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R Software Module

rwasp_multipleregression.wasp

Title produced by software

Multiple Regression

Date of computation

Thu, 24 Oct 2019 17:06:09 +0200

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Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2019/Oct/24/t1571930780xbexidy83isskrc.htm/, Retrieved Sat, 04 May 2024 13:08:37 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=318930, Retrieved Sat, 04 May 2024 13:08:37 +0000

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Estimated Impact

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

-       [Multiple Regression] [] [2019-10-24 15:06:09] [d41d8cd98f00b204e9800998ecf8427e] [Current]

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Dataseries X:

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Histogram

Boxplots

98.88 1 1 1
98.79 1 1 2
98.98 1 1 3
98.52 1 1 4
98.79 1 1 5
100 1 1 6
99.99 1 1 7
100 1 1 8
100 1 1 9
99.99 1 1 10
98.67 1 2 1
97.32 1 2 2
99.52 1 2 3
98.73 1 2 4
99.8 1 2 5
100 1 2 6
100 1 2 7
100 1 2 8
100 1 2 9
99.99 1 2 10
99.00 2 1 1
95.17 2 1 2
97.24 2 1 3
98.61 2 1 4
98.66 2 1 5
99.98 2 1 6
99.95 2 1 7
99.81 2 1 8
99.84 2 1 9
99.87 2 1 10
99.26 2 2 1
99.27 2 2 2
96.67 2 2 3
97.87 2 2 4
97.37 2 2 5
99.84 2 2 6
99.88 2 2 7
99.92 2 2 8
99.81 2 2 9
99.74 2 2 10

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	2 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 time2 seconds \tabularnewline
R ServerBig Analytics Cloud Computing Center \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=318930&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]

2 seconds[/C][/ROW] [ROW]

R Server[/C]

Big Analytics Cloud Computing Center[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=318930&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=318930&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	2 seconds
R Server	Big Analytics Cloud Computing Center

Multiple Linear Regression - Estimated Regression Equation
BCTsRes[t] = + 98.4748 -0.5105Cito[t] + 0.0795Lot[t] + 0.239076CD[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
BCTsRes[t] =  +  98.4748 -0.5105Cito[t] +  0.0795Lot[t] +  0.239076CD[t]  + e[t] \tabularnewline
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=318930&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]BCTsRes[t] =  +  98.4748 -0.5105Cito[t] +  0.0795Lot[t] +  0.239076CD[t]  + e[t][/C][/ROW]
[ROW][C][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=318930&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=318930&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
BCTsRes[t] = + 98.4748 -0.5105Cito[t] + 0.0795Lot[t] + 0.239076CD[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)	+98.47	0.6505	+1.5140e+02	4.368e-52	2.184e-52
Cito	-0.5105	0.2733	-1.8680e+00	0.06991	0.03496
Lot	+0.0795	0.2733	+2.9090e-01	0.7728	0.3864
CD	+0.2391	0.04757	+5.0260e+00	1.389e-05	6.947e-06

\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) & +98.47 &  0.6505 & +1.5140e+02 &  4.368e-52 &  2.184e-52 \tabularnewline
Cito & -0.5105 &  0.2733 & -1.8680e+00 &  0.06991 &  0.03496 \tabularnewline
Lot & +0.0795 &  0.2733 & +2.9090e-01 &  0.7728 &  0.3864 \tabularnewline
CD & +0.2391 &  0.04757 & +5.0260e+00 &  1.389e-05 &  6.947e-06 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=318930&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]+98.47[/C][C] 0.6505[/C][C]+1.5140e+02[/C][C] 4.368e-52[/C][C] 2.184e-52[/C][/ROW]
[ROW][C]Cito[/C][C]-0.5105[/C][C] 0.2733[/C][C]-1.8680e+00[/C][C] 0.06991[/C][C] 0.03496[/C][/ROW]
[ROW][C]Lot[/C][C]+0.0795[/C][C] 0.2733[/C][C]+2.9090e-01[/C][C] 0.7728[/C][C] 0.3864[/C][/ROW]
[ROW][C]CD[/C][C]+0.2391[/C][C] 0.04757[/C][C]+5.0260e+00[/C][C] 1.389e-05[/C][C] 6.947e-06[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=318930&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=318930&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)	+98.47	0.6505	+1.5140e+02	4.368e-52	2.184e-52
Cito	-0.5105	0.2733	-1.8680e+00	0.06991	0.03496
Lot	+0.0795	0.2733	+2.9090e-01	0.7728	0.3864
CD	+0.2391	0.04757	+5.0260e+00	1.389e-05	6.947e-06

