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Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationFri, 20 Nov 2009 06:10:25 -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/t1258723036q9eqtvv62p8ffu6.htm/, Retrieved Thu, 28 Mar 2024 11:49:57 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=58126, Retrieved Thu, 28 Mar 2024 11:49:57 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact125
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [Q1 The Seatbeltlaw] [2007-11-14 19:27:43] [8cd6641b921d30ebe00b648d1481bba0]
- RM D  [Multiple Regression] [Seatbelt] [2009-11-12 14:06:21] [b98453cac15ba1066b407e146608df68]
-    D      [Multiple Regression] [] [2009-11-20 13:10:25] [ed082d38031561faed979d8cebfeba4d] [Current]
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Dataseries X:
10144	112
10751	304
11752	794
13808	901
16203	1232
17432	1240
18014	1032
16956	1145
17982	1588
19435	2264
19990	2209
20154	2917
10327	243
9807	558
10862	1238
13743	1502
16458	2000
18466	2146
18810	2066
17361	2046
17411	1952
18517	2771
18525	3278
17859	4000
9499	410
9490	1107
9255	1622
10758	1986
12375	2036
14617	2400
15427	2736
14136	2901
14308	2883
15293	3747
15679	4075
16319	4996
11196	575
11169	999
12158	1411
14251	1493
16237	1846
19706	2899
18960	2372
18537	2856
19103	3468
19691	4193
19464	4440
17264	4186
8957	655
9703	1453
9166	1989
9519	2209
10535	2667
11526	3005
9630	2195
7061	2236
6021	2489
4728	2651
2657	2636
1264	2819




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time6 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 & 6 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58126&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]6 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=58126&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58126&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 time6 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135







Multiple Linear Regression - Estimated Regression Equation
Y[t] = + 7378.28355988144 + 3.95381014494266X[t] + 6461.74593137428M1[t] + 4918.47723861162M2[t] + 3506.72080584833M3[t] + 4679.62057135073M4[t] + 5504.75273192363M5[t] + 6198.70800814803M6[t] + 7252.52025307776M7[t] + 5491.07357394325M8[t] + 4915.84217683649M9[t] + 3132.54862030323M10[t] + 2278.21743653034M11[t] -215.719989563514t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Y[t] =  +  7378.28355988144 +  3.95381014494266X[t] +  6461.74593137428M1[t] +  4918.47723861162M2[t] +  3506.72080584833M3[t] +  4679.62057135073M4[t] +  5504.75273192363M5[t] +  6198.70800814803M6[t] +  7252.52025307776M7[t] +  5491.07357394325M8[t] +  4915.84217683649M9[t] +  3132.54862030323M10[t] +  2278.21743653034M11[t] -215.719989563514t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58126&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Y[t] =  +  7378.28355988144 +  3.95381014494266X[t] +  6461.74593137428M1[t] +  4918.47723861162M2[t] +  3506.72080584833M3[t] +  4679.62057135073M4[t] +  5504.75273192363M5[t] +  6198.70800814803M6[t] +  7252.52025307776M7[t] +  5491.07357394325M8[t] +  4915.84217683649M9[t] +  3132.54862030323M10[t] +  2278.21743653034M11[t] -215.719989563514t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58126&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58126&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] = + 7378.28355988144 + 3.95381014494266X[t] + 6461.74593137428M1[t] + 4918.47723861162M2[t] + 3506.72080584833M3[t] + 4679.62057135073M4[t] + 5504.75273192363M5[t] + 6198.70800814803M6[t] + 7252.52025307776M7[t] + 5491.07357394325M8[t] + 4915.84217683649M9[t] + 3132.54862030323M10[t] + 2278.21743653034M11[t] -215.719989563514t + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)7378.283559881442945.7522562.50470.0158610.00793
X3.953810144942660.8122624.86771.4e-057e-06
M16461.745931374283219.3062322.00720.0506270.025313
M24918.477238611622925.3442671.68130.0994770.049738
M33506.720805848332629.9657051.33340.1889760.094488
M44679.620571350732527.9997251.85110.0705790.03529
M55504.752731923632368.6590552.3240.0245970.012299
M66198.708008148032210.590782.80410.0073660.003683
