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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 computationSat, 12 Dec 2009 08:20:06 -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/Dec/12/t1260631279bdeftrazvjgzp45.htm/, Retrieved Thu, 31 Oct 2024 23:13:58 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=67010, Retrieved Thu, 31 Oct 2024 23:13:58 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact232
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [multiplelineairre...] [2009-11-18 15:20:48] [a9a33b1951d9ae87ed6d7d9055d41c93]
- R PD  [Multiple Regression] [Multiplelineairre...] [2009-11-18 16:10:40] [a9a33b1951d9ae87ed6d7d9055d41c93]
-    D      [Multiple Regression] [] [2009-12-12 15:20:06] [66ffaa9e54a90d3ae4874684602d24e9] [Current]
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Dataseries X:
17823.2	0
17872	0
17420.4	0
16704.4	0
15991.2	0
16583.6	0
19123.5	0
17838.7	0
17209.4	0
18586.5	0
16258.1	0
15141.6	0
19202.1	0
17746.5	0
19090.1	0
18040.3	0
17515.5	0
17751.8	0
21072.4	0
17170	0
19439.5	0
19795.4	0
17574.9	0
16165.4	0
19464.6	0
19932.1	0
19961.2	0
17343.4	0
18924.2	0
18574.1	0
21350.6	0
18594.6	0
19823.1	0
20844.4	0
19640.2	0
17735.4	0
19813.6	0
22160	0
20664.3	0
17877.4	0
20906.5	0
21164.1	0
21374.4	0
22952.3	0
21343.5	0
23899.3	0
22392.9	0
18274.1	0
22786.7	0
22321.5	0
17842.2	1
16373.5	1
15993.8	1
16446.1	1
17729	1
16643	1
16196.7	1
18252.1	1
17570.4	1
15836.8	1




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time4 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 & 4 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=67010&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]4 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=67010&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=67010&T=0

As an alternative you can also use a QR Code:  

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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time4 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135







Multiple Linear Regression - Estimated Regression Equation
y[t] = + 13885.4782857143 -5222.53071428571x[t] + 3300.83403571430M1[t] + 3383.94492857143M2[t] + 3312.40196428572M3[t] + 1479.29285714285M4[t] + 1972.46375000000M5[t] + 2104.89464285714M6[t] + 4025.66553571428M7[t] + 2430.13642857143M8[t] + 2487.58732142857M9[t] + 3855.41821428571M10[t] + 2161.90910714286M11[t] + 105.269107142857t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
y[t] =  +  13885.4782857143 -5222.53071428571x[t] +  3300.83403571430M1[t] +  3383.94492857143M2[t] +  3312.40196428572M3[t] +  1479.29285714285M4[t] +  1972.46375000000M5[t] +  2104.89464285714M6[t] +  4025.66553571428M7[t] +  2430.13642857143M8[t] +  2487.58732142857M9[t] +  3855.41821428571M10[t] +  2161.90910714286M11[t] +  105.269107142857t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=67010&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]y[t] =  +  13885.4782857143 -5222.53071428571x[t] +  3300.83403571430M1[t] +  3383.94492857143M2[t] +  3312.40196428572M3[t] +  1479.29285714285M4[t] +  1972.46375000000M5[t] +  2104.89464285714M6[t] +  4025.66553571428M7[t] +  2430.13642857143M8[t] +  2487.58732142857M9[t] +  3855.41821428571M10[t] +  2161.90910714286M11[t] +  105.269107142857t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=67010&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=67010&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] = + 13885.4782857143 -5222.53071428571x[t] + 3300.83403571430M1[t] + 3383.94492857143M2[t] + 3312.40196428572M3[t] + 1479.29285714285M4[t] + 1972.46375000000M5[t] + 2104.89464285714M6[t] + 4025.66553571428M7[t] + 2430.13642857143M8[t] + 2487.58732142857M9[t] + 3855.41821428571M10[t] + 2161.90910714286M11[t] + 105.269107142857t + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)13885.4782857143459.74841630.202300
x-5222.53071428571392.312604-13.312200
M13300.83403571430540.989566.101500
M23383.94492857143540.3965366.26200
M33312.40196428572541.3582256.118700
M41479.29285714285540.2382862.73820.0087560.004378
M51972.46375000000539.2481733.65780.0006520.000326
M62104.89464285714538.3886023.90960.0003020.000151
M74025.66553571428537.66027.487400
M82430.13642857143537.0634994.52494.2e-052.1e-05
M92487.58732142857536.598944.63583e-051.5e-05
M103855.41821428571536.2668657.189400
M112161.90910714286536.0675224.03290.0002060.000103
t105.2691071428578.44122312.470800

