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Data X:
4.35 0 1 22 48 23 12 41 7 6 6 0 2 2 52 51 6 16 9 12.7 0 1 22 50 16 45 146 7 3 6 2 2 2 16 56 4 16 11 18.1 0 1 22 150 33 37 182 7 4 6 1 2 1 46 67 8 16 12 17.85 0 1 20 154 32 37 192 9 3 6 3 2 2 56 69 5 16 12 16.6 1 0 19 109 37 108 263 7 10 6 3 2 2 52 57 4 12 7 12.6 1 1 20 68 14 10 35 6 4 6 0 0 1 55 56 17 15 12 17.1 0 1 22 194 52 68 439 8 8 5 3 1 2 50 55 4 14 12 19.1 0 0 21 158 75 72 214 8 3 6 2 2 2 59 63 4 15 12 16.1 0 1 21 159 72 143 341 9 5 5 2 0 2 60 67 8 16 10 13.35 0 0 21 67 15 9 58 7 4 6 0 0 1 52 65 4 13 15 18.4 0 0 21 147 29 55 292 6 3 5 2 0 2 44 47 7 10 10 14.7 0 1 21 39 13 17 85 8 5 5 3 0 2 67 76 4 17 15 10.6 0 1 21 100 40 37 200 6 3 6 2 0 2 52 64 4 15 10 12.6 0 1 21 111 19 27 158 6 3 4 0 0 2 55 68 5 18 15 16.2 0 1 22 138 24 37 199 9 4 5 0 0 2 37 64 7 16 9 13.6 0 1 24 101 121 58 297 6 3 6 0 0 1 54 65 4 20 15 18.9 1 1 21 131 93 66 227 9 6 6 1 1 1 72 71 4 16 12 14.1 0 1 22 101 36 21 108 8 6 5 2 2 2 51 63 7 17 13 14.5 0 1 20 114 23 19 86 8 4 6 3 2 2 48 60 11 16 12 16.15 0 0 21 165 85 78 302 9 4 6 0 0 0 60 68 7 15 12 14.75 0 1 24 114 41 35 148 6 4 6 2 0 1 50 72 4 13 8 14.8 0 1 25 111 46 48 178 4 3 4 2 1 2 63 70 4 16 9 12.45 0 1 22 75 18 27 120 8 2 6 1 2 0 33 61 4 16 15 12.65 0 1 21 82 35 43 207 5 5 5 2 0 2 67 61 4 16 12 17.35 0 1 21 121 17 30 157 7 4 6 2 2 2 46 62 4 17 12 8.6 0 1 22 32 4 25 128 9 4 6 2 0 0 54 71 4 20 15 18.4 0 0 23 150 28 69 296 9 4 5 2 0 2 59 71 6 14 11 16.1 0 1 24 117 44 72 323 8 3 4 2 1 1 61 51 8 17 12 11.6 1 1 20 71 10 23 79 6 4 5 2 2 1 33 56 23 6 6 17.75 0 1 22 165 38 13 70 8 2 6 1 1 2 47 70 4 16 14 15.25 0 1 25 154 57 61 146 3 0 0 0 0 0 69 73 8 15 12 17.65 0 1 22 126 23 43 246 8 4 6 2 1 2 52 76 6 16 12 16.35 0 0 21 149 36 51 196 7 6 6 0 2 2 55 68 4 16 12 17.65 0 0 21 145 22 67 199 9 4 4 2 0 1 41 48 7 14 11 13.6 0 1 21 120 40 36 127 4 4 6 0 0 1 73 52 4 16 12 14.35 0 0 22 109 31 44 153 6 4 5 0 1 0 52 60 4 16 12 14.75 0 0 22 132 11 45 299 3 2 1 0 1 0 50 59 4 16 12 18.25 0 1 21 172 38 34 228 8 4 5 3 2 2 51 57 10 14 12 9.9 0 0 22 169 24 36 190 8 3 5 0 0 1 60 79 6 14 8 16 0 1 23 114 37 72 180 9 6 5 2 2 0 56 60 5 16 8 18.25 0 1 21 156 37 39 212 8 6 5 3 0 2 56 60 5 16 12 16.85 0 0 21 172 22 43 269 8 4 5 0 0 2 29 59 4 15 12 14.6 1 1 21 68 15 25 130 9 5 6 2 2 1 66 62 4 16 11 13.85 1 1 19 89 2 56 179 8 4 5 0 1 2 66 