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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 17:10:12 +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/t12911369532mah8cb7bezbgm5.htm/, Retrieved Thu, 31 Oct 2024 23:36:07 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=103691, Retrieved Thu, 31 Oct 2024 23:36:07 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact174
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] [Openstaande vacat...] [2010-11-30 17:10:12] [be034431ba35f7eb1ce695fc7ca4deb9] [Current]
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Dataseries X:
27951	6,4
29781	7,7
32914	9,2
33488	8,6
35652	7,4
36488	8,6
35387	6,2
35676	6
34844	6,6
32447	5,1
31068	4,7
29010	5
29812	3,6
30951	1,9
32974	-0,1
32936	-5,7
34012	-5,6
32946	-6,4
31948	-7,7
30599	-8
27691	-11,9
25073	-15,4
23406	-15,5
22248	-13,4
22896	-10,9
25317	-10,8
26558	-7,3
26471	-6,5
27543	-5,1
26198	-5,3
24725	-6,8
25005	-8,4
23462	-8,4
20780	-9,7
19815	-8,8
19761	-9,6
21454	-11,5
23899	-11
24939	-14,9
23580	-16,2
24562	-14,4
24696	-17,3
23785	-15,7
23812	-12,6
21917	-9,4
19713	-8,1
19282	-5,4
18788	-4,6
21453	-4,9
24482	-4
27474	-3,1
27264	-1,3
27349	0
30632	-0,4
29429	3
30084	0,4
26290	1,2
24379	0,6
23335	-1,3
21346	-3,2
21106	-1,8
24514	-3,6
28353	-4,2
30805	-6,9
31348	-8
34556	-7,5
33855	-8,2
34787	-7,6
32529	-3,7
29998	-1,7
29257	-0,7
28155	0,2
30466	0,6
35704	2,2
39327	3,3
39351	5,3
42234	5,5
43630	6,3
43722	7,7
43121	6,5
37985	5,5
37135	6,9
34646	5,7
33026	6,9
35087	6,1
38846	4,8
42013	3,7
43908	5,8
42868	6,8
44423	8,5
44167	7,2
43636	5
44382	4,7
42142	2,3
43452	2,4
36912	0,1
42413	1,9
45344	1,7
44873	2
47510	-1,9
49554	0,5
47369	-1,3
45998	-3,3
48140	-2,8
48441	-8
44928	-13,9
40454	-21,9
38661	-28,8
37246	-27,6
36843	-31,4
36424	-31,8
37594	-29,4
38144	-27,6
38737	-23,6
34560	-22,8
36080	-18,2
33508	-17,8
35462	-14,2
33374	-8,8
32110	-7,9
35533	-7
35532	-7
37903	-3,6
36763	-2,4
40399	-4,9
44164	-7,7
44496	-6,5
43110	-5,1
43880	-3,4




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time5 seconds
R Server'Sir Ronald Aylmer Fisher' @ 193.190.124.24

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 5 seconds \tabularnewline
R Server & 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=103691&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]5 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Sir Ronald Aylmer Fisher' @ 193.190.124.24[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=103691&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=103691&T=0

As an alternative you can also use a 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 time5 seconds
R Server'Sir Ronald Aylmer Fisher' @ 193.190.124.24







Multiple Linear Regression - Estimated Regression Equation
Vacatures[t] = + 19808.1911422930 + 323.353687092964Ondernemersvertrouwen[t] + 1875.45382952760M1[t] + 4198.64902031432M2[t] + 6017.04319488648M3[t] + 6515.51000274085M4[t] + 7483.68743024643M5[t] + 8277.93638082915M6[t] + 7080.48854674907M7[t] + 7047.33837698085M8[t] + 5151.9492986196M9[t] + 3273.65973157583M10[t] + 1774.12559149839M11[t] + 151.237193141381t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Vacatures[t] =  +  19808.1911422930 +  323.353687092964Ondernemersvertrouwen[t] +  1875.45382952760M1[t] +  4198.64902031432M2[t] +  6017.04319488648M3[t] +  6515.51000274085M4[t] +  7483.68743024643M5[t] +  8277.93638082915M6[t] +  7080.48854674907M7[t] +  7047.33837698085M8[t] +  5151.9492986196M9[t] +  3273.65973157583M10[t] +  1774.12559149839M11[t] +  151.237193141381t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=103691&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Vacatures[t] =  +  19808.1911422930 +  323.353687092964Ondernemersvertrouwen[t] +  1875.45382952760M1[t] +  4198.64902031432M2[t] +  6017.04319488648M3[t] +  6515.51000274085M4[t] +  7483.68743024643M5[t] +  8277.93638082915M6[t] +  7080.48854674907M7[t] +  7047.33837698085M8[t] +  5151.9492986196M9[t] +  3273.65973157583M10[t] +  1774.12559149839M11[t] +  151.237193141381t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=103691&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=103691&T=1

As an alternative you can also use a QR Code:  

The GUIDs for individual cells are displayed in the table below:

Multiple Linear Regression - Estimated Regression Equation
Vacatures[t] = + 19808.1911422930 + 323.353687092964Ondernemersvertrouwen[t] + 1875.45382952760M1[t] + 4198.64902031432M2[t] + 6017.04319488648M3[t] + 6515.51000274085M4[t] + 7483.68743024643M5[t] + 8277.93638082915M6[t] + 7080.48854674907M7[t] + 7047.33837698085M8[t] + 5151.9492986196M9[t] + 3273.65973157583M10[t] + 1774.12559149839M11[t] + 151.237193141381t + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)19808.19114229301768.27246911.20200
Ondernemersvertrouwen323.35368709296448.7264296.636100
M11875.453829527602189.6090040.85650.3934890.196744
M24198.649020314322188.9658941.91810.0575780.028789
M36017.043194886482189.0531942.74870.006950.003475
M46515.510002740852188.5975472.9770.003550.001775
M57483.687430246432189.1230973.41860.0008720.000436
M68277.936380829152189.0857423.78150.0002490.000124
M77080.488546749072189.0995673.23440.0015910.000796
M87047.338376980852189.591143.21860.0016740.000837
M95151.94929861962189.8889412.35260.0203420.010171
M103273.659731575832239.921571.46150.1466040.073302
M111774.125591498392239.7744130.79210.4299330.214967
t151.23719314138112.21878512.377400

