Free Statistics

of Irreproducible Research!

Author's title

Author*Unverified author*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationWed, 14 Nov 2007 12:27:43 -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/2007/Nov/14/t1195068249tx7gkt0aclzwjiv.htm/, Retrieved Mon, 26 Feb 2024 04:02:51 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=5393, Retrieved Mon, 26 Feb 2024 04:02:51 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywordsQ1 The Seatbeltlaw
Estimated Impact872
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] [ae3f0dfb5dab6ea17524363c550229d5] [Current]
-   PD    [Multiple Regression] [The Seatbeltlaw] [2007-11-16 14:43:25] [74be16979710d4c4e7c6647856088456]
- R  D      [Multiple Regression] [seatbeltlaw] [2008-11-20 16:48:48] [74be16979710d4c4e7c6647856088456]
F   P         [Multiple Regression] [seatbelt law] [2008-11-20 17:38:41] [74be16979710d4c4e7c6647856088456]
F               [Multiple Regression] [SeatbeltlawQ1Geof...] [2008-11-24 16:26:17] [78af959f979cf98747b1ef58f3b2ffa0]
-    D    [Multiple Regression] [The Seatbeltlaw] [2007-11-16 14:47:27] [74be16979710d4c4e7c6647856088456]
F    D    [Multiple Regression] [Q1] [2008-11-13 18:17:22] [1e1d8320a8a1170c475bf6e4ce119de6]
-   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]
F    D    [Multiple Regression] [The Seatbelt Law ...] [2008-11-15 12:00:57] [93834488277b53a4510bfd06084ae13b]
-   PD      [Multiple Regression] [] [2008-11-15 18:50:27] [93834488277b53a4510bfd06084ae13b]
F   P         [Multiple Regression] [Q3 - Consumptiepr...] [2008-11-15 21:54:28] [93834488277b53a4510bfd06084ae13b]
F    D        [Multiple Regression] [q3 ] [2008-11-19 14:12:05] [44a98561a4b3e6ab8cd5a857b48b0914]
F   P           [Multiple Regression] [q3 dummie+trend] [2008-11-19 14:18:31] [44a98561a4b3e6ab8cd5a857b48b0914]
-   PD        [Multiple Regression] [Paper - Multiple ...] [2008-12-21 14:42:00] [85841a4a203c2f9589565c024425a91b]
-    D        [Multiple Regression] [Paper - Multiple ...] [2008-12-21 14:45:31] [85841a4a203c2f9589565c024425a91b]
-   PD          [Multiple Regression] [Paper - Multiple ...] [2008-12-21 15:09:11] [85841a4a203c2f9589565c024425a91b]
- RMPD            [ARIMA Forecasting] [Paper - Arima for...] [2008-12-21 19:50:13] [85841a4a203c2f9589565c024425a91b]
-   PD              [ARIMA Forecasting] [arima forecast gas] [2008-12-22 17:09:25] [44a98561a4b3e6ab8cd5a857b48b0914]
- RMPD            [ARIMA Forecasting] [Paper - Arima for...] [2008-12-21 20:01:58] [85841a4a203c2f9589565c024425a91b]
- R PD            [Multiple Regression] [Paper - Multiple ...] [2008-12-22 11:36:38] [85841a4a203c2f9589565c024425a91b]
- R  D            [Multiple Regression] [Paper - Multiple ...] [2008-12-22 11:38:40] [85841a4a203c2f9589565c024425a91b]
- R PD            [Multiple Regression] [Paper - Multiple ...] [2008-12-22 11:39:51] [85841a4a203c2f9589565c024425a91b]
-   PD              [Multiple Regression] [Paper - Multiple ...] [2008-12-22 12:06:36] [85841a4a203c2f9589565c024425a91b]
-    D              [Multiple Regression] [Paper - Multiple ...] [2008-12-22 12:08:28] [85841a4a203c2f9589565c024425a91b]
-   PD              [Multiple Regression] [Paper - Multiple ...] [2008-12-22 12:11:46] [85841a4a203c2f9589565c024425a91b]
-   PD          [Multiple Regression] [Paper - Multiple ...] [2008-12-21 15:49:26] [85841a4a203c2f9589565c024425a91b]
-   PD          [Multiple Regression] [Paper - Multiple ...] [2008-12-21 15:50:56] [85841a4a203c2f9589565c024425a91b]
F           [Multiple Regression] [seat belt q1] [2008-11-19 13:47:01] [44a98561a4b3e6ab8cd5a857b48b0914]
F R P       [Multiple Regression] [Seatbelt Law Q1] [2008-11-23 12:04:48] [3548296885df7a66ea8efc200c4aca50]
- RMP       [Univariate Explorative Data Analysis] [Seatbelt Law Q2] [2008-11-23 12:17:49] [3548296885df7a66ea8efc200c4aca50]
-           [Multiple Regression] [] [2008-11-29 13:28:42] [a4ee3bef49b119f4bd2e925060c84f5e]
-   P       [Multiple Regression] [] [2008-11-30 12:59:15] [4c8dfb519edec2da3492d7e6be9a5685]
-   P       [Multiple Regression] [] [2008-11-30 13:01:50] [4c8dfb519edec2da3492d7e6be9a5685]
-    D    [Multiple Regression] [] [2008-11-15 12:00:57] [93834488277b53a4510bfd06084ae13b]
- R PD    [Multiple Regression] [W6Q1] [2008-11-17 18:07:12] [fefc9cefce013a6daab207c2a2eec05e]
F R  D    [Multiple Regression] [] [2008-11-18 10:03:28] [d9be4962be2d3234142c279ef29acbcf]
- R  D    [Multiple Regression] [Q1_Seatbelt law] [2008-11-18 10:41:03] [f77c9ab3b413812d7baee6b7ec69a15d]
F           [Multiple Regression] [Seatbelt law] [2008-11-26 10:38:08] [f77c9ab3b413812d7baee6b7ec69a15d]
F    D    [Multiple Regression] [] [2008-11-18 12:56:00] [1376d48f59a7212e8dd85a587491a69b]
F    D    [Multiple Regression] [Q1 T6] [2008-11-18 17:59:48] [fe7291e888d31b8c4db0b24d6c0f75c6]
-   PD      [Multiple Regression] [1] [2008-12-21 13:12:19] [fe7291e888d31b8c4db0b24d6c0f75c6]
-    D      [Multiple Regression] [2] [2008-12-21 13:24:07] [fe7291e888d31b8c4db0b24d6c0f75c6]
-   PD      [Multiple Regression] [3] [2008-12-21 13:27:04] [fe7291e888d31b8c4db0b24d6c0f75c6]
-    D      [Multiple Regression] [4] [2008-12-21 13:29:10] [fe7291e888d31b8c4db0b24d6c0f75c6]
-   PD      [Multiple Regression] [5] [2008-12-21 13:31:33] [fe7291e888d31b8c4db0b24d6c0f75c6]
-    D      [Multiple Regression] [6] [2008-12-21 13:33:45] [fe7291e888d31b8c4db0b24d6c0f75c6]
-    D        [Multiple Regression] [8] [2008-12-21 13:38:48] [fe7291e888d31b8c4db0b24d6c0f75c6]
-   PD        [Multiple Regression] [7] [2008-12-21 13:40:29] [fe7291e888d31b8c4db0b24d6c0f75c6]

[Truncated]
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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
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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 compuational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time4 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135