\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) & 13885.4782857143 & 459.748416 & 30.2023 & 0 & 0 \tabularnewline
x & -5222.53071428571 & 392.312604 & -13.3122 & 0 & 0 \tabularnewline
M1 & 3300.83403571430 & 540.98956 & 6.1015 & 0 & 0 \tabularnewline
M2 & 3383.94492857143 & 540.396536 & 6.262 & 0 & 0 \tabularnewline
M3 & 3312.40196428572 & 541.358225 & 6.1187 & 0 & 0 \tabularnewline
M4 & 1479.29285714285 & 540.238286 & 2.7382 & 0.008756 & 0.004378 \tabularnewline
M5 & 1972.46375000000 & 539.248173 & 3.6578 & 0.000652 & 0.000326 \tabularnewline
M6 & 2104.89464285714 & 538.388602 & 3.9096 & 0.000302 & 0.000151 \tabularnewline
M7 & 4025.66553571428 & 537.6602 & 7.4874 & 0 & 0 \tabularnewline
M8 & 2430.13642857143 & 537.063499 & 4.5249 & 4.2e-05 & 2.1e-05 \tabularnewline
M9 & 2487.58732142857 & 536.59894 & 4.6358 & 3e-05 & 1.5e-05 \tabularnewline
M10 & 3855.41821428571 & 536.266865 & 7.1894 & 0 & 0 \tabularnewline
M11 & 2161.90910714286 & 536.067522 & 4.0329 & 0.000206 & 0.000103 \tabularnewline
t & 105.269107142857 & 8.441223 & 12.4708 & 0 & 0 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=67010&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]13885.4782857143[/C][C]459.748416[/C][C]30.2023[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]x[/C][C]-5222.53071428571[/C][C]392.312604[/C][C]-13.3122[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]M1[/C][C]3300.83403571430[/C][C]540.98956[/C][C]6.1015[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]M2[/C][C]3383.94492857143[/C][C]540.396536[/C][C]6.262[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]M3[/C][C]3312.40196428572[/C][C]541.358225[/C][C]6.1187[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]M4[/C][C]1479.29285714285[/C][C]540.238286[/C][C]2.7382[/C][C]0.008756[/C][C]0.004378[/C][/ROW]
[ROW][C]M5[/C][C]1972.46375000000[/C][C]539.248173[/C][C]3.6578[/C][C]0.000652[/C][C]0.000326[/C][/ROW]
[ROW][C]M6[/C][C]2104.89464285714[/C][C]538.388602[/C][C]3.9096[/C][C]0.000302[/C][C]0.000151[/C][/ROW]
[ROW][C]M7[/C][C]4025.66553571428[/C][C]537.6602[/C][C]7.4874[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]M8[/C][C]2430.13642857143[/C][C]537.063499[/C][C]4.5249[/C][C]4.2e-05[/C][C]2.1e-05[/C][/ROW]
[ROW][C]M9[/C][C]2487.58732142857[/C][C]536.59894[/C][C]4.6358[/C][C]3e-05[/C][C]1.5e-05[/C][/ROW]
[ROW][C]M10[/C][C]3855.41821428571[/C][C]536.266865[/C][C]7.1894[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]M11[/C][C]2161.90910714286[/C][C]536.067522[/C][C]4.0329[/C][C]0.000206[/C][C]0.000103[/C][/ROW]
[ROW][C]t[/C][C]105.269107142857[/C][C]8.441223[/C][C]12.4708[/C][C]0[/C][C]0[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=67010&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=67010&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)13885.4782857143459.74841630.202300
x-5222.53071428571392.312604-13.312200
M13300.83403571430540.989566.101500
M23383.94492857143540.3965366.26200
M33312.40196428572541.3582256.118700
M41479.29285714285540.2382862.73820.0087560.004378
M51972.46375000000539.2481733.65780.0006520.000326
M62104.89464285714538.3886023.90960.0003020.000151
M74025.66553571428537.66027.487400
M82430.13642857143537.0634994.52494.2e-052.1e-05
M92487.58732142857536.598944.63583e-051.5e-05
M103855.41821428571536.2668657.189400
M112161.90910714286536.0675224.03290.0002060.000103
t105.2691071428578.44122312.470800







Multiple Linear Regression - Regression Statistics
Multiple R0.932532816632137
R-squared0.869617454095868
Adjusted R-squared0.832770212862091
F-TEST (value)23.6006122840676
F-TEST (DF numerator)13
F-TEST (DF denominator)46
p-value4.44089209850063e-16
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation847.492085553841
Sum Squared Residuals33039170.4135144

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.932532816632137 \tabularnewline
R-squared & 0.869617454095868 \tabularnewline
Adjusted R-squared & 0.832770212862091 \tabularnewline
F-TEST (value) & 23.6006122840676 \tabularnewline
F-TEST (DF numerator) & 13 \tabularnewline
F-TEST (DF denominator) & 46 \tabularnewline
p-value & 4.44089209850063e-16 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 847.492085553841 \tabularnewline
Sum Squared Residuals & 33039170.4135144 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=67010&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.932532816632137[/C][/ROW]
[ROW][C]R-squared[/C][C]0.869617454095868[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.832770212862091[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]23.6006122840676[/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]4.44089209850063e-16[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]847.492085553841[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]33039170.4135144[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=67010&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=67010&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 R0.932532816632137
R-squared0.869617454095868
Adjusted R-squared0.832770212862091
F-TEST (value)23.6006122840676
F-TEST (DF numerator)13
F-TEST (DF denominator)46
p-value4.44089209850063e-16
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation847.492085553841
Sum Squared Residuals33039170.4135144







