.9564077370921840.08718452581563310.0435922629078166
330.9562470989436060.08750580211278810.0437529010563940
340.9583909648117240.08321807037655180.0416090351882759
350.9838071588703450.03238568225931080.0161928411296554
360.9650010548225880.06999789035482390.0349989451774120
370.9704613332197010.05907733356059730.0295386667802987
380.986247421759230.02750515648153990.0137525782407699
390.9927653287460940.01446934250781210.00723467125390604
400.9696938941405390.0606122117189220.030306105859461







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level20.090909090909091NOK
5% type I error level70.318181818181818NOK
10% type I error level130.590909090909091NOK

\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 & 2 & 0.090909090909091 & NOK \tabularnewline
5% type I error level & 7 & 0.318181818181818 & NOK \tabularnewline
10% type I error level & 13 & 0.590909090909091 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58443&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]2[/C][C]0.090909090909091[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]7[/C][C]0.318181818181818[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]13[/C][C]0.590909090909091[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58443&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58443&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 level20.090909090909091NOK
5% type I error level70.318181818181818NOK
10% type I error level130.590909090909091NOK



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')
}