\begin{tabular}{lllllllll}
\hline
Summary of compuational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 4 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=5393&T=0

[TABLE]
[ROW][C]Summary of compuational 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]4 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=5393&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=5393&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 compuational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time4 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135







Multiple Linear Regression - Estimated Regression Equation
y[t] = + 2324.06337310277 -226.385033602657x[t] -451.374973256309M1[t] -635.461053323771M2[t] -583.133697991392M3[t] -694.556342659014M4[t] -555.478987326639M5[t] -609.464131994259M6[t] -532.074276661885M7[t] -515.434421329508M8[t] -460.85706599713M9[t] -319.717210664754M10[t] -118.389855332377M11[t] -1.76485533237686t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
y[t] =  +  2324.06337310277 -226.385033602657x[t] -451.374973256309M1[t] -635.461053323771M2[t] -583.133697991392M3[t] -694.556342659014M4[t] -555.478987326639M5[t] -609.464131994259M6[t] -532.074276661885M7[t] -515.434421329508M8[t] -460.85706599713M9[t] -319.717210664754M10[t] -118.389855332377M11[t] -1.76485533237686t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=5393&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]y[t] =  +  2324.06337310277 -226.385033602657x[t] -451.374973256309M1[t] -635.461053323771M2[t] -583.133697991392M3[t] -694.556342659014M4[t] -555.478987326639M5[t] -609.464131994259M6[t] -532.074276661885M7[t] -515.434421329508M8[t] -460.85706599713M9[t] -319.717210664754M10[t] -118.389855332377M11[t] -1.76485533237686t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=5393&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=5393&T=1

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Estimated Regression Equation
y[t] = + 2324.06337310277 -226.385033602657x[t] -451.374973256309M1[t] -635.461053323771M2[t] -583.133697991392M3[t] -694.556342659014M4[t] -555.478987326639M5[t] -609.464131994259M6[t] -532.074276661885M7[t] -515.434421329508M8[t] -460.85706599713M9[t] -319.717210664754M10[t] -118.389855332377M11[t] -1.76485533237686t + 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-226.38503360265741.037226-5.516600
M1-451.37497325630953.942919-8.367600
M2-635.46105332377153.941479-11.780600
M3-583.13369799139253.931287-10.812500
M4-694.55634265901453.922166-12.880700
M5-555.47898732663953.914117-10.30300
M6-609.46413199425953.907141-11.305800
M7-532.07427666188553.901237-9.871300
M8-515.43442132950853.896405-9.563400
M9-460.8570659971353.892648-8.551400
M10-319.71721066475453.889963-5.932800
M11-118.38985533237753.888353-2.19690.0293160.014658
t-1.764855332376860.240551-7.336700

\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 & -226.385033602657 & 41.037226 & -5.5166 & 0 & 0 \tabularnewline
M1 & -451.374973256309 & 53.942919 & -8.3676 & 0 & 0 \tabularnewline
M2 & -635.461053323771 & 53.941479 & -11.7806 & 0 & 0 \tabularnewline
M3 & -583.133697991392 & 53.931287 & -10.8125 & 0 & 0 \tabularnewline
M4 & -694.556342659014 & 53.922166 & -12.8807 & 0 & 0 \tabularnewline
M5 & -555.478987326639 & 53.914117 & -10.303 & 0 & 0 \tabularnewline
M6 & -609.464131994259 & 53.907141 & -11.3058 & 0 & 0 \tabularnewline
M7 & -532.074276661885 & 53.901237 & -9.8713 & 0 & 0 \tabularnewline
M8 & -515.434421329508 & 53.896405 & -9.5634 & 0 & 0 \tabularnewline
M9 & -460.85706599713 & 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 & -1.76485533237686 & 0.240551 & -7.3367 & 0 & 0 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=5393&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]-226.385033602657[/C][C]41.037226[/C][C]-5.5166[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]M1[/C][C]-451.374973256309[/C][C]53.942919[/C][C]-8.3676[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]M2[/C][C]-635.461053323771[/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]-694.556342659014[/C][C]53.922166[/C][C]-12.8807[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]M5[/C][C]-555.478987326639[/C][C]53.914117[/C][C]-10.303[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]M6[/C][C]-609.464131994259[/C][C]53.907141[/C][C]-11.3058[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]M7[/C][C]-532.074276661885[/C][C]53.901237[/C][C]-9.8713[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]M8[/C][C]-515.434421329508[/C][C]53.896405[/C][C]-9.5634[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]M9[/C][C]-460.85706599713[/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]-1.76485533237686[/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=5393&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=5393&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)2324.0633731027744.02993952.783700
x-226.38503360265741.037226-5.516600
M1-451.37497325630953.942919-8.367600
M2-635.46105332377153.941479-11.780600
M3-583.13369799139253.931287-10.812500
M4-694.55634265901453.922166-12.880700
M5-555.47898732663953.914117-10.30300
M6-609.46413199425953.907141-11.305800
M7-532.07427666188553.901237-9.871300
M8-515.43442132950853.896405-9.563400
M9-460.8570659971353.892648-8.551400
M10-319.71721066475453.889963-5.932800
M11-118.38985533237753.888353-2.19690.0293160.014658
t-1.764855332376860.240551-7.336700







Multiple Linear Regression - Regression Statistics
Multiple R0.861322441473346
R-squared0.741876348185605
Adjusted R-squared0.723024620805902
F-TEST (value)39.3532291891914
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) & 39.3532291891914 \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=5393&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]39.3532291891914[/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=5393&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=5393&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 R0.861322441473346
