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Author

*The author of this computation has been verified*

R Software Module

rwasp_multipleregression.wasp

Title produced by software

Multiple Regression

Date of computation

Thu, 14 Dec 2017 15:12:35 +0100

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/Dec/14/t1513271790ehz6gnq5h66dz73.htm/, Retrieved Tue, 14 May 2024 21:19:43 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=309568, Retrieved Tue, 14 May 2024 21:19:43 +0000

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User-defined keywords

Estimated Impact

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)

-       [Multiple Regression] [LnTotDamg] [2017-12-14 14:12:35] [52cce9dbcec2927ac392287242c803b1] [Current]

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9,9376957239	1	0	0
6,7298240705	1	0	0
10,5039161921	1	0	0
3,9512437186	1	0	0
10,0948927281	1	0	0
5,5294290875	1	0	0
10,6951216798	1	0	0
0,6931471806	1	0	0
13,3772164263	0	0	1
10,5092232761	0	1	0
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10,0519937608	0	1	0
12,7656941477	0	1	0
12,6230413330	0	1	0
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0,6931471806	0	0	0
11,9773762054	1	0	0
11,3144989162	1	0	0
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10,1320563605	1	0	0
11,0021331740	1	0	0
9,3100952051	0	0	1
10,7364401528	1	0	0
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0,6931471806	0	1	0
10,4631604817	0	1	0
10,3890569049	0	1	0
9,2105403520	0	1	0
8,3243363327	1	0	0
10,2195748495	1	0	0
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10,4603280644	0	1	0
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10,3830392040	1	0	0
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0,6931471806	1	0	0
9,7221455159	1	0	0
5,4205349993	1	0	0
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10,8140820919	0	1	0
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0,6931471806	0	1	0
10,3641350494	1	0	0
9,7982381418	0	0	1
8,1438079768	1	0	0
9,2252289845	1	0	0
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11,4871152281	1	0	0
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9,3290119017	1	0	0
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10,8518969143	0	0	1
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11,6173034982	1	0	0
12,1395267777	0	1	0
11,3011915752	1	0	0
11,2119149623	1	0	0
9,7329364513	0	1	0
9,4102562372	1	0	0
12,0269579413	1	0	0
11,5096902373	0	1	0
9,2204887030	1	0	0
10,6635688201	1	0	0
9,6678917933	1	0	0
9,2105403520	1	0	0
13,3086769555	0	1	0
10,2920099399	1	0	0
4,6249728133	1	0	0
10,9252547933	0	1	0
10,5734428688	1	0	0
9,9759012330	0	0	1
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9,7108732063	0	0	1
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11,6325024461	1	0	0
11,2926901805	1	0	0
10,4857590368	1	0	0
0,6931471806	1	0	0
12,8346866383	0	1	0
11,0717191831	1	0	0
9,6285242525	1	0	0
10,9265318294	0	1	0
10,1267111007	1	0	0
10,5443409384	1	0	0
14,6702671045	0	1	0
9,4637415105	1	0	0
4,6249728133	1	0	0
10,8686065431	0	1	0
7,6019019599	0	1	0
11,3504300646	0	0	1
10,8517032172	1	0	0
13,5606208890	0	1	0
9,9172425178	0	0	1
11,3337987991	0	0	1
13,6530869180	0	1	0
12,9207073077	1	0	0
11,5860551016	0	0	0
10,8590374584	1	0	0
9,2105403520	1	0	0
13,0597855994	0	1	0
10,2400312152	1	0	0
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10,8114638148	1	0	0
9,7261535373	1	0	0
11,5547101959	0	1	0
10,0563373866	1	0	0
8,8848872018	1	0	0
12,2160328773	0	1	0
8,5175931114	1	0	0
8,5175931114	1	0	0
0,6931471806	1	0	0
14,5778311271	0	1	0
10,6310844678	1	0	0
9,2814509994	0	0	1
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10,1065100259	1	0	0
10,1065100259	1	0	0
9,2781859189	0	1	0
11,3163264689	0	1	0
10,9488045441	1	0	0
9,5173837979	0	0	1
12,0297351485	0	1	0
10,1267111007	1	0	0
12,5637540828	0	1	0
9,8277937082	0	0	1
9,0677394034	1	0	0
6,3007857947	1	0	0
10,3275126862	0	1	0
0,6931471806	0	1	0
10,6054458014	0	1	0
6,2186001197	0	1	0
9,2105403520	0	1	0
6,2186001197	0	1	0
10,3090193251	0	1	0
7,3145528323	0	1	0
11,4841040908	0	1	0
13,3847307188	0	1	0