Multiple Linear Regression - Regression Statistics
Multiple R	0.6669
R-squared	0.4447
Adjusted R-squared	0.3984
F-TEST (value)	9.61
F-TEST (DF numerator)	3
F-TEST (DF denominator)	36
p-value	8.473e-05
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	0.8642
Sum Squared Residuals	26.89

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R &  0.6669 \tabularnewline
R-squared &  0.4447 \tabularnewline
Adjusted R-squared &  0.3984 \tabularnewline
F-TEST (value) &  9.61 \tabularnewline
F-TEST (DF numerator) & 3 \tabularnewline
F-TEST (DF denominator) & 36 \tabularnewline
p-value &  8.473e-05 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation &  0.8642 \tabularnewline
Sum Squared Residuals &  26.89 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=318930&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C] 0.6669[/C][/ROW]
[ROW][C]R-squared[/C][C] 0.4447[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C] 0.3984[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C] 9.61[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]3[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]36[/C][/ROW]
[ROW][C]p-value[/C][C] 8.473e-05[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C] 0.8642[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C] 26.89[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=318930&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=318930&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	0.6669
R-squared	0.4447
Adjusted R-squared	0.3984
F-TEST (value)	9.61
F-TEST (DF numerator)	3
F-TEST (DF denominator)	36
p-value	8.473e-05
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	0.8642
Sum Squared Residuals	26.89

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=318930&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=318930&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=318930&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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	98.88	98.28	0.5971
2	98.79	98.52	0.268
3	98.98	98.76	0.2189
4	98.52	99	-0.4801
5	98.79	99.24	-0.4492
6	100	99.48	0.5217
7	99.99	99.72	0.2726
8	100	99.96	0.04356
9	100	100.2	-0.1955
10	99.99	100.4	-0.4446
11	98.67	98.36	0.3076
12	97.32	98.6	-1.281
13	99.52	98.84	0.6794
14	98.73	99.08	-0.3496
15	99.8	99.32	0.4813
16	100	99.56	0.4422
17	100	99.8	0.2031
18	100	100	-0.03594
19	100	100.3	-0.275
20	99.99	100.5	-0.5241
21	99	97.77	1.228
22	95.17	98.01	-2.841
23	97.24	98.25	-1.011
24	98.61	98.49	0.1204
25	98.66	98.73	-0.06871
26	99.98	98.97	1.012
27	99.95	99.21	0.7431
28	99.81	99.45	0.3641
29	99.84	99.69	0.155
30	99.87	99.92	-0.05409
31	99.26	97.85	1.408
32	99.27	98.09	1.179
33	96.67	98.33	-1.66
34	97.87	98.57	-0.6991
35	97.37	98.81	-1.438
36	99.84	99.05	0.7927
37	99.88	99.29	0.5936
38	99.92	99.53	0.3946
39	99.81	99.76	0.04548
40	99.74	100	-0.2636