\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) & 19808.1911422930 & 1768.272469 & 11.202 & 0 & 0 \tabularnewline
Ondernemersvertrouwen & 323.353687092964 & 48.726429 & 6.6361 & 0 & 0 \tabularnewline
M1 & 1875.45382952760 & 2189.609004 & 0.8565 & 0.393489 & 0.196744 \tabularnewline
M2 & 4198.64902031432 & 2188.965894 & 1.9181 & 0.057578 & 0.028789 \tabularnewline
M3 & 6017.04319488648 & 2189.053194 & 2.7487 & 0.00695 & 0.003475 \tabularnewline
M4 & 6515.51000274085 & 2188.597547 & 2.977 & 0.00355 & 0.001775 \tabularnewline
M5 & 7483.68743024643 & 2189.123097 & 3.4186 & 0.000872 & 0.000436 \tabularnewline
M6 & 8277.93638082915 & 2189.085742 & 3.7815 & 0.000249 & 0.000124 \tabularnewline
M7 & 7080.48854674907 & 2189.099567 & 3.2344 & 0.001591 & 0.000796 \tabularnewline
M8 & 7047.33837698085 & 2189.59114 & 3.2186 & 0.001674 & 0.000837 \tabularnewline
M9 & 5151.9492986196 & 2189.888941 & 2.3526 & 0.020342 & 0.010171 \tabularnewline
M10 & 3273.65973157583 & 2239.92157 & 1.4615 & 0.146604 & 0.073302 \tabularnewline
M11 & 1774.12559149839 & 2239.774413 & 0.7921 & 0.429933 & 0.214967 \tabularnewline
t & 151.237193141381 & 12.218785 & 12.3774 & 0 & 0 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=103691&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]19808.1911422930[/C][C]1768.272469[/C][C]11.202[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Ondernemersvertrouwen[/C][C]323.353687092964[/C][C]48.726429[/C][C]6.6361[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]M1[/C][C]1875.45382952760[/C][C]2189.609004[/C][C]0.8565[/C][C]0.393489[/C][C]0.196744[/C][/ROW]
[ROW][C]M2[/C][C]4198.64902031432[/C][C]2188.965894[/C][C]1.9181[/C][C]0.057578[/C][C]0.028789[/C][/ROW]
[ROW][C]M3[/C][C]6017.04319488648[/C][C]2189.053194[/C][C]2.7487[/C][C]0.00695[/C][C]0.003475[/C][/ROW]
[ROW][C]M4[/C][C]6515.51000274085[/C][C]2188.597547[/C][C]2.977[/C][C]0.00355[/C][C]0.001775[/C][/ROW]
[ROW][C]M5[/C][C]7483.68743024643[/C][C]2189.123097[/C][C]3.4186[/C][C]0.000872[/C][C]0.000436[/C][/ROW]
[ROW][C]M6[/C][C]8277.93638082915[/C][C]2189.085742[/C][C]3.7815[/C][C]0.000249[/C][C]0.000124[/C][/ROW]
[ROW][C]M7[/C][C]7080.48854674907[/C][C]2189.099567[/C][C]3.2344[/C][C]0.001591[/C][C]0.000796[/C][/ROW]
[ROW][C]M8[/C][C]7047.33837698085[/C][C]2189.59114[/C][C]3.2186[/C][C]0.001674[/C][C]0.000837[/C][/ROW]
[ROW][C]M9[/C][C]5151.9492986196[/C][C]2189.888941[/C][C]2.3526[/C][C]0.020342[/C][C]0.010171[/C][/ROW]
[ROW][C]M10[/C][C]3273.65973157583[/C][C]2239.92157[/C][C]1.4615[/C][C]0.146604[/C][C]0.073302[/C][/ROW]
[ROW][C]M11[/C][C]1774.12559149839[/C][C]2239.774413[/C][C]0.7921[/C][C]0.429933[/C][C]0.214967[/C][/ROW]
[ROW][C]t[/C][C]151.237193141381[/C][C]12.218785[/C][C]12.3774[/C][C]0[/C][C]0[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=103691&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=103691&T=2

As an alternative you can also use a 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)19808.19114229301768.27246911.20200
Ondernemersvertrouwen323.35368709296448.7264296.636100
M11875.453829527602189.6090040.85650.3934890.196744
M24198.649020314322188.9658941.91810.0575780.028789
M36017.043194886482189.0531942.74870.006950.003475
M46515.510002740852188.5975472.9770.003550.001775
M57483.687430246432189.1230973.41860.0008720.000436
M68277.936380829152189.0857423.78150.0002490.000124
M77080.488546749072189.0995673.23440.0015910.000796
M87047.338376980852189.591143.21860.0016740.000837
M95151.94929861962189.8889412.35260.0203420.010171
M103273.659731575832239.921571.46150.1466040.073302
M111774.125591498392239.7744130.79210.4299330.214967
t151.23719314138112.21878512.377400







Multiple Linear Regression - Regression Statistics
Multiple R0.801258013461579
R-squared0.642014404136396
Adjusted R-squared0.601546467212684
F-TEST (value)15.8647673427655
F-TEST (DF numerator)13
F-TEST (DF denominator)115
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation5007.90937821103
Sum Squared Residuals2884102979.14301

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.801258013461579 \tabularnewline
R-squared & 0.642014404136396 \tabularnewline
Adjusted R-squared & 0.601546467212684 \tabularnewline
F-TEST (value) & 15.8647673427655 \tabularnewline
F-TEST (DF numerator) & 13 \tabularnewline
F-TEST (DF denominator) & 115 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 5007.90937821103 \tabularnewline
Sum Squared Residuals & 2884102979.14301 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=103691&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.801258013461579[/C][/ROW]
[ROW][C]R-squared[/C][C]0.642014404136396[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.601546467212684[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]15.8647673427655[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]13[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]115[/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]5007.90937821103[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]2884102979.14301[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=103691&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=103691&T=3

As an alternative you can also use a QR Code:  

The GUIDs for individual cells are displayed in the table below:

Multiple Linear Regression - Regression Statistics
Multiple R0.801258013461579
R-squared0.642014404136396
Adjusted R-squared0.601546467212684
F-TEST (value)15.8647673427655
F-TEST (DF numerator)13
F-TEST (DF denominator)115
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation5007.90937821103
Sum Squared Residuals2884102979.14301







