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*Unverified author*

R Software Module

rwasp_multipleregression.wasp

Title produced by software

Multiple Regression

Date of computation

Sat, 05 Aug 2017 00:10:08 +0200

Cite this page as follows

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2017/Aug/05/t1501884848f0l8wc86gkc9tn2.htm/, Retrieved Fri, 10 May 2024 09:39:26 +0200

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=, Retrieved Fri, 10 May 2024 09:39:26 +0200

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Original text written by user:

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This computation is/was private until 2020-1-10

User-defined keywords

Estimated Impact

Dataseries X:

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Histogram

Boxplots

1478	1999	1	0	0	0	0	0	0	0	0	0	0	0
1323	1999	0	1	0	0	0	0	0	0	0	0	0	0
1381	1999	0	0	1	0	0	0	0	0	0	0	0	0
1411	1999	0	0	0	1	0	0	0	0	0	0	0	0
1419	1999	0	0	0	0	1	0	0	0	0	0	0	0
1375	1999	0	0	0	0	0	1	0	0	0	0	0	0
1437	1999	0	0	0	0	0	0	1	0	0	0	0	0
1421	1999	0	0	0	0	0	0	0	1	0	0	0	0
1294	1999	0	0	0	0	0	0	0	0	1	0	0	0
1406	1999	0	0	0	0	0	0	0	0	0	1	0	0
1362	1999	0	0	0	0	0	0	0	0	0	0	1	0
1292	1999	0	0	0	0	0	0	0	0	0	0	0	0
1447	2000	1	0	0	0	0	0	0	0	0	0	0	0
1318	2000	0	1	0	0	0	0	0	0	0	0	0	0
1468	2000	0	0	1	0	0	0	0	0	0	0	0	0
1372	2000	0	0	0	1	0	0	0	0	0	0	0	0
1449	2000	0	0	0	0	1	0	0	0	0	0	0	0
1415	2000	0	0	0	0	0	1	0	0	0	0	0	0
1434	2000	0	0	0	0	0	0	1	0	0	0	0	0
1410	2000	0	0	0	0	0	0	0	1	0	0	0	0
1318	2000	0	0	0	0	0	0	0	0	1	0	0	0
1383	2000	0	0	0	0	0	0	0	0	0	1	0	0
1294	2000	0	0	0	0	0	0	0	0	0	0	1	0
1278	2000	0	0	0	0	0	0	0	0	0	0	0	0
1443	2001	1	0	0	0	0	0	0	0	0	0	0	0
1328	2001	0	1	0	0	0	0	0	0	0	0	0	0
1441	2001	0	0	1	0	0	0	0	0	0	0	0	0
1438	2001	0	0	0	1	0	0	0	0	0	0	0	0
1536	2001	0	0	0	0	1	0	0	0	0	0	0	0
1340	2001	0	0	0	0	0	1	0	0	0	0	0	0
1424	2001	0	0	0	0	0	0	1	0	0	0	0	0
1441	2001	0	0	0	0	0	0	0	1	0	0	0	0
1304	2001	0	0	0	0	0	0	0	0	1	0	0	0
1432	2001	0	0	0	0	0	0	0	0	0	1	0	0
1387	2001	0	0	0	0	0	0	0	0	0	0	1	0
1355	2001	0	0	0	0	0	0	0	0	0	0	0	0
1477	2002	1	0	0	0	0	0	0	0	0	0	0	0
1332	2002	0	1	0	0	0	0	0	0	0	0	0	0
1423	2002	0	0	1	0	0	0	0	0	0	0	0	0
1510	2002	0	0	0	1	0	0	0	0	0	0	0	0
1451	2002	0	0	0	0	1	0	0	0	0	0	0	0
1408	2002	0	0	0	0	0	1	0	0	0	0	0	0
1479	2002	0	0	0	0	0	0	1	0	0	0	0	0
1480	2002	0	0	0	0	0	0	0	1	0	0	0	0
1440	2002	0	0	0	0	0	0	0	0	1	0	0	0
1427	2002	0	0	0	0	0	0	0	0	0	1	0	0
1349	2002	0	0	0	0	0	0	0	0	0	0	1	0
1332	2002	0	0	0	0	0	0	0	0	0	0	0	0
1509	2003	1	0	0	0	0	0	0	0	0	0	0	0
1260	2003	0	1	0	0	0	0	0	0	0	0	0	0
1498	2003	0	0	1	0	0	0	0	0	0	0	0	0
1467	2003	0	0	0	1	0	0	0	0	0	0	0	0
