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Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationSun, 23 Nov 2008 07:44:53 -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/2008/Nov/23/t1227451558unsbsh8j82uvpwl.htm/, Retrieved Mon, 20 May 2024 04:58:33 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=25272, Retrieved Mon, 20 May 2024 04:58:33 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact178

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
F     [Multiple Regression] [] [2007-11-19 20:22:41] [3a1956effdcb54c39e5044435310d6c8]
-    D    [Multiple Regression] [seatbelt_3.2.] [2008-11-23 14:44:53] [89a49ebb3ece8e9a225c7f9f53a14c57] [Current]
F   PD      [Multiple Regression] [seatbelt3CG2] [2008-11-23 15:00:12] [922d8ae7bd2fd460a62d9020ccd4931a]
-   PD        [Multiple Regression] [dummy] [2008-12-07 12:19:24] [922d8ae7bd2fd460a62d9020ccd4931a]
-    D          [Multiple Regression] [dummy3] [2008-12-11 14:24:38] [922d8ae7bd2fd460a62d9020ccd4931a]
- RMPD            [Standard Deviation-Mean Plot] [lambda] [2008-12-11 16:25:56] [922d8ae7bd2fd460a62d9020ccd4931a]
- RM D              [Variance Reduction Matrix] [denD] [2008-12-11 16:30:20] [922d8ae7bd2fd460a62d9020ccd4931a]
- RMP                 [(Partial) Autocorrelation Function] [autocorrelation] [2008-12-11 16:35:54] [922d8ae7bd2fd460a62d9020ccd4931a]
-   P                   [(Partial) Autocorrelation Function] [autocorrelation2] [2008-12-11 16:40:41] [922d8ae7bd2fd460a62d9020ccd4931a]
- RMP                     [Spectral Analysis] [spectrum] [2008-12-11 16:45:17] [922d8ae7bd2fd460a62d9020ccd4931a]
-   P                       [Spectral Analysis] [spectrum2] [2008-12-11 16:48:27] [922d8ae7bd2fd460a62d9020ccd4931a]
- RMP                         [(Partial) Autocorrelation Function] [autocorrelation] [2008-12-11 17:56:59] [922d8ae7bd2fd460a62d9020ccd4931a]
- RMP                           [ARIMA Backward Selection] [ARMAproces] [2008-12-11 18:10:55] [922d8ae7bd2fd460a62d9020ccd4931a]
- RMP                             [ARIMA Forecasting] [ARIMAforecasting] [2008-12-11 18:25:54] [922d8ae7bd2fd460a62d9020ccd4931a]
-   PD        [Multiple Regression] [dummy2] [2008-12-07 12:43:57] [922d8ae7bd2fd460a62d9020ccd4931a]
-   PD        [Multiple Regression] [dummy3] [2008-12-07 12:55:51] [922d8ae7bd2fd460a62d9020ccd4931a]
-   PD        [Multiple Regression] [dummy4] [2008-12-07 13:11:11] [922d8ae7bd2fd460a62d9020ccd4931a]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
78,4	0
114,6	0
113,3	0
117	0
99,6	0
99,4	0
101,9	0
115,2	0
108,5	0
113,8	0
121	0
92,2	0
90,2	0
101,5	0
126,6	0
93,9	0
89,8	0
93,4	0
101,5	0
110,4	0
105,9	0
108,4	0
113,9	0
86,1	0
69,4	0
101,2	0
100,5	0
98	0
106,6	0
90,1	0
96,9	0
125,9	0
112	0
100	0
123,9	0
79,8	0
83,4	0
113,6	0
112,9	0
104	0
109,9	0
99	0
106,3	0
128,9	0
111,1	0
102,9	0
130	0
87	0
87,5	0
117,6	0
103,4	0
110,8	0
112,6	0
102,5	0
112,4	0
135,6	0
105,1	0
127,7	0
137	0
91	0
90,5	0
122,4	0
123,3	0
124,3	0
120	0
118,1	0
119	0
142,7	0
123,6	0
129,6	0
151,6	0
110,4	1
99,2	1
130,5	1
136,2	1
129,7	1
128	1
121,6	1
135,8	1
143,8	1
147,5	1
136,2	1
156,6	1
123,3	1
100,4	1

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time3 seconds
R Server'George Udny Yule' @ 72.249.76.132

\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 & 'George Udny Yule' @ 72.249.76.132 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=25272&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]'George Udny Yule' @ 72.249.76.132[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=25272&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=25272&T=0

As an alternative you can also use a QR Code:  
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'George Udny Yule' @ 72.249.76.132






