## 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
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 compuational transaction Raw Input view raw input (R code) Raw Output view raw output of R engine Computing time 4 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 Input view raw input (R code) Raw Output view raw output of R engine Computing time 4 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 Variable Parameter S.D. T-STATH0: parameter = 0 2-tail p-value 1-tail p-value (Intercept) 2324.06337310277 44.029939 52.7837 0 0 x -226.385033602657 41.037226 -5.5166 0 0 M1 -451.374973256309 53.942919 -8.3676 0 0 M2 -635.461053323771 53.941479 -11.7806 0 0 M3 -583.133697991392 53.931287 -10.8125 0 0 M4 -694.556342659014 53.922166 -12.8807 0 0 M5 -555.478987326639 53.914117 -10.303 0 0 M6 -609.464131994259 53.907141 -11.3058 0 0 M7 -532.074276661885 53.901237 -9.8713 0 0 M8 -515.434421329508 53.896405 -9.5634 0 0 M9 -460.85706599713 53.892648 -8.5514 0 0 M10 -319.717210664754 53.889963 -5.9328 0 0 M11 -118.389855332377 53.888353 -2.1969 0.029316 0.014658 t -1.76485533237686 0.240551 -7.3367 0 0

\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 Variable Parameter S.D. T-STATH0: parameter = 0 2-tail p-value 1-tail p-value (Intercept) 2324.06337310277 44.029939 52.7837 0 0 x -226.385033602657 41.037226 -5.5166 0 0 M1 -451.374973256309 53.942919 -8.3676 0 0 M2 -635.461053323771 53.941479 -11.7806 0 0 M3 -583.133697991392 53.931287 -10.8125 0 0 M4 -694.556342659014 53.922166 -12.8807 0 0 M5 -555.478987326639 53.914117 -10.303 0 0 M6 -609.464131994259 53.907141 -11.3058 0 0 M7 -532.074276661885 53.901237 -9.8713 0 0 M8 -515.434421329508 53.896405 -9.5634 0 0 M9 -460.85706599713 53.892648 -8.5514 0 0 M10 -319.717210664754 53.889963 -5.9328 0 0 M11 -118.389855332377 53.888353 -2.1969 0.029316 0.014658 t -1.76485533237686 0.240551 -7.3367 0 0

 Multiple Linear Regression - Regression Statistics Multiple R 0.861322441473346 R-squared 0.741876348185605 Adjusted R-squared 0.723024620805902 F-TEST (value) 39.3532291891914 F-TEST (DF numerator) 13 F-TEST (DF denominator) 178 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 152.417759557721 Sum Squared Residuals 4135148.87028996

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.861322441473346 \tabularnewline
R-squared & 0.741876348185605 \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]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

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 Multiple Linear Regression - Regression Statistics Multiple R 0.861322441473346 R-squared 0.741876348185605 Adjusted R-squared 0.723024620805902 F-TEST (value) 39.3532291891914 F-TEST (DF numerator) 13 F-TEST (DF denominator) 178 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 152.417759557721 Sum Squared Residuals 4135148.87028996

 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals InterpolationForecast ResidualsPrediction Error 1 1687 1870.92354451406 -183.923544514060 2 1508 1685.07260911425 -177.072609114252 3 1507 1735.63510911425 -228.635109114254 4 1385 1622.44760911424 -237.447609114242 5 1632 1759.76010911425 -127.760109114251 6 1511 1704.01010911425 -193.010109114250 7 1559 1779.63510911425 -220.635109114248 8 1630 1794.51010911426 -164.510109114256 9 1579 1847.32260911425 -268.322609114246 10 1653 1986.69760911425 -333.697609114249 11 2152 2186.26010911425 -34.2601091142503 12 2148 2302.88510911425 -154.885109114249 13 1752 1849.74528052556 -97.7452805255622 14 1765 1663.89434512573 101.105654874272 15 1717 1714.45684512573 