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Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationThu, 24 Nov 2011 12:44:40 -0500
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2011/Nov/24/t1322156713qm6byax5fbpm91l.htm/, Retrieved Fri, 29 Mar 2024 07:26:17 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=147120, Retrieved Fri, 29 Mar 2024 07:26:17 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact63
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Multiple Regression] [berekening 1] [2011-11-24 17:44:40] [0cc546ba844126e6dd0cea8c652301ec] [Current]
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Dataseries X:
6	57	1	6	-11
3	57	3	6	-12
10	55	3	3	-10
0	65	1	4	-15
-2	65	1	7	-15
-1	64	0	5	-15
2	60	2	6	-13
8	43	2	1	-8
-6	47	-1	3	-13
-4	40	1	6	-9
4	31	0	0	-7
7	27	1	3	-4
3	24	1	4	-4
3	23	3	7	-2
8	17	2	6	0
3	16	0	6	-2
-3	15	0	6	-3
4	8	-3	6	1
-5	5	-2	2	-2
-1	6	0	2	-1
5	5	1	2	1
0	12	-1	3	-3
-6	8	-2	-1	-4
-13	17	-1	-4	-9
-15	22	-1	4	-9




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time3 seconds
R Server'Herman Ole Andreas Wold' @ wold.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 & 3 seconds \tabularnewline
R Server & 'Herman Ole Andreas Wold' @ wold.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147120&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]3 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Herman Ole Andreas Wold' @ wold.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147120&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147120&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 Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time3 seconds
R Server'Herman Ole Andreas Wold' @ wold.wessa.net







Multiple Linear Regression - Estimated Regression Equation
indicatorconsumentenvertrouwen[t] = + 0.116961332682447 + 0.278786687851773Economische[t] -0.256940649541516werkloosheid[t] + 0.124480906232951`financiële`[t] + 0.2865051862808spaarvermogen[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
indicatorconsumentenvertrouwen[t] =  +  0.116961332682447 +  0.278786687851773Economische[t] -0.256940649541516werkloosheid[t] +  0.124480906232951`financiële`[t] +  0.2865051862808spaarvermogen[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147120&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]indicatorconsumentenvertrouwen[t] =  +  0.116961332682447 +  0.278786687851773Economische[t] -0.256940649541516werkloosheid[t] +  0.124480906232951`financiële`[t] +  0.2865051862808spaarvermogen[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147120&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147120&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
indicatorconsumentenvertrouwen[t] = + 0.116961332682447 + 0.278786687851773Economische[t] -0.256940649541516werkloosheid[t] + 0.124480906232951`financiële`[t] + 0.2865051862808spaarvermogen[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)0.1169613326824470.1504490.77740.4460130.223006
Economische0.2787866878517730.01419919.633800
werkloosheid-0.2569406495415160.003963-64.838700
`financiële`0.1244809062329510.0625881.98890.0605680.030284
spaarvermogen0.28650518628080.02856510.030100

\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) & 0.116961332682447 & 0.150449 & 0.7774 & 0.446013 & 0.223006 \tabularnewline
Economische & 0.278786687851773 & 0.014199 & 19.6338 & 0 & 0 \tabularnewline
werkloosheid & -0.256940649541516 & 0.003963 & -64.8387 & 0 & 0 \tabularnewline
`financiële` & 0.124480906232951 & 0.062588 & 1.9889 & 0.060568 & 0.030284 \tabularnewline
spaarvermogen & 0.2865051862808 & 0.028565 & 10.0301 & 0 & 0 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147120&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]0.116961332682447[/C][C]0.150449[/C][C]0.7774[/C][C]0.446013[/C][C]0.223006[/C][/ROW]
[ROW][C]Economische[/C][C]0.278786687851773[/C][C]0.014199[/C][C]19.6338[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]werkloosheid[/C][C]-0.256940649541516[/C][C]0.003963[/C][C]-64.8387[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]`financiële`[/C][C]0.124480906232951[/C][C]0.062588[/C][C]1.9889[/C][C]0.060568[/C][C]0.030284[/C][/ROW]
[ROW][C]spaarvermogen[/C][C]0.2865051862808[/C][C]0.028565[/C][C]10.0301[/C][C]0[/C][C]0[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147120&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147120&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
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)0.1169613326824470.1504490.77740.4460130.223006
Economische0.2787866878517730.01419919.633800
werkloosheid-0.2569406495415160.003963-64.838700
`financiële`0.1244809062329510.0625881.98890.0605680.030284
spaarvermogen0.28650518628080.02856510.030100