Multiple Linear Regression - Estimated Regression Equation
Investeringsgoederen[t] = + 79.1305704534373 + 11.9203071672355`Wel(1)_geen(0)_financiële_crisis`[t] -6.51526592718996M1[t] + 23.2423472289940M2[t] + 25.0826883227694M3[t] + 19.3087437022591M4[t] + 17.4347990817488M5[t] + 11.1037116040956M6[t] + 17.9297669835853M7[t] + 36.0415366487892M8[t] + 23.0818777425647M9[t] + 23.5079331220543M10[t] + 39.7197027872583M11[t] + 0.27394462051032t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Investeringsgoederen[t] =  +  79.1305704534373 +  11.9203071672355`Wel(1)_geen(0)_financiële_crisis`[t] -6.51526592718996M1[t] +  23.2423472289940M2[t] +  25.0826883227694M3[t] +  19.3087437022591M4[t] +  17.4347990817488M5[t] +  11.1037116040956M6[t] +  17.9297669835853M7[t] +  36.0415366487892M8[t] +  23.0818777425647M9[t] +  23.5079331220543M10[t] +  39.7197027872583M11[t] +  0.27394462051032t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=25272&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Investeringsgoederen[t] =  +  79.1305704534373 +  11.9203071672355`Wel(1)_geen(0)_financiële_crisis`[t] -6.51526592718996M1[t] +  23.2423472289940M2[t] +  25.0826883227694M3[t] +  19.3087437022591M4[t] +  17.4347990817488M5[t] +  11.1037116040956M6[t] +  17.9297669835853M7[t] +  36.0415366487892M8[t] +  23.0818777425647M9[t] +  23.5079331220543M10[t] +  39.7197027872583M11[t] +  0.27394462051032t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=25272&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=25272&T=1

As an alternative you can also use a QR Code:  
QR Code


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

Multiple Linear Regression - Estimated Regression Equation
Investeringsgoederen[t] = + 79.1305704534373 + 11.9203071672355`Wel(1)_geen(0)_financiële_crisis`[t] -6.51526592718996M1[t] + 23.2423472289940M2[t] + 25.0826883227694M3[t] + 19.3087437022591M4[t] + 17.4347990817488M5[t] + 11.1037116040956M6[t] + 17.9297669835853M7[t] + 36.0415366487892M8[t] + 23.0818777425647M9[t] + 23.5079331220543M10[t] + 39.7197027872583M11[t] + 0.27394462051032t + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)79.13057045343733.41207323.191400
`Wel(1)_geen(0)_financiële_crisis`11.92030716723552.9968233.97760.0001668.3e-05
M1-6.515265927189964.017722-1.62160.1093150.054658
M223.24234722899404.1620295.584400
M325.08268832276944.160366.02900
M419.30874370225914.1591844.64241.5e-058e-06
M517.43479908174884.15854.19267.8e-053.9e-05
M611.10371160409564.1583092.67020.009390.004695
M717.92976698358534.158614.31155.1e-052.6e-05
M836.04153664878924.1594048.665100
M923.08187774256474.1606915.547600
M1023.50793312205434.1624695.647600
M1139.71970278725834.1647399.537100
t0.273944620510320.0452686.051600

\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) & 79.1305704534373 & 3.412073 & 23.1914 & 0 & 0 \tabularnewline
`Wel(1)_geen(0)_financiële_crisis` & 11.9203071672355 & 2.996823 & 3.9776 & 0.000166 & 8.3e-05 \tabularnewline
M1 & -6.51526592718996 & 4.017722 & -1.6216 & 0.109315 & 0.054658 \tabularnewline
M2 & 23.2423472289940 & 4.162029 & 5.5844 & 0 & 0 \tabularnewline
M3 & 25.0826883227694 & 4.16036 & 6.029 & 0 & 0 \tabularnewline
M4 & 19.3087437022591 & 4.159184 & 4.6424 & 1.5e-05 & 8e-06 \tabularnewline
M5 & 17.4347990817488 & 4.1585 & 4.1926 & 7.8e-05 & 3.9e-05 \tabularnewline
M6 & 11.1037116040956 & 4.158309 & 2.6702 & 0.00939 & 0.004695 \tabularnewline
M7 & 17.9297669835853 & 4.15861 & 4.3115 & 5.1e-05 & 2.6e-05 \tabularnewline
M8 & 36.0415366487892 & 4.159404 & 8.6651 & 0 & 0 \tabularnewline
M9 & 23.0818777425647 & 4.160691 & 5.5476 & 0 & 0 \tabularnewline
M10 & 23.5079331220543 & 4.162469 & 5.6476 & 0 & 0 \tabularnewline
M11 & 39.7197027872583 & 4.164739 & 9.5371 & 0 & 0 \tabularnewline
t & 0.27394462051032 & 0.045268 & 6.0516 & 0 & 0 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=25272&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]79.1305704534373[/C][C]3.412073[/C][C]23.1914[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]`Wel(1)_geen(0)_financiële_crisis`[/C][C]11.9203071672355[/C][C]2.996823[/C][C]3.9776[/C][C]0.000166[/C][C]8.3e-05[/C][/ROW]
[ROW][C]M1[/C][C]-6.51526592718996[/C][C]4.017722[/C][C]-1.6216[/C][C]0.109315[/C][C]0.054658[/C][/ROW]
[ROW][C]M2[/C][C]23.2423472289940[/C][C]4.162029[/C][C]5.5844[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]M3[/C][C]25.0826883227694[/C][C]4.16036[/C][C]6.029[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]M4[/C][C]19.3087437022591[/C][C]4.159184[/C][C]4.6424[/C][C]1.5e-05[/C][C]8e-06[/C][/ROW]
[ROW][C]M5[/C][C]17.4347990817488[/C][C]4.1585[/C][C]4.1926[/C][C]7.8e-05[/C][C]3.9e-05[/C][/ROW]
[ROW][C]M6[/C][C]11.1037116040956[/C][C]4.158309[/C][C]2.6702[/C][C]0.00939[/C][C]0.004695[/C][/ROW]
[ROW][C]M7[/C][C]17.9297669835853[/C][C]4.15861[/C][C]4.3115[/C][C]5.1e-05[/C][C]2.6e-05[/C][/ROW]
[ROW][C]M8[/C][C]36.0415366487892[/C][C]4.159404[/C][C]8.6651[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]M9[/C][C]23.0818777425647[/C][C]4.160691[/C][C]5.5476[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]M10[/C][C]23.5079331220543[/C][C]4.162469[/C][C]5.6476[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]M11[/C][C]39.7197027872583[/C][C]4.164739[/C][C]9.5371[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]t[/C][C]0.27394462051032[/C][C]0.045268[/C][C]6.0516[/C][C]0[/C][C]0[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=25272&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=25272&T=2

