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1 41 38 13 12 14 12 53 32 2 39 32 16 11 18 11 83 51 3 30 35 19 15 11 14 66 42 4 31 33 15 6 12 12 67 41 5 34 37 14 13 16 21 76 46 6 35 29 13 10 18 12 78 47 7 39 31 19 12 14 22 53 37 8 34 36 15 14 14 11 80 49 9 36 35 14 12 15 10 74 45 10 37 38 15 9 15 13 76 47 11 38 31 16 10 17 10 79 49 12 36 34 16 12 19 8 54 33 13 38 35 16 12 10 15 67 42 14 39 38 16 11 16 14 54 33 15 33 37 17 15 18 10 87 53 16 32 33 15 12 14 14 58 36 17 36 32 15 10 14 14 75 45 18 38 38 20 12 17 11 88 54 19 39 38 18 11 14 10 64 41 20 32 32 16 12 16 13 57 36 21 32 33 16 11 18 9.5 66 41 22 31 31 16 12 11 14 68 44 23 39 38 19 13 14 12 54 33 24 37 39 16 11 12 14 56 37 25 39 32 17 12 17 11 86 52 26 41 32 17 13 9 9 80 47 27 36 35 16 10 16 11 76 43 28 33 37 15 14 14 15 69 44 29 33 33 16 12 15 14 78 45 30 34 33 14 10 11 13 67 44 31 31 31 15 12 16 9 80 49 32 27 32 12 8 13 15 54 33 33 37 31 14 10 17 10 71 43 34 34 37 16 12 15 11 84 54 35 34 30 14 12 14 13 74 42 36 32 33 10 7 16 8 71 44 37 29 31 10 9 9 20 63 37 38 36 33 14 12 15 12 71 43 39 29 31 16 10 17 10 76 46 40 35 33 16 10 13 10 69 42 41 37 32 16 10 15 9 74 45 42 34 33 14 12 16 14 75 44 43 38 32 20 15 16 8 54 33 44 35 33 14 10 12 14 52 31 45 38 28 14 10 15 11 69 42 46 37 35 11 12 11 13 68 40 47 38 39 14 13 15 9 65 43 48 33 34 15 11 15 11 75 46 49 36 38 16 11 17 15 74 42 50 38 32 14 12 13 11 75 45 51 32 38 16 14 16 10 72 44 52 32 30 14 10 14 14 67 40 53 32 33 12 12 11 18 63 37 54 34 38 16 13 12 14 62 46 55 32 32 9 5 12 11 63 36 56 37 35 14 6 15 14.5 76 47 57 39 34 16 12 16 13 74 45 58 29 34 16 12 15 9 67 42 59 37 36 15 11 12 10 73 43 60 35 34 16 10 12 15 70 43 61 30 28 12 7 8 20 53 32 62 38 34 16 12 13 12 77 45 63 34 35 16 14 11 12 80 48 64 31 35 14 11 14 14 52 31 65 34 31 16 12 15 13 54 33 66 35 37 17 13 10 11 80 49 67 36 35 18 14 11 17 66 42 68 30 27 18 11 12 12 73 41 69 39 40 12 12 15 13 63 38 70 35 37 16 12 15 14 69 42 71 38 36 10 8 14 13 67 44 72 31 38 14 11 16 15 54 33 73 34 39 18 14 15 13 81 48 74 38 41 18 14 15 10 69 40 75 34 27 16 12 13 11 84 50 76 39 30 17 9 12 19 80 49 77 37 37 16 13 17 13 70 43 78 34 31 16 11 13 17 69 44 79 28 31 13 12 15 13 77 47 80 37 27 16 12 13 9 54 33 81 33 36 16 12 15 11 79 46 82 35 37 16 12 15 9 71 45 83 37 33 15 12 16 12 73 43 84 32 34 15 11 15 12 72 44 85 33 31 16 10 14 13 77 47 86 38 39 14 9 15 13 75 45 87 33 34 16 12 14 12 69 42 88 29 32 16 12 13 15 54 33 89 33 33 15 12 7 22 70 43 90 31 36 12 9 17 13 73 46 91 36 32 17 15 13 15 54 33 92 35 41 16 12 15 13 77 46 93 32 28 15 12 14 15 82 48 94 29 30 13 12 13 12.5 80 47 95 39 36 16 10 16 11 80 47 96 37 35 16 13 12 16 69 43 97 35 31 16 9 14 11 78 46 98 37 34 16 12 17 11 81 48 99 32 36 14 10 15 10 76 46 100 38 36 16 14 17 10 76 45 101 37 35 16 11 12 16 73 45 102 36 37 20 15 16 12 85 52 103 32 28 15 11 11 11 66 42 104 33 39 16 11 15 16 79 47 105 40 32 13 12 9 19 68 41 106 38 35 17 12 16 11 76 47 107 41 39 16 12 15 16 71 43 108 36 35 16 11 10 15 54 33 109 43 42 12 7 10 24 46 30 110 30 34 16 12 15 14 85 52 111 31 33 16 14 11 15 74 44 112 32 41 17 11 13 11 88 55 113 32 33 13 11 14 15 38 11 114 37 34 12 10 18 