1469	2003	0	0	0	0	1	0	0	0	0	0	0	0
1517	2003	0	0	0	0	0	1	0	0	0	0	0	0
1455	2003	0	0	0	0	0	0	1	0	0	0	0	0
1429	2003	0	0	0	0	0	0	0	1	0	0	0	0
1335	2003	0	0	0	0	0	0	0	0	1	0	0	0
1377	2003	0	0	0	0	0	0	0	0	0	1	0	0
1296	2003	0	0	0	0	0	0	0	0	0	0	1	0
1295	2003	0	0	0	0	0	0	0	0	0	0	0	0
1420	2004	1	0	0	0	0	0	0	0	0	0	0	0
1362	2004	0	1	0	0	0	0	0	0	0	0	0	0
1467	2004	0	0	1	0	0	0	0	0	0	0	0	0
1378	2004	0	0	0	1	0	0	0	0	0	0	0	0
1427	2004	0	0	0	0	1	0	0	0	0	0	0	0
1386	2004	0	0	0	0	0	1	0	0	0	0	0	0
1465	2004	0	0	0	0	0	0	1	0	0	0	0	0
1423	2004	0	0	0	0	0	0	0	1	0	0	0	0
1341	2004	0	0	0	0	0	0	0	0	1	0	0	0
1421	2004	0	0	0	0	0	0	0	0	0	1	0	0
1395	2004	0	0	0	0	0	0	0	0	0	0	1	0
1265	2004	0	0	0	0	0	0	0	0	0	0	0	0
1390	2005	1	0	0	0	0	0	0	0	0	0	0	0
1319	2005	0	1	0	0	0	0	0	0	0	0	0	0
1457	2005	0	0	1	0	0	0	0	0	0	0	0	0
1466	2005	0	0	0	1	0	0	0	0	0	0	0	0
1492	2005	0	0	0	0	1	0	0	0	0	0	0	0
1398	2005	0	0	0	0	0	1	0	0	0	0	0	0
1503	2005	0	0	0	0	0	0	1	0	0	0	0	0
1419	2005	0	0	0	0	0	0	0	1	0	0	0	0
1435	2005	0	0	0	0	0	0	0	0	1	0	0	0
1438	2005	0	0	0	0	0	0	0	0	0	1	0	0
1284	2005	0	0	0	0	0	0	0	0	0	0	1	0
1401	2005	0	0	0	0	0	0	0	0	0	0	0	0
1364	2006	1	0	0	0	0	0	0	0	0	0	0	0
1231	2006	0	1	0	0	0	0	0	0	0	0	0	0
1424	2006	0	0	1	0	0	0	0	0	0	0	0	0
1390	2006	0	0	0	1	0	0	0	0	0	0	0	0
1483	2006	0	0	0	0	1	0	0	0	0	0	0	0
1479	2006	0	0	0	0	0	1	0	0	0	0	0	0
1553	2006	0	0	0	0	0	0	1	0	0	0	0	0
1409	2006	0	0	0	0	0	0	0	1	0	0	0	0
1416	2006	0	0	0	0	0	0	0	0	1	0	0	0
1466	2006	0	0	0	0	0	0	0	0	0	1	0	0
1341	2006	0	0	0	0	0	0	0	0	0	0	1	0
1327	2006	0	0	0	0	0	0	0	0	0	0	0	0
1449	2007	1	0	0	0	0	0	0	0	0	0	0	0
1275	2007	0	1	0	0	0	0	0	0	0	0	0	0
1440	2007	0	0	1	0	0	0	0	0	0	0	0	0
1401	2007	0	0	0	1	0	0	0	0	0	0	0	0
1562	2007	0	0	0	0	1	0	0	0	0	0	0	0
1467	2007	0	0	0	0	0	1	0	0	0	0	0	0
1539	2007	0	0	0	0	0	0	1	0	0	0	0	0
1444	2007	0	0	0	0	0	0	0	1	0	0	0	0
1494	2007	0	0	0	0	0	0	0	0	1	0	0	0
1493	2007	0	0	0	0	0	0	0	0	0	1	0	0
1439	2007	0	0	0	0	0	0	0	0	0	0	1	0
1349	2007	0	0	0	0	0	0	0	0	0	0	0	0
1595	2008	1	0	0	0	0	0	0	0	0	0	0	0
1447	2008	0	1	0	0	0	0	0	0	0	0	0	0
1604	2008	0	0	1	0	0	0	0	0	0	0	0	0
1558	2008	0	0	0	1	0	0	0	0	0	0	0	0
1538	2008	0	0	0	0	1	0	0	0	0	0	0	0
1507	2008	0	0	0	0	0	1	0	0	0	0	0	0
1564	2008	0	0	0	0	0	0	1	0	0	0	0	0
1513	2008	0	0	0	0	0	0	0	1	0	0	0	0
1518	2008	0	0	0	0	0	0	0	0	1	0	0	0
1516	2008	0	0	0	0	0	0	0	0	0	1	0	0
1431	2008	0	0	0	0	0	0	0	0	0	0	1	0
1432	2008	0	0	0	0	0	0	0	0	0	0	0	0
1576	2009	1	0	0	0	0	0	0	0	0	0	0	0
1424	2009	0	1	0	0	0	0	0	0	0	0	0	0
1528	2009	0	0	1	0	0	0	0	0	0	0	0	0
1546	2009	0	0	0	1	0	0	0	0	0	0	0	0
1641	2009	0	0	0	0	1	0	0	0	0	0	0	0
1618	2009	0	0	0	0	0	1	0	0	0	0	0	0
1673	2009	0	0	0	0	0	0	1	0	0	0	0	0
1669	2009	0	0	0	0	0	0	0	1	0	0	0	0