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
117823.217291.5814285714531.618571428623
21787217479.9614285714392.038571428562
317420.417513.6875714286-93.287571428569
416704.415785.8475714286918.552428571425
515991.216384.2875714286-393.087571428578
616583.616621.9875714286-38.3875714285738
719123.518648.0275714286475.472428571427
817838.717157.7675714286680.932428571427
917209.417320.4875714286-111.087571428570
1018586.518793.5875714286-207.087571428575
1116258.117205.3475714286-947.247571428577
1215141.615148.7075714286-7.10757142857226
1319202.118554.8107142857647.289285714269
1417746.518743.1907142857-996.690714285716
1519090.118776.9168571429313.183142857139
1618040.317049.0768571429991.22314285714
1717515.517647.5168571429-132.016857142857
1817751.817885.2168571429-133.416857142858
1921072.419911.25685714291161.14314285714
201717018420.9968571429-1250.99685714286
2119439.518583.7168571429855.783142857141
2219795.420056.8168571429-261.416857142857
2317574.918468.5768571429-893.676857142858
2416165.416411.9368571429-246.536857142859
2519464.619818.04-353.440000000014
2619932.120006.42-74.3199999999989
2719961.220040.1461428571-78.9461428571426
2817343.418312.3061428571-968.906142857141
2918924.218910.746142857113.4538571428598
3018574.119148.4461428571-574.346142857143
3121350.621174.4861428571176.113857142855
3218594.619684.2261428571-1089.62614285714
3319823.119846.9461428571-23.8461428571441
3420844.421320.0461428571-475.646142857141
3519640.219731.8061428571-91.606142857142
3617735.417675.166142857160.233857142859
3719813.621081.2692857143-1267.66928571430
382216021269.6492857143890.350714285718
3920664.321303.3754285714-639.075428571428
4017877.419575.5354285714-1698.13542857142
4120906.520173.9754285714732.524571428576
4221164.120411.6754285714752.424571428574
4321374.422437.7154285714-1063.31542857142
4422952.320947.45542857142004.84457142857
4521343.521110.1754285714233.324571428574
4623899.322583.27542857141316.02457142857
4722392.920995.03542857141397.86457142857
4818274.118938.3954285714-664.295428571428
4922786.722344.4985714286442.201428571421
5022321.522532.8785714286-211.378571428566
5117842.217344.074498.126
5216373.515616.234757.266
5315993.816214.674-220.873999999998
5416446.116452.374-6.27399999999999
551772918478.414-749.414
561664316988.154-345.154
5716196.717150.874-954.174
5818252.118623.974-371.874000000001
5917570.417035.734534.666000000001
6015836.814979.094857.706

