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Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationThu, 13 Nov 2008 11:17:22 -0700
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2008/Nov/13/t1226600280svs9jvmqcjc3ag4.htm/, Retrieved Mon, 23 Jun 2025 19:22:15 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=24747, Retrieved Mon, 23 Jun 2025 19:22:15 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact348

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [Q1 The Seatbeltlaw] [2007-11-14 19:27:43] [8cd6641b921d30ebe00b648d1481bba0]
F    D    [Multiple Regression] [Q1] [2008-11-13 18:17:22] [fdd69703d301fae09456f660b2f52997] [Current]
-   PD      [Multiple Regression] [Q3 geen seasonal ...] [2008-11-19 18:53:58] [1e1d8320a8a1170c475bf6e4ce119de6]
-   P         [Multiple Regression] [Q3 seasonal dummi...] [2008-11-19 19:19:18] [1e1d8320a8a1170c475bf6e4ce119de6]
Feedback Forum
2008-11-30 12:16:13 [Nathalie Koulouris] [reply] 
De student heeft deze vraag correct beantwoord. Er worden maandelijks inderdaad minstens 226 mensen (x) van een dodelijk of zwaar ongeval gered. Dit is een
precies cijfer omdat er ook nog rekening wordt gehouden met seizoenaliteit dus worden er waarschijnlijk per jaar nog meer mensen gevrijwaard van ongevallen.

Post a new message

Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
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1763	1

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time6 seconds
R Server'Herman Ole Andreas Wold' @ 193.190.124.10:1001

\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 & 6 seconds \tabularnewline
R Server & 'Herman Ole Andreas Wold' @ 193.190.124.10:1001 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=24747&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]6 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Herman Ole Andreas Wold' @ 193.190.124.10:1001[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=24747&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=24747&T=0

As an alternative you can also use a QR Code:  
QR Code


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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time6 seconds
R Server'Herman Ole Andreas Wold' @ 193.190.124.10:1001






Multiple Linear Regression - Estimated Regression Equation
y[t] = + 2324.06337310277 -226.385033602658x[t] -451.374973256311M1[t] -635.461053323769M2[t] -583.133697991392M3[t] -694.556342659015M4[t] -555.478987326638M5[t] -609.464131994261M6[t] -532.074276661884M7[t] -515.434421329507M8[t] -460.857065997131M9[t] -319.717210664754M10[t] -118.389855332377M11[t] -1.76485533237685t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
y[t] =  +  2324.06337310277 -226.385033602658x[t] -451.374973256311M1[t] -635.461053323769M2[t] -583.133697991392M3[t] -694.556342659015M4[t] -555.478987326638M5[t] -609.464131994261M6[t] -532.074276661884M7[t] -515.434421329507M8[t] -460.857065997131M9[t] -319.717210664754M10[t] -118.389855332377M11[t] -1.76485533237685t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=24747&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]y[t] =  +  2324.06337310277 -226.385033602658x[t] -451.374973256311M1[t] -635.461053323769M2[t] -583.133697991392M3[t] -694.556342659015M4[t] -555.478987326638M5[t] -609.464131994261M6[t] -532.074276661884M7[t] -515.434421329507M8[t] -460.857065997131M9[t] -319.717210664754M10[t] -118.389855332377M11[t] -1.76485533237685t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=24747&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=24747&T=1

As an alternative you can also use a QR Code:  
QR Code


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

Multiple Linear Regression - Estimated Regression Equation
y[t] = + 2324.06337310277 -226.385033602658x[t] -451.374973256311M1[t] -635.461053323769M2[t] -583.133697991392M3[t] -694.556342659015M4[t] -555.478987326638M5[t] -609.464131994261M6[t] -532.074276661884M7[t] -515.434421329507M8[t] -460.857065997131M9[t] -319.717210664754M10[t] -118.389855332377M11[t] -1.76485533237685t + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)2324.0633731027744.02993952.783700
x-226.38503360265841.037226-5.516600
M1-451.37497325631153.942919-8.367600
M2-635.46105332376953.941479-11.780600
M3-583.13369799139253.931287-10.812500
M4-694.55634265901553.922166-12.880700
M5-555.47898732663853.914117-10.30300
M6-609.46413199426153.907141-11.305800
M7-532.07427666188453.901237-9.871300
M8-515.43442132950753.896405-9.563400
M9-460.85706599713153.892648-8.551400
M10-319.71721066475453.889963-5.932800
M11-118.38985533237753.888353-2.19690.0293160.014658
t-1.764855332376850.240551-7.336700

\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) & 2324.06337310277 & 44.029939 & 52.7837 & 0 & 0 \tabularnewline
x & -226.385033602658 & 41.037226 & -5.5166 & 0 & 0 \tabularnewline
M1 & -451.374973256311 & 53.942919 & -8.3676 & 0 & 0 \tabularnewline
M2 & -635.461053323769 & 53.941479 & -11.7806 & 0 & 0 \tabularnewline
M3 & -583.133697991392 & 53.931287 & -10.8125 & 0 & 0 \tabularnewline
M4 & -694.556342659015 & 53.922166 & -12.8807 & 0 & 0 \tabularnewline
M5 & -555.478987326638 & 53.914117 & -10.303 & 0 & 0 \tabularnewline
M6 & -609.464131994261 & 53.907141 & -11.3058 & 0 & 0 \tabularnewline
M7 & -532.074276661884 & 53.901237 & -9.8713 & 0 & 0 \tabularnewline
M8 & -515.434421329507 & 53.896405 & -9.5634 & 0 & 0 \tabularnewline
M9 & -460.857065997131 & 53.892648 & -8.5514 & 0 & 0 \tabularnewline
M10 & -319.717210664754 & 53.889963 & -5.9328 & 0 & 0 \tabularnewline
M11 & -118.389855332377 & 53.888353 & -2.1969 & 0.029316 & 0.014658 \tabularnewline
t & -1.76485533237685 & 0.240551 & -7.3367 & 0 & 0 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=24747&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]2324.06337310277[/C][C]44.029939[/C][C]52.7837[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]x[/C][C]-226.385033602658[/C][C]41.037226[/C][C]-5.5166[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]M1[/C][C]-451.374973256311[/C][C]53.942919[/C][C]-8.3676[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]M2[/C][C]-635.461053323769[/C][C]53.941479[/C][C]-11.7806[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]M3[/C][C]-583.133697991392[/C][C]53.931287[/C][C]-10.8125[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]M4[/C][C]-694.556342659015[/C][C]53.922166[/C][C]-12.8807[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]M5[/C][C]-555.478987326638[/C][C]53.914117[/C][C]-10.303[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]M6[/C][C]-609.464131994261[/C][C]53.907141[/C][C]-11.3058[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]M7[/C][C]-532.074276661884[/C][C]53.901237[/C][C]-9.8713[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]M8[/C][C]-515.434421329507[/C][C]53.896405[/C][C]-9.5634[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]M9[/C][C]-460.857065997131[/C][C]53.892648[/C][C]-8.5514[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]M10[/C][C]-319.717210664754[/C][C]53.889963[/C][C]-5.9328[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]M11[/C][C]-118.389855332377[/C][C]53.888353[/C][C]-2.1969[/C][C]0.029316[/C][C]0.014658[/C][/ROW]
[ROW][C]t[/C][C]-1.76485533237685[/C][C]0.240551[/C][C]-7.3367[/C][C]0[/C][C]0[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=24747&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=24747&T=2

As an alternative you can also use a QR Code:  
QR Code


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

Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)2324.0633731027744.02993952.783700
x-226.38503360265841.037226-5.516600
M1-451.37497325631153.942919-8.367600
M2-635.46105332376953.941479-11.780600
M3-583.13369799139253.931287-10.812500
M4-694.55634265901553.922166-12.880700
M5-555.47898732663853.914117-10.30300
M6-609.46413199426153.907141-11.305800
M7-532.07427666188453.901237-9.871300
M8-515.43442132950753.896405-9.563400
M9-460.85706599713153.892648-8.551400
M10-319.71721066475453.889963-5.932800
M11-118.38985533237753.888353-2.19690.0293160.014658
t-1.764855332376850.240551-7.336700






Multiple Linear Regression - Regression Statistics
Multiple R0.861322441473346
R-squared0.741876348185605
Adjusted R-squared0.723024620805902
F-TEST (value)39.3532291891913
F-TEST (DF numerator)13
F-TEST (DF denominator)178
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation152.417759557721
Sum Squared Residuals4135148.87028996

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.861322441473346 \tabularnewline
R-squared & 0.741876348185605 \tabularnewline
Adjusted R-squared & 0.723024620805902 \tabularnewline
F-TEST (value) & 39.3532291891913 \tabularnewline
F-TEST (DF numerator) & 13 \tabularnewline
F-TEST (DF denominator) & 178 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 152.417759557721 \tabularnewline
Sum Squared Residuals & 4135148.87028996 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=24747&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.861322441473346[/C][/ROW]
[ROW][C]R-squared[/C][C]0.741876348185605[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.723024620805902[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]39.3532291891913[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]13[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]178[/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]152.417759557721[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]4135148.87028996[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=24747&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=24747&T=3

