Repository of Reproducible Computations

Free Statistics

of Irreproducible Research!

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

Tue, 01 Dec 2015 12:09:45 +0000

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/2015/Dec/01/t1448972052xav2134r08jlh9r.htm/, Retrieved Thu, 16 May 2024 11:01:02 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=284702, Retrieved Thu, 16 May 2024 11:01:02 +0000

QR Codes:

Paste this QR Code to cite your computation.

Original text written by user:

IsPrivate?

No (this computation is public)

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] [] [2015-12-01 12:09:45] [5777355b7aed335e6347b7aed36a2c47] [Current]

Feedback Forum

Post a new message

Dataseries X:

Download CSV

Histogram

Boxplots

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	7 seconds
R Server	'Gwilym Jenkins' @ jenkins.wessa.net

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 7 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ jenkins.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=284702&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]7 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ jenkins.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=284702&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=284702&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	7 seconds
R Server	'Gwilym Jenkins' @ jenkins.wessa.net

Multiple Linear Regression - Estimated Regression Equation
STERFGEVALLEN[t] = + 16.1424 + 4.77001VRIJDAG_13de[t] + 13.5909DINSDAG_3de[t] + 0.246791`STERFGEVALLEN(t-1)`[t] + 0.161893`STERFGEVALLEN(t-2)`[t] + 0.141361`STERFGEVALLEN(t-3)`[t] + 0.0218881`STERFGEVALLEN(t-4)`[t] + 0.119791`STERFGEVALLEN(t-5)`[t] + 0.159053`STERFGEVALLEN(t-6)`[t] + 0.0650499`STERFGEVALLEN(t-7)`[t] + 0.0106934`STERFGEVALLEN(t-8)`[t] + 0.0425016`STERFGEVALLEN(t-9)`[t] -0.116066`STERFGEVALLEN(t-10)`[t] -0.0107002`STERFGEVALLEN(t-11)`[t] -0.000526583`STERFGEVALLEN(t-12)`[t] + 0.0485044`STERFGEVALLEN(t-13)`[t] + 0.052145`STERFGEVALLEN(t-14)`[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
STERFGEVALLEN[t] =  +  16.1424 +  4.77001VRIJDAG_13de[t] +  13.5909DINSDAG_3de[t] +  0.246791`STERFGEVALLEN(t-1)`[t] +  0.161893`STERFGEVALLEN(t-2)`[t] +  0.141361`STERFGEVALLEN(t-3)`[t] +  0.0218881`STERFGEVALLEN(t-4)`[t] +  0.119791`STERFGEVALLEN(t-5)`[t] +  0.159053`STERFGEVALLEN(t-6)`[t] +  0.0650499`STERFGEVALLEN(t-7)`[t] +  0.0106934`STERFGEVALLEN(t-8)`[t] +  0.0425016`STERFGEVALLEN(t-9)`[t] -0.116066`STERFGEVALLEN(t-10)`[t] -0.0107002`STERFGEVALLEN(t-11)`[t] -0.000526583`STERFGEVALLEN(t-12)`[t] +  0.0485044`STERFGEVALLEN(t-13)`[t] +  0.052145`STERFGEVALLEN(t-14)`[t]  + e[t] \tabularnewline