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 17823.2 & 17291.5814285714 & 531.618571428623 \tabularnewline
2 & 17872 & 17479.9614285714 & 392.038571428562 \tabularnewline
3 & 17420.4 & 17513.6875714286 & -93.287571428569 \tabularnewline
4 & 16704.4 & 15785.8475714286 & 918.552428571425 \tabularnewline
5 & 15991.2 & 16384.2875714286 & -393.087571428578 \tabularnewline
6 & 16583.6 & 16621.9875714286 & -38.3875714285738 \tabularnewline
7 & 19123.5 & 18648.0275714286 & 475.472428571427 \tabularnewline
8 & 17838.7 & 17157.7675714286 & 680.932428571427 \tabularnewline
9 & 17209.4 & 17320.4875714286 & -111.087571428570 \tabularnewline
10 & 18586.5 & 18793.5875714286 & -207.087571428575 \tabularnewline
11 & 16258.1 & 17205.3475714286 & -947.247571428577 \tabularnewline
12 & 15141.6 & 15148.7075714286 & -7.10757142857226 \tabularnewline
13 & 19202.1 & 18554.8107142857 & 647.289285714269 \tabularnewline
14 & 17746.5 & 18743.1907142857 & -996.690714285716 \tabularnewline
15 & 19090.1 & 18776.9168571429 & 313.183142857139 \tabularnewline
16 & 18040.3 & 17049.0768571429 & 991.22314285714 \tabularnewline
17 & 17515.5 & 17647.5168571429 & -132.016857142857 \tabularnewline
18 & 17751.8 & 17885.2168571429 & -133.416857142858 \tabularnewline
19 & 21072.4 & 19911.2568571429 & 1161.14314285714 \tabularnewline
20 & 17170 & 18420.9968571429 & -1250.99685714286 \tabularnewline
21 & 19439.5 & 18583.7168571429 & 855.783142857141 \tabularnewline
22 & 19795.4 & 20056.8168571429 & -261.416857142857 \tabularnewline
23 & 17574.9 & 18468.5768571429 & -893.676857142858 \tabularnewline
24 & 16165.4 & 16411.9368571429 & -246.536857142859 \tabularnewline
25 & 19464.6 & 19818.04 & -353.440000000014 \tabularnewline
26 & 19932.1 & 20006.42 & -74.3199999999989 \tabularnewline
27 & 19961.2 & 20040.1461428571 & -78.9461428571426 \tabularnewline
28 & 17343.4 & 18312.3061428571 & -968.906142857141 \tabularnewline
29 & 18924.2 & 18910.7461428571 & 13.4538571428598 \tabularnewline
30 & 18574.1 & 19148.4461428571 & -574.346142857143 \tabularnewline
31 & 21350.6 & 21174.4861428571 & 176.113857142855 \tabularnewline
32 & 18594.6 & 19684.2261428571 & -1089.62614285714 \tabularnewline
33 & 19823.1 & 19846.9461428571 & -23.8461428571441 \tabularnewline
34 & 20844.4 & 21320.0461428571 & -475.646142857141 \tabularnewline
35 & 19640.2 & 19731.8061428571 & -91.606142857142 \tabularnewline
36 & 17735.4 & 17675.1661428571 & 60.233857142859 \tabularnewline
37 & 19813.6 & 21081.2692857143 & -1267.66928571430 \tabularnewline
38 & 22160 & 21269.6492857143 & 890.350714285718 \tabularnewline
39 & 20664.3 & 21303.3754285714 & -639.075428571428 \tabularnewline
40 & 17877.4 & 19575.5354285714 & -1698.13542857142 \tabularnewline
41 & 20906.5 & 20173.9754285714 & 732.524571428576 \tabularnewline
42 & 21164.1 & 20411.6754285714 & 752.424571428574 \tabularnewline
43 & 21374.4 & 22437.7154285714 & -1063.31542857142 \tabularnewline
44 & 22952.3 & 20947.4554285714 & 2004.84457142857 \tabularnewline
45 & 21343.5 & 21110.1754285714 & 233.324571428574 \tabularnewline
46 & 23899.3 & 22583.2754285714 & 1316.02457142857 \tabularnewline
47 & 22392.9 & 20995.0354285714 & 1397.86457142857 \tabularnewline
48 & 18274.1 & 18938.3954285714 & -664.295428571428 \tabularnewline
49 & 22786.7 & 22344.4985714286 & 442.201428571421 \tabularnewline
50 & 22321.5 & 22532.8785714286 & -211.378571428566 \tabularnewline
51 & 17842.2 & 17344.074 & 498.126 \tabularnewline
52 & 16373.5 & 15616.234 & 757.266 \tabularnewline
53 & 15993.8 & 16214.674 & -220.873999999998 \tabularnewline
54 & 16446.1 & 16452.374 & -6.27399999999999 \tabularnewline
55 & 17729 & 18478.414 & -749.414 \tabularnewline
56 & 16643 & 16988.154 & -345.154 \tabularnewline
57 & 16196.7 & 17150.874 & -954.174 \tabularnewline