R-squared0.741876348185605
Adjusted R-squared0.723024620805902
F-TEST (value)39.3532291891914
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
116871870.92354451406-183.923544514060
215081685.07260911425-177.072609114252
315071735.63510911425-228.635109114254
413851622.44760911424-237.447609114242
516321759.76010911425-127.760109114251
615111704.01010911425-193.010109114250
715591779.63510911425-220.635109114248
816301794.51010911426-164.510109114256
915791847.32260911425-268.322609114246
1016531986.69760911425-333.697609114249
1121522186.26010911425-34.2601091142503
1221482302.88510911425-154.885109114249
1317521849.74528052556-97.7452805255622
1417651663.89434512573101.105654874272
1517171714.456845125732.54315487427317
1615581601.26934512573-43.2693451257275
1715751738.58184512573-163.581845125727
1815201682.83184512573-162.831845125727
1918051758.4568451257346.5431548742728
2018001773.3318451257326.6681548742733
2117191826.14434512573-107.144345125727
2220081965.5193451257342.4806548742729
2322422165.0818451257376.918154874273
2424782281.70684512573196.293154874273
2520301828.56701653704201.43298346296
2616551642.7160811372012.2839188627955
2716931693.27858113720-0.278581137204542
2816231580.0910811372142.9089188627948
2918051717.4035811372087.5964188627952
3017461661.6535811372084.3464188627952
3117951737.2785811372157.721418862795
3219261752.15358113720173.846418862796
3316191804.96608113721-185.966081137205
3419921944.3410811372047.6589188627952
3522332143.9035811372089.0964188627953
3621922260.52858113721-68.5285811372049
3720801807.38875254852272.611247451482
3817681621.53781714868146.462182851318
3918351672.10031714868162.899682851318
4015691558.9128171486810.0871828513171
4119761696.22531714868279.774682851318
4218531640.47531714868212.524682851317
4319651716.10031714868248.899682851317
4416891730.97531714868-41.9753171486821
4517781783.78781714868-5.78781714868267
4619761923.1628171486852.8371828513174
4723972122.72531714868274.274682851318
4826542239.35031714868414.649682851317
4920971786.21048856000310.789511440004
5019631600.35955316016362.64044683984
5116771650.9220531601626.0779468398400
5219411537.73455316016403.265446839839
5320031675.04705316016327.95294683984
5418131619.29705316016193.702946839840
5520121694.92205316016317.07794683984
5619121709.79705316016202.20294683984
5720841762.60955316016321.39044683984
5820801901.98455316016178.015446839840
5921182101.5470531601616.4529468398398
6021502218.17205316016-68.1720531601603
6116081765.03222457147-157.032224571473
6215031579.18128917164-76.1812891716376
6315481629.74378917164-81.7437891716377
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
7315771743.85396058295-166.853960582951
7413561558.00302518312-202.003025183115
7516521608.5655251831243.4344748168846
7613821495.37802518312-113.378025183116
7715191632.69052518312-113.690525183116
7814211576.94052518312-155.940525183116
7914421652.56552518312-210.565525183116
8015431667.44052518312-124.440525183115
8116561720.25302518312-64.2530251831158
8215611859.62802518312-298.628025183116
8319052059.19052518312-154.190525183116
8421992175.8155251831223.1844748168843
8514731722.67569659443-249.675696594429
8616551536.82476119459118.175238805407
8714071587.38726119459-180.387261194593
8813951474.19976119459-79.1997611945937
8915301611.51226119459-81.5122611945933
9013091555.76226119459-246.762261194593
9115261631.38726119459-105.387261194593
9213271646.26226119459-319.262261194593
9316271699.07476119459-72.0747611945935
9417481838.44976119459-90.4497611945934
9519582038.01226119459-80.0122611945933
9622742154.63726119459119.362738805407
9716481701.49743260591-53.4974326059064
9814011515.64649720607-114.646497206071
9914111566.20899720607-155.208997206071
10014031453.02149720607-50.0214972060714
10113941590.33399720607-196.333997206071
10215201534.58399720607-14.5839972060711
10315281610.20899720607-82.2089972060712
10416431625.0839972060717.9160027939294
10515151677.89649720607-162.896497206071
10616851817.27149720607-132.271497206071
10720002016.83399720607-16.8339972060710
10822152133.4589972060781.5410027939288
10919561680.31916861738275.680831382616
11014621494.46823321755-32.4682332175485
11115631545.0307332175517.9692667824515
11214591431.8432332175527.1567667824508
11314461569.15573321755-123.155733217549
11416221513.40573321755108.594266782451
11516571589.0307332175567.9692667824511
11616381603.9057332175534.0942667824517
11716431656.71823321755-13.7182332175489
11816831796.09323321755-113.093233217549
11920501995.6557332175554.3442667824512
12022622112.28073321755149.719266782451
12118131659.14090462886153.859095371138
12214451473.28996922903-28.2899692290262
12317621523.85246922903238.147530770974
12414611410.6649692290350.3350307709731
12515561547.977469229038.02253077097358
12614311492.22746922903-61.2274692290265
12714271567.85246922903-140.852469229027
12815541582.72746922903-28.7274692290261
12916451635.539969229039.46003077097336
13016531774.91496922903-121.914969229026
13120161974.4774692290341.5225307709736
13222072091.10246922903115.897530770973
13316651637.9626406403427.0373593596605
13413611452.11170524050-91.111705240504
13515061502.674205240503.32579475949605
13613601389.48670524050-29.4867052405046
13714531526.79920524050-73.7992052405041
13815221471.0492052405050.9507947594957
13914601546.67420524050-86.6742052405044
14015521561.54920524050-9.54920524050376
14115481614.36170524050-66.3617052405043
14218271753.7367052405073.2632947594957
14317371953.29920524050-216.299205240504
14419412069.92420524050-128.924205240504
14514741616.78437665182-142.784376651817
14614581430.9334412519827.0665587480184
14715421481.4959412519860.5040587480183
