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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 06:12:19 -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/t1229865185gopw6b26thomkl2.htm/, Retrieved Sun, 09 Nov 2025 02:00:50 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=35554, Retrieved Sun, 09 Nov 2025 02:00:50 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords1
Estimated Impact515

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] [1] [2008-12-21 13:12:19] [783db4b4a0f63b73ca8b14666b7f4329] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
519164	0
517009	0
509933	0
509127	0
500857	0
506971	0
569323	0
579714	0
577992	0
565464	0
547344	0
554788	0
562325	0
560854	0
555332	0
543599	0
536662	0
542722	0
593530	0
610763	0
612613	0
611324	0
594167	0
595454	0
590865	0
589379	0
584428	0
573100	0
567456	0
569028	0
620735	0
628884	0
628232	0
612117	0
595404	0
597141	0
593408	0
590072	0
579799	0
574205	0
572775	0
572942	0
619567	0
625809	0
619916	0
587625	0
565742	0
557274	0
560576	1
548854	1
531673	1
525919	1
511038	1
498662	1
555362	1
564591	1
541657	1
527070	1
509846	1
514258	1
516922	1
507561	1
492622	1
490243	1
469357	1
477580	1
528379	1
533590	1
517945	1
506174	1
501866	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=35554&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=35554&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=35554&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
WerklozenTotaal[t] = + 574852.791666667 -56081.2699275362Kredietcrisis[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
WerklozenTotaal[t] =  +  574852.791666667 -56081.2699275362Kredietcrisis[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=35554&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]WerklozenTotaal[t] =  +  574852.791666667 -56081.2699275362Kredietcrisis[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=35554&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=35554&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
WerklozenTotaal[t] = + 574852.791666667 -56081.2699275362Kredietcrisis[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)574852.7916666674543.819213126.513100
Kredietcrisis-56081.26992753627983.370777-7.024800

\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) & 574852.791666667 & 4543.819213 & 126.5131 & 0 & 0 \tabularnewline
Kredietcrisis & -56081.2699275362 & 7983.370777 & -7.0248 & 0 & 0 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=35554&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]574852.791666667[/C][C]4543.819213[/C][C]126.5131[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Kredietcrisis[/C][C]-56081.2699275362[/C][C]7983.370777[/C][C]-7.0248[/C][C]0[/C][C]0[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=35554&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=35554&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)574852.7916666674543.819213126.513100
Kredietcrisis-56081.26992753627983.370777-7.024800






Multiple Linear Regression - Regression Statistics
Multiple R0.645732177560642
R-squared0.416970045137209
Adjusted R-squared0.408520335646444
F-TEST (value)49.3472640204881
F-TEST (DF numerator)1
F-TEST (DF denominator)69
p-value1.19962595412915e-09
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation31480.5029515408
Sum Squared Residuals68380522559.6558

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.645732177560642 \tabularnewline
R-squared & 0.416970045137209 \tabularnewline
Adjusted R-squared & 0.408520335646444 \tabularnewline
F-TEST (value) & 49.3472640204881 \tabularnewline
