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Author's title

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 21:36:29 +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/t1513284049x92npl3ldhlbtnt.htm/, Retrieved Tue, 14 May 2024 08:01:02 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=309589, Retrieved Tue, 14 May 2024 08:01:02 +0000

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Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords

Estimated Impact

163

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

-       [Multiple Regression] [lnTrkDamg limieten] [2017-12-14 20:36:29] [52cce9dbcec2927ac392287242c803b1] [Current]

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Dataseries X:

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Histogram

Boxplots

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NA	1	0	0
NA	1	0	0
NA	1	0	0
NA	0	1	0
NA	0	1	0
8,0063675677	1	0	0
NA	1	0	0
NA	0	1	0
NA	0	1	0
NA	0	1	0
NA	1	0	0
NA	1	0	0
10,2345882454	1	0	0
7,8747391252	0	1	0
NA	1	0	0
7,0900768358	1	0	0
NA	1	0	0
8,9226582995	1	0	0
7,0900768358	0	1	0
9,1327031022	0	1	0
NA	1	0	0
NA	1	0	0
8,9586687370	1	0	0
NA	0	1	0
7,3132203871	0	1	0
8,9805497779	1	0	0
11,0389459423	0	1	0
NA	0	1	0
10,5966347331	0	1	0
8,5171931914	0	0	1
NA	1	0	0
8,3938949751	1	0	0
NA	0	1	0
10,4628175854	0	1	0
NA	0	1	0
NA	0	1	0
NA	0	1	0
NA	1	0	0
NA	1	0	0
10,5546662483	1	0	0
NA	0	1	0
8,2133817370	1	0	0
NA	1	0	0
NA	1	0	0
NA	0	1	0
7,8200379895	0	1	0
NA	0	0	1
NA	0	0	0
NA	1	0	0
NA	1	0	0
NA	1	0	0
NA	0	1	0
NA	1	0	0
NA	1	0	0
9,3022812457	1	0	0
NA	1	0	0
8,9226582995	1	0	0
NA	1	0	0
NA	1	0	0
11,3578843889	0	1	0
NA	0	0	0
8,7160440502	0	1	0
NA	0	1	0
11,2897819137	0	1	0
11,1562505210	1	0	0
NA	0	1	0
9,8754480955	1	0	0
NA	0	1	0
NA	0	1	0
NA	1	0	0
NA	1	0	0
NA	1	0	0
NA	0	0	1
8,1605182475	1	0	0
10,0168162378	0	1	0
NA	1	0	0
NA	1	0	0
NA	1	0	0
7,8240460109	1	0	0
8,5171931914	0	1	0
NA	0	1	0
NA	0	1	0
10,8589989976	0	1	0
NA	1	0	0
NA	1	0	0
NA	1	0	0
NA	1	0	0
NA	0	0	1
NA	1	0	0
NA	1	0	0
NA	1	0	0
NA	1	0	0
8,1185050676	1	0	0
8,1176107465	0	0	1
NA	1	0	0
NA	1	0	0
NA	1	0	0
NA	1	0	0
6,9077552790	1	0	0
10,1900940468	1	0	0
NA	0	1	0
9,9711462010	0	0	1
NA	0	1	0
8,6358647211	1	0	0
8,2940496401	0	0	1
NA	0	1	0
11,2252433925	0	1	0
NA	1	0	0
9,4727046364	1	0	0
8,3836617988	0	0	1
8,6125033712	1	0	0
10,2035921450	1	0	0
NA	1	0	0
NA	0	1	0
8,8566609370	0	0	1
NA	0	1	0
NA	1	0	0
11,1198828769	0	1	0
10,4114490787	0	0	0
NA	0	0	1
NA	0	0	1
9,3236690573	0	1	0
NA	1	0	0
NA	1	0	0
8,0063675677	1	0	0
10,1266311039	0	1	0
7,4121603349	1	0	0
NA	1	0	0
NA	0	0	1
9,8542447860	1	0	0
NA	1	0	0
NA	1	0	0
8,9768939277	0	0	1
9,7288960418	1	0	0