As an alternative you can also use a QR Code:  
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)79.13057045343733.41207323.191400
`Wel(1)_geen(0)_financiële_crisis`11.92030716723552.9968233.97760.0001668.3e-05
M1-6.515265927189964.017722-1.62160.1093150.054658
M223.24234722899404.1620295.584400
M325.08268832276944.160366.02900
M419.30874370225914.1591844.64241.5e-058e-06
M517.43479908174884.15854.19267.8e-053.9e-05
M611.10371160409564.1583092.67020.009390.004695
M717.92976698358534.158614.31155.1e-052.6e-05
M836.04153664878924.1594048.665100
M923.08187774256474.1606915.547600
M1023.50793312205434.1624695.647600
M1139.71970278725834.1647399.537100
t0.273944620510320.0452686.051600






Multiple Linear Regression - Regression Statistics
Multiple R0.913826884329267
R-squared0.835079574522935
Adjusted R-squared0.804882876900374
F-TEST (value)27.6546655849881
F-TEST (DF numerator)13
F-TEST (DF denominator)71
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation7.7554292617078
Sum Squared Residuals4270.41449536811

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.913826884329267 \tabularnewline
R-squared & 0.835079574522935 \tabularnewline
Adjusted R-squared & 0.804882876900374 \tabularnewline
F-TEST (value) & 27.6546655849881 \tabularnewline
F-TEST (DF numerator) & 13 \tabularnewline
F-TEST (DF denominator) & 71 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 7.7554292617078 \tabularnewline
Sum Squared Residuals & 4270.41449536811 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=25272&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.913826884329267[/C][/ROW]
[ROW][C]R-squared[/C][C]0.835079574522935[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.804882876900374[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]27.6546655849881[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]13[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]71[/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]7.7554292617078[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]4270.41449536811[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=25272&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=25272&T=3

As an alternative you can also use a QR Code:  
QR Code


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

Multiple Linear Regression - Regression Statistics
Multiple R0.913826884329267
R-squared0.835079574522935
Adjusted R-squared0.804882876900374
F-TEST (value)27.6546655849881
F-TEST (DF numerator)13
F-TEST (DF denominator)71
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation7.7554292617078
Sum Squared Residuals4270.41449536811






