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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 computationSun, 30 Nov 2008 06:01:50 -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/30/t12280501481p7kwpfnevb3xlh.htm/, Retrieved Thu, 31 Oct 2024 23:34:48 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=26492, Retrieved Thu, 31 Oct 2024 23:34:48 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact290
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] [The Seatbelt Law ...] [2008-11-15 12:00:57] [93834488277b53a4510bfd06084ae13b]
-   P       [Multiple Regression] [] [2008-11-30 13:01:50] [6d40a467de0f28bd2350f82ac9522c51] [Current]
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Dataseries X:
1687	0
1508	0
1507	0
1385	0
1632	0
1511	0
1559	0
1630	0
1579	0
1653	0
2152	0
2148	0
1752	0
1765	0
1717	0
1558	0
1575	0
1520	0
1805	0
1800	0
1719	0
2008	0
2242	0
2478	0
2030	0
1655	0
1693	0
1623	0
1805	0
1746	0
1795	0
1926	0
1619	0
1992	0
2233	0
2192	0
2080	0
1768	0
1835	0
1569	0
1976	0
1853	0
1965	0
1689	0
1778	0
1976	0
2397	0
2654	0
2097	0
1963	0
1677	0
1941	0
2003	0
1813	0
2012	0
1912	0
2084	0
2080	0
2118	0
2150	0
1608	0
1503	0
1548	0
1382	0
1731	0
1798	0
1779	0
1887	0
2004	0
2077	0
2092	0
2051	0
1577	0
1356	0
1652	0
1382	0
1519	0
1421	0
1442	0
1543	0
1656	0
1561	0
1905	0
2199	0
1473	0
1655	0
1407	0
1395	0
1530	0
1309	0
1526	0
1327	0
1627	0
1748	0
1958	0
2274	0
1648	0
1401	0
1411	0
1403	0
1394	0
1520	0
1528	0
1643	0
1515	0
1685	0
2000	0
2215	0
1956	0
1462	0
1563	0
1459	0
1446	0
1622	0
1657	0
1638	0
1643	0
1683	0
2050	0
2262	0
1813	0
1445	0
1762	0
1461	0
1556	0
1431	0
1427	0
1554	0
1645	0
1653	0
2016	0
2207	0
1665	0
1361	0
1506	0
1360	0
1453	0
1522	0
1460	0
1552	0
1548	0
1827	0
1737	0
1941	0
1474	0
1458	0
1542	0
1404	0
1522	0
1385	0
1641	0
1510	0
1681	0
1938	0
1868	0
1726	0
1456	0
1445	0
1456	0
1365	0
1487	0
1558	0
1488	0
1684	0
1594	0
1850	0
1998	0
2079	0
1494	0
1057	1
1218	1
1168	1
1236	1
1076	1
1174	1
1139	1
1427	1
1487	1
1483	1
1513	1
1357	1
1165	1
1282	1
1110	1
1297	1
1185	1
1222	1
1284	1
1444	1
1575	1
1737	1
1763	1




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

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 5 seconds \tabularnewline
R Server & 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=26492&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]5 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Sir Ronald Aylmer Fisher' @ 193.190.124.24[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=26492&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=26492&T=0

As an alternative you can also use a QR Code:  

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

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







Multiple Linear Regression - Estimated Regression Equation
Slachtoffer/maand[t] = + 2165.22639318886 -395.811145510836Dumivariabele[t] -442.550696594427M1[t] -617.8125M2[t] -567.25M3[t] -680.4375M4[t] -543.125M5[t] -598.875M6[t] -523.25M7[t] -508.375M8[t] -455.5625M9[t] -316.1875M10[t] -116.625M11[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Slachtoffer/maand[t] =  +  2165.22639318886 -395.811145510836Dumivariabele[t] -442.550696594427M1[t] -617.8125M2[t] -567.25M3[t] -680.4375M4[t] -543.125M5[t] -598.875M6[t] -523.25M7[t] -508.375M8[t] -455.5625M9[t] -316.1875M10[t] -116.625M11[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=26492&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Slachtoffer/maand[t] =  +  2165.22639318886 -395.811145510836Dumivariabele[t] -442.550696594427M1[t] -617.8125M2[t] -567.25M3[t] -680.4375M4[t] -543.125M5[t] -598.875M6[t] -523.25M7[t] -508.375M8[t] -455.5625M9[t] -316.1875M10[t] -116.625M11[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=26492&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=26492&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
Slachtoffer/maand[t] = + 2165.22639318886 -395.811145510836Dumivariabele[t] -442.550696594427M1[t] -617.8125M2[t] -567.25M3[t] -680.4375M4[t] -543.125M5[t] -598.875M6[t] -523.25M7[t] -508.375M8[t] -455.5625M9[t] -316.1875M10[t] -116.625M11[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)2165.2263931888643.63188149.624900
Dumivariabele-395.81114551083638.605577-10.252700
M1-442.55069659442761.373686-7.210800
M2-617.812561.326238-10.074200
M3-567.2561.326238-9.249700
M4-680.437561.326238-11.095400
M5-543.12561.326238-8.856300
M6-598.87561.326238-9.765400
M7-523.2561.326238-8.532200
M8-508.37561.326238-8.289700
M9-455.562561.326238-7.428500
M10-316.187561.326238-5.15581e-060
M11-116.62561.326238-1.90170.0588150.029407

\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) & 2165.22639318886 & 43.631881 & 49.6249 & 0 & 0 \tabularnewline
Dumivariabele & -395.811145510836 & 38.605577 & -10.2527 & 0 & 0 \tabularnewline
M1 & -442.550696594427 & 61.373686 & -7.2108 & 0 & 0 \tabularnewline
M2 & -617.8125 & 61.326238 & -10.0742 & 0 & 0 \tabularnewline
M3 & -567.25 & 61.326238 & -9.2497 & 0 & 0 \tabularnewline
M4 & -680.4375 & 61.326238 & -11.0954 & 0 & 0 \tabularnewline
M5 & -543.125 & 61.326238 & -8.8563 & 0 & 0 \tabularnewline
M6 & -598.875 & 61.326238 & -9.7654 & 0 & 0 \tabularnewline
M7 & -523.25 & 61.326238 & -8.5322 & 0 & 0 \tabularnewline
M8 & -508.375 & 61.326238 & -8.2897 & 0 & 0 \tabularnewline
M9 & -455.5625 & 61.326238 & -7.4285 & 0 & 0 \tabularnewline
M10 & -316.1875 & 61.326238 & -5.1558 & 1e-06 & 0 \tabularnewline
M11 & -116.625 & 61.326238 & -1.9017 & 0.058815 & 0.029407 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=26492&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]2165.22639318886[/C][C]43.631881[/C][C]49.6249[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Dumivariabele[/C][C]-395.811145510836[/C][C]38.605577[/C][C]-10.2527[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]M1[/C][C]-442.550696594427[/C][C]61.373686[/C][C]-7.2108[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]M2[/C][C]-617.8125[/C][C]61.326238[/C][C]-10.0742[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]M3[/C][C]-567.25[/C][C]61.326238[/C][C]-9.2497[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]M4[/C][C]-680.4375[/C][C]61.326238[/C][C]-11.0954[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]M5[/C][C]-543.125[/C][C]61.326238[/C][C]-8.8563[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]M6[/C][C]-598.875[/C][C]61.326238[/C][C]-9.7654[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]M7[/C][C]-523.25[/C][C]61.326238[/C][C]-8.5322[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]M8[/C][C]-508.375[/C][C]61.326238[/C][C]-8.2897[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]M9[/C][C]-455.5625[/C][C]61.326238[/C][C]-7.4285[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]M10[/C][C]-316.1875[/C][C]61.326238[/C][C]-5.1558[/C][C]1e-06[/C][C]0[/C][/ROW]
[ROW][C]M11[/C][C]-116.625[/C][C]61.326238[/C][C]-1.9017[/C][C]0.058815[/C][C]0.029407[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=26492&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=26492&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)2165.2263931888643.63188149.624900
Dumivariabele-395.81114551083638.605577-10.252700