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
12795123904.34576235694046.65423764311
22978126799.13793950592981.86206049414
33291429253.79983785883660.20016214115
43348829709.49162659883778.50837340118
53565230440.88182273425211.11817726577
63648831774.39239096994713.60760903012
73538729952.13290100815434.86709899192
83567630005.54918696265670.45081303735
93484428455.40951399866388.59048600145
103244726243.32660945676203.67339054329
113106824765.68818768356302.31181231653
122901023239.80589545435770.19410454565
132981224813.80175619324998.19824380682
143095126738.53287206324212.46712793676
153297428061.45686559094912.54313440915
163293626900.3802188666035.619781134
173401228052.13020822235959.86979177774
183294628738.9334022724207.06659772801
193194827272.36296811244675.63703188756
203059927293.44388535773305.55611464229
212769124288.21262047533402.78737952472
222507321429.42234174753643.57765825248
232340620048.79002610223357.20997389784
242224819104.94437064043143.05562935963
252289621940.0196110418955.980388958232
262531724446.7873636792870.212636320842
272655827548.1566362181-990.15663621808
282647128456.5435868882-1985.54358688820
292754330028.6533694653-2485.65336946532
302619830909.4687757708-4711.46877577082
312472529378.2276041927-4653.22760419268
322500528978.9487282171-3973.9487282171
332346227234.7968429972-3772.79684299722
342078025087.384675874-4307.38467587398
351981524030.1060473216-4215.10604732159
361976122148.5346992902-2387.53469929021
372145423560.8537164826-2106.85371648256
382389926196.9629439571-2297.96294395714
392493926905.5149320081-1966.51493200812
402358027134.859139783-3554.85913978302
412456228836.3103971973-4274.31039719731
422469628844.0708483518-4148.07084835182
432378528315.2261067619-4530.22610676187
442381229435.7095601232-5623.70956012322
452191728726.2894736008-6809.28947360083
461971327419.5968929193-7706.59689291929
471928226944.3549011342-7662.35490113424
481878825580.1494524516-6792.1494524516
492145327509.8343689927-6056.8343689927
502448230275.2850713045-5793.28507130445
512747432535.9347574017-5061.93475740167
522726433767.6753951648-6503.67539516475
532734935307.4498090326-7958.44980903258
543063236123.5944779195-5491.59447791949
552942936176.7863730969-6747.78637309687
563008435454.1538100283-5370.15381002832
572629033968.6848744828-7678.68487448282
582437932047.6202883247-7668.62028832465
592333530084.9513359120-6749.95133591196
602134627847.6909320783-6501.69093207832
612110630327.0771166775-9221.07711667746
622451432219.4728638382-7705.47286383821
632835333995.092019296-5642.09201929598
643080533771.7410651407-2966.74106514073
653134834535.4666299854-3187.46662998543
663455635642.629617256-1086.62961725601
673385534370.0713953522-515.071395352239
683478734682.1706309812104.829369018820
693252934199.0981254239-1670.09812542387
702999833118.7531257074-3120.75312570740
712925732093.8098658643-2836.80986586431
722815530761.939785891-2606.93978589097
733046632917.9722833971-2451.97228339714
743570435909.770566674-205.770566673977
753932738235.09099018981091.90900981022
763935139531.5023653715-180.502365371463
774223440715.5877234371518.41227656298
784363041919.75681683551710.24318316451
794372241326.24133782692395.75866217305
804312141056.30393668852064.69606331145
813798538988.7983643757-1003.79836437571
823713537714.4411524035-579.44115240347
833464635978.1197809559-1332.11978095586
843302634743.2558071104-1717.25580711040
853508736511.263880105-1424.26388010501
863884638565.3364708123280.663529187747
874201340179.27878272351833.72121727646
884390841508.02552661452399.97447338548
894286842950.7938343544-82.7938343544389
904442344445.9812461366-22.9812461365819
914416742979.4108119771187.58918802297
924363642386.11972374571249.88027625433
934438240544.96173239793837.03826760209
944214238041.86050947244100.1394905276
954345236725.89893124566726.10106875436
963691234359.29705257482552.70294742518
974241336968.02471201115444.97528798887
984534439377.78635852065966.21364147937
994487341444.42383236213428.57616763793
1004751040833.04845369536676.95154630474
1014955442728.51192336536825.48807663466
1024736943091.96143032214277.0385696779
1034599841399.04341519754598.95658480253
1044814041678.80728211716461.19271788288
1054844138253.216224013810187.7837759862
1064492834618.377096262910309.6229037370
1074045430683.25065258329770.74934741682
1083866126829.221813284711831.7781867153
1093724629243.93726046538002.06273953474
1103684330489.62563344016353.37436655991
1113642432329.91552631644094.08447368355
1123759433755.66837633533838.33162366469
1133814435457.11963374962686.88036625039
1143873737696.02052584561040.97947415444
1153456036908.4928345812-2348.49283458124
1163608038514.0068185820-2434.00681858204
1173350836899.1964081994-3391.19640819935
1183546236336.2173078316-874.21730783163
1193337436734.0302711976-3360.03027119758
1203211035402.1601912242-3292.16019122424
1213553337719.8695322769-2186.86953227689
1223553240194.301916205-4662.30191620499
1233790343263.3358200346-5360.33582003461
1243676344301.0642455419-7538.06424554192
1254039944612.0946484565-4213.09464845647
1264416444652.1904683203-488.190468320264
1274449643994.0042518931501.995748106872
1284311044564.7864371964-1454.78643719644
1294388043370.3358200346509.664179965391