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 &  98.88 &  98.28 &  0.5971 \tabularnewline
2 &  98.79 &  98.52 &  0.268 \tabularnewline
3 &  98.98 &  98.76 &  0.2189 \tabularnewline
4 &  98.52 &  99 & -0.4801 \tabularnewline
5 &  98.79 &  99.24 & -0.4492 \tabularnewline
6 &  100 &  99.48 &  0.5217 \tabularnewline
7 &  99.99 &  99.72 &  0.2726 \tabularnewline
8 &  100 &  99.96 &  0.04356 \tabularnewline
9 &  100 &  100.2 & -0.1955 \tabularnewline
10 &  99.99 &  100.4 & -0.4446 \tabularnewline
11 &  98.67 &  98.36 &  0.3076 \tabularnewline
12 &  97.32 &  98.6 & -1.281 \tabularnewline
13 &  99.52 &  98.84 &  0.6794 \tabularnewline
14 &  98.73 &  99.08 & -0.3496 \tabularnewline
15 &  99.8 &  99.32 &  0.4813 \tabularnewline
16 &  100 &  99.56 &  0.4422 \tabularnewline
17 &  100 &  99.8 &  0.2031 \tabularnewline
18 &  100 &  100 & -0.03594 \tabularnewline
19 &  100 &  100.3 & -0.275 \tabularnewline
20 &  99.99 &  100.5 & -0.5241 \tabularnewline
21 &  99 &  97.77 &  1.228 \tabularnewline
22 &  95.17 &  98.01 & -2.841 \tabularnewline
23 &  97.24 &  98.25 & -1.011 \tabularnewline
24 &  98.61 &  98.49 &  0.1204 \tabularnewline
25 &  98.66 &  98.73 & -0.06871 \tabularnewline
26 &  99.98 &  98.97 &  1.012 \tabularnewline
27 &  99.95 &  99.21 &  0.7431 \tabularnewline
28 &  99.81 &  99.45 &  0.3641 \tabularnewline
29 &  99.84 &  99.69 &  0.155 \tabularnewline
30 &  99.87 &  99.92 & -0.05409 \tabularnewline
31 &  99.26 &  97.85 &  1.408 \tabularnewline
32 &  99.27 &  98.09 &  1.179 \tabularnewline
33 &  96.67 &  98.33 & -1.66 \tabularnewline
34 &  97.87 &  98.57 & -0.6991 \tabularnewline
35 &  97.37 &  98.81 & -1.438 \tabularnewline
36 &  99.84 &  99.05 &  0.7927 \tabularnewline
37 &  99.88 &  99.29 &  0.5936 \tabularnewline
38 &  99.92 &  99.53 &  0.3946 \tabularnewline
39 &  99.81 &  99.76 &  0.04548 \tabularnewline
40 &  99.74 &  100 & -0.2636 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=318930&T=5