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
178.472.88924914675775.51075085324226
2114.6102.92080692345211.679193076548
3113.3105.0350926377388.26490736226228
411799.535092637737717.4649073622623
599.697.93509263773771.66490736226234
699.491.87794978059487.52205021940522
7101.998.97794978059482.92205021940517
8115.2117.363664066309-2.1636640663091
9108.5104.6779497805953.82205021940515
10113.8105.3779497805958.4220502194052
11121121.863664066309-0.863664066309158
1292.282.41790589956129.78209410043881
1390.276.176584592881514.0234154071185
14101.5106.208142369576-4.70814236957579
15126.6108.32242808386218.2775719161385
1693.9102.822428083862-8.92242808386152
1789.8101.222428083862-11.4224280838615
1893.495.1652852267187-1.76528522671867
19101.5102.265285226719-0.76528522671867
20110.4120.650999512433-10.2509995124330
21105.9107.965285226719-2.06528522671866
22108.4108.665285226719-0.265285226718667
23113.9125.150999512433-11.2509995124329
2486.185.7052413456850.394758654314984
2569.479.4639200390054-10.0639200390054
26101.2109.495477815700-8.29547781569966
27100.5111.609763529985-11.1097635299854
2898106.109763529985-8.10976352998537
29106.6104.5097635299852.09023647001462
3090.198.4526206728425-8.35262067284252
3196.9105.552620672843-8.65262067284251
32125.9123.9383349585571.9616650414432
33112111.2526206728430.747379327157493
34100111.952620672843-11.9526206728425
35123.9128.438334958557-4.53833495855679
3679.888.9925767918089-9.19257679180886
3783.482.75125548512920.648744514870806
38113.6112.7828132618240.817186738176487
39112.9114.897098976109-1.99709897610920
40104109.397098976109-5.39709897610921
41109.9107.7970989761092.10290102389079
4299101.739956118966-2.73995611896636
43106.3108.839956118966-2.53995611896636
44128.9127.2256704046811.67432959531936
45111.1114.539956118966-3.43995611896636
46102.9115.239956118966-12.3399561189664
47130131.725670404681-1.72567040468064
488792.2799122379327-5.2799122379327
4987.586.0385909312531.46140906874696
50117.6116.0701487079471.52985129205264
51103.4118.184434422233-14.7844344222330
52110.8112.684434422233-1.88443442223306
53112.6111.0844344222331.51556557776693
54102.5105.027291565090-2.52729156509021
55112.4112.1272915650900.272708434909803
56135.6130.5130058508045.0869941491955
57105.1117.827291565090-12.7272915650902
58127.7118.5272915650909.1727084349098
59137135.0130058508041.98699414919552
609195.5672476840565-4.56724768405654
6190.589.32592637737691.17407362262311
62122.4119.3574841540713.04251584592881
63123.3121.4717698683571.82823013164310
64124.3115.9717698683578.3282301316431
65120114.3717698683575.6282301316431
66118.1108.3146270112149.78537298878594
67119115.4146270112143.58537298878596
68142.7133.8003412969288.89965870307165
69123.6121.1146270112142.48537298878596
70129.6121.8146270112147.78537298878595
71151.6138.30034129692813.2996587030717
72110.4110.774890297416-0.374890297415874
7399.2104.533568990736-5.33356899073622
74130.5134.565126767431-4.06512676743053
75136.2136.679412481716-0.47941248171624
76129.7131.179412481716-1.47941248171624
77128129.579412481716-1.57941248171624
78121.6123.522269624573-1.92226962457339
79135.8130.6222696245735.17773037542662
80143.8149.007983910288-5.20798391028766
81147.5136.32226962457311.1777303754266
82136.2137.022269624573-0.822269624573396
83156.6153.5079839102883.09201608971234
84123.3114.0622257435409.23777425646027
85100.4107.82090443686-7.42090443686006