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 27951 & 23904.3457623569 & 4046.65423764311 \tabularnewline
2 & 29781 & 26799.1379395059 & 2981.86206049414 \tabularnewline
3 & 32914 & 29253.7998378588 & 3660.20016214115 \tabularnewline
4 & 33488 & 29709.4916265988 & 3778.50837340118 \tabularnewline
5 & 35652 & 30440.8818227342 & 5211.11817726577 \tabularnewline
6 & 36488 & 31774.3923909699 & 4713.60760903012 \tabularnewline
7 & 35387 & 29952.1329010081 & 5434.86709899192 \tabularnewline
8 & 35676 & 30005.5491869626 & 5670.45081303735 \tabularnewline
9 & 34844 & 28455.4095139986 & 6388.59048600145 \tabularnewline
10 & 32447 & 26243.3266094567 & 6203.67339054329 \tabularnewline
11 & 31068 & 24765.6881876835 & 6302.31181231653 \tabularnewline
12 & 29010 & 23239.8058954543 & 5770.19410454565 \tabularnewline
13 & 29812 & 24813.8017561932 & 4998.19824380682 \tabularnewline
14 & 30951 & 26738.5328720632 & 4212.46712793676 \tabularnewline
15 & 32974 & 28061.4568655909 & 4912.54313440915 \tabularnewline
16 & 32936 & 26900.380218866 & 6035.619781134 \tabularnewline
17 & 34012 & 28052.1302082223 & 5959.86979177774 \tabularnewline
18 & 32946 & 28738.933402272 & 4207.06659772801 \tabularnewline
19 & 31948 & 27272.3629681124 & 4675.63703188756 \tabularnewline
20 & 30599 & 27293.4438853577 & 3305.55611464229 \tabularnewline
21 & 27691 & 24288.2126204753 & 3402.78737952472 \tabularnewline
22 & 25073 & 21429.4223417475 & 3643.57765825248 \tabularnewline
23 & 23406 & 20048.7900261022 & 3357.20997389784 \tabularnewline
24 & 22248 & 19104.9443706404 & 3143.05562935963 \tabularnewline
25 & 22896 & 21940.0196110418 & 955.980388958232 \tabularnewline
26 & 25317 & 24446.7873636792 & 870.212636320842 \tabularnewline
27 & 26558 & 27548.1566362181 & -990.15663621808 \tabularnewline
28 & 26471 & 28456.5435868882 & -1985.54358688820 \tabularnewline
29 & 27543 & 30028.6533694653 & -2485.65336946532 \tabularnewline
30 & 26198 & 30909.4687757708 & -4711.46877577082 \tabularnewline
31 & 24725 & 29378.2276041927 & -4653.22760419268 \tabularnewline
32 & 25005 & 28978.9487282171 & -3973.9487282171 \tabularnewline
33 & 23462 & 27234.7968429972 & -3772.79684299722 \tabularnewline
34 & 20780 & 25087.384675874 & -4307.38467587398 \tabularnewline
35 & 19815 & 24030.1060473216 & -4215.10604732159 \tabularnewline
36 & 19761 & 22148.5346992902 & -2387.53469929021 \tabularnewline
37 & 21454 & 23560.8537164826 & -2106.85371648256 \tabularnewline
38 & 23899 & 26196.9629439571 & -2297.96294395714 \tabularnewline
39 & 24939 & 26905.5149320081 & -1966.51493200812 \tabularnewline
40 & 23580 & 27134.859139783 & -3554.85913978302 \tabularnewline
41 & 24562 & 28836.3103971973 & -4274.31039719731 \tabularnewline
42 & 24696 & 28844.0708483518 & -4148.07084835182 \tabularnewline
43 & 23785 & 28315.2261067619 & -4530.22610676187 \tabularnewline
44 & 23812 & 29435.7095601232 & -5623.70956012322 \tabularnewline
45 & 21917 & 28726.2894736008 & -6809.28947360083 \tabularnewline
46 & 19713 & 27419.5968929193 & -7706.59689291929 \tabularnewline
47 & 19282 & 26944.3549011342 & -7662.35490113424 \tabularnewline
48 & 18788 & 25580.1494524516 & -6792.1494524516 \tabularnewline
49 & 21453 & 27509.8343689927 & -6056.8343689927 \tabularnewline
50 & 24482 & 30275.2850713045 & -5793.28507130445 \tabularnewline
51 & 27474 & 32535.9347574017 & -5061.93475740167 \tabularnewline
52 & 27264 & 33767.6753951648 & -6503.67539516475 \tabularnewline
53 & 27349 & 35307.4498090326 & -7958.44980903258 \tabularnewline
54 & 30632 & 36123.5944779195 & -5491.59447791949 \tabularnewline
55 & 29429 & 36176.7863730969 & -6747.78637309687 \tabularnewline
56 & 30084 & 35454.1538100283 & -5370.15381002832 \tabularnewline
57 & 26290 & 33968.6848744828 & -7678.68487448282 \tabularnewline
58 & 24379 & 32047.6202883247 & -7668.62028832465 \tabularnewline
59 & 23335 & 30084.9513359120 & -6749.95133591196 \tabularnewline
60 & 21346 & 27847.6909320783 & -6501.69093207832 \tabularnewline
61 & 21106 & 30327.0771166775 & -9221.07711667746 \tabularnewline
62 & 24514 & 32219.4728638382 & -7705.47286383821 \tabularnewline
63 & 28353 & 33995.092019296 & -5642.09201929598 \tabularnewline
64 & 30805 & 33771.7410651407 & -2966.74106514073 \tabularnewline
65 & 31348 & 34535.4666299854 & -3187.46662998543 \tabularnewline
66 & 34556 & 35642.629617256 & -1086.62961725601 \tabularnewline
67 & 33855 & 34370.0713953522 & -515.071395352239 \tabularnewline
68 & 34787 & 34682.1706309812 & 104.829369018820 \tabularnewline
69 & 32529 & 34199.0981254239 & -1670.09812542387 \tabularnewline
70 & 29998 & 33118.7531257074 & -3120.75312570740 \tabularnewline
71 & 29257 & 32093.8098658643 & -2836.80986586431 \tabularnewline
72 & 28155 & 30761.939785891 & -2606.93978589097 \tabularnewline
