FreeStatistics.org

Free Statistics

of Irreproducible Research!

Description of Statistical Computation

Author's title

Author*Unverified author*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationMon, 19 Nov 2007 13:22:41 -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/2007/Nov/19/t1195503315459e66sfgpl6eem.htm/, Retrieved Fri, 03 May 2024 05:23:11 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=14479, Retrieved Fri, 03 May 2024 05:23:11 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact336

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] [94633ac7785373651a5660f7355b0c5c] [Current]
-    D    [Multiple Regression] [Q3 seasonal dummi...] [2008-11-16 22:08:10] [1e1d8320a8a1170c475bf6e4ce119de6]
F    D    [Multiple Regression] [Seatbelt Q3] [2008-11-22 14:38:00] [2bd2ad6af3eef3a703e9ec23e39bd695]
-    D    [Multiple Regression] [seatbelt_3.2.] [2008-11-23 14:44:53] [922d8ae7bd2fd460a62d9020ccd4931a]
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]
F    D    [Multiple Regression] [Q3: Eigen tijdree...] [2008-11-23 15:41:01] [1ce0d16c8f4225c977b42c8fa93bc163]
-    D    [Multiple Regression] [Q3 Berekening met...] [2008-11-24 10:34:17] [491a70d26f8c977398d8a0c1c87d3dd4]
-    D    [Multiple Regression] [Q3] [2008-11-24 15:38:06] [43d870b30ac8a7afeb5de9ee11dcfc1a]
F R  D    [Multiple Regression] [Q3] [2008-11-24 15:38:53] [7458e879e85b911182071700fff19fbd]
-    D    [Multiple Regression] [Q3] [2008-11-24 15:46:22] [43d870b30ac8a7afeb5de9ee11dcfc1a]
F R PD    [Multiple Regression] [seatbeltlaw Q3 we...] [2008-11-24 17:49:22] [f58cc3b532da25682c394745f1a82535]
F R  D    [Multiple Regression] [seatbeltlaw Q3 we...] [2008-11-24 18:04:59] [f58cc3b532da25682c394745f1a82535]
F    D    [Multiple Regression] [Q3 Seatbelt Law2] [2008-11-24 19:02:33] [db72903d7941c8279d5ce0e4e873d517]
-   PD    [Multiple Regression] [Q3 - omzet ind pr...] [2008-11-24 20:53:46] [b82ef11dce0545f3fd4676ec3ebed828]
F   P       [Multiple Regression] [Q3 - omzet ind pr...] [2008-11-24 21:04:57] [b82ef11dce0545f3fd4676ec3ebed828]
F    D    [Multiple Regression] [seatbelt] [2008-11-24 21:13:33] [005293453b571dbccb80b45226e44173]
F    D    [Multiple Regression] [Q3] [2008-11-25 07:27:34] [b641c14ac36cb6fee377f3b099dcac19]
F    D    [Multiple Regression] [workshop 3 eigen ...] [2008-11-27 13:40:54] [74be16979710d4c4e7c6647856088456]
Feedback Forum
2008-11-30 17:29:38 [Tom Ardies] [reply] 
Enkel de intercept waarde vertoont een significant verschil ten opzichte van de nulhypothese. Bijna alle andere waarden kunnen als toevallig omschreven worden.
2008-12-01 16:46:31 [Jeroen Michel] [reply] 
Ook hier verwijs ik ten zeerste naar de feedback die ik zelf heb gegeven bij Q1 en Q2:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2008/Nov/24/t1227544369l4gtmawv89d3idr.htm
, Retrieved Mon, 24 Nov 2008 16:32:53 +0000


Voorts is hier een correcte output gegenereerd en zijn de resultaten correct geïnterpreteerd en toegelicht aan de hand van grafisch materiaal en tabellen.
Ook hier zijn de conclusies juist en zeer uitgebreid.

Post a new message

Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
106.7	0
110.2	0
125.9	0
100.1	0
106.4	0
114.8	0
81.3	0
87	0
104.2	0
108	0
105	0
94.5	0
92	0
95.9	0
108.8	0
103.4	0
102.1	0
110.1	0
83.2	0
82.7	0
106.8	0
113.7	0
102.5	0
96.6	0
92.1	0
95.6	0
102.3	0
98.6	0
98.2	0
104.5	0
84	0
73.8	0
103.9	0
106	0
97.2	0
102.6	0
89	0
93.8	0
116.7	1
106.8	1
98.5	1
118.7	1
90	1
91.9	1
113.3	1
113.1	1
104.1	1
108.7	1
96.7	1
101	1
116.9	1
105.8	1
99	1
129.4	1
83	1
88.9	1
115.9	1
104.2	1
113.4	1
112.2	1
100.8	1
107.3	1
126.6	1
102.9	1
117.9	1
128.8	1
87.5	1
93.8	1
122.7	1
126.2	1
124.6	1
116.7	1
115.2	1
111.1	1
129.9	1
113.3	1
118.5	1
133.5	1
102.1	1
102.4	1