As an alternative you can also use a QR Code:  
QR Code


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

Multiple Linear Regression - Regression Statistics
Multiple R0.861322441473346
R-squared0.741876348185605
Adjusted R-squared0.723024620805902
F-TEST (value)39.3532291891913
F-TEST (DF numerator)13
F-TEST (DF denominator)178
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation152.417759557721
Sum Squared Residuals4135148.87028996






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
116871870.92354451408-183.923544514078
215081685.07260911425-177.072609114249
315071735.63510911425-228.635109114249
413851622.44760911425-237.447609114250
516321759.76010911425-127.760109114249
615111704.01010911425-193.010109114250
715591779.63510911425-220.635109114249
816301794.51010911425-164.510109114249
915791847.32260911425-268.322609114249
1016531986.69760911425-333.697609114249
1121522186.26010911425-34.2601091142491
1221482302.88510911425-154.885109114249
1317521849.74528052556-97.7452805255611
1417651663.89434512573101.105654874273
1517171714.456845125732.54315487427314
1615581601.26934512573-43.2693451257269
1715751738.58184512573-163.581845125727
1815201682.83184512573-162.831845125727
1918051758.4568451257346.5431548742731
2018001773.3318451257326.6681548742731
2117191826.14434512573-107.144345125727
2220081965.5193451257342.4806548742732
2322422165.0818451257376.9181548742732
2424782281.70684512573196.293154874273
2520301828.56701653704201.432983462961
2616551642.7160811372012.2839188627954
2716931693.27858113720-0.278581137204616
2816231580.0910811372042.9089188627954
2918051717.4035811372087.5964188627954
3017461661.6535811372084.3464188627954
3117951737.2785811372057.7214188627954
3219261752.15358113720173.846418862795
3316191804.96608113720-185.966081137205
3419921944.3410811372047.6589188627954
3522332143.9035811372089.0964188627954
3621922260.52858113720-68.5285811372045
3720801807.38875254852272.611247451483
3817681621.53781714868146.462182851318
3918351672.10031714868162.899682851318
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4119761696.22531714868279.774682851318
4218531640.47531714868212.524682851318
4319651716.10031714868248.899682851318
4416891730.97531714868-41.9753171486824
4517781783.78781714868-5.78781714868237
4619761923.1628171486852.8371828513176
4723972122.72531714868274.274682851318
4826542239.35031714868414.649682851318
4920971786.21048855999310.789511440006
5019631600.35955316016362.64044683984
5116771650.9220531601626.0779468398399
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5320031675.04705316016327.95294683984
5418131619.29705316016193.70294683984
5520121694.92205316016317.07794683984
5619121709.79705316016202.202946839840
5720841762.60955316016321.39044683984
5820801901.98455316016178.01544683984
5921182101.5470531601616.4529468398399
6021502218.17205316016-68.1720531601601
6116081765.03222457147-157.032224571472
6215031579.18128917164-76.1812891716379
6315481629.74378917164-81.7437891716379
6413821516.55628917164-134.556289171638
6517311653.8687891716477.1312108283621
6617981598.11878917164199.881210828362
6717791673.74378917164105.256210828362
6818871688.61878917164198.381210828362
6920041741.43128917164262.568710828362
7020771880.80628917164196.193710828362
7120922080.3687891716411.6312108283621
7220512196.99378917164-145.993789171638
7315771743.85396058295-166.85396058295
7413561558.00302518312-202.003025183116
7516521608.5655251831243.4344748168844
7613821495.37802518312-113.378025183116
7715191632.69052518312-113.690525183116
7814211576.94052518312-155.940525183116
7914421652.56552518312-210.565525183116
8015431667.44052518312-124.440525183116
8116561720.25302518312-64.2530251831156
8215611859.62802518312-298.628025183116
8319052059.19052518312-154.190525183116
8421992175.8155251831223.1844748168844
8514731722.67569659443-249.675696594427
8616551536.82476119459118.175238805407
8714071587.38726119459-180.387261194593
8813951474.19976119459-79.1997611945933
8915301611.51226119459-81.5122611945934
9013091555.76226119459-246.762261194593
9115261631.38726119459-105.387261194593
9213271646.26226119459-319.262261194593
9316271699.07476119459-72.0747611945934
9417481838.44976119459-90.4497611945934
9519582038.01226119459-80.0122611945934
9622742154.63726119459119.362738805407
9716481701.49743260591-53.4974326059054
9814011515.64649720607-114.646497206071
9914111566.20899720607-155.208997206071
10014031453.02149720607-50.0214972060711
10113941590.33399720607-196.333997206071
10215201534.58399720607-14.5839972060711
10315281610.20899720607-82.2089972060712
10416431625.0839972060717.9160027939289
10515151677.89649720607-162.896497206071
10616851817.27149720607-132.271497206071
10720002016.83399720607-16.8339972060712
10822152133.4589972060781.5410027939289
10919561680.31916861738275.680831382617
11014621494.46823321755-32.4682332175488
11115631545.0307332175517.9692667824511
11214591431.8432332175527.1567667824512
11314461569.15573321755-123.155733217549
11416221513.40573321755108.594266782451
11516571589.0307332175567.9692667824512
11616381603.9057332175534.0942667824511
11716431656.71823321755-13.7182332175489
11816831796.09323321755-113.093233217549
11920501995.6557332175554.3442667824512
12022622112.28073321755149.719266782451
12118131659.14090462886153.859095371139
12214451473.28996922903-28.2899692290266
12317621523.85246922903238.147530770973
12414611410.6649692290350.3350307709734
12515561547.977469229038.02253077097338
12614311492.22746922903-61.2274692290265
12714271567.85246922903-140.852469229027
12815541582.72746922903-28.7274692290267
12916451635.539969229039.4600307709734
13016531774.91496922903-121.914969229027
13120161974.4774692290341.5225307709734
13222072091.10246922903115.897530770973
13316651637.9626406403427.0373593596614
13413611452.11170524050-91.1117052405044
13515061502.674205240503.32579475949564
13613601389.48670524050-29.4867052405044
13714531526.79920524050-73.7992052405043
13815221471.0492052405050.9507947594957
13914601546.67420524050-86.6742052405043
14015521561.54920524050-9.5492052405044
14115481614.36170524050-66.3617052405044
14218271753.7367052405073.2632947594957
14317371953.29920524050-216.299205240504
14419412069.92420524050-128.924205240504
14514741616.78437665182-142.784376651816
14614581430.9334412519827.0665587480179
14715421481.4959412519860.5040587480179
14814041368.3084412519835.6915587480179
14915221505.6209412519816.3790587480179
15013851449.87094125198-64.870941251982
15116411525.49594125198115.504058748018
15215101540.37094125198-30.3709412519821
15316811593.1834412519887.816558748018
15419381732.55844125198205.441558748018
15518681932.12094125198-64.1209412519821
15617262048.74594125198-322.745941251982
15714561595.60611266329-139.606112663294
15814451409.7551772634635.2448227365402
15914561460.31767726346-4.31767726345983
16013651347.1301772634617.8698227365402
16114871484.442677263462.55732273654013
16215581428.69267726346129.307322736540
16314881504.31767726346-16.3176772634599
16416841519.19267726346164.80732273654
16515941572.0051772634621.9948227365402
16618501711.38017726346138.619822736540
16719981910.9426772634687.05732273654
16820792027.5676772634651.4323227365402
16914941574.42784867477-80.4278486747719
17010571162.19187967228-105.191879672280
17112181212.754379672285.2456203277202
17211681099.5668796722868.4331203277202
17312361236.87937967228-0.879379672279648
17410761181.12937967228-105.129379672280
17511741256.75437967228-82.7543796722796
17611391271.62937967228-132.629379672280
17714271324.44187967228102.558120327720
17814871463.8168796722823.1831203277203
17914831663.37937967228-180.379379672280
18015131780.00437967228-267.004379672280
18113571326.8645510835930.1354489164084
18211651141.0136156837623.9863843162425
18312821191.5761156837690.4238843162424
18411101078.3886156837631.6113843162425
18512971215.7011156837681.2988843162426
18611851159.9511156837625.0488843162426
18712221235.57611568376-13.5761156837574
18812841250.4511156837633.5488843162426
18914441303.26361568376140.736384316243
19015751442.63861568376132.361384316243
19117371642.2011156837694.7988843162425
19217631758.826115683764.17388431624252