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 78.4 & 72.8892491467577 & 5.51075085324226 \tabularnewline
2 & 114.6 & 102.920806923452 & 11.679193076548 \tabularnewline
3 & 113.3 & 105.035092637738 & 8.26490736226228 \tabularnewline
4 & 117 & 99.5350926377377 & 17.4649073622623 \tabularnewline
5 & 99.6 & 97.9350926377377 & 1.66490736226234 \tabularnewline
6 & 99.4 & 91.8779497805948 & 7.52205021940522 \tabularnewline
7 & 101.9 & 98.9779497805948 & 2.92205021940517 \tabularnewline
8 & 115.2 & 117.363664066309 & -2.1636640663091 \tabularnewline
9 & 108.5 & 104.677949780595 & 3.82205021940515 \tabularnewline
10 & 113.8 & 105.377949780595 & 8.4220502194052 \tabularnewline
11 & 121 & 121.863664066309 & -0.863664066309158 \tabularnewline
12 & 92.2 & 82.4179058995612 & 9.78209410043881 \tabularnewline
13 & 90.2 & 76.1765845928815 & 14.0234154071185 \tabularnewline
14 & 101.5 & 106.208142369576 & -4.70814236957579 \tabularnewline
15 & 126.6 & 108.322428083862 & 18.2775719161385 \tabularnewline
16 & 93.9 & 102.822428083862 & -8.92242808386152 \tabularnewline
17 & 89.8 & 101.222428083862 & -11.4224280838615 \tabularnewline
18 & 93.4 & 95.1652852267187 & -1.76528522671867 \tabularnewline
19 & 101.5 & 102.265285226719 & -0.76528522671867 \tabularnewline
20 & 110.4 & 120.650999512433 & -10.2509995124330 \tabularnewline
21 & 105.9 & 107.965285226719 & -2.06528522671866 \tabularnewline
22 & 108.4 & 108.665285226719 & -0.265285226718667 \tabularnewline
23 & 113.9 & 125.150999512433 & -11.2509995124329 \tabularnewline
24 & 86.1 & 85.705241345685 & 0.394758654314984 \tabularnewline
25 & 69.4 & 79.4639200390054 & -10.0639200390054 \tabularnewline
26 & 101.2 & 109.495477815700 & -8.29547781569966 \tabularnewline
27 & 100.5 & 111.609763529985 & -11.1097635299854 \tabularnewline
28 & 98 & 106.109763529985 & -8.10976352998537 \tabularnewline
29 & 106.6 & 104.509763529985 & 2.09023647001462 \tabularnewline
30 & 90.1 & 98.4526206728425 & -8.35262067284252 \tabularnewline
31 & 96.9 & 105.552620672843 & -8.65262067284251 \tabularnewline
32 & 125.9 & 123.938334958557 & 1.9616650414432 \tabularnewline
33 & 112 & 111.252620672843 & 0.747379327157493 \tabularnewline
34 & 100 & 111.952620672843 & -11.9526206728425 \tabularnewline
35 & 123.9 & 128.438334958557 & -4.53833495855679 \tabularnewline
36 & 79.8 & 88.9925767918089 & -9.19257679180886 \tabularnewline
37 & 83.4 & 82.7512554851292 & 0.648744514870806 \tabularnewline
38 & 113.6 & 112.782813261824 & 0.817186738176487 \tabularnewline
39 & 112.9 & 114.897098976109 & -1.99709897610920 \tabularnewline
40 & 104 & 109.397098976109 & -5.39709897610921 \tabularnewline
41 & 109.9 & 107.797098976109 & 2.10290102389079 \tabularnewline
42 & 99 & 101.739956118966 & -2.73995611896636 \tabularnewline
43 & 106.3 & 108.839956118966 & -2.53995611896636 \tabularnewline
44 & 128.9 & 127.225670404681 & 1.67432959531936 \tabularnewline
45 & 111.1 & 114.539956118966 & -3.43995611896636 \tabularnewline
46 & 102.9 & 115.239956118966 & -12.3399561189664 \tabularnewline
47 & 130 & 131.725670404681 & -1.72567040468064 \tabularnewline
48 & 87 & 92.2799122379327 & -5.2799122379327 \tabularnewline
49 & 87.5 & 86.038590931253 & 1.46140906874696 \tabularnewline
50 & 117.6 & 116.070148707947 & 1.52985129205264 \tabularnewline
51 & 103.4 & 118.184434422233 & -14.7844344222330 \tabularnewline
52 & 110.8 & 112.684434422233 & -1.88443442223306 \tabularnewline
53 & 112.6 & 111.084434422233 & 1.51556557776693 \tabularnewline
54 & 102.5 & 105.027291565090 & -2.52729156509021 \tabularnewline
55 & 112.4 & 112.127291565090 & 0.272708434909803 \tabularnewline
56 & 135.6 & 130.513005850804 & 5.0869941491955 \tabularnewline
57 & 105.1 & 117.827291565090 & -12.7272915650902 \tabularnewline
58 & 127.7 & 118.527291565090 & 9.1727084349098 \tabularnewline
59 & 137 & 135.013005850804 & 1.98699414919552 \tabularnewline
60 & 91 & 95.5672476840565 & -4.56724768405654 \tabularnewline
61 & 90.5 & 89.3259263773769 & 1.17407362262311 \tabularnewline
62 & 122.4 & 119.357484154071 & 3.04251584592881 \tabularnewline
63 & 123.3 & 121.471769868357 & 1.82823013164310 \tabularnewline