M77252.520253077762327.5742033.11590.0031560.001578
M85491.073573943252266.5800542.42260.0194050.009702
M94915.842176836492177.6976952.25740.0287770.014389
M103132.548620303231991.6303361.57290.1226060.061303
M112278.217436530341959.2300551.16280.2509030.125451
t-215.71998956351428.521856-7.563300

\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) & 7378.28355988144 & 2945.752256 & 2.5047 & 0.015861 & 0.00793 \tabularnewline
X & 3.95381014494266 & 0.812262 & 4.8677 & 1.4e-05 & 7e-06 \tabularnewline
M1 & 6461.74593137428 & 3219.306232 & 2.0072 & 0.050627 & 0.025313 \tabularnewline
M2 & 4918.47723861162 & 2925.344267 & 1.6813 & 0.099477 & 0.049738 \tabularnewline
M3 & 3506.72080584833 & 2629.965705 & 1.3334 & 0.188976 & 0.094488 \tabularnewline
M4 & 4679.62057135073 & 2527.999725 & 1.8511 & 0.070579 & 0.03529 \tabularnewline
M5 & 5504.75273192363 & 2368.659055 & 2.324 & 0.024597 & 0.012299 \tabularnewline
M6 & 6198.70800814803 & 2210.59078 & 2.8041 & 0.007366 & 0.003683 \tabularnewline
M7 & 7252.52025307776 & 2327.574203 & 3.1159 & 0.003156 & 0.001578 \tabularnewline
M8 & 5491.07357394325 & 2266.580054 & 2.4226 & 0.019405 & 0.009702 \tabularnewline
M9 & 4915.84217683649 & 2177.697695 & 2.2574 & 0.028777 & 0.014389 \tabularnewline
M10 & 3132.54862030323 & 1991.630336 & 1.5729 & 0.122606 & 0.061303 \tabularnewline
M11 & 2278.21743653034 & 1959.230055 & 1.1628 & 0.250903 & 0.125451 \tabularnewline
t & -215.719989563514 & 28.521856 & -7.5633 & 0 & 0 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58126&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]7378.28355988144[/C][C]2945.752256[/C][C]2.5047[/C][C]0.015861[/C][C]0.00793[/C][/ROW]
[ROW][C]X[/C][C]3.95381014494266[/C][C]0.812262[/C][C]4.8677[/C][C]1.4e-05[/C][C]7e-06[/C][/ROW]
[ROW][C]M1[/C][C]6461.74593137428[/C][C]3219.306232[/C][C]2.0072[/C][C]0.050627[/C][C]0.025313[/C][/ROW]
[ROW][C]M2[/C][C]4918.47723861162[/C][C]2925.344267[/C][C]1.6813[/C][C]0.099477[/C][C]0.049738[/C][/ROW]
[ROW][C]M3[/C][C]3506.72080584833[/C][C]2629.965705[/C][C]1.3334[/C][C]0.188976[/C][C]0.094488[/C][/ROW]
[ROW][C]M4[/C][C]4679.62057135073[/C][C]2527.999725[/C][C]1.8511[/C][C]0.070579[/C][C]0.03529[/C][/ROW]
[ROW][C]M5[/C][C]5504.75273192363[/C][C]2368.659055[/C][C]2.324[/C][C]0.024597[/C][C]0.012299[/C][/ROW]
[ROW][C]M6[/C][C]6198.70800814803[/C][C]2210.59078[/C][C]2.8041[/C][C]0.007366[/C][C]0.003683[/C][/ROW]
[ROW][C]M7[/C][C]7252.52025307776[/C][C]2327.574203[/C][C]3.1159[/C][C]0.003156[/C][C]0.001578[/C][/ROW]
[ROW][C]M8[/C][C]5491.07357394325[/C][C]2266.580054[/C][C]2.4226[/C][C]0.019405[/C][C]0.009702[/C][/ROW]
[ROW][C]M9[/C][C]4915.84217683649[/C][C]2177.697695[/C][C]2.2574[/C][C]0.028777[/C][C]0.014389[/C][/ROW]
[ROW][C]M10[/C][C]3132.54862030323[/C][C]1991.630336[/C][C]1.5729[/C][C]0.122606[/C][C]0.061303[/C][/ROW]
[ROW][C]M11[/C][C]2278.21743653034[/C][C]1959.230055[/C][C]1.1628[/C][C]0.250903[/C][C]0.125451[/C][/ROW]
[ROW][C]t[/C][C]-215.719989563514[/C][C]28.521856[/C][C]-7.5633[/C][C]0[/C][C]0[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58126&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58126&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)7378.283559881442945.7522562.50470.0158610.00793
X3.953810144942660.8122624.86771.4e-057e-06
M16461.745931374283219.3062322.00720.0506270.025313
M24918.477238611622925.3442671.68130.0994770.049738
M33506.720805848332629.9657051.33340.1889760.094488
M44679.620571350732527.9997251.85110.0705790.03529
M55504.752731923632368.6590552.3240.0245970.012299
M66198.708008148032210.590782.80410.0073660.003683
M77252.520253077762327.5742033.11590.0031560.001578
M85491.073573943252266.5800542.42260.0194050.009702
M94915.842176836492177.6976952.25740.0287770.014389
M103132.548620303231991.6303361.57290.1226060.061303
M112278.217436530341959.2300551.16280.2509030.125451
t-215.71998956351428.521856-7.563300







Multiple Linear Regression - Regression Statistics
Multiple R0.813060614890317
R-squared0.661067563485821
Adjusted R-squared0.565282309688335
F-TEST (value)6.90155882327656
F-TEST (DF numerator)13
F-TEST (DF denominator)46
p-value3.96688436321568e-07