58 & 18252.1 & 18623.974 & -371.874000000001 \tabularnewline
59 & 17570.4 & 17035.734 & 534.666000000001 \tabularnewline
60 & 15836.8 & 14979.094 & 857.706 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=67010&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]17823.2[/C][C]17291.5814285714[/C][C]531.618571428623[/C][/ROW]
[ROW][C]2[/C][C]17872[/C][C]17479.9614285714[/C][C]392.038571428562[/C][/ROW]
[ROW][C]3[/C][C]17420.4[/C][C]17513.6875714286[/C][C]-93.287571428569[/C][/ROW]
[ROW][C]4[/C][C]16704.4[/C][C]15785.8475714286[/C][C]918.552428571425[/C][/ROW]
[ROW][C]5[/C][C]15991.2[/C][C]16384.2875714286[/C][C]-393.087571428578[/C][/ROW]
[ROW][C]6[/C][C]16583.6[/C][C]16621.9875714286[/C][C]-38.3875714285738[/C][/ROW]
[ROW][C]7[/C][C]19123.5[/C][C]18648.0275714286[/C][C]475.472428571427[/C][/ROW]
[ROW][C]8[/C][C]17838.7[/C][C]17157.7675714286[/C][C]680.932428571427[/C][/ROW]
[ROW][C]9[/C][C]17209.4[/C][C]17320.4875714286[/C][C]-111.087571428570[/C][/ROW]
[ROW][C]10[/C][C]18586.5[/C][C]18793.5875714286[/C][C]-207.087571428575[/C][/ROW]
[ROW][C]11[/C][C]16258.1[/C][C]17205.3475714286[/C][C]-947.247571428577[/C][/ROW]
[ROW][C]12[/C][C]15141.6[/C][C]15148.7075714286[/C][C]-7.10757142857226[/C][/ROW]
[ROW][C]13[/C][C]19202.1[/C][C]18554.8107142857[/C][C]647.289285714269[/C][/ROW]
[ROW][C]14[/C][C]17746.5[/C][C]18743.1907142857[/C][C]-996.690714285716[/C][/ROW]
[ROW][C]15[/C][C]19090.1[/C][C]18776.9168571429[/C][C]313.183142857139[/C][/ROW]
[ROW][C]16[/C][C]18040.3[/C][C]17049.0768571429[/C][C]991.22314285714[/C][/ROW]
[ROW][C]17[/C][C]17515.5[/C][C]17647.5168571429[/C][C]-132.016857142857[/C][/ROW]
[ROW][C]18[/C][C]17751.8[/C][C]17885.2168571429[/C][C]-133.416857142858[/C][/ROW]
[ROW][C]19[/C][C]21072.4[/C][C]19911.2568571429[/C][C]1161.14314285714[/C][/ROW]
[ROW][C]20[/C][C]17170[/C][C]18420.9968571429[/C][C]-1250.99685714286[/C][/ROW]
[ROW][C]21[/C][C]19439.5[/C][C]18583.7168571429[/C][C]855.783142857141[/C][/ROW]
[ROW][C]22[/C][C]19795.4[/C][C]20056.8168571429[/C][C]-261.416857142857[/C][/ROW]
[ROW][C]23[/C][C]17574.9[/C][C]18468.5768571429[/C][C]-893.676857142858[/C][/ROW]
[ROW][C]24[/C][C]16165.4[/C][C]16411.9368571429[/C][C]-246.536857142859[/C][/ROW]
[ROW][C]25[/C][C]19464.6[/C][C]19818.04[/C][C]-353.440000000014[/C][/ROW]
[ROW][C]26[/C][C]19932.1[/C][C]20006.42[/C][C]-74.3199999999989[/C][/ROW]
[ROW][C]27[/C][C]19961.2[/C][C]20040.1461428571[/C][C]-78.9461428571426[/C][/ROW]
[ROW][C]28[/C][C]17343.4[/C][C]18312.3061428571[/C][C]-968.906142857141[/C][/ROW]
[ROW][C]29[/C][C]18924.2[/C][C]18910.7461428571[/C][C]13.4538571428598[/C][/ROW]
[ROW][C]30[/C][C]18574.1[/C][C]19148.4461428571[/C][C]-574.346142857143[/C][/ROW]
[ROW][C]31[/C][C]21350.6[/C][C]21174.4861428571[/C][C]176.113857142855[/C][/ROW]
[ROW][C]32[/C][C]18594.6[/C][C]19684.2261428571[/C][C]-1089.62614285714[/C][/ROW]
[ROW][C]33[/C][C]19823.1[/C][C]19846.9461428571[/C][C]-23.8461428571441[/C][/ROW]
[ROW][C]34[/C][C]20844.4[/C][C]21320.0461428571[/C][C]-475.646142857141[/C][/ROW]
[ROW][C]35[/C][C]19640.2[/C][C]19731.8061428571[/C][C]-91.606142857142[/C][/ROW]
[ROW][C]36[/C][C]17735.4[/C][C]17675.1661428571[/C][C]60.233857142859[/C][/ROW]
[ROW][C]37[/C][C]19813.6[/C][C]21081.2692857143[/C][C]-1267.66928571430[/C][/ROW]
[ROW][C]38[/C][C]22160[/C][C]21269.6492857143[/C][C]890.350714285718[/C][/ROW]
[ROW][C]39[/C][C]20664.3[/C][C]21303.3754285714[/C][C]-639.075428571428[/C][/ROW]
[ROW][C]40[/C][C]17877.4[/C][C]19575.5354285714[/C][C]-1698.13542857142[/C][/ROW]
[ROW][C]41[/C][C]20906.5[/C][C]20173.9754285714[/C][C]732.524571428576[/C][/ROW]
[ROW][C]42[/C][C]21164.1[/C][C]20411.6754285714[/C][C]752.424571428574[/C][/ROW]
[ROW][C]43[/C][C]21374.4[/C][C]22437.7154285714[/C][C]-1063.31542857142[/C][/ROW]
[ROW][C]44[/C][C]22952.3[/C][C]20947.4554285714[/C][C]2004.84457142857[/C][/ROW]
