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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, 07 Dec 2008 05:19:24 -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/Dec/07/t1228652517g82rga1w7b1vwlv.htm/, Retrieved Sun, 19 May 2024 10:48:10 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=29895, Retrieved Sun, 19 May 2024 10:48:10 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact255

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] [922d8ae7bd2fd460a62d9020ccd4931a]
F   PD    [Multiple Regression] [seatbelt3CG2] [2008-11-23 15:00:12] [922d8ae7bd2fd460a62d9020ccd4931a]
-   PD        [Multiple Regression] [dummy] [2008-12-07 12:19:24] [89a49ebb3ece8e9a225c7f9f53a14c57] [Current]
-    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]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
160646,8	0
160637,2	0
164997,4	0
167984,4	0
172568,7	0
178880	0
181932,9	0
187714,3	0
188076,3	0
189892,2	0
182166,5	0
196657,6	0
176794,5	0
178830,4	0
168934,5	0
167902,6	0
166530,3	0
169323,4	0
175184,6	0
172493,7	0
158829,4	0
156139,6	0
162112,3	0
186258,3	0
167201,2	0
165990,2	0
165731,8	0
168118,1	0
162460,8	0
151526,9	0
136480,9	0
135780,2	0
114973,3	0
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124920,1	0
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114088,9	0
106266,2	0
104412	0
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121798,2	0
122644,1	0
127011,6	0
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135693,2	0
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148244,4	0
154458	0
159692,2	0
163144,1	0
162107,5	0
165131,3	0
164231,2	0
170001,9	0
182144,8	0
188313,5	0
193955,3	0
200918,4	0
206409	0
214390,6	0
203845,4	0
206986,7	0
209341	0
214284	0
222753	0
224041	0
232062	0
229253	0
237632	0
244574	0
257227	0
264083	0
265331	0
269216	0
253965	0
254036	0
259733	0
266302	0
277784	0
282973	0
283398	0
300454	0
304528	0
298325	0
311393	0
316867	0
322725	0
317988	0
274957	1
267993	1
260728	1
286828	1
265985	1
263718	1
239891	1
241398	1
238924	1
246829	1
234725	1
199177	1
189131	1
198293	1
165763	1
124900	1

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time4 seconds
R Server'Sir Ronald Aylmer Fisher' @ 193.190.124.24

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 4 seconds \tabularnewline
R Server & 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=29895&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]4 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Sir Ronald Aylmer Fisher' @ 193.190.124.24[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=29895&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=29895&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 time4 seconds
R Server'Sir Ronald Aylmer Fisher' @ 193.190.124.24






Multiple Linear Regression - Estimated Regression Equation
beurskapitalisatie[t] = + 131973.098732206 -35410.2110676157`Wel(1)_geen(0)_financiële_crisis`[t] -2526.81402860159M1[t] -2951.71708695796M2[t] -4539.40903420326M3[t] -1388.57875922632M4[t] -4915.37070647162M5[t] -10071.2293203836M6[t] -12219.8089267827M7[t] -11386.3008740280M8[t] -17533.0261546066M9[t] -18421.0181018519M10[t] -6803.63999719917M11[t] + 1463.54750280084t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
beurskapitalisatie[t] =  +  131973.098732206 -35410.2110676157`Wel(1)_geen(0)_financiële_crisis`[t] -2526.81402860159M1[t] -2951.71708695796M2[t] -4539.40903420326M3[t] -1388.57875922632M4[t] -4915.37070647162M5[t] -10071.2293203836M6[t] -12219.8089267827M7[t] -11386.3008740280M8[t] -17533.0261546066M9[t] -18421.0181018519M10[t] -6803.63999719917M11[t] +  1463.54750280084t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=29895&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]beurskapitalisatie[t] =  +  131973.098732206 -35410.2110676157`Wel(1)_geen(0)_financiële_crisis`[t] -2526.81402860159M1[t] -2951.71708695796M2[t] -4539.40903420326M3[t] -1388.57875922632M4[t] -4915.37070647162M5[t] -10071.2293203836M6[t] -12219.8089267827M7[t] -11386.3008740280M8[t] -17533.0261546066M9[t] -18421.0181018519M10[t] -6803.63999719917M11[t] +  1463.54750280084t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=29895&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=29895&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
beurskapitalisatie[t] = + 131973.098732206 -35410.2110676157`Wel(1)_geen(0)_financiële_crisis`[t] -2526.81402860159M1[t] -2951.71708695796M2[t] -4539.40903420326M3[t] -1388.57875922632M4[t] -4915.37070647162M5[t] -10071.2293203836M6[t] -12219.8089267827M7[t] -11386.3008740280M8[t] -17533.0261546066M9[t] -18421.0181018519M10[t] -6803.63999719917M11[t] + 1463.54750280084t + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)131973.09873220617647.8458597.478100