[TABLE]
[ROW][C]Multiple Linear Regression - Actuals, Interpolation, and Residuals[/C][/ROW]
[ROW][C]Time or Index[/C][C]Actuals[/C][C]InterpolationForecast[/C][C]ResidualsPrediction Error[/C][/ROW]
[ROW][C]1[/C][C] 98.88[/C][C] 98.28[/C][C] 0.5971[/C][/ROW]
[ROW][C]2[/C][C] 98.79[/C][C] 98.52[/C][C] 0.268[/C][/ROW]
[ROW][C]3[/C][C] 98.98[/C][C] 98.76[/C][C] 0.2189[/C][/ROW]
[ROW][C]4[/C][C] 98.52[/C][C] 99[/C][C]-0.4801[/C][/ROW]
[ROW][C]5[/C][C] 98.79[/C][C] 99.24[/C][C]-0.4492[/C][/ROW]
[ROW][C]6[/C][C] 100[/C][C] 99.48[/C][C] 0.5217[/C][/ROW]
[ROW][C]7[/C][C] 99.99[/C][C] 99.72[/C][C] 0.2726[/C][/ROW]
[ROW][C]8[/C][C] 100[/C][C] 99.96[/C][C] 0.04356[/C][/ROW]
[ROW][C]9[/C][C] 100[/C][C] 100.2[/C][C]-0.1955[/C][/ROW]
[ROW][C]10[/C][C] 99.99[/C][C] 100.4[/C][C]-0.4446[/C][/ROW]
[ROW][C]11[/C][C] 98.67[/C][C] 98.36[/C][C] 0.3076[/C][/ROW]
[ROW][C]12[/C][C] 97.32[/C][C] 98.6[/C][C]-1.281[/C][/ROW]
[ROW][C]13[/C][C] 99.52[/C][C] 98.84[/C][C] 0.6794[/C][/ROW]
[ROW][C]14[/C][C] 98.73[/C][C] 99.08[/C][C]-0.3496[/C][/ROW]
[ROW][C]15[/C][C] 99.8[/C][C] 99.32[/C][C] 0.4813[/C][/ROW]
[ROW][C]16[/C][C] 100[/C][C] 99.56[/C][C] 0.4422[/C][/ROW]
[ROW][C]17[/C][C] 100[/C][C] 99.8[/C][C] 0.2031[/C][/ROW]
[ROW][C]18[/C][C] 100[/C][C] 100[/C][C]-0.03594[/C][/ROW]
[ROW][C]19[/C][C] 100[/C][C] 100.3[/C][C]-0.275[/C][/ROW]
[ROW][C]20[/C][C] 99.99[/C][C] 100.5[/C][C]-0.5241[/C][/ROW]
[ROW][C]21[/C][C] 99[/C][C] 97.77[/C][C] 1.228[/C][/ROW]
[ROW][C]22[/C][C] 95.17[/C][C] 98.01[/C][C]-2.841[/C][/ROW]
[ROW][C]23[/C][C] 97.24[/C][C] 98.25[/C][C]-1.011[/C][/ROW]
[ROW][C]24[/C][C] 98.61[/C][C] 98.49[/C][C] 0.1204[/C][/ROW]
[ROW][C]25[/C][C] 98.66[/C][C] 98.73[/C][C]-0.06871[/C][/ROW]
[ROW][C]26[/C][C] 99.98[/C][C] 98.97[/C][C] 1.012[/C][/ROW]
[ROW][C]27[/C][C] 99.95[/C][C] 99.21[/C][C] 0.7431[/C][/ROW]
[ROW][C]28[/C][C] 99.81[/C][C] 99.45[/C][C] 0.3641[/C][/ROW]
[ROW][C]29[/C][C] 99.84[/C][C] 99.69[/C][C] 0.155[/C][/ROW]
[ROW][C]30[/C][C] 99.87[/C][C] 99.92[/C][C]-0.05409[/C][/ROW]
[ROW][C]31[/C][C] 99.26[/C][C] 97.85[/C][C] 1.408[/C][/ROW]
[ROW][C]32[/C][C] 99.27[/C][C] 98.09[/C][C] 1.179[/C][/ROW]
[ROW][C]33[/C][C] 96.67[/C][C] 98.33[/C][C]-1.66[/C][/ROW]
[ROW][C]34[/C][C] 97.87[/C][C] 98.57[/C][C]-0.6991[/C][/ROW]
[ROW][C]35[/C][C] 97.37[/C][C] 98.81[/C][C]-1.438[/C][/ROW]
[ROW][C]36[/C][C] 99.84[/C][C] 99.05[/C][C] 0.7927[/C][/ROW]
[ROW][C]37[/C][C] 99.88[/C][C] 99.29[/C][C] 0.5936[/C][/ROW]
[ROW][C]38[/C][C] 99.92[/C][C] 99.53[/C][C] 0.3946[/C][/ROW]
[ROW][C]39[/C][C] 99.81[/C][C] 99.76[/C][C] 0.04548[/C][/ROW]
[ROW][C]40[/C][C] 99.74[/C][C] 100[/C][C]-0.2636[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=318930&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=318930&T=5

As an alternative you can also use a QR Code:

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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	98.88	98.28	0.5971
2	98.79	98.52	0.268
3	98.98	98.76	0.2189
4	98.52	99	-0.4801
5	98.79	99.24	-0.4492
6	100	99.48	0.5217
7	99.99	99.72	0.2726
8	100	99.96	0.04356
9	100	100.2	-0.1955
10	99.99	100.4	-0.4446
11	98.67	98.36	0.3076
12	97.32	98.6	-1.281
13	99.52	98.84	0.6794
14	98.73	99.08	-0.3496
15	99.8	99.32	0.4813
16	100	99.56	0.4422
17	100	99.8	0.2031
18	100	100	-0.03594
19	100	100.3	-0.275
20	99.99	100.5	-0.5241
21	99	97.77	1.228
22	95.17	98.01	-2.841
23	97.24	98.25	-1.011
24	98.61	98.49	0.1204
25	98.66	98.73	-0.06871
26	99.98	98.97	1.012
27	99.95	99.21	0.7431
28	99.81	99.45	0.3641
29	99.84	99.69	0.155
30	99.87	99.92	-0.05409
31	99.26	97.85	1.408
32	99.27	98.09	1.179
33	96.67	98.33	-1.66
34	97.87	98.57	-0.6991
35	97.37	98.81	-1.438
36	99.84	99.05	0.7927
37	99.88	99.29	0.5936
38	99.92	99.53	0.3946
39	99.81	99.76	0.04548
40	99.74	100	-0.2636