Tables (Output of Computation)





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

\begin{tabular}{lllllllll}
\hline
Summary of compuational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 4 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=14479&T=0

[TABLE]
[ROW][C]Summary of compuational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]4 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=14479&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=14479&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 compuational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time4 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135






Multiple Linear Regression - Estimated Regression Equation
y[t] = + 97.545 + 5.53874999999998x[t] -5.30886408730163M1[t] -2.22558531746031M2[t] + 12.8950148809524M3[t] -0.964563492063498M4[t] + 0.304429563492081M5[t] + 14.3591369047619M6[t] -18.4290128968254M7[t] -17.2028769841270M8[t] + 6.26683035714287M9[t] + 6.88344246031746M10[t] + 2.70005456349207M11[t] + 0.116721230158731t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
y[t] =  +  97.545 +  5.53874999999998x[t] -5.30886408730163M1[t] -2.22558531746031M2[t] +  12.8950148809524M3[t] -0.964563492063498M4[t] +  0.304429563492081M5[t] +  14.3591369047619M6[t] -18.4290128968254M7[t] -17.2028769841270M8[t] +  6.26683035714287M9[t] +  6.88344246031746M10[t] +  2.70005456349207M11[t] +  0.116721230158731t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=14479&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]y[t] =  +  97.545 +  5.53874999999998x[t] -5.30886408730163M1[t] -2.22558531746031M2[t] +  12.8950148809524M3[t] -0.964563492063498M4[t] +  0.304429563492081M5[t] +  14.3591369047619M6[t] -18.4290128968254M7[t] -17.2028769841270M8[t] +  6.26683035714287M9[t] +  6.88344246031746M10[t] +  2.70005456349207M11[t] +  0.116721230158731t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=14479&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=14479&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
y[t] = + 97.545 + 5.53874999999998x[t] -5.30886408730163M1[t] -2.22558531746031M2[t] + 12.8950148809524M3[t] -0.964563492063498M4[t] + 0.304429563492081M5[t] + 14.3591369047619M6[t] -18.4290128968254M7[t] -17.2028769841270M8[t] + 6.26683035714287M9[t] + 6.88344246031746M10[t] + 2.70005456349207M11[t] + 0.116721230158731t + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)97.5453.11727131.291800
x5.538749999999982.9694481.86520.0665930.033297
M1-5.308864087301633.660279-1.45040.1516830.075842
M2-2.225585317460313.658449-0.60830.5450490.272525
M312.89501488095243.6770183.50690.0008210.00041
M4-0.9645634920634983.671017-0.26280.7935610.39678
M50.3044295634920813.6661270.0830.9340720.467036
M614.35913690476193.6623513.92070.0002130.000106
M7-18.42901289682543.659693-5.03574e-062e-06
M8-17.20287698412703.658156-4.70261.4e-057e-06
M96.266830357142873.7990891.64960.1037850.051892
M106.883442460317463.7963861.81320.0743550.037177
M112.700054563492073.7947630.71150.4792690.239634
t0.1167212301587310.0640781.82150.0730570.036529