9,8309168597	0	1	0
NA	1	0	0
NA	0	1	0
NA	0	1	0
NA	1	0	0
NA	1	0	0
7,6009024595	0	1	0
10,0928674737	0	0	1
8,5171931914	1	0	0
NA	0	1	0
NA	1	0	0
NA	1	0	0
10,5966347331	1	0	0
NA	0	1	0
9,0020854332	1	0	0
6,9077552790	0	1	0
NA	0	1	0
NA	0	1	0
NA	1	0	0
9,2653019301	1	0	0
NA	1	0	0
NA	1	0	0
NA	1	0	0
NA	1	0	0
10,0256166858	1	0	0
NA	1	0	0
8,6995147482	1	0	0
NA	1	0	0
NA	1	0	0
NA	1	0	0
7,4383835300	0	0	1
9,2103403720	0	1	0
9,3969029230	0	1	0
9,9134378834	0	1	0
NA	0	1	0
10,8077057032	0	1	0
NA	0	0	0
NA	0	0	0
10,5966347331	1	0	0
NA	1	0	0
8,9871968207	0	1	0
8,5171931914	1	0	0
NA	1	0	0
NA	1	0	0
NA	0	0	1
6,9077552790	1	0	0
NA	0	1	0
NA	0	1	0
NA	0	1	0
10,3889953683	0	1	0
NA	0	1	0
8,3238511313	1	0	0
NA	1	0	0
9,0915568360	1	0	0
10,4602707611	0	1	0
7,0715733642	0	0	0
7,6009024595	1	0	0
6,9077552790	1	0	0
NA	1	0	0
NA	1	0	0
NA	1	0	0
NA	1	0	0
NA	1	0	0
NA	1	0	0
NA	0	1	0
10,8140418626	0	1	0
9,8708610497	0	0	1
10,3734911818	0	0	1
NA	0	1	0
NA	0	1	0
8,4118326758	1	0	0
9,3926619288	0	0	1
8,1432267504	1	0	0
NA	1	0	0
NA	1	0	0
8,4879703327	1	0	0
NA	1	0	0
NA	1	0	0
NA	0	0	1
NA	0	0	1
11,3850920935	1	0	0
NA	0	0	1
8,9435061320	0	0	1
NA	1	0	0
NA	1	0	0
NA	1	0	0
10,3182427076	0	0	1
11,0186291432	0	1	0
9,4273048722	0	0	0
6,9077552790	1	0	0
NA	0	1	0
9,6172045010	1	0	0
7,1701195434	1	0	0
8,5171931914	0	1	0
NA	1	0	0
NA	1	0	0
8,3096768960	0	1	0
NA	1	0	0
10,6362640166	1	0	0
NA	1	0	0
NA	1	0	0
NA	0	1	0
10,2529817767	1	0	0
NA	1	0	0
9,6297083853	0	1	0
NA	1	0	0
7,3132203871	0	0	1
NA	0	1	0
9,6485953029	0	0	1
NA	1	0	0
9,7470091657	1	0	0
NA	1	0	0
NA	0	1	0
8,5171931914	0	1	0
NA	1	0	0
10,5966347331	0	1	0
9,1049798563	0	1	0
NA	0	1	0
9,1049798563	1	0	0
9,9987977323	1	0	0
NA	1	0	0
NA	1	0	0
NA	0	1	0
10,8790466929	1	0	0
8,9533814722	1	0	0
10,8323193159	0	1	0
10,0212705882	1	0	0
10,1909195937	1	0	0
NA	0	1	0
NA	1	0	0
NA	1	0	0
7,8240460109	0	1	0
NA	0	1	0
9,9034875525	0	0	1
9,2103403720	1	0	0
NA	0	1	0
9,7981270369	0	0	1
9,6158054801	0	0	1
NA	0	1	0
NA	1	0	0
NA	0	0	0
10,8589989976	1	0	0
NA	1	0	0
NA	0	1	0
NA	1	0	0
NA	1	0	0
10,7142622011	1	0	0
9,6785298567	1	0	0
7,6009024595	0	1	0
NA	1	0	0
NA	1	0	0
11,4295438560	0	1	0
NA	1	0	0
8,5171931914	1	0	0
NA	1	0	0
NA	0	1	0
9,6158054801	1	0	0
9,2812647103	0	0	1
7,9373746962	0	1	0
NA	1	0	0
NA	1	0	0
NA	0	1	0
9,6158054801	0	1	0
8,2940496401	1	0	0
8,9736048671	0	0	1
NA	0	1	0
NA	1	0	0
NA	0	1	0
7,9969904058	0	0	1
NA	1	0	0
NA	1	0	0
NA	0	1	0
NA	0	1	0
NA	0	1	0
NA	0	1	0
NA	0	1	0
NA	0	1	0
NA	0	1	0
7,3132203871	0	1	0
11,3504065355	0	1	0
NA	0	1	0
NA	0	1	0
NA	0	1	0
9,2591305361	0	1	0
7,8240460109	1	0	0
9,9034875525	1	0	0
9,1900356186	0	1	0
7,8240460109	1	0	0
8,9871968207	1	0	0
NA	1	0	0
6,9077552790	1	0	0
9,7409686230	0	0	1