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
7	0.2536	0.5071	0.7464
8	0.1237	0.2475	0.8763
9	0.05585	0.1117	0.9442
10	0.02599	0.05198	0.974
11	0.01012	0.02025	0.9899
12	0.03936	0.07872	0.9606
13	0.06425	0.1285	0.9358
14	0.03475	0.06949	0.9653
15	0.02916	0.05833	0.9708
16	0.0204	0.0408	0.9796
17	0.01095	0.02189	0.9891
18	0.005221	0.01044	0.9948
19	0.002447	0.004893	0.9976
20	0.001208	0.002416	0.9988
21	0.001043	0.002086	0.999
22	0.3041	0.6082	0.6959
23	0.3151	0.6302	0.6849
24	0.2791	0.5581	0.7209
25	0.2326	0.4653	0.7674
26	0.249	0.498	0.751
27	0.2067	0.4134	0.7933
28	0.1413	0.2827	0.8587
29	0.08672	0.1734	0.9133
30	0.04822	0.09644	0.9518
31	0.08524	0.1705	0.9148
32	0.2693	0.5386	0.7307
33	0.2824	0.5649	0.7176

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
7 &  0.2536 &  0.5071 &  0.7464 \tabularnewline
8 &  0.1237 &  0.2475 &  0.8763 \tabularnewline
9 &  0.05585 &  0.1117 &  0.9442 \tabularnewline
10 &  0.02599 &  0.05198 &  0.974 \tabularnewline
11 &  0.01012 &  0.02025 &  0.9899 \tabularnewline
12 &  0.03936 &  0.07872 &  0.9606 \tabularnewline
13 &  0.06425 &  0.1285 &  0.9358 \tabularnewline
14 &  0.03475 &  0.06949 &  0.9653 \tabularnewline
15 &  0.02916 &  0.05833 &  0.9708 \tabularnewline
16 &  0.0204 &  0.0408 &  0.9796 \tabularnewline
17 &  0.01095 &  0.02189 &  0.9891 \tabularnewline
18 &  0.005221 &  0.01044 &  0.9948 \tabularnewline
19 &  0.002447 &  0.004893 &  0.9976 \tabularnewline
20 &  0.001208 &  0.002416 &  0.9988 \tabularnewline
21 &  0.001043 &  0.002086 &  0.999 \tabularnewline
22 &  0.3041 &  0.6082 &  0.6959 \tabularnewline
23 &  0.3151 &  0.6302 &  0.6849 \tabularnewline
24 &  0.2791 &  0.5581 &  0.7209 \tabularnewline
25 &  0.2326 &  0.4653 &  0.7674 \tabularnewline
26 &  0.249 &  0.498 &  0.751 \tabularnewline
27 &  0.2067 &  0.4134 &  0.7933 \tabularnewline
28 &  0.1413 &  0.2827 &  0.8587 \tabularnewline
29 &  0.08672 &  0.1734 &  0.9133 \tabularnewline
30 &  0.04822 &  0.09644 &  0.9518 \tabularnewline
31 &  0.08524 &  0.1705 &  0.9148 \tabularnewline
32 &  0.2693 &  0.5386 &  0.7307 \tabularnewline
33 &  0.2824 &  0.5649 &  0.7176 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=318930&T=6