M1-442.55069659442761.373686-7.210800
M2-617.812561.326238-10.074200
M3-567.2561.326238-9.249700
M4-680.437561.326238-11.095400
M5-543.12561.326238-8.856300
M6-598.87561.326238-9.765400
M7-523.2561.326238-8.532200
M8-508.37561.326238-8.289700
M9-455.562561.326238-7.428500
M10-316.187561.326238-5.15581e-060
M11-116.62561.326238-1.90170.0588150.029407







Multiple Linear Regression - Regression Statistics
Multiple R0.814751285561214
R-squared0.66381965732365
Adjusted R-squared0.641282427647024
F-TEST (value)29.4543591580865
F-TEST (DF numerator)12
F-TEST (DF denominator)179
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation173.456794605829
Sum Squared Residuals5385619.46749226

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.814751285561214 \tabularnewline
R-squared & 0.66381965732365 \tabularnewline
Adjusted R-squared & 0.641282427647024 \tabularnewline
F-TEST (value) & 29.4543591580865 \tabularnewline
F-TEST (DF numerator) & 12 \tabularnewline
F-TEST (DF denominator) & 179 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 173.456794605829 \tabularnewline
Sum Squared Residuals & 5385619.46749226 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=26492&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.814751285561214[/C][/ROW]
[ROW][C]R-squared[/C][C]0.66381965732365[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.641282427647024[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]29.4543591580865[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]12[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]179[/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]173.456794605829[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]5385619.46749226[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=26492&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=26492&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 R0.814751285561214
R-squared0.66381965732365
Adjusted R-squared0.641282427647024
F-TEST (value)29.4543591580865
F-TEST (DF numerator)12
F-TEST (DF denominator)179
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation173.456794605829
Sum Squared Residuals5385619.46749226







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
116871722.67569659442-35.6756965944224
215081547.41389318885-39.4138931888546
315071597.97639318885-90.9763931888542
413851484.78889318885-99.788893188855
516321622.101393188859.89860681114526
615111566.35139318885-55.3513931888549
715591641.97639318885-82.9763931888545
816301656.85139318885-26.8513931888544
915791709.66389318885-130.663893188855
1016531849.03889318885-196.038893188855
1121522048.60139318885103.398606811145
1221482165.22639318885-17.2263931888546
1317521722.6756965944329.3243034055724
1417651547.41389318885217.586106811145
1517171597.97639318885119.023606811145
1615581484.7888931888573.2111068111455
1715751622.10139318885-47.1013931888545
1815201566.35139318885-46.3513931888545
1918051641.97639318885163.023606811146
2018001656.85139318885143.148606811145
2117191709.663893188859.33610681114551
2220081849.03889318885158.961106811146
2322422048.60139318885193.398606811146
2424782165.22639318885312.773606811146
2520301722.67569659443307.324303405572
2616551547.41389318885107.586106811146
2716931597.9763931888595.0236068111455
2816231484.78889318885138.211106811146
2918051622.10139318885182.898606811145
3017461566.35139318885179.648606811145
3117951641.97639318885153.023606811146
3219261656.85139318885269.148606811145
3316191709.66389318885-90.6638931888545
3419921849.03889318885142.961106811146
3522332048.60139318885184.398606811146
3621922165.2263931888526.7736068111456
3720801722.67569659443357.324303405572
3817681547.41389318885220.586106811145
3918351597.97639318885237.023606811145
4015691484.7888931888584.2111068111455
4119761622.10139318885353.898606811146
4218531566.35139318885286.648606811146
4319651641.97639318885323.023606811145
4416891656.8513931888532.1486068111455
4517781709.6638931888568.3361068111455
4619761849.03889318885126.961106811146
4723972048.60139318885348.398606811146
4826542165.22639318885488.773606811146
4920971722.67569659443374.324303405572
5019631547.41389318885415.586106811145
5116771597.9763931888579.0236068111455
5219411484.78889318885456.211106811146
5320031622.10139318885380.898606811146
5418131566.35139318885246.648606811146
5520121641.97639318885370.023606811145
5619121656.85139318885255.148606811145
5720841709.66389318885374.336106811146
5820801849.03889318885230.961106811146
5921182048.6013931888569.3986068111455
6021502165.22639318885-15.2263931888545
6116081722.67569659443-114.675696594428
6215031547.41389318885-44.4138931888545
6315481597.97639318885-49.9763931888545
6413821484.78889318885-102.788893188854
6517311622.10139318885108.898606811145
6617981566.35139318885231.648606811145
6717791641.97639318885137.023606811145
6818871656.85139318885230.148606811145
6920041709.66389318885294.336106811146
7020771849.03889318885227.961106811146
7120922048.6013931888543.3986068111455
7220512165.22639318885-114.226393188854
7315771722.67569659443-145.675696594428
7413561547.41389318885-191.413893188855
7516521597.9763931888554.0236068111455
7613821484.78889318885-102.788893188854
7715191622.10139318885-103.101393188855
7814211566.35139318885-145.351393188854
7914421641.97639318885-199.976393188854
8015431656.85139318885-113.851393188855
8116561709.66389318885-53.6638931888545
8215611849.03889318885-288.038893188855
8319052048.60139318885-143.601393188855
8421992165.2263931888533.7736068111456
8514731722.67569659443-249.675696594428
8616551547.41389318885107.586106811146
8714071597.97639318885-190.976393188854
8813951484.78889318885-89.7888931888545
8915301622.10139318885-92.1013931888545
9013091566.35139318885-257.351393188854
9115261641.97639318885-115.976393188855
9213271656.85139318885-329.851393188854
9316271709.66389318885-82.6638931888545
9417481849.03889318885-101.038893188854
9519582048.60139318885-90.6013931888546
9622742165.22639318885108.773606811146
9716481722.67569659443-74.6756965944276
9814011547.41389318885-146.413893188855
9914111597.97639318885-186.976393188854
10014031484.78889318885-81.7888931888545
10113941622.10139318885-228.101393188854
10215201566.35139318885-46.3513931888545
10315281641.97639318885-113.976393188855
10416431656.85139318885-13.8513931888545
10515151709.66389318885-194.663893188855
10616851849.03889318885-164.038893188855
10720002048.60139318885-48.6013931888545
10822152165.2263931888549.7736068111455
10919561722.67569659443233.324303405572
11014621547.41389318885-85.4138931888544
11115631597.97639318885-34.9763931888545
11214591484.78889318885-25.7888931888545
11314461622.10139318885-176.101393188854
11416221566.3513931888555.6486068111455
11516571641.9763931888515.0236068111455
11616381656.85139318885-18.8513931888545
11716431709.66389318885-66.6638931888545
11816831849.03889318885-166.038893188855
11920502048.601393188851.39860681114554
12022622165.2263931888596.7736068111456
12118131722.6756965944390.3243034055724
12214451547.41389318885-102.413893188854
12317621597.97639318885164.023606811145
12414611484.78889318885-23.7888931888545
