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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 03:32:04 -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/t1258713153oaraz214qb980su.htm/, Retrieved Thu, 28 Mar 2024 08:56:42 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=58011, Retrieved Thu, 28 Mar 2024 08:56:42 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact192
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [Q1 The Seatbeltlaw] [2007-11-14 19:27:43] [8cd6641b921d30ebe00b648d1481bba0]
- RMPD  [Multiple Regression] [Seatbelt] [2009-11-12 13:54:52] [b98453cac15ba1066b407e146608df68]
-    D    [Multiple Regression] [W 7] [2009-11-18 20:45:25] [315ba876df544ad397193b5931d5f354]
-   PD        [Multiple Regression] [WS 7: Multiple Re...] [2009-11-20 10:32:04] [ac86848d66148c9c4c9404e0c9a511eb] [Current]
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Dataseries X:
79.8	109.87
83.4	95.74
113.6	123.06
112.9	123.39
104	120.28
109.9	115.33
99	110.4
106.3	114.49
128.9	132.03
111.1	123.16
102.9	118.82
130	128.32
87	112.24
87.5	104.53
117.6	132.57
103.4	122.52
110.8	131.8
112.6	124.55
102.5	120.96
112.4	122.6
135.6	145.52
105.1	118.57
127.7	134.25
137	136.7
91	121.37
90.5	111.63
122.4	134.42
123.3	137.65
124.3	137.86
120	119.77
118.1	130.69
119	128.28
142.7	147.45
123.6	128.42
129.6	136.9
151.6	143.95
110.4	135.64
99.2	122.48
130.5	136.83
136.2	153.04
129.7	142.71
128	123.46
121.6	144.37
135.8	146.15
143.8	147.61
147.5	158.51
136.2	147.4
156.6	165.05
123.3	154.64
104.5	126.2
139.8	157.36
136.5	154.15
112.1	123.21
118.5	113.07
94.4	110.45
102.3	113.57
111.4	122.44
99.2	114.93
87.8	111.85
115.8	126.04




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

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 3 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58011&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]3 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58011&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58011&T=0

As an alternative you can also use a QR Code:  

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

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







Multiple Linear Regression - Estimated Regression Equation
Investgoed[t] = + 2.26371392062023 + 0.970890252831042Uitvoer[t] -27.0259952474602M1[t] -18.0960455070252M2[t] -10.3481032400426M3[t] -13.9322023492286M4[t] -13.4373301649736M5[t] -0.228784107182265M6[t] -14.9263279733971M7[t] -8.48247154905135M8[t] -4.74716796666329M9[t] -9.9347654845262M10[t] -11.4879879092140M11[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Investgoed[t] =  +  2.26371392062023 +  0.970890252831042Uitvoer[t] -27.0259952474602M1[t] -18.0960455070252M2[t] -10.3481032400426M3[t] -13.9322023492286M4[t] -13.4373301649736M5[t] -0.228784107182265M6[t] -14.9263279733971M7[t] -8.48247154905135M8[t] -4.74716796666329M9[t] -9.9347654845262M10[t] -11.4879879092140M11[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58011&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Investgoed[t] =  +  2.26371392062023 +  0.970890252831042Uitvoer[t] -27.0259952474602M1[t] -18.0960455070252M2[t] -10.3481032400426M3[t] -13.9322023492286M4[t] -13.4373301649736M5[t] -0.228784107182265M6[t] -14.9263279733971M7[t] -8.48247154905135M8[t] -4.74716796666329M9[t] -9.9347654845262M10[t] -11.4879879092140M11[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58011&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58011&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
Investgoed[t] = + 2.26371392062023 + 0.970890252831042Uitvoer[t] -27.0259952474602M1[t] -18.0960455070252M2[t] -10.3481032400426M3[t] -13.9322023492286M4[t] -13.4373301649736M5[t] -0.228784107182265M6[t] -14.9263279733971M7[t] -8.48247154905135M8[t] -4.74716796666329M9[t] -9.9347654845262M10[t] -11.4879879092140M11[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)2.263713920620237.5520770.29970.7656920.382846
Uitvoer0.9708902528310420.05159218.818600
M1-27.02599524746023.190163-8.471700
M2-18.09604550702523.432269-5.27233e-062e-06
M3-10.34810324004263.120221-3.31650.0017640.000882
M4-13.93220234922863.117429-4.46914.9e-052.5e-05
M5-13.43733016497363.149149-4.2679.5e-054.8e-05
M6-0.2287841071822653.295157-0.06940.9449420.472471
M7-14.92632797339713.232022-4.61833e-051.5e-05
M8-8.482471549051353.210537-2.64210.0111560.005578
M9-4.747167966663293.116377-1.52330.1343850.067193
M10-9.93476548452623.169961-3.1340.0029680.001484
M11-11.48798790921403.159798-3.63570.0006860.000343