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation3046.67568215229
Sum Squared Residuals426982704.762034

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.813060614890317 \tabularnewline
R-squared & 0.661067563485821 \tabularnewline
Adjusted R-squared & 0.565282309688335 \tabularnewline
F-TEST (value) & 6.90155882327656 \tabularnewline
F-TEST (DF numerator) & 13 \tabularnewline
F-TEST (DF denominator) & 46 \tabularnewline
p-value & 3.96688436321568e-07 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 3046.67568215229 \tabularnewline
Sum Squared Residuals & 426982704.762034 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58126&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.813060614890317[/C][/ROW]
[ROW][C]R-squared[/C][C]0.661067563485821[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.565282309688335[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]6.90155882327656[/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]3.96688436321568e-07[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]3046.67568215229[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]426982704.762034[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58126&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58126&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 R0.813060614890317
R-squared0.661067563485821
Adjusted R-squared0.565282309688335
F-TEST (value)6.90155882327656
F-TEST (DF numerator)13
F-TEST (DF denominator)46
p-value3.96688436321568e-07
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation3046.67568215229
Sum Squared Residuals426982704.762034







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11014414067.1362379258-3923.13623792581
21075113067.2791034286-2316.2791034286
31175213377.1696521237-1625.16965212370
41380814757.4071135715-949.407113571461
51620316675.5304425569-472.530442556864
61743217185.3962103773246.603789622711
71801417201.0959555954812.904044404558
81695615670.70983327591285.29016672406
91798216631.29634081531350.70365918475
101943517305.05845269972129.94154730029
111999016017.54772139153972.45227860853
122015416322.90787791703831.09212208298
131032711996.4454921511-1669.44549215111
14980711482.9070054819-1675.90700548187
151086212544.0214817161-1682.02148171608
161374314545.0071359198-802.00713591983
171645817123.4167591107-665.416759110656
181846618178.9083269332287.091673066823
191881018700.6957707040109.304229296019
201736116644.4528991071716.547100892892
211741115481.84335881221929.15664118778
221851716721.00032142351795.99967857652
231852517655.530891573869.469108426989
241785918016.2443901278-157.244390127759
25949910068.0919115944-569.091911594363
26949011064.9089002932-1574.90890029323
27925511473.6447026119-2218.64470261189
281075813870.0113713099-3112.01137130991
291237514677.1140495664-2302.11404956642
301461716594.5362289864-1977.53622898645
311542718761.1086930534-3334.1086930534
321413617436.3206982709-3300.32069827092
331430816574.2007289917-2266.20072899166
341529317991.2791481253-2698.27914812535
351567918218.0777023301-2539.07770233015
361631919365.5994197285-3046.59941972848
37111968131.830710747733064.16928925227
38111698049.257529877253119.74247012275
39121588050.750887266824107.24911273318
40142519332.143095091014918.85690490899
411623711337.25024726514899.74975273485
421970615978.84761655073727.15238344933
431896014733.28192553214226.7180744679
441853714669.75936698633867.24063301367
451910316298.53978902102804.46021097905
461969117166.03859800762524.96140199239
471946417072.57853047202391.42146952795
481726413574.37332756283689.62667243724
4989575859.495647580983097.50435241902
5097037255.647460919052447.35253908095
5191667747.413276281511418.58672371849
5295199574.43128410779-55.4312841077877
531053511994.6885015009-1459.68850150091
541152613809.3116171524-2283.31161715242
55963011444.8176551151-1814.81765511508
5670619629.75720235971-2568.75720235971
5760219839.11978235992-3818.11978235992
5847288480.62347974385-3752.62347974385
5926577351.26515423332-4694.26515423332
6012645580.87498466397-4316.87498466397

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 10144 & 14067.1362379258 & -3923.13623792581 \tabularnewline