14814041368.3084412519835.6915587480177
14915221505.6209412519816.3790587480182
15013851449.87094125198-64.870941251982
15116411525.49594125198115.504058748018
15215101540.37094125198-30.3709412519815
15316811593.1834412519887.816558748018
15419381732.55844125198205.441558748018
15518681932.12094125198-64.1209412519819
15617262048.74594125198-322.745941251982
15714561595.60611266329-139.606112663295
15814451409.7551772634635.2448227365406
15914561460.31767726346-4.31767726345942
16013651347.1301772634617.8698227365400
16114871484.442677263462.55732273654045
16215581428.69267726346129.307322736540
16314881504.31767726346-16.3176772634598
16416841519.19267726346164.807322736541
16515941572.0051772634621.9948227365402
16618501711.38017726346138.619822736540
16719981910.9426772634687.0573227365404
16820792027.5676772634651.4323227365403
16914941574.42784867477-80.4278486747727
17010571162.19187967228-105.191879672279
17112181212.754379672285.24562032772056
17211681099.5668796722868.4331203277199
17312361236.87937967228-0.879379672279542
17410761181.12937967228-105.129379672280
17511741256.75437967228-82.7543796722797
17611391271.62937967228-132.629379672279
17714271324.44187967228102.558120327720
17814871463.8168796722823.1831203277203
17914831663.37937967228-180.379379672280
18015131780.00437967228-267.00437967228
18113571326.8645510835930.1354489164073
18211651141.0136156837623.986384316243
18312821191.5761156837690.4238843162428
18411101078.3886156837631.6113843162422
18512971215.7011156837681.2988843162427
18611851159.9511156837625.0488843162426
18712221235.57611568376-13.5761156837575
18812841250.4511156837633.548884316243
18914441303.26361568376140.736384316242
19015751442.63861568376132.361384316243
19117371642.2011156837694.7988843162427
19217631758.826115683764.17388431624253

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 1687 & 1870.92354451406 & -183.923544514060 \tabularnewline
2 & 1508 & 1685.07260911425 & -177.072609114252 \tabularnewline
3 & 1507 & 1735.63510911425 & -228.635109114254 \tabularnewline
4 & 1385 & 1622.44760911424 & -237.447609114242 \tabularnewline
5 & 1632 & 1759.76010911425 & -127.760109114251 \tabularnewline
6 & 1511 & 1704.01010911425 & -193.010109114250 \tabularnewline
7 & 1559 & 1779.63510911425 & -220.635109114248 \tabularnewline
8 & 1630 & 1794.51010911426 & -164.510109114256 \tabularnewline
9 & 1579 & 1847.32260911425 & -268.322609114246 \tabularnewline
10 & 1653 & 1986.69760911425 & -333.697609114249 \tabularnewline
11 & 2152 & 2186.26010911425 & -34.2601091142503 \tabularnewline
12 & 2148 & 2302.88510911425 & -154.885109114249 \tabularnewline
13 & 1752 & 1849.74528052556 & -97.7452805255622 \tabularnewline
14 & 1765 & 1663.89434512573 & 101.105654874272 \tabularnewline
15 & 1717 & 1714.45684512573 & 2.54315487427317 \tabularnewline
16 & 1558 & 1601.26934512573 & -43.2693451257275 \tabularnewline
17 & 1575 & 1738.58184512573 & -163.581845125727 \tabularnewline
18 & 1520 & 1682.83184512573 & -162.831845125727 \tabularnewline
19 & 1805 & 1758.45684512573 & 46.5431548742728 \tabularnewline
20 & 1800 & 1773.33184512573 & 26.6681548742733 \tabularnewline
21 & 1719 & 1826.14434512573 & -107.144345125727 \tabularnewline
22 & 2008 & 1965.51934512573 & 42.4806548742729 \tabularnewline
23 & 2242 & 2165.08184512573 & 76.918154874273 \tabularnewline
24 & 2478 & 2281.70684512573 & 196.293154874273 \tabularnewline
25 & 2030 & 1828.56701653704 & 201.43298346296 \tabularnewline
26 & 1655 & 1642.71608113720 & 12.2839188627955 \tabularnewline
27 & 1693 & 1693.27858113720 & -0.278581137204542 \tabularnewline
28 & 1623 & 1580.09108113721 & 42.9089188627948 \tabularnewline
29 & 1805 & 1717.40358113720 & 87.5964188627952 \tabularnewline
30 & 1746 & 1661.65358113720 & 84.3464188627952 \tabularnewline
31 & 1795 & 1737.27858113721 & 57.721418862795 \tabularnewline
32 & 1926 & 1752.15358113720 & 173.846418862796 \tabularnewline
33 & 1619 & 1804.96608113721 & -185.966081137205 \tabularnewline
34 & 1992 & 1944.34108113720 & 47.6589188627952 \tabularnewline
35 & 2233 & 2143.90358113720 & 89.0964188627953 \tabularnewline
36 & 2192 & 2260.52858113721 & -68.5285811372049 \tabularnewline
37 & 2080 & 1807.38875254852 & 272.611247451482 \tabularnewline
38 & 1768 & 1621.53781714868 & 146.462182851318 \tabularnewline
39 & 1835 & 1672.10031714868 & 162.899682851318 \tabularnewline
40 & 1569 & 1558.91281714868 & 10.0871828513171 \tabularnewline
41 & 1976 & 1696.22531714868 & 279.774682851318 \tabularnewline
42 & 1853 & 1640.47531714868 & 212.524682851317 \tabularnewline
43 & 1965 & 1716.10031714868 & 248.899682851317 \tabularnewline
44 & 1689 & 1730.97531714868 & -41.9753171486821 \tabularnewline
45 & 1778 & 1783.78781714868 & -5.78781714868267 \tabularnewline
46 & 1976 & 1923.16281714868 & 52.8371828513174 \tabularnewline
47 & 2397 & 2122.72531714868 & 274.274682851318 \tabularnewline
48 & 2654 & 2239.35031714868 & 414.649682851317 \tabularnewline
49 & 2097 & 1786.21048856000 & 310.789511440004 \tabularnewline
50 & 1963 & 1600.35955316016 & 362.64044683984 \tabularnewline
51 & 1677 & 1650.92205316016 & 26.0779468398400 \tabularnewline
52 & 1941 & 1537.73455316016 & 403.265446839839 \tabularnewline
53 & 2003 & 1675.04705316016 & 327.95294683984 \tabularnewline
54 & 1813 & 1619.29705316016 & 193.702946839840 \tabularnewline
55 & 2012 & 1694.92205316016 & 317.07794683984 \tabularnewline
56 & 1912 & 1709.79705316016 & 202.20294683984 \tabularnewline
57 & 2084 & 1762.60955316016 & 321.39044683984 \tabularnewline
58 & 2080 & 1901.98455316016 & 178.015446839840 \tabularnewline
59 & 2118 & 2101.54705316016 & 16.4529468398398 \tabularnewline
60 & 2150 & 2218.17205316016 & -68.1720531601603 \tabularnewline
61 & 1608 & 1765.03222457147 & -157.032224571473 \tabularnewline
62 & 1503 & 1579.18128917164 & -76.1812891716376 \tabularnewline