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=284702&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]STERFGEVALLEN[t] =  +  16.1424 +  4.77001VRIJDAG_13de[t] +  13.5909DINSDAG_3de[t] +  0.246791`STERFGEVALLEN(t-1)`[t] +  0.161893`STERFGEVALLEN(t-2)`[t] +  0.141361`STERFGEVALLEN(t-3)`[t] +  0.0218881`STERFGEVALLEN(t-4)`[t] +  0.119791`STERFGEVALLEN(t-5)`[t] +  0.159053`STERFGEVALLEN(t-6)`[t] +  0.0650499`STERFGEVALLEN(t-7)`[t] +  0.0106934`STERFGEVALLEN(t-8)`[t] +  0.0425016`STERFGEVALLEN(t-9)`[t] -0.116066`STERFGEVALLEN(t-10)`[t] -0.0107002`STERFGEVALLEN(t-11)`[t] -0.000526583`STERFGEVALLEN(t-12)`[t] +  0.0485044`STERFGEVALLEN(t-13)`[t] +  0.052145`STERFGEVALLEN(t-14)`[t]  + e[t][/C][/ROW]
[ROW][C][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=284702&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=284702&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
STERFGEVALLEN[t] = + 16.1424 + 4.77001VRIJDAG_13de[t] + 13.5909DINSDAG_3de[t] + 0.246791`STERFGEVALLEN(t-1)`[t] + 0.161893`STERFGEVALLEN(t-2)`[t] + 0.141361`STERFGEVALLEN(t-3)`[t] + 0.0218881`STERFGEVALLEN(t-4)`[t] + 0.119791`STERFGEVALLEN(t-5)`[t] + 0.159053`STERFGEVALLEN(t-6)`[t] + 0.0650499`STERFGEVALLEN(t-7)`[t] + 0.0106934`STERFGEVALLEN(t-8)`[t] + 0.0425016`STERFGEVALLEN(t-9)`[t] -0.116066`STERFGEVALLEN(t-10)`[t] -0.0107002`STERFGEVALLEN(t-11)`[t] -0.000526583`STERFGEVALLEN(t-12)`[t] + 0.0485044`STERFGEVALLEN(t-13)`[t] + 0.052145`STERFGEVALLEN(t-14)`[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)	+16.14	8.772	+1.8400e+00	0.06638	0.03319
VRIJDAG_13de	+4.77	12.3	+3.8760e-01	0.6985	0.3492
DINSDAG_3de	+13.59	12.18	+1.1160e+00	0.2652	0.1326
`STERFGEVALLEN(t-1)`	+0.2468	0.04606	+5.3580e+00	1.319e-07	6.595e-08
`STERFGEVALLEN(t-2)`	+0.1619	0.04729	+3.4240e+00	0.0006724	0.0003362
`STERFGEVALLEN(t-3)`	+0.1414	0.04784	+2.9550e+00	0.003285	0.001643
`STERFGEVALLEN(t-4)`	+0.02189	0.04835	+4.5270e-01	0.651	0.3255
`STERFGEVALLEN(t-5)`	+0.1198	0.04791	+2.5000e+00	0.01276	0.006378
`STERFGEVALLEN(t-6)`	+0.159	0.04823	+3.2980e+00	0.001049	0.0005243
`STERFGEVALLEN(t-7)`	+0.06505	0.04902	+1.3270e+00	0.1852	0.09258
`STERFGEVALLEN(t-8)`	+0.01069	0.0491	+2.1780e-01	0.8277	0.4138
`STERFGEVALLEN(t-9)`	+0.0425	0.04828	+8.8030e-01	0.3791	0.1896
`STERFGEVALLEN(t-10)`	-0.1161	0.04814	-2.4110e+00	0.0163	0.008149
`STERFGEVALLEN(t-11)`	-0.0107	0.04844	-2.2090e-01	0.8253	0.4126
`STERFGEVALLEN(t-12)`	-0.0005266	0.04807	-1.0950e-02	0.9913	0.4956
`STERFGEVALLEN(t-13)`	+0.0485	0.04745	+1.0220e+00	0.3072	0.1536
`STERFGEVALLEN(t-14)`	+0.05214	0.04622	+1.1280e+00	0.2598	0.1299