F-TEST (DF numerator) & 1 \tabularnewline
F-TEST (DF denominator) & 69 \tabularnewline
p-value & 1.19962595412915e-09 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 31480.5029515408 \tabularnewline
Sum Squared Residuals & 68380522559.6558 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=35554&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.645732177560642[/C][/ROW]
[ROW][C]R-squared[/C][C]0.416970045137209[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.408520335646444[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]49.3472640204881[/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]1.19962595412915e-09[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]31480.5029515408[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]68380522559.6558[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=35554&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=35554&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 R0.645732177560642
R-squared0.416970045137209
Adjusted R-squared0.408520335646444
F-TEST (value)49.3472640204881
F-TEST (DF numerator)1
F-TEST (DF denominator)69
p-value1.19962595412915e-09
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation31480.5029515408
Sum Squared Residuals68380522559.6558






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
1519164574852.791666667-55688.7916666668
2517009574852.791666667-57843.7916666667
3509933574852.791666667-64919.7916666667
4509127574852.791666667-65725.7916666667
5500857574852.791666667-73995.7916666667
6506971574852.791666667-67881.7916666667
7569323574852.791666667-5529.79166666666
8579714574852.7916666674861.20833333333
9577992574852.7916666673139.20833333333
10565464574852.791666667-9388.79166666666
11547344574852.791666667-27508.7916666667
12554788574852.791666667-20064.7916666667
13562325574852.791666667-12527.7916666667
14560854574852.791666667-13998.7916666667
15555332574852.791666667-19520.7916666667
16543599574852.791666667-31253.7916666667
17536662574852.791666667-38190.7916666667
18542722574852.791666667-32130.7916666667
19593530574852.79166666718677.2083333333
20610763574852.79166666735910.2083333333
21612613574852.79166666737760.2083333333
22611324574852.79166666736471.2083333333
23594167574852.79166666719314.2083333333
24595454574852.79166666720601.2083333333
25590865574852.79166666716012.2083333333
26589379574852.79166666714526.2083333333
27584428574852.7916666679575.20833333334
28573100574852.791666667-1752.79166666667
29567456574852.791666667-7396.79166666666
30569028574852.791666667-5824.79166666666
31620735574852.79166666745882.2083333333
32628884574852.79166666754031.2083333333
33628232574852.79166666753379.2083333333
34612117574852.79166666737264.2083333333
35595404574852.79166666720551.2083333333
36597141574852.79166666722288.2083333333
37593408574852.79166666718555.2083333333
38590072574852.79166666715219.2083333333
39579799574852.7916666674946.20833333333
40574205574852.791666667-647.791666666665
41572775574852.791666667-2077.79166666666
42572942574852.791666667-1910.79166666666
43619567574852.79166666744714.2083333333
44625809574852.79166666750956.2083333333
45619916574852.79166666745063.2083333333
46587625574852.79166666712772.2083333333
47565742574852.791666667-9110.79166666666
48557274574852.791666667-17578.7916666667
49560576518771.5217391341804.4782608696
50548854518771.5217391330082.4782608696
51531673518771.5217391312901.4782608696
52525919518771.521739137147.47826086957
53511038518771.52173913-7733.52173913043
54498662518771.52173913-20109.5217391304
