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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, 21 Dec 2008 06:27:04 -0700
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2008/Dec/21/t12298660685ymos03inwszz2f.htm/, Retrieved Thu, 06 Nov 2025 02:04:46 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=35567, Retrieved Thu, 06 Nov 2025 02:04:46 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords3
Estimated Impact527

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [Q1 The Seatbeltlaw] [2007-11-14 19:27:43] [8cd6641b921d30ebe00b648d1481bba0]
F    D  [Multiple Regression] [Q1 T6] [2008-11-18 17:59:48] [fe7291e888d31b8c4db0b24d6c0f75c6]
-   PD      [Multiple Regression] [3] [2008-12-21 13:27:04] [783db4b4a0f63b73ca8b14666b7f4329] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
244752	0
244576	0
241572	0
240541	0
236089	0
236997	0
264579	0
270349	0
269645	0
267037	0
258113	0
262813	0
267413	0
267366	0
264777	0
258863	0
254844	0
254868	0
277267	0
285351	0
286602	0
283042	0
276687	0
277915	0
277128	0
277103	0
275037	0
270150	0
267140	0
264993	0
287259	0
291186	0
292300	0
288186	0
281477	0
282656	0
280190	0
280408	0
276836	0
275216	0
274352	0
271311	0
289802	0
290726	0
292300	0
278506	0
269826	0
265861	0
269034	1
264176	1
255198	1
253353	1
246057	1
235372	1
258556	1
260993	1
254663	1
250643	1
243422	1
247105	1
248541	1
245039	1
237080	1
237085	1
225554	1
226839	1
247934	1
248333	1
246969	1
245098	1
246263	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'Gwilym Jenkins' @ 72.249.127.135

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 3 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=35567&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]3 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=35567&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=35567&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'Gwilym Jenkins' @ 72.249.127.135






Multiple Linear Regression - Estimated Regression Equation
WerklozenMannen[t] = + 270666.8125 -23131.7255434782Kredietcrisis[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
WerklozenMannen[t] =  +  270666.8125 -23131.7255434782Kredietcrisis[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=35567&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]WerklozenMannen[t] =  +  270666.8125 -23131.7255434782Kredietcrisis[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=35567&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=35567&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
WerklozenMannen[t] = + 270666.8125 -23131.7255434782Kredietcrisis[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)270666.81251990.606847135.97200
Kredietcrisis-23131.72554347823497.443844-6.613900

\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) & 270666.8125 & 1990.606847 & 135.972 & 0 & 0 \tabularnewline
Kredietcrisis & -23131.7255434782 & 3497.443844 & -6.6139 & 0 & 0 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=35567&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]270666.8125[/C][C]1990.606847[/C][C]135.972[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Kredietcrisis[/C][C]-23131.7255434782[/C][C]3497.443844[/C][C]-6.6139[/C][C]0[/C][C]0[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=35567&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=35567&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)270666.81251990.606847135.97200
Kredietcrisis-23131.72554347823497.443844-6.613900






Multiple Linear Regression - Regression Statistics