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 1687 & 1870.92354451408 & -183.923544514078 \tabularnewline
2 & 1508 & 1685.07260911425 & -177.072609114249 \tabularnewline
3 & 1507 & 1735.63510911425 & -228.635109114249 \tabularnewline
4 & 1385 & 1622.44760911425 & -237.447609114250 \tabularnewline
5 & 1632 & 1759.76010911425 & -127.760109114249 \tabularnewline
6 & 1511 & 1704.01010911425 & -193.010109114250 \tabularnewline
7 & 1559 & 1779.63510911425 & -220.635109114249 \tabularnewline
8 & 1630 & 1794.51010911425 & -164.510109114249 \tabularnewline
9 & 1579 & 1847.32260911425 & -268.322609114249 \tabularnewline
10 & 1653 & 1986.69760911425 & -333.697609114249 \tabularnewline
11 & 2152 & 2186.26010911425 & -34.2601091142491 \tabularnewline
12 & 2148 & 2302.88510911425 & -154.885109114249 \tabularnewline
13 & 1752 & 1849.74528052556 & -97.7452805255611 \tabularnewline
14 & 1765 & 1663.89434512573 & 101.105654874273 \tabularnewline
15 & 1717 & 1714.45684512573 & 2.54315487427314 \tabularnewline
16 & 1558 & 1601.26934512573 & -43.2693451257269 \tabularnewline
17 & 1575 & 1738.58184512573 & -163.581845125727 \tabularnewline
18 & 1520 & 1682.83184512573 & -162.831845125727 \tabularnewline
19 & 1805 & 1758.45684512573 & 46.5431548742731 \tabularnewline
20 & 1800 & 1773.33184512573 & 26.6681548742731 \tabularnewline
21 & 1719 & 1826.14434512573 & -107.144345125727 \tabularnewline
22 & 2008 & 1965.51934512573 & 42.4806548742732 \tabularnewline
23 & 2242 & 2165.08184512573 & 76.9181548742732 \tabularnewline
24 & 2478 & 2281.70684512573 & 196.293154874273 \tabularnewline
25 & 2030 & 1828.56701653704 & 201.432983462961 \tabularnewline
26 & 1655 & 1642.71608113720 & 12.2839188627954 \tabularnewline
27 & 1693 & 1693.27858113720 & -0.278581137204616 \tabularnewline
28 & 1623 & 1580.09108113720 & 42.9089188627954 \tabularnewline
29 & 1805 & 1717.40358113720 & 87.5964188627954 \tabularnewline
30 & 1746 & 1661.65358113720 & 84.3464188627954 \tabularnewline
31 & 1795 & 1737.27858113720 & 57.7214188627954 \tabularnewline
32 & 1926 & 1752.15358113720 & 173.846418862795 \tabularnewline
33 & 1619 & 1804.96608113720 & -185.966081137205 \tabularnewline
34 & 1992 & 1944.34108113720 & 47.6589188627954 \tabularnewline
35 & 2233 & 2143.90358113720 & 89.0964188627954 \tabularnewline
36 & 2192 & 2260.52858113720 & -68.5285811372045 \tabularnewline
37 & 2080 & 1807.38875254852 & 272.611247451483 \tabularnewline
38 & 1768 & 1621.53781714868 & 146.462182851318 \tabularnewline
39 & 1835 & 1672.10031714868 & 162.899682851318 \tabularnewline
40 & 1569 & 1558.91281714868 & 10.0871828513176 \tabularnewline
41 & 1976 & 1696.22531714868 & 279.774682851318 \tabularnewline
42 & 1853 & 1640.47531714868 & 212.524682851318 \tabularnewline
43 & 1965 & 1716.10031714868 & 248.899682851318 \tabularnewline
44 & 1689 & 1730.97531714868 & -41.9753171486824 \tabularnewline
45 & 1778 & 1783.78781714868 & -5.78781714868237 \tabularnewline
46 & 1976 & 1923.16281714868 & 52.8371828513176 \tabularnewline
47 & 2397 & 2122.72531714868 & 274.274682851318 \tabularnewline
48 & 2654 & 2239.35031714868 & 414.649682851318 \tabularnewline
49 & 2097 & 1786.21048855999 & 310.789511440006 \tabularnewline
50 & 1963 & 1600.35955316016 & 362.64044683984 \tabularnewline
51 & 1677 & 1650.92205316016 & 26.0779468398399 \tabularnewline
52 & 1941 & 1537.73455316016 & 403.26544683984 \tabularnewline
53 & 2003 & 1675.04705316016 & 327.95294683984 \tabularnewline
54 & 1813 & 1619.29705316016 & 193.70294683984 \tabularnewline
55 & 2012 & 1694.92205316016 & 317.07794683984 \tabularnewline
56 & 1912 & 1709.79705316016 & 202.202946839840 \tabularnewline
57 & 2084 & 1762.60955316016 & 321.39044683984 \tabularnewline
58 & 2080 & 1901.98455316016 & 178.01544683984 \tabularnewline
59 & 2118 & 2101.54705316016 & 16.4529468398399 \tabularnewline
60 & 2150 & 2218.17205316016 & -68.1720531601601 \tabularnewline
61 & 1608 & 1765.03222457147 & -157.032224571472 \tabularnewline
62 & 1503 & 1579.18128917164 & -76.1812891716379 \tabularnewline
63 & 1548 & 1629.74378917164 & -81.7437891716379 \tabularnewline
64 & 1382 & 1516.55628917164 & -134.556289171638 \tabularnewline
65 & 1731 & 1653.86878917164 & 77.1312108283621 \tabularnewline
66 & 1798 & 1598.11878917164 & 199.881210828362 \tabularnewline
67 & 1779 & 1673.74378917164 & 105.256210828362 \tabularnewline
68 & 1887 & 1688.61878917164 & 198.381210828362 \tabularnewline
69 & 2004 & 1741.43128917164 & 262.568710828362 \tabularnewline
70 & 2077 & 1880.80628917164 & 196.193710828362 \tabularnewline
71 & 2092 & 2080.36878917164 & 11.6312108283621 \tabularnewline
72 & 2051 & 2196.99378917164 & -145.993789171638 \tabularnewline
73 & 1577 & 1743.85396058295 & -166.85396058295 \tabularnewline
74 & 1356 & 1558.00302518312 & -202.003025183116 \tabularnewline
75 & 1652 & 1608.56552518312 & 43.4344748168844 \tabularnewline
76 & 1382 & 1495.37802518312 & -113.378025183116 \tabularnewline
77 & 1519 & 1632.69052518312 & -113.690525183116 \tabularnewline
78 & 1421 & 1576.94052518312 & -155.940525183116 \tabularnewline
79 & 1442 & 1652.56552518312 & -210.565525183116 \tabularnewline
80 & 1543 & 1667.44052518312 & -124.440525183116 \tabularnewline
81 & 1656 & 1720.25302518312 & -64.2530251831156 \tabularnewline
82 & 1561 & 1859.62802518312 & -298.628025183116 \tabularnewline
83 & 1905 & 2059.19052518312 & -154.190525183116 \tabularnewline
84 & 2199 & 2175.81552518312 & 23.1844748168844 \tabularnewline
85 & 1473 & 1722.67569659443 & -249.675696594427 \tabularnewline
86 & 1655 & 1536.82476119459 & 118.175238805407 \tabularnewline