\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) & +16.14 &  8.772 & +1.8400e+00 &  0.06638 &  0.03319 \tabularnewline
VRIJDAG_13de & +4.77 &  12.3 & +3.8760e-01 &  0.6985 &  0.3492 \tabularnewline
DINSDAG_3de & +13.59 &  12.18 & +1.1160e+00 &  0.2652 &  0.1326 \tabularnewline
`STERFGEVALLEN(t-1)` & +0.2468 &  0.04606 & +5.3580e+00 &  1.319e-07 &  6.595e-08 \tabularnewline
`STERFGEVALLEN(t-2)` & +0.1619 &  0.04729 & +3.4240e+00 &  0.0006724 &  0.0003362 \tabularnewline
`STERFGEVALLEN(t-3)` & +0.1414 &  0.04784 & +2.9550e+00 &  0.003285 &  0.001643 \tabularnewline
`STERFGEVALLEN(t-4)` & +0.02189 &  0.04835 & +4.5270e-01 &  0.651 &  0.3255 \tabularnewline
`STERFGEVALLEN(t-5)` & +0.1198 &  0.04791 & +2.5000e+00 &  0.01276 &  0.006378 \tabularnewline
`STERFGEVALLEN(t-6)` & +0.159 &  0.04823 & +3.2980e+00 &  0.001049 &  0.0005243 \tabularnewline
`STERFGEVALLEN(t-7)` & +0.06505 &  0.04902 & +1.3270e+00 &  0.1852 &  0.09258 \tabularnewline
`STERFGEVALLEN(t-8)` & +0.01069 &  0.0491 & +2.1780e-01 &  0.8277 &  0.4138 \tabularnewline
`STERFGEVALLEN(t-9)` & +0.0425 &  0.04828 & +8.8030e-01 &  0.3791 &  0.1896 \tabularnewline
`STERFGEVALLEN(t-10)` & -0.1161 &  0.04814 & -2.4110e+00 &  0.0163 &  0.008149 \tabularnewline
`STERFGEVALLEN(t-11)` & -0.0107 &  0.04844 & -2.2090e-01 &  0.8253 &  0.4126 \tabularnewline
`STERFGEVALLEN(t-12)` & -0.0005266 &  0.04807 & -1.0950e-02 &  0.9913 &  0.4956 \tabularnewline
`STERFGEVALLEN(t-13)` & +0.0485 &  0.04745 & +1.0220e+00 &  0.3072 &  0.1536 \tabularnewline
`STERFGEVALLEN(t-14)` & +0.05214 &  0.04622 & +1.1280e+00 &  0.2598 &  0.1299 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=284702&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]+16.14[/C][C] 8.772[/C][C]+1.8400e+00[/C][C] 0.06638[/C][C] 0.03319[/C][/ROW]
[ROW][C]VRIJDAG_13de[/C][C]+4.77[/C][C] 12.3[/C][C]+3.8760e-01[/C][C] 0.6985[/C][C] 0.3492[/C][/ROW]
[ROW][C]DINSDAG_3de[/C][C]+13.59[/C][C] 12.18[/C][C]+1.1160e+00[/C][C] 0.2652[/C][C] 0.1326[/C][/ROW]
[ROW][C]`STERFGEVALLEN(t-1)`[/C][C]+0.2468[/C][C] 0.04606[/C][C]+5.3580e+00[/C][C] 1.319e-07[/C][C] 6.595e-08[/C][/ROW]
[ROW][C]`STERFGEVALLEN(t-2)`[/C][C]+0.1619[/C][C] 0.04729[/C][C]+3.4240e+00[/C][C] 0.0006724[/C][C] 0.0003362[/C][/ROW]
[ROW][C]`STERFGEVALLEN(t-3)`[/C][C]+0.1414[/C][C] 0.04784[/C][C]+2.9550e+00[/C][C] 0.003285[/C][C] 0.001643[/C][/ROW]
[ROW][C]`STERFGEVALLEN(t-4)`[/C][C]+0.02189[/C][C] 0.04835[/C][C]+4.5270e-01[/C][C] 0.651[/C][C] 0.3255[/C][/ROW]
