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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 10:09:41 +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/t1291111723gjjse2hxdfi6hjx.htm/, Retrieved Thu, 31 Oct 2024 23:24:04 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=103284, Retrieved Thu, 31 Oct 2024 23:24:04 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact186
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] [be034431ba35f7eb1ce695fc7ca4deb9] [Current]
-    D      [Multiple Regression] [Openstaande vacat...] [2010-11-30 17:10:12] [b11c112f8986de933f8b95cd30e75cc2]
-    D      [Multiple Regression] [Vacatures, ondern...] [2010-11-30 17:16:59] [b11c112f8986de933f8b95cd30e75cc2]
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Dataseries X:
27951
29781
32914
33488
35652
36488
35387
35676
34844
32447
31068
29010
29812
30951
32974
32936
34012
32946
31948
30599
27691
25073
23406
22248
22896
25317
26558
26471
27543
26198
24725
25005
23462
20780
19815
19761
21454
23899
24939
23580
24562
24696
23785
23812
21917
19713
19282
18788
21453
24482
27474
27264
27349
30632
29429
30084
26290
24379
23335
21346
21106
24514
28353
30805
31348
34556
33855
34787
32529
29998
29257
28155
30466
35704
39327
39351
42234
43630
43722
43121
37985
37135
34646
33026
35087
38846
42013
43908
42868
44423
44167
43636
44382
42142
43452
36912
42413
45344
44873
47510
49554
47369
45998
48140
48441
44928
40454
38661
37246
36843
36424
37594
38144
38737
34560
36080
33508
35462
33374
32110
35533
35532
37903
36763
40399
44164
44496
43110
43880




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=103284&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=103284&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=103284&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 time8 seconds
R Server'George Udny Yule' @ 72.249.76.132







Multiple Linear Regression - Estimated Regression Equation
Y[t] = + 19301.6755555556 + 2240.75639730639M1[t] + 4454.02875420874M2[t] + 6371.21020202022M3[t] + 6777.39164983165M4[t] + 7917.84582491583M5[t] + 8710.93636363636M6[t] + 7509.39053872055M7[t] + 7557.39016835017M8[t] + 5687.29888888889M9[t] + 3467.63710437711M10[t] + 1939.01855218855M11[t] + 131.818552188552t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Y[t] =  +  19301.6755555556 +  2240.75639730639M1[t] +  4454.02875420874M2[t] +  6371.21020202022M3[t] +  6777.39164983165M4[t] +  7917.84582491583M5[t] +  8710.93636363636M6[t] +  7509.39053872055M7[t] +  7557.39016835017M8[t] +  5687.29888888889M9[t] +  3467.63710437711M10[t] +  1939.01855218855M11[t] +  131.818552188552t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=103284&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Y[t] =  +  19301.6755555556 +  2240.75639730639M1[t] +  4454.02875420874M2[t] +  6371.21020202022M3[t] +  6777.39164983165M4[t] +  7917.84582491583M5[t] +  8710.93636363636M6[t] +  7509.39053872055M7[t] +  7557.39016835017M8[t] +  5687.29888888889M9[t] +  3467.63710437711M10[t] +  1939.01855218855M11[t] +  131.818552188552t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=103284&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=103284&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
Y[t] = + 19301.6755555556 + 2240.75639730639M1[t] + 4454.02875420874M2[t] + 6371.21020202022M3[t] + 6777.39164983165M4[t] + 7917.84582491583M5[t] + 8710.93636363636M6[t] + 7509.39053872055M7[t] + 7557.39016835017M8[t] + 5687.29888888889M9[t] + 3467.63710437711M10[t] + 1939.01855218855M11[t] + 131.818552188552t + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)19301.67555555562068.5476089.33100
M12240.756397306392563.011670.87430.3837790.19189
M24454.028754208742562.6728731.7380.0848570.042429
M36371.210202020222562.4093342.48640.0143270.007164
M46777.391649831652562.2210762.64510.0092980.004649
M57917.845824915832562.1081143.09040.0025030.001252
M68710.936363636362562.0704593.40.0009250.000462
M77509.390538720552562.1081142.93090.0040710.002036
M87557.390168350172562.2210762.94950.003850.001925
M95687.298888888892562.4093342.21950.0283970.014198
M103467.637104377112622.5098731.32230.1886830.094342
M111939.018552188552622.3995080.73940.4611540.230577
t131.81855218855213.8906939.489700

\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) & 19301.6755555556 & 2068.547608 & 9.331 & 0 & 0 \tabularnewline
M1 & 2240.75639730639 & 2563.01167 & 0.8743 & 0.383779 & 0.19189 \tabularnewline
M2 & 4454.02875420874 & 2562.672873 & 1.738 & 0.084857 & 0.042429 \tabularnewline
M3 & 6371.21020202022 & 2562.409334 & 2.4864 & 0.014327 & 0.007164 \tabularnewline
M4 & 6777.39164983165 & 2562.221076 & 2.6451 & 0.009298 & 0.004649 \tabularnewline
M5 & 7917.84582491583 & 2562.108114 & 3.0904 & 0.002503 & 0.001252 \tabularnewline
M6 & 8710.93636363636 & 2562.070459 & 3.4 & 0.000925 & 0.000462 \tabularnewline
