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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 computationTue, 30 Nov 2010 17:16:59 +0000
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2010/Nov/30/t1291137313gxnnkcxvpby02rz.htm/, Retrieved Mon, 29 Apr 2024 15:53:51 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=103695, Retrieved Mon, 29 Apr 2024 15:53:51 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact126

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]
-  M D  [Multiple Regression] [Openstaande VDAB-...] [2010-11-30 10:09:41] [b11c112f8986de933f8b95cd30e75cc2]
-    D      [Multiple Regression] [Vacatures, ondern...] [2010-11-30 17:16:59] [be034431ba35f7eb1ce695fc7ca4deb9] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
27951	6,4	91,18
29781	7,7	91,53
32914	9,2	91,88
33488	8,6	92,05
35652	7,4	92,31
36488	8,6	92,66
35387	6,2	92,85
35676	6	92,82
34844	6,6	93,45
32447	5,1	93,23
31068	4,7	93,53
29010	5	93,29
29812	3,6	93,19
30951	1,9	93,59
32974	-0,1	93,8
32936	-5,7	94,62
34012	-5,6	95,21
32946	-6,4	95,38
31948	-7,7	95,31
30599	-8	95,3
27691	-11,9	95,57
25073	-15,4	95,42
23406	-15,5	95,52
22248	-13,4	95,32
22896	-10,9	95,9
25317	-10,8	96,06
26558	-7,3	96,31
26471	-6,5	96,33
27543	-5,1	96,48
26198	-5,3	96,21
24725	-6,8	96,53
25005	-8,4	96,5
23462	-8,4	96,77
20780	-9,7	96,66
19815	-8,8	96,58
19761	-9,6	96,63
21454	-11,5	97,06
23899	-11	97,73
24939	-14,9	98
23580	-16,2	97,76
24562	-14,4	97,48
24696	-17,3	97,77
23785	-15,7	97,96
23812	-12,6	98,22
21917	-9,4	98,51
19713	-8,1	98,19
19282	-5,4	98,37
18788	-4,6	98,31
21453	-4,9	98,6
24482	-4	98,96
27474	-3,1	99,11
27264	-1,3	99,64
27349	0	100,02
30632	-0,4	99,98
29429	3	100,32
30084	0,4	100,44
26290	1,2	100,51
24379	0,6	101
23335	-1,3	100,88
21346	-3,2	100,55
21106	-1,8	100,82
24514	-3,6	101,5
28353	-4,2	102,15
30805	-6,9	102,39
31348	-8	102,54
34556	-7,5	102,85
33855	-8,2	103,47
34787	-7,6	103,56
32529	-3,7	103,69
29998	-1,7	103,49
29257	-0,7	103,47
28155	0,2	103,45
30466	0,6	103,48
35704	2,2	103,93
39327	3,3	103,89
39351	5,3	104,4
42234	5,5	104,79
43630	6,3	104,77
43722	7,7	105,13
43121	6,5	105,26
37985	5,5	104,96
37135	6,9	104,75
34646	5,7	105,01
33026	6,9	105,15
35087	6,1	105,2
38846	4,8	105,77
42013	3,7	105,78
43908	5,8	106,26
42868	6,8	106,13
44423	8,5	106,12
44167	7,2	106,57
43636	5	106,44
44382	4,7	106,54
42142	2,3	107,1
43452	2,4	108,1
36912	0,1	108,4
42413	1,9	108,84
45344	1,7	109,62
44873	2	110,42
47510	-1,9	110,67
49554	0,5	111,66
47369	-1,3	112,28
45998	-3,3	112,87
48140	-2,8	112,18
48441	-8	112,36
44928	-13,9	112,16
40454	-21,9	111,49
38661	-28,8	111,25
37246	-27,6	111,36
36843	-31,4	111,74
36424	-31,8	111,1
37594	-29,4	111,33
38144	-27,6	111,25
38737	-23,6	111,04
34560	-22,8	110,97
36080	-18,2	111,31
33508	-17,8	111,02
35462	-14,2	111,07
33374	-8,8	111,36
32110	-7,9	111,54
35533	-7	112,05
35532	-7	112,52
37903	-3,6	112,94
36763	-2,4	113,33
40399	-4,9	113,78
44164	-7,7	113,77
44496	-6,5	113,82
43110	-5,1	113,89
43880	-3,4	114,25

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time8 seconds
R Server'George Udny Yule' @ 72.249.76.132

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 8 seconds \tabularnewline
R Server & 'George Udny Yule' @ 72.249.76.132 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=103695&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]8 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'George Udny Yule' @ 72.249.76.132[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=103695&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=103695&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 time8 seconds
R Server'George Udny Yule' @ 72.249.76.132






Multiple Linear Regression - Estimated Regression Equation
Vacatures[t] = -334812.325375605 + 363.52729951788Ondernemersvertrouwen[t] + 3918.00539786377CPI[t] + 1371.87950407921M1[t] + 2535.85091872678M2[t] + 4180.64883367968M3[t] + 4183.76149508151M4[t] + 4812.49208059306M5[t] + 5890.78995606329M6[t] + 4340.18992860402M7[t] + 4958.41592071836M8[t] + 3155.01221160367M9[t] + 2162.43607970123M10[t] + 884.882858929163M11[t] -550.550353635371t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Vacatures[t] =  -334812.325375605 +  363.52729951788Ondernemersvertrouwen[t] +  3918.00539786377CPI[t] +  1371.87950407921M1[t] +  2535.85091872678M2[t] +  4180.64883367968M3[t] +  4183.76149508151M4[t] +  4812.49208059306M5[t] +  5890.78995606329M6[t] +  4340.18992860402M7[t] +  4958.41592071836M8[t] +  3155.01221160367M9[t] +  2162.43607970123M10[t] +  884.882858929163M11[t] -550.550353635371t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=103695&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Vacatures[t] =  -334812.325375605 +  363.52729951788Ondernemersvertrouwen[t] +  3918.00539786377CPI[t] +  1371.87950407921M1[t] +  2535.85091872678M2[t] +  4180.64883367968M3[t] +  4183.76149508151M4[t] +  4812.49208059306M5[t] +  5890.78995606329M6[t] +  4340.18992860402M7[t] +  4958.41592071836M8[t] +  3155.01221160367M9[t] +  2162.43607970123M10[t] +  884.882858929163M11[t] -550.550353635371t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=103695&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=103695&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