2 & 10751 & 13067.2791034286 & -2316.2791034286 \tabularnewline
3 & 11752 & 13377.1696521237 & -1625.16965212370 \tabularnewline
4 & 13808 & 14757.4071135715 & -949.407113571461 \tabularnewline
5 & 16203 & 16675.5304425569 & -472.530442556864 \tabularnewline
6 & 17432 & 17185.3962103773 & 246.603789622711 \tabularnewline
7 & 18014 & 17201.0959555954 & 812.904044404558 \tabularnewline
8 & 16956 & 15670.7098332759 & 1285.29016672406 \tabularnewline
9 & 17982 & 16631.2963408153 & 1350.70365918475 \tabularnewline
10 & 19435 & 17305.0584526997 & 2129.94154730029 \tabularnewline
11 & 19990 & 16017.5477213915 & 3972.45227860853 \tabularnewline
12 & 20154 & 16322.9078779170 & 3831.09212208298 \tabularnewline
13 & 10327 & 11996.4454921511 & -1669.44549215111 \tabularnewline
14 & 9807 & 11482.9070054819 & -1675.90700548187 \tabularnewline
15 & 10862 & 12544.0214817161 & -1682.02148171608 \tabularnewline
16 & 13743 & 14545.0071359198 & -802.00713591983 \tabularnewline
17 & 16458 & 17123.4167591107 & -665.416759110656 \tabularnewline
18 & 18466 & 18178.9083269332 & 287.091673066823 \tabularnewline
19 & 18810 & 18700.6957707040 & 109.304229296019 \tabularnewline
20 & 17361 & 16644.4528991071 & 716.547100892892 \tabularnewline
21 & 17411 & 15481.8433588122 & 1929.15664118778 \tabularnewline
22 & 18517 & 16721.0003214235 & 1795.99967857652 \tabularnewline
23 & 18525 & 17655.530891573 & 869.469108426989 \tabularnewline
24 & 17859 & 18016.2443901278 & -157.244390127759 \tabularnewline
25 & 9499 & 10068.0919115944 & -569.091911594363 \tabularnewline
26 & 9490 & 11064.9089002932 & -1574.90890029323 \tabularnewline
27 & 9255 & 11473.6447026119 & -2218.64470261189 \tabularnewline
28 & 10758 & 13870.0113713099 & -3112.01137130991 \tabularnewline
29 & 12375 & 14677.1140495664 & -2302.11404956642 \tabularnewline
30 & 14617 & 16594.5362289864 & -1977.53622898645 \tabularnewline
31 & 15427 & 18761.1086930534 & -3334.1086930534 \tabularnewline
32 & 14136 & 17436.3206982709 & -3300.32069827092 \tabularnewline
33 & 14308 & 16574.2007289917 & -2266.20072899166 \tabularnewline
34 & 15293 & 17991.2791481253 & -2698.27914812535 \tabularnewline
35 & 15679 & 18218.0777023301 & -2539.07770233015 \tabularnewline
36 & 16319 & 19365.5994197285 & -3046.59941972848 \tabularnewline
37 & 11196 & 8131.83071074773 & 3064.16928925227 \tabularnewline
38 & 11169 & 8049.25752987725 & 3119.74247012275 \tabularnewline
39 & 12158 & 8050.75088726682 & 4107.24911273318 \tabularnewline
40 & 14251 & 9332.14309509101 & 4918.85690490899 \tabularnewline
41 & 16237 & 11337.2502472651 & 4899.74975273485 \tabularnewline
42 & 19706 & 15978.8476165507 & 3727.15238344933 \tabularnewline
43 & 18960 & 14733.2819255321 & 4226.7180744679 \tabularnewline
44 & 18537 & 14669.7593669863 & 3867.24063301367 \tabularnewline
45 & 19103 & 16298.5397890210 & 2804.46021097905 \tabularnewline
46 & 19691 & 17166.0385980076 & 2524.96140199239 \tabularnewline
47 & 19464 & 17072.5785304720 & 2391.42146952795 \tabularnewline
48 & 17264 & 13574.3733275628 & 3689.62667243724 \tabularnewline
49 & 8957 & 5859.49564758098 & 3097.50435241902 \tabularnewline
50 & 9703 & 7255.64746091905 & 2447.35253908095 \tabularnewline
51 & 9166 & 7747.41327628151 & 1418.58672371849 \tabularnewline
52 & 9519 & 9574.43128410779 & -55.4312841077877 \tabularnewline
53 & 10535 & 11994.6885015009 & -1459.68850150091 \tabularnewline
54 & 11526 & 13809.3116171524 & -2283.31161715242 \tabularnewline
55 & 9630 & 11444.8176551151 & -1814.81765511508 \tabularnewline
56 & 7061 & 9629.75720235971 & -2568.75720235971 \tabularnewline
57 & 6021 & 9839.11978235992 & -3818.11978235992 \tabularnewline
58 & 4728 & 8480.62347974385 & -3752.62347974385 \tabularnewline
59 & 2657 & 7351.26515423332 & -4694.26515423332 \tabularnewline
60 & 1264 & 5580.87498466397 & -4316.87498466397 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58126&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]10144[/C][C]14067.1362379258[/C][C]-3923.13623792581[/C][/ROW]
[ROW][C]2[/C][C]10751[/C][C]13067.2791034286[/C][C]-2316.2791034286[/C][/ROW]