59 5 16 10 18.95 0 1 21 167 43 80 243 9 6 6 3 2 2 73 61 5 18 11 15.6 0 0 21 113 31 40 190 7 6 5 2 1 2 55 71 5 15 12 14.85 1 0 19 115 29 73 299 7 9 6 2 1 2 64 57 5 16 13 11.75 1 0 18 78 45 34 121 6 4 5 2 1 0 40 66 4 16 12 18.45 1 0 19 118 25 72 137 8 8 6 3 1 2 46 63 6 16 12 15.9 1 1 21 87 4 42 305 6 5 5 3 0 2 58 69 4 17 10 17.1 0 0 22 173 31 61 157 7 4 5 3 0 0 43 58 4 14 10 16.1 0 1 22 2 -4 23 96 8 4 6 2 2 1 61 59 4 18 11 19.9 1 0 19 162 66 74 183 8 7 6 3 2 1 51 48 9 9 8 10.95 1 1 20 49 61 16 52 7 4 6 1 2 2 50 66 18 15 12 18.45 1 0 19 122 32 66 238 9 8 6 2 1 2 52 73 6 14 9 15.1 1 1 21 96 31 9 40 9 4 6 3 2 1 54 67 5 15 12 15 1 0 19 100 39 41 226 9 3 6 2 0 1 66 61 4 13 9 11.35 1 0 20 82 19 57 190 6 5 6 2 1 2 61 68 11 16 11 15.95 1 1 21 100 31 48 214 8 8 6 2 2 2 80 75 4 20 15 18.1 1 0 19 115 36 51 145 9 4 5 1 0 1 51 62 10 14 8 14.6 1 1 21 141 42 53 119 9 10 6 3 1 0 56 69 6 12 8 15.4 0 1 21 165 21 29 222 8 5 6 2 2 2 56 58 8 15 11 15.4 0 1 21 165 21 29 222 8 5 6 2 2 2 56 60 8 15 11 17.6 1 1 19 110 25 55 159 8 3 6 1 0 2 53 74 6 15 11 13.35 0 1 25 118 32 54 165 8 3 5 1 1 2 47 55 8 16 13 19.1 0 0 21 158 26 43 249 8 3 3 0 0 2 25 62 4 11 7 15.35 1 1 20 146 28 51 125 9 4 4 1 1 1 47 63 4 16 12 7.6 0 0 25 49 32 20 122 6 5 6 1 0 2 46 69 9 7 8 13.4 1 0 19 90 41 79 186 9 5 4 2 1 2 50 58 9 11 8 13.9 1 0 20 121 29 39 148 8 4 6 0 0 0 39 58 5 9 4 19.1 0 1 22 155 33 61 274 8 7 6 3 1 0 51 68 4 15 11 15.25 1 0 19 104 17 55 172 8 5 3 1 0 1 58 72 4 16 10 12.9 1 1 20 147 13 30 84 8 4 4 1 2 0 35 62 15 14 7 16.1 1 0 19 110 32 55 168 9 7 4 3 0 2 58 62 10 15 12 17.35 1 0 19 108 30 22 102 9 7 4 3 0 2 60 65 9 13 11 13.15 1 0 18 113 34 37 106 9 7 4 3 0 2 62 69 7 13 9 12.15 1 0 19 115 59 2 2 8 7 4 3 0 2 63 66 9 12 10 12.6 1 1 21 61 13 38 139 8 7 4 0 0 0 53 72 6 16 8 10.35 1 1 19 60 23 27 95 8 7 6 2 1 2 46 62 4 14 8 15.4 1 1 20 109 10 56 130 3 1 4 1 1 0 67 75 7 16 11 9.6 1 1 20 68 5 25 72 6 2 4 2 1 2 59 58 4 14 12 18.2 1 0 19 111 31 39 141 5 3 2 1 0 2 64 66 7 15 10 13.6 1 0 19 77 19 33 113 4 6 5 1 0 1 38 55 4 10 10 14.85 1 1 22 73 32 43 206 9 8 6 3 2 2 50 47 15 16 12 14.75 0 0 21 151 30 57 268 8 8 6 1 1 1 48 72 4 14 8 14.1 1 0 19 89 25 43 175 3 0 1 0 0 0 48 62 9 16 11 14.9 1 0 19 78 48 23 77 6 3 4 1 0 2 47 64 4 12 8 16.25 1 0 19 110 35 44 125 6 6 5 1 1 2 66 64 4 16 10 19.25 0 1 23 220 67 54 255 9 5 5 2 0 2 47 19 28 16 14 