Vacatures[t] = -334812.325375605 + 363.52729951788Ondernemersvertrouwen[t] + 3918.00539786377CPI[t] + 1371.87950407921M1[t] + 2535.85091872678M2[t] + 4180.64883367968M3[t] + 4183.76149508151M4[t] + 4812.49208059306M5[t] + 5890.78995606329M6[t] + 4340.18992860402M7[t] + 4958.41592071836M8[t] + 3155.01221160367M9[t] + 2162.43607970123M10[t] + 884.882858929163M11[t] -550.550353635371t + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)-334812.32537560533278.128642-10.06100
Ondernemersvertrouwen363.5272995178834.83131710.436800
CPI3918.00539786377367.40936610.663900
M11371.879504079211556.7418920.88130.3800360.190018
M22535.850918726781563.363971.6220.1075560.053778
M34180.648833679681565.1330962.67110.0086660.004333
M44183.761495081511570.6022.66380.0088460.004423
M54812.492080593061575.7176893.05420.002810.001405
M65890.789956063291571.6770493.74810.0002810.000141
M74340.189928604021576.7443122.75260.0068810.00344
M84958.415920718361568.2945843.16170.002010.001005
M93155.012211603671567.4506482.01280.0464890.023245
M102162.436079701231595.1868481.35560.1779060.088953
M11884.8828589291631593.8579610.55520.5798570.289929
t-550.55035363537166.380213-8.293900

\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) & -334812.325375605 & 33278.128642 & -10.061 & 0 & 0 \tabularnewline
Ondernemersvertrouwen & 363.52729951788 & 34.831317 & 10.4368 & 0 & 0 \tabularnewline
CPI & 3918.00539786377 & 367.409366 & 10.6639 & 0 & 0 \tabularnewline
M1 & 1371.87950407921 & 1556.741892 & 0.8813 & 0.380036 & 0.190018 \tabularnewline
M2 & 2535.85091872678 & 1563.36397 & 1.622 & 0.107556 & 0.053778 \tabularnewline
M3 & 4180.64883367968 & 1565.133096 & 2.6711 & 0.008666 & 0.004333 \tabularnewline
M4 & 4183.76149508151 & 1570.602 & 2.6638 & 0.008846 & 0.004423 \tabularnewline
M5 & 4812.49208059306 & 1575.717689 & 3.0542 & 0.00281 & 0.001405 \tabularnewline
M6 & 5890.78995606329 & 1571.677049 & 3.7481 & 0.000281 & 0.000141 \tabularnewline
M7 & 4340.18992860402 & 1576.744312 & 2.7526 & 0.006881 & 0.00344 \tabularnewline
M8 & 4958.41592071836 & 1568.294584 & 3.1617 & 0.00201 & 0.001005 \tabularnewline
M9 & 3155.01221160367 & 1567.450648 & 2.0128 & 0.046489 & 0.023245 \tabularnewline
M10 & 2162.43607970123 & 1595.186848 & 1.3556 & 0.177906 & 0.088953 \tabularnewline
M11 & 884.882858929163 & 1593.857961 & 0.5552 & 0.579857 & 0.289929 \tabularnewline
t & -550.550353635371 & 66.380213 & -8.2939 & 0 & 0 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=103695&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]-334812.325375605[/C][C]33278.128642[/C][C]-10.061[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Ondernemersvertrouwen[/C][C]363.52729951788[/C][C]34.831317[/C][C]10.4368[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]CPI[/C][C]3918.00539786377[/C][C]367.409366[/C][C]10.6639[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]M1[/C][C]1371.87950407921[/C][C]1556.741892[/C][C]0.8813[/C][C]0.380036[/C][C]0.190018[/C][/ROW]
[ROW][C]M2[/C][C]2535.85091872678[/C][C]1563.36397[/C][C]1.622[/C][C]0.107556[/C][C]0.053778[/C][/ROW]
[ROW][C]M3[/C][C]4180.64883367968[/C][C]1565.133096[/C][C]2.6711[/C][C]0.008666[/C][C]0.004333[/C][/ROW]
[ROW][C]M4[/C][C]4183.76149508151[/C][C]1570.602[/C][C]2.6638[/C][C]0.008846[/C][C]0.004423[/C][/ROW]
[ROW][C]M5[/C][C]4812.49208059306[/C][C]1575.717689[/C][C]3.0542[/C][C]0.00281[/C][C]0.001405[/C][/ROW]
[ROW][C]M6[/C][C]5890.78995606329[/C][C]1571.677049[/C][C]3.7481[/C][C]0.000281[/C][C]0.000141[/C][/ROW]
[ROW][C]M7[/C][C]4340.18992860402[/C][C]1576.744312[/C][C]2.7526[/C][C]0.006881[/C][C]0.00344[/C][/ROW]
[ROW][C]M8[/C][C]4958.41592071836[/C][C]1568.294584[/C][C]3.1617[/C][C]0.00201[/C][C]0.001005[/C][/ROW]
[ROW][C]M9[/C][C]3155.01221160367[/C][C]1567.450648[/C][C]2.0128[/C][C]0.046489[/C][C]0.023245[/C][/ROW]
[ROW][C]M10[/C][C]2162.43607970123[/C][C]1595.186848[/C][C]1.3556[/C][C]0.177906[/C][C]0.088953[/C][/ROW]
[ROW][C]M11[/C][C]884.882858929163[/C][C]1593.857961[/C][C]0.5552[/C][C]0.579857[/C][C]0.289929[/C][/ROW]
[ROW][C]t[/C][C]-550.550353635371[/C][C]66.380213[/C][C]-8.2939[/C][C]0[/C][C]0[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=103695&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=103695&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)-334812.32537560533278.128642-10.06100
Ondernemersvertrouwen363.5272995178834.83131710.436800
CPI3918.00539786377367.40936610.663900
M11371.879504079211556.7418920.88130.3800360.190018
M22535.850918726781563.363971.6220.1075560.053778
M34180.648833679681565.1330962.67110.0086660.004333
M44183.761495081511570.6022.66380.0088460.004423
M54812.492080593061575.7176893.05420.002810.001405
M65890.789956063291571.6770493.74810.0002810.000141
M74340.189928604021576.7443122.75260.0068810.00344
M84958.415920718361568.2945843.16170.002010.001005
M93155.012211603671567.4506482.01280.0464890.023245
M102162.436079701231595.1868481.35560.1779060.088953
M11884.8828589291631593.8579610.55520.5798570.289929
t-550.55035363537166.380213-8.293900






Multiple Linear Regression - Regression Statistics
Multiple R0.905972198675616
R-squared0.82078562477313
Adjusted R-squared0.798776841850532