Multiple Linear Regression - Regression Statistics
Multiple R0.99824477810679
R-squared0.996492637017474
Adjusted R-squared0.995791164420969
F-TEST (value)1420.57243858422
F-TEST (DF numerator)4
F-TEST (DF denominator)20
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.343432092816606
Sum Squared Residuals2.35891204752788

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.99824477810679 \tabularnewline
R-squared & 0.996492637017474 \tabularnewline
Adjusted R-squared & 0.995791164420969 \tabularnewline
F-TEST (value) & 1420.57243858422 \tabularnewline
F-TEST (DF numerator) & 4 \tabularnewline
F-TEST (DF denominator) & 20 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 0.343432092816606 \tabularnewline
Sum Squared Residuals & 2.35891204752788 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147120&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.99824477810679[/C][/ROW]
[ROW][C]R-squared[/C][C]0.996492637017474[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.995791164420969[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]1420.57243858422[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]4[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]20[/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]0.343432092816606[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]2.35891204752788[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147120&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147120&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 R0.99824477810679
R-squared0.996492637017474
Adjusted R-squared0.995791164420969
F-TEST (value)1420.57243858422
F-TEST (DF numerator)4
F-TEST (DF denominator)20
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.343432092816606
Sum Squared Residuals2.35891204752788







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
1-11-11.01242354015560.0124235401555497
2-12-11.599821791245-0.40017820875503
3-10-9.99394923604193-0.00605076395807319
4-15-15.31367923615990.313679236159916
5-15-15.01173705302110.0117370530210595
6-15-15.17350099442230.173500994422324
7-13-12.7739113339542-0.22608866604576
8-8-8.165726096041840.16572609604184
9-13-12.89693467027-0.103065329730028
10-9-9.432299376467510.432299376467514
11-7-6.73305205169745-0.266947948302554
12-4-3.88493292490071-0.115067075099287
13-4-3.94275254140246-0.0572474585975439
14-2-2.577334520552640.577334520552637
150-0.05274327655843150.0527432765584315
16-2-1.43869787874168-0.561302121258318
17-3-2.8544773563108-0.145522643689198
1810.5221712867433630.477828713256637
19-2-2.23762679418830.237626794188295
20-1-1.130458879856820.130458879856818
2110.9236828030282860.0763171969717143
22-3-2.23129180920629-0.76870819079371
23-4-4.146750989507020.146750989507016
24-9-9.145758302952520.145758302952515
25-9-8.69599343611723-0.304006563882766

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & -11 & -11.0124235401556 & 0.0124235401555497 \tabularnewline
2 & -12 & -11.599821791245 & -0.40017820875503 \tabularnewline
3 & -10 & -9.99394923604193 & -0.00605076395807319 \tabularnewline
4 & -15 & -15.3136792361599 & 0.313679236159916 \tabularnewline
5 & -15 & -15.0117370530211 & 0.0117370530210595 \tabularnewline
6 & -15 & -15.1735009944223 & 0.173500994422324 \tabularnewline
7 & -13 & -12.7739113339542 & -0.22608866604576 \tabularnewline
8 & -8 & -8.16572609604184 & 0.16572609604184 \tabularnewline
9 & -13 & -12.89693467027 & -0.103065329730028 \tabularnewline
10 & -9 & -9.43229937646751 & 0.432299376467514 \tabularnewline
11 & -7 & -6.73305205169745 & -0.266947948302554 \tabularnewline
12 & -4 & -3.88493292490071 & -0.115067075099287 \tabularnewline
13 & -4 & -3.94275254140246 & -0.0572474585975439 \tabularnewline
14 & -2 & -2.57733452055264 & 0.577334520552637 \tabularnewline
15 & 0 & -0.0527432765584315 & 0.0527432765584315 \tabularnewline
16 & -2 & -1.43869787874168 & -0.561302121258318 \tabularnewline
17 & -3 & -2.8544773563108 & -0.145522643689198 \tabularnewline
18 & 1 & 0.522171286743363 & 0.477828713256637 \tabularnewline
19 & -2 & -2.2376267941883 & 0.237626794188295 \tabularnewline
20 & -1 & -1.13045887985682 & 0.130458879856818 \tabularnewline
21 & 1 & 0.923682803028286 & 0.0763171969717143 \tabularnewline
22 & -3 & -2.23129180920629 & -0.76870819079371 \tabularnewline
23 & -4 & -4.14675098950702 & 0.146750989507016 \tabularnewline
24 & -9 & -9.14575830295252 & 0.145758302952515 \tabularnewline
25 & -9 & -8.69599343611723 & -0.304006563882766 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147120&T=4