`Wel(1)_geen(0)_financiële_crisis`-35410.211067615715247.995633-2.32230.0224240.011212
M1-2526.8140286015921251.948231-0.11890.9056150.452808
M2-2951.7170869579621246.389231-0.13890.8898110.444905
M3-4539.4090342032621242.307384-0.21370.8312570.415628
M4-1388.5787592263221239.703543-0.06540.9480160.474008
M5-4915.3707064716221238.578252-0.23140.817490.408745
M6-10071.229320383621238.931745-0.47420.636490.318245
M7-12219.808926782721282.656219-0.57420.5672560.283628
M8-11386.300874028021277.234673-0.53510.5938440.296922
M9-17533.026154606621273.2882-0.82420.4119690.205984
M10-18421.018101851921270.817622-0.8660.388730.194365
M11-6803.6399971991721854.312576-0.31130.7562630.378132
t1463.54750280084177.2229538.258200

\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) & 131973.098732206 & 17647.845859 & 7.4781 & 0 & 0 \tabularnewline
`Wel(1)_geen(0)_financiële_crisis` & -35410.2110676157 & 15247.995633 & -2.3223 & 0.022424 & 0.011212 \tabularnewline
M1 & -2526.81402860159 & 21251.948231 & -0.1189 & 0.905615 & 0.452808 \tabularnewline
M2 & -2951.71708695796 & 21246.389231 & -0.1389 & 0.889811 & 0.444905 \tabularnewline
M3 & -4539.40903420326 & 21242.307384 & -0.2137 & 0.831257 & 0.415628 \tabularnewline
M4 & -1388.57875922632 & 21239.703543 & -0.0654 & 0.948016 & 0.474008 \tabularnewline
M5 & -4915.37070647162 & 21238.578252 & -0.2314 & 0.81749 & 0.408745 \tabularnewline
M6 & -10071.2293203836 & 21238.931745 & -0.4742 & 0.63649 & 0.318245 \tabularnewline
M7 & -12219.8089267827 & 21282.656219 & -0.5742 & 0.567256 & 0.283628 \tabularnewline
M8 & -11386.3008740280 & 21277.234673 & -0.5351 & 0.593844 & 0.296922 \tabularnewline
M9 & -17533.0261546066 & 21273.2882 & -0.8242 & 0.411969 & 0.205984 \tabularnewline
M10 & -18421.0181018519 & 21270.817622 & -0.866 & 0.38873 & 0.194365 \tabularnewline
M11 & -6803.63999719917 & 21854.312576 & -0.3113 & 0.756263 & 0.378132 \tabularnewline
t & 1463.54750280084 & 177.222953 & 8.2582 & 0 & 0 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=29895&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]131973.098732206[/C][C]17647.845859[/C][C]7.4781[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]`Wel(1)_geen(0)_financiële_crisis`[/C][C]-35410.2110676157[/C][C]15247.995633[/C][C]-2.3223[/C][C]0.022424[/C][C]0.011212[/C][/ROW]
[ROW][C]M1[/C][C]-2526.81402860159[/C][C]21251.948231[/C][C]-0.1189[/C][C]0.905615[/C][C]0.452808[/C][/ROW]
[ROW][C]M2[/C][C]-2951.71708695796[/C][C]21246.389231[/C][C]-0.1389[/C][C]0.889811[/C][C]0.444905[/C][/ROW]
[ROW][C]M3[/C][C]-4539.40903420326[/C][C]21242.307384[/C][C]-0.2137[/C][C]0.831257[/C][C]0.415628[/C][/ROW]
[ROW][C]M4[/C][C]-1388.57875922632[/C][C]21239.703543[/C][C]-0.0654[/C][C]0.948016[/C][C]0.474008[/C][/ROW]
[ROW][C]M5[/C][C]-4915.37070647162[/C][C]21238.578252[/C][C]-0.2314[/C][C]0.81749[/C][C]0.408745[/C][/ROW]
[ROW][C]M6[/C][C]-10071.2293203836[/C][C]21238.931745[/C][C]-0.4742[/C][C]0.63649[/C][C]0.318245[/C][/ROW]
[ROW][C]M7[/C][C]-12219.8089267827[/C][C]21282.656219[/C][C]-0.5742[/C][C]0.567256[/C][C]0.283628[/C][/ROW]
[ROW][C]M8[/C][C]-11386.3008740280[/C][C]21277.234673[/C][C]-0.5351[/C][C]0.593844[/C][C]0.296922[/C][/ROW]
[ROW][C]M9[/C][C]-17533.0261546066[/C][C]21273.2882[/C][C]-0.8242[/C][C]0.411969[/C][C]0.205984[/C][/ROW]
[ROW][C]M10[/C][C]-18421.0181018519[/C][C]21270.817622[/C][C]-0.866[/C][C]0.38873[/C][C]0.194365[/C][/ROW]
[ROW][C]M11[/C][C]-6803.63999719917[/C][C]21854.312576[/C][C]-0.3113[/C][C]0.756263[/C][C]0.378132[/C][/ROW]
[ROW][C]t[/C][C]1463.54750280084[/C][C]177.222953[/C][C]8.2582[/C][C]0[/C][C]0[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=29895&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=29895&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)131973.09873220617647.8458597.478100
`Wel(1)_geen(0)_financiële_crisis`-35410.211067615715247.995633-2.32230.0224240.011212
M1-2526.8140286015921251.948231-0.11890.9056150.452808
M2-2951.7170869579621246.389231-0.13890.8898110.444905
M3-4539.4090342032621242.307384-0.21370.8312570.415628
M4-1388.5787592263221239.703543-0.06540.9480160.474008
M5-4915.3707064716221238.578252-0.23140.817490.408745
M6-10071.229320383621238.931745-0.47420.636490.318245
M7-12219.808926782721282.656219-0.57420.5672560.283628
M8-11386.300874028021277.234673-0.53510.5938440.296922
M9-17533.026154606621273.2882-0.82420.4119690.205984
M10-18421.018101851921270.817622-0.8660.388730.194365
M11-6803.6399971991721854.312576-0.31130.7562630.378132