M7 & 7509.39053872055 & 2562.108114 & 2.9309 & 0.004071 & 0.002036 \tabularnewline
M8 & 7557.39016835017 & 2562.221076 & 2.9495 & 0.00385 & 0.001925 \tabularnewline
M9 & 5687.29888888889 & 2562.409334 & 2.2195 & 0.028397 & 0.014198 \tabularnewline
M10 & 3467.63710437711 & 2622.509873 & 1.3223 & 0.188683 & 0.094342 \tabularnewline
M11 & 1939.01855218855 & 2622.399508 & 0.7394 & 0.461154 & 0.230577 \tabularnewline
t & 131.818552188552 & 13.890693 & 9.4897 & 0 & 0 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=103284&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]19301.6755555556[/C][C]2068.547608[/C][C]9.331[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]M1[/C][C]2240.75639730639[/C][C]2563.01167[/C][C]0.8743[/C][C]0.383779[/C][C]0.19189[/C][/ROW]
[ROW][C]M2[/C][C]4454.02875420874[/C][C]2562.672873[/C][C]1.738[/C][C]0.084857[/C][C]0.042429[/C][/ROW]
[ROW][C]M3[/C][C]6371.21020202022[/C][C]2562.409334[/C][C]2.4864[/C][C]0.014327[/C][C]0.007164[/C][/ROW]
[ROW][C]M4[/C][C]6777.39164983165[/C][C]2562.221076[/C][C]2.6451[/C][C]0.009298[/C][C]0.004649[/C][/ROW]
[ROW][C]M5[/C][C]7917.84582491583[/C][C]2562.108114[/C][C]3.0904[/C][C]0.002503[/C][C]0.001252[/C][/ROW]
[ROW][C]M6[/C][C]8710.93636363636[/C][C]2562.070459[/C][C]3.4[/C][C]0.000925[/C][C]0.000462[/C][/ROW]
[ROW][C]M7[/C][C]7509.39053872055[/C][C]2562.108114[/C][C]2.9309[/C][C]0.004071[/C][C]0.002036[/C][/ROW]
[ROW][C]M8[/C][C]7557.39016835017[/C][C]2562.221076[/C][C]2.9495[/C][C]0.00385[/C][C]0.001925[/C][/ROW]
[ROW][C]M9[/C][C]5687.29888888889[/C][C]2562.409334[/C][C]2.2195[/C][C]0.028397[/C][C]0.014198[/C][/ROW]
[ROW][C]M10[/C][C]3467.63710437711[/C][C]2622.509873[/C][C]1.3223[/C][C]0.188683[/C][C]0.094342[/C][/ROW]
[ROW][C]M11[/C][C]1939.01855218855[/C][C]2622.399508[/C][C]0.7394[/C][C]0.461154[/C][C]0.230577[/C][/ROW]
[ROW][C]t[/C][C]131.818552188552[/C][C]13.890693[/C][C]9.4897[/C][C]0[/C][C]0[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=103284&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=103284&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)19301.67555555562068.5476089.33100
M12240.756397306392563.011670.87430.3837790.19189
M24454.028754208742562.6728731.7380.0848570.042429
M36371.210202020222562.4093342.48640.0143270.007164
M46777.391649831652562.2210762.64510.0092980.004649
M57917.845824915832562.1081143.09040.0025030.001252
M68710.936363636362562.0704593.40.0009250.000462
M77509.390538720552562.1081142.93090.0040710.002036
M87557.390168350172562.2210762.94950.003850.001925
M95687.298888888892562.4093342.21950.0283970.014198
M103467.637104377112622.5098731.32230.1886830.094342
M111939.018552188552622.3995080.73940.4611540.230577
t131.81855218855213.8906939.489700







Multiple Linear Regression - Regression Statistics
Multiple R0.71058290556023
R-squared0.504928065674419
Adjusted R-squared0.453713727640738
F-TEST (value)9.85911533880132
F-TEST (DF numerator)12
F-TEST (DF denominator)116
p-value4.90607554581857e-13
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation5863.78129990337
Sum Squared Residuals3988536011.43919

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.71058290556023 \tabularnewline
R-squared & 0.504928065674419 \tabularnewline
Adjusted R-squared & 0.453713727640738 \tabularnewline
F-TEST (value) & 9.85911533880132 \tabularnewline
F-TEST (DF numerator) & 12 \tabularnewline
F-TEST (DF denominator) & 116 \tabularnewline
p-value & 4.90607554581857e-13 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 5863.78129990337 \tabularnewline
Sum Squared Residuals & 3988536011.43919 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=103284&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.71058290556023[/C][/ROW]
[ROW][C]R-squared[/C][C]0.504928065674419[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.453713727640738[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]9.85911533880132[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]12[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]116[/C][/ROW]
[ROW][C]p-value[/C][C]4.90607554581857e-13[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]5863.78129990337[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]3988536011.43919[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=103284&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=103284&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 R0.71058290556023
R-squared0.504928065674419
Adjusted R-squared0.453713727640738
F-TEST (value)9.85911533880132
F-TEST (DF numerator)12
F-TEST (DF denominator)116
p-value4.90607554581857e-13
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation5863.78129990337
Sum Squared Residuals3988536011.43919







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
12795121674.25050505066276.7494949494
22978124019.34141414145761.65858585858
33291426068.34141414146845.65858585862
43348826606.34141414146881.65858585857
53565227878.61414141417773.38585858585