9,2635967383	0	1	0
NA	0	1	0
9,6631342595	0	1	0
NA	0	1	0
NA	0	1	0
NA	0	1	0
NA	0	1	0
7,7719102564	0	1	0
9,1442005695	0	0	1
NA	1	0	0
8,8536654280	1	0	0
10,4954875511	1	0	0
NA	1	0	0
9,1655521892	0	1	0
NA	1	0	0
NA	1	0	0
9,6158054801	0	1	0
NA	1	0	0
NA	1	0	0
10,8333852878	0	1	0
NA	0	0	1
NA	0	1	0
NA	0	1	0
NA	1	0	0
7,8240460109	1	0	0
10,5907675548	0	1	0
NA	0	0	0
NA	0	0	0
9,1292387705	1	0	0
8,2940496401	0	1	0
NA	0	1	0
10,2479633890	1	0	0
NA	1	0	0
NA	1	0	0
NA	0	1	0
NA	1	0	0
9,9034875525	1	0	0
NA	1	0	0
NA	1	0	0
NA	0	1	0
NA	0	1	0
NA	0	1	0
NA	0	1	0
8,0381891800	0	1	0
NA	0	1	0
9,2103403720	0	1	0
7,0121152943	1	0	0
NA	1	0	0
NA	0	0	1
NA	0	0	1
8,6125033712	0	1	0
NA	0	1	0
8,5171931914	0	1	0
NA	1	0	0
7,4955419439	1	0	0
8,5171931914	0	0	1
NA	0	1	0
NA	1	0	0
NA	1	0	0
NA	1	0	0
NA	0	1	0
NA	0	1	0
NA	0	1	0
NA	1	0	0
NA	1	0	0
10,4884368626	0	1	0
7,7977020355	1	0	0
NA	1	0	0
11,1061895497	0	1	0
8,8536654280	1	0	0
11,4626842485	0	0	0
NA	0	1	0
NA	0	1	0
NA	0	1	0
9,3847137589	1	0	0
NA	1	0	0
NA	0	1	0
8,5171931914	0	1	0
NA	0	1	0
8,3428398043	0	1	0
10,3089526606	1	0	0
9,9831763514	1	0	0
6,9077552790	1	0	0
10,0732303271	0	1	0
NA	0	1	0
9,7981270369	1	0	0
NA	1	0	0
11,0020998412	0	1	0
7,5331588075	1	0	0
8,2586814963	1	0	0
NA	0	1	0
NA	0	1	0
8,5171931914	1	0	0
NA	0	1	0
NA	1	0	0
NA	1	0	0
8,1684864171	1	0	0
NA	0	1	0
NA	0	1	0
8,9871968207	0	1	0
7,6009024595	1	0	0
7,6962126393	1	0	0
NA	0	1	0
NA	0	1	0
10,5746447152	0	1	0
NA	0	1	0
NA	1	0	0
NA	1	0	0
NA	0	1	0
NA	1	0	0
NA	1	0	0
NA	1	0	0
7,0900768358	1	0	0
NA	1	0	0
NA	1	0	0
NA	0	0	0
NA	0	1	0
NA	1	0	0
11,0020998412	1	0	0
NA	1	0	0
10,8197782844	0	1	0
9,3056505518	0	0	0
9,2103403720	0	1	0
9,0478214425	0	0	0
NA	1	0	0
NA	1	0	0
NA	1	0	0
NA	1	0	0
NA	1	0	0
10,1266311039	0	0	0
NA	1	0	0
NA	1	0	0
7,8240460109	1	0	0
NA	1	0	0
9,2179116375	0	1	0
NA	1	0	0
NA	1	0	0
9,1010833860	1	0	0
NA	0	1	0
NA	0	0	1
7,2442275156	0	1	0
8,9226582995	0	0	1
8,5171931914	0	1	0
9,0825070005	1	0	0
NA	0	0	1
NA	0	1	0
9,6027202405	1	0	0
8,0709060888	0	1	0
8,6125033712	1	0	0
NA	0	0	1
NA	1	0	0
9,0191798665	0	1	0
8,0063675677	1	0	0
NA	0	1	0
8,8769633403	0	1	0
NA	0	0	1
9,0825070005	0	1	0
NA	0	1	0
NA	0	1	0
9,9034875525	0	1	0
NA	0	1	0
10,2255710517	0	0	0
7,7097568645	0	0	1
9,2591305361	0	0	1
7,6009024595	0	1	0
NA	1	0	0
8,8945331358	0	1	0
NA	1	0	0
8,1605182475	1	0	0
7,7025561133	1	0	0
NA	1	0	0
10,9962494275	1	0	0
8,0391573905	1	0	0
NA	0	1	0
NA	1	0	0
NA	1	0	0
NA	1	0	0
11,2897819137	0	1	0
9,9034875525	1	0	0
10,3254819626	1	0	0
9,4562625102	0	0	1
9,7394380405	1	0	0
9,7471260974	1	0	0
NA	0	0	1
NA	0	0	1
8,6995147482	1	0	0
NA	0	1	0
9,9987977323	1	0	0
NA	1	0	0
NA	1	0	0
NA	1	0	0
9,0757798786	0	1	0
NA	0	1	0
8,3308636132	0	1	0