Multiple R0.622889950233052
R-squared0.387991890101335
Adjusted R-squared0.37912220734918
F-TEST (value)43.7436040209089
F-TEST (DF numerator)1
F-TEST (DF denominator)69
p-value6.61418353420373e-09
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation13791.3287907150
Sum Squared Residuals13123851737.1386

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.622889950233052 \tabularnewline
R-squared & 0.387991890101335 \tabularnewline
Adjusted R-squared & 0.37912220734918 \tabularnewline
F-TEST (value) & 43.7436040209089 \tabularnewline
F-TEST (DF numerator) & 1 \tabularnewline
F-TEST (DF denominator) & 69 \tabularnewline
p-value & 6.61418353420373e-09 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 13791.3287907150 \tabularnewline
Sum Squared Residuals & 13123851737.1386 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=35567&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.622889950233052[/C][/ROW]
[ROW][C]R-squared[/C][C]0.387991890101335[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.37912220734918[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]43.7436040209089[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]1[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]69[/C][/ROW]
[ROW][C]p-value[/C][C]6.61418353420373e-09[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]13791.3287907150[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]13123851737.1386[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=35567&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=35567&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 R0.622889950233052
R-squared0.387991890101335
Adjusted R-squared0.37912220734918
F-TEST (value)43.7436040209089
F-TEST (DF numerator)1
F-TEST (DF denominator)69
p-value6.61418353420373e-09
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation13791.3287907150
Sum Squared Residuals13123851737.1386






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
1244752270666.812500001-25914.8125000009
2244576270666.8125-26090.8125
3241572270666.8125-29094.8125
4240541270666.8125-30125.8125
5236089270666.8125-34577.8125
6236997270666.8125-33669.8125
7264579270666.8125-6087.81249999998
8270349270666.8125-317.812499999982
9269645270666.8125-1021.81249999998
10267037270666.8125-3629.81249999998
11258113270666.8125-12553.8125000000
12262813270666.8125-7853.81249999998
13267413270666.8125-3253.81249999998
14267366270666.8125-3300.81249999998
15264777270666.8125-5889.81249999998
16258863270666.8125-11803.8125000000
17254844270666.8125-15822.8125000000
18254868270666.8125-15798.8125000000
19277267270666.81256600.18750000002
20285351270666.812514684.1875000000
21286602270666.812515935.1875000000
22283042270666.812512375.1875000000
23276687270666.81256020.18750000002
24277915270666.81257248.18750000002
25277128270666.81256461.18750000002
26277103270666.81256436.18750000002
27275037270666.81254370.18750000002
28270150270666.8125-516.812499999982
29267140270666.8125-3526.81249999998
30264993270666.8125-5673.81249999998
31287259270666.812516592.1875000000
32291186270666.812520519.1875
33292300270666.812521633.1875000000
34288186270666.812517519.1875000000
35281477270666.812510810.1875000000
36282656270666.812511989.1875000000
37280190270666.81259523.18750000002
38280408270666.81259741.18750000002
39276836270666.81256169.18750000002
40275216270666.81254549.18750000002
41274352270666.81253685.18750000002
42271311270666.8125644.187500000018
43289802270666.812519135.1875
44290726270666.812520059.1875000000
45292300270666.812521633.1875000000