1605	2009	0	0	0	0	0	0	0	0	1	0	0	0
1615	2009	0	0	0	0	0	0	0	0	0	1	0	0
1416	2009	0	0	0	0	0	0	0	0	0	0	1	0
1424	2009	0	0	0	0	0	0	0	0	0	0	0	0
1568	2010	1	0	0	0	0	0	0	0	0	0	0	0
1426	2010	0	1	0	0	0	0	0	0	0	0	0	0
1661	2010	0	0	1	0	0	0	0	0	0	0	0	0
1618	2010	0	0	0	1	0	0	0	0	0	0	0	0
1653	2010	0	0	0	0	1	0	0	0	0	0	0	0
1656	2010	0	0	0	0	0	1	0	0	0	0	0	0
1774	2010	0	0	0	0	0	0	1	0	0	0	0	0
1723	2010	0	0	0	0	0	0	0	1	0	0	0	0
1555	2010	0	0	0	0	0	0	0	0	1	0	0	0
1655	2010	0	0	0	0	0	0	0	0	0	1	0	0
1526	2010	0	0	0	0	0	0	0	0	0	0	1	0
1577	2010	0	0	0	0	0	0	0	0	0	0	0	0
1588	2011	1	0	0	0	0	0	0	0	0	0	0	0
1394	2011	0	1	0	0	0	0	0	0	0	0	0	0
1758	2011	0	0	1	0	0	0	0	0	0	0	0	0
1784	2011	0	0	0	1	0	0	0	0	0	0	0	0
1723	2011	0	0	0	0	1	0	0	0	0	0	0	0
1717	2011	0	0	0	0	0	1	0	0	0	0	0	0
1818	2011	0	0	0	0	0	0	1	0	0	0	0	0
1770	2011	0	0	0	0	0	0	0	1	0	0	0	0
1618	2011	0	0	0	0	0	0	0	0	1	0	0	0
1690	2011	0	0	0	0	0	0	0	0	0	1	0	0
1554	2011	0	0	0	0	0	0	0	0	0	0	1	0
1576	2011	0	0	0	0	0	0	0	0	0	0	0	0
1707	2012	1	0	0	0	0	0	0	0	0	0	0	0
1549	2012	0	1	0	0	0	0	0	0	0	0	0	0
1707	2012	0	0	1	0	0	0	0	0	0	0	0	0
1745	2012	0	0	0	1	0	0	0	0	0	0	0	0
1932	2012	0	0	0	0	1	0	0	0	0	0	0	0
1724	2012	0	0	0	0	0	1	0	0	0	0	0	0
1789	2012	0	0	0	0	0	0	1	0	0	0	0	0
1796	2012	0	0	0	0	0	0	0	1	0	0	0	0
1717	2012	0	0	0	0	0	0	0	0	1	0	0	0
1696	2012	0	0	0	0	0	0	0	0	0	1	0	0
1680	2012	0	0	0	0	0	0	0	0	0	0	1	0
1624	2012	0	0	0	0	0	0	0	0	0	0	0	0
1797	2013	1	0	0	0	0	0	0	0	0	0	0	0
1592	2013	0	1	0	0	0	0	0	0	0	0	0	0
1870	2013	0	0	1	0	0	0	0	0	0	0	0	0
1819	2013	0	0	0	1	0	0	0	0	0	0	0	0
1791	2013	0	0	0	0	1	0	0	0	0	0	0	0
1797	2013	0	0	0	0	0	1	0	0	0	0	0	0
1893	2013	0	0	0	0	0	0	1	0	0	0	0	0
1786	2013	0	0	0	0	0	0	0	1	0	0	0	0
1728	2013	0	0	0	0	0	0	0	0	1	0	0	0
1741	2013	0	0	0	0	0	0	0	0	0	1	0	0
1698	2013	0	0	0	0	0	0	0	0	0	0	1	0
1663	2013	0	0	0	0	0	0	0	0	0	0	0	0
1716	2014	1	0	0	0	0	0	0	0	0	0	0	0
1592	2014	0	1	0	0	0	0	0	0	0	0	0	0
1733	2014	0	0	1	0	0	0	0	0	0	0	0	0
1874	2014	0	0	0	1	0	0	0	0	0	0	0	0
1784	2014	0	0	0	0	1	0	0	0	0	0	0	0
1848	2014	0	0	0	0	0	1	0	0	0	0	0	0
1843	2014	0	0	0	0	0	0	1	0	0	0	0	0
1863	2014	0	0	0	0	0	0	0	1	0	0	0	1
1901	2014	0	0	0	0	0	0	0	0	1	0	0	1
1822	2014	0	0	0	0	0	0	0	0	0	1	0	0
1712	2014	0	0	0	0	0	0	0	0	0	0	1	0
1698	2014	0	0	0	0	0	0	0	0	0	0	0	0
1819	2015	1	0	0	0	0	0	0	0	0	0	0	0
1632	2015	0	1	0	0	0	0	0	0	0	0	0	0
1941	2015	0	0	1	0	0	0	0	0	0	0	0	0
1932	2015	0	0	0	1	0	0	0	0	0	0	0	0
1942	2015	0	0	0	0	1	0	0	0	0	0	0	0
1838	2015	0	0	0	0	0	1	0	0	0	0	0	0
1968	2015	0	0	0	0	0	0	1	0	0	0	0	0
1905	2015	0	0	0	0	0	0	0	1	0	0	0	0
1778	2015	0	0	0	0	0	0	0	0	1	0	0	0
1806	2015	0	0	0	0	0	0	0	0	0	1	0	0
1687	2015	0	0	0	0	0	0	0	0	0	0	1	0
1770	2015	0	0	0	0	0	0	0	0	0	0	0	0