F-TEST (value)37.2935490190313
F-TEST (DF numerator)14
F-TEST (DF denominator)114
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation3558.82473961745
Sum Squared Residuals1443836622.11371

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.905972198675616 \tabularnewline
R-squared & 0.82078562477313 \tabularnewline
Adjusted R-squared & 0.798776841850532 \tabularnewline
F-TEST (value) & 37.2935490190313 \tabularnewline
F-TEST (DF numerator) & 14 \tabularnewline
F-TEST (DF denominator) & 114 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 3558.82473961745 \tabularnewline
Sum Squared Residuals & 1443836622.11371 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=103695&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.905972198675616[/C][/ROW]
[ROW][C]R-squared[/C][C]0.82078562477313[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.798776841850532[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]37.2935490190313[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]14[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]114[/C][/ROW]
[ROW][C]p-value[/C][C]0[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]3558.82473961745[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]1443836622.11371[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=103695&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=103695&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 R0.905972198675616
R-squared0.82078562477313
Adjusted R-squared0.798776841850532
F-TEST (value)37.2935490190313
F-TEST (DF numerator)14
F-TEST (DF denominator)114
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation3558.82473961745
Sum Squared Residuals1443836622.11371






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
12795125579.31066896932371.68933103072
22978128036.61910860941744.38089139064
33291431047.45950845601866.54049154404
43348830947.96635414862540.03364585141
53565231608.59523004794043.40476995208
63648833943.87740055652544.12259944347
73538731714.68252621313672.31747378692
83567631592.11254285254083.88745714745
93484431924.61826046752919.38173953255
103244728974.23963812283472.76036187722
113106828176.12676326732891.87323673267
122901025909.43044507093100.56955492914
132981225830.02083640333981.97916359675
143095127392.64764738063558.3523526194
153297428582.62174321384391.37825678624
163293629212.19559992843723.80440007162
173401231638.35174649592373.64825350406
183294632541.3383463533404.661653646666
193194829693.3420980352254.65790196499
203059929612.77949268986.22050732004
212769126898.9304192334792.069580766621
222507323495.75757570351577.24242429655
232340622023.10181113061382.89818886942
242224820567.47484798081680.52515201917
252289624570.0653779804-1674.06537798040
262531725846.7200326026-529.720032602583
272655829192.8144916986-2634.81449169864
282647129014.5587470367-2543.55874703665
292754330189.3780079175-2646.37800791746
302619829586.5586124255-3388.55861242547
312472528193.8790093704-3468.87900937045
322500527562.3708066849-2557.37080668489
332346226266.2782013580-2804.27820135804
342078023819.5856326820-3039.58563268197
351981522005.2161960115-2190.21619601153
361976120474.8614137259-713.861413725869
372145422290.2310161672-836.23101616718
382389925710.4793435071-1811.47934350706
392493926444.8318941281-1505.83189412806
402358024484.487417034-904.48741703399
412456224119.9752766405442.024723359507
422469624729.7151952540-33.7151952539551
432378523954.6295189820-169.629518982032
442381226167.9211894110-2355.92118941103
452191726113.4760504987-4196.47605049871
461971323789.1733270177-4076.17332701770
471928223647.8344329240-4365.83443292405
481878822268.142736102-3480.14273610198
492145324116.6352620709-2663.63526207092
502448226467.7128358802-1985.71283588017
512747428476.8357764434-1002.83577644338
522726430660.2900842098-3396.29008420982
532734932699.8978566475-5350.89785664746
543063232925.5142427606-2293.51424276064
552942933392.4785153004-3963.47851530043
563008432985.1438227766-2901.14382277659
572629031196.2719774913-4906.27197749133
582437931354.851757196-6975.85175719601
592333528365.8856659609-5030.88566596094
602134624946.8088030174-3600.80880301739
612110627334.9376302095-6228.93763020946
622451429958.2532226369-5444.25322263687
632835333381.0879128552-5028.08791285515
643080532792.4478074106-1987.44780741062
653134833058.4488194967-1710.44881949672
663455634982.5416644282-426.541664428237
673385535056.0855203466-1201.08552034664
683478735694.4980243441-907.49802434409
693252935267.641131436-2738.64113143604
702999833667.9681653612-3669.96816536121
712925732125.0317825144-2868.0317825144
722815530938.4130315587-2783.41303155869
733046632022.6932637456-1556.69326374560
743570434980.8604330251723.139566974875
753932736318.26780789773008.73219210226
763935138496.0674676105854.932532389493
774223440174.97526455712059.02473544286
784363040915.1845180492714.81548195102
794372240733.45429951032988.54570048968
804312140874.23788029022246.76211970983
813798536981.35489866311003.64510133695
823713535124.38549889892010.6145011011
833464633878.7305685146767.269431485386
843302633428.0508710725-402.05087107246
853508734154.4584517952932.541548204829
863884636528.55710021652317.44289978354