55555362518771.5217391336590.4782608696
56564591518771.5217391345819.4782608696
57541657518771.5217391322885.4782608696
58527070518771.521739138298.47826086956
59509846518771.52173913-8925.52173913044
60514258518771.52173913-4513.52173913043
61516922518771.52173913-1849.52173913043
62507561518771.52173913-11210.5217391304
63492622518771.52173913-26149.5217391304
64490243518771.52173913-28528.5217391304
65469357518771.52173913-49414.5217391304
66477580518771.52173913-41191.5217391304
67528379518771.521739139607.47826086956
68533590518771.5217391314818.4782608696
69517945518771.52173913-826.521739130435
70506174518771.52173913-12597.5217391304
71501866518771.52173913-16905.5217391304

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 519164 & 574852.791666667 & -55688.7916666668 \tabularnewline
2 & 517009 & 574852.791666667 & -57843.7916666667 \tabularnewline
3 & 509933 & 574852.791666667 & -64919.7916666667 \tabularnewline
4 & 509127 & 574852.791666667 & -65725.7916666667 \tabularnewline
5 & 500857 & 574852.791666667 & -73995.7916666667 \tabularnewline
6 & 506971 & 574852.791666667 & -67881.7916666667 \tabularnewline
7 & 569323 & 574852.791666667 & -5529.79166666666 \tabularnewline
8 & 579714 & 574852.791666667 & 4861.20833333333 \tabularnewline
9 & 577992 & 574852.791666667 & 3139.20833333333 \tabularnewline
10 & 565464 & 574852.791666667 & -9388.79166666666 \tabularnewline
11 & 547344 & 574852.791666667 & -27508.7916666667 \tabularnewline
12 & 554788 & 574852.791666667 & -20064.7916666667 \tabularnewline
13 & 562325 & 574852.791666667 & -12527.7916666667 \tabularnewline
14 & 560854 & 574852.791666667 & -13998.7916666667 \tabularnewline
15 & 555332 & 574852.791666667 & -19520.7916666667 \tabularnewline
16 & 543599 & 574852.791666667 & -31253.7916666667 \tabularnewline
17 & 536662 & 574852.791666667 & -38190.7916666667 \tabularnewline
18 & 542722 & 574852.791666667 & -32130.7916666667 \tabularnewline
19 & 593530 & 574852.791666667 & 18677.2083333333 \tabularnewline
20 & 610763 & 574852.791666667 & 35910.2083333333 \tabularnewline
21 & 612613 & 574852.791666667 & 37760.2083333333 \tabularnewline
22 & 611324 & 574852.791666667 & 36471.2083333333 \tabularnewline
23 & 594167 & 574852.791666667 & 19314.2083333333 \tabularnewline
24 & 595454 & 574852.791666667 & 20601.2083333333 \tabularnewline
25 & 590865 & 574852.791666667 & 16012.2083333333 \tabularnewline
26 & 589379 & 574852.791666667 & 14526.2083333333 \tabularnewline
27 & 584428 & 574852.791666667 & 9575.20833333334 \tabularnewline
28 & 573100 & 574852.791666667 & -1752.79166666667 \tabularnewline
29 & 567456 & 574852.791666667 & -7396.79166666666 \tabularnewline
30 & 569028 & 574852.791666667 & -5824.79166666666 \tabularnewline
31 & 620735 & 574852.791666667 & 45882.2083333333 \tabularnewline
32 & 628884 & 574852.791666667 & 54031.2083333333 \tabularnewline
33 & 628232 & 574852.791666667 & 53379.2083333333 \tabularnewline
34 & 612117 & 574852.791666667 & 37264.2083333333 \tabularnewline
35 & 595404 & 574852.791666667 & 20551.2083333333 \tabularnewline
36 & 597141 & 574852.791666667 & 22288.2083333333 \tabularnewline
37 & 593408 & 574852.791666667 & 18555.2083333333 \tabularnewline
38 & 590072 & 574852.791666667 & 15219.2083333333 \tabularnewline
39 & 579799 & 574852.791666667 & 4946.20833333333 \tabularnewline
40 & 574205 & 574852.791666667 & -647.791666666665 \tabularnewline
41 & 572775 & 574852.791666667 & -2077.79166666666 \tabularnewline
42 & 572942 & 574852.791666667 & -1910.79166666666 \tabularnewline
43 & 619567 & 574852.791666667 & 44714.2083333333 \tabularnewline