73 & 30466 & 32917.9722833971 & -2451.97228339714 \tabularnewline
74 & 35704 & 35909.770566674 & -205.770566673977 \tabularnewline
75 & 39327 & 38235.0909901898 & 1091.90900981022 \tabularnewline
76 & 39351 & 39531.5023653715 & -180.502365371463 \tabularnewline
77 & 42234 & 40715.587723437 & 1518.41227656298 \tabularnewline
78 & 43630 & 41919.7568168355 & 1710.24318316451 \tabularnewline
79 & 43722 & 41326.2413378269 & 2395.75866217305 \tabularnewline
80 & 43121 & 41056.3039366885 & 2064.69606331145 \tabularnewline
81 & 37985 & 38988.7983643757 & -1003.79836437571 \tabularnewline
82 & 37135 & 37714.4411524035 & -579.44115240347 \tabularnewline
83 & 34646 & 35978.1197809559 & -1332.11978095586 \tabularnewline
84 & 33026 & 34743.2558071104 & -1717.25580711040 \tabularnewline
85 & 35087 & 36511.263880105 & -1424.26388010501 \tabularnewline
86 & 38846 & 38565.3364708123 & 280.663529187747 \tabularnewline
87 & 42013 & 40179.2787827235 & 1833.72121727646 \tabularnewline
88 & 43908 & 41508.0255266145 & 2399.97447338548 \tabularnewline
89 & 42868 & 42950.7938343544 & -82.7938343544389 \tabularnewline
90 & 44423 & 44445.9812461366 & -22.9812461365819 \tabularnewline
91 & 44167 & 42979.410811977 & 1187.58918802297 \tabularnewline
92 & 43636 & 42386.1197237457 & 1249.88027625433 \tabularnewline
93 & 44382 & 40544.9617323979 & 3837.03826760209 \tabularnewline
94 & 42142 & 38041.8605094724 & 4100.1394905276 \tabularnewline
95 & 43452 & 36725.8989312456 & 6726.10106875436 \tabularnewline
96 & 36912 & 34359.2970525748 & 2552.70294742518 \tabularnewline
97 & 42413 & 36968.0247120111 & 5444.97528798887 \tabularnewline
98 & 45344 & 39377.7863585206 & 5966.21364147937 \tabularnewline
99 & 44873 & 41444.4238323621 & 3428.57616763793 \tabularnewline
100 & 47510 & 40833.0484536953 & 6676.95154630474 \tabularnewline
101 & 49554 & 42728.5119233653 & 6825.48807663466 \tabularnewline
102 & 47369 & 43091.9614303221 & 4277.0385696779 \tabularnewline
103 & 45998 & 41399.0434151975 & 4598.95658480253 \tabularnewline
104 & 48140 & 41678.8072821171 & 6461.19271788288 \tabularnewline
105 & 48441 & 38253.2162240138 & 10187.7837759862 \tabularnewline
106 & 44928 & 34618.3770962629 & 10309.6229037370 \tabularnewline
107 & 40454 & 30683.2506525832 & 9770.74934741682 \tabularnewline
108 & 38661 & 26829.2218132847 & 11831.7781867153 \tabularnewline
109 & 37246 & 29243.9372604653 & 8002.06273953474 \tabularnewline
110 & 36843 & 30489.6256334401 & 6353.37436655991 \tabularnewline
111 & 36424 & 32329.9155263164 & 4094.08447368355 \tabularnewline
112 & 37594 & 33755.6683763353 & 3838.33162366469 \tabularnewline
113 & 38144 & 35457.1196337496 & 2686.88036625039 \tabularnewline
114 & 38737 & 37696.0205258456 & 1040.97947415444 \tabularnewline
115 & 34560 & 36908.4928345812 & -2348.49283458124 \tabularnewline
116 & 36080 & 38514.0068185820 & -2434.00681858204 \tabularnewline
117 & 33508 & 36899.1964081994 & -3391.19640819935 \tabularnewline
118 & 35462 & 36336.2173078316 & -874.21730783163 \tabularnewline
119 & 33374 & 36734.0302711976 & -3360.03027119758 \tabularnewline
120 & 32110 & 35402.1601912242 & -3292.16019122424 \tabularnewline
121 & 35533 & 37719.8695322769 & -2186.86953227689 \tabularnewline
122 & 35532 & 40194.301916205 & -4662.30191620499 \tabularnewline
123 & 37903 & 43263.3358200346 & -5360.33582003461 \tabularnewline
124 & 36763 & 44301.0642455419 & -7538.06424554192 \tabularnewline
125 & 40399 & 44612.0946484565 & -4213.09464845647 \tabularnewline
126 & 44164 & 44652.1904683203 & -488.190468320264 \tabularnewline
127 & 44496 & 43994.0042518931 & 501.995748106872 \tabularnewline
128 & 43110 & 44564.7864371964 & -1454.78643719644 \tabularnewline
129 & 43880 & 43370.3358200346 & 509.664179965391 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=103691&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]23904.3457623569[/C][C]4046.65423764311[/C][/ROW]
[ROW][C]2[/C][C]29781[/C][C]26799.1379395059[/C][C]2981.86206049414[/C][/ROW]
[ROW][C]3[/C][C]32914[/C][C]29253.7998378588[/C][C]3660.20016214115[/C][/ROW]
[ROW][C]4[/C][C]33488[/C][C]29709.4916265988[/C][C]3778.50837340118[/C][/ROW]
[ROW][C]5[/C][C]35652[/C][C]30440.8818227342[/C][C]5211.11817726577[/C][/ROW]
[ROW][C]6[/C][C]36488[/C][C]31774.3923909699[/C][C]4713.60760903012[/C][/ROW]
[ROW][C]7[/C][C]35387[/C][C]29952.1329010081[/C][C]5434.86709899192[/C][/ROW]
[ROW][C]8[/C][C]35676[/C][C]30005.5491869626[/C][C]5670.45081303735[/C][/ROW]
[ROW][C]9[/C][C]34844[/C][C]28455.4095139986[/C][C]6388.59048600145[/C][/ROW]
[ROW][C]10[/C][C]32447[/C][C]26243.3266094567[/C][C]6203.67339054329[/C][/ROW]
[ROW][C]11[/C][C]31068[/C][C]24765.6881876835[/C][C]6302.31181231653[/C][/ROW]
[ROW][C]12[/C][C]29010[/C][C]23239.8058954543[/C][C]5770.19410454565[/C][/ROW]