874201337262.1046860434750.89531395702
884390839358.71691377164549.2830862284
894286839291.08374344333576.91625655666
904442340397.647620484025.35237952001
914416739587.01417905084579.98582094924
924363638345.58905686815290.41094313188
934438236274.37734404918107.62265595089
944214236052.86836247206089.13163752795
954345238179.12291588025272.87708411983
963691237082.9785337837-170.978533783692
974241340282.57919841982130.42080158023
984534443879.33900986211464.66099013785
994487348217.049079326-3344.04907932604
1004751047231.3562684387278.643731561285
1014955452060.8273630429-2506.82736304293
1024736954363.3890924211-6994.38909242114
1034599853846.8072970304-7848.80729703039
1044814051392.8228607423-3252.8228607423
1054844147853.7678121147587.232187885282
1064492843382.22917984861545.77082015135
1074045436020.84359272944433.15640727056
1083866131136.75071800427524.24928199576
1093724632825.29322163464420.70677836545
1103684333546.1525956673296.84740433297
1113642431987.46578254464436.53421745541
1123759433213.63485066264380.36514933736
1133814433632.72378984194511.27621015809
1143873734791.79937619693945.20062380308
1153456032707.21045686611852.78954313392
1163608035779.233508401300.766491599007
1173350832434.46880007761073.53119992243
1183546232395.94086269733066.05913730270
1193337433667.1062710669-293.106271066941
1203211033264.088599684-1154.088599684
1213553336410.7750726044-877.775072604417
1223553238865.6586706126-3333.65867061258
1233790342841.4613173937-4938.4613173937
1243676344258.2784897485-7495.27848974849
1254039945190.7429018687-4791.74290186867
1264416444661.4339310748-497.433931074808
1274449643192.41657929481303.58342070519
1284311044043.2908149493-933.290814949295
1294388043717.8151046106162.184895389403

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 27951 & 25579.3106689693 & 2371.68933103072 \tabularnewline
2 & 29781 & 28036.6191086094 & 1744.38089139064 \tabularnewline
3 & 32914 & 31047.4595084560 & 1866.54049154404 \tabularnewline
4 & 33488 & 30947.9663541486 & 2540.03364585141 \tabularnewline
5 & 35652 & 31608.5952300479 & 4043.40476995208 \tabularnewline
6 & 36488 & 33943.8774005565 & 2544.12259944347 \tabularnewline
7 & 35387 & 31714.6825262131 & 3672.31747378692 \tabularnewline
8 & 35676 & 31592.1125428525 & 4083.88745714745 \tabularnewline
9 & 34844 & 31924.6182604675 & 2919.38173953255 \tabularnewline
10 & 32447 & 28974.2396381228 & 3472.76036187722 \tabularnewline
11 & 31068 & 28176.1267632673 & 2891.87323673267 \tabularnewline
12 & 29010 & 25909.4304450709 & 3100.56955492914 \tabularnewline
13 & 29812 & 25830.0208364033 & 3981.97916359675 \tabularnewline
14 & 30951 & 27392.6476473806 & 3558.3523526194 \tabularnewline
15 & 32974 & 28582.6217432138 & 4391.37825678624 \tabularnewline
16 & 32936 & 29212.1955999284 & 3723.80440007162 \tabularnewline
17 & 34012 & 31638.3517464959 & 2373.64825350406 \tabularnewline
18 & 32946 & 32541.3383463533 & 404.661653646666 \tabularnewline
19 & 31948 & 29693.342098035 & 2254.65790196499 \tabularnewline
20 & 30599 & 29612.77949268 & 986.22050732004 \tabularnewline
21 & 27691 & 26898.9304192334 & 792.069580766621 \tabularnewline
22 & 25073 & 23495.7575757035 & 1577.24242429655 \tabularnewline
23 & 23406 & 22023.1018111306 & 1382.89818886942 \tabularnewline
24 & 22248 & 20567.4748479808 & 1680.52515201917 \tabularnewline
25 & 22896 & 24570.0653779804 & -1674.06537798040 \tabularnewline
26 & 25317 & 25846.7200326026 & -529.720032602583 \tabularnewline
27 & 26558 & 29192.8144916986 & -2634.81449169864 \tabularnewline
28 & 26471 & 29014.5587470367 & -2543.55874703665 \tabularnewline
29 & 27543 & 30189.3780079175 & -2646.37800791746 \tabularnewline
30 & 26198 & 29586.5586124255 & -3388.55861242547 \tabularnewline
31 & 24725 & 28193.8790093704 & -3468.87900937045 \tabularnewline
32 & 25005 & 27562.3708066849 & -2557.37080668489 \tabularnewline
33 & 23462 & 26266.2782013580 & -2804.27820135804 \tabularnewline
34 & 20780 & 23819.5856326820 & -3039.58563268197 \tabularnewline
35 & 19815 & 22005.2161960115 & -2190.21619601153 \tabularnewline
36 & 19761 & 20474.8614137259 & -713.861413725869 \tabularnewline
37 & 21454 & 22290.2310161672 & -836.23101616718 \tabularnewline
38 & 23899 & 25710.4793435071 & -1811.47934350706 \tabularnewline
39 & 24939 & 26444.8318941281 & -1505.83189412806 \tabularnewline
40 & 23580 & 24484.487417034 & -904.48741703399 \tabularnewline
41 & 24562 & 24119.9752766405 & 442.024723359507 \tabularnewline
42 & 24696 & 24729.7151952540 & -33.7151952539551 \tabularnewline
43 & 23785 & 23954.6295189820 & -169.629518982032 \tabularnewline
44 & 23812 & 26167.9211894110 & -2355.92118941103 \tabularnewline
45 & 21917 & 26113.4760504987 & -4196.47605049871 \tabularnewline
46 & 19713 & 23789.1733270177 & -4076.17332701770 \tabularnewline
47 & 19282 & 23647.8344329240 & -4365.83443292405 \tabularnewline
48 & 18788 & 22268.142736102 & -3480.14273610198 \tabularnewline
49 & 21453 & 24116.6352620709 & -2663.63526207092 \tabularnewline
50 & 24482 & 26467.7128358802 & -1985.71283588017 \tabularnewline
51 & 27474 & 28476.8357764434 & -1002.83577644338 \tabularnewline
52 & 27264 & 30660.2900842098 & -3396.29008420982 \tabularnewline