63 & 1548 & 1629.74378917164 & -81.7437891716377 \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 & -166.853960582951 \tabularnewline
74 & 1356 & 1558.00302518312 & -202.003025183115 \tabularnewline
75 & 1652 & 1608.56552518312 & 43.4344748168846 \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 & -124.440525183115 \tabularnewline
81 & 1656 & 1720.25302518312 & -64.2530251831158 \tabularnewline
82 & 1561 & 1859.62802518312 & -298.628025183116 \tabularnewline
83 & 1905 & 2059.19052518312 & -154.190525183116 \tabularnewline
84 & 2199 & 2175.81552518312 & 23.1844748168843 \tabularnewline
85 & 1473 & 1722.67569659443 & -249.675696594429 \tabularnewline
86 & 1655 & 1536.82476119459 & 118.175238805407 \tabularnewline
87 & 1407 & 1587.38726119459 & -180.387261194593 \tabularnewline
88 & 1395 & 1474.19976119459 & -79.1997611945937 \tabularnewline
89 & 1530 & 1611.51226119459 & -81.5122611945933 \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 & -72.0747611945935 \tabularnewline
94 & 1748 & 1838.44976119459 & -90.4497611945934 \tabularnewline
95 & 1958 & 2038.01226119459 & -80.0122611945933 \tabularnewline
96 & 2274 & 2154.63726119459 & 119.362738805407 \tabularnewline
97 & 1648 & 1701.49743260591 & -53.4974326059064 \tabularnewline
98 & 1401 & 1515.64649720607 & -114.646497206071 \tabularnewline
99 & 1411 & 1566.20899720607 & -155.208997206071 \tabularnewline
100 & 1403 & 1453.02149720607 & -50.0214972060714 \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 & 17.9160027939294 \tabularnewline
105 & 1515 & 1677.89649720607 & -162.896497206071 \tabularnewline
106 & 1685 & 1817.27149720607 & -132.271497206071 \tabularnewline
107 & 2000 & 2016.83399720607 & -16.8339972060710 \tabularnewline
108 & 2215 & 2133.45899720607 & 81.5410027939288 \tabularnewline
109 & 1956 & 1680.31916861738 & 275.680831382616 \tabularnewline
110 & 1462 & 1494.46823321755 & -32.4682332175485 \tabularnewline
111 & 1563 & 1545.03073321755 & 17.9692667824515 \tabularnewline
112 & 1459 & 1431.84323321755 & 27.1567667824508 \tabularnewline
113 & 1446 & 1569.15573321755 & -123.155733217549 \tabularnewline
114 & 1622 & 1513.40573321755 & 108.594266782451 \tabularnewline
115 & 1657 & 1589.03073321755 & 67.9692667824511 \tabularnewline
116 & 1638 & 1603.90573321755 & 34.0942667824517 \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 & 153.859095371138 \tabularnewline
122 & 1445 & 1473.28996922903 & -28.2899692290262 \tabularnewline
123 & 1762 & 1523.85246922903 & 238.147530770974 \tabularnewline
124 & 1461 & 1410.66496922903 & 50.3350307709731 \tabularnewline
125 & 1556 & 1547.97746922903 & 8.02253077097358 \tabularnewline
126 & 1431 & 1492.22746922903 & -61.2274692290265 \tabularnewline
127 & 1427 & 1567.85246922903 & -140.852469229027 \tabularnewline
128 & 1554 & 1582.72746922903 & -28.7274692290261 \tabularnewline
129 & 1645 & 1635.53996922903 & 9.46003077097336 \tabularnewline
130 & 1653 & 1774.91496922903 & -121.914969229026 \tabularnewline
131 & 2016 & 1974.47746922903 & 41.5225307709736 \tabularnewline
132 & 2207 & 2091.10246922903 & 115.897530770973 \tabularnewline
133 & 1665 & 1637.96264064034 & 27.0373593596605 \tabularnewline
134 & 1361 & 1452.11170524050 & -91.111705240504 \tabularnewline
135 & 1506 & 1502.67420524050 & 3.32579475949605 \tabularnewline
136 & 1360 & 1389.48670524050 & -29.4867052405046 \tabularnewline
137 & 1453 & 1526.79920524050 & -73.7992052405041 \tabularnewline
138 & 1522 & 1471.04920524050 & 50.9507947594957 \tabularnewline
139 & 1460 & 1546.67420524050 & -86.6742052405044 \tabularnewline
140 & 1552 & 1561.54920524050 & -9.54920524050376 \tabularnewline
141 & 1548 & 1614.36170524050 & -66.3617052405043 \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 & -142.784376651817 \tabularnewline
146 & 1458 & 1430.93344125198 & 27.0665587480184 \tabularnewline
147 & 1542 & 1481.49594125198 & 60.5040587480183 \tabularnewline
148 & 1404 & 1368.30844125198 & 35.6915587480177 \tabularnewline
149 & 1522 & 1505.62094125198 & 16.3790587480182 \tabularnewline
150 & 1385 & 1449.87094125198 & -64.870941251982 \tabularnewline
151 & 1641 & 1525.49594125198 & 115.504058748018 \tabularnewline
152 & 1510 & 1540.37094125198 & -30.3709412519815 \tabularnewline
153 & 1681 & 1593.18344125198 & 87.816558748018 \tabularnewline
154 & 1938 & 1732.55844125198 & 205.441558748018 \tabularnewline
155 & 1868 & 1932.12094125198 & -64.1209412519819 \tabularnewline
156 & 1726 & 2048.74594125198 & -322.745941251982 \tabularnewline
157 & 1456 & 1595.60611266329 & -139.606112663295 \tabularnewline
158 & 1445 & 1409.75517726346 & 35.2448227365406 \tabularnewline
159 & 1456 & 1460.31767726346 & -4.31767726345942 \tabularnewline
160 & 1365 & 1347.13017726346 & 17.8698227365400 \tabularnewline
161 & 1487 & 1484.44267726346 & 2.55732273654045 \tabularnewline
162 & 1558 & 1428.69267726346 & 129.307322736540 \tabularnewline
163 & 1488 & 1504.31767726346 & -16.3176772634598 \tabularnewline
164 & 1684 & 1519.19267726346 & 164.807322736541 \tabularnewline
165 & 1594 & 1572.00517726346 & 21.9948227365402 \tabularnewline
166 & 1850 & 1711.38017726346 & 138.619822736540 \tabularnewline
167 & 1998 & 1910.94267726346 & 87.0573227365404 \tabularnewline
168 & 2079 & 2027.56767726346 & 51.4323227365403 \tabularnewline
169 & 1494 & 1574.42784867477 & -80.4278486747727 \tabularnewline
170 & 1057 & 1162.19187967228 & -105.191879672279 \tabularnewline
171 & 1218 & 1212.75437967228 & 5.24562032772056 \tabularnewline
172 & 1168 & 1099.56687967228 & 68.4331203277199 \tabularnewline
173 & 1236 & 1236.87937967228 & -0.879379672279542 \tabularnewline
174 & 1076 & 1181.12937967228 & -105.129379672280 \tabularnewline
175 & 1174 & 1256.75437967228 & -82.7543796722797 \tabularnewline
176 & 1139 & 1271.62937967228 & -132.629379672279 \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 & -267.00437967228 \tabularnewline
181 & 1357 & 1326.86455108359 & 30.1354489164073 \tabularnewline