[ROW][C]45[/C][C]21343.5[/C][C]21110.1754285714[/C][C]233.324571428574[/C][/ROW]
[ROW][C]46[/C][C]23899.3[/C][C]22583.2754285714[/C][C]1316.02457142857[/C][/ROW]
[ROW][C]47[/C][C]22392.9[/C][C]20995.0354285714[/C][C]1397.86457142857[/C][/ROW]
[ROW][C]48[/C][C]18274.1[/C][C]18938.3954285714[/C][C]-664.295428571428[/C][/ROW]
[ROW][C]49[/C][C]22786.7[/C][C]22344.4985714286[/C][C]442.201428571421[/C][/ROW]
[ROW][C]50[/C][C]22321.5[/C][C]22532.8785714286[/C][C]-211.378571428566[/C][/ROW]
[ROW][C]51[/C][C]17842.2[/C][C]17344.074[/C][C]498.126[/C][/ROW]
[ROW][C]52[/C][C]16373.5[/C][C]15616.234[/C][C]757.266[/C][/ROW]
[ROW][C]53[/C][C]15993.8[/C][C]16214.674[/C][C]-220.873999999998[/C][/ROW]
[ROW][C]54[/C][C]16446.1[/C][C]16452.374[/C][C]-6.27399999999999[/C][/ROW]
[ROW][C]55[/C][C]17729[/C][C]18478.414[/C][C]-749.414[/C][/ROW]
[ROW][C]56[/C][C]16643[/C][C]16988.154[/C][C]-345.154[/C][/ROW]
[ROW][C]57[/C][C]16196.7[/C][C]17150.874[/C][C]-954.174[/C][/ROW]
[ROW][C]58[/C][C]18252.1[/C][C]18623.974[/C][C]-371.874000000001[/C][/ROW]
[ROW][C]59[/C][C]17570.4[/C][C]17035.734[/C][C]534.666000000001[/C][/ROW]
[ROW][C]60[/C][C]15836.8[/C][C]14979.094[/C][C]857.706[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=67010&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=67010&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
117823.217291.5814285714531.618571428623
21787217479.9614285714392.038571428562
317420.417513.6875714286-93.287571428569
416704.415785.8475714286918.552428571425
515991.216384.2875714286-393.087571428578
616583.616621.9875714286-38.3875714285738
719123.518648.0275714286475.472428571427
817838.717157.7675714286680.932428571427
917209.417320.4875714286-111.087571428570
1018586.518793.5875714286-207.087571428575
1116258.117205.3475714286-947.247571428577
1215141.615148.7075714286-7.10757142857226
1319202.118554.8107142857647.289285714269
1417746.518743.1907142857-996.690714285716
1519090.118776.9168571429313.183142857139
1618040.317049.0768571429991.22314285714
1717515.517647.5168571429-132.016857142857
1817751.817885.2168571429-133.416857142858
1921072.419911.25685714291161.14314285714
201717018420.9968571429-1250.99685714286
2119439.518583.7168571429855.783142857141
2219795.420056.8168571429-261.416857142857
2317574.918468.5768571429-893.676857142858
2416165.416411.9368571429-246.536857142859
2519464.619818.04-353.440000000014
2619932.120006.42-74.3199999999989
2719961.220040.1461428571-78.9461428571426
2817343.418312.3061428571-968.906142857141
2918924.218910.746142857113.4538571428598
3018574.119148.4461428571-574.346142857143
3121350.621174.4861428571176.113857142855
3218594.619684.2261428571-1089.62614285714
3319823.119846.9461428571-23.8461428571441
3420844.421320.0461428571-475.646142857141
3519640.219731.8061428571-91.606142857142
3617735.417675.166142857160.233857142859
3719813.621081.2692857143-1267.66928571430
382216021269.6492857143890.350714285718
3920664.321303.3754285714-639.075428571428
4017877.419575.5354285714-1698.13542857142
4120906.520173.9754285714732.524571428576
4221164.120411.6754285714752.424571428574
4321374.422437.7154285714-1063.31542857142
4422952.320947.45542857142004.84457142857
4521343.521110.1754285714233.324571428574
4623899.322583.27542857141316.02457142857
4722392.920995.03542857141397.86457142857
4818274.118938.3954285714-664.295428571428
4922786.722344.4985714286442.201428571421
5022321.522532.8785714286-211.378571428566
5117842.217344.074498.126
5216373.515616.234757.266
5315993.816214.674-220.873999999998
5416446.116452.374-6.27399999999999
551772918478.414-749.414
561664316988.154-345.154
5716196.717150.874-954.174
5818252.118623.974-371.874000000001
5917570.417035.734534.666000000001
6015836.814979.094857.706