63648828803.52323232337684.47676767675
73538727733.79595959597653.20404040405
83567627913.61414141427762.38585858584
93484426175.34141414148668.65858585859
103244724087.49818181828359.50181818182
113106822690.69818181828377.30181818182
122901020883.49818181828126.50181818182
132981223256.07313131316555.92686868692
143095125601.16404040405349.83595959596
153297427650.16404040405323.83595959596
163293628188.16404040404747.83595959597
173401229460.43676767684551.56323232323
183294630385.34585858592560.65414141414
193194829315.61858585862632.38141414141
203059929495.43676767681103.56323232324
212769127757.1640404040-66.1640404040355
222507325669.3208080808-596.320808080806
232340624272.5208080808-866.52080808081
242224822465.3208080808-217.320808080808
252289624837.8957575757-1941.89575757575
262531727182.9866666667-1865.98666666666
272655829231.9866666667-2673.98666666667
282647129769.9866666667-3298.98666666666
292754331042.2593939394-3499.25939393939
302619831967.1684848485-5769.16848484848
312472530897.4412121212-6172.44121212121
322500531077.2593939394-6072.25939393939
332346229338.9866666667-5876.98666666667
342078027251.1434343434-6471.14343434344
351981525854.3434343434-6039.34343434344
361976124047.1434343434-4286.14343434343
372145426419.7183838384-4965.71838383838
382389928764.8092929293-4865.80929292929
392493930813.8092929293-5874.8092929293
402358031351.8092929293-7771.80929292929
412456232624.082020202-8062.08202020202
422469633548.9911111111-8852.99111111111
432378532479.2638383838-8694.26383838384
442381232659.082020202-8847.08202020202
452191730920.8092929293-9003.80929292929
461971328832.9660606061-9119.96606060606
471928227436.1660606061-8154.16606060606
481878825628.9660606061-6840.96606060606
492145328001.541010101-6548.541010101
502448230346.6319191919-5864.63191919192
512747432395.6319191919-4921.63191919192
522726432933.6319191919-5669.63191919191
532734934205.9046464646-6856.90464646465
543063235130.8137373737-4498.81373737374
552942934061.0864646465-4632.08646464647
563008434240.9046464646-4156.90464646464
572629032502.6319191919-6212.63191919192
582437930414.7886868687-6035.78868686869
592333529017.9886868687-5682.98868686869
602134627210.7886868687-5864.78868686869
612110629583.3636363636-8477.36363636363
622451431928.4545454545-7414.45454545454
632835333977.4545454546-5624.45454545455
643080534515.4545454545-3710.45454545454
653134835787.7272727273-4439.72727272727
663455636712.6363636364-2156.63636363637
673385535642.9090909091-1787.90909090909
683478735822.7272727273-1035.72727272727
693252934084.4545454545-1555.45454545454
702999831996.6113131313-1998.61131313131
712925730599.8113131313-1342.81131313131
722815528792.6113131313-637.611313131313
733046631165.1862626263-699.186262626257
743570433510.27717171722193.72282828283
753932735559.27717171723767.72282828282
763935136097.27717171723253.72282828283
774223437369.54989898994864.4501010101
784363038294.4589898995335.54101010101
794372237224.73171717176497.26828282828
804312137404.54989898995716.4501010101
813798535666.27717171722318.72282828283
823713533578.43393939393556.56606060606
833464632181.63393939392464.36606060606
843302630374.43393939392651.56606060606
853508732747.00888888892339.99111111112
863884635092.09979797983753.9002020202
874201337141.09979797984871.9002020202
884390837679.09979797986228.9002020202
894286838951.37252525253916.62747474747
904442339876.28161616164546.71838383838
914416738806.55434343435360.44565656565
924363638986.37252525254649.62747474748
934438237248.09979797987133.9002020202
944214235160.25656565666981.74343434343
954345233763.45656565669688.54343434343
963691231956.25656565664955.74343434343
974241334328.83151515158084.16848484849
984534436673.92242424248670.07757575758
994487338722.92242424246150.07757575757
1004751039260.92242424248249.07757575758
1014955440533.19515151529020.80484848485
1024736941458.10424242425910.89575757576
1034599840388.3769696975609.62303030303
1044814040568.19515151527571.80484848485
1054844138829.92242424249611.07757575757
1064492836742.07919191928185.9208080808
1074045435345.27919191925108.72080808081
1083866133538.07919191925122.92080808081
1093724635910.65414141411335.34585858586
1103684338255.7450505051-1412.74505050505
1113642440304.7450505051-3880.74505050506
1123759440842.745050505-3248.74505050505
1133814442115.0177777778-3971.01777777778
1143873743039.9268686869-4302.92686868687
1153456041970.1995959596-7410.1995959596
1163608042150.0177777778-6070.01777777778
1173350840411.7450505051-6903.74505050505
1183546238323.9018181818-2861.90181818182
1193337436927.1018181818-3553.10181818182
1203211035119.9018181818-3009.90181818182
1213553337492.4767676768-1959.47676767676
1223553239837.5676767677-4305.56767676768
1233790341886.5676767677-3983.56767676769
1243676342424.5676767677-5661.56767676768
1254039943696.8404040404-3297.84040404041