[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]-11[/C][C]-11.0124235401556[/C][C]0.0124235401555497[/C][/ROW]
[ROW][C]2[/C][C]-12[/C][C]-11.599821791245[/C][C]-0.40017820875503[/C][/ROW]
[ROW][C]3[/C][C]-10[/C][C]-9.99394923604193[/C][C]-0.00605076395807319[/C][/ROW]
[ROW][C]4[/C][C]-15[/C][C]-15.3136792361599[/C][C]0.313679236159916[/C][/ROW]
[ROW][C]5[/C][C]-15[/C][C]-15.0117370530211[/C][C]0.0117370530210595[/C][/ROW]
[ROW][C]6[/C][C]-15[/C][C]-15.1735009944223[/C][C]0.173500994422324[/C][/ROW]
[ROW][C]7[/C][C]-13[/C][C]-12.7739113339542[/C][C]-0.22608866604576[/C][/ROW]
[ROW][C]8[/C][C]-8[/C][C]-8.16572609604184[/C][C]0.16572609604184[/C][/ROW]
[ROW][C]9[/C][C]-13[/C][C]-12.89693467027[/C][C]-0.103065329730028[/C][/ROW]
[ROW][C]10[/C][C]-9[/C][C]-9.43229937646751[/C][C]0.432299376467514[/C][/ROW]
[ROW][C]11[/C][C]-7[/C][C]-6.73305205169745[/C][C]-0.266947948302554[/C][/ROW]
[ROW][C]12[/C][C]-4[/C][C]-3.88493292490071[/C][C]-0.115067075099287[/C][/ROW]
[ROW][C]13[/C][C]-4[/C][C]-3.94275254140246[/C][C]-0.0572474585975439[/C][/ROW]
[ROW][C]14[/C][C]-2[/C][C]-2.57733452055264[/C][C]0.577334520552637[/C][/ROW]
[ROW][C]15[/C][C]0[/C][C]-0.0527432765584315[/C][C]0.0527432765584315[/C][/ROW]
[ROW][C]16[/C][C]-2[/C][C]-1.43869787874168[/C][C]-0.561302121258318[/C][/ROW]
[ROW][C]17[/C][C]-3[/C][C]-2.8544773563108[/C][C]-0.145522643689198[/C][/ROW]
[ROW][C]18[/C][C]1[/C][C]0.522171286743363[/C][C]0.477828713256637[/C][/ROW]
[ROW][C]19[/C][C]-2[/C][C]-2.2376267941883[/C][C]0.237626794188295[/C][/ROW]
[ROW][C]20[/C][C]-1[/C][C]-1.13045887985682[/C][C]0.130458879856818[/C][/ROW]
[ROW][C]21[/C][C]1[/C][C]0.923682803028286[/C][C]0.0763171969717143[/C][/ROW]
[ROW][C]22[/C][C]-3[/C][C]-2.23129180920629[/C][C]-0.76870819079371[/C][/ROW]
[ROW][C]23[/C][C]-4[/C][C]-4.14675098950702[/C][C]0.146750989507016[/C][/ROW]
[ROW][C]24[/C][C]-9[/C][C]-9.14575830295252[/C][C]0.145758302952515[/C][/ROW]
[ROW][C]25[/C][C]-9[/C][C]-8.69599343611723[/C][C]-0.304006563882766[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147120&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147120&T=4