12515561622.10139318885-66.1013931888545
12614311566.35139318885-135.351393188854
12714271641.97639318885-214.976393188854
12815541656.85139318885-102.851393188855
12916451709.66389318885-64.6638931888545
13016531849.03889318885-196.038893188855
13120162048.60139318885-32.6013931888545
13222072165.2263931888541.7736068111456
13316651722.67569659443-57.6756965944276
13413611547.41389318885-186.413893188855
13515061597.97639318885-91.9763931888545
13613601484.78889318885-124.788893188854
13714531622.10139318885-169.101393188854
13815221566.35139318885-44.3513931888545
13914601641.97639318885-181.976393188854
14015521656.85139318885-104.851393188855
14115481709.66389318885-161.663893188855
14218271849.03889318885-22.0388931888545
14317372048.60139318885-311.601393188855
14419412165.22639318885-224.226393188855
14514741722.67569659443-248.675696594428
14614581547.41389318885-89.4138931888544
14715421597.97639318885-55.9763931888545
14814041484.78889318885-80.7888931888545
14915221622.10139318885-100.101393188855
15013851566.35139318885-181.351393188854
15116411641.97639318885-0.976393188854502
15215101656.85139318885-146.851393188855
15316811709.66389318885-28.6638931888545
15419381849.0388931888588.9611068111455
15518682048.60139318885-180.601393188855
15617262165.22639318885-439.226393188855
15714561722.67569659443-266.675696594428
15814451547.41389318885-102.413893188854
15914561597.97639318885-141.976393188855
16013651484.78889318885-119.788893188854
16114871622.10139318885-135.101393188855
16215581566.35139318885-8.35139318885452
16314881641.97639318885-153.976393188855
16416841656.8513931888527.1486068111455
16515941709.66389318885-115.663893188855
16618501849.038893188850.961106811145528
16719982048.60139318885-50.6013931888545
16820792165.22639318885-86.2263931888545
16914941722.67569659443-228.675696594428
17010571151.60274767802-94.6027476780185
17112181202.1652476780215.8347523219813
17211681088.9777476780279.0222523219813
17312361226.290247678029.70975232198147
17410761170.54024767802-94.5402476780185
17511741246.16524767802-72.1652476780185
17611391261.04024767802-122.040247678018
17714271313.85274767802113.147252321981
17814871453.2277476780233.7722523219815
17914831652.79024767802-169.790247678019
18015131769.41524767802-256.415247678019
18113571326.8645510835930.1354489164084
18211651151.6027476780213.3972523219814
18312821202.1652476780279.8347523219813
18411101088.9777476780221.0222523219814
18512971226.2902476780270.7097523219815
18611851170.5402476780214.4597523219814
18712221246.16524767802-24.1652476780185
18812841261.0402476780222.9597523219815
18914441313.85274767802130.147252321981
19015751453.22774767802121.772252321981
19117371652.7902476780284.2097523219814
19217631769.41524767802-6.41524767801863

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 1687 & 1722.67569659442 & -35.6756965944224 \tabularnewline
2 & 1508 & 1547.41389318885 & -39.4138931888546 \tabularnewline
3 & 1507 & 1597.97639318885 & -90.9763931888542 \tabularnewline
4 & 1385 & 1484.78889318885 & -99.788893188855 \tabularnewline
5 & 1632 & 1622.10139318885 & 9.89860681114526 \tabularnewline
6 & 1511 & 1566.35139318885 & -55.3513931888549 \tabularnewline
7 & 1559 & 1641.97639318885 & -82.9763931888545 \tabularnewline
8 & 1630 & 1656.85139318885 & -26.8513931888544 \tabularnewline
9 & 1579 & 1709.66389318885 & -130.663893188855 \tabularnewline
10 & 1653 & 1849.03889318885 & -196.038893188855 \tabularnewline
11 & 2152 & 2048.60139318885 & 103.398606811145 \tabularnewline
12 & 2148 & 2165.22639318885 & -17.2263931888546 \tabularnewline
13 & 1752 & 1722.67569659443 & 29.3243034055724 \tabularnewline
14 & 1765 & 1547.41389318885 & 217.586106811145 \tabularnewline
15 & 1717 & 1597.97639318885 & 119.023606811145 \tabularnewline
16 & 1558 & 1484.78889318885 & 73.2111068111455 \tabularnewline
17 & 1575 & 1622.10139318885 & -47.1013931888545 \tabularnewline
18 & 1520 & 1566.35139318885 & -46.3513931888545 \tabularnewline
19 & 1805 & 1641.97639318885 & 163.023606811146 \tabularnewline
20 & 1800 & 1656.85139318885 & 143.148606811145 \tabularnewline
21 & 1719 & 1709.66389318885 & 9.33610681114551 \tabularnewline
22 & 2008 & 1849.03889318885 & 158.961106811146 \tabularnewline
23 & 2242 & 2048.60139318885 & 193.398606811146 \tabularnewline
24 & 2478 & 2165.22639318885 & 312.773606811146 \tabularnewline
25 & 2030 & 1722.67569659443 & 307.324303405572 \tabularnewline
26 & 1655 & 1547.41389318885 & 107.586106811146 \tabularnewline
27 & 1693 & 1597.97639318885 & 95.0236068111455 \tabularnewline
28 & 1623 & 1484.78889318885 & 138.211106811146 \tabularnewline
29 & 1805 & 1622.10139318885 & 182.898606811145 \tabularnewline
30 & 1746 & 1566.35139318885 & 179.648606811145 \tabularnewline
31 & 1795 & 1641.97639318885 & 153.023606811146 \tabularnewline
32 & 1926 & 1656.85139318885 & 269.148606811145 \tabularnewline
33 & 1619 & 1709.66389318885 & -90.6638931888545 \tabularnewline
34 & 1992 & 1849.03889318885 & 142.961106811146 \tabularnewline
35 & 2233 & 2048.60139318885 & 184.398606811146 \tabularnewline
36 & 2192 & 2165.22639318885 & 26.7736068111456 \tabularnewline
37 & 2080 & 1722.67569659443 & 357.324303405572 \tabularnewline
38 & 1768 & 1547.41389318885 & 220.586106811145 \tabularnewline
39 & 1835 & 1597.97639318885 & 237.023606811145 \tabularnewline
40 & 1569 & 1484.78889318885 & 84.2111068111455 \tabularnewline
41 & 1976 & 1622.10139318885 & 353.898606811146 \tabularnewline
42 & 1853 & 1566.35139318885 & 286.648606811146 \tabularnewline
43 & 1965 & 1641.97639318885 & 323.023606811145 \tabularnewline
44 & 1689 & 1656.85139318885 & 32.1486068111455 \tabularnewline
45 & 1778 & 1709.66389318885 & 68.3361068111455 \tabularnewline
46 & 1976 & 1849.03889318885 & 126.961106811146 \tabularnewline
47 & 2397 & 2048.60139318885 & 348.398606811146 \tabularnewline
48 & 2654 & 2165.22639318885 & 488.773606811146 \tabularnewline
49 & 2097 & 1722.67569659443 & 374.324303405572 \tabularnewline
50 & 1963 & 1547.41389318885 & 415.586106811145 \tabularnewline
51 & 1677 & 1597.97639318885 & 79.0236068111455 \tabularnewline
52 & 1941 & 1484.78889318885 & 456.211106811146 \tabularnewline
53 & 2003 & 1622.10139318885 & 380.898606811146 \tabularnewline
54 & 1813 & 1566.35139318885 & 246.648606811146 \tabularnewline
55 & 2012 & 1641.97639318885 & 370.023606811145 \tabularnewline
56 & 1912 & 1656.85139318885 & 255.148606811145 \tabularnewline
57 & 2084 & 1709.66389318885 & 374.336106811146 \tabularnewline
58 & 2080 & 1849.03889318885 & 230.961106811146 \tabularnewline
59 & 2118 & 2048.60139318885 & 69.3986068111455 \tabularnewline
60 & 2150 & 2165.22639318885 & -15.2263931888545 \tabularnewline
61 & 1608 & 1722.67569659443 & -114.675696594428 \tabularnewline
62 & 1503 & 1547.41389318885 & -44.4138931888545 \tabularnewline
63 & 1548 & 1597.97639318885 & -49.9763931888545 \tabularnewline
64 & 1382 & 1484.78889318885 & -102.788893188854 \tabularnewline
65 & 1731 & 1622.10139318885 & 108.898606811145 \tabularnewline
66 & 1798 & 1566.35139318885 & 231.648606811145 \tabularnewline
67 & 1779 & 1641.97639318885 & 137.023606811145 \tabularnewline