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	15 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 time15 seconds \tabularnewline
R ServerBig Analytics Cloud Computing Center \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=309589&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]

15 seconds[/C][/ROW] [ROW]

R Server[/C]

Big Analytics Cloud Computing Center[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=309589&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=309589&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	15 seconds
R Server	Big Analytics Cloud Computing Center

Multiple Linear Regression - Estimated Regression Equation
lnTrkDamg[t] = + 9.55946 -0.53312Yard[t] + 0.0330033Main[t] -0.541921Industry[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
lnTrkDamg[t] =  +  9.55946 -0.53312Yard[t] +  0.0330033Main[t] -0.541921Industry[t]  + e[t] \tabularnewline
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=309589&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]lnTrkDamg[t] =  +  9.55946 -0.53312Yard[t] +  0.0330033Main[t] -0.541921Industry[t]  + e[t][/C][/ROW]
[ROW][C][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=309589&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=309589&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
lnTrkDamg[t] = + 9.55946 -0.53312Yard[t] + 0.0330033Main[t] -0.541921Industry[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)	+9.559	0.2158	+4.4300e+01	3.149e-255	1.574e-255
Yard	-0.5331	0.2212	-2.4100e+00	0.01612	0.008058
Main	+0.033	0.224	+1.4740e-01	0.8829	0.4414
Industry	-0.5419	0.2395	-2.2630e+00	0.02383	0.01192