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
170.2639782269845740.5279564539691480.736021773015426
180.1301708543016850.2603417086033690.869829145698315
190.1209622161408780.2419244322817550.879037783859122
200.3071663032408420.6143326064816840.692833696759158
210.3439555744127070.6879111488254140.656044425587293
220.2366929487386630.4733858974773260.763307051261337
230.1670704634525610.3341409269051210.832929536547439
240.1045902709351490.2091805418702990.89540972906485
250.08555826926348850.1711165385269770.914441730736511
260.05710236019801930.1142047203960390.94289763980198
270.03248934251669430.06497868503338860.967510657483306
280.0666640483101320.1333280966202640.933335951689868
290.04706696077331430.09413392154662860.952933039226686
300.02796404125014270.05592808250028540.972035958749857
310.02490833416400350.04981666832800710.975091665835996
320.02244890607589260.04489781215178530.977551093924107
330.01366743748035000.02733487496069990.98633256251965
340.007406071199741950.01481214239948390.992593928800258
350.009034118932957550.01806823786591510.990965881067043
360.004901642416740410.009803284833480830.99509835758326
370.01077916222173230.02155832444346460.989220837778268
380.01418180392878660.02836360785757310.985818196071213
390.01188602941869500.02377205883739010.988113970581305
400.1387377560533520.2774755121067040.861262243946648
410.1113071237045720.2226142474091440.888692876295428
420.0790211681072020.1580423362144040.920978831892798
430.0709908396997880.1419816793995760.929009160300212