\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) & 97.545 & 3.117271 & 31.2918 & 0 & 0 \tabularnewline
x & 5.53874999999998 & 2.969448 & 1.8652 & 0.066593 & 0.033297 \tabularnewline
M1 & -5.30886408730163 & 3.660279 & -1.4504 & 0.151683 & 0.075842 \tabularnewline
M2 & -2.22558531746031 & 3.658449 & -0.6083 & 0.545049 & 0.272525 \tabularnewline
M3 & 12.8950148809524 & 3.677018 & 3.5069 & 0.000821 & 0.00041 \tabularnewline
M4 & -0.964563492063498 & 3.671017 & -0.2628 & 0.793561 & 0.39678 \tabularnewline
M5 & 0.304429563492081 & 3.666127 & 0.083 & 0.934072 & 0.467036 \tabularnewline
M6 & 14.3591369047619 & 3.662351 & 3.9207 & 0.000213 & 0.000106 \tabularnewline
M7 & -18.4290128968254 & 3.659693 & -5.0357 & 4e-06 & 2e-06 \tabularnewline
M8 & -17.2028769841270 & 3.658156 & -4.7026 & 1.4e-05 & 7e-06 \tabularnewline
M9 & 6.26683035714287 & 3.799089 & 1.6496 & 0.103785 & 0.051892 \tabularnewline
M10 & 6.88344246031746 & 3.796386 & 1.8132 & 0.074355 & 0.037177 \tabularnewline
M11 & 2.70005456349207 & 3.794763 & 0.7115 & 0.479269 & 0.239634 \tabularnewline
t & 0.116721230158731 & 0.064078 & 1.8215 & 0.073057 & 0.036529 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=14479&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]97.545[/C][C]3.117271[/C][C]31.2918[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]x[/C][C]5.53874999999998[/C][C]2.969448[/C][C]1.8652[/C][C]0.066593[/C][C]0.033297[/C][/ROW]
[ROW][C]M1[/C][C]-5.30886408730163[/C][C]3.660279[/C][C]-1.4504[/C][C]0.151683[/C][C]0.075842[/C][/ROW]
[ROW][C]M2[/C][C]-2.22558531746031[/C][C]3.658449[/C][C]-0.6083[/C][C]0.545049[/C][C]0.272525[/C][/ROW]
[ROW][C]M3[/C][C]12.8950148809524[/C][C]3.677018[/C][C]3.5069[/C][C]0.000821[/C][C]0.00041[/C][/ROW]
[ROW][C]M4[/C][C]-0.964563492063498[/C][C]3.671017[/C][C]-0.2628[/C][C]0.793561[/C][C]0.39678[/C][/ROW]
[ROW][C]M5[/C][C]0.304429563492081[/C][C]3.666127[/C][C]0.083[/C][C]0.934072[/C][C]0.467036[/C][/ROW]
[ROW][C]M6[/C][C]14.3591369047619[/C][C]3.662351[/C][C]3.9207[/C][C]0.000213[/C][C]0.000106[/C][/ROW]
[ROW][C]M7[/C][C]-18.4290128968254[/C][C]3.659693[/C][C]-5.0357[/C][C]4e-06[/C][C]2e-06[/C][/ROW]
[ROW][C]M8[/C][C]-17.2028769841270[/C][C]3.658156[/C][C]-4.7026[/C][C]1.4e-05[/C][C]7e-06[/C][/ROW]
[ROW][C]M9[/C][C]6.26683035714287[/C][C]3.799089[/C][C]1.6496[/C][C]0.103785[/C][C]0.051892[/C][/ROW]
[ROW][C]M10[/C][C]6.88344246031746[/C][C]3.796386[/C][C]1.8132[/C][C]0.074355[/C][C]0.037177[/C][/ROW]
[ROW][C]M11[/C][C]2.70005456349207[/C][C]3.794763[/C][C]0.7115[/C][C]0.479269[/C][C]0.239634[/C][/ROW]
[ROW][C]t[/C][C]0.116721230158731[/C][C]0.064078[/C][C]1.8215[/C][C]0.073057[/C][C]0.036529[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=14479&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=14479&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)97.5453.11727131.291800
x5.538749999999982.9694481.86520.0665930.033297
M1-5.308864087301633.660279-1.45040.1516830.075842
M2-2.225585317460313.658449-0.60830.5450490.272525
M312.89501488095243.6770183.50690.0008210.00041
M4-0.9645634920634983.671017-0.26280.7935610.39678
M50.3044295634920813.6661270.0830.9340720.467036
M614.35913690476193.6623513.92070.0002130.000106
M7-18.42901289682543.659693-5.03574e-062e-06
M8-17.20287698412703.658156-4.70261.4e-057e-06
M96.266830357142873.7990891.64960.1037850.051892
M106.883442460317463.7963861.81320.0743550.037177
M112.700054563492073.7947630.71150.4792690.239634
t0.1167212301587310.0640781.82150.0730570.036529






Multiple Linear Regression - Regression Statistics
Multiple R0.881461940737189
R-squared0.776975152968172
Adjusted R-squared0.733046016431599
F-TEST (value)17.6870117244694
F-TEST (DF numerator)13
F-TEST (DF denominator)66
p-value1.11022302462516e-16
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation6.57178533306778
Sum Squared Residuals2850.43192261904

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.881461940737189 \tabularnewline
R-squared & 0.776975152968172 \tabularnewline
Adjusted R-squared & 0.733046016431599 \tabularnewline
F-TEST (value) & 17.6870117244694 \tabularnewline
F-TEST (DF numerator) & 13 \tabularnewline
F-TEST (DF denominator) & 66 \tabularnewline
p-value & 1.11022302462516e-16 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 6.57178533306778 \tabularnewline
Sum Squared Residuals & 2850.43192261904 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=14479&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.881461940737189[/C][/ROW]
[ROW][C]R-squared[/C][C]0.776975152968172[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.733046016431599[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]17.6870117244694[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]13[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]66[/C][/ROW]
[ROW][C]p-value[/C][C]1.11022302462516e-16[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]6.57178533306778[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]2850.43192261904[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=14479&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=14479&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 R0.881461940737189
R-squared0.776975152968172
Adjusted R-squared0.733046016431599
F-TEST (value)17.6870117244694
F-TEST (DF numerator)13
F-TEST (DF denominator)66
p-value1.11022302462516e-16
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation6.57178533306778
Sum Squared Residuals2850.43192261904