[ROW][C]13[/C][C]29812[/C][C]24813.8017561932[/C][C]4998.19824380682[/C][/ROW]
[ROW][C]14[/C][C]30951[/C][C]26738.5328720632[/C][C]4212.46712793676[/C][/ROW]
[ROW][C]15[/C][C]32974[/C][C]28061.4568655909[/C][C]4912.54313440915[/C][/ROW]
[ROW][C]16[/C][C]32936[/C][C]26900.380218866[/C][C]6035.619781134[/C][/ROW]
[ROW][C]17[/C][C]34012[/C][C]28052.1302082223[/C][C]5959.86979177774[/C][/ROW]
[ROW][C]18[/C][C]32946[/C][C]28738.933402272[/C][C]4207.06659772801[/C][/ROW]
[ROW][C]19[/C][C]31948[/C][C]27272.3629681124[/C][C]4675.63703188756[/C][/ROW]
[ROW][C]20[/C][C]30599[/C][C]27293.4438853577[/C][C]3305.55611464229[/C][/ROW]
[ROW][C]21[/C][C]27691[/C][C]24288.2126204753[/C][C]3402.78737952472[/C][/ROW]
[ROW][C]22[/C][C]25073[/C][C]21429.4223417475[/C][C]3643.57765825248[/C][/ROW]
[ROW][C]23[/C][C]23406[/C][C]20048.7900261022[/C][C]3357.20997389784[/C][/ROW]
[ROW][C]24[/C][C]22248[/C][C]19104.9443706404[/C][C]3143.05562935963[/C][/ROW]
[ROW][C]25[/C][C]22896[/C][C]21940.0196110418[/C][C]955.980388958232[/C][/ROW]
[ROW][C]26[/C][C]25317[/C][C]24446.7873636792[/C][C]870.212636320842[/C][/ROW]
[ROW][C]27[/C][C]26558[/C][C]27548.1566362181[/C][C]-990.15663621808[/C][/ROW]
[ROW][C]28[/C][C]26471[/C][C]28456.5435868882[/C][C]-1985.54358688820[/C][/ROW]
[ROW][C]29[/C][C]27543[/C][C]30028.6533694653[/C][C]-2485.65336946532[/C][/ROW]
[ROW][C]30[/C][C]26198[/C][C]30909.4687757708[/C][C]-4711.46877577082[/C][/ROW]
[ROW][C]31[/C][C]24725[/C][C]29378.2276041927[/C][C]-4653.22760419268[/C][/ROW]
[ROW][C]32[/C][C]25005[/C][C]28978.9487282171[/C][C]-3973.9487282171[/C][/ROW]
[ROW][C]33[/C][C]23462[/C][C]27234.7968429972[/C][C]-3772.79684299722[/C][/ROW]
[ROW][C]34[/C][C]20780[/C][C]25087.384675874[/C][C]-4307.38467587398[/C][/ROW]
[ROW][C]35[/C][C]19815[/C][C]24030.1060473216[/C][C]-4215.10604732159[/C][/ROW]
[ROW][C]36[/C][C]19761[/C][C]22148.5346992902[/C][C]-2387.53469929021[/C][/ROW]
[ROW][C]37[/C][C]21454[/C][C]23560.8537164826[/C][C]-2106.85371648256[/C][/ROW]
[ROW][C]38[/C][C]23899[/C][C]26196.9629439571[/C][C]-2297.96294395714[/C][/ROW]
[ROW][C]39[/C][C]24939[/C][C]26905.5149320081[/C][C]-1966.51493200812[/C][/ROW]
[ROW][C]40[/C][C]23580[/C][C]27134.859139783[/C][C]-3554.85913978302[/C][/ROW]
[ROW][C]41[/C][C]24562[/C][C]28836.3103971973[/C][C]-4274.31039719731[/C][/ROW]
[ROW][C]42[/C][C]24696[/C][C]28844.0708483518[/C][C]-4148.07084835182[/C][/ROW]
[ROW][C]43[/C][C]23785[/C][C]28315.2261067619[/C][C]-4530.22610676187[/C][/ROW]
[ROW][C]44[/C][C]23812[/C][C]29435.7095601232[/C][C]-5623.70956012322[/C][/ROW]
[ROW][C]45[/C][C]21917[/C][C]28726.2894736008[/C][C]-6809.28947360083[/C][/ROW]
[ROW][C]46[/C][C]19713[/C][C]27419.5968929193[/C][C]-7706.59689291929[/C][/ROW]
[ROW][C]47[/C][C]19282[/C][C]26944.3549011342[/C][C]-7662.35490113424[/C][/ROW]
[ROW][C]48[/C][C]18788[/C][C]25580.1494524516[/C][C]-6792.1494524516[/C][/ROW]
[ROW][C]49[/C][C]21453[/C][C]27509.8343689927[/C][C]-6056.8343689927[/C][/ROW]
[ROW][C]50[/C][C]24482[/C][C]30275.2850713045[/C][C]-5793.28507130445[/C][/ROW]
[ROW][C]51[/C][C]27474[/C][C]32535.9347574017[/C][C]-5061.93475740167[/C][/ROW]
[ROW][C]52[/C][C]27264[/C][C]33767.6753951648[/C][C]-6503.67539516475[/C][/ROW]
[ROW][C]53[/C][C]27349[/C][C]35307.4498090326[/C][C]-7958.44980903258[/C][/ROW]
[ROW][C]54[/C][C]30632[/C][C]36123.5944779195[/C][C]-5491.59447791949[/C][/ROW]
[ROW][C]55[/C][C]29429[/C][C]36176.7863730969[/C][C]-6747.78637309687[/C][/ROW]
[ROW][C]56[/C][C]30084[/C][C]35454.1538100283[/C][C]-5370.15381002832[/C][/ROW]
[ROW][C]57[/C][C]26290[/C][C]33968.6848744828[/C][C]-7678.68487448282[/C][/ROW]
[ROW][C]58[/C][C]24379[/C][C]32047.6202883247[/C][C]-7668.62028832465[/C][/ROW]
[ROW][C]59[/C][C]23335[/C][C]30084.9513359120[/C][C]-6749.95133591196[/C][/ROW]
[ROW][C]60[/C][C]21346[/C][C]27847.6909320783[/C][C]-6501.69093207832[/C][/ROW]
[ROW][C]61[/C][C]21106[/C][C]30327.0771166775[/C][C]-9221.07711667746[/C][/ROW]
[ROW][C]62[/C][C]24514[/C][C]32219.4728638382[/C][C]-7705.47286383821[/C][/ROW]
[ROW][C]63[/C][C]28353[/C][C]33995.092019296[/C][C]-5642.09201929598[/C][/ROW]
[ROW][C]64[/C][C]30805[/C][C]33771.7410651407[/C][C]-2966.74106514073[/C][/ROW]
[ROW][C]65[/C][C]31348[/C][C]34535.4666299854[/C][C]-3187.46662998543[/C][/ROW]
[ROW][C]66[/C][C]34556[/C][C]35642.629617256[/C][C]-1086.62961725601[/C][/ROW]
[ROW][C]67[/C][C]33855[/C][C]34370.0713953522[/C][C]-515.071395352239[/C][/ROW]
[ROW][C]68[/C][C]34787[/C][C]34682.1706309812[/C][C]104.829369018820[/C][/ROW]
[ROW][C]69[/C][C]32529[/C][C]34199.0981254239[/C][C]-1670.09812542387[/C][/ROW]
[ROW][C]70[/C][C]29998[/C][C]33118.7531257074[/C][C]-3120.75312570740[/C][/ROW]
[ROW][C]71[/C][C]29257[/C][C]32093.8098658643[/C][C]-2836.80986586431[/C][/ROW]