68 & 1887 & 1656.85139318885 & 230.148606811145 \tabularnewline
69 & 2004 & 1709.66389318885 & 294.336106811146 \tabularnewline
70 & 2077 & 1849.03889318885 & 227.961106811146 \tabularnewline
71 & 2092 & 2048.60139318885 & 43.3986068111455 \tabularnewline
72 & 2051 & 2165.22639318885 & -114.226393188854 \tabularnewline
73 & 1577 & 1722.67569659443 & -145.675696594428 \tabularnewline
74 & 1356 & 1547.41389318885 & -191.413893188855 \tabularnewline
75 & 1652 & 1597.97639318885 & 54.0236068111455 \tabularnewline
76 & 1382 & 1484.78889318885 & -102.788893188854 \tabularnewline
77 & 1519 & 1622.10139318885 & -103.101393188855 \tabularnewline
78 & 1421 & 1566.35139318885 & -145.351393188854 \tabularnewline
79 & 1442 & 1641.97639318885 & -199.976393188854 \tabularnewline
80 & 1543 & 1656.85139318885 & -113.851393188855 \tabularnewline
81 & 1656 & 1709.66389318885 & -53.6638931888545 \tabularnewline
82 & 1561 & 1849.03889318885 & -288.038893188855 \tabularnewline
83 & 1905 & 2048.60139318885 & -143.601393188855 \tabularnewline
84 & 2199 & 2165.22639318885 & 33.7736068111456 \tabularnewline
85 & 1473 & 1722.67569659443 & -249.675696594428 \tabularnewline
86 & 1655 & 1547.41389318885 & 107.586106811146 \tabularnewline
87 & 1407 & 1597.97639318885 & -190.976393188854 \tabularnewline
88 & 1395 & 1484.78889318885 & -89.7888931888545 \tabularnewline
89 & 1530 & 1622.10139318885 & -92.1013931888545 \tabularnewline
90 & 1309 & 1566.35139318885 & -257.351393188854 \tabularnewline
91 & 1526 & 1641.97639318885 & -115.976393188855 \tabularnewline
92 & 1327 & 1656.85139318885 & -329.851393188854 \tabularnewline
93 & 1627 & 1709.66389318885 & -82.6638931888545 \tabularnewline
94 & 1748 & 1849.03889318885 & -101.038893188854 \tabularnewline
95 & 1958 & 2048.60139318885 & -90.6013931888546 \tabularnewline
96 & 2274 & 2165.22639318885 & 108.773606811146 \tabularnewline
97 & 1648 & 1722.67569659443 & -74.6756965944276 \tabularnewline
98 & 1401 & 1547.41389318885 & -146.413893188855 \tabularnewline
99 & 1411 & 1597.97639318885 & -186.976393188854 \tabularnewline
100 & 1403 & 1484.78889318885 & -81.7888931888545 \tabularnewline
101 & 1394 & 1622.10139318885 & -228.101393188854 \tabularnewline
102 & 1520 & 1566.35139318885 & -46.3513931888545 \tabularnewline
103 & 1528 & 1641.97639318885 & -113.976393188855 \tabularnewline
104 & 1643 & 1656.85139318885 & -13.8513931888545 \tabularnewline
105 & 1515 & 1709.66389318885 & -194.663893188855 \tabularnewline
106 & 1685 & 1849.03889318885 & -164.038893188855 \tabularnewline
107 & 2000 & 2048.60139318885 & -48.6013931888545 \tabularnewline
108 & 2215 & 2165.22639318885 & 49.7736068111455 \tabularnewline
109 & 1956 & 1722.67569659443 & 233.324303405572 \tabularnewline
110 & 1462 & 1547.41389318885 & -85.4138931888544 \tabularnewline
111 & 1563 & 1597.97639318885 & -34.9763931888545 \tabularnewline
112 & 1459 & 1484.78889318885 & -25.7888931888545 \tabularnewline
113 & 1446 & 1622.10139318885 & -176.101393188854 \tabularnewline
114 & 1622 & 1566.35139318885 & 55.6486068111455 \tabularnewline
115 & 1657 & 1641.97639318885 & 15.0236068111455 \tabularnewline
116 & 1638 & 1656.85139318885 & -18.8513931888545 \tabularnewline
117 & 1643 & 1709.66389318885 & -66.6638931888545 \tabularnewline
118 & 1683 & 1849.03889318885 & -166.038893188855 \tabularnewline
119 & 2050 & 2048.60139318885 & 1.39860681114554 \tabularnewline
120 & 2262 & 2165.22639318885 & 96.7736068111456 \tabularnewline
121 & 1813 & 1722.67569659443 & 90.3243034055724 \tabularnewline
122 & 1445 & 1547.41389318885 & -102.413893188854 \tabularnewline
123 & 1762 & 1597.97639318885 & 164.023606811145 \tabularnewline
124 & 1461 & 1484.78889318885 & -23.7888931888545 \tabularnewline
125 & 1556 & 1622.10139318885 & -66.1013931888545 \tabularnewline
126 & 1431 & 1566.35139318885 & -135.351393188854 \tabularnewline
127 & 1427 & 1641.97639318885 & -214.976393188854 \tabularnewline
128 & 1554 & 1656.85139318885 & -102.851393188855 \tabularnewline
129 & 1645 & 1709.66389318885 & -64.6638931888545 \tabularnewline
130 & 1653 & 1849.03889318885 & -196.038893188855 \tabularnewline
131 & 2016 & 2048.60139318885 & -32.6013931888545 \tabularnewline
132 & 2207 & 2165.22639318885 & 41.7736068111456 \tabularnewline
133 & 1665 & 1722.67569659443 & -57.6756965944276 \tabularnewline
134 & 1361 & 1547.41389318885 & -186.413893188855 \tabularnewline
135 & 1506 & 1597.97639318885 & -91.9763931888545 \tabularnewline
136 & 1360 & 1484.78889318885 & -124.788893188854 \tabularnewline
137 & 1453 & 1622.10139318885 & -169.101393188854 \tabularnewline
138 & 1522 & 1566.35139318885 & -44.3513931888545 \tabularnewline
139 & 1460 & 1641.97639318885 & -181.976393188854 \tabularnewline
140 & 1552 & 1656.85139318885 & -104.851393188855 \tabularnewline
141 & 1548 & 1709.66389318885 & -161.663893188855 \tabularnewline
142 & 1827 & 1849.03889318885 & -22.0388931888545 \tabularnewline
143 & 1737 & 2048.60139318885 & -311.601393188855 \tabularnewline
144 & 1941 & 2165.22639318885 & -224.226393188855 \tabularnewline
145 & 1474 & 1722.67569659443 & -248.675696594428 \tabularnewline
146 & 1458 & 1547.41389318885 & -89.4138931888544 \tabularnewline
147 & 1542 & 1597.97639318885 & -55.9763931888545 \tabularnewline
148 & 1404 & 1484.78889318885 & -80.7888931888545 \tabularnewline
149 & 1522 & 1622.10139318885 & -100.101393188855 \tabularnewline
150 & 1385 & 1566.35139318885 & -181.351393188854 \tabularnewline
151 & 1641 & 1641.97639318885 & -0.976393188854502 \tabularnewline
152 & 1510 & 1656.85139318885 & -146.851393188855 \tabularnewline
153 & 1681 & 1709.66389318885 & -28.6638931888545 \tabularnewline
154 & 1938 & 1849.03889318885 & 88.9611068111455 \tabularnewline
155 & 1868 & 2048.60139318885 & -180.601393188855 \tabularnewline
156 & 1726 & 2165.22639318885 & -439.226393188855 \tabularnewline
157 & 1456 & 1722.67569659443 & -266.675696594428 \tabularnewline
158 & 1445 & 1547.41389318885 & -102.413893188854 \tabularnewline
159 & 1456 & 1597.97639318885 & -141.976393188855 \tabularnewline
160 & 1365 & 1484.78889318885 & -119.788893188854 \tabularnewline
161 & 1487 & 1622.10139318885 & -135.101393188855 \tabularnewline
162 & 1558 & 1566.35139318885 & -8.35139318885452 \tabularnewline
163 & 1488 & 1641.97639318885 & -153.976393188855 \tabularnewline
164 & 1684 & 1656.85139318885 & 27.1486068111455 \tabularnewline
165 & 1594 & 1709.66389318885 & -115.663893188855 \tabularnewline
166 & 1850 & 1849.03889318885 & 0.961106811145528 \tabularnewline
167 & 1998 & 2048.60139318885 & -50.6013931888545 \tabularnewline
168 & 2079 & 2165.22639318885 & -86.2263931888545 \tabularnewline
169 & 1494 & 1722.67569659443 & -228.675696594428 \tabularnewline
170 & 1057 & 1151.60274767802 & -94.6027476780185 \tabularnewline
171 & 1218 & 1202.16524767802 & 15.8347523219813 \tabularnewline
172 & 1168 & 1088.97774767802 & 79.0222523219813 \tabularnewline
173 & 1236 & 1226.29024767802 & 9.70975232198147 \tabularnewline
174 & 1076 & 1170.54024767802 & -94.5402476780185 \tabularnewline
175 & 1174 & 1246.16524767802 & -72.1652476780185 \tabularnewline
176 & 1139 & 1261.04024767802 & -122.040247678018 \tabularnewline