[ROW][C]3[/C][C]11752[/C][C]13377.1696521237[/C][C]-1625.16965212370[/C][/ROW]
[ROW][C]4[/C][C]13808[/C][C]14757.4071135715[/C][C]-949.407113571461[/C][/ROW]
[ROW][C]5[/C][C]16203[/C][C]16675.5304425569[/C][C]-472.530442556864[/C][/ROW]
[ROW][C]6[/C][C]17432[/C][C]17185.3962103773[/C][C]246.603789622711[/C][/ROW]
[ROW][C]7[/C][C]18014[/C][C]17201.0959555954[/C][C]812.904044404558[/C][/ROW]
[ROW][C]8[/C][C]16956[/C][C]15670.7098332759[/C][C]1285.29016672406[/C][/ROW]
[ROW][C]9[/C][C]17982[/C][C]16631.2963408153[/C][C]1350.70365918475[/C][/ROW]
[ROW][C]10[/C][C]19435[/C][C]17305.0584526997[/C][C]2129.94154730029[/C][/ROW]
[ROW][C]11[/C][C]19990[/C][C]16017.5477213915[/C][C]3972.45227860853[/C][/ROW]
[ROW][C]12[/C][C]20154[/C][C]16322.9078779170[/C][C]3831.09212208298[/C][/ROW]
[ROW][C]13[/C][C]10327[/C][C]11996.4454921511[/C][C]-1669.44549215111[/C][/ROW]
[ROW][C]14[/C][C]9807[/C][C]11482.9070054819[/C][C]-1675.90700548187[/C][/ROW]
[ROW][C]15[/C][C]10862[/C][C]12544.0214817161[/C][C]-1682.02148171608[/C][/ROW]
[ROW][C]16[/C][C]13743[/C][C]14545.0071359198[/C][C]-802.00713591983[/C][/ROW]
[ROW][C]17[/C][C]16458[/C][C]17123.4167591107[/C][C]-665.416759110656[/C][/ROW]
[ROW][C]18[/C][C]18466[/C][C]18178.9083269332[/C][C]287.091673066823[/C][/ROW]
[ROW][C]19[/C][C]18810[/C][C]18700.6957707040[/C][C]109.304229296019[/C][/ROW]
[ROW][C]20[/C][C]17361[/C][C]16644.4528991071[/C][C]716.547100892892[/C][/ROW]
[ROW][C]21[/C][C]17411[/C][C]15481.8433588122[/C][C]1929.15664118778[/C][/ROW]
[ROW][C]22[/C][C]18517[/C][C]16721.0003214235[/C][C]1795.99967857652[/C][/ROW]
[ROW][C]23[/C][C]18525[/C][C]17655.530891573[/C][C]869.469108426989[/C][/ROW]
[ROW][C]24[/C][C]17859[/C][C]18016.2443901278[/C][C]-157.244390127759[/C][/ROW]
[ROW][C]25[/C][C]9499[/C][C]10068.0919115944[/C][C]-569.091911594363[/C][/ROW]
[ROW][C]26[/C][C]9490[/C][C]11064.9089002932[/C][C]-1574.90890029323[/C][/ROW]
[ROW][C]27[/C][C]9255[/C][C]11473.6447026119[/C][C]-2218.64470261189[/C][/ROW]
[ROW][C]28[/C][C]10758[/C][C]13870.0113713099[/C][C]-3112.01137130991[/C][/ROW]
[ROW][C]29[/C][C]12375[/C][C]14677.1140495664[/C][C]-2302.11404956642[/C][/ROW]
[ROW][C]30[/C][C]14617[/C][C]16594.5362289864[/C][C]-1977.53622898645[/C][/ROW]
[ROW][C]31[/C][C]15427[/C][C]18761.1086930534[/C][C]-3334.1086930534[/C][/ROW]
[ROW][C]32[/C][C]14136[/C][C]17436.3206982709[/C][C]-3300.32069827092[/C][/ROW]
[ROW][C]33[/C][C]14308[/C][C]16574.2007289917[/C][C]-2266.20072899166[/C][/ROW]
[ROW][C]34[/C][C]15293[/C][C]17991.2791481253[/C][C]-2698.27914812535[/C][/ROW]
[ROW][C]35[/C][C]15679[/C][C]18218.0777023301[/C][C]-2539.07770233015[/C][/ROW]
[ROW][C]36[/C][C]16319[/C][C]19365.5994197285[/C][C]-3046.59941972848[/C][/ROW]
[ROW][C]37[/C][C]11196[/C][C]8131.83071074773[/C][C]3064.16928925227[/C][/ROW]
[ROW][C]38[/C][C]11169[/C][C]8049.25752987725[/C][C]3119.74247012275[/C][/ROW]
[ROW][C]39[/C][C]12158[/C][C]8050.75088726682[/C][C]4107.24911273318[/C][/ROW]
[ROW][C]40[/C][C]14251[/C][C]9332.14309509101[/C][C]4918.85690490899[/C][/ROW]
[ROW][C]41[/C][C]16237[/C][C]11337.2502472651[/C][C]4899.74975273485[/C][/ROW]
[ROW][C]42[/C][C]19706[/C][C]15978.8476165507[/C][C]3727.15238344933[/C][/ROW]
[ROW][C]43[/C][C]18960[/C][C]14733.2819255321[/C][C]4226.7180744679[/C][/ROW]
[ROW][C]44[/C][C]18537[/C][C]14669.7593669863[/C][C]3867.24063301367[/C][/ROW]
[ROW][C]45[/C][C]19103[/C][C]16298.5397890210[/C][C]2804.46021097905[/C][/ROW]
[ROW][C]46[/C][C]19691[/C][C]17166.0385980076[/C][C]2524.96140199239[/C][/ROW]
[ROW][C]47[/C][C]19464[/C][C]17072.5785304720[/C][C]2391.42146952795[/C][/ROW]
[ROW][C]48[/C][C]17264[/C][C]13574.3733275628[/C][C]3689.62667243724[/C][/ROW]
[ROW][C]49[/C][C]8957[/C][C]5859.49564758098[/C][C]3097.50435241902[/C][/ROW]
[ROW][C]50[/C][C]9703[/C][C]7255.64746091905[/C][C]2447.35253908095[/C][/ROW]
[ROW][C]51[/C][C]9166[/C][C]7747.41327628151[/C][C]1418.58672371849[/C][/ROW]
[ROW][C]52[/C][C]9519[/C][C]9574.43128410779[/C][C]-55.4312841077877[/C][/ROW]
[ROW][C]53[/C][C]10535[/C][C]11994.6885015009[/C][C]-1459.68850150091[/C][/ROW]
[ROW][C]54[/C][C]11526[/C][C]13809.3116171524[/C][C]-2283.31161715242[/C][/ROW]