\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) & 2.26371392062023 & 7.552077 & 0.2997 & 0.765692 & 0.382846 \tabularnewline
Uitvoer & 0.970890252831042 & 0.051592 & 18.8186 & 0 & 0 \tabularnewline
M1 & -27.0259952474602 & 3.190163 & -8.4717 & 0 & 0 \tabularnewline
M2 & -18.0960455070252 & 3.432269 & -5.2723 & 3e-06 & 2e-06 \tabularnewline
M3 & -10.3481032400426 & 3.120221 & -3.3165 & 0.001764 & 0.000882 \tabularnewline
M4 & -13.9322023492286 & 3.117429 & -4.4691 & 4.9e-05 & 2.5e-05 \tabularnewline
M5 & -13.4373301649736 & 3.149149 & -4.267 & 9.5e-05 & 4.8e-05 \tabularnewline
M6 & -0.228784107182265 & 3.295157 & -0.0694 & 0.944942 & 0.472471 \tabularnewline
M7 & -14.9263279733971 & 3.232022 & -4.6183 & 3e-05 & 1.5e-05 \tabularnewline
M8 & -8.48247154905135 & 3.210537 & -2.6421 & 0.011156 & 0.005578 \tabularnewline
M9 & -4.74716796666329 & 3.116377 & -1.5233 & 0.134385 & 0.067193 \tabularnewline
M10 & -9.9347654845262 & 3.169961 & -3.134 & 0.002968 & 0.001484 \tabularnewline
M11 & -11.4879879092140 & 3.159798 & -3.6357 & 0.000686 & 0.000343 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58011&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]2.26371392062023[/C][C]7.552077[/C][C]0.2997[/C][C]0.765692[/C][C]0.382846[/C][/ROW]
[ROW][C]Uitvoer[/C][C]0.970890252831042[/C][C]0.051592[/C][C]18.8186[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]M1[/C][C]-27.0259952474602[/C][C]3.190163[/C][C]-8.4717[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]M2[/C][C]-18.0960455070252[/C][C]3.432269[/C][C]-5.2723[/C][C]3e-06[/C][C]2e-06[/C][/ROW]
[ROW][C]M3[/C][C]-10.3481032400426[/C][C]3.120221[/C][C]-3.3165[/C][C]0.001764[/C][C]0.000882[/C][/ROW]
[ROW][C]M4[/C][C]-13.9322023492286[/C][C]3.117429[/C][C]-4.4691[/C][C]4.9e-05[/C][C]2.5e-05[/C][/ROW]
[ROW][C]M5[/C][C]-13.4373301649736[/C][C]3.149149[/C][C]-4.267[/C][C]9.5e-05[/C][C]4.8e-05[/C][/ROW]
[ROW][C]M6[/C][C]-0.228784107182265[/C][C]3.295157[/C][C]-0.0694[/C][C]0.944942[/C][C]0.472471[/C][/ROW]
[ROW][C]M7[/C][C]-14.9263279733971[/C][C]3.232022[/C][C]-4.6183[/C][C]3e-05[/C][C]1.5e-05[/C][/ROW]
[ROW][C]M8[/C][C]-8.48247154905135[/C][C]3.210537[/C][C]-2.6421[/C][C]0.011156[/C][C]0.005578[/C][/ROW]
[ROW][C]M9[/C][C]-4.74716796666329[/C][C]3.116377[/C][C]-1.5233[/C][C]0.134385[/C][C]0.067193[/C][/ROW]
[ROW][C]M10[/C][C]-9.9347654845262[/C][C]3.169961[/C][C]-3.134[/C][C]0.002968[/C][C]0.001484[/C][/ROW]
[ROW][C]M11[/C][C]-11.4879879092140[/C][C]3.159798[/C][C]-3.6357[/C][C]0.000686[/C][C]0.000343[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58011&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58011&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)2.263713920620237.5520770.29970.7656920.382846
Uitvoer0.9708902528310420.05159218.818600
M1-27.02599524746023.190163-8.471700
M2-18.09604550702523.432269-5.27233e-062e-06
M3-10.34810324004263.120221-3.31650.0017640.000882
M4-13.93220234922863.117429-4.46914.9e-052.5e-05
M5-13.43733016497363.149149-4.2679.5e-054.8e-05
M6-0.2287841071822653.295157-0.06940.9449420.472471
M7-14.92632797339713.232022-4.61833e-051.5e-05
M8-8.482471549051353.210537-2.64210.0111560.005578
M9-4.747167966663293.116377-1.52330.1343850.067193
M10-9.93476548452623.169961-3.1340.0029680.001484
M11-11.48798790921403.159798-3.63570.0006860.000343







Multiple Linear Regression - Regression Statistics
Multiple R0.969120209849607
R-squared0.939193981138947
Adjusted R-squared0.923669040153146
F-TEST (value)60.4958165057075
F-TEST (DF numerator)12
F-TEST (DF denominator)47
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation4.92674642846338
Sum Squared Residuals1140.82302740770