177 & 1427 & 1313.85274767802 & 113.147252321981 \tabularnewline
178 & 1487 & 1453.22774767802 & 33.7722523219815 \tabularnewline
179 & 1483 & 1652.79024767802 & -169.790247678019 \tabularnewline
180 & 1513 & 1769.41524767802 & -256.415247678019 \tabularnewline
181 & 1357 & 1326.86455108359 & 30.1354489164084 \tabularnewline
182 & 1165 & 1151.60274767802 & 13.3972523219814 \tabularnewline
183 & 1282 & 1202.16524767802 & 79.8347523219813 \tabularnewline
184 & 1110 & 1088.97774767802 & 21.0222523219814 \tabularnewline
185 & 1297 & 1226.29024767802 & 70.7097523219815 \tabularnewline
186 & 1185 & 1170.54024767802 & 14.4597523219814 \tabularnewline
187 & 1222 & 1246.16524767802 & -24.1652476780185 \tabularnewline
188 & 1284 & 1261.04024767802 & 22.9597523219815 \tabularnewline
189 & 1444 & 1313.85274767802 & 130.147252321981 \tabularnewline
190 & 1575 & 1453.22774767802 & 121.772252321981 \tabularnewline
191 & 1737 & 1652.79024767802 & 84.2097523219814 \tabularnewline
192 & 1763 & 1769.41524767802 & -6.41524767801863 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=26492&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]1722.67569659442[/C][C]-35.6756965944224[/C][/ROW]
[ROW][C]2[/C][C]1508[/C][C]1547.41389318885[/C][C]-39.4138931888546[/C][/ROW]
[ROW][C]3[/C][C]1507[/C][C]1597.97639318885[/C][C]-90.9763931888542[/C][/ROW]
[ROW][C]4[/C][C]1385[/C][C]1484.78889318885[/C][C]-99.788893188855[/C][/ROW]
[ROW][C]5[/C][C]1632[/C][C]1622.10139318885[/C][C]9.89860681114526[/C][/ROW]
[ROW][C]6[/C][C]1511[/C][C]1566.35139318885[/C][C]-55.3513931888549[/C][/ROW]
[ROW][C]7[/C][C]1559[/C][C]1641.97639318885[/C][C]-82.9763931888545[/C][/ROW]
[ROW][C]8[/C][C]1630[/C][C]1656.85139318885[/C][C]-26.8513931888544[/C][/ROW]
[ROW][C]9[/C][C]1579[/C][C]1709.66389318885[/C][C]-130.663893188855[/C][/ROW]
[ROW][C]10[/C][C]1653[/C][C]1849.03889318885[/C][C]-196.038893188855[/C][/ROW]
[ROW][C]11[/C][C]2152[/C][C]2048.60139318885[/C][C]103.398606811145[/C][/ROW]
[ROW][C]12[/C][C]2148[/C][C]2165.22639318885[/C][C]-17.2263931888546[/C][/ROW]
[ROW][C]13[/C][C]1752[/C][C]1722.67569659443[/C][C]29.3243034055724[/C][/ROW]
[ROW][C]14[/C][C]1765[/C][C]1547.41389318885[/C][C]217.586106811145[/C][/ROW]
[ROW][C]15[/C][C]1717[/C][C]1597.97639318885[/C][C]119.023606811145[/C][/ROW]
[ROW][C]16[/C][C]1558[/C][C]1484.78889318885[/C][C]73.2111068111455[/C][/ROW]
[ROW][C]17[/C][C]1575[/C][C]1622.10139318885[/C][C]-47.1013931888545[/C][/ROW]
[ROW][C]18[/C][C]1520[/C][C]1566.35139318885[/C][C]-46.3513931888545[/C][/ROW]
[ROW][C]19[/C][C]1805[/C][C]1641.97639318885[/C][C]163.023606811146[/C][/ROW]
[ROW][C]20[/C][C]1800[/C][C]1656.85139318885[/C][C]143.148606811145[/C][/ROW]
[ROW][C]21[/C][C]1719[/C][C]1709.66389318885[/C][C]9.33610681114551[/C][/ROW]
[ROW][C]22[/C][C]2008[/C][C]1849.03889318885[/C][C]158.961106811146[/C][/ROW]
[ROW][C]23[/C][C]2242[/C][C]2048.60139318885[/C][C]193.398606811146[/C][/ROW]
[ROW][C]24[/C][C]2478[/C][C]2165.22639318885[/C][C]312.773606811146[/C][/ROW]
[ROW][C]25[/C][C]2030[/C][C]1722.67569659443[/C][C]307.324303405572[/C][/ROW]
[ROW][C]26[/C][C]1655[/C][C]1547.41389318885[/C][C]107.586106811146[/C][/ROW]
[ROW][C]27[/C][C]1693[/C][C]1597.97639318885[/C][C]95.0236068111455[/C][/ROW]
[ROW][C]28[/C][C]1623[/C][C]1484.78889318885[/C][C]138.211106811146[/C][/ROW]
[ROW][C]29[/C][C]1805[/C][C]1622.10139318885[/C][C]182.898606811145[/C][/ROW]
[ROW][C]30[/C][C]1746[/C][C]1566.35139318885[/C][C]179.648606811145[/C][/ROW]
[ROW][C]31[/C][C]1795[/C][C]1641.97639318885[/C][C]153.023606811146[/C][/ROW]
[ROW][C]32[/C][C]1926[/C][C]1656.85139318885[/C][C]269.148606811145[/C][/ROW]
[ROW][C]33[/C][C]1619[/C][C]1709.66389318885[/C][C]-90.6638931888545[/C][/ROW]
[ROW][C]34[/C][C]1992[/C][C]1849.03889318885[/C][C]142.961106811146[/C][/ROW]
[ROW][C]35[/C][C]2233[/C][C]2048.60139318885[/C][C]184.398606811146[/C][/ROW]
[ROW][C]36[/C][C]2192[/C][C]2165.22639318885[/C][C]26.7736068111456[/C][/ROW]
[ROW][C]37[/C][C]2080[/C][C]1722.67569659443[/C][C]357.324303405572[/C][/ROW]
[ROW][C]38[/C][C]1768[/C][C]1547.41389318885[/C][C]220.586106811145[/C][/ROW]
[ROW][C]39[/C][C]1835[/C][C]1597.97639318885[/C][C]237.023606811145[/C][/ROW]
[ROW][C]40[/C][C]1569[/C][C]1484.78889318885[/C][C]84.2111068111455[/C][/ROW]
[ROW][C]41[/C][C]1976[/C][C]1622.10139318885[/C][C]353.898606811146[/C][/ROW]
[ROW][C]42[/C][C]1853[/C][C]1566.35139318885[/C][C]286.648606811146[/C][/ROW]
[ROW][C]43[/C][C]1965[/C][C]1641.97639318885[/C][C]323.023606811145[/C][/ROW]
[ROW][C]44[/C][C]1689[/C][C]1656.85139318885[/C][C]32.1486068111455[/C][/ROW]
[ROW][C]45[/C][C]1778[/C][C]1709.66389318885[/C][C]68.3361068111455[/C][/ROW]
[ROW][C]46[/C][C]1976[/C][C]1849.03889318885[/C][C]126.961106811146[/C][/ROW]
[ROW][C]47[/C][C]2397[/C][C]2048.60139318885[/C][C]348.398606811146[/C][/ROW]
[ROW][C]48[/C][C]2654[/C][C]2165.22639318885[/C][C]488.773606811146[/C][/ROW]
[ROW][C]49[/C][C]2097[/C][C]1722.67569659443[/C][C]374.324303405572[/C][/ROW]
[ROW][C]50[/C][C]1963[/C][C]1547.41389318885[/C][C]415.586106811145[/C][/ROW]
[ROW][C]51[/C][C]1677[/C][C]1597.97639318885[/C][C]79.0236068111455[/C][/ROW]
[ROW][C]52[/C][C]1941[/C][C]1484.78889318885[/C][C]456.211106811146[/C][/ROW]
[ROW][C]53[/C][C]2003[/C][C]1622.10139318885[/C][C]380.898606811146[/C][/ROW]
[ROW][C]54[/C][C]1813[/C][C]1566.35139318885[/C][C]246.648606811146[/C][/ROW]
[ROW][C]55[/C][C]2012[/C][C]1641.97639318885[/C][C]370.023606811145[/C][/ROW]
[ROW][C]56[/C][C]1912[/C][C]1656.85139318885[/C][C]255.148606811145[/C][/ROW]
[ROW][C]57[/C][C]2084[/C][C]1709.66389318885[/C][C]374.336106811146[/C][/ROW]
[ROW][C]58[/C][C]2080[/C][C]1849.03889318885[/C][C]230.961106811146[/C][/ROW]
[ROW][C]59[/C][C]2118[/C][C]2048.60139318885[/C][C]69.3986068111455[/C][/ROW]
[ROW][C]60[/C][C]2150[/C][C]2165.22639318885[/C][C]-15.2263931888545[/C][/ROW]
[ROW][C]61[/C][C]1608[/C][C]1722.67569659443[/C][C]-114.675696594428[/C][/ROW]
[ROW][C]62[/C][C]1503[/C][C]1547.41389318885[/C][C]-44.4138931888545[/C][/ROW]
[ROW][C]63[/C][C]1548[/C][C]1597.97639318885[/C][C]-49.9763931888545[/C][/ROW]
[ROW][C]64[/C][C]1382[/C][C]1484.78889318885[/C][C]-102.788893188854[/C][/ROW]
[ROW][C]65[/C][C]1731[/C][C]1622.10139318885[/C][C]108.898606811145[/C][/ROW]
[ROW][C]66[/C][C]1798[/C][C]1566.35139318885[/C][C]231.648606811145[/C][/ROW]
[ROW][C]67[/C][C]1779[/C][C]1641.97639318885[/C][C]137.023606811145[/C][/ROW]
[ROW][C]68[/C][C]1887[/C][C]1656.85139318885[/C][C]230.148606811145[/C][/ROW]
[ROW][C]69[/C][C]2004[/C][C]1709.66389318885[/C][C]294.336106811146[/C][/ROW]
[ROW][C]70[/C][C]2077[/C][C]1849.03889318885[/C][C]227.961106811146[/C][/ROW]
[ROW][C]71[/C][C]2092[/C][C]2048.60139318885[/C][C]43.3986068111455[/C][/ROW]
[ROW][C]72[/C][C]2051[/C][C]2165.22639318885[/C][C]-114.226393188854[/C][/ROW]
[ROW][C]73[/C][C]1577[/C][C]1722.67569659443[/C][C]-145.675696594428[/C][/ROW]
[ROW][C]74[/C][C]1356[/C][C]1547.41389318885[/C][C]-191.413893188855[/C][/ROW]
[ROW][C]75[/C][C]1652[/C][C]1597.97639318885[/C][C]54.0236068111455[/C][/ROW]
[ROW][C]76[/C][C]1382[/C][C]1484.78889318885[/C][C]-102.788893188854[/C][/ROW]
[ROW][C]77[/C][C]1519[/C][C]1622.10139318885[/C][C]-103.101393188855[/C][/ROW]