12 76 47 115 37 32 18 13 16 10 86 53 116 33 40 14 13 14 14 54 33 117 34 40 14 8 14 13 67 44 118 33 35 13 11 14 9 69 42 119 38 36 16 12 14 15 90 55 120 33 37 13 11 12 15 54 33 121 31 27 16 13 14 14 76 46 122 38 39 13 12 15 11 89 54 123 37 38 16 14 15 8 76 47 124 36 31 15 13 15 11 73 45 125 31 33 16 15 13 11 79 47 126 39 32 15 10 17 8 90 55 127 44 39 17 11 17 10 74 44 128 33 36 15 9 19 11 81 53 129 35 33 12 11 15 13 72 44 130 32 33 16 10 13 11 71 42 131 28 32 10 11 9 20 66 40 132 40 37 16 8 15 10 77 46 133 27 30 12 11 15 15 65 40 134 37 38 14 12 15 12 74 46 135 32 29 15 12 16 14 85 53 136 28 22 13 9 11 23 54 33 137 34 35 15 11 14 14 63 42 138 30 35 11 10 11 16 54 35 139 35 34 12 8 15 11 64 40 140 31 35 11 9 13 12 69 41 141 32 34 16 8 15 10 54 33 142 30 37 15 9 16 14 84 51 143 30 35 17 15 14 12 86 53 144 31 23 16 11 15 12 77 46 145 40 31 10 8 16 11 89 55 146 32 27 18 13 16 12 76 47 147 36 36 13 12 11 13 60 38 148 32 31 16 12 12 11 75 46 149 35 32 13 9 9 19 73 46 150 38 39 10 7 16 12 85 53 151 42 37 15 13 13 17 79 47 152 34 38 16 9 16 9 71 41 153 35 39 16 6 12 12 72 44 154 38 34 14 8 9 19 69 43 155 33 31 10 8 13 18 78 51 156 36 32 17 15 13 15 54 33 157 32 37 13 6 14 14 69 43 158 33 36 15 9 19 11 81 53 159 34 32 16 11 13 9 84 51 160 32 38 12 8 12 18 84 50 161 34 36 13 8 13 16 69 46 162 27 26 13 10 10 24 66 43 163 31 26 12 8 14 14 81 47 164 38 33 17 14 16 20 82 50 165 34 39 15 10 10 18 72 43 166 24 30 10 8 11 23 54 33 167 30 33 14 11 14 12 78 48 168 26 25 11 12 12 14 74 44 169 34 38 13 12 9 16 82 50 170 27 37 16 12 9 18 73 41 171 37 31 12 5 11 20 55 34 172 36 37 16 12 16 12 72 44 173 41 35 12 10 9 12 78 47 174 29 25 9 7 13 17 59 35 175 36 28 12 12 16 13 72 44 176 32 35 15 11 13 9 78 44 177 37 33 12 8 9 16 68 43 178 30 30 12 9 12 18 69 41 179 31 31 14 10 16 10 67 41 180 38 37 12 9 11 14 74 42 181 36 36 16 12 14 11 54 33 182 35 30 11 6 13 9 67 41 183 31 36 19 15 15 11 70 44 184 38 32 15 12 14 10 80 48 185 22 28 8 12 16 11 89 55 186 32 36 16 12 13 19 76 44 187 36 34 17 11 14 14 74 43 188 39 31 12 7 15 12 87 52 189 28 28 11 7 13 14 54 30 190 32 36 11 5 11 21 61 39 191 32 36 14 12 11 13 38 11 192 38 40 16 12 14 10 75 44 193 32 33 12 3 15 15 69 42 194 35 37 16 11 11 16 62 41 195 32 32 13 10 15 14 72 44 196 37 38 15 12 12 12 70 44 197 34 31 16 9 14 19 79 48 198 33 37 16 12 14 15 87 53 199 33 33 14 9 8 19 62 37 200 26 32 16 12 13 13 77 44 201 30 30 16 12 9 17 69 44 202 24 30 14 10 15 12 69 40 203 34 31 11 9 17 11 75 42 204 34 32 12 12 13 14 54 35 205 33 34 15 8 15 11 72 43 206 34 36 15 11 15 13 74 45 207 35 37 16 11 14 12 85 55 208 35 36 16 12 16 15 52 31 209 36 33 11 10 13 14 70 44 210 34 33 15 10 16 12 84 50 211 34 33 12 12 9 17 64 40 212 41 44 12 12 16 11 84 53 213 32 39 15 11 11 18 87 54 214 30 32 15 8 10 13 79 49 215 35 35 16 12 11 17 67 40 216 28 25 14 10 15 13 65 41 217 33 35 17 11 17 11 85 52 218 39 34 14 10 14 12 83 52 219 36 35 13 8 8 22 61 36 220 36 39 15 12 15 14 82 52 221 35 33 13 12 11 12 76 46 222 38 36 14 10 16 12 58 31 223 33 32 15 12 10 17 72 44 224 31 32 12 9 15 9 72 44 225 34 36 13 9 9 21 38 11 226 32 36 8 6 16 10 78 46 227 31 32 14 10 19 11 