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	9 seconds
R Server	Big Analytics Cloud Computing Center

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input view raw input (R code)  \tabularnewline
Raw Outputview raw output of R engine  \tabularnewline
Computing time9 seconds \tabularnewline
R ServerBig Analytics Cloud Computing Center \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=&T=0

[TABLE]
[ROW]

Summary of computational transaction[/C][/ROW] [ROW]

Raw Input[/C]

view raw input (R code) [/C][/ROW] [ROW]

Raw Output[/C]

view raw output of R engine [/C][/ROW] [ROW]

Computing time[/C]

9 seconds[/C][/ROW] [ROW]

R Server[/C]

Big Analytics Cloud Computing Center[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=&T=0

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

As an alternative you can also use a QR Code:

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

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	9 seconds
R Server	Big Analytics Cloud Computing Center

Multiple Linear Regression - Estimated Regression Equation
firearm[t] = -57480.6 + 29.3627year[t] + 99.1176jan[t] -50.2353feb[t] + 126.059mar[t] + 120.647apr[t] + 154.941may[t] + 107.765jun[t] + 173.706jul[t] + 124.034aug[t] + 59.0925sep[t] + 101.529oct[t] + 11.3529nov[t] + 134.427RW[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
firearm[t] =  -57480.6 +  29.3627year[t] +  99.1176jan[t] -50.2353feb[t] +  126.059mar[t] +  120.647apr[t] +  154.941may[t] +  107.765jun[t] +  173.706jul[t] +  124.034aug[t] +  59.0925sep[t] +  101.529oct[t] +  11.3529nov[t] +  134.427RW[t]  + e[t] \tabularnewline
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]firearm[t] =  -57480.6 +  29.3627year[t] +  99.1176jan[t] -50.2353feb[t] +  126.059mar[t] +  120.647apr[t] +  154.941may[t] +  107.765jun[t] +  173.706jul[t] +  124.034aug[t] +  59.0925sep[t] +  101.529oct[t] +  11.3529nov[t] +  134.427RW[t]  + e[t][/C][/ROW]
[ROW][C][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=&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
firearm[t] = -57480.6 + 29.3627year[t] + 99.1176jan[t] -50.2353feb[t] + 126.059mar[t] + 120.647apr[t] + 154.941may[t] + 107.765jun[t] + 173.706jul[t] + 124.034aug[t] + 59.0925sep[t] + 101.529oct[t] + 11.3529nov[t] + 134.427RW[t] + 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)	-5.748e+04	2116	-2.7170e+01	2.311e-67	1.156e-67
year	+29.36	1.054	+2.7860e+01	5.196e-69	2.598e-69
jan	+99.12	25.03	+3.9610e+00	0.0001057	5.283e-05
feb	-50.23	25.03	-2.0070e+00	0.04613	0.02307
mar	+126.1	25.03	+5.0370e+00	1.095e-06	5.476e-07
apr	+120.7	25.03	+4.8210e+00	2.92e-06	1.46e-06
may	+154.9	25.03	+6.1910e+00	3.605e-09	1.803e-09
jun	+107.8	25.03	+4.3060e+00	2.66e-05	1.33e-05
jul	+173.7	25.03	+6.9410e+00	5.979e-11	2.99e-11
aug	+124	25.23	+4.9170e+00	1.895e-06	9.473e-07
sep	+59.09	25.23	+2.3430e+00	0.02018	0.01009
oct	+101.5	25.03	+4.0570e+00	7.255e-05	3.627e-05
nov	+11.35	25.03	+4.5360e-01	0.6506	0.3253
RW	+134.4	53.76	+2.5010e+00	0.01324	0.00662

\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) & -5.748e+04 &  2116 & -2.7170e+01 &  2.311e-67 &  1.156e-67 \tabularnewline
year & +29.36 &  1.054 & +2.7860e+01 &  5.196e-69 &  2.598e-69 \tabularnewline
jan & +99.12 &  25.03 & +3.9610e+00 &  0.0001057 &  5.283e-05 \tabularnewline
feb & -50.23 &  25.03 & -2.0070e+00 &  0.04613 &  0.02307 \tabularnewline
mar & +126.1 &  25.03 & +5.0370e+00 &  1.095e-06 &  5.476e-07 \tabularnewline
apr & +120.7 &  25.03 & +4.8210e+00 &  2.92e-06 &  1.46e-06 \tabularnewline
may & +154.9 &  25.03 & +6.1910e+00 &  3.605e-09 &  1.803e-09 \tabularnewline
jun & +107.8 &  25.03 & +4.3060e+00 &  2.66e-05 &  1.33e-05 \tabularnewline
jul & +173.7 &  25.03 & +6.9410e+00 &  5.979e-11 &  2.99e-11 \tabularnewline
aug & +124 &  25.23 & +4.9170e+00 &  1.895e-06 &  9.473e-07 \tabularnewline
sep & +59.09 &  25.23 & +2.3430e+00 &  0.02018 &  0.01009 \tabularnewline
oct & +101.5 &  25.03 & +4.0570e+00 &  7.255e-05 &  3.627e-05 \tabularnewline
nov & +11.35 &  25.03 & +4.5360e-01 &  0.6506 &  0.3253 \tabularnewline
RW & +134.4 &  53.76 & +2.5010e+00 &  0.01324 &  0.00662 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=&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]-5.748e+04[/C][C] 2116[/C][C]-2.7170e+01[/C][C] 2.311e-67[/C][C] 1.156e-67[/C][/ROW]
[ROW][C]year[/C][C]+29.36[/C][C] 1.054[/C][C]+2.7860e+01[/C][C] 5.196e-69[/C][C] 2.598e-69[/C][/ROW]
[ROW][C]jan[/C][C]+99.12[/C][C] 25.03[/C][C]+3.9610e+00[/C][C] 0.0001057[/C][C] 5.283e-05[/C][/ROW]
[ROW][C]feb[/C][C]-50.23[/C][C] 25.03[/C][C]-2.0070e+00[/C][C] 0.04613[/C][C] 0.02307[/C][/ROW]
[ROW][C]mar[/C][C]+126.1[/C][C] 25.03[/C][C]+5.0370e+00[/C][C] 1.095e-06[/C][C] 5.476e-07[/C][/ROW]
[ROW][C]apr[/C][C]+120.7[/C][C] 25.03[/C][C]+4.8210e+00[/C][C] 2.92e-06[/C][C] 1.46e-06[/C][/ROW]
[ROW][C]may[/C][C]+154.9[/C][C] 25.03[/C][C]+6.1910e+00[/C][C] 3.605e-09[/C][C] 1.803e-09[/C][/ROW]
[ROW][C]jun[/C][C]+107.8[/C][C] 25.03[/C][C]+4.3060e+00[/C][C] 2.66e-05[/C][C] 1.33e-05[/C][/ROW]
[ROW][C]jul[/C][C]+173.7[/C][C] 25.03[/C][C]+6.9410e+00[/C][C] 5.979e-11[/C][C] 2.99e-11[/C][/ROW]
[ROW][C]aug[/C][C]+124[/C][C] 25.23[/C][C]+4.9170e+00[/C][C] 1.895e-06[/C][C] 9.473e-07[/C][/ROW]
[ROW][C]sep[/C][C]+59.09[/C][C] 25.23[/C][C]+2.3430e+00[/C][C] 0.02018[/C][C] 0.01009[/C][/ROW]
[ROW][C]oct[/C][C]+101.5[/C][C] 25.03[/C][C]+4.0570e+00[/C][C] 7.255e-05[/C][C] 3.627e-05[/C][/ROW]
[ROW][C]nov[/C][C]+11.35[/C][C] 25.03[/C][C]+4.5360e-01[/C][C] 0.6506[/C][C] 0.3253[/C][/ROW]
[ROW][C]RW[/C][C]+134.4[/C][C] 53.76[/C][C]+2.5010e+00[/C][C] 0.01324[/C][C] 0.00662[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=&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)	-5.748e+04	2116	-2.7170e+01	2.311e-67	1.156e-67
year	+29.36	1.054	+2.7860e+01	5.196e-69	2.598e-69
jan	+99.12	25.03	+3.9610e+00	0.0001057	5.283e-05
feb	-50.23	25.03	-2.0070e+00	0.04613	0.02307
mar	+126.1	25.03	+5.0370e+00	1.095e-06	5.476e-07
apr	+120.7	25.03	+4.8210e+00	2.92e-06	1.46e-06
may	+154.9	25.03	+6.1910e+00	3.605e-09	1.803e-09
jun	+107.8	25.03	+4.3060e+00	2.66e-05	1.33e-05
jul	+173.7	25.03	+6.9410e+00	5.979e-11	2.99e-11
aug	+124	25.23	+4.9170e+00	1.895e-06	9.473e-07
sep	+59.09	25.23	+2.3430e+00	0.02018	0.01009
oct	+101.5	25.03	+4.0570e+00	7.255e-05	3.627e-05
nov	+11.35	25.03	+4.5360e-01	0.6506	0.3253
RW	+134.4	53.76	+2.5010e+00	0.01324	0.00662