t1463.54750280084177.2229538.258200






Multiple Linear Regression - Regression Statistics
Multiple R0.686345413966058
R-squared0.471070027272240
Adjusted R-squared0.396329922430273
F-TEST (value)6.30277450464234
F-TEST (DF numerator)13
F-TEST (DF denominator)92
p-value2.42904676373001e-08
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation43707.1879760203
Sum Squared Residuals175749281830.948

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.686345413966058 \tabularnewline
R-squared & 0.471070027272240 \tabularnewline
Adjusted R-squared & 0.396329922430273 \tabularnewline
F-TEST (value) & 6.30277450464234 \tabularnewline
F-TEST (DF numerator) & 13 \tabularnewline
F-TEST (DF denominator) & 92 \tabularnewline
p-value & 2.42904676373001e-08 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 43707.1879760203 \tabularnewline
Sum Squared Residuals & 175749281830.948 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=29895&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.686345413966058[/C][/ROW]
[ROW][C]R-squared[/C][C]0.471070027272240[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.396329922430273[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]6.30277450464234[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]13[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]92[/C][/ROW]
[ROW][C]p-value[/C][C]2.42904676373001e-08[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]43707.1879760203[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]175749281830.948[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=29895&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=29895&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 R0.686345413966058
R-squared0.471070027272240
Adjusted R-squared0.396329922430273
F-TEST (value)6.30277450464234
F-TEST (DF numerator)13
F-TEST (DF denominator)92
p-value2.42904676373001e-08
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation43707.1879760203
Sum Squared Residuals175749281830.948






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
1160646.8130909.83220640629736.9677935942
2160637.2131948.4766508528688.7233491499
3164997.4131824.33220640633173.0677935943
4167984.4136438.70998418331545.6900158165
5172568.7134375.46553973938193.234460261
6178880130683.15442862848196.8455713721
7181932.9129998.12232503051934.7776749703
8187714.3132295.17788058555419.1221194148
9188076.3127612.00010280760464.2998971926
10189892.2128187.55565836361704.644341637
11182166.5141268.48126581740898.0187341835
12196657.6149535.66876581747121.9312341834
13176794.5148472.40224001628322.0977599842
14178830.4149511.04668446029319.3533155397
15168934.5149386.90224001619547.5977599842
16167902.6154001.28001779413901.3199822064
17166530.3151938.03557334914592.2644266508
18169323.4148245.72446223821077.6755377620
19175184.6147560.69235864027623.9076413602
20172493.7149857.74791419522635.9520858047
21158829.4145174.57013641813654.8298635824
22156139.6145750.12569197310389.4743080269
23162112.3158831.0512994273281.24870057334
24186258.3167098.23879942719160.0612005733
25167201.2166034.9722736261166.22772637408
26165990.2167073.616718070-1083.41671807037
27165731.8166949.472273626-1217.67227362594
28168118.1171563.850051404-3445.75005140372
29162460.8169500.605606959-7039.80560695927
30151526.9165808.294495848-14281.3944958482
31136480.9165123.26239225-28642.3623922499
32135780.2167420.317947805-31640.1179478054
33114973.3162737.140170028-47763.8401700277
34121127.6163312.695725583-42185.0957255832
35124920.1176393.621333037-51473.5213330367
36122454.3184660.808833037-62206.5088330368
37114088.9183597.542307236-69508.642307236
38106266.2184636.186751681-78369.9867516805
39104412184512.042307236-80100.042307236
40117742.8189126.420085014-71383.6200850139
41121798.2187063.175640569-65264.9756405694
42122644.1183370.864529458-60726.7645294583
43127011.6182685.83242586-55674.23242586
44129469.6184982.887981416-55513.2879814156
45129579.3180299.710203638-50720.4102036378
46135693.2180875.265759193-45182.0657591933
47136320.4193956.191366647-57635.7913666469
48137592.5202223.378866647-64630.8788666469
49148244.4201160.112340846-52915.7123408462
50154458202198.756785291-47740.7567852906
51159692.2202074.612340846-42382.4123408462
52163144.1206688.990118624-43544.8901186240
53162107.5204625.745674180-42518.2456741795
54165131.3200933.434563068-35802.1345630684
55164231.2200248.40245947-36017.2024594701
56170001.9202545.458015026-32543.5580150257
57182144.8197862.280237248-15717.4802372479
58188313.5198437.835792803-10124.3357928035