53 & 27349 & 32699.8978566475 & -5350.89785664746 \tabularnewline
54 & 30632 & 32925.5142427606 & -2293.51424276064 \tabularnewline
55 & 29429 & 33392.4785153004 & -3963.47851530043 \tabularnewline
56 & 30084 & 32985.1438227766 & -2901.14382277659 \tabularnewline
57 & 26290 & 31196.2719774913 & -4906.27197749133 \tabularnewline
58 & 24379 & 31354.851757196 & -6975.85175719601 \tabularnewline
59 & 23335 & 28365.8856659609 & -5030.88566596094 \tabularnewline
60 & 21346 & 24946.8088030174 & -3600.80880301739 \tabularnewline
61 & 21106 & 27334.9376302095 & -6228.93763020946 \tabularnewline
62 & 24514 & 29958.2532226369 & -5444.25322263687 \tabularnewline
63 & 28353 & 33381.0879128552 & -5028.08791285515 \tabularnewline
64 & 30805 & 32792.4478074106 & -1987.44780741062 \tabularnewline
65 & 31348 & 33058.4488194967 & -1710.44881949672 \tabularnewline
66 & 34556 & 34982.5416644282 & -426.541664428237 \tabularnewline
67 & 33855 & 35056.0855203466 & -1201.08552034664 \tabularnewline
68 & 34787 & 35694.4980243441 & -907.49802434409 \tabularnewline
69 & 32529 & 35267.641131436 & -2738.64113143604 \tabularnewline
70 & 29998 & 33667.9681653612 & -3669.96816536121 \tabularnewline
71 & 29257 & 32125.0317825144 & -2868.0317825144 \tabularnewline
72 & 28155 & 30938.4130315587 & -2783.41303155869 \tabularnewline
73 & 30466 & 32022.6932637456 & -1556.69326374560 \tabularnewline
74 & 35704 & 34980.8604330251 & 723.139566974875 \tabularnewline
75 & 39327 & 36318.2678078977 & 3008.73219210226 \tabularnewline
76 & 39351 & 38496.0674676105 & 854.932532389493 \tabularnewline
77 & 42234 & 40174.9752645571 & 2059.02473544286 \tabularnewline
78 & 43630 & 40915.184518049 & 2714.81548195102 \tabularnewline
79 & 43722 & 40733.4542995103 & 2988.54570048968 \tabularnewline
80 & 43121 & 40874.2378802902 & 2246.76211970983 \tabularnewline
81 & 37985 & 36981.3548986631 & 1003.64510133695 \tabularnewline
82 & 37135 & 35124.3854988989 & 2010.6145011011 \tabularnewline
83 & 34646 & 33878.7305685146 & 767.269431485386 \tabularnewline
84 & 33026 & 33428.0508710725 & -402.05087107246 \tabularnewline
85 & 35087 & 34154.4584517952 & 932.541548204829 \tabularnewline
86 & 38846 & 36528.5571002165 & 2317.44289978354 \tabularnewline
87 & 42013 & 37262.104686043 & 4750.89531395702 \tabularnewline
88 & 43908 & 39358.7169137716 & 4549.2830862284 \tabularnewline
89 & 42868 & 39291.0837434433 & 3576.91625655666 \tabularnewline
90 & 44423 & 40397.64762048 & 4025.35237952001 \tabularnewline
91 & 44167 & 39587.0141790508 & 4579.98582094924 \tabularnewline
92 & 43636 & 38345.5890568681 & 5290.41094313188 \tabularnewline
93 & 44382 & 36274.3773440491 & 8107.62265595089 \tabularnewline
94 & 42142 & 36052.8683624720 & 6089.13163752795 \tabularnewline
95 & 43452 & 38179.1229158802 & 5272.87708411983 \tabularnewline
96 & 36912 & 37082.9785337837 & -170.978533783692 \tabularnewline
97 & 42413 & 40282.5791984198 & 2130.42080158023 \tabularnewline
98 & 45344 & 43879.3390098621 & 1464.66099013785 \tabularnewline
99 & 44873 & 48217.049079326 & -3344.04907932604 \tabularnewline
100 & 47510 & 47231.3562684387 & 278.643731561285 \tabularnewline
101 & 49554 & 52060.8273630429 & -2506.82736304293 \tabularnewline
102 & 47369 & 54363.3890924211 & -6994.38909242114 \tabularnewline
103 & 45998 & 53846.8072970304 & -7848.80729703039 \tabularnewline
104 & 48140 & 51392.8228607423 & -3252.8228607423 \tabularnewline
105 & 48441 & 47853.7678121147 & 587.232187885282 \tabularnewline
106 & 44928 & 43382.2291798486 & 1545.77082015135 \tabularnewline
107 & 40454 & 36020.8435927294 & 4433.15640727056 \tabularnewline
108 & 38661 & 31136.7507180042 & 7524.24928199576 \tabularnewline
109 & 37246 & 32825.2932216346 & 4420.70677836545 \tabularnewline
110 & 36843 & 33546.152595667 & 3296.84740433297 \tabularnewline
111 & 36424 & 31987.4657825446 & 4436.53421745541 \tabularnewline
112 & 37594 & 33213.6348506626 & 4380.36514933736 \tabularnewline
113 & 38144 & 33632.7237898419 & 4511.27621015809 \tabularnewline
114 & 38737 & 34791.7993761969 & 3945.20062380308 \tabularnewline
115 & 34560 & 32707.2104568661 & 1852.78954313392 \tabularnewline
116 & 36080 & 35779.233508401 & 300.766491599007 \tabularnewline
117 & 33508 & 32434.4688000776 & 1073.53119992243 \tabularnewline
118 & 35462 & 32395.9408626973 & 3066.05913730270 \tabularnewline
119 & 33374 & 33667.1062710669 & -293.106271066941 \tabularnewline
120 & 32110 & 33264.088599684 & -1154.088599684 \tabularnewline
121 & 35533 & 36410.7750726044 & -877.775072604417 \tabularnewline
122 & 35532 & 38865.6586706126 & -3333.65867061258 \tabularnewline
123 & 37903 & 42841.4613173937 & -4938.4613173937 \tabularnewline
124 & 36763 & 44258.2784897485 & -7495.27848974849 \tabularnewline
125 & 40399 & 45190.7429018687 & -4791.74290186867 \tabularnewline
126 & 44164 & 44661.4339310748 & -497.433931074808 \tabularnewline
127 & 44496 & 43192.4165792948 & 1303.58342070519 \tabularnewline
128 & 43110 & 44043.2908149493 & -933.290814949295 \tabularnewline
129 & 43880 & 43717.8151046106 & 162.184895389403 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=103695&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]27951[/C][C]25579.3106689693[/C][C]2371.68933103072[/C][/ROW]