2.54315487427317 16 1558 1601.26934512573 -43.2693451257275 17 1575 1738.58184512573 -163.581845125727 18 1520 1682.83184512573 -162.831845125727 19 1805 1758.45684512573 46.5431548742728 20 1800 1773.33184512573 26.6681548742733 21 1719 1826.14434512573 -107.144345125727 22 2008 1965.51934512573 42.4806548742729 23 2242 2165.08184512573 76.918154874273 24 2478 2281.70684512573 196.293154874273 25 2030 1828.56701653704 201.43298346296 26 1655 1642.71608113720 12.2839188627955 27 1693 1693.27858113720 -0.278581137204542 28 1623 1580.09108113721 42.9089188627948 29 1805 1717.40358113720 87.5964188627952 30 1746 1661.65358113720 84.3464188627952 31 1795 1737.27858113721 57.721418862795 32 1926 1752.15358113720 173.846418862796 33 1619 1804.96608113721 -185.966081137205 34 1992 1944.34108113720 47.6589188627952 35 2233 2143.90358113720 89.0964188627953 36 2192 2260.52858113721 -68.5285811372049 37 2080 1807.38875254852 272.611247451482 38 1768 1621.53781714868 146.462182851318 39 1835 1672.10031714868 162.899682851318 40 1569 1558.91281714868 10.0871828513171 41 1976 1696.22531714868 279.774682851318 42 1853 1640.47531714868 212.524682851317 43 1965 1716.10031714868 248.899682851317 44 1689 1730.97531714868 -41.9753171486821 45 1778 1783.78781714868 -5.78781714868267 46 1976 1923.16281714868 52.8371828513174 47 2397 2122.72531714868 274.274682851318 48 2654 2239.35031714868 414.649682851317 49 2097 1786.21048856000 310.789511440004 50 1963 1600.35955316016 362.64044683984 51 1677 1650.92205316016 26.0779468398400 52 1941 1537.73455316016 403.265446839839 53 2003 1675.04705316016 327.95294683984 54 1813 1619.29705316016 193.702946839840 55 2012 1694.92205316016 317.07794683984 56 1912 1709.79705316016 202.20294683984 57 2084 1762.60955316016 321.39044683984 58 2080 1901.98455316016 178.015446839840 59 2118 2101.54705316016 16.4529468398398 60 2150 2218.17205316016 -68.1720531601603 61 1608 1765.03222457147 -157.032224571473 62 1503 1579.18128917164 -76.1812891716376 63 1548 1629.74378917164 -81.7437891716377 64 1382 1516.55628917164 -134.556289171638 65 1731 1653.86878917164 77.1312108283621 66 1798 1598.11878917164 199.881210828362 67 1779 1673.74378917164 105.256210828362 68 1887 1688.61878917164 198.381210828362 69 2004 1741.43128917164 262.568710828362 70 2077 1880.80628917164 196.193710828362 71 2092 2080.36878917164 11.6312108283621 72 2051 2196.99378917164 -145.993789171638 73 1577 1743.85396058295 -166.853960582951 74 1356 1558.00302518312 -202.003025183115 75 1652 1608.56552518312 43.4344748168846 76 1382 1495.37802518312 -113.378025183116 77 1519 1632.69052518312 -113.690525183116 78 1421 1576.94052518312 -155.940525183116 79 1442 1652.56552518312 -210.565525183116 80 1543 1667.44052518312 -124.440525183115 81 1656 1720.25302518312 -64.2530251831158 82 1561 1859.62802518312 -298.628025183116 83 1905 2059.19052518312 -154.190525183116 84 2199 2175.81552518312 23.1844748168843 85 1473 1722.67569659443 -249.675696594429 86 1655 1536.82476119459 118.175238805407 87 1407 1587.38726119459 -180.387261194593 88 1395 1474.19976119459 -79.1997611945937 89 1530 1611.51226119459 -81.5122611945933 90 1309 1555.76226119459 -246.762261194593 91 1526 1631.38726119459 -105.387261194593 92 1327 1646.26226119459 -319.262261194593 93 1627 1699.07476119459 -72.0747611945935 94 1748 1838.44976119459 -90.4497611945934 95 1958 2038.01226119459 -80.0122611945933 96 2274 2154.63726119459 119.362738805407 97 1648 1701.49743260591 -53.4974326059064 98 1401 1515.64649720607 -114.646497206071 99 1411 1566.20899720607 -155.208997206071 100 1403 1453.02149720607 -50.0214972060714 101 1394 1590.33399720607 -196.333997206071 102 1520 1534.58399720607 -14.5839972060711 103 1528 1610.20899720607 -82.2089972060712 104 1643 1625.08399720607 17.9160027939294 105 1515 1677.89649720607 -162.896497206071 106 1685 1817.27149720607 -132.271497206071 107 2000 2016.83399720607 -16.8339972060710 108 2215 2133.45899720607 81.5410027939288 109 1956 1680.31916861738 275.680831382616 110 1462 1494.46823321755 -32.4682332175485 111 1563 1545.03073321755 17.9692667824515 112 1459 1431.84323321755 27.1567667824508 113 1446 1569.15573321755 -123.155733217549 114 1622 1513.40573321755 108.594266782451 115 1657 1589.03073321755 67.9692667824511 116 1638 1603.90573321755 34.0942667824517 117 1643 1656.71823321755 -13.7182332175489 118 1683 1796.09323321755 -113.093233217549 119 2050 1995.65573321755 54.3442667824512 120 2262 2112.28073321755 149.719266782451 121 1813 1659.14090462886 153.859095371138 122 1445 1473.28996922903 -28.2899692290262 123 1762 1523.85246922903 238.147530770974 124 1461 1410.66496922903 50.3350307709731 125 1556 1547.97746922903 8.02253077097358 126 1431 1492.22746922903 -61.2274692290265 127 1427 1567.85246922903 -140.852469229027 128 1554 1582.72746922903 -28.7274692290261 129 1645 1635.53996922903 9.46003077097336 130 1653 1774.91496922903 -121.914969229026 131 2016 1974.47746922903 41.5225307709736 132 2207 2091.10246922903 115.897530770973 133 1665 1637.96264064034 27.0373593596605 134 1361 1452.11170524050 -91.111705240504 135 1506 1502.67420524050 3.32579475949605 136 1360 1389.48670524050 -29.4867052405046 137 1453 1526.79920524050 -73.7992052405041 138 1522 1471.04920524050 50.9507947594957 139 1460 1546.67420524050 -86.6742052405044 140 1552 1561.54920524050 -9.54920524050376 141 1548 1614.36170524050 -66.3617052405043 142 1827 1753.73670524050 73.2632947594957 143 1737 1953.29920524050 -216.299205240504 144 1941 2069.92420524050 -128.924205240504 145 1474 1616.78437665182 -142.784376651817 146 1458 1430.93344125198 27.0665587480184 147 1542 1481.49594125198 60.5040587480183 148 1404 1368.30844125198 35.6915587480177 149 1522 1505.62094125198 16.3790587480182 150 1385 1449.87094125198 -64.870941251982 151 1641 1525.49594125198 115.504058748018 152 1510 1540.37094125198 -30.3709412519815 153 1681 1593.18344125198 87.816558748018 154 1938 1732.55844125198 205.441558748018 155 1868 1932.12094125198 -64.1209412519819 156 1726 2048.74594125198 -322.745941251982 157 1456 1595.60611266329 -139.606112663295 158 1445 1409.75517726346 35.2448227365406 159 1456 1460.31767726346 -4.31767726345942 160 1365 1347.13017726346 17.8698227365400 161 1487 1484.44267726346 2.55732273654045 162 1558 1428.69267726346 129.307322736540 163 1488 1504.31767726346 -16.3176772634598 164 1684 1519.19267726346 164.807322736541 165 1594 1572.00517726346 21.9948227365402 166 1850 1711.38017726346 138.619822736540 167 1998 1910.94267726346 87.0573227365404 168 2079 2027.56767726346 51.4323227365403 169 1494 1574.42784867477 -80.4278486747727 170 1057 1162.19187967228 -105.191879672279 171 1218 1212.75437967228 5.24562032772056 172 1168 1099.56687967228 68.4331203277199 173 1236 1236.87937967228 -0.879379672279542 174 1076 1181.12937967228 -105.129379672280 175 1174 1256.75437967228 -82.7543796722797 176 1139 1271.62937967228 -132.629379672279 177 1427 1324.44187967228 102.558120327720 178 1487 1463.81687967228 23.1831203277203 179 1483 1663.37937967228 -180.379379672280 180 1513 1780.00437967228 -267.00437967228 181 1357 1326.86455108359 30.1354489164073 182 1165 1141.01361568376 23.986384316243 183 1282 1191.57611568376 90.4238843162428 184 1110 1078.38861568376 31.6113843162422 185 1297 1215.70111568376 81.2988843162427 186 1185 1159.95111568376 25.0488843162426 187 1222 1235.57611568376 -13.5761156837575 188 1284 1250.45111568376 33.548884316243 189 1444 1303.26361568376 140.736384316242 190 1575 1442.63861568376 132.361384316243 191 1737 1642.20111568376 94.7988843162427 192 1763 1758.82611568376 4.17388431624253