87 & 1407 & 1587.38726119459 & -180.387261194593 \tabularnewline
88 & 1395 & 1474.19976119459 & -79.1997611945933 \tabularnewline
89 & 1530 & 1611.51226119459 & -81.5122611945934 \tabularnewline
90 & 1309 & 1555.76226119459 & -246.762261194593 \tabularnewline
91 & 1526 & 1631.38726119459 & -105.387261194593 \tabularnewline
92 & 1327 & 1646.26226119459 & -319.262261194593 \tabularnewline
93 & 1627 & 1699.07476119459 & -72.0747611945934 \tabularnewline
94 & 1748 & 1838.44976119459 & -90.4497611945934 \tabularnewline
95 & 1958 & 2038.01226119459 & -80.0122611945934 \tabularnewline
96 & 2274 & 2154.63726119459 & 119.362738805407 \tabularnewline
97 & 1648 & 1701.49743260591 & -53.4974326059054 \tabularnewline
98 & 1401 & 1515.64649720607 & -114.646497206071 \tabularnewline
99 & 1411 & 1566.20899720607 & -155.208997206071 \tabularnewline
100 & 1403 & 1453.02149720607 & -50.0214972060711 \tabularnewline
101 & 1394 & 1590.33399720607 & -196.333997206071 \tabularnewline
102 & 1520 & 1534.58399720607 & -14.5839972060711 \tabularnewline
103 & 1528 & 1610.20899720607 & -82.2089972060712 \tabularnewline
104 & 1643 & 1625.08399720607 & 17.9160027939289 \tabularnewline
105 & 1515 & 1677.89649720607 & -162.896497206071 \tabularnewline
106 & 1685 & 1817.27149720607 & -132.271497206071 \tabularnewline
107 & 2000 & 2016.83399720607 & -16.8339972060712 \tabularnewline
108 & 2215 & 2133.45899720607 & 81.5410027939289 \tabularnewline
109 & 1956 & 1680.31916861738 & 275.680831382617 \tabularnewline
110 & 1462 & 1494.46823321755 & -32.4682332175488 \tabularnewline
111 & 1563 & 1545.03073321755 & 17.9692667824511 \tabularnewline
112 & 1459 & 1431.84323321755 & 27.1567667824512 \tabularnewline
113 & 1446 & 1569.15573321755 & -123.155733217549 \tabularnewline
114 & 1622 & 1513.40573321755 & 108.594266782451 \tabularnewline
115 & 1657 & 1589.03073321755 & 67.9692667824512 \tabularnewline
116 & 1638 & 1603.90573321755 & 34.0942667824511 \tabularnewline
117 & 1643 & 1656.71823321755 & -13.7182332175489 \tabularnewline
118 & 1683 & 1796.09323321755 & -113.093233217549 \tabularnewline
119 & 2050 & 1995.65573321755 & 54.3442667824512 \tabularnewline
120 & 2262 & 2112.28073321755 & 149.719266782451 \tabularnewline
121 & 1813 & 1659.14090462886 & 153.859095371139 \tabularnewline
122 & 1445 & 1473.28996922903 & -28.2899692290266 \tabularnewline
123 & 1762 & 1523.85246922903 & 238.147530770973 \tabularnewline
124 & 1461 & 1410.66496922903 & 50.3350307709734 \tabularnewline
125 & 1556 & 1547.97746922903 & 8.02253077097338 \tabularnewline
126 & 1431 & 1492.22746922903 & -61.2274692290265 \tabularnewline
127 & 1427 & 1567.85246922903 & -140.852469229027 \tabularnewline
128 & 1554 & 1582.72746922903 & -28.7274692290267 \tabularnewline
129 & 1645 & 1635.53996922903 & 9.4600307709734 \tabularnewline
130 & 1653 & 1774.91496922903 & -121.914969229027 \tabularnewline
131 & 2016 & 1974.47746922903 & 41.5225307709734 \tabularnewline
132 & 2207 & 2091.10246922903 & 115.897530770973 \tabularnewline
133 & 1665 & 1637.96264064034 & 27.0373593596614 \tabularnewline
134 & 1361 & 1452.11170524050 & -91.1117052405044 \tabularnewline
135 & 1506 & 1502.67420524050 & 3.32579475949564 \tabularnewline
136 & 1360 & 1389.48670524050 & -29.4867052405044 \tabularnewline
137 & 1453 & 1526.79920524050 & -73.7992052405043 \tabularnewline
138 & 1522 & 1471.04920524050 & 50.9507947594957 \tabularnewline
139 & 1460 & 1546.67420524050 & -86.6742052405043 \tabularnewline
140 & 1552 & 1561.54920524050 & -9.5492052405044 \tabularnewline
141 & 1548 & 1614.36170524050 & -66.3617052405044 \tabularnewline
142 & 1827 & 1753.73670524050 & 73.2632947594957 \tabularnewline
143 & 1737 & 1953.29920524050 & -216.299205240504 \tabularnewline
144 & 1941 & 2069.92420524050 & -128.924205240504 \tabularnewline
145 & 1474 & 1616.78437665182 & -142.784376651816 \tabularnewline
146 & 1458 & 1430.93344125198 & 27.0665587480179 \tabularnewline
147 & 1542 & 1481.49594125198 & 60.5040587480179 \tabularnewline
148 & 1404 & 1368.30844125198 & 35.6915587480179 \tabularnewline
149 & 1522 & 1505.62094125198 & 16.3790587480179 \tabularnewline
150 & 1385 & 1449.87094125198 & -64.870941251982 \tabularnewline
151 & 1641 & 1525.49594125198 & 115.504058748018 \tabularnewline
152 & 1510 & 1540.37094125198 & -30.3709412519821 \tabularnewline
153 & 1681 & 1593.18344125198 & 87.816558748018 \tabularnewline
154 & 1938 & 1732.55844125198 & 205.441558748018 \tabularnewline
155 & 1868 & 1932.12094125198 & -64.1209412519821 \tabularnewline
156 & 1726 & 2048.74594125198 & -322.745941251982 \tabularnewline
157 & 1456 & 1595.60611266329 & -139.606112663294 \tabularnewline
158 & 1445 & 1409.75517726346 & 35.2448227365402 \tabularnewline
159 & 1456 & 1460.31767726346 & -4.31767726345983 \tabularnewline
160 & 1365 & 1347.13017726346 & 17.8698227365402 \tabularnewline
161 & 1487 & 1484.44267726346 & 2.55732273654013 \tabularnewline
162 & 1558 & 1428.69267726346 & 129.307322736540 \tabularnewline
163 & 1488 & 1504.31767726346 & -16.3176772634599 \tabularnewline
164 & 1684 & 1519.19267726346 & 164.80732273654 \tabularnewline
165 & 1594 & 1572.00517726346 & 21.9948227365402 \tabularnewline
166 & 1850 & 1711.38017726346 & 138.619822736540 \tabularnewline
167 & 1998 & 1910.94267726346 & 87.05732273654 \tabularnewline
168 & 2079 & 2027.56767726346 & 51.4323227365402 \tabularnewline
169 & 1494 & 1574.42784867477 & -80.4278486747719 \tabularnewline
170 & 1057 & 1162.19187967228 & -105.191879672280 \tabularnewline
171 & 1218 & 1212.75437967228 & 5.2456203277202 \tabularnewline