46278506270666.81257839.18750000002
47269826270666.8125-840.812499999982
48265861270666.8125-4805.81249999998
49269034247535.08695652221498.9130434783
50264176247535.08695652216640.9130434783
51255198247535.0869565227662.91304347826
52253353247535.0869565225817.91304347826
53246057247535.086956522-1478.08695652174
54235372247535.086956522-12163.0869565217
55258556247535.08695652211020.9130434783
56260993247535.08695652213457.9130434783
57254663247535.0869565227127.91304347826
58250643247535.0869565223107.91304347826
59243422247535.086956522-4113.08695652174
60247105247535.086956522-430.086956521739
61248541247535.0869565221005.91304347826
62245039247535.086956522-2496.08695652174
63237080247535.086956522-10455.0869565217
64237085247535.086956522-10450.0869565217
65225554247535.086956522-21981.0869565217
66226839247535.086956522-20696.0869565217
67247934247535.086956522398.913043478261
68248333247535.086956522797.913043478261
69246969247535.086956522-566.086956521739
70245098247535.086956522-2437.08695652174
71246263247535.086956522-1272.08695652174

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 244752 & 270666.812500001 & -25914.8125000009 \tabularnewline
2 & 244576 & 270666.8125 & -26090.8125 \tabularnewline
3 & 241572 & 270666.8125 & -29094.8125 \tabularnewline
4 & 240541 & 270666.8125 & -30125.8125 \tabularnewline
5 & 236089 & 270666.8125 & -34577.8125 \tabularnewline
6 & 236997 & 270666.8125 & -33669.8125 \tabularnewline
7 & 264579 & 270666.8125 & -6087.81249999998 \tabularnewline
8 & 270349 & 270666.8125 & -317.812499999982 \tabularnewline
9 & 269645 & 270666.8125 & -1021.81249999998 \tabularnewline
10 & 267037 & 270666.8125 & -3629.81249999998 \tabularnewline
11 & 258113 & 270666.8125 & -12553.8125000000 \tabularnewline
12 & 262813 & 270666.8125 & -7853.81249999998 \tabularnewline
13 & 267413 & 270666.8125 & -3253.81249999998 \tabularnewline
14 & 267366 & 270666.8125 & -3300.81249999998 \tabularnewline
15 & 264777 & 270666.8125 & -5889.81249999998 \tabularnewline
16 & 258863 & 270666.8125 & -11803.8125000000 \tabularnewline
17 & 254844 & 270666.8125 & -15822.8125000000 \tabularnewline
18 & 254868 & 270666.8125 & -15798.8125000000 \tabularnewline
19 & 277267 & 270666.8125 & 6600.18750000002 \tabularnewline
20 & 285351 & 270666.8125 & 14684.1875000000 \tabularnewline
21 & 286602 & 270666.8125 & 15935.1875000000 \tabularnewline
22 & 283042 & 270666.8125 & 12375.1875000000 \tabularnewline
23 & 276687 & 270666.8125 & 6020.18750000002 \tabularnewline
24 & 277915 & 270666.8125 & 7248.18750000002 \tabularnewline
25 & 277128 & 270666.8125 & 6461.18750000002 \tabularnewline
26 & 277103 & 270666.8125 & 6436.18750000002 \tabularnewline
27 & 275037 & 270666.8125 & 4370.18750000002 \tabularnewline
28 & 270150 & 270666.8125 & -516.812499999982 \tabularnewline
29 & 267140 & 270666.8125 & -3526.81249999998 \tabularnewline
30 & 264993 & 270666.8125 & -5673.81249999998 \tabularnewline
31 & 287259 & 270666.8125 & 16592.1875000000 \tabularnewline
32 & 291186 & 270666.8125 & 20519.1875 \tabularnewline
33 & 292300 & 270666.8125 & 21633.1875000000 \tabularnewline
34 & 288186 & 270666.8125 & 17519.1875000000 \tabularnewline
35 & 281477 & 270666.8125 & 10810.1875000000 \tabularnewline
36 & 282656 & 270666.8125 & 11989.1875000000 \tabularnewline
37 & 280190 & 270666.8125 & 9523.18750000002 \tabularnewline
38 & 280408 & 270666.8125 & 9741.18750000002 \tabularnewline
39 & 276836 & 270666.8125 & 6169.18750000002 \tabularnewline
40 & 275216 & 270666.8125 & 4549.18750000002 \tabularnewline