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
1106.792.352857142857414.3471428571426
2110.295.552857142857114.6471428571429
3125.9110.79017857142915.1098214285714
4100.197.04732142857143.05267857142857
5106.498.43303571428577.9669642857143
6114.8112.6044642857142.19553571428574
781.379.93303571428571.36696428571428
88781.27589285714285.72410714285718
9104.2104.862321428571-0.662321428571399
10108105.5956547619052.40434523809523
11105101.5289880952383.47101190476192
1294.598.9456547619047-4.44565476190473
139293.7535119047619-1.75351190476185
1495.996.9535119047619-1.0535119047619
15108.8112.190833333333-3.39083333333333
16103.498.44797619047624.95202380952382
17102.199.83369047619052.26630952380952
18110.1114.005119047619-3.90511904761906
1983.281.33369047619051.86630952380953
2082.782.67654761904760.0234523809523808
21106.8106.2629761904760.537023809523803
22113.7106.9963095238106.70369047619048
23102.5102.929642857143-0.429642857142853
2496.6100.346309523810-3.74630952380952
2592.195.1541666666666-3.05416666666662
2695.698.3541666666667-2.75416666666667
27102.3113.591488095238-11.2914880952381
2898.699.848630952381-1.24863095238096
2998.2101.234345238095-3.03434523809524
30104.5115.405773809524-10.9057738095238
318482.73434523809521.26565476190476
3273.884.0772023809524-10.2772023809524
33103.9107.663630952381-3.76363095238096
34106108.396964285714-2.39696428571429
3597.2104.330297619048-7.13029761904762
36102.6101.7469642857140.85303571428571
378996.5548214285714-7.55482142857139
3893.899.7548214285714-5.95482142857143
39116.7120.530892857143-3.83089285714285
40106.8106.7880357142860.0119642857142910
4198.5108.17375-9.67375
42118.7122.345178571429-3.64517857142857
439089.673750.326250000000014
4491.991.01660714285710.883392857142863
45113.3114.603035714286-1.30303571428572
46113.1115.336369047619-2.23636904761905
47104.1111.269702380952-7.16970238095238
48108.7108.6863690476190.0136309523809659
4996.7103.494226190476-6.79422619047614
50101106.694226190476-5.69422619047618
51116.9121.931547619048-5.03154761904762
52105.8108.188690476190-2.38869047619048
5399109.574404761905-10.5744047619048
54129.4123.7458333333335.65416666666667
558391.0744047619048-8.07440476190475
5688.992.4172619047619-3.51726190476191
57115.9116.003690476190-0.103690476190482
58104.2116.737023809524-12.5370238095238
59113.4112.6703571428570.72964285714286
60112.2110.0870238095242.11297619047620
61100.8104.894880952381-4.09488095238091
62107.3108.094880952381-0.794880952380956
63126.6123.3322023809523.2677976190476
64102.9109.589345238095-6.68934523809524
65117.9110.9750595238106.92494047619047
66128.8125.1464880952383.6535119047619
6787.592.4750595238095-4.97505952380952
6893.893.8179166666667-0.0179166666666888
69122.7117.4043452380955.29565476190475
70126.2118.1376785714298.06232142857142
71124.6114.07101190476210.5289880952381
72116.7111.4876785714295.21232142857142
73115.2106.2955357142868.90446428571433
74111.1109.4955357142861.60446428571427
75129.9124.7328571428575.16714285714284
76113.3110.992.30999999999998
77118.5112.3757142857146.1242857142857
78133.5126.5471428571436.95285714285712
79102.193.87571428571438.2242857142857
80102.495.21857142857157.18142857142855