1264416444621.7494949495-457.7494949495
1274449643552.0222222222943.97777777777
1284311043731.8404040404-621.840404040406
1294388041993.56767676771886.43232323232

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 27951 & 21674.2505050506 & 6276.7494949494 \tabularnewline
2 & 29781 & 24019.3414141414 & 5761.65858585858 \tabularnewline
3 & 32914 & 26068.3414141414 & 6845.65858585862 \tabularnewline
4 & 33488 & 26606.3414141414 & 6881.65858585857 \tabularnewline
5 & 35652 & 27878.6141414141 & 7773.38585858585 \tabularnewline
6 & 36488 & 28803.5232323233 & 7684.47676767675 \tabularnewline
7 & 35387 & 27733.7959595959 & 7653.20404040405 \tabularnewline
8 & 35676 & 27913.6141414142 & 7762.38585858584 \tabularnewline
9 & 34844 & 26175.3414141414 & 8668.65858585859 \tabularnewline
10 & 32447 & 24087.4981818182 & 8359.50181818182 \tabularnewline
11 & 31068 & 22690.6981818182 & 8377.30181818182 \tabularnewline
12 & 29010 & 20883.4981818182 & 8126.50181818182 \tabularnewline
13 & 29812 & 23256.0731313131 & 6555.92686868692 \tabularnewline
14 & 30951 & 25601.1640404040 & 5349.83595959596 \tabularnewline
15 & 32974 & 27650.1640404040 & 5323.83595959596 \tabularnewline
16 & 32936 & 28188.1640404040 & 4747.83595959597 \tabularnewline
17 & 34012 & 29460.4367676768 & 4551.56323232323 \tabularnewline
18 & 32946 & 30385.3458585859 & 2560.65414141414 \tabularnewline
19 & 31948 & 29315.6185858586 & 2632.38141414141 \tabularnewline
20 & 30599 & 29495.4367676768 & 1103.56323232324 \tabularnewline
21 & 27691 & 27757.1640404040 & -66.1640404040355 \tabularnewline
22 & 25073 & 25669.3208080808 & -596.320808080806 \tabularnewline
23 & 23406 & 24272.5208080808 & -866.52080808081 \tabularnewline
24 & 22248 & 22465.3208080808 & -217.320808080808 \tabularnewline
25 & 22896 & 24837.8957575757 & -1941.89575757575 \tabularnewline
26 & 25317 & 27182.9866666667 & -1865.98666666666 \tabularnewline
27 & 26558 & 29231.9866666667 & -2673.98666666667 \tabularnewline
28 & 26471 & 29769.9866666667 & -3298.98666666666 \tabularnewline
29 & 27543 & 31042.2593939394 & -3499.25939393939 \tabularnewline
30 & 26198 & 31967.1684848485 & -5769.16848484848 \tabularnewline
31 & 24725 & 30897.4412121212 & -6172.44121212121 \tabularnewline
32 & 25005 & 31077.2593939394 & -6072.25939393939 \tabularnewline
33 & 23462 & 29338.9866666667 & -5876.98666666667 \tabularnewline
34 & 20780 & 27251.1434343434 & -6471.14343434344 \tabularnewline
35 & 19815 & 25854.3434343434 & -6039.34343434344 \tabularnewline
36 & 19761 & 24047.1434343434 & -4286.14343434343 \tabularnewline
37 & 21454 & 26419.7183838384 & -4965.71838383838 \tabularnewline
38 & 23899 & 28764.8092929293 & -4865.80929292929 \tabularnewline
39 & 24939 & 30813.8092929293 & -5874.8092929293 \tabularnewline
40 & 23580 & 31351.8092929293 & -7771.80929292929 \tabularnewline
41 & 24562 & 32624.082020202 & -8062.08202020202 \tabularnewline
42 & 24696 & 33548.9911111111 & -8852.99111111111 \tabularnewline
43 & 23785 & 32479.2638383838 & -8694.26383838384 \tabularnewline
44 & 23812 & 32659.082020202 & -8847.08202020202 \tabularnewline
45 & 21917 & 30920.8092929293 & -9003.80929292929 \tabularnewline
46 & 19713 & 28832.9660606061 & -9119.96606060606 \tabularnewline
47 & 19282 & 27436.1660606061 & -8154.16606060606 \tabularnewline
48 & 18788 & 25628.9660606061 & -6840.96606060606 \tabularnewline
49 & 21453 & 28001.541010101 & -6548.541010101 \tabularnewline
50 & 24482 & 30346.6319191919 & -5864.63191919192 \tabularnewline
51 & 27474 & 32395.6319191919 & -4921.63191919192 \tabularnewline
52 & 27264 & 32933.6319191919 & -5669.63191919191 \tabularnewline
53 & 27349 & 34205.9046464646 & -6856.90464646465 \tabularnewline
54 & 30632 & 35130.8137373737 & -4498.81373737374 \tabularnewline
55 & 29429 & 34061.0864646465 & -4632.08646464647 \tabularnewline
56 & 30084 & 34240.9046464646 & -4156.90464646464 \tabularnewline
57 & 26290 & 32502.6319191919 & -6212.63191919192 \tabularnewline
58 & 24379 & 30414.7886868687 & -6035.78868686869 \tabularnewline
59 & 23335 & 29017.9886868687 & -5682.98868686869 \tabularnewline
60 & 21346 & 27210.7886868687 & -5864.78868686869 \tabularnewline
61 & 21106 & 29583.3636363636 & -8477.36363636363 \tabularnewline
62 & 24514 & 31928.4545454545 & -7414.45454545454 \tabularnewline
63 & 28353 & 33977.4545454546 & -5624.45454545455 \tabularnewline
64 & 30805 & 34515.4545454545 & -3710.45454545454 \tabularnewline
65 & 31348 & 35787.7272727273 & -4439.72727272727 \tabularnewline
66 & 34556 & 36712.6363636364 & -2156.63636363637 \tabularnewline
67 & 33855 & 35642.9090909091 & -1787.90909090909 \tabularnewline
68 & 34787 & 35822.7272727273 & -1035.72727272727 \tabularnewline
69 & 32529 & 34084.4545454545 & -1555.45454545454 \tabularnewline
70 & 29998 & 31996.6113131313 & -1998.61131313131 \tabularnewline
71 & 29257 & 30599.8113131313 & -1342.81131313131 \tabularnewline