[ROW][C]55[/C][C]9630[/C][C]11444.8176551151[/C][C]-1814.81765511508[/C][/ROW]
[ROW][C]56[/C][C]7061[/C][C]9629.75720235971[/C][C]-2568.75720235971[/C][/ROW]
[ROW][C]57[/C][C]6021[/C][C]9839.11978235992[/C][C]-3818.11978235992[/C][/ROW]
[ROW][C]58[/C][C]4728[/C][C]8480.62347974385[/C][C]-3752.62347974385[/C][/ROW]
[ROW][C]59[/C][C]2657[/C][C]7351.26515423332[/C][C]-4694.26515423332[/C][/ROW]
[ROW][C]60[/C][C]1264[/C][C]5580.87498466397[/C][C]-4316.87498466397[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58126&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58126&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
11014414067.1362379258-3923.13623792581
21075113067.2791034286-2316.2791034286
31175213377.1696521237-1625.16965212370
41380814757.4071135715-949.407113571461
51620316675.5304425569-472.530442556864
61743217185.3962103773246.603789622711
71801417201.0959555954812.904044404558
81695615670.70983327591285.29016672406
91798216631.29634081531350.70365918475
101943517305.05845269972129.94154730029
111999016017.54772139153972.45227860853
122015416322.90787791703831.09212208298
131032711996.4454921511-1669.44549215111
14980711482.9070054819-1675.90700548187
151086212544.0214817161-1682.02148171608
161374314545.0071359198-802.00713591983
171645817123.4167591107-665.416759110656
181846618178.9083269332287.091673066823
191881018700.6957707040109.304229296019
201736116644.4528991071716.547100892892
211741115481.84335881221929.15664118778
221851716721.00032142351795.99967857652
231852517655.530891573869.469108426989
241785918016.2443901278-157.244390127759
25949910068.0919115944-569.091911594363
26949011064.9089002932-1574.90890029323
27925511473.6447026119-2218.64470261189
281075813870.0113713099-3112.01137130991
291237514677.1140495664-2302.11404956642
301461716594.5362289864-1977.53622898645
311542718761.1086930534-3334.1086930534
321413617436.3206982709-3300.32069827092
331430816574.2007289917-2266.20072899166
341529317991.2791481253-2698.27914812535
351567918218.0777023301-2539.07770233015
361631919365.5994197285-3046.59941972848
37111968131.830710747733064.16928925227
38111698049.257529877253119.74247012275
39121588050.750887266824107.24911273318
40142519332.143095091014918.85690490899
411623711337.25024726514899.74975273485
421970615978.84761655073727.15238344933
431896014733.28192553214226.7180744679
441853714669.75936698633867.24063301367
451910316298.53978902102804.46021097905
461969117166.03859800762524.96140199239
471946417072.57853047202391.42146952795
481726413574.37332756283689.62667243724
4989575859.495647580983097.50435241902
5097037255.647460919052447.35253908095
5191667747.413276281511418.58672371849
5295199574.43128410779-55.4312841077877
531053511994.6885015009-1459.68850150091
541152613809.3116171524-2283.31161715242
55963011444.8176551151-1814.81765511508
5670619629.75720235971-2568.75720235971
5760219839.11978235992-3818.11978235992
5847288480.62347974385-3752.62347974385
5926577351.26515423332-4694.26515423332
6012645580.87498466397-4316.87498466397







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
170.003568091536812260.007136183073624520.996431908463188
180.000750224310472290.001500448620944580.999249775689528
198.56748506512946e-050.0001713497013025890.999914325149349
209.2675548917958e-061.85351097835916e-050.999990732445108
211.12815764249729e-062.25631528499459e-060.999998871842358
223.74303397903608e-077.48606795807215e-070.999999625696602
232.74098465785763e-065.48196931571526e-060.999997259015342
247.34818240664762e-061.46963648132952e-050.999992651817593
251.48352498926296e-062.96704997852593e-060.99999851647501
263.11337802941747e-076.22675605883494e-070.999999688662197
272.25773476370176e-074.51546952740352e-070.999999774226524
287.62023095888406e-071.52404619177681e-060.999999237976904
292.7765244417018e-065.5530488834036e-060.999997223475558
302.22377507220397e-064.44755014440795e-060.999997776224928
313.16853245085323e-066.33706490170646e-060.99999683146755
324.59469066445211e-069.18938132890421e-060.999995405309336
334.51110223721642e-069.02220447443284e-060.999995488897763
349.63390611245494e-061.92678122249099e-050.999990366093888