\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 Index Actuals InterpolationForecast ResidualsPrediction Error 1 1687 1870.92354451406 -183.923544514060 2 1508 1685.07260911425 -177.072609114252 3 1507 1735.63510911425 -228.635109114254 4 1385 1622.44760911424 -237.447609114242 5 1632 1759.76010911425 -127.760109114251 6 1511 1704.01010911425 -193.010109114250 7 1559 1779.63510911425 -220.635109114248 8 1630 1794.51010911426 -164.510109114256 9 1579 1847.32260911425 -268.322609114246 10 1653 1986.69760911425 -333.697609114249 11 2152 2186.26010911425 -34.2601091142503 12 2148 2302.88510911425 -154.885109114249 13 1752 1849.74528052556 -97.7452805255622 14 1765 1663.89434512573 101.105654874272 15 1717 1714.45684512573 2.54315487427317 16 1558 1601.26934512573 -43.2693451257275 17 1575 1738.58184512573 -163.581845125727 18 1520 1682.83184512573 -162.831845125727 19 1805 1758.45684512573 46.5431548742728 20 1800 1773.33184512573 26.6681548742733 21 1719 1826.14434512573 -107.144345125727 22 2008 1965.51934512573 42.4806548742729 23 2242 2165.08184512573 76.918154874273 24 2478 2281.70684512573 196.293154874273 25 2030 1828.56701653704 201.43298346296 26 1655 1642.71608113720 12.2839188627955 27 1693 1693.27858113720 -0.278581137204542 28 1623 1580.09108113721 42.9089188627948 29 1805 1717.40358113720 87.5964188627952 30 1746 1661.65358113720 84.3464188627952 31 1795 1737.27858113721 57.721418862795 32 1926 1752.15358113720 173.846418862796 33 1619 1804.96608113721 -185.966081137205 34 1992 1944.34108113720 47.6589188627952 35 2233 2143.90358113720 89.0964188627953 36 2192 2260.52858113721 -68.5285811372049 37 2080 1807.38875254852 272.611247451482 38 1768 1621.53781714868 146.462182851318 39 1835 1672.10031714868 162.899682851318 40 1569 1558.91281714868 10.0871828513171 41 1976 1696.22531714868 279.774682851318 42 1853 1640.47531714868 212.524682851317 43 1965 1716.10031714868 248.899682851317 44 1689 1730.97531714868 -41.9753171486821 45 1778 1783.78781714868 -5.78781714868267 46 1976 1923.16281714868 52.8371828513174 47 2397 2122.72531714868 274.274682851318 48 2654 2239.35031714868 414.649682851317 49 2097 1786.21048856000 310.789511440004 50 1963 1600.35955316016 362.64044683984 51 1677 1650.92205316016 26.0779468398400 52 1941 1537.73455316016 403.265446839839 53 2003 1675.04705316016 327.95294683984 54 1813 1619.29705316016 193.702946839840 55 2012 1694.92205316016 317.07794683984 56 1912 1709.79705316016 202.20294683984 57 2084 1762.60955316016 321.39044683984 58 2080 1901.98455316016 178.015446839840 59 2118 2101.54705316016 16.4529468398398 60 2150 2218.17205316016 -68.1720531601603 61 1608 1765.03222457147 -157.032224571473 62 1503 1579.18128917164 -76.1812891716376 63 1548 1629.74378917164 -81.7437891716377 64 1382 1516.55628917164 -134.556289171638 65 1731 1653.86878917164 77.1312108283621 66 1798 1598.11878917164 199.881210828362 67 1779 1673.74378917164 105.256210828362 68 1887 1688.61878917164 198.381210828362 69 2004 1741.43128917164 262.568710828362 70 2077 1880.80628917164 196.193710828362 71 2092 