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 106.7 & 92.3528571428574 & 14.3471428571426 \tabularnewline
2 & 110.2 & 95.5528571428571 & 14.6471428571429 \tabularnewline
3 & 125.9 & 110.790178571429 & 15.1098214285714 \tabularnewline
4 & 100.1 & 97.0473214285714 & 3.05267857142857 \tabularnewline
5 & 106.4 & 98.4330357142857 & 7.9669642857143 \tabularnewline
6 & 114.8 & 112.604464285714 & 2.19553571428574 \tabularnewline
7 & 81.3 & 79.9330357142857 & 1.36696428571428 \tabularnewline
8 & 87 & 81.2758928571428 & 5.72410714285718 \tabularnewline
9 & 104.2 & 104.862321428571 & -0.662321428571399 \tabularnewline
10 & 108 & 105.595654761905 & 2.40434523809523 \tabularnewline
11 & 105 & 101.528988095238 & 3.47101190476192 \tabularnewline
12 & 94.5 & 98.9456547619047 & -4.44565476190473 \tabularnewline
13 & 92 & 93.7535119047619 & -1.75351190476185 \tabularnewline
14 & 95.9 & 96.9535119047619 & -1.0535119047619 \tabularnewline
15 & 108.8 & 112.190833333333 & -3.39083333333333 \tabularnewline
16 & 103.4 & 98.4479761904762 & 4.95202380952382 \tabularnewline
17 & 102.1 & 99.8336904761905 & 2.26630952380952 \tabularnewline
18 & 110.1 & 114.005119047619 & -3.90511904761906 \tabularnewline
19 & 83.2 & 81.3336904761905 & 1.86630952380953 \tabularnewline
20 & 82.7 & 82.6765476190476 & 0.0234523809523808 \tabularnewline
21 & 106.8 & 106.262976190476 & 0.537023809523803 \tabularnewline
22 & 113.7 & 106.996309523810 & 6.70369047619048 \tabularnewline
23 & 102.5 & 102.929642857143 & -0.429642857142853 \tabularnewline
24 & 96.6 & 100.346309523810 & -3.74630952380952 \tabularnewline
25 & 92.1 & 95.1541666666666 & -3.05416666666662 \tabularnewline
26 & 95.6 & 98.3541666666667 & -2.75416666666667 \tabularnewline
27 & 102.3 & 113.591488095238 & -11.2914880952381 \tabularnewline
28 & 98.6 & 99.848630952381 & -1.24863095238096 \tabularnewline
29 & 98.2 & 101.234345238095 & -3.03434523809524 \tabularnewline
30 & 104.5 & 115.405773809524 & -10.9057738095238 \tabularnewline
31 & 84 & 82.7343452380952 & 1.26565476190476 \tabularnewline
32 & 73.8 & 84.0772023809524 & -10.2772023809524 \tabularnewline
33 & 103.9 & 107.663630952381 & -3.76363095238096 \tabularnewline
34 & 106 & 108.396964285714 & -2.39696428571429 \tabularnewline
35 & 97.2 & 104.330297619048 & -7.13029761904762 \tabularnewline
36 & 102.6 & 101.746964285714 & 0.85303571428571 \tabularnewline
37 & 89 & 96.5548214285714 & -7.55482142857139 \tabularnewline
38 & 93.8 & 99.7548214285714 & -5.95482142857143 \tabularnewline
39 & 116.7 & 120.530892857143 & -3.83089285714285 \tabularnewline
40 & 106.8 & 106.788035714286 & 0.0119642857142910 \tabularnewline
41 & 98.5 & 108.17375 & -9.67375 \tabularnewline
42 & 118.7 & 122.345178571429 & -3.64517857142857 \tabularnewline
43 & 90 & 89.67375 & 0.326250000000014 \tabularnewline
44 & 91.9 & 91.0166071428571 & 0.883392857142863 \tabularnewline
45 & 113.3 & 114.603035714286 & -1.30303571428572 \tabularnewline
46 & 113.1 & 115.336369047619 & -2.23636904761905 \tabularnewline
47 & 104.1 & 111.269702380952 & -7.16970238095238 \tabularnewline
48 & 108.7 & 108.686369047619 & 0.0136309523809659 \tabularnewline
49 & 96.7 & 103.494226190476 & -6.79422619047614 \tabularnewline
50 & 101 & 106.694226190476 & -5.69422619047618 \tabularnewline
51 & 116.9 & 121.931547619048 & -5.03154761904762 \tabularnewline
52 & 105.8 & 108.188690476190 & -2.38869047619048 \tabularnewline
53 & 99 & 109.574404761905 & -10.5744047619048 \tabularnewline
54 & 129.4 & 123.745833333333 & 5.65416666666667 \tabularnewline
55 & 83 & 91.0744047619048 & -8.07440476190475 \tabularnewline
56 & 88.9 & 92.4172619047619 & -3.51726190476191 \tabularnewline
57 & 115.9 & 116.003690476190 & -0.103690476190482 \tabularnewline
58 & 104.2 & 116.737023809524 & -12.5370238095238 \tabularnewline
59 & 113.4 & 112.670357142857 & 0.72964285714286 \tabularnewline
60 & 112.2 & 110.087023809524 & 2.11297619047620 \tabularnewline