44 & 625809 & 574852.791666667 & 50956.2083333333 \tabularnewline
45 & 619916 & 574852.791666667 & 45063.2083333333 \tabularnewline
46 & 587625 & 574852.791666667 & 12772.2083333333 \tabularnewline
47 & 565742 & 574852.791666667 & -9110.79166666666 \tabularnewline
48 & 557274 & 574852.791666667 & -17578.7916666667 \tabularnewline
49 & 560576 & 518771.52173913 & 41804.4782608696 \tabularnewline
50 & 548854 & 518771.52173913 & 30082.4782608696 \tabularnewline
51 & 531673 & 518771.52173913 & 12901.4782608696 \tabularnewline
52 & 525919 & 518771.52173913 & 7147.47826086957 \tabularnewline
53 & 511038 & 518771.52173913 & -7733.52173913043 \tabularnewline
54 & 498662 & 518771.52173913 & -20109.5217391304 \tabularnewline
55 & 555362 & 518771.52173913 & 36590.4782608696 \tabularnewline
56 & 564591 & 518771.52173913 & 45819.4782608696 \tabularnewline
57 & 541657 & 518771.52173913 & 22885.4782608696 \tabularnewline
58 & 527070 & 518771.52173913 & 8298.47826086956 \tabularnewline
59 & 509846 & 518771.52173913 & -8925.52173913044 \tabularnewline
60 & 514258 & 518771.52173913 & -4513.52173913043 \tabularnewline
61 & 516922 & 518771.52173913 & -1849.52173913043 \tabularnewline
62 & 507561 & 518771.52173913 & -11210.5217391304 \tabularnewline
63 & 492622 & 518771.52173913 & -26149.5217391304 \tabularnewline
64 & 490243 & 518771.52173913 & -28528.5217391304 \tabularnewline
65 & 469357 & 518771.52173913 & -49414.5217391304 \tabularnewline
66 & 477580 & 518771.52173913 & -41191.5217391304 \tabularnewline
67 & 528379 & 518771.52173913 & 9607.47826086956 \tabularnewline
68 & 533590 & 518771.52173913 & 14818.4782608696 \tabularnewline
69 & 517945 & 518771.52173913 & -826.521739130435 \tabularnewline
70 & 506174 & 518771.52173913 & -12597.5217391304 \tabularnewline
71 & 501866 & 518771.52173913 & -16905.5217391304 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=35554&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]519164[/C][C]574852.791666667[/C][C]-55688.7916666668[/C][/ROW]
[ROW][C]2[/C][C]517009[/C][C]574852.791666667[/C][C]-57843.7916666667[/C][/ROW]
[ROW][C]3[/C][C]509933[/C][C]574852.791666667[/C][C]-64919.7916666667[/C][/ROW]
[ROW][C]4[/C][C]509127[/C][C]574852.791666667[/C][C]-65725.7916666667[/C][/ROW]
[ROW][C]5[/C][C]500857[/C][C]574852.791666667[/C][C]-73995.7916666667[/C][/ROW]
[ROW][C]6[/C][C]506971[/C][C]574852.791666667[/C][C]-67881.7916666667[/C][/ROW]
[ROW][C]7[/C][C]569323[/C][C]574852.791666667[/C][C]-5529.79166666666[/C][/ROW]
[ROW][C]8[/C][C]579714[/C][C]574852.791666667[/C][C]4861.20833333333[/C][/ROW]
[ROW][C]9[/C][C]577992[/C][C]574852.791666667[/C][C]3139.20833333333[/C][/ROW]
[ROW][C]10[/C][C]565464[/C][C]574852.791666667[/C][C]-9388.79166666666[/C][/ROW]
[ROW][C]11[/C][C]547344[/C][C]574852.791666667[/C][C]-27508.7916666667[/C][/ROW]
[ROW][C]12[/C][C]554788[/C][C]574852.791666667[/C][C]-20064.7916666667[/C][/ROW]
[ROW][C]13[/C][C]562325[/C][C]574852.791666667[/C][C]-12527.7916666667[/C][/ROW]
[ROW][C]14[/C][C]560854[/C][C]574852.791666667[/C][C]-13998.7916666667[/C][/ROW]
[ROW][C]15[/C][C]555332[/C][C]574852.791666667[/C][C]-19520.7916666667[/C][/ROW]
[ROW][C]16[/C][C]543599[/C][C]574852.791666667[/C][C]-31253.7916666667[/C][/ROW]
[ROW][C]17[/C][C]536662[/C][C]574852.791666667[/C][C]-38190.7916666667[/C][/ROW]
[ROW][C]18[/C][C]542722[/C][C]574852.791666667[/C][C]-32130.7916666667[/C][/ROW]
[ROW][C]19[/C][C]593530[/C][C]574852.791666667[/C][C]18677.2083333333[/C][/ROW]
[ROW][C]20[/C][C]610763[/C][C]574852.791666667[/C][C]35910.2083333333[/C][/ROW]
[ROW][C]21[/C][C]612613[/C][C]574852.791666667[/C][C]37760.2083333333[/C][/ROW]