182 & 1165 & 1141.01361568376 & 23.986384316243 \tabularnewline
183 & 1282 & 1191.57611568376 & 90.4238843162428 \tabularnewline
184 & 1110 & 1078.38861568376 & 31.6113843162422 \tabularnewline
185 & 1297 & 1215.70111568376 & 81.2988843162427 \tabularnewline
186 & 1185 & 1159.95111568376 & 25.0488843162426 \tabularnewline
187 & 1222 & 1235.57611568376 & -13.5761156837575 \tabularnewline
188 & 1284 & 1250.45111568376 & 33.548884316243 \tabularnewline
189 & 1444 & 1303.26361568376 & 140.736384316242 \tabularnewline
190 & 1575 & 1442.63861568376 & 132.361384316243 \tabularnewline
191 & 1737 & 1642.20111568376 & 94.7988843162427 \tabularnewline
192 & 1763 & 1758.82611568376 & 4.17388431624253 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=5393&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]1870.92354451406[/C][C]-183.923544514060[/C][/ROW]
[ROW][C]2[/C][C]1508[/C][C]1685.07260911425[/C][C]-177.072609114252[/C][/ROW]
[ROW][C]3[/C][C]1507[/C][C]1735.63510911425[/C][C]-228.635109114254[/C][/ROW]
[ROW][C]4[/C][C]1385[/C][C]1622.44760911424[/C][C]-237.447609114242[/C][/ROW]
[ROW][C]5[/C][C]1632[/C][C]1759.76010911425[/C][C]-127.760109114251[/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]-220.635109114248[/C][/ROW]
[ROW][C]8[/C][C]1630[/C][C]1794.51010911426[/C][C]-164.510109114256[/C][/ROW]
[ROW][C]9[/C][C]1579[/C][C]1847.32260911425[/C][C]-268.322609114246[/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]-34.2601091142503[/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]-97.7452805255622[/C][/ROW]
[ROW][C]14[/C][C]1765[/C][C]1663.89434512573[/C][C]101.105654874272[/C][/ROW]
[ROW][C]15[/C][C]1717[/C][C]1714.45684512573[/C][C]2.54315487427317[/C][/ROW]
[ROW][C]16[/C][C]1558[/C][C]1601.26934512573[/C][C]-43.2693451257275[/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]46.5431548742728[/C][/ROW]
[ROW][C]20[/C][C]1800[/C][C]1773.33184512573[/C][C]26.6681548742733[/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]42.4806548742729[/C][/ROW]
[ROW][C]23[/C][C]2242[/C][C]2165.08184512573[/C][C]76.918154874273[/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]201.43298346296[/C][/ROW]
[ROW][C]26[/C][C]1655[/C][C]1642.71608113720[/C][C]12.2839188627955[/C][/ROW]
[ROW][C]27[/C][C]1693[/C][C]1693.27858113720[/C][C]-0.278581137204542[/C][/ROW]
[ROW][C]28[/C][C]1623[/C][C]1580.09108113721[/C][C]42.9089188627948[/C][/ROW]
[ROW][C]29[/C][C]1805[/C][C]1717.40358113720[/C][C]87.5964188627952[/C][/ROW]
[ROW][C]30[/C][C]1746[/C][C]1661.65358113720[/C][C]84.3464188627952[/C][/ROW]
[ROW][C]31[/C][C]1795[/C][C]1737.27858113721[/C][C]57.721418862795[/C][/ROW]
[ROW][C]32[/C][C]1926[/C][C]1752.15358113720[/C][C]173.846418862796[/C][/ROW]
[ROW][C]33[/C][C]1619[/C][C]1804.96608113721[/C][C]-185.966081137205[/C][/ROW]
[ROW][C]34[/C][C]1992[/C][C]1944.34108113720[/C][C]47.6589188627952[/C][/ROW]
[ROW][C]35[/C][C]2233[/C][C]2143.90358113720[/C][C]89.0964188627953[/C][/ROW]
[ROW][C]36[/C][C]2192[/C][C]2260.52858113721[/C][C]-68.5285811372049[/C][/ROW]
[ROW][C]37[/C][C]2080[/C][C]1807.38875254852[/C][C]272.611247451482[/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]10.0871828513171[/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]212.524682851317[/C][/ROW]
[ROW][C]43[/C][C]1965[/C][C]1716.10031714868[/C][C]248.899682851317[/C][/ROW]
[ROW][C]44[/C][C]1689[/C][C]1730.97531714868[/C][C]-41.9753171486821[/C][/ROW]
[ROW][C]45[/C][C]1778[/C][C]1783.78781714868[/C][C]-5.78781714868267[/C][/ROW]
[ROW][C]46[/C][C]1976[/C][C]1923.16281714868[/C][C]52.8371828513174[/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]414.649682851317[/C][/ROW]
[ROW][C]49[/C][C]2097[/C][C]1786.21048856000[/C][C]310.789511440004[/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]26.0779468398400[/C][/ROW]
[ROW][C]52[/C][C]1941[/C][C]1537.73455316016[/C][C]403.265446839839[/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]193.702946839840[/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]202.20294683984[/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]178.015446839840[/C][/ROW]
[ROW][C]59[/C][C]2118[/C][C]2101.54705316016[/C][C]16.4529468398398[/C][/ROW]
[ROW][C]60[/C][C]2150[/C][C]2218.17205316016[/C][C]-68.1720531601603[/C][/ROW]
[ROW][C]61[/C][C]1608[/C][C]1765.03222457147[/C][C]-157.032224571473[/C][/ROW]
[ROW][C]62[/C][C]1503[/C][C]1579.18128917164[/C][C]-76.1812891716376[/C][/ROW]
[ROW][C]63[/C][C]1548[/C][C]1629.74378917164[/C][C]-81.7437891716377[/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]-166.853960582951[/C][/ROW]
[ROW][C]74[/C][C]1356[/C][C]1558.00302518312[/C][C]-202.003025183115[/C][/ROW]
[ROW][C]75[/C][C]1652[/C][C]1608.56552518312[/C][C]43.4344748168846[/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]-124.440525183115[/C][/ROW]
[ROW][C]81[/C][C]1656[/C][C]1720.25302518312[/C][C]-64.2530251831158[/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]23.1844748168843[/C][/ROW]
[ROW][C]85[/C][C]1473[/C][C]1722.67569659443[/C][C]-249.675696594429[/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]-79.1997611945937[/C][/ROW]
[ROW][C]89[/C][C]1530[/C][C]1611.51226119459[/C][C]-81.5122611945933[/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]-72.0747611945935[/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]-80.0122611945933[/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]-53.4974326059064[/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]-50.0214972060714[/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]17.9160027939294[/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]-16.8339972060710[/C][/ROW]
[ROW][C]108[/C][C]2215[/C][C]2133.45899720607[/C][C]81.5410027939288[/C][/ROW]
[ROW][C]109[/C][C]1956[/C][C]1680.31916861738[/C][C]275.680831382616[/C][/ROW]
[ROW][C]110[/C][C]1462[/C][C]1494.46823321755[/C][C]-32.4682332175485[/C][/ROW]
[ROW][C]111[/C][C]1563[/C][C]1545.03073321755[/C][C]17.9692667824515[/C][/ROW]