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.969120209849607 \tabularnewline
R-squared & 0.939193981138947 \tabularnewline
Adjusted R-squared & 0.923669040153146 \tabularnewline
F-TEST (value) & 60.4958165057075 \tabularnewline
F-TEST (DF numerator) & 12 \tabularnewline
F-TEST (DF denominator) & 47 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 4.92674642846338 \tabularnewline
Sum Squared Residuals & 1140.82302740770 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58011&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.969120209849607[/C][/ROW]
[ROW][C]R-squared[/C][C]0.939193981138947[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.923669040153146[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]60.4958165057075[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]12[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]47[/C][/ROW]
[ROW][C]p-value[/C][C]0[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]4.92674642846338[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]1140.82302740770[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58011&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58011&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 R0.969120209849607
R-squared0.939193981138947
Adjusted R-squared0.923669040153146
F-TEST (value)60.4958165057075
F-TEST (DF numerator)12
F-TEST (DF denominator)47
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation4.92674642846338
Sum Squared Residuals1140.82302740770







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
179.881.9094307517059-2.10943075170587
283.477.12070121963886.27929878036116
3113.6111.3933651939662.20663480603439
4112.9108.1296598682144.77034013178617
5104105.605063366164-1.60506336616429
6109.9114.007702672442-4.10770267244194
79994.523669859774.47633014022993
8106.3104.9384674181951.36153258180521
9128.9125.7031860352393.19681396476068
10111.1111.903791974765-0.80379197476507
11102.9106.136905852791-3.23690585279057
12130126.8483511638993.15164883610054
138784.2104406509162.78955934908395
1487.585.65482654202371.84517345797628
15117.6120.626531498389-3.0265314983888
16103.4107.284985348251-3.8849853482508
17110.8116.789719078778-5.9897190787779
18112.6122.959310803544-10.3593108035442
19102.5104.776270929666-2.27627092966585
20112.4112.812387368655-0.412387368654536
21135.6138.80049554593-3.20049554593010
22105.1107.447405714271-2.34740571427059
23127.7121.1177424539746.58225754602643
24137134.9844114826242.01558851737642
259193.0746686592635-2.07466865926346
2690.592.5481473371241-2.04814733712411
27122.4122.422678466126-0.0226784661262075
28123.3121.9745548735841.32544512641552
29124.3122.6733140109341.62668598906599
30120118.3184553950121.68154460498822
31118.1114.2230330897123.87696691028810
32119118.3270440047350.672955995265134
33142.7140.6743137338942.02568626610601
34123.6117.0106747046566.58932529534365
35129.6123.6906016239765.90939837602416
36151.6142.0233658156499.57663418435136
37110.4106.9292725671623.47072743283759
3899.2103.082306580341-3.88230658034092
39130.5124.7625239754495.73747602455095
40136.2136.916555864654-0.716555864654212
41129.7127.3821317371652.31786826283543
42128121.9010404279586.09895957204168
43121.6127.504811748441-5.90481174844057
44135.8135.6768528228260.123147177174423
45143.8140.8296561743472.97034382565304
46147.5146.2247624123421.27523758765761
47136.2133.8849492787022.31505072129821
48156.6162.509150150384-5.90915015038364
49123.3125.376187370952-2.07618737095221
50104.5106.694018320872-2.1940183208724
51139.8144.694900866070-4.89490086607033
52136.5137.994244045297-1.49424404529667
53112.1108.4497718069593.65022819304077
54118.5111.8134907010446.68650929895621
5594.494.5722143724116-0.172214372411607
56102.3104.045248385590-1.74524838559023
57111.4116.392348510590-4.99234851058963
5899.2103.913365193966-4.7133651939656
5987.899.3698007905582-11.5698007905582
60115.8124.634721387445-8.83472138744469