[ROW][C]22[/C][C]611324[/C][C]574852.791666667[/C][C]36471.2083333333[/C][/ROW]
[ROW][C]23[/C][C]594167[/C][C]574852.791666667[/C][C]19314.2083333333[/C][/ROW]
[ROW][C]24[/C][C]595454[/C][C]574852.791666667[/C][C]20601.2083333333[/C][/ROW]
[ROW][C]25[/C][C]590865[/C][C]574852.791666667[/C][C]16012.2083333333[/C][/ROW]
[ROW][C]26[/C][C]589379[/C][C]574852.791666667[/C][C]14526.2083333333[/C][/ROW]
[ROW][C]27[/C][C]584428[/C][C]574852.791666667[/C][C]9575.20833333334[/C][/ROW]
[ROW][C]28[/C][C]573100[/C][C]574852.791666667[/C][C]-1752.79166666667[/C][/ROW]
[ROW][C]29[/C][C]567456[/C][C]574852.791666667[/C][C]-7396.79166666666[/C][/ROW]
[ROW][C]30[/C][C]569028[/C][C]574852.791666667[/C][C]-5824.79166666666[/C][/ROW]
[ROW][C]31[/C][C]620735[/C][C]574852.791666667[/C][C]45882.2083333333[/C][/ROW]
[ROW][C]32[/C][C]628884[/C][C]574852.791666667[/C][C]54031.2083333333[/C][/ROW]
[ROW][C]33[/C][C]628232[/C][C]574852.791666667[/C][C]53379.2083333333[/C][/ROW]
[ROW][C]34[/C][C]612117[/C][C]574852.791666667[/C][C]37264.2083333333[/C][/ROW]
[ROW][C]35[/C][C]595404[/C][C]574852.791666667[/C][C]20551.2083333333[/C][/ROW]
[ROW][C]36[/C][C]597141[/C][C]574852.791666667[/C][C]22288.2083333333[/C][/ROW]
[ROW][C]37[/C][C]593408[/C][C]574852.791666667[/C][C]18555.2083333333[/C][/ROW]
[ROW][C]38[/C][C]590072[/C][C]574852.791666667[/C][C]15219.2083333333[/C][/ROW]
[ROW][C]39[/C][C]579799[/C][C]574852.791666667[/C][C]4946.20833333333[/C][/ROW]
[ROW][C]40[/C][C]574205[/C][C]574852.791666667[/C][C]-647.791666666665[/C][/ROW]
[ROW][C]41[/C][C]572775[/C][C]574852.791666667[/C][C]-2077.79166666666[/C][/ROW]
[ROW][C]42[/C][C]572942[/C][C]574852.791666667[/C][C]-1910.79166666666[/C][/ROW]
[ROW][C]43[/C][C]619567[/C][C]574852.791666667[/C][C]44714.2083333333[/C][/ROW]
[ROW][C]44[/C][C]625809[/C][C]574852.791666667[/C][C]50956.2083333333[/C][/ROW]
[ROW][C]45[/C][C]619916[/C][C]574852.791666667[/C][C]45063.2083333333[/C][/ROW]
[ROW][C]46[/C][C]587625[/C][C]574852.791666667[/C][C]12772.2083333333[/C][/ROW]
[ROW][C]47[/C][C]565742[/C][C]574852.791666667[/C][C]-9110.79166666666[/C][/ROW]
[ROW][C]48[/C][C]557274[/C][C]574852.791666667[/C][C]-17578.7916666667[/C][/ROW]
[ROW][C]49[/C][C]560576[/C][C]518771.52173913[/C][C]41804.4782608696[/C][/ROW]
[ROW][C]50[/C][C]548854[/C][C]518771.52173913[/C][C]30082.4782608696[/C][/ROW]
[ROW][C]51[/C][C]531673[/C][C]518771.52173913[/C][C]12901.4782608696[/C][/ROW]
[ROW][C]52[/C][C]525919[/C][C]518771.52173913[/C][C]7147.47826086957[/C][/ROW]
[ROW][C]53[/C][C]511038[/C][C]518771.52173913[/C][C]-7733.52173913043[/C][/ROW]
[ROW][C]54[/C][C]498662[/C][C]518771.52173913[/C][C]-20109.5217391304[/C][/ROW]
[ROW][C]55[/C][C]555362[/C][C]518771.52173913[/C][C]36590.4782608696[/C][/ROW]
[ROW][C]56[/C][C]564591[/C][C]518771.52173913[/C][C]45819.4782608696[/C][/ROW]
[ROW][C]57[/C][C]541657[/C][C]518771.52173913[/C][C]22885.4782608696[/C][/ROW]
[ROW][C]58[/C][C]527070[/C][C]518771.52173913[/C][C]8298.47826086956[/C][/ROW]
[ROW][C]59[/C][C]509846[/C][C]518771.52173913[/C][C]-8925.52173913044[/C][/ROW]
[ROW][C]60[/C][C]514258[/C][C]518771.52173913[/C][C]-4513.52173913043[/C][/ROW]
[ROW][C]61[/C][C]516922[/C][C]518771.52173913[/C][C]-1849.52173913043[/C][/ROW]
[ROW][C]62[/C][C]507561[/C][C]518771.52173913[/C][C]-11210.5217391304[/C][/ROW]
[ROW][C]63[/C][C]492622[/C][C]518771.52173913[/C][C]-26149.5217391304[/C][/ROW]
[ROW][C]64[/C][C]490243[/C][C]518771.52173913[/C][C]-28528.5217391304[/C][/ROW]
[ROW][C]65[/C][C]469357[/C][C]518771.52173913[/C][C]-49414.5217391304[/C][/ROW]