[ROW][C]2[/C][C]29781[/C][C]28036.6191086094[/C][C]1744.38089139064[/C][/ROW]
[ROW][C]3[/C][C]32914[/C][C]31047.4595084560[/C][C]1866.54049154404[/C][/ROW]
[ROW][C]4[/C][C]33488[/C][C]30947.9663541486[/C][C]2540.03364585141[/C][/ROW]
[ROW][C]5[/C][C]35652[/C][C]31608.5952300479[/C][C]4043.40476995208[/C][/ROW]
[ROW][C]6[/C][C]36488[/C][C]33943.8774005565[/C][C]2544.12259944347[/C][/ROW]
[ROW][C]7[/C][C]35387[/C][C]31714.6825262131[/C][C]3672.31747378692[/C][/ROW]
[ROW][C]8[/C][C]35676[/C][C]31592.1125428525[/C][C]4083.88745714745[/C][/ROW]
[ROW][C]9[/C][C]34844[/C][C]31924.6182604675[/C][C]2919.38173953255[/C][/ROW]
[ROW][C]10[/C][C]32447[/C][C]28974.2396381228[/C][C]3472.76036187722[/C][/ROW]
[ROW][C]11[/C][C]31068[/C][C]28176.1267632673[/C][C]2891.87323673267[/C][/ROW]
[ROW][C]12[/C][C]29010[/C][C]25909.4304450709[/C][C]3100.56955492914[/C][/ROW]
[ROW][C]13[/C][C]29812[/C][C]25830.0208364033[/C][C]3981.97916359675[/C][/ROW]
[ROW][C]14[/C][C]30951[/C][C]27392.6476473806[/C][C]3558.3523526194[/C][/ROW]
[ROW][C]15[/C][C]32974[/C][C]28582.6217432138[/C][C]4391.37825678624[/C][/ROW]
[ROW][C]16[/C][C]32936[/C][C]29212.1955999284[/C][C]3723.80440007162[/C][/ROW]
[ROW][C]17[/C][C]34012[/C][C]31638.3517464959[/C][C]2373.64825350406[/C][/ROW]
[ROW][C]18[/C][C]32946[/C][C]32541.3383463533[/C][C]404.661653646666[/C][/ROW]
[ROW][C]19[/C][C]31948[/C][C]29693.342098035[/C][C]2254.65790196499[/C][/ROW]
[ROW][C]20[/C][C]30599[/C][C]29612.77949268[/C][C]986.22050732004[/C][/ROW]
[ROW][C]21[/C][C]27691[/C][C]26898.9304192334[/C][C]792.069580766621[/C][/ROW]
[ROW][C]22[/C][C]25073[/C][C]23495.7575757035[/C][C]1577.24242429655[/C][/ROW]
[ROW][C]23[/C][C]23406[/C][C]22023.1018111306[/C][C]1382.89818886942[/C][/ROW]
[ROW][C]24[/C][C]22248[/C][C]20567.4748479808[/C][C]1680.52515201917[/C][/ROW]
[ROW][C]25[/C][C]22896[/C][C]24570.0653779804[/C][C]-1674.06537798040[/C][/ROW]
[ROW][C]26[/C][C]25317[/C][C]25846.7200326026[/C][C]-529.720032602583[/C][/ROW]
[ROW][C]27[/C][C]26558[/C][C]29192.8144916986[/C][C]-2634.81449169864[/C][/ROW]
[ROW][C]28[/C][C]26471[/C][C]29014.5587470367[/C][C]-2543.55874703665[/C][/ROW]
[ROW][C]29[/C][C]27543[/C][C]30189.3780079175[/C][C]-2646.37800791746[/C][/ROW]
[ROW][C]30[/C][C]26198[/C][C]29586.5586124255[/C][C]-3388.55861242547[/C][/ROW]
[ROW][C]31[/C][C]24725[/C][C]28193.8790093704[/C][C]-3468.87900937045[/C][/ROW]
[ROW][C]32[/C][C]25005[/C][C]27562.3708066849[/C][C]-2557.37080668489[/C][/ROW]
[ROW][C]33[/C][C]23462[/C][C]26266.2782013580[/C][C]-2804.27820135804[/C][/ROW]
[ROW][C]34[/C][C]20780[/C][C]23819.5856326820[/C][C]-3039.58563268197[/C][/ROW]
[ROW][C]35[/C][C]19815[/C][C]22005.2161960115[/C][C]-2190.21619601153[/C][/ROW]
[ROW][C]36[/C][C]19761[/C][C]20474.8614137259[/C][C]-713.861413725869[/C][/ROW]
[ROW][C]37[/C][C]21454[/C][C]22290.2310161672[/C][C]-836.23101616718[/C][/ROW]
[ROW][C]38[/C][C]23899[/C][C]25710.4793435071[/C][C]-1811.47934350706[/C][/ROW]
[ROW][C]39[/C][C]24939[/C][C]26444.8318941281[/C][C]-1505.83189412806[/C][/ROW]
[ROW][C]40[/C][C]23580[/C][C]24484.487417034[/C][C]-904.48741703399[/C][/ROW]
[ROW][C]41[/C][C]24562[/C][C]24119.9752766405[/C][C]442.024723359507[/C][/ROW]
[ROW][C]42[/C][C]24696[/C][C]24729.7151952540[/C][C]-33.7151952539551[/C][/ROW]
[ROW][C]43[/C][C]23785[/C][C]23954.6295189820[/C][C]-169.629518982032[/C][/ROW]
[ROW][C]44[/C][C]23812[/C][C]26167.9211894110[/C][C]-2355.92118941103[/C][/ROW]
[ROW][C]45[/C][C]21917[/C][C]26113.4760504987[/C][C]-4196.47605049871[/C][/ROW]
[ROW][C]46[/C][C]19713[/C][C]23789.1733270177[/C][C]-4076.17332701770[/C][/ROW]
[ROW][C]47[/C][C]19282[/C][C]23647.8344329240[/C][C]-4365.83443292405[/C][/ROW]
[ROW][C]48[/C][C]18788[/C][C]22268.142736102[/C][C]-3480.14273610198[/C][/ROW]
[ROW][C]49[/C][C]21453[/C][C]24116.6352620709[/C][C]-2663.63526207092[/C][/ROW]
[ROW][C]50[/C][C]24482[/C][C]26467.7128358802[/C][C]-1985.71283588017[/C][/ROW]
[ROW][C]51[/C][C]27474[/C][C]28476.8357764434[/C][C]-1002.83577644338[/C][/ROW]
[ROW][C]52[/C][C]27264[/C][C]30660.2900842098[/C][C]-3396.29008420982[/C][/ROW]
[ROW][C]53[/C][C]27349[/C][C]32699.8978566475[/C][C]-5350.89785664746[/C][/ROW]
[ROW][C]54[/C][C]30632[/C][C]32925.5142427606[/C][C]-2293.51424276064[/C][/ROW]
[ROW][C]55[/C][C]29429[/C][C]33392.4785153004[/C][C]-3963.47851530043[/C][/ROW]
[ROW][C]56[/C][C]30084[/C][C]32985.1438227766[/C][C]-2901.14382277659[/C][/ROW]
[ROW][C]57[/C][C]26290[/C][C]31196.2719774913[/C][C]-4906.27197749133[/C][/ROW]
[ROW][C]58[/C][C]24379[/C][C]31354.851757196[/C][C]-6975.85175719601[/C][/ROW]
[ROW][C]59[/C][C]23335[/C][C]28365.8856659609[/C][C]-5030.88566596094[/C][/ROW]
[ROW][C]60[/C][C]21346[/C][C]24946.8088030174[/C][C]-3600.80880301739[/C][/ROW]
[ROW][C]61[/C][C]21106[/C][C]27334.9376302095[/C][C]-6228.93763020946[/C][/ROW]
[ROW][C]62[/C][C]24514[/C][C]29958.2532226369[/C][C]-5444.25322263687[/C][/ROW]
[ROW][C]63[/C][C]28353[/C][C]33381.0879128552[/C][C]-5028.08791285515[/C][/ROW]
[ROW][C]64[/C][C]30805[/C][C]32792.4478074106[/C][C]-1987.44780741062[/C][/ROW]
[ROW][C]65[/C][C]31348[/C][C]33058.4488194967[/C][C]-1710.44881949672[/C][/ROW]
[ROW][C]66[/C][C]34556[/C][C]34982.5416644282[/C][C]-426.541664428237[/C][/ROW]
[ROW][C]67[/C][C]33855[/C][C]35056.0855203466[/C][C]-1201.08552034664[/C][/ROW]