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
17 & 0.263978226984574 & 0.527956453969148 & 0.736021773015426 \tabularnewline
18 & 0.130170854301685 & 0.260341708603369 & 0.869829145698315 \tabularnewline
19 & 0.120962216140878 & 0.241924432281755 & 0.879037783859122 \tabularnewline
20 & 0.307166303240842 & 0.614332606481684 & 0.692833696759158 \tabularnewline
21 & 0.343955574412707 & 0.687911148825414 & 0.656044425587293 \tabularnewline
22 & 0.236692948738663 & 0.473385897477326 & 0.763307051261337 \tabularnewline
23 & 0.167070463452561 & 0.334140926905121 & 0.832929536547439 \tabularnewline
24 & 0.104590270935149 & 0.209180541870299 & 0.89540972906485 \tabularnewline
25 & 0.0855582692634885 & 0.171116538526977 & 0.914441730736511 \tabularnewline
26 & 0.0571023601980193 & 0.114204720396039 & 0.94289763980198 \tabularnewline
27 & 0.0324893425166943 & 0.0649786850333886 & 0.967510657483306 \tabularnewline
28 & 0.066664048310132 & 0.133328096620264 & 0.933335951689868 \tabularnewline
29 & 0.0470669607733143 & 0.0941339215466286 & 0.952933039226686 \tabularnewline
30 & 0.0279640412501427 & 0.0559280825002854 & 0.972035958749857 \tabularnewline
31 & 0.0249083341640035 & 0.0498166683280071 & 0.975091665835996 \tabularnewline
32 & 0.0224489060758926 & 0.0448978121517853 & 0.977551093924107 \tabularnewline
33 & 0.0136674374803500 & 0.0273348749606999 & 0.98633256251965 \tabularnewline
34 & 0.00740607119974195 & 0.0148121423994839 & 0.992593928800258 \tabularnewline
35 & 0.00903411893295755 & 0.0180682378659151 & 0.990965881067043 \tabularnewline
36 & 0.00490164241674041 & 0.00980328483348083 & 0.99509835758326 \tabularnewline
37 & 0.0107791622217323 & 0.0215583244434646 & 0.989220837778268 \tabularnewline
38 & 0.0141818039287866 & 0.0283636078575731 & 0.985818196071213 \tabularnewline
39 & 0.0118860294186950 & 0.0237720588373901 & 0.988113970581305 \tabularnewline
40 & 0.138737756053352 & 0.277475512106704 & 0.861262243946648 \tabularnewline
41 & 0.111307123704572 & 0.222614247409144 & 0.888692876295428 \tabularnewline
42 & 0.079021168107202 & 0.158042336214404 & 0.920978831892798 \tabularnewline
43 & 0.070990839699788 & 0.141981679399576 & 0.929009160300212 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=67010&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]0.263978226984574[/C][C]0.527956453969148[/C][C]0.736021773015426[/C][/ROW]
[ROW][C]18[/C][C]0.130170854301685[/C][C]0.260341708603369[/C][C]0.869829145698315[/C][/ROW]
[ROW][C]19[/C][C]0.120962216140878[/C][C]0.241924432281755[/C][C]0.879037783859122[/C][/ROW]
[ROW][C]20[/C][C]0.307166303240842[/C][C]0.614332606481684[/C][C]0.692833696759158[/C][/ROW]
[ROW][C]21[/C][C]0.343955574412707[/C][C]0.687911148825414[/C][C]0.656044425587293[/C][/ROW]
[ROW][C]22[/C][C]0.236692948738663[/C][C]0.473385897477326[/C][C]0.763307051261337[/C][/ROW]
[ROW][C]23[/C][C]0.167070463452561[/C][C]0.334140926905121[/C][C]0.832929536547439[/C][/ROW]
[ROW][C]24[/C][C]0.104590270935149[/C][C]0.209180541870299[/C][C]0.89540972906485[/C][/ROW]
[ROW][C]25[/C][C]0.0855582692634885[/C][C]0.171116538526977[/C][C]0.914441730736511[/C][/ROW]
[ROW][C]26[/C][C]0.0571023601980193[/C][C]0.114204720396039[/C][C]0.94289763980198[/C][/ROW]
[ROW][C]27[/C][C]0.0324893425166943[/C][C]0.0649786850333886[/C][C]0.967510657483306[/C][/ROW]
[ROW][C]28[/C][C]0.066664048310132[/C][C]0.133328096620264[/C][C]0.933335951689868[/C][/ROW]
[ROW][C]29[/C][C]0.0470669607733143[/C][C]0.0941339215466286[/C][C]0.952933039226686[/C][/ROW]
[ROW][C]30[/C][C]0.0279640412501427[/C][C]0.0559280825002854[/C][C]0.972035958749857[/C][/ROW]
[ROW][C]31[/C][C]0.0249083341640035[/C][C]0.0498166683280071[/C][C]0.975091665835996[/C][/ROW]
[ROW][C]32[/C][C]0.0224489060758926[/C][C]0.0448978121517853[/C][C]0.977551093924107[/C][/ROW]
[ROW][C]33[/C][C]0.0136674374803500[/C][C]0.0273348749606999[/C][C]0.98633256251965[/C][/ROW]
[ROW][C]34[/C][C]0.00740607119974195[/C][C]0.0148121423994839[/C][C]0.992593928800258[/C][/ROW]
[ROW][C]35[/C][C]0.00903411893295755[/C][C]0.0180682378659151[/C][C]0.990965881067043[/C][/ROW]
[ROW][C]36[/C][C]0.00490164241674041[/C][C]0.00980328483348083[/C][C]0.99509835758326[/C][/ROW]
[ROW][C]37[/C][C]0.0107791622217323[/C][C]0.0215583244434646[/C][C]0.989220837778268[/C][/ROW]
[ROW][C]38[/C][C]0.0141818039287866[/C][C]0.0283636078575731[/C][C]0.985818196071213[/C][/ROW]
[ROW][C]39[/C][C]0.0118860294186950[/C][C]0.0237720588373901[/C][C]0.988113970581305[/C][/ROW]
[ROW][C]40[/C][C]0.138737756053352[/C][C]0.277475512106704[/C][C]0.861262243946648[/C][/ROW]
[ROW][C]41[/C][C]0.111307123704572[/C][C]0.222614247409144[/C][C]0.888692876295428[/C][/ROW]
[ROW][C]42[/C][C]0.079021168107202[/C][C]0.158042336214404[/C][C]0.920978831892798[/C][/ROW]
[ROW][C]43[/C][C]0.070990839699788[/C][C]0.141981679399576[/C][C]0.929009160300212[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=67010&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=67010&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
170.2639782269845740.5279564539691480.736021773015426
180.1301708543016850.2603417086033690.869829145698315
190.1209622161408780.2419244322817550.879037783859122
200.3071663032408420.6143326064816840.692833696759158
210.3439555744127070.6879111488254140.656044425587293
220.2366929487386630.4733858974773260.763307051261337
230.1670704634525610.3341409269051210.832929536547439
240.1045902709351490.2091805418702990.89540972906485
250.08555826926348850.1711165385269770.914441730736511
260.05710236019801930.1142047203960390.94289763980198
270.03248934251669430.06497868503338860.967510657483306
280.0666640483101320.1333280966202640.933335951689868
290.04706696077331430.09413392154662860.952933039226686
300.02796404125014270.05592808250028540.972035958749857
310.02490833416400350.04981666832800710.975091665835996
320.02244890607589260.04489781215178530.977551093924107
330.01366743748035000.02733487496069990.98633256251965
340.007406071199741950.01481214239948390.992593928800258
350.009034118932957550.01806823786591510.990965881067043
360.004901642416740410.009803284833480830.99509835758326
370.01077916222173230.02155832444346460.989220837778268
380.01418180392878660.02836360785757310.985818196071213
390.01188602941869500.02377205883739010.988113970581305
400.1387377560533520.2774755121067040.861262243946648
410.1113071237045720.2226142474091440.888692876295428
420.0790211681072020.1580423362144040.920978831892798
430.0709908396997880.1419816793995760.929009160300212







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level10.0370370370370370NOK
5% type I error level90.333333333333333NOK
10% type I error level120.444444444444444NOK

\begin{tabular}{lllllllll}
\hline
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
Description & # significant tests & % significant tests & OK/NOK \tabularnewline
1% type I error level & 1 & 0.0370370370370370 & NOK \tabularnewline
5% type I error level & 9 & 0.333333333333333 & NOK \tabularnewline
10% type I error level & 12 & 0.444444444444444 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=67010&T=6

[TABLE]
[ROW][C]Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]Description[/C][C]# significant tests[/C][C]% significant tests[/C][C]OK/NOK[/C][/ROW]
[ROW][C]1% type I error level[/C][C]1[/C][C]0.0370370370370370[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]9[/C][C]0.333333333333333[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]12[/C][C]0.444444444444444[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=67010&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=67010&T=6

As an alternative you can also use a QR Code:  

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

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level10.0370370370370370NOK
5% type I error level90.333333333333333NOK
10% type I error level120.444444444444444NOK



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')
}