Multiple Linear Regression - Regression Statistics
Multiple R	0.9152
R-squared	0.8376
Adjusted R-squared	0.8265
F-TEST (value)	75.39
F-TEST (DF numerator)	13
F-TEST (DF denominator)	190
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	72.96
Sum Squared Residuals	1.011e+06

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R &  0.9152 \tabularnewline
R-squared &  0.8376 \tabularnewline
Adjusted R-squared &  0.8265 \tabularnewline
F-TEST (value) &  75.39 \tabularnewline
F-TEST (DF numerator) & 13 \tabularnewline
F-TEST (DF denominator) & 190 \tabularnewline
p-value &  0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation &  72.96 \tabularnewline
Sum Squared Residuals &  1.011e+06 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C] 0.9152[/C][/ROW]
[ROW][C]R-squared[/C][C] 0.8376[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C] 0.8265[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C] 75.39[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]13[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]190[/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] 72.96[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C] 1.011e+06[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=&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 R	0.9152
R-squared	0.8376
Adjusted R-squared	0.8265
F-TEST (value)	75.39
F-TEST (DF numerator)	13
F-TEST (DF denominator)	190
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	72.96
Sum Squared Residuals	1.011e+06

Menu of Residual Diagnostics
Description	Link
Histogram	Compute
Central Tendency	Compute
QQ Plot	Compute
Kernel Density Plot	Compute
Skewness/Kurtosis Test	Compute
Skewness-Kurtosis Plot	Compute
Harrell-Davis Plot	Compute
Bootstrap Plot -- Central Tendency	Compute
Blocked Bootstrap Plot -- Central Tendency	Compute
(Partial) Autocorrelation Plot	Compute
Spectral Analysis	Compute
Tukey lambda PPCC Plot	Compute
Box-Cox Normality Plot	Compute
Summary Statistics	Compute

\begin{tabular}{lllllllll}
\hline
Menu of Residual Diagnostics \tabularnewline
Description & Link \tabularnewline
Histogram & Compute \tabularnewline
Central Tendency & Compute \tabularnewline
QQ Plot & Compute \tabularnewline
Kernel Density Plot & Compute \tabularnewline
Skewness/Kurtosis Test & Compute \tabularnewline
Skewness-Kurtosis Plot & Compute \tabularnewline
Harrell-Davis Plot & Compute \tabularnewline
Bootstrap Plot -- Central Tendency & Compute \tabularnewline
Blocked Bootstrap Plot -- Central Tendency & Compute \tabularnewline
(Partial) Autocorrelation Plot & Compute \tabularnewline
Spectral Analysis & Compute \tabularnewline
Tukey lambda PPCC Plot & Compute \tabularnewline
Box-Cox Normality Plot & Compute \tabularnewline
Summary Statistics & Compute \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=&T=4