59193955.3211518.761400257-17563.4614002570
60200918.4219785.948900257-18867.5489002570
61206409218722.682374456-12313.6823744563
62214390.6219761.326818901-5370.72681890074
63203845.4219637.182374456-15791.7823744563
64206986.7224251.560152234-17264.8601522341
65209341222188.315707790-12847.3157077896
66214284218496.004596679-4212.00459667853
67222753217810.9724930804942.02750691974
68224041220108.0280486363932.97195136419
69232062215424.85027085816637.1497291420
70229253216000.40582641413252.5941735864
71237632229081.3314338678550.66856613287
72244574237348.5189338677225.48106613285
73257227236285.25240806620941.7475919336
74264083237323.89685251126759.1031474891
75265331237199.75240806628131.2475919336
76269216241814.13018584427401.8698141558
77253965239750.88574140014214.1142586003
78254036236058.57463028917977.4253697113
79259733235373.54252669024359.4574733096
80266302237670.59808224628631.4019177540
81277784232987.42030446844796.5796955318
82282973233562.97586002449410.0241399763
83283398246643.90146747736754.0985325228
84300454254911.08896747745542.9110325227
85304528253847.82244167750680.1775583235
86298325254886.46688612143438.533113879
87311393254762.32244167756630.6775583234
88316867259376.70021945457490.2997805457
89322725257313.4557750165411.5442249901
90317988253621.14466389964366.8553361012
91274957217525.90149268557431.0985073152
92267993219822.95704824048170.0429517596
93260728215139.77927046345588.2207295374
94286828215715.33482601871112.6651739818
95265985228796.26043347237188.7395665283
96263718237063.44793347226654.5520665283
97239891236000.1814076713890.81859232901
98241398237038.8258521154359.17414788453
99238924236914.6814076712009.31859232899
100246829241529.0591854495299.9408145512
101234725239465.814741004-4740.81474100434
102199177235773.503629893-36596.5036298933
103189131235088.471526295-45957.471526295
104198293237385.527081851-39092.5270818506
105165763232702.349304073-66939.3493040727
106124900233277.904859628-108377.904859628

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 160646.8 & 130909.832206406 & 29736.9677935942 \tabularnewline
2 & 160637.2 & 131948.47665085 & 28688.7233491499 \tabularnewline
3 & 164997.4 & 131824.332206406 & 33173.0677935943 \tabularnewline
4 & 167984.4 & 136438.709984183 & 31545.6900158165 \tabularnewline
5 & 172568.7 & 134375.465539739 & 38193.234460261 \tabularnewline
6 & 178880 & 130683.154428628 & 48196.8455713721 \tabularnewline
7 & 181932.9 & 129998.122325030 & 51934.7776749703 \tabularnewline
8 & 187714.3 & 132295.177880585 & 55419.1221194148 \tabularnewline
9 & 188076.3 & 127612.000102807 & 60464.2998971926 \tabularnewline
10 & 189892.2 & 128187.555658363 & 61704.644341637 \tabularnewline
11 & 182166.5 & 141268.481265817 & 40898.0187341835 \tabularnewline
12 & 196657.6 & 149535.668765817 & 47121.9312341834 \tabularnewline
13 & 176794.5 & 148472.402240016 & 28322.0977599842 \tabularnewline
14 & 178830.4 & 149511.046684460 & 29319.3533155397 \tabularnewline
15 & 168934.5 & 149386.902240016 & 19547.5977599842 \tabularnewline
16 & 167902.6 & 154001.280017794 & 13901.3199822064 \tabularnewline
17 & 166530.3 & 151938.035573349 & 14592.2644266508 \tabularnewline
18 & 169323.4 & 148245.724462238 & 21077.6755377620 \tabularnewline
19 & 175184.6 & 147560.692358640 & 27623.9076413602 \tabularnewline
20 & 172493.7 & 149857.747914195 & 22635.9520858047 \tabularnewline
21 & 158829.4 & 145174.570136418 & 13654.8298635824 \tabularnewline
22 & 156139.6 & 145750.125691973 & 10389.4743080269 \tabularnewline
23 & 162112.3 & 158831.051299427 & 3281.24870057334 \tabularnewline
24 & 186258.3 & 167098.238799427 & 19160.0612005733 \tabularnewline
25 & 167201.2 & 166034.972273626 & 1166.22772637408 \tabularnewline
26 & 165990.2 & 167073.616718070 & -1083.41671807037 \tabularnewline
27 & 165731.8 & 166949.472273626 & -1217.67227362594 \tabularnewline
28 & 168118.1 & 171563.850051404 & -3445.75005140372 \tabularnewline
29 & 162460.8 & 169500.605606959 & -7039.80560695927 \tabularnewline
30 & 151526.9 & 165808.294495848 & -14281.3944958482 \tabularnewline
31 & 136480.9 & 165123.26239225 & -28642.3623922499 \tabularnewline
32 & 135780.2 & 167420.317947805 & -31640.1179478054 \tabularnewline
33 & 114973.3 & 162737.140170028 & -47763.8401700277 \tabularnewline
34 & 121127.6 & 163312.695725583 & -42185.0957255832 \tabularnewline
35 & 124920.1 & 176393.621333037 & -51473.5213330367 \tabularnewline
36 & 122454.3 & 184660.808833037 & -62206.5088330368 \tabularnewline
37 & 114088.9 & 183597.542307236 & -69508.642307236 \tabularnewline
38 & 106266.2 & 184636.186751681 & -78369.9867516805 \tabularnewline