41 & 274352 & 270666.8125 & 3685.18750000002 \tabularnewline
42 & 271311 & 270666.8125 & 644.187500000018 \tabularnewline
43 & 289802 & 270666.8125 & 19135.1875 \tabularnewline
44 & 290726 & 270666.8125 & 20059.1875000000 \tabularnewline
45 & 292300 & 270666.8125 & 21633.1875000000 \tabularnewline
46 & 278506 & 270666.8125 & 7839.18750000002 \tabularnewline
47 & 269826 & 270666.8125 & -840.812499999982 \tabularnewline
48 & 265861 & 270666.8125 & -4805.81249999998 \tabularnewline
49 & 269034 & 247535.086956522 & 21498.9130434783 \tabularnewline
50 & 264176 & 247535.086956522 & 16640.9130434783 \tabularnewline
51 & 255198 & 247535.086956522 & 7662.91304347826 \tabularnewline
52 & 253353 & 247535.086956522 & 5817.91304347826 \tabularnewline
53 & 246057 & 247535.086956522 & -1478.08695652174 \tabularnewline
54 & 235372 & 247535.086956522 & -12163.0869565217 \tabularnewline
55 & 258556 & 247535.086956522 & 11020.9130434783 \tabularnewline
56 & 260993 & 247535.086956522 & 13457.9130434783 \tabularnewline
57 & 254663 & 247535.086956522 & 7127.91304347826 \tabularnewline
58 & 250643 & 247535.086956522 & 3107.91304347826 \tabularnewline
59 & 243422 & 247535.086956522 & -4113.08695652174 \tabularnewline
60 & 247105 & 247535.086956522 & -430.086956521739 \tabularnewline
61 & 248541 & 247535.086956522 & 1005.91304347826 \tabularnewline
62 & 245039 & 247535.086956522 & -2496.08695652174 \tabularnewline
63 & 237080 & 247535.086956522 & -10455.0869565217 \tabularnewline
64 & 237085 & 247535.086956522 & -10450.0869565217 \tabularnewline
65 & 225554 & 247535.086956522 & -21981.0869565217 \tabularnewline
66 & 226839 & 247535.086956522 & -20696.0869565217 \tabularnewline
67 & 247934 & 247535.086956522 & 398.913043478261 \tabularnewline
68 & 248333 & 247535.086956522 & 797.913043478261 \tabularnewline
69 & 246969 & 247535.086956522 & -566.086956521739 \tabularnewline
70 & 245098 & 247535.086956522 & -2437.08695652174 \tabularnewline
71 & 246263 & 247535.086956522 & -1272.08695652174 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=35567&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]244752[/C][C]270666.812500001[/C][C]-25914.8125000009[/C][/ROW]
[ROW][C]2[/C][C]244576[/C][C]270666.8125[/C][C]-26090.8125[/C][/ROW]
[ROW][C]3[/C][C]241572[/C][C]270666.8125[/C][C]-29094.8125[/C][/ROW]
[ROW][C]4[/C][C]240541[/C][C]270666.8125[/C][C]-30125.8125[/C][/ROW]
[ROW][C]5[/C][C]236089[/C][C]270666.8125[/C][C]-34577.8125[/C][/ROW]
[ROW][C]6[/C][C]236997[/C][C]270666.8125[/C][C]-33669.8125[/C][/ROW]
[ROW][C]7[/C][C]264579[/C][C]270666.8125[/C][C]-6087.81249999998[/C][/ROW]
[ROW][C]8[/C][C]270349[/C][C]270666.8125[/C][C]-317.812499999982[/C][/ROW]
[ROW][C]9[/C][C]269645[/C][C]270666.8125[/C][C]-1021.81249999998[/C][/ROW]
[ROW][C]10[/C][C]267037[/C][C]270666.8125[/C][C]-3629.81249999998[/C][/ROW]
[ROW][C]11[/C][C]258113[/C][C]270666.8125[/C][C]-12553.8125000000[/C][/ROW]
[ROW][C]12[/C][C]262813[/C][C]270666.8125[/C][C]-7853.81249999998[/C][/ROW]
[ROW][C]13[/C][C]267413[/C][C]270666.8125[/C][C]-3253.81249999998[/C][/ROW]
[ROW][C]14[/C][C]267366[/C][C]270666.8125[/C][C]-3300.81249999998[/C][/ROW]
[ROW][C]15[/C][C]264777[/C][C]270666.8125[/C][C]-5889.81249999998[/C][/ROW]
[ROW][C]16[/C][C]258863[/C][C]270666.8125[/C][C]-11803.8125000000[/C][/ROW]
[ROW][C]17[/C][C]254844[/C][C]270666.8125[/C][C]-15822.8125000000[/C][/ROW]
[ROW][C]18[/C][C]254868[/C][C]270666.8125[/C][C]-15798.8125000000[/C][/ROW]
[ROW][C]19[/C][C]277267[/C][C]270666.8125[/C][C]6600.18750000002[/C][/ROW]