[TABLE]
[ROW][C]Menu of Residual Diagnostics[/C][/ROW]
[ROW][C]Description[/C][C]Link[/C][/ROW]
[ROW][C]Histogram[/C][C]Compute[/C][/ROW]
[ROW][C]Central Tendency[/C][C]Compute[/C][/ROW]
[ROW][C]QQ Plot[/C][C]Compute[/C][/ROW]
[ROW][C]Kernel Density Plot[/C][C]Compute[/C][/ROW]
[ROW][C]Skewness/Kurtosis Test[/C][C]Compute[/C][/ROW]
[ROW][C]Skewness-Kurtosis Plot[/C][C]Compute[/C][/ROW]
[ROW][C]Harrell-Davis Plot[/C][C]Compute[/C][/ROW]
[ROW][C]Bootstrap Plot -- Central Tendency[/C][C]Compute[/C][/ROW]
[ROW][C]Blocked Bootstrap Plot -- Central Tendency[/C][C]Compute[/C][/ROW]
[ROW][C](Partial) Autocorrelation Plot[/C][C]Compute[/C][/ROW]
[ROW][C]Spectral Analysis[/C][C]Compute[/C][/ROW]
[ROW][C]Tukey lambda PPCC Plot[/C][C]Compute[/C][/ROW]
[ROW][C]Box-Cox Normality Plot[/C][C]Compute[/C][/ROW]
[ROW][C]Summary Statistics[/C][C]Compute[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=&T=4

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

As an alternative you can also use a QR Code:

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

Menu of Residual Diagnostics
Description	Link
Histogram	Compute
Central Tendency	Compute
QQ Plot	Compute
Kernel Density Plot	Compute
Skewness/Kurtosis Test	Compute
Skewness-Kurtosis Plot	Compute
Harrell-Davis Plot	Compute
Bootstrap Plot -- Central Tendency	Compute
Blocked Bootstrap Plot -- Central Tendency	Compute
(Partial) Autocorrelation Plot	Compute
Spectral Analysis	Compute
Tukey lambda PPCC Plot	Compute
Box-Cox Normality Plot	Compute
Summary Statistics	Compute

Ramsey RESET F-Test for powers (2 and 3) of fitted values
> reset_test_fitted RESET test data: mylm RESET = 54.374, df1 = 2, df2 = 188, p-value < 2.2e-16
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors RESET test data: mylm RESET = 5.2879, df1 = 26, df2 = 164, p-value = 9.654e-12
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components RESET test data: mylm RESET = 78.803, df1 = 2, df2 = 188, p-value < 2.2e-16

\begin{tabular}{lllllllll}
\hline
Ramsey RESET F-Test for powers (2 and 3) of fitted values \tabularnewline
> reset_test_fitted
	RESET test
data:  mylm
RESET = 54.374, df1 = 2, df2 = 188, p-value < 2.2e-16
 \tabularnewline
Ramsey RESET F-Test for powers (2 and 3) of regressors \tabularnewline
> reset_test_regressors
	RESET test
data:  mylm
RESET = 5.2879, df1 = 26, df2 = 164, p-value = 9.654e-12
 \tabularnewline
Ramsey RESET F-Test for powers (2 and 3) of principal components \tabularnewline
> reset_test_principal_components
	RESET test
data:  mylm
RESET = 78.803, df1 = 2, df2 = 188, p-value < 2.2e-16
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=&T=5

[TABLE]
[ROW][C]Ramsey RESET F-Test for powers (2 and 3) of fitted values[/C][/ROW]
[ROW][C]> reset_test_fitted
	RESET test
data:  mylm
RESET = 54.374, df1 = 2, df2 = 188, p-value < 2.2e-16
[/C][/ROW]
[ROW][C]Ramsey RESET F-Test for powers (2 and 3) of regressors[/C][/ROW]
[ROW][C]> reset_test_regressors
	RESET test
data:  mylm
RESET = 5.2879, df1 = 26, df2 = 164, p-value = 9.654e-12
[/C][/ROW]
[ROW][C]Ramsey RESET F-Test for powers (2 and 3) of principal components[/C][/ROW]
[ROW][C]> reset_test_principal_components
	RESET test
data:  mylm
RESET = 78.803, df1 = 2, df2 = 188, p-value < 2.2e-16
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=&T=5

As an alternative you can also use a QR Code:

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

Ramsey RESET F-Test for powers (2 and 3) of fitted values
> reset_test_fitted RESET test data: mylm RESET = 54.374, df1 = 2, df2 = 188, p-value < 2.2e-16
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors RESET test data: mylm RESET = 5.2879, df1 = 26, df2 = 164, p-value = 9.654e-12
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components RESET test data: mylm RESET = 78.803, df1 = 2, df2 = 188, p-value < 2.2e-16

Variance Inflation Factors (Multicollinearity)
> vif year jan feb mar apr may jun jul 1.021729 1.833333 1.833333 1.833333 1.833333 1.833333 1.833333 1.833333 aug sep oct nov RW 1.862602 1.862602 1.833333 1.833333 1.074945

\begin{tabular}{lllllllll}
\hline
Variance Inflation Factors (Multicollinearity) \tabularnewline
> vif
    year      jan      feb      mar      apr      may      jun      jul 
1.021729 1.833333 1.833333 1.833333 1.833333 1.833333 1.833333 1.833333 
     aug      sep      oct      nov       RW 
1.862602 1.862602 1.833333 1.833333 1.074945 
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=&T=6

[TABLE]
[ROW][C]Variance Inflation Factors (Multicollinearity)[/C][/ROW]
[ROW][C]> vif
    year      jan      feb      mar      apr      may      jun      jul 
1.021729 1.833333 1.833333 1.833333 1.833333 1.833333 1.833333 1.833333 
     aug      sep      oct      nov       RW 
1.862602 1.862602 1.833333 1.833333 1.074945 
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=&T=6

As an alternative you can also use a QR Code:

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

Variance Inflation Factors (Multicollinearity)
> vif year jan feb mar apr may jun jul 1.021729 1.833333 1.833333 1.833333 1.833333 1.833333 1.833333 1.833333 aug sep oct nov RW 1.862602 1.862602 1.833333 1.833333 1.074945

Figure 1

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Figure 10

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Parameters (Session):

par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ; par4 = 1 ; par6 = 12 ;

Parameters (R input):

par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ; par4 = ; par5 = ; par6 = 12 ;

R code (references can be found in the software module):

par6 <- '12'
par5 <- ''
par4 <- ''
par3 <- 'No Linear Trend'
par2 <- 'Do not include Seasonal Dummies'
par1 <- '1'
library(lattice)
library(lmtest)
library(car)
library(MASS)
n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
mywarning <- ''
par6 <- as.numeric(par6)
if(is.na(par6)) {
par6 <- 12
mywarning = 'Warning: you did not specify the seasonality. The seasonal period was set to s = 12.'
}
par1 <- as.numeric(par1)
if(is.na(par1)) {
par1 <- 1
mywarning = 'Warning: you did not specify the column number of the endogenous series! The first column was selected by default.'
}
if (par4=='') par4 <- 0
par4 <- as.numeric(par4)
if (!is.numeric(par4)) par4 <- 0
if (par5=='') par5 <- 0
par5 <- as.numeric(par5)
if (!is.numeric(par5)) par5 <- 0
x <- na.omit(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'){
(n <- n -1)
x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-B)',colnames(x),sep='')))
for (i in 1:n) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
}
if (par3 == 'Seasonal Differences (s)'){
(n <- n - par6)
x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-Bs)',colnames(x),sep='')))
for (i in 1:n) {
for (j in 1:k) {
x2[i,j] <- x[i+par6,j] - x[i,j]
}
}
x <- x2
}
if (par3 == 'First and Seasonal Differences (s)'){
(n <- n -1)
x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-B)',colnames(x),sep='')))
for (i in 1:n) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
(n <- n - par6)
x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-Bs)',colnames(x),sep='')))
for (i in 1:n) {
for (j in 1:k) {
x2[i,j] <- x[i+par6,j] - x[i,j]
}
}
x <- x2
}
if(par4 > 0) {
x2 <- array(0, dim=c(n-par4,par4), dimnames=list(1:(n-par4), paste(colnames(x)[par1],'(t-',1:par4,')',sep='')))
for (i in 1:(n-par4)) {
for (j in 1:par4) {
x2[i,j] <- x[i+par4-j,par1]
}
}
x <- cbind(x[(par4+1):n,], x2)
n <- n - par4
}
if(par5 > 0) {
x2 <- array(0, dim=c(n-par5*par6,par5), dimnames=list(1:(n-par5*par6), paste(colnames(x)[par1],'(t-',1:par5,'s)',sep='')))
for (i in 1:(n-par5*par6)) {
for (j in 1:par5) {
x2[i,j] <- x[i+par5*par6-j*par6,par1]
}
}
x <- cbind(x[(par5*par6+1):n,], x2)
n <- n - par5*par6
}
if (par2 == 'Include Seasonal Dummies'){
x2 <- array(0, dim=c(n,par6-1), dimnames=list(1:n, paste('M', seq(1:(par6-1)), sep ='')))
for (i in 1:(par6-1)){
x2[seq(i,n,par6),i] <- 1
}
x <- cbind(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[n,]))
if (par3 == 'Linear Trend'){
x <- cbind(x, c(1:n))
colnames(x)[k+1] <- 't'
}
print(x)
(k <- length(x[n,]))
head(x)
df <- as.data.frame(x)
(mylm <- lm(df))
(mysum <- summary(mylm))
if (n > n25) {
kp3 <- k + 3
nmkm3 <- n - k - 3
gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
numgqtests <- 0
numsignificant1 <- 0
numsignificant5 <- 0
numsignificant10 <- 0
for (mypoint in kp3:nmkm3) {
j <- 0
numgqtests <- numgqtests + 1
for (myalt in c('greater', 'two.sided', 'less')) {
j <- j + 1
gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
}
if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
}
gqarr
}
bitmap(file='test0.png')
plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
points(x[,1]-mysum$resid)
grid()
dev.off()
bitmap(file='test1.png')
plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
grid()
dev.off()
bitmap(file='test2.png')
sresid <- studres(mylm)
hist(sresid, freq=FALSE, main='Distribution of Studentized Residuals')
xfit<-seq(min(sresid),max(sresid),length=40)
yfit<-dnorm(xfit)
lines(xfit, yfit)
grid()
dev.off()
bitmap(file='test3.png')
densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
dev.off()
bitmap(file='test4.png')
qqPlot(mylm, main='QQ Plot')
grid()
dev.off()
(myerror <- as.ts(mysum$resid))
bitmap(file='test5.png')
dum <- cbind(lag(myerror,k=1),myerror)
dum
dum1 <- dum[2:length(myerror),]
dum1
z <- as.data.frame(dum1)
print(z)
plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
lines(lowess(z))
abline(lm(z))
grid()
dev.off()
bitmap(file='test6.png')
acf(mysum$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$resid)/2, main='Residual Partial Autocorrelation Function')
grid()
dev.off()
bitmap(file='test8.png')
opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
plot(mylm, las = 1, sub='Residual Diagnostics')
par(opar)
dev.off()
if (n > n25) {
bitmap(file='test9.png')
plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
grid()
dev.off()
}
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
a<-table.row.end(a)
myeq <- colnames(x)[1]
myeq <- paste(myeq, '[t] = ', sep='')
for (i in 1:k){
if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
myeq <- paste(myeq, signif(mysum$coefficients[i,1],6), sep=' ')
if (rownames(mysum$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.row.start(a)
a<-table.element(a, mywarning)
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,'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$coefficients)[i],header=TRUE)
a<-table.element(a,formatC(signif(mysum$coefficients[i,1],5),format='g',flag='+'))
a<-table.element(a,formatC(signif(mysum$coefficients[i,2],5),format='g',flag=' '))
a<-table.element(a,formatC(signif(mysum$coefficients[i,3],4),format='e',flag='+'))
a<-table.element(a,formatC(signif(mysum$coefficients[i,4],4),format='g',flag=' '))
a<-table.element(a,formatC(signif(mysum$coefficients[i,4]/2,4),format='g',flag=' '))
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,formatC(signif(sqrt(mysum$r.squared),6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'R-squared',1,TRUE)
a<-table.element(a,formatC(signif(mysum$r.squared,6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Adjusted R-squared',1,TRUE)
a<-table.element(a,formatC(signif(mysum$adj.r.squared,6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (value)',1,TRUE)
a<-table.element(a,formatC(signif(mysum$fstatistic[1],6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