9,3928285815	0	1	0
8,5175931114	0	1	0
9,6357388178	0	1	0
10,4602707611	1	0	0
11,0210360802	1	0	0
12,6486440535	0	1	0
9,5820418499	1	0	0
10,8057199272	1	0	0
6,5539334040	1	0	0
9,4651376171	1	0	0
9,7537106272	0	0	1
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9,6159388045	0	1	0
9,6945550443	0	1	0
9,2105403520	0	1	0
0,6931471806	0	1	0
9,2303390585	0	1	0
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9,4371571687	0	1	0
9,1714955882	0	0	1
9,6159388045	1	0	0
9,3928285815	1	0	0
10,4955428715	1	0	0
11,4752755175	1	0	0
10,2090218220	0	1	0
10,1267111007	1	0	0
0,6931471806	1	0	0
9,6678917933	0	1	0
10,2783903551	1	0	0
6,6080006253	1	0	0
11,8117700046	0	1	0
10,4970906028	0	0	1
9,6652938132	0	1	0
7,8808043447	0	1	0
12,0505526953	1	0	0
10,7376567508	1	0	0
11,3862732138	0	1	0
9,5410819339	0	0	0
0,6931471806	0	0	0
9,2948653270	1	0	0
10,0433364477	0	1	0
10,7655754984	0	1	0
10,2559737359	1	0	0
10,1986168256	1	0	0
9,7158317942	1	0	0
9,7842534646	0	1	0
10,6454725152	1	0	0
9,9035875475	1	0	0
9,1954297592	1	0	0
9,6159388045	1	0	0
12,8434639576	0	1	0
12,8779885287	0	1	0
9,7982381418	0	1	0
9,8522995158	0	1	0
8,0388347578	0	1	0
10,6365282968	0	1	0
9,3928285815	0	1	0
9,6873196150	1	0	0
7,5098830612	1	0	0
11,0059258460	0	0	1
9,2105403520	0	0	1
9,3722040869	0	1	0
9,5469554555	0	1	0
8,5175931114	0	1	0
9,3058323534	1	0	0
7,4966524382	1	0	0
9,8517204617	0	0	1
10,4663267113	0	1	0
9,1379847098	1	0	0
9,9354221711	1	0	0
5,9939614273	1	0	0
12,7738011505	0	1	0
10,2946508759	0	1	0
10,5453940689	0	1	0
10,6689321386	1	0	0
9,6159388045	1	0	0
10,7713230641	0	1	0
9,6215887248	1	0	0
8,0070340122	1	0	0
11,1862253255	0	1	0
9,2400933301	1	0	0
11,4627052788	0	0	0
14,0654510428	0	1	0
9,1634583861	0	1	0
12,8223441813	0	1	0
9,3890721599	1	0	0
9,6658012664	1	0	0
13,0079329654	0	1	0
11,5129454648	0	1	0
9,5150270432	0	1	0
8,3433158814	0	1	0
10,8264162046	1	0	0
9,9987977323	1	0	0
9,5105931016	1	0	0
11,5874483510	0	1	0
10,3735536798	0	1	0
10,8003915706	1	0	0
5,1298987149	1	0	0
12,0951522579	0	1	0
10,1340832674	1	0	0
8,2591993627	1	0	0
13,1342959008	0	1	0
0,6931471806	0	1	0
9,3238476128	1	0	0
13,4888298277	0	1	0
9,3542675408	1	0	0
7,4283331942	1	0	0
9,4358810479	1	0	0
9,7325212798	0	1	0
8,5175931114	0	1	0
8,9874467894	0	1	0
10,9141607613	1	0	0
9,8627697193	1	0	0
9,6394569004	0	1	0
0,6931471806	0	1	0
11,0686987383	0	1	0
9,7241216236	0	1	0
11,3265597415	1	0	0
8,2945495151	1	0	0
13,2971952900	0	1	0
10,0167269481	1	0	0
8,5517878362	1	0	0
11,3093884433	1	0	0
8,4029040450	1	0	0
9,8653702304	1	0	0
3,7376696183	1	0	0
10,1356701285	0	0	0
9,6487243268	0	1	0
11,6771933641	1	0	0
11,0021331740	1	0	0
10,0674329641	1	0	0
13,2605832619	0	1	0
11,2064138916	0	0	0
10,3090193251	0	1	0
9,2593209942	0	0	0
11,6654233511	1	0	0
10,2923828023	1	0	0
6,9097532816	1	0	0
10,3735536798	1	0	0
8,8539511015	1	0	0
10,6454725152	0	0	0
12,0492141362	1	0	0
6,2186001197	1	0	0
9,6293138301	1	0	0
8,9025916374	1	0	0
9,3127164386	0	1	0
9,9109595674	1	0	0
9,6995950058	1	0	0
11,6220668242	1	0	0
10,4918851419	0	1	0
9,7982381418	0	0	1
11,4762822273	0	1	0
9,2593209942	0	0	1
10,4631604817	0	1	0
9,2204887030	1	0	0
10,1676962688	0	0	1
13,2720722068	0	1	0
9,7786610196	1	0	0
10,4337922218	0	1	0
10,9059926772	1	0	0
13,8098515761	0	0	1
11,7760970957	1	0	0
9,2362030315	0	1	0
9,3432090451	1	0	0
8,7323045710	0	1	0
8,8772424360	0	1	0
8,8020713365	0	0	1
9,0827342474	0	1	0
12,7001650466	0	1	0
15,0693874772	0	1	0
11,5310698554	0	1	0
9,9424197625	0	1	0
11,7566713238	0	0	0
9,7286578615	0	0	1
11,0104317013	0	0	1
10,0411167875	0	1	0
9,3582430019	1	0	0
9,2585589442	0	1	0
12,2784026094	1	0	0
10,2484238676	1	0	0
9,2090395262	1	0	0
7,9915922821	1	0	0
11,3091555274	1	0	0
9,8356368856	1	0	0
11,6082538264	0	1	0
9,3860568297	1	0	0
9,6159388045	1	0	0
8,0833286088	1	0	0
11,2898069133	0	1	0
11,0821733176	1	0	0
12,0179462733	1	0	0
11,1338003922	0	0	1
10,8585373526	1	0	0
9,9670724942	1	0	0
9,7549295379	0	0	1
8,1945055098	0	0	1
10,2220867276	1	0	0
10,0433364477	0	1	0
11,3919887116	1	0	0
10,2201943040	1	0	0
5,6021188209	1	0	0
10,8406391751	1	0	0
9,2121387539	0	1	0
13,3568013064	0	1	0
11,6881552680	0	1	0