[ROW][C]20[/C][C]285351[/C][C]270666.8125[/C][C]14684.1875000000[/C][/ROW]
[ROW][C]21[/C][C]286602[/C][C]270666.8125[/C][C]15935.1875000000[/C][/ROW]
[ROW][C]22[/C][C]283042[/C][C]270666.8125[/C][C]12375.1875000000[/C][/ROW]
[ROW][C]23[/C][C]276687[/C][C]270666.8125[/C][C]6020.18750000002[/C][/ROW]
[ROW][C]24[/C][C]277915[/C][C]270666.8125[/C][C]7248.18750000002[/C][/ROW]
[ROW][C]25[/C][C]277128[/C][C]270666.8125[/C][C]6461.18750000002[/C][/ROW]
[ROW][C]26[/C][C]277103[/C][C]270666.8125[/C][C]6436.18750000002[/C][/ROW]
[ROW][C]27[/C][C]275037[/C][C]270666.8125[/C][C]4370.18750000002[/C][/ROW]
[ROW][C]28[/C][C]270150[/C][C]270666.8125[/C][C]-516.812499999982[/C][/ROW]
[ROW][C]29[/C][C]267140[/C][C]270666.8125[/C][C]-3526.81249999998[/C][/ROW]
[ROW][C]30[/C][C]264993[/C][C]270666.8125[/C][C]-5673.81249999998[/C][/ROW]
[ROW][C]31[/C][C]287259[/C][C]270666.8125[/C][C]16592.1875000000[/C][/ROW]
[ROW][C]32[/C][C]291186[/C][C]270666.8125[/C][C]20519.1875[/C][/ROW]
[ROW][C]33[/C][C]292300[/C][C]270666.8125[/C][C]21633.1875000000[/C][/ROW]
[ROW][C]34[/C][C]288186[/C][C]270666.8125[/C][C]17519.1875000000[/C][/ROW]
[ROW][C]35[/C][C]281477[/C][C]270666.8125[/C][C]10810.1875000000[/C][/ROW]
[ROW][C]36[/C][C]282656[/C][C]270666.8125[/C][C]11989.1875000000[/C][/ROW]
[ROW][C]37[/C][C]280190[/C][C]270666.8125[/C][C]9523.18750000002[/C][/ROW]
[ROW][C]38[/C][C]280408[/C][C]270666.8125[/C][C]9741.18750000002[/C][/ROW]
[ROW][C]39[/C][C]276836[/C][C]270666.8125[/C][C]6169.18750000002[/C][/ROW]
[ROW][C]40[/C][C]275216[/C][C]270666.8125[/C][C]4549.18750000002[/C][/ROW]
[ROW][C]41[/C][C]274352[/C][C]270666.8125[/C][C]3685.18750000002[/C][/ROW]
[ROW][C]42[/C][C]271311[/C][C]270666.8125[/C][C]644.187500000018[/C][/ROW]
[ROW][C]43[/C][C]289802[/C][C]270666.8125[/C][C]19135.1875[/C][/ROW]
[ROW][C]44[/C][C]290726[/C][C]270666.8125[/C][C]20059.1875000000[/C][/ROW]
[ROW][C]45[/C][C]292300[/C][C]270666.8125[/C][C]21633.1875000000[/C][/ROW]
[ROW][C]46[/C][C]278506[/C][C]270666.8125[/C][C]7839.18750000002[/C][/ROW]
[ROW][C]47[/C][C]269826[/C][C]270666.8125[/C][C]-840.812499999982[/C][/ROW]
[ROW][C]48[/C][C]265861[/C][C]270666.8125[/C][C]-4805.81249999998[/C][/ROW]
[ROW][C]49[/C][C]269034[/C][C]247535.086956522[/C][C]21498.9130434783[/C][/ROW]
[ROW][C]50[/C][C]264176[/C][C]247535.086956522[/C][C]16640.9130434783[/C][/ROW]
[ROW][C]51[/C][C]255198[/C][C]247535.086956522[/C][C]7662.91304347826[/C][/ROW]
[ROW][C]52[/C][C]253353[/C][C]247535.086956522[/C][C]5817.91304347826[/C][/ROW]
[ROW][C]53[/C][C]246057[/C][C]247535.086956522[/C][C]-1478.08695652174[/C][/ROW]
[ROW][C]54[/C][C]235372[/C][C]247535.086956522[/C][C]-12163.0869565217[/C][/ROW]
[ROW][C]55[/C][C]258556[/C][C]247535.086956522[/C][C]11020.9130434783[/C][/ROW]
[ROW][C]56[/C][C]260993[/C][C]247535.086956522[/C][C]13457.9130434783[/C][/ROW]
[ROW][C]57[/C][C]254663[/C][C]247535.086956522[/C][C]7127.91304347826[/C][/ROW]
[ROW][C]58[/C][C]250643[/C][C]247535.086956522[/C][C]3107.91304347826[/C][/ROW]
[ROW][C]59[/C][C]243422[/C][C]247535.086956522[/C][C]-4113.08695652174[/C][/ROW]
[ROW][C]60[/C][C]247105[/C][C]247535.086956522[/C][C]-430.086956521739[/C][/ROW]
[ROW][C]61[/C][C]248541[/C][C]247535.086956522[/C][C]1005.91304347826[/C][/ROW]
[ROW][C]62[/C][C]245039[/C][C]247535.086956522[/C][C]-2496.08695652174[/C][/ROW]
[ROW][C]63[/C][C]237080[/C][C]247535.086956522[/C][C]-10455.0869565217[/C][/ROW]
[ROW][C]64[/C][C]237085[/C][C]247535.086956522[/C][C]-10450.0869565217[/C][/ROW]
[ROW][C]65[/C][C]225554[/C][C]247535.086956522[/C][C]-21981.0869565217[/C][/ROW]