[ROW][C]68[/C][C]34787[/C][C]35694.4980243441[/C][C]-907.49802434409[/C][/ROW]
[ROW][C]69[/C][C]32529[/C][C]35267.641131436[/C][C]-2738.64113143604[/C][/ROW]
[ROW][C]70[/C][C]29998[/C][C]33667.9681653612[/C][C]-3669.96816536121[/C][/ROW]
[ROW][C]71[/C][C]29257[/C][C]32125.0317825144[/C][C]-2868.0317825144[/C][/ROW]
[ROW][C]72[/C][C]28155[/C][C]30938.4130315587[/C][C]-2783.41303155869[/C][/ROW]
[ROW][C]73[/C][C]30466[/C][C]32022.6932637456[/C][C]-1556.69326374560[/C][/ROW]
[ROW][C]74[/C][C]35704[/C][C]34980.8604330251[/C][C]723.139566974875[/C][/ROW]
[ROW][C]75[/C][C]39327[/C][C]36318.2678078977[/C][C]3008.73219210226[/C][/ROW]
[ROW][C]76[/C][C]39351[/C][C]38496.0674676105[/C][C]854.932532389493[/C][/ROW]
[ROW][C]77[/C][C]42234[/C][C]40174.9752645571[/C][C]2059.02473544286[/C][/ROW]
[ROW][C]78[/C][C]43630[/C][C]40915.184518049[/C][C]2714.81548195102[/C][/ROW]
[ROW][C]79[/C][C]43722[/C][C]40733.4542995103[/C][C]2988.54570048968[/C][/ROW]
[ROW][C]80[/C][C]43121[/C][C]40874.2378802902[/C][C]2246.76211970983[/C][/ROW]
[ROW][C]81[/C][C]37985[/C][C]36981.3548986631[/C][C]1003.64510133695[/C][/ROW]
[ROW][C]82[/C][C]37135[/C][C]35124.3854988989[/C][C]2010.6145011011[/C][/ROW]
[ROW][C]83[/C][C]34646[/C][C]33878.7305685146[/C][C]767.269431485386[/C][/ROW]
[ROW][C]84[/C][C]33026[/C][C]33428.0508710725[/C][C]-402.05087107246[/C][/ROW]
[ROW][C]85[/C][C]35087[/C][C]34154.4584517952[/C][C]932.541548204829[/C][/ROW]
[ROW][C]86[/C][C]38846[/C][C]36528.5571002165[/C][C]2317.44289978354[/C][/ROW]
[ROW][C]87[/C][C]42013[/C][C]37262.104686043[/C][C]4750.89531395702[/C][/ROW]
[ROW][C]88[/C][C]43908[/C][C]39358.7169137716[/C][C]4549.2830862284[/C][/ROW]
[ROW][C]89[/C][C]42868[/C][C]39291.0837434433[/C][C]3576.91625655666[/C][/ROW]
[ROW][C]90[/C][C]44423[/C][C]40397.64762048[/C][C]4025.35237952001[/C][/ROW]
[ROW][C]91[/C][C]44167[/C][C]39587.0141790508[/C][C]4579.98582094924[/C][/ROW]
[ROW][C]92[/C][C]43636[/C][C]38345.5890568681[/C][C]5290.41094313188[/C][/ROW]
[ROW][C]93[/C][C]44382[/C][C]36274.3773440491[/C][C]8107.62265595089[/C][/ROW]
[ROW][C]94[/C][C]42142[/C][C]36052.8683624720[/C][C]6089.13163752795[/C][/ROW]
[ROW][C]95[/C][C]43452[/C][C]38179.1229158802[/C][C]5272.87708411983[/C][/ROW]
[ROW][C]96[/C][C]36912[/C][C]37082.9785337837[/C][C]-170.978533783692[/C][/ROW]
[ROW][C]97[/C][C]42413[/C][C]40282.5791984198[/C][C]2130.42080158023[/C][/ROW]
[ROW][C]98[/C][C]45344[/C][C]43879.3390098621[/C][C]1464.66099013785[/C][/ROW]
[ROW][C]99[/C][C]44873[/C][C]48217.049079326[/C][C]-3344.04907932604[/C][/ROW]
[ROW][C]100[/C][C]47510[/C][C]47231.3562684387[/C][C]278.643731561285[/C][/ROW]
[ROW][C]101[/C][C]49554[/C][C]52060.8273630429[/C][C]-2506.82736304293[/C][/ROW]
[ROW][C]102[/C][C]47369[/C][C]54363.3890924211[/C][C]-6994.38909242114[/C][/ROW]
[ROW][C]103[/C][C]45998[/C][C]53846.8072970304[/C][C]-7848.80729703039[/C][/ROW]
[ROW][C]104[/C][C]48140[/C][C]51392.8228607423[/C][C]-3252.8228607423[/C][/ROW]
[ROW][C]105[/C][C]48441[/C][C]47853.7678121147[/C][C]587.232187885282[/C][/ROW]
[ROW][C]106[/C][C]44928[/C][C]43382.2291798486[/C][C]1545.77082015135[/C][/ROW]
[ROW][C]107[/C][C]40454[/C][C]36020.8435927294[/C][C]4433.15640727056[/C][/ROW]
[ROW][C]108[/C][C]38661[/C][C]31136.7507180042[/C][C]7524.24928199576[/C][/ROW]
[ROW][C]109[/C][C]37246[/C][C]32825.2932216346[/C][C]4420.70677836545[/C][/ROW]
[ROW][C]110[/C][C]36843[/C][C]33546.152595667[/C][C]3296.84740433297[/C][/ROW]
[ROW][C]111[/C][C]36424[/C][C]31987.4657825446[/C][C]4436.53421745541[/C][/ROW]
[ROW][C]112[/C][C]37594[/C][C]33213.6348506626[/C][C]4380.36514933736[/C][/ROW]
[ROW][C]113[/C][C]38144[/C][C]33632.7237898419[/C][C]4511.27621015809[/C][/ROW]
[ROW][C]114[/C][C]38737[/C][C]34791.7993761969[/C][C]3945.20062380308[/C][/ROW]
[ROW][C]115[/C][C]34560[/C][C]32707.2104568661[/C][C]1852.78954313392[/C][/ROW]
[ROW][C]116[/C][C]36080[/C][C]35779.233508401[/C][C]300.766491599007[/C][/ROW]
[ROW][C]117[/C][C]33508[/C][C]32434.4688000776[/C][C]1073.53119992243[/C][/ROW]
[ROW][C]118[/C][C]35462[/C][C]32395.9408626973[/C][C]3066.05913730270[/C][/ROW]
[ROW][C]119[/C][C]33374[/C][C]33667.1062710669[/C][C]-293.106271066941[/C][/ROW]
[ROW][C]120[/C][C]32110[/C][C]33264.088599684[/C][C]-1154.088599684[/C][/ROW]
[ROW][C]121[/C][C]35533[/C][C]36410.7750726044[/C][C]-877.775072604417[/C][/ROW]
[ROW][C]122[/C][C]35532[/C][C]38865.6586706126[/C][C]-3333.65867061258[/C][/ROW]
[ROW][C]123[/C][C]37903[/C][C]42841.4613173937[/C][C]-4938.4613173937[/C][/ROW]
[ROW][C]124[/C][C]36763[/C][C]44258.2784897485[/C][C]-7495.27848974849[/C][/ROW]
[ROW][C]125[/C][C]40399[/C][C]45190.7429018687[/C][C]-4791.74290186867[/C][/ROW]
[ROW][C]126[/C][C]44164[/C][C]44661.4339310748[/C][C]-497.433931074808[/C][/ROW]
[ROW][C]127[/C][C]44496[/C][C]43192.4165792948[/C][C]1303.58342070519[/C][/ROW]
[ROW][C]128[/C][C]43110[/C][C]44043.2908149493[/C][C]-933.290814949295[/C][/ROW]
[ROW][C]129[/C][C]43880[/C][C]43717.8151046106[/C][C]162.184895389403[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=103695&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=103695&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
12795125579.31066896932371.68933103072
22978128036.61910860941744.38089139064