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	31 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 time31 seconds \tabularnewline
R ServerBig Analytics Cloud Computing Center \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=309568&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]

31 seconds[/C][/ROW] [ROW]

R Server[/C]

Big Analytics Cloud Computing Center[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=309568&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=309568&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	31 seconds
R Server	Big Analytics Cloud Computing Center

Multiple Linear Regression - Estimated Regression Equation
lnTotDamg[t] = + 10.2801 -0.588864Yard[t] -0.0785709Main[t] -0.457664Industry[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
lnTotDamg[t] =  +  10.2801 -0.588864Yard[t] -0.0785709Main[t] -0.457664Industry[t]  + e[t] \tabularnewline
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=309568&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]lnTotDamg[t] =  +  10.2801 -0.588864Yard[t] -0.0785709Main[t] -0.457664Industry[t]  + e[t][/C][/ROW]
[ROW][C][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=309568&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=309568&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
lnTotDamg[t] = + 10.2801 -0.588864Yard[t] -0.0785709Main[t] -0.457664Industry[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)	+10.28	0.2951	+3.4840e+01	8.301e-219	4.15e-219
Yard	-0.5889	0.3015	-1.9530e+00	0.05092	0.02546
Main	-0.07857	0.3039	-2.5860e-01	0.796	0.398
Industry	-0.4577	0.3284	-1.3930e+00	0.1636	0.0818

\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) & +10.28 &  0.2951 & +3.4840e+01 &  8.301e-219 &  4.15e-219 \tabularnewline
Yard & -0.5889 &  0.3015 & -1.9530e+00 &  0.05092 &  0.02546 \tabularnewline
Main & -0.07857 &  0.3039 & -2.5860e-01 &  0.796 &  0.398 \tabularnewline
Industry & -0.4577 &  0.3284 & -1.3930e+00 &  0.1636 &  0.0818 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=309568&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]+10.28[/C][C] 0.2951[/C][C]+3.4840e+01[/C][C] 8.301e-219[/C][C] 4.15e-219[/C][/ROW]
[ROW][C]Yard[/C][C]-0.5889[/C][C] 0.3015[/C][C]-1.9530e+00[/C][C] 0.05092[/C][C] 0.02546[/C][/ROW]
[ROW][C]Main[/C][C]-0.07857[/C][C] 0.3039[/C][C]-2.5860e-01[/C][C] 0.796[/C][C] 0.398[/C][/ROW]
[ROW][C]Industry[/C][C]-0.4577[/C][C] 0.3284[/C][C]-1.3930e+00[/C][C] 0.1636[/C][C] 0.0818[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=309568&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=309568&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)	+10.28	0.2951	+3.4840e+01	8.301e-219	4.15e-219
Yard	-0.5889	0.3015	-1.9530e+00	0.05092	0.02546
Main	-0.07857	0.3039	-2.5860e-01	0.796	0.398
Industry	-0.4577	0.3284	-1.3930e+00	0.1636	0.0818