\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) & +9.559 &  0.2158 & +4.4300e+01 &  3.149e-255 &  1.574e-255 \tabularnewline
Yard & -0.5331 &  0.2212 & -2.4100e+00 &  0.01612 &  0.008058 \tabularnewline
Main & +0.033 &  0.224 & +1.4740e-01 &  0.8829 &  0.4414 \tabularnewline
Industry & -0.5419 &  0.2395 & -2.2630e+00 &  0.02383 &  0.01192 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=309589&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]+9.559[/C][C] 0.2158[/C][C]+4.4300e+01[/C][C] 3.149e-255[/C][C] 1.574e-255[/C][/ROW]
[ROW][C]Yard[/C][C]-0.5331[/C][C] 0.2212[/C][C]-2.4100e+00[/C][C] 0.01612[/C][C] 0.008058[/C][/ROW]
[ROW][C]Main[/C][C]+0.033[/C][C] 0.224[/C][C]+1.4740e-01[/C][C] 0.8829[/C][C] 0.4414[/C][/ROW]
[ROW][C]Industry[/C][C]-0.5419[/C][C] 0.2395[/C][C]-2.2630e+00[/C][C] 0.02383[/C][C] 0.01192[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=309589&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=309589&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)	+9.559	0.2158	+4.4300e+01	3.149e-255	1.574e-255
Yard	-0.5331	0.2212	-2.4100e+00	0.01612	0.008058
Main	+0.033	0.224	+1.4740e-01	0.8829	0.4414
Industry	-0.5419	0.2395	-2.2630e+00	0.02383	0.01192

Multiple Linear Regression - Regression Statistics
Multiple R	0.2185
R-squared	0.04776
Adjusted R-squared	0.04539
F-TEST (value)	20.16
F-TEST (DF numerator)	3
F-TEST (DF denominator)	1206
p-value	9.394e-13
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	1.221
Sum Squared Residuals	1797

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R &  0.2185 \tabularnewline
R-squared &  0.04776 \tabularnewline
Adjusted R-squared &  0.04539 \tabularnewline
F-TEST (value) &  20.16 \tabularnewline
F-TEST (DF numerator) & 3 \tabularnewline
F-TEST (DF denominator) & 1206 \tabularnewline
p-value &  9.394e-13 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation &  1.221 \tabularnewline
Sum Squared Residuals &  1797 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=309589&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C] 0.2185[/C][/ROW]
[ROW][C]R-squared[/C][C] 0.04776[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C] 0.04539[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C] 20.16[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]3[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]1206[/C][/ROW]
[ROW][C]p-value[/C][C] 9.394e-13[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C] 1.221[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C] 1797[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=309589&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=309589&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	0.2185
R-squared	0.04776
Adjusted R-squared	0.04539
F-TEST (value)	20.16
F-TEST (DF numerator)	3
F-TEST (DF denominator)	1206
p-value	9.394e-13
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	1.221
Sum Squared Residuals	1797

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=309589&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=309589&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=309589&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 = 1204, 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 = 1200, 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 = 1204, 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 = 1204, 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 = 1200, 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 = 1204, p-value = 1
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=309589&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 = 1204, 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 = 1200, 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 = 1204, p-value = 1
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=309589&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=309589&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 = 1204, 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 = 1200, 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 = 1204, p-value = 1

Variance Inflation Factors (Multicollinearity)
> vif Yard Main Industry 9.926265 9.177815 4.706612

\begin{tabular}{lllllllll}
\hline
Variance Inflation Factors (Multicollinearity) \tabularnewline
> vif
    Yard     Main Industry 
9.926265 9.177815 4.706612 
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=309589&T=6

[TABLE]
[ROW][C]Variance Inflation Factors (Multicollinearity)[/C][/ROW]
[ROW][C]> vif
    Yard     Main Industry 
9.926265 9.177815 4.706612 
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=309589&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=309589&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 9.926265 9.177815 4.706612

Figure 1

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Figure 2

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Figure 3

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Figure 4

PNG link

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Figure 5

PNG link

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Figure 6

PNG link

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Figure 7

PNG link

Postscript link

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Figure 8

PNG link

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Figure 9

PNG link

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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):

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