64 & 124.3 & 115.971769868357 & 8.3282301316431 \tabularnewline
65 & 120 & 114.371769868357 & 5.6282301316431 \tabularnewline
66 & 118.1 & 108.314627011214 & 9.78537298878594 \tabularnewline
67 & 119 & 115.414627011214 & 3.58537298878596 \tabularnewline
68 & 142.7 & 133.800341296928 & 8.89965870307165 \tabularnewline
69 & 123.6 & 121.114627011214 & 2.48537298878596 \tabularnewline
70 & 129.6 & 121.814627011214 & 7.78537298878595 \tabularnewline
71 & 151.6 & 138.300341296928 & 13.2996587030717 \tabularnewline
72 & 110.4 & 110.774890297416 & -0.374890297415874 \tabularnewline
73 & 99.2 & 104.533568990736 & -5.33356899073622 \tabularnewline
74 & 130.5 & 134.565126767431 & -4.06512676743053 \tabularnewline
75 & 136.2 & 136.679412481716 & -0.47941248171624 \tabularnewline
76 & 129.7 & 131.179412481716 & -1.47941248171624 \tabularnewline
77 & 128 & 129.579412481716 & -1.57941248171624 \tabularnewline
78 & 121.6 & 123.522269624573 & -1.92226962457339 \tabularnewline
79 & 135.8 & 130.622269624573 & 5.17773037542662 \tabularnewline
80 & 143.8 & 149.007983910288 & -5.20798391028766 \tabularnewline
81 & 147.5 & 136.322269624573 & 11.1777303754266 \tabularnewline
82 & 136.2 & 137.022269624573 & -0.822269624573396 \tabularnewline
83 & 156.6 & 153.507983910288 & 3.09201608971234 \tabularnewline
84 & 123.3 & 114.062225743540 & 9.23777425646027 \tabularnewline
85 & 100.4 & 107.82090443686 & -7.42090443686006 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=25272&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]78.4[/C][C]72.8892491467577[/C][C]5.51075085324226[/C][/ROW]
[ROW][C]2[/C][C]114.6[/C][C]102.920806923452[/C][C]11.679193076548[/C][/ROW]
[ROW][C]3[/C][C]113.3[/C][C]105.035092637738[/C][C]8.26490736226228[/C][/ROW]
[ROW][C]4[/C][C]117[/C][C]99.5350926377377[/C][C]17.4649073622623[/C][/ROW]
[ROW][C]5[/C][C]99.6[/C][C]97.9350926377377[/C][C]1.66490736226234[/C][/ROW]
[ROW][C]6[/C][C]99.4[/C][C]91.8779497805948[/C][C]7.52205021940522[/C][/ROW]
[ROW][C]7[/C][C]101.9[/C][C]98.9779497805948[/C][C]2.92205021940517[/C][/ROW]
[ROW][C]8[/C][C]115.2[/C][C]117.363664066309[/C][C]-2.1636640663091[/C][/ROW]
[ROW][C]9[/C][C]108.5[/C][C]104.677949780595[/C][C]3.82205021940515[/C][/ROW]
[ROW][C]10[/C][C]113.8[/C][C]105.377949780595[/C][C]8.4220502194052[/C][/ROW]
[ROW][C]11[/C][C]121[/C][C]121.863664066309[/C][C]-0.863664066309158[/C][/ROW]
[ROW][C]12[/C][C]92.2[/C][C]82.4179058995612[/C][C]9.78209410043881[/C][/ROW]
[ROW][C]13[/C][C]90.2[/C][C]76.1765845928815[/C][C]14.0234154071185[/C][/ROW]
[ROW][C]14[/C][C]101.5[/C][C]106.208142369576[/C][C]-4.70814236957579[/C][/ROW]
[ROW][C]15[/C][C]126.6[/C][C]108.322428083862[/C][C]18.2775719161385[/C][/ROW]
[ROW][C]16[/C][C]93.9[/C][C]102.822428083862[/C][C]-8.92242808386152[/C][/ROW]
[ROW][C]17[/C][C]89.8[/C][C]101.222428083862[/C][C]-11.4224280838615[/C][/ROW]
[ROW][C]18[/C][C]93.4[/C][C]95.1652852267187[/C][C]-1.76528522671867[/C][/ROW]
[ROW][C]19[/C][C]101.5[/C][C]102.265285226719[/C][C]-0.76528522671867[/C][/ROW]
[ROW][C]20[/C][C]110.4[/C][C]120.650999512433[/C][C]-10.2509995124330[/C][/ROW]
[ROW][C]21[/C][C]105.9[/C][C]107.965285226719[/C][C]-2.06528522671866[/C][/ROW]
[ROW][C]22[/C][C]108.4[/C][C]108.665285226719[/C][C]-0.265285226718667[/C][/ROW]
[ROW][C]23[/C][C]113.9[/C][C]125.150999512433[/C][C]-11.2509995124329[/C][/ROW]
[ROW][C]24[/C][C]86.1[/C][C]85.705241345685[/C][C]0.394758654314984[/C][/ROW]
[ROW][C]25[/C][C]69.4[/C][C]79.4639200390054[/C][C]-10.0639200390054[/C][/ROW]
[ROW][C]26[/C][C]101.2[/C][C]109.495477815700[/C][C]-8.29547781569966[/C][/ROW]
[ROW][C]27[/C][C]100.5[/C][C]111.609763529985[/C][C]-11.1097635299854[/C][/ROW]
[ROW][C]28[/C][C]98[/C][C]106.109763529985[/C][C]-8.10976352998537[/C][/ROW]
[ROW][C]29[/C][C]106.6[/C][C]104.509763529985[/C][C]2.09023647001462[/C][/ROW]
[ROW][C]30[/C][C]90.1[/C][C]98.4526206728425[/C][C]-8.35262067284252[/C][/ROW]
[ROW][C]31[/C][C]96.9[/C][C]105.552620672843[/C][C]-8.65262067284251[/C][/ROW]
[ROW][C]32[/C][C]125.9[/C][C]123.938334958557[/C][C]1.9616650414432[/C][/ROW]
[ROW][C]33[/C][C]112[/C][C]111.252620672843[/C][C]0.747379327157493[/C][/ROW]
[ROW][C]34[/C][C]100[/C][C]111.952620672843[/C][C]-11.9526206728425[/C][/ROW]