2080.36878917164 11.6312108283621 72 2051 2196.99378917164 -145.993789171638 73 1577 1743.85396058295 -166.853960582951 74 1356 1558.00302518312 -202.003025183115 75 1652 1608.56552518312 43.4344748168846 76 1382 1495.37802518312 -113.378025183116 77 1519 1632.69052518312 -113.690525183116 78 1421 1576.94052518312 -155.940525183116 79 1442 1652.56552518312 -210.565525183116 80 1543 1667.44052518312 -124.440525183115 81 1656 1720.25302518312 -64.2530251831158 82 1561 1859.62802518312 -298.628025183116 83 1905 2059.19052518312 -154.190525183116 84 2199 2175.81552518312 23.1844748168843 85 1473 1722.67569659443 -249.675696594429 86 1655 1536.82476119459 118.175238805407 87 1407 1587.38726119459 -180.387261194593 88 1395 1474.19976119459 -79.1997611945937 89 1530 1611.51226119459 -81.5122611945933 90 1309 1555.76226119459 -246.762261194593 91 1526 1631.38726119459 -105.387261194593 92 1327 1646.26226119459 -319.262261194593 93 1627 1699.07476119459 -72.0747611945935 94 1748 1838.44976119459 -90.4497611945934 95 1958 2038.01226119459 -80.0122611945933 96 2274 2154.63726119459 119.362738805407 97 1648 1701.49743260591 -53.4974326059064 98 1401 1515.64649720607 -114.646497206071 99 1411 1566.20899720607 -155.208997206071 100 1403 1453.02149720607 -50.0214972060714 101 1394 1590.33399720607 -196.333997206071 102 1520 1534.58399720607 -14.5839972060711 103 1528 1610.20899720607 -82.2089972060712 104 1643 1625.08399720607 17.9160027939294 105 1515 1677.89649720607 -162.896497206071 106 1685 1817.27149720607 -132.271497206071 107 2000 2016.83399720607 -16.8339972060710 108 2215 2133.45899720607 81.5410027939288 109 1956 1680.31916861738 275.680831382616 110 1462 1494.46823321755 -32.4682332175485 111 1563 1545.03073321755 17.9692667824515 112 1459 1431.84323321755 27.1567667824508 113 1446 1569.15573321755 -123.155733217549 114 1622 1513.40573321755 108.594266782451 115 1657 1589.03073321755 67.9692667824511 116 1638 1603.90573321755 34.0942667824517 117 1643 1656.71823321755 -13.7182332175489 118 1683 1796.09323321755 -113.093233217549 119 2050 1995.65573321755 54.3442667824512 120 2262 2112.28073321755 149.719266782451 121 1813 1659.14090462886 153.859095371138 122 1445 1473.28996922903 -28.2899692290262 123 1762 1523.85246922903 238.147530770974 124 1461 1410.66496922903 50.3350307709731 125 1556 1547.97746922903 8.02253077097358 126 1431 1492.22746922903 -61.2274692290265 127 1427 1567.85246922903 -140.852469229027 128 1554 1582.72746922903 -28.7274692290261 129 1645 1635.53996922903 9.46003077097336 130 1653 1774.91496922903 -121.914969229026 131 2016 1974.47746922903 41.5225307709736 132 2207 2091.10246922903 115.897530770973 133 1665 1637.96264064034 27.0373593596605 134 1361 1452.11170524050 -91.111705240504 135 1506 1502.67420524050 3.32579475949605 136 1360 1389.48670524050 -29.4867052405046 137 1453 1526.79920524050 -73.7992052405041 138 1522 1471.04920524050 50.9507947594957 139 1460 1546.67420524050 -86.6742052405044 140 1552 1561.54920524050 -9.54920524050376 141 1548 1614.36170524050 -66.3617052405043 142 1827 1753.73670524050 73.2632947594957 143 1737 1953.29920524050 -216.299205240504 144 1941 2069.92420524050 -128.924205240504 145 1474 1616.78437665182 -142.784376651817 146 1458 1430.93344125198 27.0665587480184 147 1542 1481.49594125198 60.5040587480183 148 1404 1368.30844125198 35.6915587480177 149 1522 1505.62094125198 16.3790587480182 150 1385 1449.87094125198 -64.870941251982 151 1641 1525.49594125198 115.504058748018 152 1510 1540.37094125198 -30.3709412519815 153 1681 1593.18344125198 