72 & 28155 & 28792.6113131313 & -637.611313131313 \tabularnewline
73 & 30466 & 31165.1862626263 & -699.186262626257 \tabularnewline
74 & 35704 & 33510.2771717172 & 2193.72282828283 \tabularnewline
75 & 39327 & 35559.2771717172 & 3767.72282828282 \tabularnewline
76 & 39351 & 36097.2771717172 & 3253.72282828283 \tabularnewline
77 & 42234 & 37369.5498989899 & 4864.4501010101 \tabularnewline
78 & 43630 & 38294.458989899 & 5335.54101010101 \tabularnewline
79 & 43722 & 37224.7317171717 & 6497.26828282828 \tabularnewline
80 & 43121 & 37404.5498989899 & 5716.4501010101 \tabularnewline
81 & 37985 & 35666.2771717172 & 2318.72282828283 \tabularnewline
82 & 37135 & 33578.4339393939 & 3556.56606060606 \tabularnewline
83 & 34646 & 32181.6339393939 & 2464.36606060606 \tabularnewline
84 & 33026 & 30374.4339393939 & 2651.56606060606 \tabularnewline
85 & 35087 & 32747.0088888889 & 2339.99111111112 \tabularnewline
86 & 38846 & 35092.0997979798 & 3753.9002020202 \tabularnewline
87 & 42013 & 37141.0997979798 & 4871.9002020202 \tabularnewline
88 & 43908 & 37679.0997979798 & 6228.9002020202 \tabularnewline
89 & 42868 & 38951.3725252525 & 3916.62747474747 \tabularnewline
90 & 44423 & 39876.2816161616 & 4546.71838383838 \tabularnewline
91 & 44167 & 38806.5543434343 & 5360.44565656565 \tabularnewline
92 & 43636 & 38986.3725252525 & 4649.62747474748 \tabularnewline
93 & 44382 & 37248.0997979798 & 7133.9002020202 \tabularnewline
94 & 42142 & 35160.2565656566 & 6981.74343434343 \tabularnewline
95 & 43452 & 33763.4565656566 & 9688.54343434343 \tabularnewline
96 & 36912 & 31956.2565656566 & 4955.74343434343 \tabularnewline
97 & 42413 & 34328.8315151515 & 8084.16848484849 \tabularnewline
98 & 45344 & 36673.9224242424 & 8670.07757575758 \tabularnewline
99 & 44873 & 38722.9224242424 & 6150.07757575757 \tabularnewline
100 & 47510 & 39260.9224242424 & 8249.07757575758 \tabularnewline
101 & 49554 & 40533.1951515152 & 9020.80484848485 \tabularnewline
102 & 47369 & 41458.1042424242 & 5910.89575757576 \tabularnewline
103 & 45998 & 40388.376969697 & 5609.62303030303 \tabularnewline
104 & 48140 & 40568.1951515152 & 7571.80484848485 \tabularnewline
105 & 48441 & 38829.9224242424 & 9611.07757575757 \tabularnewline
106 & 44928 & 36742.0791919192 & 8185.9208080808 \tabularnewline
107 & 40454 & 35345.2791919192 & 5108.72080808081 \tabularnewline
108 & 38661 & 33538.0791919192 & 5122.92080808081 \tabularnewline
109 & 37246 & 35910.6541414141 & 1335.34585858586 \tabularnewline
110 & 36843 & 38255.7450505051 & -1412.74505050505 \tabularnewline
111 & 36424 & 40304.7450505051 & -3880.74505050506 \tabularnewline
112 & 37594 & 40842.745050505 & -3248.74505050505 \tabularnewline
113 & 38144 & 42115.0177777778 & -3971.01777777778 \tabularnewline
114 & 38737 & 43039.9268686869 & -4302.92686868687 \tabularnewline
115 & 34560 & 41970.1995959596 & -7410.1995959596 \tabularnewline
116 & 36080 & 42150.0177777778 & -6070.01777777778 \tabularnewline
117 & 33508 & 40411.7450505051 & -6903.74505050505 \tabularnewline
118 & 35462 & 38323.9018181818 & -2861.90181818182 \tabularnewline
119 & 33374 & 36927.1018181818 & -3553.10181818182 \tabularnewline
120 & 32110 & 35119.9018181818 & -3009.90181818182 \tabularnewline
121 & 35533 & 37492.4767676768 & -1959.47676767676 \tabularnewline
122 & 35532 & 39837.5676767677 & -4305.56767676768 \tabularnewline
123 & 37903 & 41886.5676767677 & -3983.56767676769 \tabularnewline
124 & 36763 & 42424.5676767677 & -5661.56767676768 \tabularnewline
125 & 40399 & 43696.8404040404 & -3297.84040404041 \tabularnewline
126 & 44164 & 44621.7494949495 & -457.7494949495 \tabularnewline
127 & 44496 & 43552.0222222222 & 943.97777777777 \tabularnewline
128 & 43110 & 43731.8404040404 & -621.840404040406 \tabularnewline
129 & 43880 & 41993.5676767677 & 1886.43232323232 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=103284&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]21674.2505050506[/C][C]6276.7494949494[/C][/ROW]
[ROW][C]2[/C][C]29781[/C][C]24019.3414141414[/C][C]5761.65858585858[/C][/ROW]
[ROW][C]3[/C][C]32914[/C][C]26068.3414141414[/C][C]6845.65858585862[/C][/ROW]
[ROW][C]4[/C][C]33488[/C][C]26606.3414141414[/C][C]6881.65858585857[/C][/ROW]
[ROW][C]5[/C][C]35652[/C][C]27878.6141414141[/C][C]7773.38585858585[/C][/ROW]
[ROW][C]6[/C][C]36488[/C][C]28803.5232323233[/C][C]7684.47676767675[/C][/ROW]
[ROW][C]7[/C][C]35387[/C][C]27733.7959595959[/C][C]7653.20404040405[/C][/ROW]
[ROW][C]8[/C][C]35676[/C][C]27913.6141414142[/C][C]7762.38585858584[/C][/ROW]
[ROW][C]9[/C][C]34844[/C][C]26175.3414141414[/C][C]8668.65858585859[/C][/ROW]
[ROW][C]10[/C][C]32447[/C][C]24087.4981818182[/C][C]8359.50181818182[/C][/ROW]
[ROW][C]11[/C][C]31068[/C][C]22690.6981818182[/C][C]8377.30181818182[/C][/ROW]
[ROW][C]12[/C][C]29010[/C][C]20883.4981818182[/C][C]8126.50181818182[/C][/ROW]
[ROW][C]13[/C][C]29812[/C][C]23256.0731313131[/C][C]6555.92686868692[/C][/ROW]