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 79.8 & 81.9094307517059 & -2.10943075170587 \tabularnewline
2 & 83.4 & 77.1207012196388 & 6.27929878036116 \tabularnewline
3 & 113.6 & 111.393365193966 & 2.20663480603439 \tabularnewline
4 & 112.9 & 108.129659868214 & 4.77034013178617 \tabularnewline
5 & 104 & 105.605063366164 & -1.60506336616429 \tabularnewline
6 & 109.9 & 114.007702672442 & -4.10770267244194 \tabularnewline
7 & 99 & 94.52366985977 & 4.47633014022993 \tabularnewline
8 & 106.3 & 104.938467418195 & 1.36153258180521 \tabularnewline
9 & 128.9 & 125.703186035239 & 3.19681396476068 \tabularnewline
10 & 111.1 & 111.903791974765 & -0.80379197476507 \tabularnewline
11 & 102.9 & 106.136905852791 & -3.23690585279057 \tabularnewline
12 & 130 & 126.848351163899 & 3.15164883610054 \tabularnewline
13 & 87 & 84.210440650916 & 2.78955934908395 \tabularnewline
14 & 87.5 & 85.6548265420237 & 1.84517345797628 \tabularnewline
15 & 117.6 & 120.626531498389 & -3.0265314983888 \tabularnewline
16 & 103.4 & 107.284985348251 & -3.8849853482508 \tabularnewline
17 & 110.8 & 116.789719078778 & -5.9897190787779 \tabularnewline
18 & 112.6 & 122.959310803544 & -10.3593108035442 \tabularnewline
19 & 102.5 & 104.776270929666 & -2.27627092966585 \tabularnewline
20 & 112.4 & 112.812387368655 & -0.412387368654536 \tabularnewline
21 & 135.6 & 138.80049554593 & -3.20049554593010 \tabularnewline
22 & 105.1 & 107.447405714271 & -2.34740571427059 \tabularnewline
23 & 127.7 & 121.117742453974 & 6.58225754602643 \tabularnewline
24 & 137 & 134.984411482624 & 2.01558851737642 \tabularnewline
25 & 91 & 93.0746686592635 & -2.07466865926346 \tabularnewline
26 & 90.5 & 92.5481473371241 & -2.04814733712411 \tabularnewline
27 & 122.4 & 122.422678466126 & -0.0226784661262075 \tabularnewline
28 & 123.3 & 121.974554873584 & 1.32544512641552 \tabularnewline
29 & 124.3 & 122.673314010934 & 1.62668598906599 \tabularnewline
30 & 120 & 118.318455395012 & 1.68154460498822 \tabularnewline
31 & 118.1 & 114.223033089712 & 3.87696691028810 \tabularnewline
32 & 119 & 118.327044004735 & 0.672955995265134 \tabularnewline
33 & 142.7 & 140.674313733894 & 2.02568626610601 \tabularnewline
34 & 123.6 & 117.010674704656 & 6.58932529534365 \tabularnewline
35 & 129.6 & 123.690601623976 & 5.90939837602416 \tabularnewline
36 & 151.6 & 142.023365815649 & 9.57663418435136 \tabularnewline
37 & 110.4 & 106.929272567162 & 3.47072743283759 \tabularnewline
38 & 99.2 & 103.082306580341 & -3.88230658034092 \tabularnewline
39 & 130.5 & 124.762523975449 & 5.73747602455095 \tabularnewline
40 & 136.2 & 136.916555864654 & -0.716555864654212 \tabularnewline
41 & 129.7 & 127.382131737165 & 2.31786826283543 \tabularnewline
42 & 128 & 121.901040427958 & 6.09895957204168 \tabularnewline
43 & 121.6 & 127.504811748441 & -5.90481174844057 \tabularnewline
44 & 135.8 & 135.676852822826 & 0.123147177174423 \tabularnewline
45 & 143.8 & 140.829656174347 & 2.97034382565304 \tabularnewline
46 & 147.5 & 146.224762412342 & 1.27523758765761 \tabularnewline
47 & 136.2 & 133.884949278702 & 2.31505072129821 \tabularnewline
48 & 156.6 & 162.509150150384 & -5.90915015038364 \tabularnewline
49 & 123.3 & 125.376187370952 & -2.07618737095221 \tabularnewline
50 & 104.5 & 106.694018320872 & -2.1940183208724 \tabularnewline
51 & 139.8 & 144.694900866070 & -4.89490086607033 \tabularnewline
52 & 136.5 & 137.994244045297 & -1.49424404529667 \tabularnewline
53 & 112.1 & 108.449771806959 & 3.65022819304077 \tabularnewline
54 & 118.5 & 111.813490701044 & 6.68650929895621 \tabularnewline
55 & 94.4 & 94.5722143724116 & -0.172214372411607 \tabularnewline
56 & 102.3 & 104.045248385590 & -1.74524838559023 \tabularnewline
57 & 111.4 & 116.392348510590 & -4.99234851058963 \tabularnewline
