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Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationMon, 05 Nov 2012 05:03:37 -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/2012/Nov/05/t13521098488ofejsde6t9b2yb.htm/, Retrieved Fri, 03 Feb 2023 11:07:25 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=185975, Retrieved Fri, 03 Feb 2023 11:07:25 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact125
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [Competence to learn] [2010-11-17 07:43:53] [b98453cac15ba1066b407e146608df68]
-   PD    [Multiple Regression] [] [2012-11-05 10:03:37] [7d61013405aa85534cb0146e7095f1e4] [Current]
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Dataseries X:
95	2768	252	22	324	8760219	438465,0625
150	4108	333	29	308	8760195	438474,0625
4	4045	62	5	249	8760168	438480,0625
0	4572	85	8	14	8760135	438489,0625
0	4614	115	10	63	8760105	438495,0625
80	4321	176	16	130	8760072	438498,0625
95	3886	72	6	199	8760039	438504,0625
20	4206	57	5	32	8760012	438507,0625
90	4192	266	23	197	8759985	438513,0625
10	4051	69	6	113	8759955	4385190625
10	3746	62	5	149	8759922	438519,0625
50	3789	42	3	218	8759895	438525,0625
45	3771	44	4	53	8759865	438531,0625
60	3796	48	4	101	8759838	438534,0625
55	3885	77	7	332	8759811	438537,0625
3	4295	113	10	18	8759787	438540,0625
33	4467	147	13	50	8759760	438546,0625
0	4764	12	1	276	8759730	438552,0625
35	4313	38	3	350	8759703	438552,0625
45	4387	40	3	46	8759673	438558,0625




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time6 seconds
R Server'Sir Maurice George Kendall' @ kendall.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 & 6 seconds \tabularnewline
R Server & 'Sir Maurice George Kendall' @ kendall.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185975&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]6 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Sir Maurice George Kendall' @ kendall.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185975&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185975&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 time6 seconds
R Server'Sir Maurice George Kendall' @ kendall.wessa.net







Multiple Linear Regression - Estimated Regression Equation
Sneeuwhoogte[t] = + 333606.354642066 -0.0243680823015372`hoogte(berg)`[t] + 0.920194778952218ruwheid[t] -6.77604704872255helling[t] + 0.0622850543689997Orientering[t] -0.0380717709913641breedtegraad[t] -4.67807588830331e-09lengte[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Sneeuwhoogte[t] =  +  333606.354642066 -0.0243680823015372`hoogte(berg)`[t] +  0.920194778952218ruwheid[t] -6.77604704872255helling[t] +  0.0622850543689997Orientering[t] -0.0380717709913641breedtegraad[t] -4.67807588830331e-09lengte[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185975&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Sneeuwhoogte[t] =  +  333606.354642066 -0.0243680823015372`hoogte(berg)`[t] +  0.920194778952218ruwheid[t] -6.77604704872255helling[t] +  0.0622850543689997Orientering[t] -0.0380717709913641breedtegraad[t] -4.67807588830331e-09lengte[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185975&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185975&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
Sneeuwhoogte[t] = + 333606.354642066 -0.0243680823015372`hoogte(berg)`[t] + 0.920194778952218ruwheid[t] -6.77604704872255helling[t] + 0.0622850543689997Orientering[t] -0.0380717709913641breedtegraad[t] -4.67807588830331e-09lengte[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)333606.354642066438413.8408460.76090.4602740.230137
`hoogte(berg)`-0.02436808230153720.018019-1.35240.1993180.099659
ruwheid0.9201947789522182.0369870.45170.6588990.329449
helling-6.7760470487225523.040433-0.29410.7733290.386665
Orientering0.06228505436899970.0674630.92320.3726980.186349
breedtegraad-0.03807177099136410.050046-0.76070.4603890.230195
lengte-4.67807588830331e-090-0.66660.5166650.258333