[ROW][C]112[/C][C]1459[/C][C]1431.84323321755[/C][C]27.1567667824508[/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]67.9692667824511[/C][/ROW]
[ROW][C]116[/C][C]1638[/C][C]1603.90573321755[/C][C]34.0942667824517[/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]153.859095371138[/C][/ROW]
[ROW][C]122[/C][C]1445[/C][C]1473.28996922903[/C][C]-28.2899692290262[/C][/ROW]
[ROW][C]123[/C][C]1762[/C][C]1523.85246922903[/C][C]238.147530770974[/C][/ROW]
[ROW][C]124[/C][C]1461[/C][C]1410.66496922903[/C][C]50.3350307709731[/C][/ROW]
[ROW][C]125[/C][C]1556[/C][C]1547.97746922903[/C][C]8.02253077097358[/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]-28.7274692290261[/C][/ROW]
[ROW][C]129[/C][C]1645[/C][C]1635.53996922903[/C][C]9.46003077097336[/C][/ROW]
[ROW][C]130[/C][C]1653[/C][C]1774.91496922903[/C][C]-121.914969229026[/C][/ROW]
[ROW][C]131[/C][C]2016[/C][C]1974.47746922903[/C][C]41.5225307709736[/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]27.0373593596605[/C][/ROW]
[ROW][C]134[/C][C]1361[/C][C]1452.11170524050[/C][C]-91.111705240504[/C][/ROW]
[ROW][C]135[/C][C]1506[/C][C]1502.67420524050[/C][C]3.32579475949605[/C][/ROW]
[ROW][C]136[/C][C]1360[/C][C]1389.48670524050[/C][C]-29.4867052405046[/C][/ROW]
[ROW][C]137[/C][C]1453[/C][C]1526.79920524050[/C][C]-73.7992052405041[/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]-86.6742052405044[/C][/ROW]
[ROW][C]140[/C][C]1552[/C][C]1561.54920524050[/C][C]-9.54920524050376[/C][/ROW]
[ROW][C]141[/C][C]1548[/C][C]1614.36170524050[/C][C]-66.3617052405043[/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]-142.784376651817[/C][/ROW]
[ROW][C]146[/C][C]1458[/C][C]1430.93344125198[/C][C]27.0665587480184[/C][/ROW]
[ROW][C]147[/C][C]1542[/C][C]1481.49594125198[/C][C]60.5040587480183[/C][/ROW]
[ROW][C]148[/C][C]1404[/C][C]1368.30844125198[/C][C]35.6915587480177[/C][/ROW]
[ROW][C]149[/C][C]1522[/C][C]1505.62094125198[/C][C]16.3790587480182[/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]-30.3709412519815[/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]-64.1209412519819[/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]-139.606112663295[/C][/ROW]
[ROW][C]158[/C][C]1445[/C][C]1409.75517726346[/C][C]35.2448227365406[/C][/ROW]
[ROW][C]159[/C][C]1456[/C][C]1460.31767726346[/C][C]-4.31767726345942[/C][/ROW]
[ROW][C]160[/C][C]1365[/C][C]1347.13017726346[/C][C]17.8698227365400[/C][/ROW]
[ROW][C]161[/C][C]1487[/C][C]1484.44267726346[/C][C]2.55732273654045[/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]-16.3176772634598[/C][/ROW]
[ROW][C]164[/C][C]1684[/C][C]1519.19267726346[/C][C]164.807322736541[/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]87.0573227365404[/C][/ROW]
[ROW][C]168[/C][C]2079[/C][C]2027.56767726346[/C][C]51.4323227365403[/C][/ROW]
[ROW][C]169[/C][C]1494[/C][C]1574.42784867477[/C][C]-80.4278486747727[/C][/ROW]
[ROW][C]170[/C][C]1057[/C][C]1162.19187967228[/C][C]-105.191879672279[/C][/ROW]
[ROW][C]171[/C][C]1218[/C][C]1212.75437967228[/C][C]5.24562032772056[/C][/ROW]
[ROW][C]172[/C][C]1168[/C][C]1099.56687967228[/C][C]68.4331203277199[/C][/ROW]
[ROW][C]173[/C][C]1236[/C][C]1236.87937967228[/C][C]-0.879379672279542[/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]-82.7543796722797[/C][/ROW]
[ROW][C]176[/C][C]1139[/C][C]1271.62937967228[/C][C]-132.629379672279[/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]-267.00437967228[/C][/ROW]
[ROW][C]181[/C][C]1357[/C][C]1326.86455108359[/C][C]30.1354489164073[/C][/ROW]
[ROW][C]182[/C][C]1165[/C][C]1141.01361568376[/C][C]23.986384316243[/C][/ROW]
[ROW][C]183[/C][C]1282[/C][C]1191.57611568376[/C][C]90.4238843162428[/C][/ROW]
[ROW][C]184[/C][C]1110[/C][C]1078.38861568376[/C][C]31.6113843162422[/C][/ROW]
[ROW][C]185[/C][C]1297[/C][C]1215.70111568376[/C][C]81.2988843162427[/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]-13.5761156837575[/C][/ROW]
[ROW][C]188[/C][C]1284[/C][C]1250.45111568376[/C][C]33.548884316243[/C][/ROW]
[ROW][C]189[/C][C]1444[/C][C]1303.26361568376[/C][C]140.736384316242[/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]94.7988843162427[/C][/ROW]
[ROW][C]192[/C][C]1763[/C][C]1758.82611568376[/C][C]4.17388431624253[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=5393&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=5393&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
116871870.92354451406-183.923544514060
215081685.07260911425-177.072609114252
315071735.63510911425-228.635109114254
413851622.44760911424-237.447609114242
516321759.76010911425-127.760109114251
615111704.01010911425-193.010109114250
715591779.63510911425-220.635109114248
816301794.51010911426-164.510109114256
915791847.32260911425-268.322609114246
1016531986.69760911425-333.697609114249
1121522186.26010911425-34.2601091142503
1221482302.88510911425-154.885109114249
1317521849.74528052556-97.7452805255622
1417651663.89434512573101.105654874272
1517171714.456845125732.54315487427317
1615581601.26934512573-43.2693451257275
1715751738.58184512573-163.581845125727
1815201682.83184512573-162.831845125727
1918051758.4568451257346.5431548742728
2018001773.3318451257326.6681548742733
2117191826.14434512573-107.144345125727
2220081965.5193451257342.4806548742729
2322422165.0818451257376.918154874273
2424782281.70684512573196.293154874273
2520301828.56701653704201.43298346296
2616551642.7160811372012.2839188627955
2716931693.27858113720-0.278581137204542
2816231580.0910811372142.9089188627948
2918051717.4035811372087.5964188627952
3017461661.6535811372084.3464188627952
3117951737.2785811372157.721418862795
3219261752.15358113720173.846418862796
3316191804.96608113721-185.966081137205
3419921944.3410811372047.6589188627952
3522332143.9035811372089.0964188627953
3621922260.52858113721-68.5285811372049
3720801807.38875254852272.611247451482
3817681621.53781714868146.462182851318
3918351672.10031714868162.899682851318