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 IndexActualsInterpolationForecastResidualsPrediction Error
1-11-11.01242354015560.0124235401555497
2-12-11.599821791245-0.40017820875503
3-10-9.99394923604193-0.00605076395807319
4-15-15.31367923615990.313679236159916
5-15-15.01173705302110.0117370530210595
6-15-15.17350099442230.173500994422324
7-13-12.7739113339542-0.22608866604576
8-8-8.165726096041840.16572609604184
9-13-12.89693467027-0.103065329730028
10-9-9.432299376467510.432299376467514
11-7-6.73305205169745-0.266947948302554
12-4-3.88493292490071-0.115067075099287
13-4-3.94275254140246-0.0572474585975439
14-2-2.577334520552640.577334520552637
150-0.05274327655843150.0527432765584315
16-2-1.43869787874168-0.561302121258318
17-3-2.8544773563108-0.145522643689198
1810.5221712867433630.477828713256637
19-2-2.23762679418830.237626794188295
20-1-1.130458879856820.130458879856818
2110.9236828030282860.0763171969717143
22-3-2.23129180920629-0.76870819079371
23-4-4.146750989507020.146750989507016
24-9-9.145758302952520.145758302952515
25-9-8.69599343611723-0.304006563882766



Parameters (Session):
par1 = 5 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
Parameters (R input):
par1 = 5 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
R code (references can be found in the software module):
library(lattice)
library(lmtest)
n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
par1 <- as.numeric(par1)
x <- 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 (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,])
if (par3 == 'Linear Trend'){
x <- cbind(x, c(1:n))
colnames(x)[k+1] <- 't'
}
x
k <- length(x[1,])
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$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ')
if (rownames(mysum$coefficients)[i] != '(Intercept)') {
myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
}
}
myeq <- paste(myeq, ' + e[t]')
a<-table.row.start(a)
a<-table.element(a, myeq)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable1.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,hyperlink('ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Variable',header=TRUE)
a<-table.element(a,'Parameter',header=TRUE)
a<-table.element(a,'S.D.',header=TRUE)
a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
a<-table.element(a,'2-tail p-value',header=TRUE)
a<-table.element(a,'1-tail p-value',header=TRUE)
a<-table.row.end(a)
for (i in 1:k){
a<-table.row.start(a)
a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
a<-table.element(a,mysum$coefficients[i,1])
a<-table.element(a, round(mysum$coefficients[i,2],6))
a<-table.element(a, round(mysum$coefficients[i,3],4))
a<-table.element(a, round(mysum$coefficients[i,4],6))
a<-table.element(a, round(mysum$coefficients[i,4]/2,6))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable2.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple R',1,TRUE)
a<-table.element(a, sqrt(mysum$r.squared))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'R-squared',1,TRUE)
a<-table.element(a, mysum$r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Adjusted R-squared',1,TRUE)
a<-table.element(a, mysum$adj.r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (value)',1,TRUE)
a<-table.element(a, mysum$fstatistic[1])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[2])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[3])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'p-value',1,TRUE)
a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]))
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, mysum$sigma)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
a<-table.element(a, sum(myerror*myerror))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,x[i])
a<-table.element(a,x[i]-mysum$resid[i])
a<-table.element(a,mysum$resid[i])
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,gqarr[mypoint-kp3+1,1])
a<-table.element(a,gqarr[mypoint-kp3+1,2])
a<-table.element(a,gqarr[mypoint-kp3+1,3])
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,numsignificant1)
a<-table.element(a,numsignificant1/numgqtests)
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,numsignificant5)
a<-table.element(a,numsignificant5/numgqtests)
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,numsignificant10)
a<-table.element(a,numsignificant10/numgqtests)
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')
}