[ROW][C]66[/C][C]226839[/C][C]247535.086956522[/C][C]-20696.0869565217[/C][/ROW]
[ROW][C]67[/C][C]247934[/C][C]247535.086956522[/C][C]398.913043478261[/C][/ROW]
[ROW][C]68[/C][C]248333[/C][C]247535.086956522[/C][C]797.913043478261[/C][/ROW]
[ROW][C]69[/C][C]246969[/C][C]247535.086956522[/C][C]-566.086956521739[/C][/ROW]
[ROW][C]70[/C][C]245098[/C][C]247535.086956522[/C][C]-2437.08695652174[/C][/ROW]
[ROW][C]71[/C][C]246263[/C][C]247535.086956522[/C][C]-1272.08695652174[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=35567&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=35567&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
1244752270666.812500001-25914.8125000009
2244576270666.8125-26090.8125
3241572270666.8125-29094.8125
4240541270666.8125-30125.8125
5236089270666.8125-34577.8125
6236997270666.8125-33669.8125
7264579270666.8125-6087.81249999998
8270349270666.8125-317.812499999982
9269645270666.8125-1021.81249999998
10267037270666.8125-3629.81249999998
11258113270666.8125-12553.8125000000
12262813270666.8125-7853.81249999998
13267413270666.8125-3253.81249999998
14267366270666.8125-3300.81249999998
15264777270666.8125-5889.81249999998
16258863270666.8125-11803.8125000000
17254844270666.8125-15822.8125000000
18254868270666.8125-15798.8125000000
19277267270666.81256600.18750000002
20285351270666.812514684.1875000000
21286602270666.812515935.1875000000
22283042270666.812512375.1875000000
23276687270666.81256020.18750000002
24277915270666.81257248.18750000002
25277128270666.81256461.18750000002
26277103270666.81256436.18750000002
27275037270666.81254370.18750000002
28270150270666.8125-516.812499999982
29267140270666.8125-3526.81249999998
30264993270666.8125-5673.81249999998
31287259270666.812516592.1875000000
32291186270666.812520519.1875
33292300270666.812521633.1875000000
34288186270666.812517519.1875000000
35281477270666.812510810.1875000000
36282656270666.812511989.1875000000
37280190270666.81259523.18750000002
38280408270666.81259741.18750000002
39276836270666.81256169.18750000002
40275216270666.81254549.18750000002
41274352270666.81253685.18750000002
42271311270666.8125644.187500000018
43289802270666.812519135.1875
44290726270666.812520059.1875000000
45292300270666.812521633.1875000000
46278506270666.81257839.18750000002
47269826270666.8125-840.812499999982
48265861270666.8125-4805.81249999998
49269034247535.08695652221498.9130434783
50264176247535.08695652216640.9130434783
51255198247535.0869565227662.91304347826
52253353247535.0869565225817.91304347826
53246057247535.086956522-1478.08695652174
54235372247535.086956522-12163.0869565217
55258556247535.08695652211020.9130434783
56260993247535.08695652213457.9130434783
57254663247535.0869565227127.91304347826
58250643247535.0869565223107.91304347826
59243422247535.086956522-4113.08695652174
60247105247535.086956522-430.086956521739
61248541247535.0869565221005.91304347826
62245039247535.086956522-2496.08695652174
63237080247535.086956522-10455.0869565217
64237085247535.086956522-10450.0869565217
65225554247535.086956522-21981.0869565217
66226839247535.086956522-20696.0869565217
67247934247535.086956522398.913043478261
68248333247535.086956522797.913043478261
69246969247535.086956522-566.086956521739
70245098247535.086956522-2437.08695652174
71246263247535.086956522-1272.08695652174


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 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
Parameters (R input):
par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No 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')