4015691558.9128171486810.0871828513171
4119761696.22531714868279.774682851318
4218531640.47531714868212.524682851317
4319651716.10031714868248.899682851317
4416891730.97531714868-41.9753171486821
4517781783.78781714868-5.78781714868267
4619761923.1628171486852.8371828513174
4723972122.72531714868274.274682851318
4826542239.35031714868414.649682851317
4920971786.21048856000310.789511440004
5019631600.35955316016362.64044683984
5116771650.9220531601626.0779468398400
5219411537.73455316016403.265446839839
5320031675.04705316016327.95294683984
5418131619.29705316016193.702946839840
5520121694.92205316016317.07794683984
5619121709.79705316016202.20294683984
5720841762.60955316016321.39044683984
5820801901.98455316016178.015446839840
5921182101.5470531601616.4529468398398
6021502218.17205316016-68.1720531601603
6116081765.03222457147-157.032224571473
6215031579.18128917164-76.1812891716376
6315481629.74378917164-81.7437891716377
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
7315771743.85396058295-166.853960582951
7413561558.00302518312-202.003025183115
7516521608.5655251831243.4344748168846
7613821495.37802518312-113.378025183116
7715191632.69052518312-113.690525183116
7814211576.94052518312-155.940525183116
7914421652.56552518312-210.565525183116
8015431667.44052518312-124.440525183115
8116561720.25302518312-64.2530251831158
8215611859.62802518312-298.628025183116
8319052059.19052518312-154.190525183116
8421992175.8155251831223.1844748168843
8514731722.67569659443-249.675696594429
8616551536.82476119459118.175238805407
8714071587.38726119459-180.387261194593
8813951474.19976119459-79.1997611945937
8915301611.51226119459-81.5122611945933
9013091555.76226119459-246.762261194593
9115261631.38726119459-105.387261194593
9213271646.26226119459-319.262261194593
9316271699.07476119459-72.0747611945935
9417481838.44976119459-90.4497611945934
9519582038.01226119459-80.0122611945933
9622742154.63726119459119.362738805407
9716481701.49743260591-53.4974326059064
9814011515.64649720607-114.646497206071
9914111566.20899720607-155.208997206071
10014031453.02149720607-50.0214972060714
10113941590.33399720607-196.333997206071
10215201534.58399720607-14.5839972060711
10315281610.20899720607-82.2089972060712
10416431625.0839972060717.9160027939294
10515151677.89649720607-162.896497206071
10616851817.27149720607-132.271497206071
10720002016.83399720607-16.8339972060710
10822152133.4589972060781.5410027939288
10919561680.31916861738275.680831382616
11014621494.46823321755-32.4682332175485
11115631545.0307332175517.9692667824515
11214591431.8432332175527.1567667824508
11314461569.15573321755-123.155733217549
11416221513.40573321755108.594266782451
11516571589.0307332175567.9692667824511
11616381603.9057332175534.0942667824517
11716431656.71823321755-13.7182332175489
11816831796.09323321755-113.093233217549
11920501995.6557332175554.3442667824512
12022622112.28073321755149.719266782451
12118131659.14090462886153.859095371138
12214451473.28996922903-28.2899692290262
12317621523.85246922903238.147530770974
12414611410.6649692290350.3350307709731
12515561547.977469229038.02253077097358
12614311492.22746922903-61.2274692290265
12714271567.85246922903-140.852469229027
12815541582.72746922903-28.7274692290261
12916451635.539969229039.46003077097336
13016531774.91496922903-121.914969229026
13120161974.4774692290341.5225307709736
13222072091.10246922903115.897530770973
13316651637.9626406403427.0373593596605
13413611452.11170524050-91.111705240504
13515061502.674205240503.32579475949605
13613601389.48670524050-29.4867052405046
13714531526.79920524050-73.7992052405041
13815221471.0492052405050.9507947594957
13914601546.67420524050-86.6742052405044
14015521561.54920524050-9.54920524050376
14115481614.36170524050-66.3617052405043
14218271753.7367052405073.2632947594957
14317371953.29920524050-216.299205240504
14419412069.92420524050-128.924205240504
14514741616.78437665182-142.784376651817
14614581430.9334412519827.0665587480184
14715421481.4959412519860.5040587480183
14814041368.3084412519835.6915587480177
14915221505.6209412519816.3790587480182
15013851449.87094125198-64.870941251982
15116411525.49594125198115.504058748018
15215101540.37094125198-30.3709412519815
15316811593.1834412519887.816558748018
15419381732.55844125198205.441558748018
15518681932.12094125198-64.1209412519819
15617262048.74594125198-322.745941251982
15714561595.60611266329-139.606112663295
15814451409.7551772634635.2448227365406
15914561460.31767726346-4.31767726345942
16013651347.1301772634617.8698227365400
16114871484.442677263462.55732273654045
16215581428.69267726346129.307322736540
16314881504.31767726346-16.3176772634598
16416841519.19267726346164.807322736541
16515941572.0051772634621.9948227365402
16618501711.38017726346138.619822736540
16719981910.9426772634687.0573227365404
16820792027.5676772634651.4323227365403
16914941574.42784867477-80.4278486747727
17010571162.19187967228-105.191879672279
17112181212.754379672285.24562032772056
17211681099.5668796722868.4331203277199
17312361236.87937967228-0.879379672279542
17410761181.12937967228-105.129379672280
17511741256.75437967228-82.7543796722797
17611391271.62937967228-132.629379672279
17714271324.44187967228102.558120327720
17814871463.8168796722823.1831203277203
17914831663.37937967228-180.379379672280
18015131780.00437967228-267.00437967228
18113571326.8645510835930.1354489164073
18211651141.0136156837623.986384316243
18312821191.5761156837690.4238843162428
18411101078.3886156837631.6113843162422
18512971215.7011156837681.2988843162427
18611851159.9511156837625.0488843162426
18712221235.57611568376-13.5761156837575
18812841250.4511156837633.548884316243
18914441303.26361568376140.736384316242
19015751442.63861568376132.361384316243
19117371642.2011156837694.7988843162427
19217631758.826115683764.17388431624253



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')