[ROW][C]`STERFGEVALLEN(t-5)`[/C][C]+0.1198[/C][C] 0.04791[/C][C]+2.5000e+00[/C][C] 0.01276[/C][C] 0.006378[/C][/ROW]
[ROW][C]`STERFGEVALLEN(t-6)`[/C][C]+0.159[/C][C] 0.04823[/C][C]+3.2980e+00[/C][C] 0.001049[/C][C] 0.0005243[/C][/ROW]
[ROW][C]`STERFGEVALLEN(t-7)`[/C][C]+0.06505[/C][C] 0.04902[/C][C]+1.3270e+00[/C][C] 0.1852[/C][C] 0.09258[/C][/ROW]
[ROW][C]`STERFGEVALLEN(t-8)`[/C][C]+0.01069[/C][C] 0.0491[/C][C]+2.1780e-01[/C][C] 0.8277[/C][C] 0.4138[/C][/ROW]
[ROW][C]`STERFGEVALLEN(t-9)`[/C][C]+0.0425[/C][C] 0.04828[/C][C]+8.8030e-01[/C][C] 0.3791[/C][C] 0.1896[/C][/ROW]
[ROW][C]`STERFGEVALLEN(t-10)`[/C][C]-0.1161[/C][C] 0.04814[/C][C]-2.4110e+00[/C][C] 0.0163[/C][C] 0.008149[/C][/ROW]
[ROW][C]`STERFGEVALLEN(t-11)`[/C][C]-0.0107[/C][C] 0.04844[/C][C]-2.2090e-01[/C][C] 0.8253[/C][C] 0.4126[/C][/ROW]
[ROW][C]`STERFGEVALLEN(t-12)`[/C][C]-0.0005266[/C][C] 0.04807[/C][C]-1.0950e-02[/C][C] 0.9913[/C][C] 0.4956[/C][/ROW]
[ROW][C]`STERFGEVALLEN(t-13)`[/C][C]+0.0485[/C][C] 0.04745[/C][C]+1.0220e+00[/C][C] 0.3072[/C][C] 0.1536[/C][/ROW]
[ROW][C]`STERFGEVALLEN(t-14)`[/C][C]+0.05214[/C][C] 0.04622[/C][C]+1.1280e+00[/C][C] 0.2598[/C][C] 0.1299[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=284702&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=284702&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)	+16.14	8.772	+1.8400e+00	0.06638	0.03319
VRIJDAG_13de	+4.77	12.3	+3.8760e-01	0.6985	0.3492
DINSDAG_3de	+13.59	12.18	+1.1160e+00	0.2652	0.1326
`STERFGEVALLEN(t-1)`	+0.2468	0.04606	+5.3580e+00	1.319e-07	6.595e-08
`STERFGEVALLEN(t-2)`	+0.1619	0.04729	+3.4240e+00	0.0006724	0.0003362
`STERFGEVALLEN(t-3)`	+0.1414	0.04784	+2.9550e+00	0.003285	0.001643
`STERFGEVALLEN(t-4)`	+0.02189	0.04835	+4.5270e-01	0.651	0.3255
`STERFGEVALLEN(t-5)`	+0.1198	0.04791	+2.5000e+00	0.01276	0.006378
`STERFGEVALLEN(t-6)`	+0.159	0.04823	+3.2980e+00	0.001049	0.0005243
`STERFGEVALLEN(t-7)`	+0.06505	0.04902	+1.3270e+00	0.1852	0.09258
`STERFGEVALLEN(t-8)`	+0.01069	0.0491	+2.1780e-01	0.8277	0.4138
`STERFGEVALLEN(t-9)`	+0.0425	0.04828	+8.8030e-01	0.3791	0.1896
`STERFGEVALLEN(t-10)`	-0.1161	0.04814	-2.4110e+00	0.0163	0.008149
`STERFGEVALLEN(t-11)`	-0.0107	0.04844	-2.2090e-01	0.8253	0.4126
`STERFGEVALLEN(t-12)`	-0.0005266	0.04807	-1.0950e-02	0.9913	0.4956
`STERFGEVALLEN(t-13)`	+0.0485	0.04745	+1.0220e+00	0.3072	0.1536
`STERFGEVALLEN(t-14)`	+0.05214	0.04622	+1.1280e+00	0.2598	0.1299