a<-table.element(a, signif(mysum$fstatistic[2],6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
a<-table.element(a, signif(mysum$fstatistic[3],6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'p-value',1,TRUE)
a<-table.element(a,formatC(signif(1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]),6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
a<-table.element(a,formatC(signif(mysum$sigma,6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
a<-table.element(a,formatC(signif(sum(myerror*myerror),6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
myr <- as.numeric(mysum$resid)
myr
a <-table.start()
a <- table.row.start(a)
a <- table.element(a,'Menu of Residual Diagnostics',2,TRUE)
a <- table.row.end(a)
a <- table.row.start(a)
a <- table.element(a,'Description',1,TRUE)
a <- table.element(a,'Link',1,TRUE)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Histogram',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_histogram.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Central Tendency',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_centraltendency.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'QQ Plot',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_fitdistrnorm.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Kernel Density Plot',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_density.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Skewness/Kurtosis Test',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_skewness_kurtosis.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Skewness-Kurtosis Plot',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_skewness_kurtosis_plot.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Harrell-Davis Plot',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_harrell_davis.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Bootstrap Plot -- Central Tendency',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_bootstrapplot1.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Blocked Bootstrap Plot -- Central Tendency',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_bootstrapplot.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'(Partial) Autocorrelation Plot',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_autocorrelation.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Spectral Analysis',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_spectrum.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Tukey lambda PPCC Plot',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_tukeylambda.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Box-Cox Normality Plot',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_boxcoxnorm.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <- table.element(a,'Summary Statistics',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_summary1.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable7.tab')
if(n < 200) {
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,formatC(signif(x[i],6),format='g',flag=' '))
a<-table.element(a,formatC(signif(x[i]-mysum$resid[i],6),format='g',flag=' '))
a<-table.element(a,formatC(signif(mysum$resid[i],6),format='g',flag=' '))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable4.tab')
if (n > n25) {
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'p-values',header=TRUE)
a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'breakpoint index',header=TRUE)
a<-table.element(a,'greater',header=TRUE)
a<-table.element(a,'2-sided',header=TRUE)
a<-table.element(a,'less',header=TRUE)
a<-table.row.end(a)
for (mypoint in kp3:nmkm3) {
a<-table.row.start(a)
a<-table.element(a,mypoint,header=TRUE)
a<-table.element(a,formatC(signif(gqarr[mypoint-kp3+1,1],6),format='g',flag=' '))
a<-table.element(a,formatC(signif(gqarr[mypoint-kp3+1,2],6),format='g',flag=' '))
a<-table.element(a,formatC(signif(gqarr[mypoint-kp3+1,3],6),format='g',flag=' '))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable5.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Description',header=TRUE)
a<-table.element(a,'# significant tests',header=TRUE)
a<-table.element(a,'% significant tests',header=TRUE)
a<-table.element(a,'OK/NOK',header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'1% type I error level',header=TRUE)
a<-table.element(a,signif(numsignificant1,6))
a<-table.element(a,formatC(signif(numsignificant1/numgqtests,6),format='g',flag=' '))
if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'5% type I error level',header=TRUE)
a<-table.element(a,signif(numsignificant5,6))
a<-table.element(a,signif(numsignificant5/numgqtests,6))
if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'10% type I error level',header=TRUE)
a<-table.element(a,signif(numsignificant10,6))
a<-table.element(a,signif(numsignificant10/numgqtests,6))
if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable6.tab')
}
}
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Ramsey RESET F-Test for powers (2 and 3) of fitted values',1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
reset_test_fitted <- resettest(mylm,power=2:3,type='fitted')
a<-table.element(a,paste('',RC.texteval('reset_test_fitted'),'
',sep=''))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Ramsey RESET F-Test for powers (2 and 3) of regressors',1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
reset_test_regressors <- resettest(mylm,power=2:3,type='regressor')
a<-table.element(a,paste('',RC.texteval('reset_test_regressors'),'
',sep=''))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Ramsey RESET F-Test for powers (2 and 3) of principal components',1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
reset_test_principal_components <- resettest(mylm,power=2:3,type='princomp')
a<-table.element(a,paste('',RC.texteval('reset_test_principal_components'),'
',sep=''))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable8.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Variance Inflation Factors (Multicollinearity)',1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
vif <- vif(mylm)
a<-table.element(a,paste('',RC.texteval('vif'),'
',sep=''))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable9.tab')

Free Statistics

Description of Statistical Computation

Tree of Dependent Computations

Dataset

Tables (Output of Computation)

Figures (Output of Computation)

Input Parameters & R Code