[ROW][C]66[/C][C]477580[/C][C]518771.52173913[/C][C]-41191.5217391304[/C][/ROW]
[ROW][C]67[/C][C]528379[/C][C]518771.52173913[/C][C]9607.47826086956[/C][/ROW]
[ROW][C]68[/C][C]533590[/C][C]518771.52173913[/C][C]14818.4782608696[/C][/ROW]
[ROW][C]69[/C][C]517945[/C][C]518771.52173913[/C][C]-826.521739130435[/C][/ROW]
[ROW][C]70[/C][C]506174[/C][C]518771.52173913[/C][C]-12597.5217391304[/C][/ROW]
[ROW][C]71[/C][C]501866[/C][C]518771.52173913[/C][C]-16905.5217391304[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=35554&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=35554&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
1519164574852.791666667-55688.7916666668
2517009574852.791666667-57843.7916666667
3509933574852.791666667-64919.7916666667
4509127574852.791666667-65725.7916666667
5500857574852.791666667-73995.7916666667
6506971574852.791666667-67881.7916666667
7569323574852.791666667-5529.79166666666
8579714574852.7916666674861.20833333333
9577992574852.7916666673139.20833333333
10565464574852.791666667-9388.79166666666
11547344574852.791666667-27508.7916666667
12554788574852.791666667-20064.7916666667
13562325574852.791666667-12527.7916666667
14560854574852.791666667-13998.7916666667
15555332574852.791666667-19520.7916666667
16543599574852.791666667-31253.7916666667
17536662574852.791666667-38190.7916666667
18542722574852.791666667-32130.7916666667
19593530574852.79166666718677.2083333333
20610763574852.79166666735910.2083333333
21612613574852.79166666737760.2083333333
22611324574852.79166666736471.2083333333
23594167574852.79166666719314.2083333333
24595454574852.79166666720601.2083333333
25590865574852.79166666716012.2083333333
26589379574852.79166666714526.2083333333
27584428574852.7916666679575.20833333334
28573100574852.791666667-1752.79166666667
29567456574852.791666667-7396.79166666666
30569028574852.791666667-5824.79166666666
31620735574852.79166666745882.2083333333
32628884574852.79166666754031.2083333333
33628232574852.79166666753379.2083333333
34612117574852.79166666737264.2083333333
35595404574852.79166666720551.2083333333
36597141574852.79166666722288.2083333333
37593408574852.79166666718555.2083333333
38590072574852.79166666715219.2083333333
39579799574852.7916666674946.20833333333
40574205574852.791666667-647.791666666665
41572775574852.791666667-2077.79166666666
42572942574852.791666667-1910.79166666666
43619567574852.79166666744714.2083333333
44625809574852.79166666750956.2083333333
45619916574852.79166666745063.2083333333
46587625574852.79166666712772.2083333333
47565742574852.791666667-9110.79166666666
48557274574852.791666667-17578.7916666667
49560576518771.5217391341804.4782608696
50548854518771.5217391330082.4782608696
51531673518771.5217391312901.4782608696
52525919518771.521739137147.47826086957
53511038518771.52173913-7733.52173913043
54498662518771.52173913-20109.5217391304
55555362518771.5217391336590.4782608696
56564591518771.5217391345819.4782608696
57541657518771.5217391322885.4782608696
58527070518771.521739138298.47826086956
59509846518771.52173913-8925.52173913044
60514258518771.52173913-4513.52173913043
61516922518771.52173913-1849.52173913043
62507561518771.52173913-11210.5217391304
63492622518771.52173913-26149.5217391304
64490243518771.52173913-28528.5217391304
65469357518771.52173913-49414.5217391304
66477580518771.52173913-41191.5217391304
67528379518771.521739139607.47826086956
68533590518771.5217391314818.4782608696
69517945518771.52173913-826.521739130435
70506174518771.52173913-12597.5217391304
71501866518771.52173913-16905.5217391304


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')