172 & 1168 & 1099.56687967228 & 68.4331203277202 \tabularnewline
173 & 1236 & 1236.87937967228 & -0.879379672279648 \tabularnewline
174 & 1076 & 1181.12937967228 & -105.129379672280 \tabularnewline
175 & 1174 & 1256.75437967228 & -82.7543796722796 \tabularnewline
176 & 1139 & 1271.62937967228 & -132.629379672280 \tabularnewline
177 & 1427 & 1324.44187967228 & 102.558120327720 \tabularnewline
178 & 1487 & 1463.81687967228 & 23.1831203277203 \tabularnewline
179 & 1483 & 1663.37937967228 & -180.379379672280 \tabularnewline
180 & 1513 & 1780.00437967228 & -267.004379672280 \tabularnewline
181 & 1357 & 1326.86455108359 & 30.1354489164084 \tabularnewline
182 & 1165 & 1141.01361568376 & 23.9863843162425 \tabularnewline
183 & 1282 & 1191.57611568376 & 90.4238843162424 \tabularnewline
184 & 1110 & 1078.38861568376 & 31.6113843162425 \tabularnewline
185 & 1297 & 1215.70111568376 & 81.2988843162426 \tabularnewline
186 & 1185 & 1159.95111568376 & 25.0488843162426 \tabularnewline
187 & 1222 & 1235.57611568376 & -13.5761156837574 \tabularnewline
188 & 1284 & 1250.45111568376 & 33.5488843162426 \tabularnewline
189 & 1444 & 1303.26361568376 & 140.736384316243 \tabularnewline
190 & 1575 & 1442.63861568376 & 132.361384316243 \tabularnewline
191 & 1737 & 1642.20111568376 & 94.7988843162425 \tabularnewline
192 & 1763 & 1758.82611568376 & 4.17388431624252 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=24747&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]1687[/C][C]1870.92354451408[/C][C]-183.923544514078[/C][/ROW]
[ROW][C]2[/C][C]1508[/C][C]1685.07260911425[/C][C]-177.072609114249[/C][/ROW]
[ROW][C]3[/C][C]1507[/C][C]1735.63510911425[/C][C]-228.635109114249[/C][/ROW]
[ROW][C]4[/C][C]1385[/C][C]1622.44760911425[/C][C]-237.447609114250[/C][/ROW]
[ROW][C]5[/C][C]1632[/C][C]1759.76010911425[/C][C]-127.760109114249[/C][/ROW]
[ROW][C]6[/C][C]1511[/C][C]1704.01010911425[/C][C]-193.010109114250[/C][/ROW]
[ROW][C]7[/C][C]1559[/C][C]1779.63510911425[/C][C]-220.635109114249[/C][/ROW]
[ROW][C]8[/C][C]1630[/C][C]1794.51010911425[/C][C]-164.510109114249[/C][/ROW]
[ROW][C]9[/C][C]1579[/C][C]1847.32260911425[/C][C]-268.322609114249[/C][/ROW]
[ROW][C]10[/C][C]1653[/C][C]1986.69760911425[/C][C]-333.697609114249[/C][/ROW]
[ROW][C]11[/C][C]2152[/C][C]2186.26010911425[/C][C]-34.2601091142491[/C][/ROW]
[ROW][C]12[/C][C]2148[/C][C]2302.88510911425[/C][C]-154.885109114249[/C][/ROW]
[ROW][C]13[/C][C]1752[/C][C]1849.74528052556[/C][C]-97.7452805255611[/C][/ROW]
[ROW][C]14[/C][C]1765[/C][C]1663.89434512573[/C][C]101.105654874273[/C][/ROW]
[ROW][C]15[/C][C]1717[/C][C]1714.45684512573[/C][C]2.54315487427314[/C][/ROW]
[ROW][C]16[/C][C]1558[/C][C]1601.26934512573[/C][C]-43.2693451257269[/C][/ROW]
[ROW][C]17[/C][C]1575[/C][C]1738.58184512573[/C][C]-163.581845125727[/C][/ROW]
[ROW][C]18[/C][C]1520[/C][C]1682.83184512573[/C][C]-162.831845125727[/C][/ROW]
[ROW][C]19[/C][C]1805[/C][C]1758.45684512573[/C][C]46.5431548742731[/C][/ROW]
[ROW][C]20[/C][C]1800[/C][C]1773.33184512573[/C][C]26.6681548742731[/C][/ROW]
[ROW][C]21[/C][C]1719[/C][C]1826.14434512573[/C][C]-107.144345125727[/C][/ROW]
[ROW][C]22[/C][C]2008[/C][C]1965.51934512573[/C][C]42.4806548742732[/C][/ROW]
[ROW][C]23[/C][C]2242[/C][C]2165.08184512573[/C][C]76.9181548742732[/C][/ROW]
[ROW][C]24[/C][C]2478[/C][C]2281.70684512573[/C][C]196.293154874273[/C][/ROW]
[ROW][C]25[/C][C]2030[/C][C]1828.56701653704[/C][C]201.432983462961[/C][/ROW]
[ROW][C]26[/C][C]1655[/C][C]1642.71608113720[/C][C]12.2839188627954[/C][/ROW]
[ROW][C]27[/C][C]1693[/C][C]1693.27858113720[/C][C]-0.278581137204616[/C][/ROW]
[ROW][C]28[/C][C]1623[/C][C]1580.09108113720[/C][C]42.9089188627954[/C][/ROW]
[ROW][C]29[/C][C]1805[/C][C]1717.40358113720[/C][C]87.5964188627954[/C][/ROW]
[ROW][C]30[/C][C]1746[/C][C]1661.65358113720[/C][C]84.3464188627954[/C][/ROW]
[ROW][C]31[/C][C]1795[/C][C]1737.27858113720[/C][C]57.7214188627954[/C][/ROW]
[ROW][C]32[/C][C]1926[/C][C]1752.15358113720[/C][C]173.846418862795[/C][/ROW]
[ROW][C]33[/C][C]1619[/C][C]1804.96608113720[/C][C]-185.966081137205[/C][/ROW]
[ROW][C]34[/C][C]1992[/C][C]1944.34108113720[/C][C]47.6589188627954[/C][/ROW]
[ROW][C]35[/C][C]2233[/C][C]2143.90358113720[/C][C]89.0964188627954[/C][/ROW]
[ROW][C]36[/C][C]2192[/C][C]2260.52858113720[/C][C]-68.5285811372045[/C][/ROW]
[ROW][C]37[/C][C]2080[/C][C]1807.38875254852[/C][C]272.611247451483[/C][/ROW]
[ROW][C]38[/C][C]1768[/C][C]1621.53781714868[/C][C]146.462182851318[/C][/ROW]
[ROW][C]39[/C][C]1835[/C][C]1672.10031714868[/C][C]162.899682851318[/C][/ROW]
[ROW][C]40[/C][C]1569[/C][C]1558.91281714868[/C][C]10.0871828513176[/C][/ROW]
[ROW][C]41[/C][C]1976[/C][C]1696.22531714868[/C][C]279.774682851318[/C][/ROW]
[ROW][C]42[/C][C]1853[/C][C]1640.47531714868[/C][C]212.524682851318[/C][/ROW]
[ROW][C]43[/C][C]1965[/C][C]1716.10031714868[/C][C]248.899682851318[/C][/ROW]
[ROW][C]44[/C][C]1689[/C][C]1730.97531714868[/C][C]-41.9753171486824[/C][/ROW]
[ROW][C]45[/C][C]1778[/C][C]1783.78781714868[/C][C]-5.78781714868237[/C][/ROW]
[ROW][C]46[/C][C]1976[/C][C]1923.16281714868[/C][C]52.8371828513176[/C][/ROW]
[ROW][C]47[/C][C]2397[/C][C]2122.72531714868[/C][C]274.274682851318[/C][/ROW]
[ROW][C]48[/C][C]2654[/C][C]2239.35031714868[/C][C]414.649682851318[/C][/ROW]
[ROW][C]49[/C][C]2097[/C][C]1786.21048855999[/C][C]310.789511440006[/C][/ROW]
[ROW][C]50[/C][C]1963[/C][C]1600.35955316016[/C][C]362.64044683984[/C][/ROW]
[ROW][C]51[/C][C]1677[/C][C]1650.92205316016[/C][C]26.0779468398399[/C][/ROW]
[ROW][C]52[/C][C]1941[/C][C]1537.73455316016[/C][C]403.26544683984[/C][/ROW]
[ROW][C]53[/C][C]2003[/C][C]1675.04705316016[/C][C]327.95294683984[/C][/ROW]