Multiple Linear Regression - Regression Statistics
Multiple R	0.1074
R-squared	0.01154
Adjusted R-squared	0.01041
F-TEST (value)	10.18
F-TEST (DF numerator)	3
F-TEST (DF denominator)	2617
p-value	1.149e-06
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	2.267
Sum Squared Residuals	1.344e+04

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R &  0.1074 \tabularnewline
R-squared &  0.01154 \tabularnewline
Adjusted R-squared &  0.01041 \tabularnewline
F-TEST (value) &  10.18 \tabularnewline
F-TEST (DF numerator) & 3 \tabularnewline
F-TEST (DF denominator) & 2617 \tabularnewline
p-value &  1.149e-06 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation &  2.267 \tabularnewline
Sum Squared Residuals &  1.344e+04 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=309568&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C] 0.1074[/C][/ROW]
[ROW][C]R-squared[/C][C] 0.01154[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C] 0.01041[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C] 10.18[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]3[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]2617[/C][/ROW]
[ROW][C]p-value[/C][C] 1.149e-06[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C] 2.267[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C] 1.344e+04[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=309568&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=309568&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	0.1074
R-squared	0.01154
Adjusted R-squared	0.01041
F-TEST (value)	10.18
F-TEST (DF numerator)	3
F-TEST (DF denominator)	2617
p-value	1.149e-06
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	2.267
Sum Squared Residuals	1.344e+04

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=309568&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=309568&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=309568&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 = 0, df1 = 2, df2 = 2615, p-value = 1
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors RESET test data: mylm RESET = 0, df1 = 6, df2 = 2611, p-value = 1
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components RESET test data: mylm RESET = 0, df1 = 2, df2 = 2615, p-value = 1

\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 = 0, df1 = 2, df2 = 2615, p-value = 1
 \tabularnewline
Ramsey RESET F-Test for powers (2 and 3) of regressors \tabularnewline
> reset_test_regressors
	RESET test
data:  mylm
RESET = 0, df1 = 6, df2 = 2611, p-value = 1
 \tabularnewline
Ramsey RESET F-Test for powers (2 and 3) of principal components \tabularnewline
> reset_test_principal_components
	RESET test
data:  mylm
RESET = 0, df1 = 2, df2 = 2615, p-value = 1
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=309568&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 = 0, df1 = 2, df2 = 2615, p-value = 1
[/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 = 0, df1 = 6, df2 = 2611, p-value = 1
[/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 = 0, df1 = 2, df2 = 2615, p-value = 1
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=309568&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=309568&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 = 0, df1 = 2, df2 = 2615, p-value = 1
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors RESET test data: mylm RESET = 0, df1 = 6, df2 = 2611, p-value = 1
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components RESET test data: mylm RESET = 0, df1 = 2, df2 = 2615, p-value = 1

Variance Inflation Factors (Multicollinearity)
> vif Yard Main Industry 11.589049 11.006046 4.697677

\begin{tabular}{lllllllll}
\hline
Variance Inflation Factors (Multicollinearity) \tabularnewline
> vif
     Yard      Main  Industry 
11.589049 11.006046  4.697677 
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=309568&T=6

[TABLE]
[ROW][C]Variance Inflation Factors (Multicollinearity)[/C][/ROW]
[ROW][C]> vif
     Yard      Main  Industry 
11.589049 11.006046  4.697677 
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=309568&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=309568&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 Yard Main Industry 11.589049 11.006046 4.697677

Figure 1

PNG link

Postscript link

PDF link

Figure 2

PNG link

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Figure 3

PNG link

Postscript link

PDF link

Figure 4

PNG link

Postscript link

PDF link

Figure 5

PNG link

Postscript link

PDF link

Figure 6

PNG link

Postscript link

PDF link

Figure 7

PNG link

Postscript link

PDF link

Figure 8

PNG link

Postscript link

PDF link

Figure 9

PNG link

Postscript link

PDF link

Figure 10

PNG link

Postscript link

PDF link

Parameters (Session):

par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ; par4 = 0 ; par5 = 0 ; par6 = 12 ;

Parameters (R input):

par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ; par4 = 0 ; par5 = 0 ; par6 = 12 ;

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

par6 <- '12'
par5 <- '0'
par4 <- '0'
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