\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) & 333606.354642066 & 438413.840846 & 0.7609 & 0.460274 & 0.230137 \tabularnewline
`hoogte(berg)` & -0.0243680823015372 & 0.018019 & -1.3524 & 0.199318 & 0.099659 \tabularnewline
ruwheid & 0.920194778952218 & 2.036987 & 0.4517 & 0.658899 & 0.329449 \tabularnewline
helling & -6.77604704872255 & 23.040433 & -0.2941 & 0.773329 & 0.386665 \tabularnewline
Orientering & 0.0622850543689997 & 0.067463 & 0.9232 & 0.372698 & 0.186349 \tabularnewline
breedtegraad & -0.0380717709913641 & 0.050046 & -0.7607 & 0.460389 & 0.230195 \tabularnewline
lengte & -4.67807588830331e-09 & 0 & -0.6666 & 0.516665 & 0.258333 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185975&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]333606.354642066[/C][C]438413.840846[/C][C]0.7609[/C][C]0.460274[/C][C]0.230137[/C][/ROW]
[ROW][C]`hoogte(berg)`[/C][C]-0.0243680823015372[/C][C]0.018019[/C][C]-1.3524[/C][C]0.199318[/C][C]0.099659[/C][/ROW]
[ROW][C]ruwheid[/C][C]0.920194778952218[/C][C]2.036987[/C][C]0.4517[/C][C]0.658899[/C][C]0.329449[/C][/ROW]
[ROW][C]helling[/C][C]-6.77604704872255[/C][C]23.040433[/C][C]-0.2941[/C][C]0.773329[/C][C]0.386665[/C][/ROW]
[ROW][C]Orientering[/C][C]0.0622850543689997[/C][C]0.067463[/C][C]0.9232[/C][C]0.372698[/C][C]0.186349[/C][/ROW]
[ROW][C]breedtegraad[/C][C]-0.0380717709913641[/C][C]0.050046[/C][C]-0.7607[/C][C]0.460389[/C][C]0.230195[/C][/ROW]
[ROW][C]lengte[/C][C]-4.67807588830331e-09[/C][C]0[/C][C]-0.6666[/C][C]0.516665[/C][C]0.258333[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185975&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185975&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)333606.354642066438413.8408460.76090.4602740.230137
`hoogte(berg)`-0.02436808230153720.018019-1.35240.1993180.099659
ruwheid0.9201947789522182.0369870.45170.6588990.329449
helling-6.7760470487225523.040433-0.29410.7733290.386665
Orientering0.06228505436899970.0674630.92320.3726980.186349
breedtegraad-0.03807177099136410.050046-0.76070.4603890.230195
lengte-4.67807588830331e-090-0.66660.5166650.258333







Multiple Linear Regression - Regression Statistics
Multiple R0.805791839803273
R-squared0.649300489093543
Adjusted R-squared0.487439176367486
F-TEST (value)4.01146190005548
F-TEST (DF numerator)6
F-TEST (DF denominator)13
p-value0.017073352218789
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation29.5844227876884
Sum Squared Residuals11378.0949318491

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.805791839803273 \tabularnewline
R-squared & 0.649300489093543 \tabularnewline
Adjusted R-squared & 0.487439176367486 \tabularnewline
F-TEST (value) & 4.01146190005548 \tabularnewline
F-TEST (DF numerator) & 6 \tabularnewline
F-TEST (DF denominator) & 13 \tabularnewline
p-value & 0.017073352218789 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 29.5844227876884 \tabularnewline
Sum Squared Residuals & 11378.0949318491 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185975&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.805791839803273[/C][/ROW]
[ROW][C]R-squared[/C][C]0.649300489093543[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.487439176367486[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]4.01146190005548[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]6[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]13[/C][/ROW]
[ROW][C]p-value[/C][C]0.017073352218789[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]29.5844227876884[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]11378.0949318491[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185975&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185975&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 R0.805791839803273
R-squared0.649300489093543
Adjusted R-squared0.487439176367486
F-TEST (value)4.01146190005548
F-TEST (DF numerator)6
F-TEST (DF denominator)13
p-value0.017073352218789
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation29.5844227876884
Sum Squared Residuals11378.0949318491







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
195124.846543725242-29.8465437252424
2150119.21392278708230.7860772129185
3431.3545756262958-27.3545756262958
405.96831564701642-5.96831564701642
5023.1927262272268-23.1927262272268
68051.23764063673128.762359363269
79539.45200708020255.547992919798
82015.2536798312594.74632016874101
99097.2516666670716-7.25166666707162
101010.0000000779878-7.79877778246153e-08
111041.7777822789861-31.7777822789861
125041.203759798588.79624020141997
134527.571846919977117.4281530800229
146034.661044390692125.3389556093079
155554.26557787127360.734422128726433
16338.4297504416481-35.4297504416481
173348.2179811524978-15.2179811524978
18013.2855055241288-13.2855055241288
193540.2855126375075-5.28551263750749
204522.530160678594722.4698393214053