61 & 100.8 & 104.894880952381 & -4.09488095238091 \tabularnewline
62 & 107.3 & 108.094880952381 & -0.794880952380956 \tabularnewline
63 & 126.6 & 123.332202380952 & 3.2677976190476 \tabularnewline
64 & 102.9 & 109.589345238095 & -6.68934523809524 \tabularnewline
65 & 117.9 & 110.975059523810 & 6.92494047619047 \tabularnewline
66 & 128.8 & 125.146488095238 & 3.6535119047619 \tabularnewline
67 & 87.5 & 92.4750595238095 & -4.97505952380952 \tabularnewline
68 & 93.8 & 93.8179166666667 & -0.0179166666666888 \tabularnewline
69 & 122.7 & 117.404345238095 & 5.29565476190475 \tabularnewline
70 & 126.2 & 118.137678571429 & 8.06232142857142 \tabularnewline
71 & 124.6 & 114.071011904762 & 10.5289880952381 \tabularnewline
72 & 116.7 & 111.487678571429 & 5.21232142857142 \tabularnewline
73 & 115.2 & 106.295535714286 & 8.90446428571433 \tabularnewline
74 & 111.1 & 109.495535714286 & 1.60446428571427 \tabularnewline
75 & 129.9 & 124.732857142857 & 5.16714285714284 \tabularnewline
76 & 113.3 & 110.99 & 2.30999999999998 \tabularnewline
77 & 118.5 & 112.375714285714 & 6.1242857142857 \tabularnewline
78 & 133.5 & 126.547142857143 & 6.95285714285712 \tabularnewline
79 & 102.1 & 93.8757142857143 & 8.2242857142857 \tabularnewline
80 & 102.4 & 95.2185714285715 & 7.18142857142855 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=14479&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]106.7[/C][C]92.3528571428574[/C][C]14.3471428571426[/C][/ROW]
[ROW][C]2[/C][C]110.2[/C][C]95.5528571428571[/C][C]14.6471428571429[/C][/ROW]
[ROW][C]3[/C][C]125.9[/C][C]110.790178571429[/C][C]15.1098214285714[/C][/ROW]
[ROW][C]4[/C][C]100.1[/C][C]97.0473214285714[/C][C]3.05267857142857[/C][/ROW]
[ROW][C]5[/C][C]106.4[/C][C]98.4330357142857[/C][C]7.9669642857143[/C][/ROW]
[ROW][C]6[/C][C]114.8[/C][C]112.604464285714[/C][C]2.19553571428574[/C][/ROW]
[ROW][C]7[/C][C]81.3[/C][C]79.9330357142857[/C][C]1.36696428571428[/C][/ROW]
[ROW][C]8[/C][C]87[/C][C]81.2758928571428[/C][C]5.72410714285718[/C][/ROW]
[ROW][C]9[/C][C]104.2[/C][C]104.862321428571[/C][C]-0.662321428571399[/C][/ROW]
[ROW][C]10[/C][C]108[/C][C]105.595654761905[/C][C]2.40434523809523[/C][/ROW]
[ROW][C]11[/C][C]105[/C][C]101.528988095238[/C][C]3.47101190476192[/C][/ROW]
[ROW][C]12[/C][C]94.5[/C][C]98.9456547619047[/C][C]-4.44565476190473[/C][/ROW]
[ROW][C]13[/C][C]92[/C][C]93.7535119047619[/C][C]-1.75351190476185[/C][/ROW]
[ROW][C]14[/C][C]95.9[/C][C]96.9535119047619[/C][C]-1.0535119047619[/C][/ROW]
[ROW][C]15[/C][C]108.8[/C][C]112.190833333333[/C][C]-3.39083333333333[/C][/ROW]
[ROW][C]16[/C][C]103.4[/C][C]98.4479761904762[/C][C]4.95202380952382[/C][/ROW]
[ROW][C]17[/C][C]102.1[/C][C]99.8336904761905[/C][C]2.26630952380952[/C][/ROW]
[ROW][C]18[/C][C]110.1[/C][C]114.005119047619[/C][C]-3.90511904761906[/C][/ROW]
[ROW][C]19[/C][C]83.2[/C][C]81.3336904761905[/C][C]1.86630952380953[/C][/ROW]
[ROW][C]20[/C][C]82.7[/C][C]82.6765476190476[/C][C]0.0234523809523808[/C][/ROW]
[ROW][C]21[/C][C]106.8[/C][C]106.262976190476[/C][C]0.537023809523803[/C][/ROW]
[ROW][C]22[/C][C]113.7[/C][C]106.996309523810[/C][C]6.70369047619048[/C][/ROW]
[ROW][C]23[/C][C]102.5[/C][C]102.929642857143[/C][C]-0.429642857142853[/C][/ROW]
[ROW][C]24[/C][C]96.6[/C][C]100.346309523810[/C][C]-3.74630952380952[/C][/ROW]
[ROW][C]25[/C][C]92.1[/C][C]95.1541666666666[/C][C]-3.05416666666662[/C][/ROW]
[ROW][C]26[/C][C]95.6[/C][C]98.3541666666667[/C][C]-2.75416666666667[/C][/ROW]
[ROW][C]27[/C][C]102.3[/C][C]113.591488095238[/C][C]-11.2914880952381[/C][/ROW]
[ROW][C]28[/C][C]98.6[/C][C]99.848630952381[/C][C]-1.24863095238096[/C][/ROW]
[ROW][C]29[/C][C]98.2[/C][C]101.234345238095[/C][C]-3.03434523809524[/C][/ROW]
[ROW][C]30[/C][C]104.5[/C][C]115.405773809524[/C][C]-10.9057738095238[/C][/ROW]
[ROW][C]31[/C][C]84[/C][C]82.7343452380952[/C][C]1.26565476190476[/C][/ROW]
[ROW][C]32[/C][C]73.8[/C][C]84.0772023809524[/C][C]-10.2772023809524[/C][/ROW]