[ROW][C]14[/C][C]30951[/C][C]25601.1640404040[/C][C]5349.83595959596[/C][/ROW]
[ROW][C]15[/C][C]32974[/C][C]27650.1640404040[/C][C]5323.83595959596[/C][/ROW]
[ROW][C]16[/C][C]32936[/C][C]28188.1640404040[/C][C]4747.83595959597[/C][/ROW]
[ROW][C]17[/C][C]34012[/C][C]29460.4367676768[/C][C]4551.56323232323[/C][/ROW]
[ROW][C]18[/C][C]32946[/C][C]30385.3458585859[/C][C]2560.65414141414[/C][/ROW]
[ROW][C]19[/C][C]31948[/C][C]29315.6185858586[/C][C]2632.38141414141[/C][/ROW]
[ROW][C]20[/C][C]30599[/C][C]29495.4367676768[/C][C]1103.56323232324[/C][/ROW]
[ROW][C]21[/C][C]27691[/C][C]27757.1640404040[/C][C]-66.1640404040355[/C][/ROW]
[ROW][C]22[/C][C]25073[/C][C]25669.3208080808[/C][C]-596.320808080806[/C][/ROW]
[ROW][C]23[/C][C]23406[/C][C]24272.5208080808[/C][C]-866.52080808081[/C][/ROW]
[ROW][C]24[/C][C]22248[/C][C]22465.3208080808[/C][C]-217.320808080808[/C][/ROW]
[ROW][C]25[/C][C]22896[/C][C]24837.8957575757[/C][C]-1941.89575757575[/C][/ROW]
[ROW][C]26[/C][C]25317[/C][C]27182.9866666667[/C][C]-1865.98666666666[/C][/ROW]
[ROW][C]27[/C][C]26558[/C][C]29231.9866666667[/C][C]-2673.98666666667[/C][/ROW]
[ROW][C]28[/C][C]26471[/C][C]29769.9866666667[/C][C]-3298.98666666666[/C][/ROW]
[ROW][C]29[/C][C]27543[/C][C]31042.2593939394[/C][C]-3499.25939393939[/C][/ROW]
[ROW][C]30[/C][C]26198[/C][C]31967.1684848485[/C][C]-5769.16848484848[/C][/ROW]
[ROW][C]31[/C][C]24725[/C][C]30897.4412121212[/C][C]-6172.44121212121[/C][/ROW]
[ROW][C]32[/C][C]25005[/C][C]31077.2593939394[/C][C]-6072.25939393939[/C][/ROW]
[ROW][C]33[/C][C]23462[/C][C]29338.9866666667[/C][C]-5876.98666666667[/C][/ROW]
[ROW][C]34[/C][C]20780[/C][C]27251.1434343434[/C][C]-6471.14343434344[/C][/ROW]
[ROW][C]35[/C][C]19815[/C][C]25854.3434343434[/C][C]-6039.34343434344[/C][/ROW]
[ROW][C]36[/C][C]19761[/C][C]24047.1434343434[/C][C]-4286.14343434343[/C][/ROW]
[ROW][C]37[/C][C]21454[/C][C]26419.7183838384[/C][C]-4965.71838383838[/C][/ROW]
[ROW][C]38[/C][C]23899[/C][C]28764.8092929293[/C][C]-4865.80929292929[/C][/ROW]
[ROW][C]39[/C][C]24939[/C][C]30813.8092929293[/C][C]-5874.8092929293[/C][/ROW]
[ROW][C]40[/C][C]23580[/C][C]31351.8092929293[/C][C]-7771.80929292929[/C][/ROW]
[ROW][C]41[/C][C]24562[/C][C]32624.082020202[/C][C]-8062.08202020202[/C][/ROW]
[ROW][C]42[/C][C]24696[/C][C]33548.9911111111[/C][C]-8852.99111111111[/C][/ROW]
[ROW][C]43[/C][C]23785[/C][C]32479.2638383838[/C][C]-8694.26383838384[/C][/ROW]
[ROW][C]44[/C][C]23812[/C][C]32659.082020202[/C][C]-8847.08202020202[/C][/ROW]
[ROW][C]45[/C][C]21917[/C][C]30920.8092929293[/C][C]-9003.80929292929[/C][/ROW]
[ROW][C]46[/C][C]19713[/C][C]28832.9660606061[/C][C]-9119.96606060606[/C][/ROW]
[ROW][C]47[/C][C]19282[/C][C]27436.1660606061[/C][C]-8154.16606060606[/C][/ROW]
[ROW][C]48[/C][C]18788[/C][C]25628.9660606061[/C][C]-6840.96606060606[/C][/ROW]
[ROW][C]49[/C][C]21453[/C][C]28001.541010101[/C][C]-6548.541010101[/C][/ROW]
[ROW][C]50[/C][C]24482[/C][C]30346.6319191919[/C][C]-5864.63191919192[/C][/ROW]
[ROW][C]51[/C][C]27474[/C][C]32395.6319191919[/C][C]-4921.63191919192[/C][/ROW]
[ROW][C]52[/C][C]27264[/C][C]32933.6319191919[/C][C]-5669.63191919191[/C][/ROW]
[ROW][C]53[/C][C]27349[/C][C]34205.9046464646[/C][C]-6856.90464646465[/C][/ROW]
[ROW][C]54[/C][C]30632[/C][C]35130.8137373737[/C][C]-4498.81373737374[/C][/ROW]
[ROW][C]55[/C][C]29429[/C][C]34061.0864646465[/C][C]-4632.08646464647[/C][/ROW]
[ROW][C]56[/C][C]30084[/C][C]34240.9046464646[/C][C]-4156.90464646464[/C][/ROW]
[ROW][C]57[/C][C]26290[/C][C]32502.6319191919[/C][C]-6212.63191919192[/C][/ROW]
[ROW][C]58[/C][C]24379[/C][C]30414.7886868687[/C][C]-6035.78868686869[/C][/ROW]
[ROW][C]59[/C][C]23335[/C][C]29017.9886868687[/C][C]-5682.98868686869[/C][/ROW]
[ROW][C]60[/C][C]21346[/C][C]27210.7886868687[/C][C]-5864.78868686869[/C][/ROW]
[ROW][C]61[/C][C]21106[/C][C]29583.3636363636[/C][C]-8477.36363636363[/C][/ROW]
[ROW][C]62[/C][C]24514[/C][C]31928.4545454545[/C][C]-7414.45454545454[/C][/ROW]
[ROW][C]63[/C][C]28353[/C][C]33977.4545454546[/C][C]-5624.45454545455[/C][/ROW]
[ROW][C]64[/C][C]30805[/C][C]34515.4545454545[/C][C]-3710.45454545454[/C][/ROW]
[ROW][C]65[/C][C]31348[/C][C]35787.7272727273[/C][C]-4439.72727272727[/C][/ROW]
[ROW][C]66[/C][C]34556[/C][C]36712.6363636364[/C][C]-2156.63636363637[/C][/ROW]
[ROW][C]67[/C][C]33855[/C][C]35642.9090909091[/C][C]-1787.90909090909[/C][/ROW]
[ROW][C]68[/C][C]34787[/C][C]35822.7272727273[/C][C]-1035.72727272727[/C][/ROW]
[ROW][C]69[/C][C]32529[/C][C]34084.4545454545[/C][C]-1555.45454545454[/C][/ROW]
[ROW][C]70[/C][C]29998[/C][C]31996.6113131313[/C][C]-1998.61131313131[/C][/ROW]
[ROW][C]71[/C][C]29257[/C][C]30599.8113131313[/C][C]-1342.81131313131[/C][/ROW]
[ROW][C]72[/C][C]28155[/C][C]28792.6113131313[/C][C]-637.611313131313[/C][/ROW]
[ROW][C]73[/C][C]30466[/C][C]31165.1862626263[/C][C]-699.186262626257[/C][/ROW]
[ROW][C]74[/C][C]35704[/C][C]33510.2771717172[/C][C]2193.72282828283[/C][/ROW]
[ROW][C]75[/C][C]39327[/C][C]35559.2771717172[/C][C]3767.72282828282[/C][/ROW]