353.00676300578268e-056.01352601156536e-050.999969932369942
360.03836778137742910.07673556275485820.96163221862257
370.3351519647863860.6703039295727730.664848035213614
380.8617727857465880.2764544285068250.138227214253412
390.99295691693240.01408616613519980.00704308306759989
400.9915179767822840.01696404643543220.00848202321771608
410.982560334622060.03487933075587880.0174396653779394
420.9717263727608080.0565472544783840.028273627239192
430.9188654412945730.1622691174108540.081134558705427

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
17 & 0.00356809153681226 & 0.00713618307362452 & 0.996431908463188 \tabularnewline
18 & 0.00075022431047229 & 0.00150044862094458 & 0.999249775689528 \tabularnewline
19 & 8.56748506512946e-05 & 0.000171349701302589 & 0.999914325149349 \tabularnewline
20 & 9.2675548917958e-06 & 1.85351097835916e-05 & 0.999990732445108 \tabularnewline
21 & 1.12815764249729e-06 & 2.25631528499459e-06 & 0.999998871842358 \tabularnewline
22 & 3.74303397903608e-07 & 7.48606795807215e-07 & 0.999999625696602 \tabularnewline
23 & 2.74098465785763e-06 & 5.48196931571526e-06 & 0.999997259015342 \tabularnewline
24 & 7.34818240664762e-06 & 1.46963648132952e-05 & 0.999992651817593 \tabularnewline
25 & 1.48352498926296e-06 & 2.96704997852593e-06 & 0.99999851647501 \tabularnewline
26 & 3.11337802941747e-07 & 6.22675605883494e-07 & 0.999999688662197 \tabularnewline
27 & 2.25773476370176e-07 & 4.51546952740352e-07 & 0.999999774226524 \tabularnewline
28 & 7.62023095888406e-07 & 1.52404619177681e-06 & 0.999999237976904 \tabularnewline
29 & 2.7765244417018e-06 & 5.5530488834036e-06 & 0.999997223475558 \tabularnewline
30 & 2.22377507220397e-06 & 4.44755014440795e-06 & 0.999997776224928 \tabularnewline
31 & 3.16853245085323e-06 & 6.33706490170646e-06 & 0.99999683146755 \tabularnewline
32 & 4.59469066445211e-06 & 9.18938132890421e-06 & 0.999995405309336 \tabularnewline
33 & 4.51110223721642e-06 & 9.02220447443284e-06 & 0.999995488897763 \tabularnewline
34 & 9.63390611245494e-06 & 1.92678122249099e-05 & 0.999990366093888 \tabularnewline
35 & 3.00676300578268e-05 & 6.01352601156536e-05 & 0.999969932369942 \tabularnewline
36 & 0.0383677813774291 & 0.0767355627548582 & 0.96163221862257 \tabularnewline
37 & 0.335151964786386 & 0.670303929572773 & 0.664848035213614 \tabularnewline
38 & 0.861772785746588 & 0.276454428506825 & 0.138227214253412 \tabularnewline
39 & 0.9929569169324 & 0.0140861661351998 & 0.00704308306759989 \tabularnewline
40 & 0.991517976782284 & 0.0169640464354322 & 0.00848202321771608 \tabularnewline
41 & 0.98256033462206 & 0.0348793307558788 & 0.0174396653779394 \tabularnewline
42 & 0.971726372760808 & 0.056547254478384 & 0.028273627239192 \tabularnewline
43 & 0.918865441294573 & 0.162269117410854 & 0.081134558705427 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58126&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]0.00356809153681226[/C][C]0.00713618307362452[/C][C]0.996431908463188[/C][/ROW]
[ROW][C]18[/C][C]0.00075022431047229[/C][C]0.00150044862094458[/C][C]0.999249775689528[/C][/ROW]
[ROW][C]19[/C][C]8.56748506512946e-05[/C][C]0.000171349701302589[/C][C]0.999914325149349[/C][/ROW]
[ROW][C]20[/C][C]9.2675548917958e-06[/C][C]1.85351097835916e-05[/C][C]0.999990732445108[/C][/ROW]
[ROW][C]21[/C][C]1.12815764249729e-06[/C][C]2.25631528499459e-06[/C][C]0.999998871842358[/C][/ROW]
[ROW][C]22[/C][C]3.74303397903608e-07[/C][C]7.48606795807215e-07[/C][C]0.999999625696602[/C][/ROW]
[ROW][C]23[/C][C]2.74098465785763e-06[/C][C]5.48196931571526e-06[/C][C]0.999997259015342[/C][/ROW]
[ROW][C]24[/C][C]7.34818240664762e-06[/C][C]1.46963648132952e-05[/C][C]0.999992651817593[/C][/ROW]
[ROW][C]25[/C][C]1.48352498926296e-06[/C][C]2.96704997852593e-06[/C][C]0.99999851647501[/C][/ROW]
[ROW][C]26[/C][C]3.11337802941747e-07[/C][C]6.22675605883494e-07[/C][C]0.999999688662197[/C][/ROW]
[ROW][C]27[/C][C]2.25773476370176e-07[/C][C]4.51546952740352e-07[/C][C]0.999999774226524[/C][/ROW]
[ROW][C]28[/C][C]7.62023095888406e-07[/C][C]1.52404619177681e-06[/C][C]0.999999237976904[/C][/ROW]
[ROW][C]29[/C][C]2.7765244417018e-06[/C][C]5.5530488834036e-06[/C][C]0.999997223475558[/C][/ROW]