[ROW][C]33[/C][C]103.9[/C][C]107.663630952381[/C][C]-3.76363095238096[/C][/ROW]
[ROW][C]34[/C][C]106[/C][C]108.396964285714[/C][C]-2.39696428571429[/C][/ROW]
[ROW][C]35[/C][C]97.2[/C][C]104.330297619048[/C][C]-7.13029761904762[/C][/ROW]
[ROW][C]36[/C][C]102.6[/C][C]101.746964285714[/C][C]0.85303571428571[/C][/ROW]
[ROW][C]37[/C][C]89[/C][C]96.5548214285714[/C][C]-7.55482142857139[/C][/ROW]
[ROW][C]38[/C][C]93.8[/C][C]99.7548214285714[/C][C]-5.95482142857143[/C][/ROW]
[ROW][C]39[/C][C]116.7[/C][C]120.530892857143[/C][C]-3.83089285714285[/C][/ROW]
[ROW][C]40[/C][C]106.8[/C][C]106.788035714286[/C][C]0.0119642857142910[/C][/ROW]
[ROW][C]41[/C][C]98.5[/C][C]108.17375[/C][C]-9.67375[/C][/ROW]
[ROW][C]42[/C][C]118.7[/C][C]122.345178571429[/C][C]-3.64517857142857[/C][/ROW]
[ROW][C]43[/C][C]90[/C][C]89.67375[/C][C]0.326250000000014[/C][/ROW]
[ROW][C]44[/C][C]91.9[/C][C]91.0166071428571[/C][C]0.883392857142863[/C][/ROW]
[ROW][C]45[/C][C]113.3[/C][C]114.603035714286[/C][C]-1.30303571428572[/C][/ROW]
[ROW][C]46[/C][C]113.1[/C][C]115.336369047619[/C][C]-2.23636904761905[/C][/ROW]
[ROW][C]47[/C][C]104.1[/C][C]111.269702380952[/C][C]-7.16970238095238[/C][/ROW]
[ROW][C]48[/C][C]108.7[/C][C]108.686369047619[/C][C]0.0136309523809659[/C][/ROW]
[ROW][C]49[/C][C]96.7[/C][C]103.494226190476[/C][C]-6.79422619047614[/C][/ROW]
[ROW][C]50[/C][C]101[/C][C]106.694226190476[/C][C]-5.69422619047618[/C][/ROW]
[ROW][C]51[/C][C]116.9[/C][C]121.931547619048[/C][C]-5.03154761904762[/C][/ROW]
[ROW][C]52[/C][C]105.8[/C][C]108.188690476190[/C][C]-2.38869047619048[/C][/ROW]
[ROW][C]53[/C][C]99[/C][C]109.574404761905[/C][C]-10.5744047619048[/C][/ROW]
[ROW][C]54[/C][C]129.4[/C][C]123.745833333333[/C][C]5.65416666666667[/C][/ROW]
[ROW][C]55[/C][C]83[/C][C]91.0744047619048[/C][C]-8.07440476190475[/C][/ROW]
[ROW][C]56[/C][C]88.9[/C][C]92.4172619047619[/C][C]-3.51726190476191[/C][/ROW]
[ROW][C]57[/C][C]115.9[/C][C]116.003690476190[/C][C]-0.103690476190482[/C][/ROW]
[ROW][C]58[/C][C]104.2[/C][C]116.737023809524[/C][C]-12.5370238095238[/C][/ROW]
[ROW][C]59[/C][C]113.4[/C][C]112.670357142857[/C][C]0.72964285714286[/C][/ROW]
[ROW][C]60[/C][C]112.2[/C][C]110.087023809524[/C][C]2.11297619047620[/C][/ROW]
[ROW][C]61[/C][C]100.8[/C][C]104.894880952381[/C][C]-4.09488095238091[/C][/ROW]
[ROW][C]62[/C][C]107.3[/C][C]108.094880952381[/C][C]-0.794880952380956[/C][/ROW]
[ROW][C]63[/C][C]126.6[/C][C]123.332202380952[/C][C]3.2677976190476[/C][/ROW]
[ROW][C]64[/C][C]102.9[/C][C]109.589345238095[/C][C]-6.68934523809524[/C][/ROW]
[ROW][C]65[/C][C]117.9[/C][C]110.975059523810[/C][C]6.92494047619047[/C][/ROW]
[ROW][C]66[/C][C]128.8[/C][C]125.146488095238[/C][C]3.6535119047619[/C][/ROW]
[ROW][C]67[/C][C]87.5[/C][C]92.4750595238095[/C][C]-4.97505952380952[/C][/ROW]
[ROW][C]68[/C][C]93.8[/C][C]93.8179166666667[/C][C]-0.0179166666666888[/C][/ROW]
[ROW][C]69[/C][C]122.7[/C][C]117.404345238095[/C][C]5.29565476190475[/C][/ROW]
[ROW][C]70[/C][C]126.2[/C][C]118.137678571429[/C][C]8.06232142857142[/C][/ROW]
[ROW][C]71[/C][C]124.6[/C][C]114.071011904762[/C][C]10.5289880952381[/C][/ROW]
[ROW][C]72[/C][C]116.7[/C][C]111.487678571429[/C][C]5.21232142857142[/C][/ROW]
[ROW][C]73[/C][C]115.2[/C][C]106.295535714286[/C][C]8.90446428571433[/C][/ROW]
[ROW][C]74[/C][C]111.1[/C][C]109.495535714286[/C][C]1.60446428571427[/C][/ROW]
[ROW][C]75[/C][C]129.9[/C][C]124.732857142857[/C][C]5.16714285714284[/C][/ROW]
[ROW][C]76[/C][C]113.3[/C][C]110.99[/C][C]2.30999999999998[/C][/ROW]
[ROW][C]77[/C][C]118.5[/C][C]112.375714285714[/C][C]6.1242857142857[/C][/ROW]
[ROW][C]78[/C][C]133.5[/C][C]126.547142857143[/C][C]6.95285714285712[/C][/ROW]
[ROW][C]79[/C][C]102.1[/C][C]93.8757142857143[/C][C]8.2242857142857[/C][/ROW]
[ROW][C]80[/C][C]102.4[/C][C]95.2185714285715[/C][C]7.18142857142855[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=14479&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=14479&T=4