39 & 104412 & 184512.042307236 & -80100.042307236 \tabularnewline
40 & 117742.8 & 189126.420085014 & -71383.6200850139 \tabularnewline
41 & 121798.2 & 187063.175640569 & -65264.9756405694 \tabularnewline
42 & 122644.1 & 183370.864529458 & -60726.7645294583 \tabularnewline
43 & 127011.6 & 182685.83242586 & -55674.23242586 \tabularnewline
44 & 129469.6 & 184982.887981416 & -55513.2879814156 \tabularnewline
45 & 129579.3 & 180299.710203638 & -50720.4102036378 \tabularnewline
46 & 135693.2 & 180875.265759193 & -45182.0657591933 \tabularnewline
47 & 136320.4 & 193956.191366647 & -57635.7913666469 \tabularnewline
48 & 137592.5 & 202223.378866647 & -64630.8788666469 \tabularnewline
49 & 148244.4 & 201160.112340846 & -52915.7123408462 \tabularnewline
50 & 154458 & 202198.756785291 & -47740.7567852906 \tabularnewline
51 & 159692.2 & 202074.612340846 & -42382.4123408462 \tabularnewline
52 & 163144.1 & 206688.990118624 & -43544.8901186240 \tabularnewline
53 & 162107.5 & 204625.745674180 & -42518.2456741795 \tabularnewline
54 & 165131.3 & 200933.434563068 & -35802.1345630684 \tabularnewline
55 & 164231.2 & 200248.40245947 & -36017.2024594701 \tabularnewline
56 & 170001.9 & 202545.458015026 & -32543.5580150257 \tabularnewline
57 & 182144.8 & 197862.280237248 & -15717.4802372479 \tabularnewline
58 & 188313.5 & 198437.835792803 & -10124.3357928035 \tabularnewline
59 & 193955.3 & 211518.761400257 & -17563.4614002570 \tabularnewline
60 & 200918.4 & 219785.948900257 & -18867.5489002570 \tabularnewline
61 & 206409 & 218722.682374456 & -12313.6823744563 \tabularnewline
62 & 214390.6 & 219761.326818901 & -5370.72681890074 \tabularnewline
63 & 203845.4 & 219637.182374456 & -15791.7823744563 \tabularnewline
64 & 206986.7 & 224251.560152234 & -17264.8601522341 \tabularnewline
65 & 209341 & 222188.315707790 & -12847.3157077896 \tabularnewline
66 & 214284 & 218496.004596679 & -4212.00459667853 \tabularnewline
67 & 222753 & 217810.972493080 & 4942.02750691974 \tabularnewline
68 & 224041 & 220108.028048636 & 3932.97195136419 \tabularnewline
69 & 232062 & 215424.850270858 & 16637.1497291420 \tabularnewline
70 & 229253 & 216000.405826414 & 13252.5941735864 \tabularnewline
71 & 237632 & 229081.331433867 & 8550.66856613287 \tabularnewline
72 & 244574 & 237348.518933867 & 7225.48106613285 \tabularnewline
73 & 257227 & 236285.252408066 & 20941.7475919336 \tabularnewline
74 & 264083 & 237323.896852511 & 26759.1031474891 \tabularnewline
75 & 265331 & 237199.752408066 & 28131.2475919336 \tabularnewline
76 & 269216 & 241814.130185844 & 27401.8698141558 \tabularnewline
77 & 253965 & 239750.885741400 & 14214.1142586003 \tabularnewline
78 & 254036 & 236058.574630289 & 17977.4253697113 \tabularnewline
79 & 259733 & 235373.542526690 & 24359.4574733096 \tabularnewline
80 & 266302 & 237670.598082246 & 28631.4019177540 \tabularnewline
81 & 277784 & 232987.420304468 & 44796.5796955318 \tabularnewline
82 & 282973 & 233562.975860024 & 49410.0241399763 \tabularnewline
83 & 283398 & 246643.901467477 & 36754.0985325228 \tabularnewline
84 & 300454 & 254911.088967477 & 45542.9110325227 \tabularnewline
85 & 304528 & 253847.822441677 & 50680.1775583235 \tabularnewline
86 & 298325 & 254886.466886121 & 43438.533113879 \tabularnewline
87 & 311393 & 254762.322441677 & 56630.6775583234 \tabularnewline
88 & 316867 & 259376.700219454 & 57490.2997805457 \tabularnewline
89 & 322725 & 257313.45577501 & 65411.5442249901 \tabularnewline
90 & 317988 & 253621.144663899 & 64366.8553361012 \tabularnewline
91 & 274957 & 217525.901492685 & 57431.0985073152 \tabularnewline
92 & 267993 & 219822.957048240 & 48170.0429517596 \tabularnewline
93 & 260728 & 215139.779270463 & 45588.2207295374 \tabularnewline
94 & 286828 & 215715.334826018 & 71112.6651739818 \tabularnewline
95 & 265985 & 228796.260433472 & 37188.7395665283 \tabularnewline
96 & 263718 & 237063.447933472 & 26654.5520665283 \tabularnewline
97 & 239891 & 236000.181407671 & 3890.81859232901 \tabularnewline
98 & 241398 & 237038.825852115 & 4359.17414788453 \tabularnewline
99 & 238924 & 236914.681407671 & 2009.31859232899 \tabularnewline
100 & 246829 & 241529.059185449 & 5299.9408145512 \tabularnewline
101 & 234725 & 239465.814741004 & -4740.81474100434 \tabularnewline
102 & 199177 & 235773.503629893 & -36596.5036298933 \tabularnewline
103 & 189131 & 235088.471526295 & -45957.471526295 \tabularnewline
104 & 198293 & 237385.527081851 & -39092.5270818506 \tabularnewline
105 & 165763 & 232702.349304073 & -66939.3493040727 \tabularnewline