[ROW][C]78[/C][C]1421[/C][C]1566.35139318885[/C][C]-145.351393188854[/C][/ROW]
[ROW][C]79[/C][C]1442[/C][C]1641.97639318885[/C][C]-199.976393188854[/C][/ROW]
[ROW][C]80[/C][C]1543[/C][C]1656.85139318885[/C][C]-113.851393188855[/C][/ROW]
[ROW][C]81[/C][C]1656[/C][C]1709.66389318885[/C][C]-53.6638931888545[/C][/ROW]
[ROW][C]82[/C][C]1561[/C][C]1849.03889318885[/C][C]-288.038893188855[/C][/ROW]
[ROW][C]83[/C][C]1905[/C][C]2048.60139318885[/C][C]-143.601393188855[/C][/ROW]
[ROW][C]84[/C][C]2199[/C][C]2165.22639318885[/C][C]33.7736068111456[/C][/ROW]
[ROW][C]85[/C][C]1473[/C][C]1722.67569659443[/C][C]-249.675696594428[/C][/ROW]
[ROW][C]86[/C][C]1655[/C][C]1547.41389318885[/C][C]107.586106811146[/C][/ROW]
[ROW][C]87[/C][C]1407[/C][C]1597.97639318885[/C][C]-190.976393188854[/C][/ROW]
[ROW][C]88[/C][C]1395[/C][C]1484.78889318885[/C][C]-89.7888931888545[/C][/ROW]
[ROW][C]89[/C][C]1530[/C][C]1622.10139318885[/C][C]-92.1013931888545[/C][/ROW]
[ROW][C]90[/C][C]1309[/C][C]1566.35139318885[/C][C]-257.351393188854[/C][/ROW]
[ROW][C]91[/C][C]1526[/C][C]1641.97639318885[/C][C]-115.976393188855[/C][/ROW]
[ROW][C]92[/C][C]1327[/C][C]1656.85139318885[/C][C]-329.851393188854[/C][/ROW]
[ROW][C]93[/C][C]1627[/C][C]1709.66389318885[/C][C]-82.6638931888545[/C][/ROW]
[ROW][C]94[/C][C]1748[/C][C]1849.03889318885[/C][C]-101.038893188854[/C][/ROW]
[ROW][C]95[/C][C]1958[/C][C]2048.60139318885[/C][C]-90.6013931888546[/C][/ROW]
[ROW][C]96[/C][C]2274[/C][C]2165.22639318885[/C][C]108.773606811146[/C][/ROW]
[ROW][C]97[/C][C]1648[/C][C]1722.67569659443[/C][C]-74.6756965944276[/C][/ROW]
[ROW][C]98[/C][C]1401[/C][C]1547.41389318885[/C][C]-146.413893188855[/C][/ROW]
[ROW][C]99[/C][C]1411[/C][C]1597.97639318885[/C][C]-186.976393188854[/C][/ROW]
[ROW][C]100[/C][C]1403[/C][C]1484.78889318885[/C][C]-81.7888931888545[/C][/ROW]
[ROW][C]101[/C][C]1394[/C][C]1622.10139318885[/C][C]-228.101393188854[/C][/ROW]
[ROW][C]102[/C][C]1520[/C][C]1566.35139318885[/C][C]-46.3513931888545[/C][/ROW]
[ROW][C]103[/C][C]1528[/C][C]1641.97639318885[/C][C]-113.976393188855[/C][/ROW]
[ROW][C]104[/C][C]1643[/C][C]1656.85139318885[/C][C]-13.8513931888545[/C][/ROW]
[ROW][C]105[/C][C]1515[/C][C]1709.66389318885[/C][C]-194.663893188855[/C][/ROW]
[ROW][C]106[/C][C]1685[/C][C]1849.03889318885[/C][C]-164.038893188855[/C][/ROW]
[ROW][C]107[/C][C]2000[/C][C]2048.60139318885[/C][C]-48.6013931888545[/C][/ROW]
[ROW][C]108[/C][C]2215[/C][C]2165.22639318885[/C][C]49.7736068111455[/C][/ROW]
[ROW][C]109[/C][C]1956[/C][C]1722.67569659443[/C][C]233.324303405572[/C][/ROW]
[ROW][C]110[/C][C]1462[/C][C]1547.41389318885[/C][C]-85.4138931888544[/C][/ROW]
[ROW][C]111[/C][C]1563[/C][C]1597.97639318885[/C][C]-34.9763931888545[/C][/ROW]
[ROW][C]112[/C][C]1459[/C][C]1484.78889318885[/C][C]-25.7888931888545[/C][/ROW]
[ROW][C]113[/C][C]1446[/C][C]1622.10139318885[/C][C]-176.101393188854[/C][/ROW]
[ROW][C]114[/C][C]1622[/C][C]1566.35139318885[/C][C]55.6486068111455[/C][/ROW]
[ROW][C]115[/C][C]1657[/C][C]1641.97639318885[/C][C]15.0236068111455[/C][/ROW]
[ROW][C]116[/C][C]1638[/C][C]1656.85139318885[/C][C]-18.8513931888545[/C][/ROW]
[ROW][C]117[/C][C]1643[/C][C]1709.66389318885[/C][C]-66.6638931888545[/C][/ROW]
[ROW][C]118[/C][C]1683[/C][C]1849.03889318885[/C][C]-166.038893188855[/C][/ROW]
[ROW][C]119[/C][C]2050[/C][C]2048.60139318885[/C][C]1.39860681114554[/C][/ROW]
[ROW][C]120[/C][C]2262[/C][C]2165.22639318885[/C][C]96.7736068111456[/C][/ROW]
[ROW][C]121[/C][C]1813[/C][C]1722.67569659443[/C][C]90.3243034055724[/C][/ROW]
[ROW][C]122[/C][C]1445[/C][C]1547.41389318885[/C][C]-102.413893188854[/C][/ROW]
[ROW][C]123[/C][C]1762[/C][C]1597.97639318885[/C][C]164.023606811145[/C][/ROW]
[ROW][C]124[/C][C]1461[/C][C]1484.78889318885[/C][C]-23.7888931888545[/C][/ROW]
[ROW][C]125[/C][C]1556[/C][C]1622.10139318885[/C][C]-66.1013931888545[/C][/ROW]
[ROW][C]126[/C][C]1431[/C][C]1566.35139318885[/C][C]-135.351393188854[/C][/ROW]
[ROW][C]127[/C][C]1427[/C][C]1641.97639318885[/C][C]-214.976393188854[/C][/ROW]
[ROW][C]128[/C][C]1554[/C][C]1656.85139318885[/C][C]-102.851393188855[/C][/ROW]
[ROW][C]129[/C][C]1645[/C][C]1709.66389318885[/C][C]-64.6638931888545[/C][/ROW]
[ROW][C]130[/C][C]1653[/C][C]1849.03889318885[/C][C]-196.038893188855[/C][/ROW]
[ROW][C]131[/C][C]2016[/C][C]2048.60139318885[/C][C]-32.6013931888545[/C][/ROW]
[ROW][C]132[/C][C]2207[/C][C]2165.22639318885[/C][C]41.7736068111456[/C][/ROW]
[ROW][C]133[/C][C]1665[/C][C]1722.67569659443[/C][C]-57.6756965944276[/C][/ROW]
[ROW][C]134[/C][C]1361[/C][C]1547.41389318885[/C][C]-186.413893188855[/C][/ROW]
[ROW][C]135[/C][C]1506[/C][C]1597.97639318885[/C][C]-91.9763931888545[/C][/ROW]
[ROW][C]136[/C][C]1360[/C][C]1484.78889318885[/C][C]-124.788893188854[/C][/ROW]
[ROW][C]137[/C][C]1453[/C][C]1622.10139318885[/C][C]-169.101393188854[/C][/ROW]
[ROW][C]138[/C][C]1522[/C][C]1566.35139318885[/C][C]-44.3513931888545[/C][/ROW]
[ROW][C]139[/C][C]1460[/C][C]1641.97639318885[/C][C]-181.976393188854[/C][/ROW]
[ROW][C]140[/C][C]1552[/C][C]1656.85139318885[/C][C]-104.851393188855[/C][/ROW]
[ROW][C]141[/C][C]1548[/C][C]1709.66389318885[/C][C]-161.663893188855[/C][/ROW]
[ROW][C]142[/C][C]1827[/C][C]1849.03889318885[/C][C]-22.0388931888545[/C][/ROW]
[ROW][C]143[/C][C]1737[/C][C]2048.60139318885[/C][C]-311.601393188855[/C][/ROW]
[ROW][C]144[/C][C]1941[/C][C]2165.22639318885[/C][C]-224.226393188855[/C][/ROW]
[ROW][C]145[/C][C]1474[/C][C]1722.67569659443[/C][C]-248.675696594428[/C][/ROW]
[ROW][C]146[/C][C]1458[/C][C]1547.41389318885[/C][C]-89.4138931888544[/C][/ROW]
[ROW][C]147[/C][C]1542[/C][C]1597.97639318885[/C][C]-55.9763931888545[/C][/ROW]
[ROW][C]148[/C][C]1404[/C][C]1484.78889318885[/C][C]-80.7888931888545[/C][/ROW]
[ROW][C]149[/C][C]1522[/C][C]1622.10139318885[/C][C]-100.101393188855[/C][/ROW]
[ROW][C]150[/C][C]1385[/C][C]1566.35139318885[/C][C]-181.351393188854[/C][/ROW]
[ROW][C]151[/C][C]1641[/C][C]1641.97639318885[/C][C]-0.976393188854502[/C][/ROW]
[ROW][C]152[/C][C]1510[/C][C]1656.85139318885[/C][C]-146.851393188855[/C][/ROW]
[ROW][C]153[/C][C]1681[/C][C]1709.66389318885[/C][C]-28.6638931888545[/C][/ROW]
[ROW][C]154[/C][C]1938[/C][C]1849.03889318885[/C][C]88.9611068111455[/C][/ROW]
[ROW][C]155[/C][C]1868[/C][C]2048.60139318885[/C][C]-180.601393188855[/C][/ROW]
[ROW][C]156[/C][C]1726[/C][C]2165.22639318885[/C][C]-439.226393188855[/C][/ROW]
[ROW][C]157[/C][C]1456[/C][C]1722.67569659443[/C][C]-266.675696594428[/C][/ROW]
[ROW][C]158[/C][C]1445[/C][C]1547.41389318885[/C][C]-102.413893188854[/C][/ROW]
[ROW][C]159[/C][C]1456[/C][C]1597.97639318885[/C][C]-141.976393188855[/C][/ROW]
[ROW][C]160[/C][C]1365[/C][C]1484.78889318885[/C][C]-119.788893188854[/C][/ROW]
[ROW][C]161[/C][C]1487[/C][C]1622.10139318885[/C][C]-135.101393188855[/C][/ROW]
[ROW][C]162[/C][C]1558[/C][C]1566.35139318885[/C][C]-8.35139318885452[/C][/ROW]
[ROW][C]163[/C][C]1488[/C][C]1641.97639318885[/C][C]-153.976393188855[/C][/ROW]
[ROW][C]164[/C][C]1684[/C][C]1656.85139318885[/C][C]27.1486068111455[/C][/ROW]
[ROW][C]165[/C][C]1594[/C][C]1709.66389318885[/C][C]-115.663893188855[/C][/ROW]
[ROW][C]166[/C][C]1850[/C][C]1849.03889318885[/C][C]0.961106811145528[/C][/ROW]
[ROW][C]167[/C][C]1998[/C][C]2048.60139318885[/C][C]-50.6013931888545[/C][/ROW]