[ROW][C]35[/C][C]123.9[/C][C]128.438334958557[/C][C]-4.53833495855679[/C][/ROW]
[ROW][C]36[/C][C]79.8[/C][C]88.9925767918089[/C][C]-9.19257679180886[/C][/ROW]
[ROW][C]37[/C][C]83.4[/C][C]82.7512554851292[/C][C]0.648744514870806[/C][/ROW]
[ROW][C]38[/C][C]113.6[/C][C]112.782813261824[/C][C]0.817186738176487[/C][/ROW]
[ROW][C]39[/C][C]112.9[/C][C]114.897098976109[/C][C]-1.99709897610920[/C][/ROW]
[ROW][C]40[/C][C]104[/C][C]109.397098976109[/C][C]-5.39709897610921[/C][/ROW]
[ROW][C]41[/C][C]109.9[/C][C]107.797098976109[/C][C]2.10290102389079[/C][/ROW]
[ROW][C]42[/C][C]99[/C][C]101.739956118966[/C][C]-2.73995611896636[/C][/ROW]
[ROW][C]43[/C][C]106.3[/C][C]108.839956118966[/C][C]-2.53995611896636[/C][/ROW]
[ROW][C]44[/C][C]128.9[/C][C]127.225670404681[/C][C]1.67432959531936[/C][/ROW]
[ROW][C]45[/C][C]111.1[/C][C]114.539956118966[/C][C]-3.43995611896636[/C][/ROW]
[ROW][C]46[/C][C]102.9[/C][C]115.239956118966[/C][C]-12.3399561189664[/C][/ROW]
[ROW][C]47[/C][C]130[/C][C]131.725670404681[/C][C]-1.72567040468064[/C][/ROW]
[ROW][C]48[/C][C]87[/C][C]92.2799122379327[/C][C]-5.2799122379327[/C][/ROW]
[ROW][C]49[/C][C]87.5[/C][C]86.038590931253[/C][C]1.46140906874696[/C][/ROW]
[ROW][C]50[/C][C]117.6[/C][C]116.070148707947[/C][C]1.52985129205264[/C][/ROW]
[ROW][C]51[/C][C]103.4[/C][C]118.184434422233[/C][C]-14.7844344222330[/C][/ROW]
[ROW][C]52[/C][C]110.8[/C][C]112.684434422233[/C][C]-1.88443442223306[/C][/ROW]
[ROW][C]53[/C][C]112.6[/C][C]111.084434422233[/C][C]1.51556557776693[/C][/ROW]
[ROW][C]54[/C][C]102.5[/C][C]105.027291565090[/C][C]-2.52729156509021[/C][/ROW]
[ROW][C]55[/C][C]112.4[/C][C]112.127291565090[/C][C]0.272708434909803[/C][/ROW]
[ROW][C]56[/C][C]135.6[/C][C]130.513005850804[/C][C]5.0869941491955[/C][/ROW]
[ROW][C]57[/C][C]105.1[/C][C]117.827291565090[/C][C]-12.7272915650902[/C][/ROW]
[ROW][C]58[/C][C]127.7[/C][C]118.527291565090[/C][C]9.1727084349098[/C][/ROW]
[ROW][C]59[/C][C]137[/C][C]135.013005850804[/C][C]1.98699414919552[/C][/ROW]
[ROW][C]60[/C][C]91[/C][C]95.5672476840565[/C][C]-4.56724768405654[/C][/ROW]
[ROW][C]61[/C][C]90.5[/C][C]89.3259263773769[/C][C]1.17407362262311[/C][/ROW]
[ROW][C]62[/C][C]122.4[/C][C]119.357484154071[/C][C]3.04251584592881[/C][/ROW]
[ROW][C]63[/C][C]123.3[/C][C]121.471769868357[/C][C]1.82823013164310[/C][/ROW]
[ROW][C]64[/C][C]124.3[/C][C]115.971769868357[/C][C]8.3282301316431[/C][/ROW]
[ROW][C]65[/C][C]120[/C][C]114.371769868357[/C][C]5.6282301316431[/C][/ROW]
[ROW][C]66[/C][C]118.1[/C][C]108.314627011214[/C][C]9.78537298878594[/C][/ROW]
[ROW][C]67[/C][C]119[/C][C]115.414627011214[/C][C]3.58537298878596[/C][/ROW]
[ROW][C]68[/C][C]142.7[/C][C]133.800341296928[/C][C]8.89965870307165[/C][/ROW]
[ROW][C]69[/C][C]123.6[/C][C]121.114627011214[/C][C]2.48537298878596[/C][/ROW]
[ROW][C]70[/C][C]129.6[/C][C]121.814627011214[/C][C]7.78537298878595[/C][/ROW]
[ROW][C]71[/C][C]151.6[/C][C]138.300341296928[/C][C]13.2996587030717[/C][/ROW]
[ROW][C]72[/C][C]110.4[/C][C]110.774890297416[/C][C]-0.374890297415874[/C][/ROW]
[ROW][C]73[/C][C]99.2[/C][C]104.533568990736[/C][C]-5.33356899073622[/C][/ROW]
[ROW][C]74[/C][C]130.5[/C][C]134.565126767431[/C][C]-4.06512676743053[/C][/ROW]
[ROW][C]75[/C][C]136.2[/C][C]136.679412481716[/C][C]-0.47941248171624[/C][/ROW]
[ROW][C]76[/C][C]129.7[/C][C]131.179412481716[/C][C]-1.47941248171624[/C][/ROW]
[ROW][C]77[/C][C]128[/C][C]129.579412481716[/C][C]-1.57941248171624[/C][/ROW]
[ROW][C]78[/C][C]121.6[/C][C]123.522269624573[/C][C]-1.92226962457339[/C][/ROW]
[ROW][C]79[/C][C]135.8[/C][C]130.622269624573[/C][C]5.17773037542662[/C][/ROW]
[ROW][C]80[/C][C]143.8[/C][C]149.007983910288[/C][C]-5.20798391028766[/C][/ROW]
[ROW][C]81[/C][C]147.5[/C][C]136.322269624573[/C][C]11.1777303754266[/C][/ROW]
[ROW][C]82[/C][C]136.2[/C][C]137.022269624573[/C][C]-0.822269624573396[/C][/ROW]
[ROW][C]83[/C][C]156.6[/C][C]153.507983910288[/C][C]3.09201608971234[/C][/ROW]
[ROW][C]84[/C][C]123.3[/C][C]114.062225743540[/C][C]9.23777425646027[/C][/ROW]
[ROW][C]85[/C][C]100.4[/C][C]107.82090443686[/C][C]-7.42090443686006[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=25272&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=25272&T=4