[ROW][C]54[/C][C]1813[/C][C]1619.29705316016[/C][C]193.70294683984[/C][/ROW]
[ROW][C]55[/C][C]2012[/C][C]1694.92205316016[/C][C]317.07794683984[/C][/ROW]
[ROW][C]56[/C][C]1912[/C][C]1709.79705316016[/C][C]202.202946839840[/C][/ROW]
[ROW][C]57[/C][C]2084[/C][C]1762.60955316016[/C][C]321.39044683984[/C][/ROW]
[ROW][C]58[/C][C]2080[/C][C]1901.98455316016[/C][C]178.01544683984[/C][/ROW]
[ROW][C]59[/C][C]2118[/C][C]2101.54705316016[/C][C]16.4529468398399[/C][/ROW]
[ROW][C]60[/C][C]2150[/C][C]2218.17205316016[/C][C]-68.1720531601601[/C][/ROW]
[ROW][C]61[/C][C]1608[/C][C]1765.03222457147[/C][C]-157.032224571472[/C][/ROW]
[ROW][C]62[/C][C]1503[/C][C]1579.18128917164[/C][C]-76.1812891716379[/C][/ROW]
[ROW][C]63[/C][C]1548[/C][C]1629.74378917164[/C][C]-81.7437891716379[/C][/ROW]
[ROW][C]64[/C][C]1382[/C][C]1516.55628917164[/C][C]-134.556289171638[/C][/ROW]
[ROW][C]65[/C][C]1731[/C][C]1653.86878917164[/C][C]77.1312108283621[/C][/ROW]
[ROW][C]66[/C][C]1798[/C][C]1598.11878917164[/C][C]199.881210828362[/C][/ROW]
[ROW][C]67[/C][C]1779[/C][C]1673.74378917164[/C][C]105.256210828362[/C][/ROW]
[ROW][C]68[/C][C]1887[/C][C]1688.61878917164[/C][C]198.381210828362[/C][/ROW]
[ROW][C]69[/C][C]2004[/C][C]1741.43128917164[/C][C]262.568710828362[/C][/ROW]
[ROW][C]70[/C][C]2077[/C][C]1880.80628917164[/C][C]196.193710828362[/C][/ROW]
[ROW][C]71[/C][C]2092[/C][C]2080.36878917164[/C][C]11.6312108283621[/C][/ROW]
[ROW][C]72[/C][C]2051[/C][C]2196.99378917164[/C][C]-145.993789171638[/C][/ROW]
[ROW][C]73[/C][C]1577[/C][C]1743.85396058295[/C][C]-166.85396058295[/C][/ROW]
[ROW][C]74[/C][C]1356[/C][C]1558.00302518312[/C][C]-202.003025183116[/C][/ROW]
[ROW][C]75[/C][C]1652[/C][C]1608.56552518312[/C][C]43.4344748168844[/C][/ROW]
[ROW][C]76[/C][C]1382[/C][C]1495.37802518312[/C][C]-113.378025183116[/C][/ROW]
[ROW][C]77[/C][C]1519[/C][C]1632.69052518312[/C][C]-113.690525183116[/C][/ROW]
[ROW][C]78[/C][C]1421[/C][C]1576.94052518312[/C][C]-155.940525183116[/C][/ROW]
[ROW][C]79[/C][C]1442[/C][C]1652.56552518312[/C][C]-210.565525183116[/C][/ROW]
[ROW][C]80[/C][C]1543[/C][C]1667.44052518312[/C][C]-124.440525183116[/C][/ROW]
[ROW][C]81[/C][C]1656[/C][C]1720.25302518312[/C][C]-64.2530251831156[/C][/ROW]
[ROW][C]82[/C][C]1561[/C][C]1859.62802518312[/C][C]-298.628025183116[/C][/ROW]
[ROW][C]83[/C][C]1905[/C][C]2059.19052518312[/C][C]-154.190525183116[/C][/ROW]
[ROW][C]84[/C][C]2199[/C][C]2175.81552518312[/C][C]23.1844748168844[/C][/ROW]
[ROW][C]85[/C][C]1473[/C][C]1722.67569659443[/C][C]-249.675696594427[/C][/ROW]
[ROW][C]86[/C][C]1655[/C][C]1536.82476119459[/C][C]118.175238805407[/C][/ROW]
[ROW][C]87[/C][C]1407[/C][C]1587.38726119459[/C][C]-180.387261194593[/C][/ROW]
[ROW][C]88[/C][C]1395[/C][C]1474.19976119459[/C][C]-79.1997611945933[/C][/ROW]
[ROW][C]89[/C][C]1530[/C][C]1611.51226119459[/C][C]-81.5122611945934[/C][/ROW]
[ROW][C]90[/C][C]1309[/C][C]1555.76226119459[/C][C]-246.762261194593[/C][/ROW]
[ROW][C]91[/C][C]1526[/C][C]1631.38726119459[/C][C]-105.387261194593[/C][/ROW]
[ROW][C]92[/C][C]1327[/C][C]1646.26226119459[/C][C]-319.262261194593[/C][/ROW]
[ROW][C]93[/C][C]1627[/C][C]1699.07476119459[/C][C]-72.0747611945934[/C][/ROW]
[ROW][C]94[/C][C]1748[/C][C]1838.44976119459[/C][C]-90.4497611945934[/C][/ROW]
[ROW][C]95[/C][C]1958[/C][C]2038.01226119459[/C][C]-80.0122611945934[/C][/ROW]
[ROW][C]96[/C][C]2274[/C][C]2154.63726119459[/C][C]119.362738805407[/C][/ROW]
[ROW][C]97[/C][C]1648[/C][C]1701.49743260591[/C][C]-53.4974326059054[/C][/ROW]
[ROW][C]98[/C][C]1401[/C][C]1515.64649720607[/C][C]-114.646497206071[/C][/ROW]
[ROW][C]99[/C][C]1411[/C][C]1566.20899720607[/C][C]-155.208997206071[/C][/ROW]
[ROW][C]100[/C][C]1403[/C][C]1453.02149720607[/C][C]-50.0214972060711[/C][/ROW]
[ROW][C]101[/C][C]1394[/C][C]1590.33399720607[/C][C]-196.333997206071[/C][/ROW]
[ROW][C]102[/C][C]1520[/C][C]1534.58399720607[/C][C]-14.5839972060711[/C][/ROW]
[ROW][C]103[/C][C]1528[/C][C]1610.20899720607[/C][C]-82.2089972060712[/C][/ROW]
[ROW][C]104[/C][C]1643[/C][C]1625.08399720607[/C][C]17.9160027939289[/C][/ROW]
[ROW][C]105[/C][C]1515[/C][C]1677.89649720607[/C][C]-162.896497206071[/C][/ROW]
[ROW][C]106[/C][C]1685[/C][C]1817.27149720607[/C][C]-132.271497206071[/C][/ROW]
[ROW][C]107[/C][C]2000[/C][C]2016.83399720607[/C][C]-16.8339972060712[/C][/ROW]
[ROW][C]108[/C][C]2215[/C][C]2133.45899720607[/C][C]81.5410027939289[/C][/ROW]
[ROW][C]109[/C][C]1956[/C][C]1680.31916861738[/C][C]275.680831382617[/C][/ROW]
[ROW][C]110[/C][C]1462[/C][C]1494.46823321755[/C][C]-32.4682332175488[/C][/ROW]
[ROW][C]111[/C][C]1563[/C][C]1545.03073321755[/C][C]17.9692667824511[/C][/ROW]
[ROW][C]112[/C][C]1459[/C][C]1431.84323321755[/C][C]27.1567667824512[/C][/ROW]
[ROW][C]113[/C][C]1446[/C][C]1569.15573321755[/C][C]-123.155733217549[/C][/ROW]
[ROW][C]114[/C][C]1622[/C][C]1513.40573321755[/C][C]108.594266782451[/C][/ROW]
[ROW][C]115[/C][C]1657[/C][C]1589.03073321755[/C][C]67.9692667824512[/C][/ROW]
[ROW][C]116[/C][C]1638[/C][C]1603.90573321755[/C][C]34.0942667824511[/C][/ROW]
[ROW][C]117[/C][C]1643[/C][C]1656.71823321755[/C][C]-13.7182332175489[/C][/ROW]
[ROW][C]118[/C][C]1683[/C][C]1796.09323321755[/C][C]-113.093233217549[/C][/ROW]
[ROW][C]119[/C][C]2050[/C][C]1995.65573321755[/C][C]54.3442667824512[/C][/ROW]
[ROW][C]120[/C][C]2262[/C][C]2112.28073321755[/C][C]149.719266782451[/C][/ROW]
[ROW][C]121[/C][C]1813[/C][C]1659.14090462886[/C][C]153.859095371139[/C][/ROW]
[ROW][C]122[/C][C]1445[/C][C]1473.28996922903[/C][C]-28.2899692290266[/C][/ROW]
[ROW][C]123[/C][C]1762[/C][C]1523.85246922903[/C][C]238.147530770973[/C][/ROW]