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 - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
1106.792.352857142857414.3471428571426
2110.295.552857142857114.6471428571429
3125.9110.79017857142915.1098214285714
4100.197.04732142857143.05267857142857
5106.498.43303571428577.9669642857143
6114.8112.6044642857142.19553571428574
781.379.93303571428571.36696428571428
88781.27589285714285.72410714285718
9104.2104.862321428571-0.662321428571399
10108105.5956547619052.40434523809523
11105101.5289880952383.47101190476192
1294.598.9456547619047-4.44565476190473
139293.7535119047619-1.75351190476185
1495.996.9535119047619-1.0535119047619
15108.8112.190833333333-3.39083333333333
16103.498.44797619047624.95202380952382
17102.199.83369047619052.26630952380952
18110.1114.005119047619-3.90511904761906
1983.281.33369047619051.86630952380953
2082.782.67654761904760.0234523809523808
21106.8106.2629761904760.537023809523803
22113.7106.9963095238106.70369047619048
23102.5102.929642857143-0.429642857142853
2496.6100.346309523810-3.74630952380952
2592.195.1541666666666-3.05416666666662
2695.698.3541666666667-2.75416666666667
27102.3113.591488095238-11.2914880952381
2898.699.848630952381-1.24863095238096
2998.2101.234345238095-3.03434523809524
30104.5115.405773809524-10.9057738095238
318482.73434523809521.26565476190476
3273.884.0772023809524-10.2772023809524
33103.9107.663630952381-3.76363095238096
34106108.396964285714-2.39696428571429
3597.2104.330297619048-7.13029761904762
36102.6101.7469642857140.85303571428571
378996.5548214285714-7.55482142857139
3893.899.7548214285714-5.95482142857143
39116.7120.530892857143-3.83089285714285
40106.8106.7880357142860.0119642857142910
4198.5108.17375-9.67375
42118.7122.345178571429-3.64517857142857
439089.673750.326250000000014
4491.991.01660714285710.883392857142863
45113.3114.603035714286-1.30303571428572
46113.1115.336369047619-2.23636904761905
47104.1111.269702380952-7.16970238095238
48108.7108.6863690476190.0136309523809659
4996.7103.494226190476-6.79422619047614
50101106.694226190476-5.69422619047618
51116.9121.931547619048-5.03154761904762
52105.8108.188690476190-2.38869047619048
5399109.574404761905-10.5744047619048
54129.4123.7458333333335.65416666666667
558391.0744047619048-8.07440476190475
5688.992.4172619047619-3.51726190476191
57115.9116.003690476190-0.103690476190482
58104.2116.737023809524-12.5370238095238
59113.4112.6703571428570.72964285714286
60112.2110.0870238095242.11297619047620
61100.8104.894880952381-4.09488095238091
62107.3108.094880952381-0.794880952380956
63126.6123.3322023809523.2677976190476
64102.9109.589345238095-6.68934523809524
65117.9110.9750595238106.92494047619047
66128.8125.1464880952383.6535119047619
6787.592.4750595238095-4.97505952380952
6893.893.8179166666667-0.0179166666666888
69122.7117.4043452380955.29565476190475
70126.2118.1376785714298.06232142857142
71124.6114.07101190476210.5289880952381
72116.7111.4876785714295.21232142857142
73115.2106.2955357142868.90446428571433
74111.1109.4955357142861.60446428571427
75129.9124.7328571428575.16714285714284
76113.3110.992.30999999999998
77118.5112.3757142857146.1242857142857
78133.5126.5471428571436.95285714285712
79102.193.87571428571438.2242857142857
80102.495.21857142857157.18142857142855


Figures (Output of Computation)

Figure 1
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 2
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 3
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 4
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 5
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 6
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 7
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 8
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 9
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


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')