87.816558748018 154 1938 1732.55844125198 205.441558748018 155 1868 1932.12094125198 -64.1209412519819 156 1726 2048.74594125198 -322.745941251982 157 1456 1595.60611266329 -139.606112663295 158 1445 1409.75517726346 35.2448227365406 159 1456 1460.31767726346 -4.31767726345942 160 1365 1347.13017726346 17.8698227365400 161 1487 1484.44267726346 2.55732273654045 162 1558 1428.69267726346 129.307322736540 163 1488 1504.31767726346 -16.3176772634598 164 1684 1519.19267726346 164.807322736541 165 1594 1572.00517726346 21.9948227365402 166 1850 1711.38017726346 138.619822736540 167 1998 1910.94267726346 87.0573227365404 168 2079 2027.56767726346 51.4323227365403 169 1494 1574.42784867477 -80.4278486747727 170 1057 1162.19187967228 -105.191879672279 171 1218 1212.75437967228 5.24562032772056 172 1168 1099.56687967228 68.4331203277199 173 1236 1236.87937967228 -0.879379672279542 174 1076 1181.12937967228 -105.129379672280 175 1174 1256.75437967228 -82.7543796722797 176 1139 1271.62937967228 -132.629379672279 177 1427 1324.44187967228 102.558120327720 178 1487 1463.81687967228 23.1831203277203 179 1483 1663.37937967228 -180.379379672280 180 1513 1780.00437967228 -267.00437967228 181 1357 1326.86455108359 30.1354489164073 182 1165 1141.01361568376 23.986384316243 183 1282 1191.57611568376 90.4238843162428 184 1110 1078.38861568376 31.6113843162422 185 1297 1215.70111568376 81.2988843162427 186 1185 1159.95111568376 25.0488843162426 187 1222 1235.57611568376 -13.5761156837575 188 1284 1250.45111568376 33.548884316243 189 1444 1303.26361568376 140.736384316242 190 1575 1442.63861568376 132.361384316243 191 1737 1642.20111568376 94.7988843162427 192 1763 1758.82611568376 4.17388431624253

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 <- x1if (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'}xk <- 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)dumdum1 <- dum[2:length(myerror),]dum1z <- as.data.frame(dum1)zplot(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$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')}}myeq <- paste(myeq, ' + e[t]')a<-table.row.start(a)a<-table.element(a, myeq)a<-table.row.end(a)a<-table.end(a)table.save(a,file='mytable1.tab')a<-table.start()a<-table.row.start(a)a<-table.element(a,hyperlink('ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)a<-table.row.end(a)a<-table.row.start(a)a<-table.element(a,'Variable',header=TRUE)a<-table.element(a,'Parameter',header=TRUE)a<-table.element(a,'S.D.',header=TRUE)a<-table.element(a,'T-STATH0: parameter = 0',header=TRUE)a<-table.element(a,'2-tail p-value',header=TRUE)a<-table.element(a,'1-tail p-value',header=TRUE)a<-table.row.end(a)for (i in 1:k){a<-table.row.start(a)a<-table.element(a,rownames(mysum$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$sigma)a<-table.row.end(a)a<-table.row.start(a)a<-table.element(a, 'Sum Squared Residuals',1,TRUE)a<-table.element(a, sum(myerror*myerror))a<-table.row.end(a)a<-table.end(a)table.save(a,file='mytable3.tab')a<-table.start()a<-table.row.start(a)a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)a<-table.row.end(a)a<-table.row.start(a)a<-table.element(a, 'Time or Index', 1, TRUE)a<-table.element(a, 'Actuals', 1, TRUE)a<-table.element(a, 'InterpolationForecast', 1, TRUE)a<-table.element(a, 'ResidualsPrediction Error', 1, TRUE)a<-table.row.end(a)for (i in 1:n) {a<-table.row.start(a)a<-table.element(a,i, 1, TRUE)a<-table.element(a,x[i])a<-table.element(a,x[i]-mysum$resid[i])a<-table.element(a,mysum\$resid[i])a<-table.row.end(a)}a<-table.end(a)table.save(a,file='mytable4.tab')