[ROW][C]76[/C][C]39351[/C][C]36097.2771717172[/C][C]3253.72282828283[/C][/ROW]
[ROW][C]77[/C][C]42234[/C][C]37369.5498989899[/C][C]4864.4501010101[/C][/ROW]
[ROW][C]78[/C][C]43630[/C][C]38294.458989899[/C][C]5335.54101010101[/C][/ROW]
[ROW][C]79[/C][C]43722[/C][C]37224.7317171717[/C][C]6497.26828282828[/C][/ROW]
[ROW][C]80[/C][C]43121[/C][C]37404.5498989899[/C][C]5716.4501010101[/C][/ROW]
[ROW][C]81[/C][C]37985[/C][C]35666.2771717172[/C][C]2318.72282828283[/C][/ROW]
[ROW][C]82[/C][C]37135[/C][C]33578.4339393939[/C][C]3556.56606060606[/C][/ROW]
[ROW][C]83[/C][C]34646[/C][C]32181.6339393939[/C][C]2464.36606060606[/C][/ROW]
[ROW][C]84[/C][C]33026[/C][C]30374.4339393939[/C][C]2651.56606060606[/C][/ROW]
[ROW][C]85[/C][C]35087[/C][C]32747.0088888889[/C][C]2339.99111111112[/C][/ROW]
[ROW][C]86[/C][C]38846[/C][C]35092.0997979798[/C][C]3753.9002020202[/C][/ROW]
[ROW][C]87[/C][C]42013[/C][C]37141.0997979798[/C][C]4871.9002020202[/C][/ROW]
[ROW][C]88[/C][C]43908[/C][C]37679.0997979798[/C][C]6228.9002020202[/C][/ROW]
[ROW][C]89[/C][C]42868[/C][C]38951.3725252525[/C][C]3916.62747474747[/C][/ROW]
[ROW][C]90[/C][C]44423[/C][C]39876.2816161616[/C][C]4546.71838383838[/C][/ROW]
[ROW][C]91[/C][C]44167[/C][C]38806.5543434343[/C][C]5360.44565656565[/C][/ROW]
[ROW][C]92[/C][C]43636[/C][C]38986.3725252525[/C][C]4649.62747474748[/C][/ROW]
[ROW][C]93[/C][C]44382[/C][C]37248.0997979798[/C][C]7133.9002020202[/C][/ROW]
[ROW][C]94[/C][C]42142[/C][C]35160.2565656566[/C][C]6981.74343434343[/C][/ROW]
[ROW][C]95[/C][C]43452[/C][C]33763.4565656566[/C][C]9688.54343434343[/C][/ROW]
[ROW][C]96[/C][C]36912[/C][C]31956.2565656566[/C][C]4955.74343434343[/C][/ROW]
[ROW][C]97[/C][C]42413[/C][C]34328.8315151515[/C][C]8084.16848484849[/C][/ROW]
[ROW][C]98[/C][C]45344[/C][C]36673.9224242424[/C][C]8670.07757575758[/C][/ROW]
[ROW][C]99[/C][C]44873[/C][C]38722.9224242424[/C][C]6150.07757575757[/C][/ROW]
[ROW][C]100[/C][C]47510[/C][C]39260.9224242424[/C][C]8249.07757575758[/C][/ROW]
[ROW][C]101[/C][C]49554[/C][C]40533.1951515152[/C][C]9020.80484848485[/C][/ROW]
[ROW][C]102[/C][C]47369[/C][C]41458.1042424242[/C][C]5910.89575757576[/C][/ROW]
[ROW][C]103[/C][C]45998[/C][C]40388.376969697[/C][C]5609.62303030303[/C][/ROW]
[ROW][C]104[/C][C]48140[/C][C]40568.1951515152[/C][C]7571.80484848485[/C][/ROW]
[ROW][C]105[/C][C]48441[/C][C]38829.9224242424[/C][C]9611.07757575757[/C][/ROW]
[ROW][C]106[/C][C]44928[/C][C]36742.0791919192[/C][C]8185.9208080808[/C][/ROW]
[ROW][C]107[/C][C]40454[/C][C]35345.2791919192[/C][C]5108.72080808081[/C][/ROW]
[ROW][C]108[/C][C]38661[/C][C]33538.0791919192[/C][C]5122.92080808081[/C][/ROW]
[ROW][C]109[/C][C]37246[/C][C]35910.6541414141[/C][C]1335.34585858586[/C][/ROW]
[ROW][C]110[/C][C]36843[/C][C]38255.7450505051[/C][C]-1412.74505050505[/C][/ROW]
[ROW][C]111[/C][C]36424[/C][C]40304.7450505051[/C][C]-3880.74505050506[/C][/ROW]
[ROW][C]112[/C][C]37594[/C][C]40842.745050505[/C][C]-3248.74505050505[/C][/ROW]
[ROW][C]113[/C][C]38144[/C][C]42115.0177777778[/C][C]-3971.01777777778[/C][/ROW]
[ROW][C]114[/C][C]38737[/C][C]43039.9268686869[/C][C]-4302.92686868687[/C][/ROW]
[ROW][C]115[/C][C]34560[/C][C]41970.1995959596[/C][C]-7410.1995959596[/C][/ROW]
[ROW][C]116[/C][C]36080[/C][C]42150.0177777778[/C][C]-6070.01777777778[/C][/ROW]
[ROW][C]117[/C][C]33508[/C][C]40411.7450505051[/C][C]-6903.74505050505[/C][/ROW]
[ROW][C]118[/C][C]35462[/C][C]38323.9018181818[/C][C]-2861.90181818182[/C][/ROW]
[ROW][C]119[/C][C]33374[/C][C]36927.1018181818[/C][C]-3553.10181818182[/C][/ROW]
[ROW][C]120[/C][C]32110[/C][C]35119.9018181818[/C][C]-3009.90181818182[/C][/ROW]
[ROW][C]121[/C][C]35533[/C][C]37492.4767676768[/C][C]-1959.47676767676[/C][/ROW]
[ROW][C]122[/C][C]35532[/C][C]39837.5676767677[/C][C]-4305.56767676768[/C][/ROW]
[ROW][C]123[/C][C]37903[/C][C]41886.5676767677[/C][C]-3983.56767676769[/C][/ROW]
[ROW][C]124[/C][C]36763[/C][C]42424.5676767677[/C][C]-5661.56767676768[/C][/ROW]
[ROW][C]125[/C][C]40399[/C][C]43696.8404040404[/C][C]-3297.84040404041[/C][/ROW]
[ROW][C]126[/C][C]44164[/C][C]44621.7494949495[/C][C]-457.7494949495[/C][/ROW]
[ROW][C]127[/C][C]44496[/C][C]43552.0222222222[/C][C]943.97777777777[/C][/ROW]
[ROW][C]128[/C][C]43110[/C][C]43731.8404040404[/C][C]-621.840404040406[/C][/ROW]
[ROW][C]129[/C][C]43880[/C][C]41993.5676767677[/C][C]1886.43232323232[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=103284&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=103284&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
12795121674.25050505066276.7494949494
22978124019.34141414145761.65858585858
33291426068.34141414146845.65858585862
43348826606.34141414146881.65858585857
53565227878.61414141417773.38585858585
63648828803.52323232337684.47676767675
73538727733.79595959597653.20404040405
83567627913.61414141427762.38585858584
93484426175.34141414148668.65858585859
103244724087.49818181828359.50181818182