58 & 99.2 & 103.913365193966 & -4.7133651939656 \tabularnewline
59 & 87.8 & 99.3698007905582 & -11.5698007905582 \tabularnewline
60 & 115.8 & 124.634721387445 & -8.83472138744469 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58011&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]79.8[/C][C]81.9094307517059[/C][C]-2.10943075170587[/C][/ROW]
[ROW][C]2[/C][C]83.4[/C][C]77.1207012196388[/C][C]6.27929878036116[/C][/ROW]
[ROW][C]3[/C][C]113.6[/C][C]111.393365193966[/C][C]2.20663480603439[/C][/ROW]
[ROW][C]4[/C][C]112.9[/C][C]108.129659868214[/C][C]4.77034013178617[/C][/ROW]
[ROW][C]5[/C][C]104[/C][C]105.605063366164[/C][C]-1.60506336616429[/C][/ROW]
[ROW][C]6[/C][C]109.9[/C][C]114.007702672442[/C][C]-4.10770267244194[/C][/ROW]
[ROW][C]7[/C][C]99[/C][C]94.52366985977[/C][C]4.47633014022993[/C][/ROW]
[ROW][C]8[/C][C]106.3[/C][C]104.938467418195[/C][C]1.36153258180521[/C][/ROW]
[ROW][C]9[/C][C]128.9[/C][C]125.703186035239[/C][C]3.19681396476068[/C][/ROW]
[ROW][C]10[/C][C]111.1[/C][C]111.903791974765[/C][C]-0.80379197476507[/C][/ROW]
[ROW][C]11[/C][C]102.9[/C][C]106.136905852791[/C][C]-3.23690585279057[/C][/ROW]
[ROW][C]12[/C][C]130[/C][C]126.848351163899[/C][C]3.15164883610054[/C][/ROW]
[ROW][C]13[/C][C]87[/C][C]84.210440650916[/C][C]2.78955934908395[/C][/ROW]
[ROW][C]14[/C][C]87.5[/C][C]85.6548265420237[/C][C]1.84517345797628[/C][/ROW]
[ROW][C]15[/C][C]117.6[/C][C]120.626531498389[/C][C]-3.0265314983888[/C][/ROW]
[ROW][C]16[/C][C]103.4[/C][C]107.284985348251[/C][C]-3.8849853482508[/C][/ROW]
[ROW][C]17[/C][C]110.8[/C][C]116.789719078778[/C][C]-5.9897190787779[/C][/ROW]
[ROW][C]18[/C][C]112.6[/C][C]122.959310803544[/C][C]-10.3593108035442[/C][/ROW]
[ROW][C]19[/C][C]102.5[/C][C]104.776270929666[/C][C]-2.27627092966585[/C][/ROW]
[ROW][C]20[/C][C]112.4[/C][C]112.812387368655[/C][C]-0.412387368654536[/C][/ROW]
[ROW][C]21[/C][C]135.6[/C][C]138.80049554593[/C][C]-3.20049554593010[/C][/ROW]
[ROW][C]22[/C][C]105.1[/C][C]107.447405714271[/C][C]-2.34740571427059[/C][/ROW]
[ROW][C]23[/C][C]127.7[/C][C]121.117742453974[/C][C]6.58225754602643[/C][/ROW]
[ROW][C]24[/C][C]137[/C][C]134.984411482624[/C][C]2.01558851737642[/C][/ROW]
[ROW][C]25[/C][C]91[/C][C]93.0746686592635[/C][C]-2.07466865926346[/C][/ROW]
[ROW][C]26[/C][C]90.5[/C][C]92.5481473371241[/C][C]-2.04814733712411[/C][/ROW]
[ROW][C]27[/C][C]122.4[/C][C]122.422678466126[/C][C]-0.0226784661262075[/C][/ROW]
[ROW][C]28[/C][C]123.3[/C][C]121.974554873584[/C][C]1.32544512641552[/C][/ROW]
[ROW][C]29[/C][C]124.3[/C][C]122.673314010934[/C][C]1.62668598906599[/C][/ROW]
[ROW][C]30[/C][C]120[/C][C]118.318455395012[/C][C]1.68154460498822[/C][/ROW]
[ROW][C]31[/C][C]118.1[/C][C]114.223033089712[/C][C]3.87696691028810[/C][/ROW]
[ROW][C]32[/C][C]119[/C][C]118.327044004735[/C][C]0.672955995265134[/C][/ROW]
[ROW][C]33[/C][C]142.7[/C][C]140.674313733894[/C][C]2.02568626610601[/C][/ROW]
[ROW][C]34[/C][C]123.6[/C][C]117.010674704656[/C][C]6.58932529534365[/C][/ROW]
[ROW][C]35[/C][C]129.6[/C][C]123.690601623976[/C][C]5.90939837602416[/C][/ROW]
[ROW][C]36[/C][C]151.6[/C][C]142.023365815649[/C][C]9.57663418435136[/C][/ROW]
[ROW][C]37[/C][C]110.4[/C][C]106.929272567162[/C][C]3.47072743283759[/C][/ROW]
[ROW][C]38[/C][C]99.2[/C][C]103.082306580341[/C][C]-3.88230658034092[/C][/ROW]
[ROW][C]39[/C][C]130.5[/C][C]124.762523975449[/C][C]5.73747602455095[/C][/ROW]
[ROW][C]40[/C][C]136.2[/C][C]136.916555864654[/C][C]-0.716555864654212[/C][/ROW]
[ROW][C]41[/C][C]129.7[/C][C]127.382131737165[/C][C]2.31786826283543[/C][/ROW]
[ROW][C]42[/C][C]128[/C][C]121.901040427958[/C][C]6.09895957204168[/C][/ROW]
[ROW][C]43[/C][C]121.6[/C][C]127.504811748441[/C][C]-5.90481174844057[/C][/ROW]
[ROW][C]44[/C][C]135.8[/C][C]135.676852822826[/C][C]0.123147177174423[/C][/ROW]
[ROW][C]45[/C][C]143.8[/C][C]140.829656174347[/C][C]2.97034382565304[/C][/ROW]