[ROW][C]30[/C][C]2.22377507220397e-06[/C][C]4.44755014440795e-06[/C][C]0.999997776224928[/C][/ROW]
[ROW][C]31[/C][C]3.16853245085323e-06[/C][C]6.33706490170646e-06[/C][C]0.99999683146755[/C][/ROW]
[ROW][C]32[/C][C]4.59469066445211e-06[/C][C]9.18938132890421e-06[/C][C]0.999995405309336[/C][/ROW]
[ROW][C]33[/C][C]4.51110223721642e-06[/C][C]9.02220447443284e-06[/C][C]0.999995488897763[/C][/ROW]
[ROW][C]34[/C][C]9.63390611245494e-06[/C][C]1.92678122249099e-05[/C][C]0.999990366093888[/C][/ROW]
[ROW][C]35[/C][C]3.00676300578268e-05[/C][C]6.01352601156536e-05[/C][C]0.999969932369942[/C][/ROW]
[ROW][C]36[/C][C]0.0383677813774291[/C][C]0.0767355627548582[/C][C]0.96163221862257[/C][/ROW]
[ROW][C]37[/C][C]0.335151964786386[/C][C]0.670303929572773[/C][C]0.664848035213614[/C][/ROW]
[ROW][C]38[/C][C]0.861772785746588[/C][C]0.276454428506825[/C][C]0.138227214253412[/C][/ROW]
[ROW][C]39[/C][C]0.9929569169324[/C][C]0.0140861661351998[/C][C]0.00704308306759989[/C][/ROW]
[ROW][C]40[/C][C]0.991517976782284[/C][C]0.0169640464354322[/C][C]0.00848202321771608[/C][/ROW]
[ROW][C]41[/C][C]0.98256033462206[/C][C]0.0348793307558788[/C][C]0.0174396653779394[/C][/ROW]
[ROW][C]42[/C][C]0.971726372760808[/C][C]0.056547254478384[/C][C]0.028273627239192[/C][/ROW]
[ROW][C]43[/C][C]0.918865441294573[/C][C]0.162269117410854[/C][C]0.081134558705427[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58126&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58126&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
170.003568091536812260.007136183073624520.996431908463188
180.000750224310472290.001500448620944580.999249775689528
198.56748506512946e-050.0001713497013025890.999914325149349
209.2675548917958e-061.85351097835916e-050.999990732445108
211.12815764249729e-062.25631528499459e-060.999998871842358
223.74303397903608e-077.48606795807215e-070.999999625696602
232.74098465785763e-065.48196931571526e-060.999997259015342
247.34818240664762e-061.46963648132952e-050.999992651817593
251.48352498926296e-062.96704997852593e-060.99999851647501
263.11337802941747e-076.22675605883494e-070.999999688662197
272.25773476370176e-074.51546952740352e-070.999999774226524
287.62023095888406e-071.52404619177681e-060.999999237976904
292.7765244417018e-065.5530488834036e-060.999997223475558
302.22377507220397e-064.44755014440795e-060.999997776224928
313.16853245085323e-066.33706490170646e-060.99999683146755
324.59469066445211e-069.18938132890421e-060.999995405309336
334.51110223721642e-069.02220447443284e-060.999995488897763
349.63390611245494e-061.92678122249099e-050.999990366093888
353.00676300578268e-056.01352601156536e-050.999969932369942
360.03836778137742910.07673556275485820.96163221862257
370.3351519647863860.6703039295727730.664848035213614
380.8617727857465880.2764544285068250.138227214253412
390.99295691693240.01408616613519980.00704308306759989
400.9915179767822840.01696404643543220.00848202321771608
410.982560334622060.03487933075587880.0174396653779394
420.9717263727608080.0565472544783840.028273627239192
430.9188654412945730.1622691174108540.081134558705427







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level190.703703703703704NOK
5% type I error level220.814814814814815NOK
10% type I error level240.888888888888889NOK

\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 & 19 & 0.703703703703704 & NOK \tabularnewline
5% type I error level & 22 & 0.814814814814815 & NOK \tabularnewline
10% type I error level & 24 & 0.888888888888889 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58126&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]19[/C][C]0.703703703703704[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]22[/C][C]0.814814814814815[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]24[/C][C]0.888888888888889[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58126&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58126&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 level190.703703703703704NOK
5% type I error level220.814814814814815NOK
10% type I error level240.888888888888889NOK



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')
}