[ROW][C]72[/C][C]28155[/C][C]30761.939785891[/C][C]-2606.93978589097[/C][/ROW]
[ROW][C]73[/C][C]30466[/C][C]32917.9722833971[/C][C]-2451.97228339714[/C][/ROW]
[ROW][C]74[/C][C]35704[/C][C]35909.770566674[/C][C]-205.770566673977[/C][/ROW]
[ROW][C]75[/C][C]39327[/C][C]38235.0909901898[/C][C]1091.90900981022[/C][/ROW]
[ROW][C]76[/C][C]39351[/C][C]39531.5023653715[/C][C]-180.502365371463[/C][/ROW]
[ROW][C]77[/C][C]42234[/C][C]40715.587723437[/C][C]1518.41227656298[/C][/ROW]
[ROW][C]78[/C][C]43630[/C][C]41919.7568168355[/C][C]1710.24318316451[/C][/ROW]
[ROW][C]79[/C][C]43722[/C][C]41326.2413378269[/C][C]2395.75866217305[/C][/ROW]
[ROW][C]80[/C][C]43121[/C][C]41056.3039366885[/C][C]2064.69606331145[/C][/ROW]
[ROW][C]81[/C][C]37985[/C][C]38988.7983643757[/C][C]-1003.79836437571[/C][/ROW]
[ROW][C]82[/C][C]37135[/C][C]37714.4411524035[/C][C]-579.44115240347[/C][/ROW]
[ROW][C]83[/C][C]34646[/C][C]35978.1197809559[/C][C]-1332.11978095586[/C][/ROW]
[ROW][C]84[/C][C]33026[/C][C]34743.2558071104[/C][C]-1717.25580711040[/C][/ROW]
[ROW][C]85[/C][C]35087[/C][C]36511.263880105[/C][C]-1424.26388010501[/C][/ROW]
[ROW][C]86[/C][C]38846[/C][C]38565.3364708123[/C][C]280.663529187747[/C][/ROW]
[ROW][C]87[/C][C]42013[/C][C]40179.2787827235[/C][C]1833.72121727646[/C][/ROW]
[ROW][C]88[/C][C]43908[/C][C]41508.0255266145[/C][C]2399.97447338548[/C][/ROW]
[ROW][C]89[/C][C]42868[/C][C]42950.7938343544[/C][C]-82.7938343544389[/C][/ROW]
[ROW][C]90[/C][C]44423[/C][C]44445.9812461366[/C][C]-22.9812461365819[/C][/ROW]
[ROW][C]91[/C][C]44167[/C][C]42979.410811977[/C][C]1187.58918802297[/C][/ROW]
[ROW][C]92[/C][C]43636[/C][C]42386.1197237457[/C][C]1249.88027625433[/C][/ROW]
[ROW][C]93[/C][C]44382[/C][C]40544.9617323979[/C][C]3837.03826760209[/C][/ROW]
[ROW][C]94[/C][C]42142[/C][C]38041.8605094724[/C][C]4100.1394905276[/C][/ROW]
[ROW][C]95[/C][C]43452[/C][C]36725.8989312456[/C][C]6726.10106875436[/C][/ROW]
[ROW][C]96[/C][C]36912[/C][C]34359.2970525748[/C][C]2552.70294742518[/C][/ROW]
[ROW][C]97[/C][C]42413[/C][C]36968.0247120111[/C][C]5444.97528798887[/C][/ROW]
[ROW][C]98[/C][C]45344[/C][C]39377.7863585206[/C][C]5966.21364147937[/C][/ROW]
[ROW][C]99[/C][C]44873[/C][C]41444.4238323621[/C][C]3428.57616763793[/C][/ROW]
[ROW][C]100[/C][C]47510[/C][C]40833.0484536953[/C][C]6676.95154630474[/C][/ROW]
[ROW][C]101[/C][C]49554[/C][C]42728.5119233653[/C][C]6825.48807663466[/C][/ROW]
[ROW][C]102[/C][C]47369[/C][C]43091.9614303221[/C][C]4277.0385696779[/C][/ROW]
[ROW][C]103[/C][C]45998[/C][C]41399.0434151975[/C][C]4598.95658480253[/C][/ROW]
[ROW][C]104[/C][C]48140[/C][C]41678.8072821171[/C][C]6461.19271788288[/C][/ROW]
[ROW][C]105[/C][C]48441[/C][C]38253.2162240138[/C][C]10187.7837759862[/C][/ROW]
[ROW][C]106[/C][C]44928[/C][C]34618.3770962629[/C][C]10309.6229037370[/C][/ROW]
[ROW][C]107[/C][C]40454[/C][C]30683.2506525832[/C][C]9770.74934741682[/C][/ROW]
[ROW][C]108[/C][C]38661[/C][C]26829.2218132847[/C][C]11831.7781867153[/C][/ROW]
[ROW][C]109[/C][C]37246[/C][C]29243.9372604653[/C][C]8002.06273953474[/C][/ROW]
[ROW][C]110[/C][C]36843[/C][C]30489.6256334401[/C][C]6353.37436655991[/C][/ROW]
[ROW][C]111[/C][C]36424[/C][C]32329.9155263164[/C][C]4094.08447368355[/C][/ROW]
[ROW][C]112[/C][C]37594[/C][C]33755.6683763353[/C][C]3838.33162366469[/C][/ROW]
[ROW][C]113[/C][C]38144[/C][C]35457.1196337496[/C][C]2686.88036625039[/C][/ROW]
[ROW][C]114[/C][C]38737[/C][C]37696.0205258456[/C][C]1040.97947415444[/C][/ROW]
[ROW][C]115[/C][C]34560[/C][C]36908.4928345812[/C][C]-2348.49283458124[/C][/ROW]
[ROW][C]116[/C][C]36080[/C][C]38514.0068185820[/C][C]-2434.00681858204[/C][/ROW]
[ROW][C]117[/C][C]33508[/C][C]36899.1964081994[/C][C]-3391.19640819935[/C][/ROW]
[ROW][C]118[/C][C]35462[/C][C]36336.2173078316[/C][C]-874.21730783163[/C][/ROW]
[ROW][C]119[/C][C]33374[/C][C]36734.0302711976[/C][C]-3360.03027119758[/C][/ROW]
[ROW][C]120[/C][C]32110[/C][C]35402.1601912242[/C][C]-3292.16019122424[/C][/ROW]
[ROW][C]121[/C][C]35533[/C][C]37719.8695322769[/C][C]-2186.86953227689[/C][/ROW]
[ROW][C]122[/C][C]35532[/C][C]40194.301916205[/C][C]-4662.30191620499[/C][/ROW]
[ROW][C]123[/C][C]37903[/C][C]43263.3358200346[/C][C]-5360.33582003461[/C][/ROW]
[ROW][C]124[/C][C]36763[/C][C]44301.0642455419[/C][C]-7538.06424554192[/C][/ROW]
[ROW][C]125[/C][C]40399[/C][C]44612.0946484565[/C][C]-4213.09464845647[/C][/ROW]
[ROW][C]126[/C][C]44164[/C][C]44652.1904683203[/C][C]-488.190468320264[/C][/ROW]
[ROW][C]127[/C][C]44496[/C][C]43994.0042518931[/C][C]501.995748106872[/C][/ROW]
[ROW][C]128[/C][C]43110[/C][C]44564.7864371964[/C][C]-1454.78643719644[/C][/ROW]
[ROW][C]129[/C][C]43880[/C][C]43370.3358200346[/C][C]509.664179965391[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=103691&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=103691&T=4