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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
178.472.88924914675775.51075085324226
2114.6102.92080692345211.679193076548
3113.3105.0350926377388.26490736226228
411799.535092637737717.4649073622623
599.697.93509263773771.66490736226234
699.491.87794978059487.52205021940522
7101.998.97794978059482.92205021940517
8115.2117.363664066309-2.1636640663091
9108.5104.6779497805953.82205021940515
10113.8105.3779497805958.4220502194052
11121121.863664066309-0.863664066309158
1292.282.41790589956129.78209410043881
1390.276.176584592881514.0234154071185
14101.5106.208142369576-4.70814236957579
15126.6108.32242808386218.2775719161385
1693.9102.822428083862-8.92242808386152
1789.8101.222428083862-11.4224280838615
1893.495.1652852267187-1.76528522671867
19101.5102.265285226719-0.76528522671867
20110.4120.650999512433-10.2509995124330
21105.9107.965285226719-2.06528522671866
22108.4108.665285226719-0.265285226718667
23113.9125.150999512433-11.2509995124329
2486.185.7052413456850.394758654314984
2569.479.4639200390054-10.0639200390054
26101.2109.495477815700-8.29547781569966
27100.5111.609763529985-11.1097635299854
2898106.109763529985-8.10976352998537
29106.6104.5097635299852.09023647001462
3090.198.4526206728425-8.35262067284252
3196.9105.552620672843-8.65262067284251
32125.9123.9383349585571.9616650414432
33112111.2526206728430.747379327157493
34100111.952620672843-11.9526206728425
35123.9128.438334958557-4.53833495855679
3679.888.9925767918089-9.19257679180886
3783.482.75125548512920.648744514870806
38113.6112.7828132618240.817186738176487
39112.9114.897098976109-1.99709897610920
40104109.397098976109-5.39709897610921
41109.9107.7970989761092.10290102389079
4299101.739956118966-2.73995611896636
43106.3108.839956118966-2.53995611896636
44128.9127.2256704046811.67432959531936
45111.1114.539956118966-3.43995611896636
46102.9115.239956118966-12.3399561189664
47130131.725670404681-1.72567040468064
488792.2799122379327-5.2799122379327
4987.586.0385909312531.46140906874696
50117.6116.0701487079471.52985129205264
51103.4118.184434422233-14.7844344222330
52110.8112.684434422233-1.88443442223306
53112.6111.0844344222331.51556557776693
54102.5105.027291565090-2.52729156509021
55112.4112.1272915650900.272708434909803
56135.6130.5130058508045.0869941491955
57105.1117.827291565090-12.7272915650902
58127.7118.5272915650909.1727084349098
59137135.0130058508041.98699414919552
609195.5672476840565-4.56724768405654
6190.589.32592637737691.17407362262311
62122.4119.3574841540713.04251584592881
63123.3121.4717698683571.82823013164310
64124.3115.9717698683578.3282301316431
65120114.3717698683575.6282301316431
66118.1108.3146270112149.78537298878594
67119115.4146270112143.58537298878596
68142.7133.8003412969288.89965870307165
69123.6121.1146270112142.48537298878596
70129.6121.8146270112147.78537298878595
71151.6138.30034129692813.2996587030717
72110.4110.774890297416-0.374890297415874
7399.2104.533568990736-5.33356899073622
74130.5134.565126767431-4.06512676743053
75136.2136.679412481716-0.47941248171624
76129.7131.179412481716-1.47941248171624
77128129.579412481716-1.57941248171624
78121.6123.522269624573-1.92226962457339
79135.8130.6222696245735.17773037542662
80143.8149.007983910288-5.20798391028766
81147.5136.32226962457311.1777303754266
82136.2137.022269624573-0.822269624573396
83156.6153.5079839102883.09201608971234
84123.3114.0622257435409.23777425646027
85100.4107.82090443686-7.42090443686006


Figures (Output of Computation)

Figure 1
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Figure 2
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Figure 7
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Figure 8
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Figure 9
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Input Parameters & R Code

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)
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))
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$resid, main='Residual Normal Q-Q Plot')
grid()
dev.off()
(myerror <- as.ts(mysum$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()
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')