106 & 124900 & 233277.904859628 & -108377.904859628 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=29895&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]160646.8[/C][C]130909.832206406[/C][C]29736.9677935942[/C][/ROW]
[ROW][C]2[/C][C]160637.2[/C][C]131948.47665085[/C][C]28688.7233491499[/C][/ROW]
[ROW][C]3[/C][C]164997.4[/C][C]131824.332206406[/C][C]33173.0677935943[/C][/ROW]
[ROW][C]4[/C][C]167984.4[/C][C]136438.709984183[/C][C]31545.6900158165[/C][/ROW]
[ROW][C]5[/C][C]172568.7[/C][C]134375.465539739[/C][C]38193.234460261[/C][/ROW]
[ROW][C]6[/C][C]178880[/C][C]130683.154428628[/C][C]48196.8455713721[/C][/ROW]
[ROW][C]7[/C][C]181932.9[/C][C]129998.122325030[/C][C]51934.7776749703[/C][/ROW]
[ROW][C]8[/C][C]187714.3[/C][C]132295.177880585[/C][C]55419.1221194148[/C][/ROW]
[ROW][C]9[/C][C]188076.3[/C][C]127612.000102807[/C][C]60464.2998971926[/C][/ROW]
[ROW][C]10[/C][C]189892.2[/C][C]128187.555658363[/C][C]61704.644341637[/C][/ROW]
[ROW][C]11[/C][C]182166.5[/C][C]141268.481265817[/C][C]40898.0187341835[/C][/ROW]
[ROW][C]12[/C][C]196657.6[/C][C]149535.668765817[/C][C]47121.9312341834[/C][/ROW]
[ROW][C]13[/C][C]176794.5[/C][C]148472.402240016[/C][C]28322.0977599842[/C][/ROW]
[ROW][C]14[/C][C]178830.4[/C][C]149511.046684460[/C][C]29319.3533155397[/C][/ROW]
[ROW][C]15[/C][C]168934.5[/C][C]149386.902240016[/C][C]19547.5977599842[/C][/ROW]
[ROW][C]16[/C][C]167902.6[/C][C]154001.280017794[/C][C]13901.3199822064[/C][/ROW]
[ROW][C]17[/C][C]166530.3[/C][C]151938.035573349[/C][C]14592.2644266508[/C][/ROW]
[ROW][C]18[/C][C]169323.4[/C][C]148245.724462238[/C][C]21077.6755377620[/C][/ROW]
[ROW][C]19[/C][C]175184.6[/C][C]147560.692358640[/C][C]27623.9076413602[/C][/ROW]
[ROW][C]20[/C][C]172493.7[/C][C]149857.747914195[/C][C]22635.9520858047[/C][/ROW]
[ROW][C]21[/C][C]158829.4[/C][C]145174.570136418[/C][C]13654.8298635824[/C][/ROW]
[ROW][C]22[/C][C]156139.6[/C][C]145750.125691973[/C][C]10389.4743080269[/C][/ROW]
[ROW][C]23[/C][C]162112.3[/C][C]158831.051299427[/C][C]3281.24870057334[/C][/ROW]
[ROW][C]24[/C][C]186258.3[/C][C]167098.238799427[/C][C]19160.0612005733[/C][/ROW]
[ROW][C]25[/C][C]167201.2[/C][C]166034.972273626[/C][C]1166.22772637408[/C][/ROW]
[ROW][C]26[/C][C]165990.2[/C][C]167073.616718070[/C][C]-1083.41671807037[/C][/ROW]
[ROW][C]27[/C][C]165731.8[/C][C]166949.472273626[/C][C]-1217.67227362594[/C][/ROW]
[ROW][C]28[/C][C]168118.1[/C][C]171563.850051404[/C][C]-3445.75005140372[/C][/ROW]
[ROW][C]29[/C][C]162460.8[/C][C]169500.605606959[/C][C]-7039.80560695927[/C][/ROW]
[ROW][C]30[/C][C]151526.9[/C][C]165808.294495848[/C][C]-14281.3944958482[/C][/ROW]
[ROW][C]31[/C][C]136480.9[/C][C]165123.26239225[/C][C]-28642.3623922499[/C][/ROW]
[ROW][C]32[/C][C]135780.2[/C][C]167420.317947805[/C][C]-31640.1179478054[/C][/ROW]
[ROW][C]33[/C][C]114973.3[/C][C]162737.140170028[/C][C]-47763.8401700277[/C][/ROW]
[ROW][C]34[/C][C]121127.6[/C][C]163312.695725583[/C][C]-42185.0957255832[/C][/ROW]
[ROW][C]35[/C][C]124920.1[/C][C]176393.621333037[/C][C]-51473.5213330367[/C][/ROW]
[ROW][C]36[/C][C]122454.3[/C][C]184660.808833037[/C][C]-62206.5088330368[/C][/ROW]
[ROW][C]37[/C][C]114088.9[/C][C]183597.542307236[/C][C]-69508.642307236[/C][/ROW]
[ROW][C]38[/C][C]106266.2[/C][C]184636.186751681[/C][C]-78369.9867516805[/C][/ROW]
[ROW][C]39[/C][C]104412[/C][C]184512.042307236[/C][C]-80100.042307236[/C][/ROW]
[ROW][C]40[/C][C]117742.8[/C][C]189126.420085014[/C][C]-71383.6200850139[/C][/ROW]
[ROW][C]41[/C][C]121798.2[/C][C]187063.175640569[/C][C]-65264.9756405694[/C][/ROW]
[ROW][C]42[/C][C]122644.1[/C][C]183370.864529458[/C][C]-60726.7645294583[/C][/ROW]
[ROW][C]43[/C][C]127011.6[/C][C]182685.83242586[/C][C]-55674.23242586[/C][/ROW]
[ROW][C]44[/C][C]129469.6[/C][C]184982.887981416[/C][C]-55513.2879814156[/C][/ROW]
[ROW][C]45[/C][C]129579.3[/C][C]180299.710203638[/C][C]-50720.4102036378[/C][/ROW]
[ROW][C]46[/C][C]135693.2[/C][C]180875.265759193[/C][C]-45182.0657591933[/C][/ROW]
[ROW][C]47[/C][C]136320.4[/C][C]193956.191366647[/C][C]-57635.7913666469[/C][/ROW]
[ROW][C]48[/C][C]137592.5[/C][C]202223.378866647[/C][C]-64630.8788666469[/C][/ROW]
[ROW][C]49[/C][C]148244.4[/C][C]201160.112340846[/C][C]-52915.7123408462[/C][/ROW]
[ROW][C]50[/C][C]154458[/C][C]202198.756785291[/C][C]-47740.7567852906[/C][/ROW]
[ROW][C]51[/C][C]159692.2[/C][C]202074.612340846[/C][C]-42382.4123408462[/C][/ROW]
[ROW][C]52[/C][C]163144.1[/C][C]206688.990118624[/C][C]-43544.8901186240[/C][/ROW]
[ROW][C]53[/C][C]162107.5[/C][C]204625.745674180[/C][C]-42518.2456741795[/C][/ROW]
[ROW][C]54[/C][C]165131.3[/C][C]200933.434563068[/C][C]-35802.1345630684[/C][/ROW]
[ROW][C]55[/C][C]164231.2[/C][C]200248.40245947[/C][C]-36017.2024594701[/C][/ROW]