[ROW][C]46[/C][C]147.5[/C][C]146.224762412342[/C][C]1.27523758765761[/C][/ROW]
[ROW][C]47[/C][C]136.2[/C][C]133.884949278702[/C][C]2.31505072129821[/C][/ROW]
[ROW][C]48[/C][C]156.6[/C][C]162.509150150384[/C][C]-5.90915015038364[/C][/ROW]
[ROW][C]49[/C][C]123.3[/C][C]125.376187370952[/C][C]-2.07618737095221[/C][/ROW]
[ROW][C]50[/C][C]104.5[/C][C]106.694018320872[/C][C]-2.1940183208724[/C][/ROW]
[ROW][C]51[/C][C]139.8[/C][C]144.694900866070[/C][C]-4.89490086607033[/C][/ROW]
[ROW][C]52[/C][C]136.5[/C][C]137.994244045297[/C][C]-1.49424404529667[/C][/ROW]
[ROW][C]53[/C][C]112.1[/C][C]108.449771806959[/C][C]3.65022819304077[/C][/ROW]
[ROW][C]54[/C][C]118.5[/C][C]111.813490701044[/C][C]6.68650929895621[/C][/ROW]
[ROW][C]55[/C][C]94.4[/C][C]94.5722143724116[/C][C]-0.172214372411607[/C][/ROW]
[ROW][C]56[/C][C]102.3[/C][C]104.045248385590[/C][C]-1.74524838559023[/C][/ROW]
[ROW][C]57[/C][C]111.4[/C][C]116.392348510590[/C][C]-4.99234851058963[/C][/ROW]
[ROW][C]58[/C][C]99.2[/C][C]103.913365193966[/C][C]-4.7133651939656[/C][/ROW]
[ROW][C]59[/C][C]87.8[/C][C]99.3698007905582[/C][C]-11.5698007905582[/C][/ROW]
[ROW][C]60[/C][C]115.8[/C][C]124.634721387445[/C][C]-8.83472138744469[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58011&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58011&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
179.881.9094307517059-2.10943075170587
283.477.12070121963886.27929878036116
3113.6111.3933651939662.20663480603439
4112.9108.1296598682144.77034013178617
5104105.605063366164-1.60506336616429
6109.9114.007702672442-4.10770267244194
79994.523669859774.47633014022993
8106.3104.9384674181951.36153258180521
9128.9125.7031860352393.19681396476068
10111.1111.903791974765-0.80379197476507
11102.9106.136905852791-3.23690585279057
12130126.8483511638993.15164883610054
138784.2104406509162.78955934908395
1487.585.65482654202371.84517345797628
15117.6120.626531498389-3.0265314983888
16103.4107.284985348251-3.8849853482508
17110.8116.789719078778-5.9897190787779
18112.6122.959310803544-10.3593108035442
19102.5104.776270929666-2.27627092966585
20112.4112.812387368655-0.412387368654536
21135.6138.80049554593-3.20049554593010
22105.1107.447405714271-2.34740571427059
23127.7121.1177424539746.58225754602643
24137134.9844114826242.01558851737642
259193.0746686592635-2.07466865926346
2690.592.5481473371241-2.04814733712411
27122.4122.422678466126-0.0226784661262075
28123.3121.9745548735841.32544512641552
29124.3122.6733140109341.62668598906599
30120118.3184553950121.68154460498822
31118.1114.2230330897123.87696691028810
32119118.3270440047350.672955995265134
33142.7140.6743137338942.02568626610601
34123.6117.0106747046566.58932529534365
35129.6123.6906016239765.90939837602416
36151.6142.0233658156499.57663418435136
37110.4106.9292725671623.47072743283759
3899.2103.082306580341-3.88230658034092
39130.5124.7625239754495.73747602455095
40136.2136.916555864654-0.716555864654212
41129.7127.3821317371652.31786826283543
42128121.9010404279586.09895957204168
43121.6127.504811748441-5.90481174844057
44135.8135.6768528228260.123147177174423
45143.8140.8296561743472.97034382565304
46147.5146.2247624123421.27523758765761
47136.2133.8849492787022.31505072129821
48156.6162.509150150384-5.90915015038364
49123.3125.376187370952-2.07618737095221
50104.5106.694018320872-2.1940183208724
51139.8144.694900866070-4.89490086607033
52136.5137.994244045297-1.49424404529667
53112.1108.4497718069593.65022819304077
54118.5111.8134907010446.68650929895621
5594.494.5722143724116-0.172214372411607
56102.3104.045248385590-1.74524838559023
57111.4116.392348510590-4.99234851058963
5899.2103.913365193966-4.7133651939656
5987.899.3698007905582-11.5698007905582
60115.8124.634721387445-8.83472138744469