[ROW][C]124[/C][C]1461[/C][C]1410.66496922903[/C][C]50.3350307709734[/C][/ROW]
[ROW][C]125[/C][C]1556[/C][C]1547.97746922903[/C][C]8.02253077097338[/C][/ROW]
[ROW][C]126[/C][C]1431[/C][C]1492.22746922903[/C][C]-61.2274692290265[/C][/ROW]
[ROW][C]127[/C][C]1427[/C][C]1567.85246922903[/C][C]-140.852469229027[/C][/ROW]
[ROW][C]128[/C][C]1554[/C][C]1582.72746922903[/C][C]-28.7274692290267[/C][/ROW]
[ROW][C]129[/C][C]1645[/C][C]1635.53996922903[/C][C]9.4600307709734[/C][/ROW]
[ROW][C]130[/C][C]1653[/C][C]1774.91496922903[/C][C]-121.914969229027[/C][/ROW]
[ROW][C]131[/C][C]2016[/C][C]1974.47746922903[/C][C]41.5225307709734[/C][/ROW]
[ROW][C]132[/C][C]2207[/C][C]2091.10246922903[/C][C]115.897530770973[/C][/ROW]
[ROW][C]133[/C][C]1665[/C][C]1637.96264064034[/C][C]27.0373593596614[/C][/ROW]
[ROW][C]134[/C][C]1361[/C][C]1452.11170524050[/C][C]-91.1117052405044[/C][/ROW]
[ROW][C]135[/C][C]1506[/C][C]1502.67420524050[/C][C]3.32579475949564[/C][/ROW]
[ROW][C]136[/C][C]1360[/C][C]1389.48670524050[/C][C]-29.4867052405044[/C][/ROW]
[ROW][C]137[/C][C]1453[/C][C]1526.79920524050[/C][C]-73.7992052405043[/C][/ROW]
[ROW][C]138[/C][C]1522[/C][C]1471.04920524050[/C][C]50.9507947594957[/C][/ROW]
[ROW][C]139[/C][C]1460[/C][C]1546.67420524050[/C][C]-86.6742052405043[/C][/ROW]
[ROW][C]140[/C][C]1552[/C][C]1561.54920524050[/C][C]-9.5492052405044[/C][/ROW]
[ROW][C]141[/C][C]1548[/C][C]1614.36170524050[/C][C]-66.3617052405044[/C][/ROW]
[ROW][C]142[/C][C]1827[/C][C]1753.73670524050[/C][C]73.2632947594957[/C][/ROW]
[ROW][C]143[/C][C]1737[/C][C]1953.29920524050[/C][C]-216.299205240504[/C][/ROW]
[ROW][C]144[/C][C]1941[/C][C]2069.92420524050[/C][C]-128.924205240504[/C][/ROW]
[ROW][C]145[/C][C]1474[/C][C]1616.78437665182[/C][C]-142.784376651816[/C][/ROW]
[ROW][C]146[/C][C]1458[/C][C]1430.93344125198[/C][C]27.0665587480179[/C][/ROW]
[ROW][C]147[/C][C]1542[/C][C]1481.49594125198[/C][C]60.5040587480179[/C][/ROW]
[ROW][C]148[/C][C]1404[/C][C]1368.30844125198[/C][C]35.6915587480179[/C][/ROW]
[ROW][C]149[/C][C]1522[/C][C]1505.62094125198[/C][C]16.3790587480179[/C][/ROW]
[ROW][C]150[/C][C]1385[/C][C]1449.87094125198[/C][C]-64.870941251982[/C][/ROW]
[ROW][C]151[/C][C]1641[/C][C]1525.49594125198[/C][C]115.504058748018[/C][/ROW]
[ROW][C]152[/C][C]1510[/C][C]1540.37094125198[/C][C]-30.3709412519821[/C][/ROW]
[ROW][C]153[/C][C]1681[/C][C]1593.18344125198[/C][C]87.816558748018[/C][/ROW]
[ROW][C]154[/C][C]1938[/C][C]1732.55844125198[/C][C]205.441558748018[/C][/ROW]
[ROW][C]155[/C][C]1868[/C][C]1932.12094125198[/C][C]-64.1209412519821[/C][/ROW]
[ROW][C]156[/C][C]1726[/C][C]2048.74594125198[/C][C]-322.745941251982[/C][/ROW]
[ROW][C]157[/C][C]1456[/C][C]1595.60611266329[/C][C]-139.606112663294[/C][/ROW]
[ROW][C]158[/C][C]1445[/C][C]1409.75517726346[/C][C]35.2448227365402[/C][/ROW]
[ROW][C]159[/C][C]1456[/C][C]1460.31767726346[/C][C]-4.31767726345983[/C][/ROW]
[ROW][C]160[/C][C]1365[/C][C]1347.13017726346[/C][C]17.8698227365402[/C][/ROW]
[ROW][C]161[/C][C]1487[/C][C]1484.44267726346[/C][C]2.55732273654013[/C][/ROW]
[ROW][C]162[/C][C]1558[/C][C]1428.69267726346[/C][C]129.307322736540[/C][/ROW]
[ROW][C]163[/C][C]1488[/C][C]1504.31767726346[/C][C]-16.3176772634599[/C][/ROW]
[ROW][C]164[/C][C]1684[/C][C]1519.19267726346[/C][C]164.80732273654[/C][/ROW]
[ROW][C]165[/C][C]1594[/C][C]1572.00517726346[/C][C]21.9948227365402[/C][/ROW]
[ROW][C]166[/C][C]1850[/C][C]1711.38017726346[/C][C]138.619822736540[/C][/ROW]
[ROW][C]167[/C][C]1998[/C][C]1910.94267726346[/C][C]87.05732273654[/C][/ROW]
[ROW][C]168[/C][C]2079[/C][C]2027.56767726346[/C][C]51.4323227365402[/C][/ROW]
[ROW][C]169[/C][C]1494[/C][C]1574.42784867477[/C][C]-80.4278486747719[/C][/ROW]
[ROW][C]170[/C][C]1057[/C][C]1162.19187967228[/C][C]-105.191879672280[/C][/ROW]
[ROW][C]171[/C][C]1218[/C][C]1212.75437967228[/C][C]5.2456203277202[/C][/ROW]
[ROW][C]172[/C][C]1168[/C][C]1099.56687967228[/C][C]68.4331203277202[/C][/ROW]
[ROW][C]173[/C][C]1236[/C][C]1236.87937967228[/C][C]-0.879379672279648[/C][/ROW]
[ROW][C]174[/C][C]1076[/C][C]1181.12937967228[/C][C]-105.129379672280[/C][/ROW]
[ROW][C]175[/C][C]1174[/C][C]1256.75437967228[/C][C]-82.7543796722796[/C][/ROW]
[ROW][C]176[/C][C]1139[/C][C]1271.62937967228[/C][C]-132.629379672280[/C][/ROW]
[ROW][C]177[/C][C]1427[/C][C]1324.44187967228[/C][C]102.558120327720[/C][/ROW]
[ROW][C]178[/C][C]1487[/C][C]1463.81687967228[/C][C]23.1831203277203[/C][/ROW]
[ROW][C]179[/C][C]1483[/C][C]1663.37937967228[/C][C]-180.379379672280[/C][/ROW]
[ROW][C]180[/C][C]1513[/C][C]1780.00437967228[/C][C]-267.004379672280[/C][/ROW]
[ROW][C]181[/C][C]1357[/C][C]1326.86455108359[/C][C]30.1354489164084[/C][/ROW]
[ROW][C]182[/C][C]1165[/C][C]1141.01361568376[/C][C]23.9863843162425[/C][/ROW]
[ROW][C]183[/C][C]1282[/C][C]1191.57611568376[/C][C]90.4238843162424[/C][/ROW]
[ROW][C]184[/C][C]1110[/C][C]1078.38861568376[/C][C]31.6113843162425[/C][/ROW]
[ROW][C]185[/C][C]1297[/C][C]1215.70111568376[/C][C]81.2988843162426[/C][/ROW]
[ROW][C]186[/C][C]1185[/C][C]1159.95111568376[/C][C]25.0488843162426[/C][/ROW]
[ROW][C]187[/C][C]1222[/C][C]1235.57611568376[/C][C]-13.5761156837574[/C][/ROW]
[ROW][C]188[/C][C]1284[/C][C]1250.45111568376[/C][C]33.5488843162426[/C][/ROW]
[ROW][C]189[/C][C]1444[/C][C]1303.26361568376[/C][C]140.736384316243[/C][/ROW]
[ROW][C]190[/C][C]1575[/C][C]1442.63861568376[/C][C]132.361384316243[/C][/ROW]
[ROW][C]191[/C][C]1737[/C][C]1642.20111568376[/C][C]94.7988843162425[/C][/ROW]
[ROW][C]192[/C][C]1763[/C][C]1758.82611568376[/C][C]4.17388431624252[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=24747&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=24747&T=4