[ROW][C]56[/C][C]170001.9[/C][C]202545.458015026[/C][C]-32543.5580150257[/C][/ROW]
[ROW][C]57[/C][C]182144.8[/C][C]197862.280237248[/C][C]-15717.4802372479[/C][/ROW]
[ROW][C]58[/C][C]188313.5[/C][C]198437.835792803[/C][C]-10124.3357928035[/C][/ROW]
[ROW][C]59[/C][C]193955.3[/C][C]211518.761400257[/C][C]-17563.4614002570[/C][/ROW]
[ROW][C]60[/C][C]200918.4[/C][C]219785.948900257[/C][C]-18867.5489002570[/C][/ROW]
[ROW][C]61[/C][C]206409[/C][C]218722.682374456[/C][C]-12313.6823744563[/C][/ROW]
[ROW][C]62[/C][C]214390.6[/C][C]219761.326818901[/C][C]-5370.72681890074[/C][/ROW]
[ROW][C]63[/C][C]203845.4[/C][C]219637.182374456[/C][C]-15791.7823744563[/C][/ROW]
[ROW][C]64[/C][C]206986.7[/C][C]224251.560152234[/C][C]-17264.8601522341[/C][/ROW]
[ROW][C]65[/C][C]209341[/C][C]222188.315707790[/C][C]-12847.3157077896[/C][/ROW]
[ROW][C]66[/C][C]214284[/C][C]218496.004596679[/C][C]-4212.00459667853[/C][/ROW]
[ROW][C]67[/C][C]222753[/C][C]217810.972493080[/C][C]4942.02750691974[/C][/ROW]
[ROW][C]68[/C][C]224041[/C][C]220108.028048636[/C][C]3932.97195136419[/C][/ROW]
[ROW][C]69[/C][C]232062[/C][C]215424.850270858[/C][C]16637.1497291420[/C][/ROW]
[ROW][C]70[/C][C]229253[/C][C]216000.405826414[/C][C]13252.5941735864[/C][/ROW]
[ROW][C]71[/C][C]237632[/C][C]229081.331433867[/C][C]8550.66856613287[/C][/ROW]
[ROW][C]72[/C][C]244574[/C][C]237348.518933867[/C][C]7225.48106613285[/C][/ROW]
[ROW][C]73[/C][C]257227[/C][C]236285.252408066[/C][C]20941.7475919336[/C][/ROW]
[ROW][C]74[/C][C]264083[/C][C]237323.896852511[/C][C]26759.1031474891[/C][/ROW]
[ROW][C]75[/C][C]265331[/C][C]237199.752408066[/C][C]28131.2475919336[/C][/ROW]
[ROW][C]76[/C][C]269216[/C][C]241814.130185844[/C][C]27401.8698141558[/C][/ROW]
[ROW][C]77[/C][C]253965[/C][C]239750.885741400[/C][C]14214.1142586003[/C][/ROW]
[ROW][C]78[/C][C]254036[/C][C]236058.574630289[/C][C]17977.4253697113[/C][/ROW]
[ROW][C]79[/C][C]259733[/C][C]235373.542526690[/C][C]24359.4574733096[/C][/ROW]
[ROW][C]80[/C][C]266302[/C][C]237670.598082246[/C][C]28631.4019177540[/C][/ROW]
[ROW][C]81[/C][C]277784[/C][C]232987.420304468[/C][C]44796.5796955318[/C][/ROW]
[ROW][C]82[/C][C]282973[/C][C]233562.975860024[/C][C]49410.0241399763[/C][/ROW]
[ROW][C]83[/C][C]283398[/C][C]246643.901467477[/C][C]36754.0985325228[/C][/ROW]
[ROW][C]84[/C][C]300454[/C][C]254911.088967477[/C][C]45542.9110325227[/C][/ROW]
[ROW][C]85[/C][C]304528[/C][C]253847.822441677[/C][C]50680.1775583235[/C][/ROW]
[ROW][C]86[/C][C]298325[/C][C]254886.466886121[/C][C]43438.533113879[/C][/ROW]
[ROW][C]87[/C][C]311393[/C][C]254762.322441677[/C][C]56630.6775583234[/C][/ROW]
[ROW][C]88[/C][C]316867[/C][C]259376.700219454[/C][C]57490.2997805457[/C][/ROW]
[ROW][C]89[/C][C]322725[/C][C]257313.45577501[/C][C]65411.5442249901[/C][/ROW]
[ROW][C]90[/C][C]317988[/C][C]253621.144663899[/C][C]64366.8553361012[/C][/ROW]
[ROW][C]91[/C][C]274957[/C][C]217525.901492685[/C][C]57431.0985073152[/C][/ROW]
[ROW][C]92[/C][C]267993[/C][C]219822.957048240[/C][C]48170.0429517596[/C][/ROW]
[ROW][C]93[/C][C]260728[/C][C]215139.779270463[/C][C]45588.2207295374[/C][/ROW]
[ROW][C]94[/C][C]286828[/C][C]215715.334826018[/C][C]71112.6651739818[/C][/ROW]
[ROW][C]95[/C][C]265985[/C][C]228796.260433472[/C][C]37188.7395665283[/C][/ROW]
[ROW][C]96[/C][C]263718[/C][C]237063.447933472[/C][C]26654.5520665283[/C][/ROW]
[ROW][C]97[/C][C]239891[/C][C]236000.181407671[/C][C]3890.81859232901[/C][/ROW]
[ROW][C]98[/C][C]241398[/C][C]237038.825852115[/C][C]4359.17414788453[/C][/ROW]
[ROW][C]99[/C][C]238924[/C][C]236914.681407671[/C][C]2009.31859232899[/C][/ROW]
[ROW][C]100[/C][C]246829[/C][C]241529.059185449[/C][C]5299.9408145512[/C][/ROW]
[ROW][C]101[/C][C]234725[/C][C]239465.814741004[/C][C]-4740.81474100434[/C][/ROW]
[ROW][C]102[/C][C]199177[/C][C]235773.503629893[/C][C]-36596.5036298933[/C][/ROW]
[ROW][C]103[/C][C]189131[/C][C]235088.471526295[/C][C]-45957.471526295[/C][/ROW]
[ROW][C]104[/C][C]198293[/C][C]237385.527081851[/C][C]-39092.5270818506[/C][/ROW]
[ROW][C]105[/C][C]165763[/C][C]232702.349304073[/C][C]-66939.3493040727[/C][/ROW]
[ROW][C]106[/C][C]124900[/C][C]233277.904859628[/C][C]-108377.904859628[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=29895&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=29895&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
1160646.8130909.83220640629736.9677935942
2160637.2131948.4766508528688.7233491499
3164997.4131824.33220640633173.0677935943
4167984.4136438.70998418331545.6900158165
5172568.7134375.46553973938193.234460261
6178880130683.15442862848196.8455713721
7181932.9129998.12232503051934.7776749703
8187714.3132295.17788058555419.1221194148
9188076.3127612.00010280760464.2998971926