[ROW][C]168[/C][C]2079[/C][C]2165.22639318885[/C][C]-86.2263931888545[/C][/ROW]
[ROW][C]169[/C][C]1494[/C][C]1722.67569659443[/C][C]-228.675696594428[/C][/ROW]
[ROW][C]170[/C][C]1057[/C][C]1151.60274767802[/C][C]-94.6027476780185[/C][/ROW]
[ROW][C]171[/C][C]1218[/C][C]1202.16524767802[/C][C]15.8347523219813[/C][/ROW]
[ROW][C]172[/C][C]1168[/C][C]1088.97774767802[/C][C]79.0222523219813[/C][/ROW]
[ROW][C]173[/C][C]1236[/C][C]1226.29024767802[/C][C]9.70975232198147[/C][/ROW]
[ROW][C]174[/C][C]1076[/C][C]1170.54024767802[/C][C]-94.5402476780185[/C][/ROW]
[ROW][C]175[/C][C]1174[/C][C]1246.16524767802[/C][C]-72.1652476780185[/C][/ROW]
[ROW][C]176[/C][C]1139[/C][C]1261.04024767802[/C][C]-122.040247678018[/C][/ROW]
[ROW][C]177[/C][C]1427[/C][C]1313.85274767802[/C][C]113.147252321981[/C][/ROW]
[ROW][C]178[/C][C]1487[/C][C]1453.22774767802[/C][C]33.7722523219815[/C][/ROW]
[ROW][C]179[/C][C]1483[/C][C]1652.79024767802[/C][C]-169.790247678019[/C][/ROW]
[ROW][C]180[/C][C]1513[/C][C]1769.41524767802[/C][C]-256.415247678019[/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]1151.60274767802[/C][C]13.3972523219814[/C][/ROW]
[ROW][C]183[/C][C]1282[/C][C]1202.16524767802[/C][C]79.8347523219813[/C][/ROW]
[ROW][C]184[/C][C]1110[/C][C]1088.97774767802[/C][C]21.0222523219814[/C][/ROW]
[ROW][C]185[/C][C]1297[/C][C]1226.29024767802[/C][C]70.7097523219815[/C][/ROW]
[ROW][C]186[/C][C]1185[/C][C]1170.54024767802[/C][C]14.4597523219814[/C][/ROW]
[ROW][C]187[/C][C]1222[/C][C]1246.16524767802[/C][C]-24.1652476780185[/C][/ROW]
[ROW][C]188[/C][C]1284[/C][C]1261.04024767802[/C][C]22.9597523219815[/C][/ROW]
[ROW][C]189[/C][C]1444[/C][C]1313.85274767802[/C][C]130.147252321981[/C][/ROW]
[ROW][C]190[/C][C]1575[/C][C]1453.22774767802[/C][C]121.772252321981[/C][/ROW]
[ROW][C]191[/C][C]1737[/C][C]1652.79024767802[/C][C]84.2097523219814[/C][/ROW]
[ROW][C]192[/C][C]1763[/C][C]1769.41524767802[/C][C]-6.41524767801863[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=26492&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=26492&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
116871722.67569659442-35.6756965944224
215081547.41389318885-39.4138931888546
315071597.97639318885-90.9763931888542
413851484.78889318885-99.788893188855
516321622.101393188859.89860681114526
615111566.35139318885-55.3513931888549
715591641.97639318885-82.9763931888545
816301656.85139318885-26.8513931888544
915791709.66389318885-130.663893188855
1016531849.03889318885-196.038893188855
1121522048.60139318885103.398606811145
1221482165.22639318885-17.2263931888546
1317521722.6756965944329.3243034055724
1417651547.41389318885217.586106811145
1517171597.97639318885119.023606811145
1615581484.7888931888573.2111068111455
1715751622.10139318885-47.1013931888545
1815201566.35139318885-46.3513931888545
1918051641.97639318885163.023606811146
2018001656.85139318885143.148606811145
2117191709.663893188859.33610681114551
2220081849.03889318885158.961106811146
2322422048.60139318885193.398606811146
2424782165.22639318885312.773606811146
2520301722.67569659443307.324303405572
2616551547.41389318885107.586106811146
2716931597.9763931888595.0236068111455
2816231484.78889318885138.211106811146
2918051622.10139318885182.898606811145
3017461566.35139318885179.648606811145
3117951641.97639318885153.023606811146
3219261656.85139318885269.148606811145
3316191709.66389318885-90.6638931888545
3419921849.03889318885142.961106811146
3522332048.60139318885184.398606811146
3621922165.2263931888526.7736068111456
3720801722.67569659443357.324303405572
3817681547.41389318885220.586106811145
3918351597.97639318885237.023606811145
4015691484.7888931888584.2111068111455
4119761622.10139318885353.898606811146
4218531566.35139318885286.648606811146
4319651641.97639318885323.023606811145
4416891656.8513931888532.1486068111455
4517781709.6638931888568.3361068111455
4619761849.03889318885126.961106811146
4723972048.60139318885348.398606811146
4826542165.22639318885488.773606811146
4920971722.67569659443374.324303405572
5019631547.41389318885415.586106811145
5116771597.9763931888579.0236068111455
5219411484.78889318885456.211106811146
5320031622.10139318885380.898606811146
5418131566.35139318885246.648606811146
5520121641.97639318885370.023606811145
5619121656.85139318885255.148606811145
5720841709.66389318885374.336106811146
5820801849.03889318885230.961106811146
5921182048.6013931888569.3986068111455
6021502165.22639318885-15.2263931888545
6116081722.67569659443-114.675696594428
6215031547.41389318885-44.4138931888545
6315481597.97639318885-49.9763931888545
6413821484.78889318885-102.788893188854
6517311622.10139318885108.898606811145
6617981566.35139318885231.648606811145
6717791641.97639318885137.023606811145
6818871656.85139318885230.148606811145
6920041709.66389318885294.336106811146
7020771849.03889318885227.961106811146
7120922048.6013931888543.3986068111455
7220512165.22639318885-114.226393188854
7315771722.67569659443-145.675696594428
7413561547.41389318885-191.413893188855
7516521597.9763931888554.0236068111455
7613821484.78889318885-102.788893188854
7715191622.10139318885-103.101393188855
7814211566.35139318885-145.351393188854
7914421641.97639318885-199.976393188854
8015431656.85139318885-113.851393188855
8116561709.66389318885-53.6638931888545
8215611849.03889318885-288.038893188855
8319052048.60139318885-143.601393188855
8421992165.2263931888533.7736068111456
8514731722.67569659443-249.675696594428
8616551547.41389318885107.586106811146
8714071597.97639318885-190.976393188854
8813951484.78889318885-89.7888931888545
8915301622.10139318885-92.1013931888545
9013091566.35139318885-257.351393188854
9115261641.97639318885-115.976393188855
9213271656.85139318885-329.851393188854
9316271709.66389318885-82.6638931888545
9417481849.03889318885-101.038893188854
9519582048.60139318885-90.6013931888546
9622742165.22639318885108.773606811146
9716481722.67569659443-74.6756965944276
9814011547.41389318885-146.413893188855
9914111597.97639318885-186.976393188854
10014031484.78889318885-81.7888931888545
10113941622.10139318885-228.101393188854
10215201566.35139318885-46.3513931888545
10315281641.97639318885-113.976393188855
10416431656.85139318885-13.8513931888545
10515151709.66389318885-194.663893188855
10616851849.03889318885-164.038893188855
10720002048.60139318885-48.6013931888545
10822152165.2263931888549.7736068111455
10919561722.67569659443233.324303405572
11014621547.41389318885-85.4138931888544
11115631597.97639318885-34.9763931888545
11214591484.78889318885-25.7888931888545
11314461622.10139318885-176.101393188854
11416221566.3513931888555.6486068111455
11516571641.9763931888515.0236068111455
11616381656.85139318885-18.8513931888545
11716431709.66389318885-66.6638931888545
11816831849.03889318885-166.038893188855
11920502048.601393188851.39860681114554
12022622165.2263931888596.7736068111456
12118131722.6756965944390.3243034055724
12214451547.41389318885-102.413893188854
12317621597.97639318885164.023606811145
12414611484.78889318885-23.7888931888545
12515561622.10139318885-66.1013931888545
12614311566.35139318885-135.351393188854
12714271641.97639318885-214.976393188854
12815541656.85139318885-102.851393188855