33291431047.45950845601866.54049154404
43348830947.96635414862540.03364585141
53565231608.59523004794043.40476995208
63648833943.87740055652544.12259944347
73538731714.68252621313672.31747378692
83567631592.11254285254083.88745714745
93484431924.61826046752919.38173953255
103244728974.23963812283472.76036187722
113106828176.12676326732891.87323673267
122901025909.43044507093100.56955492914
132981225830.02083640333981.97916359675
143095127392.64764738063558.3523526194
153297428582.62174321384391.37825678624
163293629212.19559992843723.80440007162
173401231638.35174649592373.64825350406
183294632541.3383463533404.661653646666
193194829693.3420980352254.65790196499
203059929612.77949268986.22050732004
212769126898.9304192334792.069580766621
222507323495.75757570351577.24242429655
232340622023.10181113061382.89818886942
242224820567.47484798081680.52515201917
252289624570.0653779804-1674.06537798040
262531725846.7200326026-529.720032602583
272655829192.8144916986-2634.81449169864
282647129014.5587470367-2543.55874703665
292754330189.3780079175-2646.37800791746
302619829586.5586124255-3388.55861242547
312472528193.8790093704-3468.87900937045
322500527562.3708066849-2557.37080668489
332346226266.2782013580-2804.27820135804
342078023819.5856326820-3039.58563268197
351981522005.2161960115-2190.21619601153
361976120474.8614137259-713.861413725869
372145422290.2310161672-836.23101616718
382389925710.4793435071-1811.47934350706
392493926444.8318941281-1505.83189412806
402358024484.487417034-904.48741703399
412456224119.9752766405442.024723359507
422469624729.7151952540-33.7151952539551
432378523954.6295189820-169.629518982032
442381226167.9211894110-2355.92118941103
452191726113.4760504987-4196.47605049871
461971323789.1733270177-4076.17332701770
471928223647.8344329240-4365.83443292405
481878822268.142736102-3480.14273610198
492145324116.6352620709-2663.63526207092
502448226467.7128358802-1985.71283588017
512747428476.8357764434-1002.83577644338
522726430660.2900842098-3396.29008420982
532734932699.8978566475-5350.89785664746
543063232925.5142427606-2293.51424276064
552942933392.4785153004-3963.47851530043
563008432985.1438227766-2901.14382277659
572629031196.2719774913-4906.27197749133
582437931354.851757196-6975.85175719601
592333528365.8856659609-5030.88566596094
602134624946.8088030174-3600.80880301739
612110627334.9376302095-6228.93763020946
622451429958.2532226369-5444.25322263687
632835333381.0879128552-5028.08791285515
643080532792.4478074106-1987.44780741062
653134833058.4488194967-1710.44881949672
663455634982.5416644282-426.541664428237
673385535056.0855203466-1201.08552034664
683478735694.4980243441-907.49802434409
693252935267.641131436-2738.64113143604
702999833667.9681653612-3669.96816536121
712925732125.0317825144-2868.0317825144
722815530938.4130315587-2783.41303155869
733046632022.6932637456-1556.69326374560
743570434980.8604330251723.139566974875
753932736318.26780789773008.73219210226
763935138496.0674676105854.932532389493
774223440174.97526455712059.02473544286
784363040915.1845180492714.81548195102
794372240733.45429951032988.54570048968
804312140874.23788029022246.76211970983
813798536981.35489866311003.64510133695
823713535124.38549889892010.6145011011
833464633878.7305685146767.269431485386
843302633428.0508710725-402.05087107246
853508734154.4584517952932.541548204829
863884636528.55710021652317.44289978354
874201337262.1046860434750.89531395702
884390839358.71691377164549.2830862284
894286839291.08374344333576.91625655666
904442340397.647620484025.35237952001
914416739587.01417905084579.98582094924
924363638345.58905686815290.41094313188
934438236274.37734404918107.62265595089
944214236052.86836247206089.13163752795
954345238179.12291588025272.87708411983
963691237082.9785337837-170.978533783692
974241340282.57919841982130.42080158023
984534443879.33900986211464.66099013785
994487348217.049079326-3344.04907932604
1004751047231.3562684387278.643731561285
1014955452060.8273630429-2506.82736304293
1024736954363.3890924211-6994.38909242114
1034599853846.8072970304-7848.80729703039
1044814051392.8228607423-3252.8228607423
1054844147853.7678121147587.232187885282
1064492843382.22917984861545.77082015135
1074045436020.84359272944433.15640727056
1083866131136.75071800427524.24928199576
1093724632825.29322163464420.70677836545
1103684333546.1525956673296.84740433297
1113642431987.46578254464436.53421745541
1123759433213.63485066264380.36514933736
1133814433632.72378984194511.27621015809
1143873734791.79937619693945.20062380308
1153456032707.21045686611852.78954313392
1163608035779.233508401300.766491599007
1173350832434.46880007761073.53119992243
1183546232395.94086269733066.05913730270
1193337433667.1062710669-293.106271066941
1203211033264.088599684-1154.088599684
1213553336410.7750726044-877.775072604417
1223553238865.6586706126-3333.65867061258
1233790342841.4613173937-4938.4613173937
1243676344258.2784897485-7495.27848974849
1254039945190.7429018687-4791.74290186867
1264416444661.4339310748-497.433931074808
1274449643192.41657929481303.58342070519
1284311044043.2908149493-933.290814949295
1294388043717.8151046106162.184895389403


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')