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
160.425679275372330.851358550744660.57432072462767
170.2782051418201660.5564102836403330.721794858179833
180.2507011336688460.5014022673376910.749298866331154
190.1543075490998680.3086150981997370.845692450900132
200.09200354258702730.1840070851740550.907996457412973
210.04977211422702820.09954422845405650.950227885772972
220.03388400997234980.06776801994469960.96611599002765
230.2820279845853770.5640559691707550.717972015414623
240.2072924833079930.4145849666159850.792707516692007
250.1393214621063800.2786429242127600.86067853789362
260.1044065984542360.2088131969084720.895593401545764
270.06723098832767810.1344619766553560.932769011672322
280.05050265782884770.1010053156576950.949497342171152
290.05574844223013610.1114968844602720.944251557769864
300.08156843135463970.1631368627092790.91843156864536
310.07232234218642740.1446446843728550.927677657813573
320.04469218709039540.08938437418079080.955307812909605
330.02828042142364560.05656084284729120.971719578576354
340.04895140566106330.09790281132212650.951048594338937
350.06250160579232790.1250032115846560.937498394207672
360.3478753994331270.6957507988662530.652124600566873
370.3405464947805740.6810929895611470.659453505219426
380.2987969018214190.5975938036428390.70120309817858
390.57415960426030.85168079147940.4258403957397
400.4587893121923270.9175786243846550.541210687807673
410.385085294235090.770170588470180.61491470576491
420.3532110782014320.7064221564028650.646788921798568
430.6341708562921490.7316582874157020.365829143707851
440.5400251370826650.919949725834670.459974862917335