10189892.2128187.55565836361704.644341637
11182166.5141268.48126581740898.0187341835
12196657.6149535.66876581747121.9312341834
13176794.5148472.40224001628322.0977599842
14178830.4149511.04668446029319.3533155397
15168934.5149386.90224001619547.5977599842
16167902.6154001.28001779413901.3199822064
17166530.3151938.03557334914592.2644266508
18169323.4148245.72446223821077.6755377620
19175184.6147560.69235864027623.9076413602
20172493.7149857.74791419522635.9520858047
21158829.4145174.57013641813654.8298635824
22156139.6145750.12569197310389.4743080269
23162112.3158831.0512994273281.24870057334
24186258.3167098.23879942719160.0612005733
25167201.2166034.9722736261166.22772637408
26165990.2167073.616718070-1083.41671807037
27165731.8166949.472273626-1217.67227362594
28168118.1171563.850051404-3445.75005140372
29162460.8169500.605606959-7039.80560695927
30151526.9165808.294495848-14281.3944958482
31136480.9165123.26239225-28642.3623922499
32135780.2167420.317947805-31640.1179478054
33114973.3162737.140170028-47763.8401700277
34121127.6163312.695725583-42185.0957255832
35124920.1176393.621333037-51473.5213330367
36122454.3184660.808833037-62206.5088330368
37114088.9183597.542307236-69508.642307236
38106266.2184636.186751681-78369.9867516805
39104412184512.042307236-80100.042307236
40117742.8189126.420085014-71383.6200850139
41121798.2187063.175640569-65264.9756405694
42122644.1183370.864529458-60726.7645294583
43127011.6182685.83242586-55674.23242586
44129469.6184982.887981416-55513.2879814156
45129579.3180299.710203638-50720.4102036378
46135693.2180875.265759193-45182.0657591933
47136320.4193956.191366647-57635.7913666469
48137592.5202223.378866647-64630.8788666469
49148244.4201160.112340846-52915.7123408462
50154458202198.756785291-47740.7567852906
51159692.2202074.612340846-42382.4123408462
52163144.1206688.990118624-43544.8901186240
53162107.5204625.745674180-42518.2456741795
54165131.3200933.434563068-35802.1345630684
55164231.2200248.40245947-36017.2024594701
56170001.9202545.458015026-32543.5580150257
57182144.8197862.280237248-15717.4802372479
58188313.5198437.835792803-10124.3357928035
59193955.3211518.761400257-17563.4614002570
60200918.4219785.948900257-18867.5489002570
61206409218722.682374456-12313.6823744563
62214390.6219761.326818901-5370.72681890074
63203845.4219637.182374456-15791.7823744563
64206986.7224251.560152234-17264.8601522341
65209341222188.315707790-12847.3157077896
66214284218496.004596679-4212.00459667853
67222753217810.9724930804942.02750691974
68224041220108.0280486363932.97195136419
69232062215424.85027085816637.1497291420
70229253216000.40582641413252.5941735864
71237632229081.3314338678550.66856613287
72244574237348.5189338677225.48106613285
73257227236285.25240806620941.7475919336
74264083237323.89685251126759.1031474891
75265331237199.75240806628131.2475919336
76269216241814.13018584427401.8698141558
77253965239750.88574140014214.1142586003
78254036236058.57463028917977.4253697113
79259733235373.54252669024359.4574733096
80266302237670.59808224628631.4019177540
81277784232987.42030446844796.5796955318
82282973233562.97586002449410.0241399763
83283398246643.90146747736754.0985325228
84300454254911.08896747745542.9110325227
85304528253847.82244167750680.1775583235
86298325254886.46688612143438.533113879
87311393254762.32244167756630.6775583234
88316867259376.70021945457490.2997805457
89322725257313.4557750165411.5442249901
90317988253621.14466389964366.8553361012
91274957217525.90149268557431.0985073152
92267993219822.95704824048170.0429517596
93260728215139.77927046345588.2207295374
94286828215715.33482601871112.6651739818
95265985228796.26043347237188.7395665283
96263718237063.44793347226654.5520665283
97239891236000.1814076713890.81859232901
98241398237038.8258521154359.17414788453
99238924236914.6814076712009.31859232899
100246829241529.0591854495299.9408145512
101234725239465.814741004-4740.81474100434
102199177235773.503629893-36596.5036298933
103189131235088.471526295-45957.471526295
104198293237385.527081851-39092.5270818506
105165763232702.349304073-66939.3493040727
106124900233277.904859628-108377.904859628


Figures (Output of Computation)

Figure 1
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Figure 2
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Figure 3
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Figure 4
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Figure 5
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Figure 6
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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')