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 95 & 124.846543725242 & -29.8465437252424 \tabularnewline
2 & 150 & 119.213922787082 & 30.7860772129185 \tabularnewline
3 & 4 & 31.3545756262958 & -27.3545756262958 \tabularnewline
4 & 0 & 5.96831564701642 & -5.96831564701642 \tabularnewline
5 & 0 & 23.1927262272268 & -23.1927262272268 \tabularnewline
6 & 80 & 51.237640636731 & 28.762359363269 \tabularnewline
7 & 95 & 39.452007080202 & 55.547992919798 \tabularnewline
8 & 20 & 15.253679831259 & 4.74632016874101 \tabularnewline
9 & 90 & 97.2516666670716 & -7.25166666707162 \tabularnewline
10 & 10 & 10.0000000779878 & -7.79877778246153e-08 \tabularnewline
11 & 10 & 41.7777822789861 & -31.7777822789861 \tabularnewline
12 & 50 & 41.20375979858 & 8.79624020141997 \tabularnewline
13 & 45 & 27.5718469199771 & 17.4281530800229 \tabularnewline
14 & 60 & 34.6610443906921 & 25.3389556093079 \tabularnewline
15 & 55 & 54.2655778712736 & 0.734422128726433 \tabularnewline
16 & 3 & 38.4297504416481 & -35.4297504416481 \tabularnewline
17 & 33 & 48.2179811524978 & -15.2179811524978 \tabularnewline
18 & 0 & 13.2855055241288 & -13.2855055241288 \tabularnewline
19 & 35 & 40.2855126375075 & -5.28551263750749 \tabularnewline
20 & 45 & 22.5301606785947 & 22.4698393214053 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185975&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]95[/C][C]124.846543725242[/C][C]-29.8465437252424[/C][/ROW]
[ROW][C]2[/C][C]150[/C][C]119.213922787082[/C][C]30.7860772129185[/C][/ROW]
[ROW][C]3[/C][C]4[/C][C]31.3545756262958[/C][C]-27.3545756262958[/C][/ROW]
[ROW][C]4[/C][C]0[/C][C]5.96831564701642[/C][C]-5.96831564701642[/C][/ROW]
[ROW][C]5[/C][C]0[/C][C]23.1927262272268[/C][C]-23.1927262272268[/C][/ROW]
[ROW][C]6[/C][C]80[/C][C]51.237640636731[/C][C]28.762359363269[/C][/ROW]
[ROW][C]7[/C][C]95[/C][C]39.452007080202[/C][C]55.547992919798[/C][/ROW]
[ROW][C]8[/C][C]20[/C][C]15.253679831259[/C][C]4.74632016874101[/C][/ROW]
[ROW][C]9[/C][C]90[/C][C]97.2516666670716[/C][C]-7.25166666707162[/C][/ROW]
[ROW][C]10[/C][C]10[/C][C]10.0000000779878[/C][C]-7.79877778246153e-08[/C][/ROW]
[ROW][C]11[/C][C]10[/C][C]41.7777822789861[/C][C]-31.7777822789861[/C][/ROW]
[ROW][C]12[/C][C]50[/C][C]41.20375979858[/C][C]8.79624020141997[/C][/ROW]
[ROW][C]13[/C][C]45[/C][C]27.5718469199771[/C][C]17.4281530800229[/C][/ROW]
[ROW][C]14[/C][C]60[/C][C]34.6610443906921[/C][C]25.3389556093079[/C][/ROW]
[ROW][C]15[/C][C]55[/C][C]54.2655778712736[/C][C]0.734422128726433[/C][/ROW]
[ROW][C]16[/C][C]3[/C][C]38.4297504416481[/C][C]-35.4297504416481[/C][/ROW]
[ROW][C]17[/C][C]33[/C][C]48.2179811524978[/C][C]-15.2179811524978[/C][/ROW]
[ROW][C]18[/C][C]0[/C][C]13.2855055241288[/C][C]-13.2855055241288[/C][/ROW]
[ROW][C]19[/C][C]35[/C][C]40.2855126375075[/C][C]-5.28551263750749[/C][/ROW]
[ROW][C]20[/C][C]45[/C][C]22.5301606785947[/C][C]22.4698393214053[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185975&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185975&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
195124.846543725242-29.8465437252424
2150119.21392278708230.7860772129185
3431.3545756262958-27.3545756262958
405.96831564701642-5.96831564701642
5023.1927262272268-23.1927262272268
68051.23764063673128.762359363269
79539.45200708020255.547992919798
82015.2536798312594.74632016874101
99097.2516666670716-7.25166666707162
101010.0000000779878-7.79877778246153e-08
111041.7777822789861-31.7777822789861
125041.203759798588.79624020141997
134527.571846919977117.4281530800229
146034.661044390692125.3389556093079
155554.26557787127360.734422128726433
16338.4297504416481-35.4297504416481
173348.2179811524978-15.2179811524978
18013.2855055241288-13.2855055241288
193540.2855126375075-5.28551263750749
204522.530160678594722.4698393214053



Parameters (Session):
par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
Parameters (R input):
par1 = 1 ; 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')
}