As an alternative you can also use a QR Code:  
QR Code


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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
116871870.92354451408-183.923544514078
215081685.07260911425-177.072609114249
315071735.63510911425-228.635109114249
413851622.44760911425-237.447609114250
516321759.76010911425-127.760109114249
615111704.01010911425-193.010109114250
715591779.63510911425-220.635109114249
816301794.51010911425-164.510109114249
915791847.32260911425-268.322609114249
1016531986.69760911425-333.697609114249
1121522186.26010911425-34.2601091142491
1221482302.88510911425-154.885109114249
1317521849.74528052556-97.7452805255611
1417651663.89434512573101.105654874273
1517171714.456845125732.54315487427314
1615581601.26934512573-43.2693451257269
1715751738.58184512573-163.581845125727
1815201682.83184512573-162.831845125727
1918051758.4568451257346.5431548742731
2018001773.3318451257326.6681548742731
2117191826.14434512573-107.144345125727
2220081965.5193451257342.4806548742732
2322422165.0818451257376.9181548742732
2424782281.70684512573196.293154874273
2520301828.56701653704201.432983462961
2616551642.7160811372012.2839188627954
2716931693.27858113720-0.278581137204616
2816231580.0910811372042.9089188627954
2918051717.4035811372087.5964188627954
3017461661.6535811372084.3464188627954
3117951737.2785811372057.7214188627954
3219261752.15358113720173.846418862795
3316191804.96608113720-185.966081137205
3419921944.3410811372047.6589188627954
3522332143.9035811372089.0964188627954
3621922260.52858113720-68.5285811372045
3720801807.38875254852272.611247451483
3817681621.53781714868146.462182851318
3918351672.10031714868162.899682851318
4015691558.9128171486810.0871828513176
4119761696.22531714868279.774682851318
4218531640.47531714868212.524682851318
4319651716.10031714868248.899682851318
4416891730.97531714868-41.9753171486824
4517781783.78781714868-5.78781714868237
4619761923.1628171486852.8371828513176
4723972122.72531714868274.274682851318
4826542239.35031714868414.649682851318
4920971786.21048855999310.789511440006
5019631600.35955316016362.64044683984
5116771650.9220531601626.0779468398399
5219411537.73455316016403.26544683984
5320031675.04705316016327.95294683984
5418131619.29705316016193.70294683984
5520121694.92205316016317.07794683984
5619121709.79705316016202.202946839840
5720841762.60955316016321.39044683984
5820801901.98455316016178.01544683984
5921182101.5470531601616.4529468398399
6021502218.17205316016-68.1720531601601
6116081765.03222457147-157.032224571472
6215031579.18128917164-76.1812891716379
6315481629.74378917164-81.7437891716379
6413821516.55628917164-134.556289171638
6517311653.8687891716477.1312108283621
6617981598.11878917164199.881210828362
6717791673.74378917164105.256210828362
6818871688.61878917164198.381210828362
6920041741.43128917164262.568710828362
7020771880.80628917164196.193710828362
7120922080.3687891716411.6312108283621
7220512196.99378917164-145.993789171638
7315771743.85396058295-166.85396058295
7413561558.00302518312-202.003025183116
7516521608.5655251831243.4344748168844
7613821495.37802518312-113.378025183116
7715191632.69052518312-113.690525183116
7814211576.94052518312-155.940525183116
7914421652.56552518312-210.565525183116
8015431667.44052518312-124.440525183116
8116561720.25302518312-64.2530251831156
8215611859.62802518312-298.628025183116
8319052059.19052518312-154.190525183116
8421992175.8155251831223.1844748168844
8514731722.67569659443-249.675696594427
8616551536.82476119459118.175238805407
8714071587.38726119459-180.387261194593
8813951474.19976119459-79.1997611945933
8915301611.51226119459-81.5122611945934
9013091555.76226119459-246.762261194593
9115261631.38726119459-105.387261194593
9213271646.26226119459-319.262261194593
9316271699.07476119459-72.0747611945934
9417481838.44976119459-90.4497611945934
9519582038.01226119459-80.0122611945934
9622742154.63726119459119.362738805407
9716481701.49743260591-53.4974326059054
9814011515.64649720607-114.646497206071
9914111566.20899720607-155.208997206071
10014031453.02149720607-50.0214972060711
10113941590.33399720607-196.333997206071
10215201534.58399720607-14.5839972060711
10315281610.20899720607-82.2089972060712
10416431625.0839972060717.9160027939289
10515151677.89649720607-162.896497206071
10616851817.27149720607-132.271497206071
10720002016.83399720607-16.8339972060712
10822152133.4589972060781.5410027939289
10919561680.31916861738275.680831382617
11014621494.46823321755-32.4682332175488
11115631545.0307332175517.9692667824511
11214591431.8432332175527.1567667824512
11314461569.15573321755-123.155733217549
11416221513.40573321755108.594266782451
11516571589.0307332175567.9692667824512
11616381603.9057332175534.0942667824511
11716431656.71823321755-13.7182332175489
11816831796.09323321755-113.093233217549
11920501995.6557332175554.3442667824512
12022622112.28073321755149.719266782451
12118131659.14090462886153.859095371139
12214451473.28996922903-28.2899692290266
12317621523.85246922903238.147530770973
12414611410.6649692290350.3350307709734
12515561547.977469229038.02253077097338
12614311492.22746922903-61.2274692290265
12714271567.85246922903-140.852469229027
12815541582.72746922903-28.7274692290267
12916451635.539969229039.4600307709734
13016531774.91496922903-121.914969229027
13120161974.4774692290341.5225307709734
13222072091.10246922903115.897530770973
13316651637.9626406403427.0373593596614
13413611452.11170524050-91.1117052405044
13515061502.674205240503.32579475949564
13613601389.48670524050-29.4867052405044
13714531526.79920524050-73.7992052405043
13815221471.0492052405050.9507947594957
13914601546.67420524050-86.6742052405043
14015521561.54920524050-9.5492052405044
14115481614.36170524050-66.3617052405044
14218271753.7367052405073.2632947594957
14317371953.29920524050-216.299205240504
14419412069.92420524050-128.924205240504
14514741616.78437665182-142.784376651816
14614581430.9334412519827.0665587480179
14715421481.4959412519860.5040587480179
14814041368.3084412519835.6915587480179
14915221505.6209412519816.3790587480179
15013851449.87094125198-64.870941251982
15116411525.49594125198115.504058748018
15215101540.37094125198-30.3709412519821
15316811593.1834412519887.816558748018
15419381732.55844125198205.441558748018
15518681932.12094125198-64.1209412519821
15617262048.74594125198-322.745941251982
15714561595.60611266329-139.606112663294
15814451409.7551772634635.2448227365402
15914561460.31767726346-4.31767726345983
16013651347.1301772634617.8698227365402
16114871484.442677263462.55732273654013
16215581428.69267726346129.307322736540
16314881504.31767726346-16.3176772634599
16416841519.19267726346164.80732273654
16515941572.0051772634621.9948227365402
16618501711.38017726346138.619822736540
16719981910.9426772634687.05732273654
16820792027.5676772634651.4323227365402
16914941574.42784867477-80.4278486747719
17010571162.19187967228-105.191879672280
17112181212.754379672285.2456203277202
17211681099.5668796722868.4331203277202
17312361236.87937967228-0.879379672279648
17410761181.12937967228-105.129379672280
17511741256.75437967228-82.7543796722796
17611391271.62937967228-132.629379672280
17714271324.44187967228102.558120327720
17814871463.8168796722823.1831203277203
17914831663.37937967228-180.379379672280
18015131780.00437967228-267.004379672280
18113571326.8645510835930.1354489164084
18211651141.0136156837623.9863843162425
18312821191.5761156837690.4238843162424
18411101078.3886156837631.6113843162425
18512971215.7011156837681.2988843162426
18611851159.9511156837625.0488843162426
18712221235.57611568376-13.5761156837574
18812841250.4511156837633.5488843162426
18914441303.26361568376140.736384316243
19015751442.63861568376132.361384316243
19117371642.2011156837694.7988843162425
19217631758.826115683764.17388431624252


Figures (Output of Computation)

Figure 1
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Figure 2
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Figure 3
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Figure 4
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Figure 5
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Figure 6
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Figure 7
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Figure 8
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Figure 9
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Input Parameters & R Code

Parameters (Session):
par1 = 1 ; par2 = Include Monthly Dummies ; par3 = Linear Trend ;
Parameters (R input):
par1 = 1 ; par2 = Include Monthly Dummies ; par3 = Linear Trend ;
R code (references can be found in the software module):
library(lattice)
par1 <- as.numeric(par1)
x <- t(y)
k <- length(x[1,])
n <- length(x[,1])
x1 <- cbind(x[,par1], x[,1:k!=par1])
mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
colnames(x1) <- mycolnames #colnames(x)[par1]
x <- x1
if (par3 == 'First Differences'){
x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
for (i in 1:n-1) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
}
if (par2 == 'Include Monthly Dummies'){
x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
for (i in 1:11){
x2[seq(i,n,12),i] <- 1
}
x <- cbind(x, x2)
}
if (par2 == 'Include Quarterly Dummies'){
x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
for (i in 1:3){
x2[seq(i,n,4),i] <- 1
}
x <- cbind(x, x2)
}
k <- length(x[1,])
if (par3 == 'Linear Trend'){
x <- cbind(x, c(1:n))
colnames(x)[k+1] <- 't'
}
x
k <- length(x[1,])
df <- as.data.frame(x)
(mylm <- lm(df))
(mysum <- summary(mylm))
bitmap(file='test0.png')
plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
points(x[,1]-mysum$resid)
grid()
dev.off()
bitmap(file='test1.png')
plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
grid()
dev.off()
bitmap(file='test2.png')
hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
grid()
dev.off()
bitmap(file='test3.png')
densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
dev.off()
bitmap(file='test4.png')
qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
grid()
dev.off()
(myerror <- as.ts(mysum$resid))
bitmap(file='test5.png')
dum <- cbind(lag(myerror,k=1),myerror)
dum
dum1 <- dum[2:length(myerror),]
dum1
z <- as.data.frame(dum1)
z
plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
lines(lowess(z))
abline(lm(z))
grid()
dev.off()
bitmap(file='test6.png')
acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
grid()
dev.off()
bitmap(file='test7.png')
pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
grid()
dev.off()
bitmap(file='test8.png')
opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
plot(mylm, las = 1, sub='Residual Diagnostics')
par(opar)
dev.off()
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
a<-table.row.end(a)
myeq <- colnames(x)[1]
myeq <- paste(myeq, '[t] = ', sep='')
for (i in 1:k){
if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ')
if (rownames(mysum$coefficients)[i] != '(Intercept)') {
myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
}
}
myeq <- paste(myeq, ' + e[t]')
a<-table.row.start(a)
a<-table.element(a, myeq)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable1.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,hyperlink('ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Variable',header=TRUE)
a<-table.element(a,'Parameter',header=TRUE)
a<-table.element(a,'S.D.',header=TRUE)
a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
a<-table.element(a,'2-tail p-value',header=TRUE)
a<-table.element(a,'1-tail p-value',header=TRUE)
a<-table.row.end(a)
for (i in 1:k){
a<-table.row.start(a)
a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
a<-table.element(a,mysum$coefficients[i,1])
a<-table.element(a, round(mysum$coefficients[i,2],6))
a<-table.element(a, round(mysum$coefficients[i,3],4))
a<-table.element(a, round(mysum$coefficients[i,4],6))
a<-table.element(a, round(mysum$coefficients[i,4]/2,6))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable2.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple R',1,TRUE)
a<-table.element(a, sqrt(mysum$r.squared))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'R-squared',1,TRUE)
a<-table.element(a, mysum$r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Adjusted R-squared',1,TRUE)
a<-table.element(a, mysum$adj.r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (value)',1,TRUE)
a<-table.element(a, mysum$fstatistic[1])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[2])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[3])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'p-value',1,TRUE)
a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
a<-table.element(a, mysum$sigma)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
a<-table.element(a, sum(myerror*myerror))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,x[i])
a<-table.element(a,x[i]-mysum$resid[i])
a<-table.element(a,mysum$resid[i])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable4.tab')