[TABLE]
[ROW][C]Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]p-values[/C][C]Alternative Hypothesis[/C][/ROW]
[ROW][C]breakpoint index[/C][C]greater[/C][C]2-sided[/C][C]less[/C][/ROW]
[ROW][C]7[/C][C] 0.2536[/C][C] 0.5071[/C][C] 0.7464[/C][/ROW]
[ROW][C]8[/C][C] 0.1237[/C][C] 0.2475[/C][C] 0.8763[/C][/ROW]
[ROW][C]9[/C][C] 0.05585[/C][C] 0.1117[/C][C] 0.9442[/C][/ROW]
[ROW][C]10[/C][C] 0.02599[/C][C] 0.05198[/C][C] 0.974[/C][/ROW]
[ROW][C]11[/C][C] 0.01012[/C][C] 0.02025[/C][C] 0.9899[/C][/ROW]
[ROW][C]12[/C][C] 0.03936[/C][C] 0.07872[/C][C] 0.9606[/C][/ROW]
[ROW][C]13[/C][C] 0.06425[/C][C] 0.1285[/C][C] 0.9358[/C][/ROW]
[ROW][C]14[/C][C] 0.03475[/C][C] 0.06949[/C][C] 0.9653[/C][/ROW]
[ROW][C]15[/C][C] 0.02916[/C][C] 0.05833[/C][C] 0.9708[/C][/ROW]
[ROW][C]16[/C][C] 0.0204[/C][C] 0.0408[/C][C] 0.9796[/C][/ROW]
[ROW][C]17[/C][C] 0.01095[/C][C] 0.02189[/C][C] 0.9891[/C][/ROW]
[ROW][C]18[/C][C] 0.005221[/C][C] 0.01044[/C][C] 0.9948[/C][/ROW]
[ROW][C]19[/C][C] 0.002447[/C][C] 0.004893[/C][C] 0.9976[/C][/ROW]
[ROW][C]20[/C][C] 0.001208[/C][C] 0.002416[/C][C] 0.9988[/C][/ROW]
[ROW][C]21[/C][C] 0.001043[/C][C] 0.002086[/C][C] 0.999[/C][/ROW]
[ROW][C]22[/C][C] 0.3041[/C][C] 0.6082[/C][C] 0.6959[/C][/ROW]
[ROW][C]23[/C][C] 0.3151[/C][C] 0.6302[/C][C] 0.6849[/C][/ROW]
[ROW][C]24[/C][C] 0.2791[/C][C] 0.5581[/C][C] 0.7209[/C][/ROW]
[ROW][C]25[/C][C] 0.2326[/C][C] 0.4653[/C][C] 0.7674[/C][/ROW]
[ROW][C]26[/C][C] 0.249[/C][C] 0.498[/C][C] 0.751[/C][/ROW]
[ROW][C]27[/C][C] 0.2067[/C][C] 0.4134[/C][C] 0.7933[/C][/ROW]
[ROW][C]28[/C][C] 0.1413[/C][C] 0.2827[/C][C] 0.8587[/C][/ROW]
[ROW][C]29[/C][C] 0.08672[/C][C] 0.1734[/C][C] 0.9133[/C][/ROW]
[ROW][C]30[/C][C] 0.04822[/C][C] 0.09644[/C][C] 0.9518[/C][/ROW]
[ROW][C]31[/C][C] 0.08524[/C][C] 0.1705[/C][C] 0.9148[/C][/ROW]
[ROW][C]32[/C][C] 0.2693[/C][C] 0.5386[/C][C] 0.7307[/C][/ROW]
[ROW][C]33[/C][C] 0.2824[/C][C] 0.5649[/C][C] 0.7176[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=318930&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=318930&T=6

As an alternative you can also use a QR Code:

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

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
7	0.2536	0.5071	0.7464
8	0.1237	0.2475	0.8763
9	0.05585	0.1117	0.9442
10	0.02599	0.05198	0.974
11	0.01012	0.02025	0.9899
12	0.03936	0.07872	0.9606
13	0.06425	0.1285	0.9358
14	0.03475	0.06949	0.9653
15	0.02916	0.05833	0.9708
16	0.0204	0.0408	0.9796
17	0.01095	0.02189	0.9891
18	0.005221	0.01044	0.9948
19	0.002447	0.004893	0.9976
20	0.001208	0.002416	0.9988
21	0.001043	0.002086	0.999
22	0.3041	0.6082	0.6959
23	0.3151	0.6302	0.6849
24	0.2791	0.5581	0.7209
25	0.2326	0.4653	0.7674
26	0.249	0.498	0.751
27	0.2067	0.4134	0.7933
28	0.1413	0.2827	0.8587
29	0.08672	0.1734	0.9133
30	0.04822	0.09644	0.9518
31	0.08524	0.1705	0.9148
32	0.2693	0.5386	0.7307
33	0.2824	0.5649	0.7176

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	3	0.1111	NOK
5% type I error level	7	0.259259	NOK
10% type I error level	12	0.444444	NOK

\begin{tabular}{lllllllll}
\hline
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
Description & # significant tests & % significant tests & OK/NOK \tabularnewline
1% type I error level & 3 &  0.1111 & NOK \tabularnewline
5% type I error level & 7 & 0.259259 & NOK \tabularnewline
10% type I error level & 12 & 0.444444 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=318930&T=7

[TABLE]
[ROW][C]Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]Description[/C][C]# significant tests[/C][C]% significant tests[/C][C]OK/NOK[/C][/ROW]
[ROW][C]1% type I error level[/C][C]3[/C][C] 0.1111[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]7[/C][C]0.259259[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]12[/C][C]0.444444[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=318930&T=7

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=318930&T=7

As an alternative you can also use a QR Code:

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

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	3	0.1111	NOK
5% type I error level	7	0.259259	NOK
10% type I error level	12	0.444444	NOK

Ramsey RESET F-Test for powers (2 and 3) of fitted values
> reset_test_fitted RESET test data: mylm RESET = 2.9896, df1 = 2, df2 = 34, p-value = 0.06367
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors RESET test data: mylm RESET = 1.6308, df1 = 6, df2 = 30, p-value = 0.1731
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components RESET test data: mylm RESET = 5.5448, df1 = 2, df2 = 34, p-value = 0.008239

\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 = 2.9896, df1 = 2, df2 = 34, p-value = 0.06367
 \tabularnewline
Ramsey RESET F-Test for powers (2 and 3) of regressors \tabularnewline
> reset_test_regressors
	RESET test
data:  mylm
RESET = 1.6308, df1 = 6, df2 = 30, p-value = 0.1731
 \tabularnewline
Ramsey RESET F-Test for powers (2 and 3) of principal components \tabularnewline
> reset_test_principal_components
	RESET test
data:  mylm
RESET = 5.5448, df1 = 2, df2 = 34, p-value = 0.008239
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=318930&T=8

[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 = 2.9896, df1 = 2, df2 = 34, p-value = 0.06367
[/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 = 1.6308, df1 = 6, df2 = 30, p-value = 0.1731
[/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 = 5.5448, df1 = 2, df2 = 34, p-value = 0.008239
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=318930&T=8

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=318930&T=8

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 = 2.9896, df1 = 2, df2 = 34, p-value = 0.06367
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors RESET test data: mylm RESET = 1.6308, df1 = 6, df2 = 30, p-value = 0.1731
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components RESET test data: mylm RESET = 5.5448, df1 = 2, df2 = 34, p-value = 0.008239

Variance Inflation Factors (Multicollinearity)
> vif Cito Lot CD 1 1 1

\begin{tabular}{lllllllll}
\hline
Variance Inflation Factors (Multicollinearity) \tabularnewline
> vif
Cito  Lot   CD 
   1    1    1 
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=318930&T=9

[TABLE]
[ROW][C]Variance Inflation Factors (Multicollinearity)[/C][/ROW]
[ROW][C]> vif
Cito  Lot   CD 
   1    1    1 
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=318930&T=9

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=318930&T=9

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 Cito Lot CD 1 1 1

Figure 1

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Figure 2

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Figure 3

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Figure 4

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Figure 5

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Figure 6

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Figure 7

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Figure 8

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Figure 9

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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 = 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