Multiple Linear Regression - Regression Statistics
Multiple R	0.8337
R-squared	0.6951
Adjusted R-squared	0.6847
F-TEST (value)	66.82
F-TEST (DF numerator)	16
F-TEST (DF denominator)	469
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	20.83
Sum Squared Residuals	2.035e+05

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R &  0.8337 \tabularnewline
R-squared &  0.6951 \tabularnewline
Adjusted R-squared &  0.6847 \tabularnewline
F-TEST (value) &  66.82 \tabularnewline
F-TEST (DF numerator) & 16 \tabularnewline
F-TEST (DF denominator) & 469 \tabularnewline
p-value &  0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation &  20.83 \tabularnewline
Sum Squared Residuals &  2.035e+05 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=284702&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C] 0.8337[/C][/ROW]
[ROW][C]R-squared[/C][C] 0.6951[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C] 0.6847[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C] 66.82[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]16[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]469[/C][/ROW]
[ROW][C]p-value[/C][C] 0[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C] 20.83[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C] 2.035e+05[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=284702&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=284702&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	0.8337
R-squared	0.6951
Adjusted R-squared	0.6847
F-TEST (value)	66.82
F-TEST (DF numerator)	16
F-TEST (DF denominator)	469
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	20.83
Sum Squared Residuals	2.035e+05

Figure 1

PNG link

Postscript link

PDF link

Figure 2

PNG link

Postscript link

PDF link

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 = 14 ;

Parameters (R input):

par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ; par4 = 14 ; par5 = ;

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

library(lattice)
library(lmtest)
n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
mywarning <- ''
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 (par5=='') par5 <- 0
par5 <- as.numeric(par5)
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'){
x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
for (i in 1:n-1) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
}
if(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 (par2 == 'Include Monthly Dummies'){
x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
for (i in 1:11){
x2[seq(i,n,12),i] <- 1
}
x <- cbind(x, x2)
}
if (par2 == 'Include Quarterly Dummies'){
x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
for (i in 1:3){
x2[seq(i,n,4),i] <- 1
}
x <- cbind(x, x2)
}
k <- length(x[1+par4,])
if (par3 == 'Linear Trend'){
x <- cbind(x, c(1:n))
colnames(x)[k+1] <- 't'
}
x
k <- length(x[1+par4,])
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$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
grid()
dev.off()
bitmap(file='test2.png')
hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
grid()
dev.off()
bitmap(file='test3.png')
densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
dev.off()
bitmap(file='test4.png')
qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
qqline(mysum$resid)
grid()
dev.off()
(myerror <- as.ts(mysum$resid))
bitmap(file='test5.png')
dum <- cbind(lag(myerror,k=1),myerror)
dum
dum1 <- dum[2:length(myerror),]
dum1
z <- as.data.frame(dum1)
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$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
}
}
myeq <- paste(myeq, ' + e[t]')
a<-table.row.start(a)
a<-table.element(a, myeq)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, mywarning)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable1.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,hyperlink('ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Variable',header=TRUE)
a<-table.element(a,'Parameter',header=TRUE)
a<-table.element(a,'S.D.',header=TRUE)
a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
a<-table.element(a,'2-tail p-value',header=TRUE)
a<-table.element(a,'1-tail p-value',header=TRUE)
a<-table.row.end(a)
for (i in 1:k){
a<-table.row.start(a)
a<-table.element(a,rownames(mysum$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$sigma,6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
a<-table.element(a,formatC(signif(sum(myerror*myerror),6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
if(n < 200) {
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,formatC(signif(x[i],6),format='g',flag=' '))
a<-table.element(a,formatC(signif(x[i]-mysum$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')
}
}

Free Statistics

Description of Statistical Computation

Tree of Dependent Computations

Dataset

Tables (Output of Computation)

Figures (Output of Computation)

Input Parameters & R Code