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
16 & 0.42567927537233 & 0.85135855074466 & 0.57432072462767 \tabularnewline
17 & 0.278205141820166 & 0.556410283640333 & 0.721794858179833 \tabularnewline
18 & 0.250701133668846 & 0.501402267337691 & 0.749298866331154 \tabularnewline
19 & 0.154307549099868 & 0.308615098199737 & 0.845692450900132 \tabularnewline
20 & 0.0920035425870273 & 0.184007085174055 & 0.907996457412973 \tabularnewline
21 & 0.0497721142270282 & 0.0995442284540565 & 0.950227885772972 \tabularnewline
22 & 0.0338840099723498 & 0.0677680199446996 & 0.96611599002765 \tabularnewline
23 & 0.282027984585377 & 0.564055969170755 & 0.717972015414623 \tabularnewline
24 & 0.207292483307993 & 0.414584966615985 & 0.792707516692007 \tabularnewline
25 & 0.139321462106380 & 0.278642924212760 & 0.86067853789362 \tabularnewline
26 & 0.104406598454236 & 0.208813196908472 & 0.895593401545764 \tabularnewline
27 & 0.0672309883276781 & 0.134461976655356 & 0.932769011672322 \tabularnewline
28 & 0.0505026578288477 & 0.101005315657695 & 0.949497342171152 \tabularnewline
29 & 0.0557484422301361 & 0.111496884460272 & 0.944251557769864 \tabularnewline
30 & 0.0815684313546397 & 0.163136862709279 & 0.91843156864536 \tabularnewline
31 & 0.0723223421864274 & 0.144644684372855 & 0.927677657813573 \tabularnewline
32 & 0.0446921870903954 & 0.0893843741807908 & 0.955307812909605 \tabularnewline
33 & 0.0282804214236456 & 0.0565608428472912 & 0.971719578576354 \tabularnewline
34 & 0.0489514056610633 & 0.0979028113221265 & 0.951048594338937 \tabularnewline
35 & 0.0625016057923279 & 0.125003211584656 & 0.937498394207672 \tabularnewline
36 & 0.347875399433127 & 0.695750798866253 & 0.652124600566873 \tabularnewline
37 & 0.340546494780574 & 0.681092989561147 & 0.659453505219426 \tabularnewline
38 & 0.298796901821419 & 0.597593803642839 & 0.70120309817858 \tabularnewline
39 & 0.5741596042603 & 0.8516807914794 & 0.4258403957397 \tabularnewline
40 & 0.458789312192327 & 0.917578624384655 & 0.541210687807673 \tabularnewline
41 & 0.38508529423509 & 0.77017058847018 & 0.61491470576491 \tabularnewline
42 & 0.353211078201432 & 0.706422156402865 & 0.646788921798568 \tabularnewline
43 & 0.634170856292149 & 0.731658287415702 & 0.365829143707851 \tabularnewline
44 & 0.540025137082665 & 0.91994972583467 & 0.459974862917335 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58011&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]16[/C][C]0.42567927537233[/C][C]0.85135855074466[/C][C]0.57432072462767[/C][/ROW]
[ROW][C]17[/C][C]0.278205141820166[/C][C]0.556410283640333[/C][C]0.721794858179833[/C][/ROW]
[ROW][C]18[/C][C]0.250701133668846[/C][C]0.501402267337691[/C][C]0.749298866331154[/C][/ROW]
[ROW][C]19[/C][C]0.154307549099868[/C][C]0.308615098199737[/C][C]0.845692450900132[/C][/ROW]
[ROW][C]20[/C][C]0.0920035425870273[/C][C]0.184007085174055[/C][C]0.907996457412973[/C][/ROW]
[ROW][C]21[/C][C]0.0497721142270282[/C][C]0.0995442284540565[/C][C]0.950227885772972[/C][/ROW]
[ROW][C]22[/C][C]0.0338840099723498[/C][C]0.0677680199446996[/C][C]0.96611599002765[/C][/ROW]
[ROW][C]23[/C][C]0.282027984585377[/C][C]0.564055969170755[/C][C]0.717972015414623[/C][/ROW]
[ROW][C]24[/C][C]0.207292483307993[/C][C]0.414584966615985[/C][C]0.792707516692007[/C][/ROW]
[ROW][C]25[/C][C]0.139321462106380[/C][C]0.278642924212760[/C][C]0.86067853789362[/C][/ROW]
[ROW][C]26[/C][C]0.104406598454236[/C][C]0.208813196908472[/C][C]0.895593401545764[/C][/ROW]
[ROW][C]27[/C][C]0.0672309883276781[/C][C]0.134461976655356[/C][C]0.932769011672322[/C][/ROW]
[ROW][C]28[/C][C]0.0505026578288477[/C][C]0.101005315657695[/C][C]0.949497342171152[/C][/ROW]
[ROW][C]29[/C][C]0.0557484422301361[/C][C]0.111496884460272[/C][C]0.944251557769864[/C][/ROW]
[ROW][C]30[/C][C]0.0815684313546397[/C][C]0.163136862709279[/C][C]0.91843156864536[/C][/ROW]
[ROW][C]31[/C][C]0.0723223421864274[/C][C]0.144644684372855[/C][C]0.927677657813573[/C][/ROW]
[ROW][C]32[/C][C]0.0446921870903954[/C][C]0.0893843741807908[/C][C]0.955307812909605[/C][/ROW]
[ROW][C]33[/C][C]0.0282804214236456[/C][C]0.0565608428472912[/C][C]0.971719578576354[/C][/ROW]
[ROW][C]34[/C][C]0.0489514056610633[/C][C]0.0979028113221265[/C][C]0.951048594338937[/C][/ROW]
[ROW][C]35[/C][C]0.0625016057923279[/C][C]0.125003211584656[/C][C]0.937498394207672[/C][/ROW]
[ROW][C]36[/C][C]0.347875399433127[/C][C]0.695750798866253[/C][C]0.652124600566873[/C][/ROW]
[ROW][C]37[/C][C]0.340546494780574[/C][C]0.681092989561147[/C][C]0.659453505219426[/C][/ROW]
[ROW][C]38[/C][C]0.298796901821419[/C][C]0.597593803642839[/C][C]0.70120309817858[/C][/ROW]
[ROW][C]39[/C][C]0.5741596042603[/C][C]0.8516807914794[/C][C]0.4258403957397[/C][/ROW]
[ROW][C]40[/C][C]0.458789312192327[/C][C]0.917578624384655[/C][C]0.541210687807673[/C][/ROW]
[ROW][C]41[/C][C]0.38508529423509[/C][C]0.77017058847018[/C][C]0.61491470576491[/C][/ROW]
[ROW][C]42[/C][C]0.353211078201432[/C][C]0.706422156402865[/C][C]0.646788921798568[/C][/ROW]
[ROW][C]43[/C][C]0.634170856292149[/C][C]0.731658287415702[/C][C]0.365829143707851[/C][/ROW]
[ROW][C]44[/C][C]0.540025137082665[/C][C]0.91994972583467[/C][C]0.459974862917335[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58011&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58011&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
160.425679275372330.851358550744660.57432072462767
170.2782051418201660.5564102836403330.721794858179833
180.2507011336688460.5014022673376910.749298866331154
190.1543075490998680.3086150981997370.845692450900132
200.09200354258702730.1840070851740550.907996457412973
210.04977211422702820.09954422845405650.950227885772972
220.03388400997234980.06776801994469960.96611599002765
230.2820279845853770.5640559691707550.717972015414623
240.2072924833079930.4145849666159850.792707516692007
250.1393214621063800.2786429242127600.86067853789362
260.1044065984542360.2088131969084720.895593401545764
270.06723098832767810.1344619766553560.932769011672322
280.05050265782884770.1010053156576950.949497342171152
290.05574844223013610.1114968844602720.944251557769864
300.08156843135463970.1631368627092790.91843156864536
310.07232234218642740.1446446843728550.927677657813573
320.04469218709039540.08938437418079080.955307812909605
330.02828042142364560.05656084284729120.971719578576354
340.04895140566106330.09790281132212650.951048594338937
350.06250160579232790.1250032115846560.937498394207672
360.3478753994331270.6957507988662530.652124600566873
370.3405464947805740.6810929895611470.659453505219426
380.2987969018214190.5975938036428390.70120309817858
390.57415960426030.85168079147940.4258403957397
400.4587893121923270.9175786243846550.541210687807673
410.385085294235090.770170588470180.61491470576491
420.3532110782014320.7064221564028650.646788921798568
430.6341708562921490.7316582874157020.365829143707851
440.5400251370826650.919949725834670.459974862917335







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level00OK
10% type I error level50.172413793103448NOK

\begin{tabular}{lllllllll}
\hline
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
Description & # significant tests & % significant tests & OK/NOK \tabularnewline
1% type I error level & 0 & 0 & OK \tabularnewline
5% type I error level & 0 & 0 & OK \tabularnewline
10% type I error level & 5 & 0.172413793103448 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58011&T=6

[TABLE]
[ROW][C]Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]Description[/C][C]# significant tests[/C][C]% significant tests[/C][C]OK/NOK[/C][/ROW]
[ROW][C]1% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]5[/C][C]0.172413793103448[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58011&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58011&T=6

As an alternative you can also use a QR Code:  

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

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level00OK
10% type I error level50.172413793103448NOK



Parameters (Session):
par1 = 1 ; par2 = Include Monthly Dummies ; par3 = No Linear Trend ;
Parameters (R input):
par1 = 1 ; par2 = Include Monthly Dummies ; par3 = No 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')
}