54 33 228 33 34 14 9 12 12 63 34 229 34 33 11 9 8 23 66 42 230 34 35 12 9 11 13 70 43 231 34 30 13 6 14 12 71 43 232 33 38 10 10 9 16 67 44 233 32 34 16 6 15 9 58 36 234 41 33 18 14 13 17 72 46 235 34 32 13 10 16 9 72 44 236 36 31 11 10 11 14 70 43 237 37 30 4 6 12 17 76 50 238 36 27 13 12 13 13 50 33 239 29 31 16 12 10 11 72 43 240 37 30 10 7 11 12 72 44 241 27 32 12 8 12 10 88 53 242 35 35 12 11 8 19 53 34 243 28 28 10 3 12 16 58 35 244 35 33 13 6 12 16 66 40 245 37 31 15 10 15 14 82 53 246 29 35 12 8 11 20 69 42 247 32 35 14 9 13 15 68 43 248 36 32 10 9 14 23 44 29 249 19 21 12 8 10 20 56 36 250 21 20 12 9 12 16 53 30 251 31 34 11 7 15 14 70 42 252 33 32 10 7 13 17 78 47 253 36 34 12 6 13 11 71 44 254 33 32 16 9 13 13 72 45 255 37 33 12 10 12 17 68 44 256 34 33 14 11 12 15 67 43 257 35 37 16 12 9 21 75 43 258 31 32 14 8 9 18 62 40 259 37 34 13 11 15 15 67 41 260 35 30 4 3 10 8 83 52 261 27 30 15 11 14 12 64 38 262 34 38 11 12 15 12 68 41 263 40 36 11 7 7 22 62 39 264 29 32 14 9 14 12 72 43
Names of X columns:
t Connected Separate Learning Software Happiness Depression Belonging Belonging_Final
Sample Range:
(leave blank to include all observations)
From:
To:
Column Number of Endogenous Series
(?)
Fixed Seasonal Effects
Do not include Seasonal Dummies
Do not include Seasonal Dummies
Include Seasonal Dummies
Type of Equation
No Linear Trend
No Linear Trend
Linear Trend
First Differences
Seasonal Differences (s)
First and Seasonal Differences (s)
Degree of Predetermination (lagged endogenous variables)
Degree of Seasonal Predetermination
Seasonality
12
1
2
3
4
5
6
7
8
9
10
11
12
Chart options
R Code
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$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='') } } myeq <- paste(myeq, ' + e[t]') a<-table.row.start(a) a<-table.element(a, myeq) a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable1.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Variable',header=TRUE) a<-table.element(a,'Parameter',header=TRUE) a<-table.element(a,'S.D.',header=TRUE) a<-table.element(a,'T-STAT<br />H0: parameter = 0',header=TRUE) a<-table.element(a,'2-tail p-value',header=TRUE) a<-table.element(a,'1-tail p-value',header=TRUE) a<-table.row.end(a) for (i in 1:k){ a<-table.row.start(a) a<-table.element(a,rownames(mysum$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$sigma) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Sum Squared Residuals',1,TRUE) a<-table.element(a, sum(myerror*myerror)) a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable3.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Time or Index', 1, TRUE) a<-table.element(a, 'Actuals', 1, TRUE) a<-table.element(a, 'Interpolation<br />Forecast', 1, TRUE) a<-table.element(a, 'Residuals<br />Prediction Error', 1, TRUE) a<-table.row.end(a) for (i in 1:n) { a<-table.row.start(a) a<-table.element(a,i, 1, TRUE) a<-table.element(a,x[i]) a<-table.element(a,x[i]-mysum$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') }
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Raw Output
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