12916451709.66389318885-64.6638931888545
13016531849.03889318885-196.038893188855
13120162048.60139318885-32.6013931888545
13222072165.2263931888541.7736068111456
13316651722.67569659443-57.6756965944276
13413611547.41389318885-186.413893188855
13515061597.97639318885-91.9763931888545
13613601484.78889318885-124.788893188854
13714531622.10139318885-169.101393188854
13815221566.35139318885-44.3513931888545
13914601641.97639318885-181.976393188854
14015521656.85139318885-104.851393188855
14115481709.66389318885-161.663893188855
14218271849.03889318885-22.0388931888545
14317372048.60139318885-311.601393188855
14419412165.22639318885-224.226393188855
14514741722.67569659443-248.675696594428
14614581547.41389318885-89.4138931888544
14715421597.97639318885-55.9763931888545
14814041484.78889318885-80.7888931888545
14915221622.10139318885-100.101393188855
15013851566.35139318885-181.351393188854
15116411641.97639318885-0.976393188854502
15215101656.85139318885-146.851393188855
15316811709.66389318885-28.6638931888545
15419381849.0388931888588.9611068111455
15518682048.60139318885-180.601393188855
15617262165.22639318885-439.226393188855
15714561722.67569659443-266.675696594428
15814451547.41389318885-102.413893188854
15914561597.97639318885-141.976393188855
16013651484.78889318885-119.788893188854
16114871622.10139318885-135.101393188855
16215581566.35139318885-8.35139318885452
16314881641.97639318885-153.976393188855
16416841656.8513931888527.1486068111455
16515941709.66389318885-115.663893188855
16618501849.038893188850.961106811145528
16719982048.60139318885-50.6013931888545
16820792165.22639318885-86.2263931888545
16914941722.67569659443-228.675696594428
17010571151.60274767802-94.6027476780185
17112181202.1652476780215.8347523219813
17211681088.9777476780279.0222523219813
17312361226.290247678029.70975232198147
17410761170.54024767802-94.5402476780185
17511741246.16524767802-72.1652476780185
17611391261.04024767802-122.040247678018
17714271313.85274767802113.147252321981
17814871453.2277476780233.7722523219815
17914831652.79024767802-169.790247678019
18015131769.41524767802-256.415247678019
18113571326.8645510835930.1354489164084
18211651151.6027476780213.3972523219814
18312821202.1652476780279.8347523219813
18411101088.9777476780221.0222523219814
18512971226.2902476780270.7097523219815
18611851170.5402476780214.4597523219814
18712221246.16524767802-24.1652476780185
18812841261.0402476780222.9597523219815
18914441313.85274767802130.147252321981
19015751453.22774767802121.772252321981
19117371652.7902476780284.2097523219814
19217631769.41524767802-6.41524767801863



Parameters (Session):
par1 = 1 ; par2 = Include Monthly Dummies ; par3 = No Linear Trend ;
Parameters (R input):
par1 = 1 ; par2 = Include Monthly Dummies ; par3 = No Linear Trend ;
R code (references can be found in the software module):
library(lattice)
par1 <- as.numeric(par1)
x <- t(y)
k <- length(x[1,])
n <- length(x[,1])
x1 <- cbind(x[,par1], x[,1:k!=par1])
mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
colnames(x1) <- mycolnames #colnames(x)[par1]
x <- x1
if (par3 == 'First Differences'){
x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
for (i in 1:n-1) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
}
if (par2 == 'Include Monthly Dummies'){
x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
for (i in 1:11){
x2[seq(i,n,12),i] <- 1
}
x <- cbind(x, x2)
}
if (par2 == 'Include Quarterly Dummies'){
x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
for (i in 1:3){
x2[seq(i,n,4),i] <- 1
}
x <- cbind(x, x2)
}
k <- length(x[1,])
if (par3 == 'Linear Trend'){
x <- cbind(x, c(1:n))
colnames(x)[k+1] <- 't'
}
x
k <- length(x[1,])
df <- as.data.frame(x)
(mylm <- lm(df))
(mysum <- summary(mylm))
bitmap(file='test0.png')
plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
points(x[,1]-mysum$resid)
grid()
dev.off()
bitmap(file='test1.png')
plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
grid()
dev.off()
bitmap(file='test2.png')
hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
grid()
dev.off()
bitmap(file='test3.png')
densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
dev.off()
bitmap(file='test4.png')
qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
grid()
dev.off()
(myerror <- as.ts(mysum$resid))
bitmap(file='test5.png')
dum <- cbind(lag(myerror,k=1),myerror)
dum
dum1 <- dum[2:length(myerror),]
dum1
z <- as.data.frame(dum1)
z
plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
lines(lowess(z))
abline(lm(z))
grid()
dev.off()
bitmap(file='test6.png')
acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
grid()
dev.off()
bitmap(file='test7.png')
pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
grid()
dev.off()
bitmap(file='test8.png')
opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
plot(mylm, las = 1, sub='Residual Diagnostics')
par(opar)
dev.off()
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
a<-table.row.end(a)
myeq <- colnames(x)[1]
myeq <- paste(myeq, '[t] = ', sep='')
for (i in 1:k){
if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ')
if (rownames(mysum$coefficients)[i] != '(Intercept)') {
myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
}
}
myeq <- paste(myeq, ' + e[t]')
a<-table.row.start(a)
a<-table.element(a, myeq)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable1.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,hyperlink('ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Variable',header=TRUE)
a<-table.element(a,'Parameter',header=TRUE)
a<-table.element(a,'S.D.',header=TRUE)
a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
a<-table.element(a,'2-tail p-value',header=TRUE)
a<-table.element(a,'1-tail p-value',header=TRUE)
a<-table.row.end(a)
for (i in 1:k){
a<-table.row.start(a)
a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
a<-table.element(a,mysum$coefficients[i,1])
a<-table.element(a, round(mysum$coefficients[i,2],6))
a<-table.element(a, round(mysum$coefficients[i,3],4))
a<-table.element(a, round(mysum$coefficients[i,4],6))
a<-table.element(a, round(mysum$coefficients[i,4]/2,6))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable2.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple R',1,TRUE)
a<-table.element(a, sqrt(mysum$r.squared))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'R-squared',1,TRUE)
a<-table.element(a, mysum$r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Adjusted R-squared',1,TRUE)
a<-table.element(a, mysum$adj.r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (value)',1,TRUE)
a<-table.element(a, mysum$fstatistic[1])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[2])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[3])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'p-value',1,TRUE)
a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
a<-table.element(a, mysum$sigma)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
a<-table.element(a, sum(myerror*myerror))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,x[i])
a<-table.element(a,x[i]-mysum$resid[i])
a<-table.element(a,mysum$resid[i])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable4.tab')