113106822690.69818181828377.30181818182
122901020883.49818181828126.50181818182
132981223256.07313131316555.92686868692
143095125601.16404040405349.83595959596
153297427650.16404040405323.83595959596
163293628188.16404040404747.83595959597
173401229460.43676767684551.56323232323
183294630385.34585858592560.65414141414
193194829315.61858585862632.38141414141
203059929495.43676767681103.56323232324
212769127757.1640404040-66.1640404040355
222507325669.3208080808-596.320808080806
232340624272.5208080808-866.52080808081
242224822465.3208080808-217.320808080808
252289624837.8957575757-1941.89575757575
262531727182.9866666667-1865.98666666666
272655829231.9866666667-2673.98666666667
282647129769.9866666667-3298.98666666666
292754331042.2593939394-3499.25939393939
302619831967.1684848485-5769.16848484848
312472530897.4412121212-6172.44121212121
322500531077.2593939394-6072.25939393939
332346229338.9866666667-5876.98666666667
342078027251.1434343434-6471.14343434344
351981525854.3434343434-6039.34343434344
361976124047.1434343434-4286.14343434343
372145426419.7183838384-4965.71838383838
382389928764.8092929293-4865.80929292929
392493930813.8092929293-5874.8092929293
402358031351.8092929293-7771.80929292929
412456232624.082020202-8062.08202020202
422469633548.9911111111-8852.99111111111
432378532479.2638383838-8694.26383838384
442381232659.082020202-8847.08202020202
452191730920.8092929293-9003.80929292929
461971328832.9660606061-9119.96606060606
471928227436.1660606061-8154.16606060606
481878825628.9660606061-6840.96606060606
492145328001.541010101-6548.541010101
502448230346.6319191919-5864.63191919192
512747432395.6319191919-4921.63191919192
522726432933.6319191919-5669.63191919191
532734934205.9046464646-6856.90464646465
543063235130.8137373737-4498.81373737374
552942934061.0864646465-4632.08646464647
563008434240.9046464646-4156.90464646464
572629032502.6319191919-6212.63191919192
582437930414.7886868687-6035.78868686869
592333529017.9886868687-5682.98868686869
602134627210.7886868687-5864.78868686869
612110629583.3636363636-8477.36363636363
622451431928.4545454545-7414.45454545454
632835333977.4545454546-5624.45454545455
643080534515.4545454545-3710.45454545454
653134835787.7272727273-4439.72727272727
663455636712.6363636364-2156.63636363637
673385535642.9090909091-1787.90909090909
683478735822.7272727273-1035.72727272727
693252934084.4545454545-1555.45454545454
702999831996.6113131313-1998.61131313131
712925730599.8113131313-1342.81131313131
722815528792.6113131313-637.611313131313
733046631165.1862626263-699.186262626257
743570433510.27717171722193.72282828283
753932735559.27717171723767.72282828282
763935136097.27717171723253.72282828283
774223437369.54989898994864.4501010101
784363038294.4589898995335.54101010101
794372237224.73171717176497.26828282828
804312137404.54989898995716.4501010101
813798535666.27717171722318.72282828283
823713533578.43393939393556.56606060606
833464632181.63393939392464.36606060606
843302630374.43393939392651.56606060606
853508732747.00888888892339.99111111112
863884635092.09979797983753.9002020202
874201337141.09979797984871.9002020202
884390837679.09979797986228.9002020202
894286838951.37252525253916.62747474747
904442339876.28161616164546.71838383838
914416738806.55434343435360.44565656565
924363638986.37252525254649.62747474748
934438237248.09979797987133.9002020202
944214235160.25656565666981.74343434343
954345233763.45656565669688.54343434343
963691231956.25656565664955.74343434343
974241334328.83151515158084.16848484849
984534436673.92242424248670.07757575758
994487338722.92242424246150.07757575757
1004751039260.92242424248249.07757575758
1014955440533.19515151529020.80484848485
1024736941458.10424242425910.89575757576
1034599840388.3769696975609.62303030303
1044814040568.19515151527571.80484848485
1054844138829.92242424249611.07757575757
1064492836742.07919191928185.9208080808
1074045435345.27919191925108.72080808081
1083866133538.07919191925122.92080808081
1093724635910.65414141411335.34585858586
1103684338255.7450505051-1412.74505050505
1113642440304.7450505051-3880.74505050506
1123759440842.745050505-3248.74505050505
1133814442115.0177777778-3971.01777777778
1143873743039.9268686869-4302.92686868687
1153456041970.1995959596-7410.1995959596
1163608042150.0177777778-6070.01777777778
1173350840411.7450505051-6903.74505050505
1183546238323.9018181818-2861.90181818182
1193337436927.1018181818-3553.10181818182
1203211035119.9018181818-3009.90181818182
1213553337492.4767676768-1959.47676767676
1223553239837.5676767677-4305.56767676768
1233790341886.5676767677-3983.56767676769
1243676342424.5676767677-5661.56767676768
1254039943696.8404040404-3297.84040404041
1264416444621.7494949495-457.7494949495
1274449643552.0222222222943.97777777777
1284311043731.8404040404-621.840404040406
1294388041993.56767676771886.43232323232



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')