As an alternative you can also use a QR Code:  

The GUIDs for individual cells are displayed in the table below:

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
12795123904.34576235694046.65423764311
22978126799.13793950592981.86206049414
33291429253.79983785883660.20016214115
43348829709.49162659883778.50837340118
53565230440.88182273425211.11817726577
63648831774.39239096994713.60760903012
73538729952.13290100815434.86709899192
83567630005.54918696265670.45081303735
93484428455.40951399866388.59048600145
103244726243.32660945676203.67339054329
113106824765.68818768356302.31181231653
122901023239.80589545435770.19410454565
132981224813.80175619324998.19824380682
143095126738.53287206324212.46712793676
153297428061.45686559094912.54313440915
163293626900.3802188666035.619781134
173401228052.13020822235959.86979177774
183294628738.9334022724207.06659772801
193194827272.36296811244675.63703188756
203059927293.44388535773305.55611464229
212769124288.21262047533402.78737952472
222507321429.42234174753643.57765825248
232340620048.79002610223357.20997389784
242224819104.94437064043143.05562935963
252289621940.0196110418955.980388958232
262531724446.7873636792870.212636320842
272655827548.1566362181-990.15663621808
282647128456.5435868882-1985.54358688820
292754330028.6533694653-2485.65336946532
302619830909.4687757708-4711.46877577082
312472529378.2276041927-4653.22760419268
322500528978.9487282171-3973.9487282171
332346227234.7968429972-3772.79684299722
342078025087.384675874-4307.38467587398
351981524030.1060473216-4215.10604732159
361976122148.5346992902-2387.53469929021
372145423560.8537164826-2106.85371648256
382389926196.9629439571-2297.96294395714
392493926905.5149320081-1966.51493200812
402358027134.859139783-3554.85913978302
412456228836.3103971973-4274.31039719731
422469628844.0708483518-4148.07084835182
432378528315.2261067619-4530.22610676187
442381229435.7095601232-5623.70956012322
452191728726.2894736008-6809.28947360083
461971327419.5968929193-7706.59689291929
471928226944.3549011342-7662.35490113424
481878825580.1494524516-6792.1494524516
492145327509.8343689927-6056.8343689927
502448230275.2850713045-5793.28507130445
512747432535.9347574017-5061.93475740167
522726433767.6753951648-6503.67539516475
532734935307.4498090326-7958.44980903258
543063236123.5944779195-5491.59447791949
552942936176.7863730969-6747.78637309687
563008435454.1538100283-5370.15381002832
572629033968.6848744828-7678.68487448282
582437932047.6202883247-7668.62028832465
592333530084.9513359120-6749.95133591196
602134627847.6909320783-6501.69093207832
612110630327.0771166775-9221.07711667746
622451432219.4728638382-7705.47286383821
632835333995.092019296-5642.09201929598
643080533771.7410651407-2966.74106514073
653134834535.4666299854-3187.46662998543
663455635642.629617256-1086.62961725601
673385534370.0713953522-515.071395352239
683478734682.1706309812104.829369018820
693252934199.0981254239-1670.09812542387
702999833118.7531257074-3120.75312570740
712925732093.8098658643-2836.80986586431
722815530761.939785891-2606.93978589097
733046632917.9722833971-2451.97228339714
743570435909.770566674-205.770566673977
753932738235.09099018981091.90900981022
763935139531.5023653715-180.502365371463
774223440715.5877234371518.41227656298
784363041919.75681683551710.24318316451
794372241326.24133782692395.75866217305
804312141056.30393668852064.69606331145
813798538988.7983643757-1003.79836437571
823713537714.4411524035-579.44115240347
833464635978.1197809559-1332.11978095586
843302634743.2558071104-1717.25580711040
853508736511.263880105-1424.26388010501
863884638565.3364708123280.663529187747
874201340179.27878272351833.72121727646
884390841508.02552661452399.97447338548
894286842950.7938343544-82.7938343544389
904442344445.9812461366-22.9812461365819
914416742979.4108119771187.58918802297
924363642386.11972374571249.88027625433
934438240544.96173239793837.03826760209
944214238041.86050947244100.1394905276
954345236725.89893124566726.10106875436
963691234359.29705257482552.70294742518
974241336968.02471201115444.97528798887
984534439377.78635852065966.21364147937
994487341444.42383236213428.57616763793
1004751040833.04845369536676.95154630474
1014955442728.51192336536825.48807663466
1024736943091.96143032214277.0385696779
1034599841399.04341519754598.95658480253
1044814041678.80728211716461.19271788288
1054844138253.216224013810187.7837759862
1064492834618.377096262910309.6229037370
1074045430683.25065258329770.74934741682
1083866126829.221813284711831.7781867153
1093724629243.93726046538002.06273953474
1103684330489.62563344016353.37436655991
1113642432329.91552631644094.08447368355
1123759433755.66837633533838.33162366469
1133814435457.11963374962686.88036625039
1143873737696.02052584561040.97947415444
1153456036908.4928345812-2348.49283458124
1163608038514.0068185820-2434.00681858204
1173350836899.1964081994-3391.19640819935
1183546236336.2173078316-874.21730783163
1193337436734.0302711976-3360.03027119758
1203211035402.1601912242-3292.16019122424
1213553337719.8695322769-2186.86953227689
1223553240194.301916205-4662.30191620499
1233790343263.3358200346-5360.33582003461
1243676344301.0642455419-7538.06424554192
1254039944612.0946484565-4213.09464845647
1264416444652.1904683203-488.190468320264
1274449643994.0042518931501.995748106872
1284311044564.7864371964-1454.78643719644
1294388043370.3358200346509.664179965391



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')