13.6 1 1 19 65 15 28 111 7 7 6 1 0 1 63 50 4 15 9 13.6 0 0 20 141 22 36 132 6 3 5 0 1 2 58 68 4 14 9 15.65 1 0 19 117 18 39 211 9 3 6 2 0 0 44 70 4 16 10 12.75 0 1 22 122 33 16 92 7 4 6 2 0 1 51 79 5 11 13 14.6 1 0 19 63 46 23 76 8 4 5 3 0 2 43 69 4 15 12 9.85 0 1 25 44 24 40 171 8 1 5 0 0 2 55 71 4 18 13 12.65 1 1 19 52 14 24 83 8 5 6 2 0 2 38 48 12 13 8 19.2 1 0 19 131 12 78 266 0 0 0 0 0 0 45 73 4 7 3 16.6 1 1 19 101 38 57 186 6 4 6 1 1 0 50 74 6 7 8 11.2 1 1 20 42 12 37 50 9 6 5 2 2 1 54 66 6 17 12 15.25 0 1 20 152 28 27 117 9 4 6 1 1 2 57 71 5 18 11 11.9 0 0 21 107 41 61 219 6 1 2 0 1 2 60 74 4 15 9 13.2 1 0 19 77 12 27 246 8 3 5 0 0 2 55 78 4 8 12 16.35 0 0 21 154 31 69 279 8 7 5 2 0 2 56 75 4 13 12 12.4 0 1 23 103 33 34 148 5 3 1 0 0 2 49 53 10 13 12 15.85 1 1 19 96 34 44 137 6 5 5 1 1 0 37 60 7 15 10 18.15 0 1 22 175 21 34 181 9 3 5 2 2 2 59 70 4 18 13 11.15 1 1 20 57 20 39 98 9 6 4 2 1 2 46 69 7 16 9 15.65 1 0 18 112 44 51 226 9 9 6 3 0 2 51 65 4 14 12 17.75 0 0 21 143 52 34 234 6 4 5 0 1 2 58 78 4 15 11 7.65 1 0 20 49 7 31 138 4 3 6 0 1 1 64 78 12 19 14 12.35 0 1 21 110 29 13 85 8 9 6 2 2 2 53 59 5 16 11 15.6 0 1 21 131 11 12 66 4 5 6 0 1 0 48 72 8 12 9 19.3 0 0 21 167 26 51 236 5 3 6 3 1 1 51 70 6 16 12 15.2 1 0 19 56 24 24 106 8 6 5 2 0 1 47 63 17 11 8 17.1 0 0 21 137 7 19 135 6 2 6 1 0 1 59 63 4 16 15 15.6 1 1 19 86 60 30 122 8 4 5 3 1 2 62 71 5 15 12 18.4 0 1 21 121 13 81 218 9 5 5 2 1 1 62 74 4 19 14 19.05 0 0 21 149 20 42 199 7 4 5 2 0 1 51 67 5 15 12 18.55 0 0 22 168 52 22 112 4 0 0 0 0 0 64 66 5 14 9 19.1 0 0 21 140 28 85 278 8 2 6 1 1 2 52 62 6 14 9 13.1 1 1 22 88 25 27 94 8 5 6 2 1 2 67 80 4 17 13 12.85 0 1 22 168 39 25 113 8 3 6 2 0 1 50 73 4 16 13 9.5 0 1 22 94 9 22 84 4 0 0 0 0 0 54 67 4 20 15 4.5 0 1 22 51 19 19 86 9 5 5 3 0 2 58 61 6 16 11 11.85 1 0 21 48 13 14 62 8 6 5 1 0 2 56 73 8 9 7 13.6 0 1 22 145 60 45 222 6 3 5 0 1 1 63 74 10 13 10 11.7 0 1 23 66 19 45 167 3 0 0 0 0 0 31 32 4 15 11 12.4 1 1 19 85 34 28 82 7 3 4 0 1 0 65 69 5 19 14 13.35 0 0 22 109 14 51 207 8 5 6 2 1 2 71 69 4 16 14 11.4 1 0 21 63 17 41 184 7 4 4 0 0 2 50 84 4 17 13 14.9 1 1 19 102 45 31 83 7 5 5 2 0 1 57 64 4 16 12 19.9 1 0 19 162 66 74 183 8 7 6 3 2 1 47 58 16 9 8 11.2 1 1 20 86 48 19 89 7 8 6 2 1 2 47 59 7 11 13 14.6 1 1 18 114 29 51 225 7 6 6 1 1 2 57 78 4 14 9 17.6 0 0 21 164 -2 73 237 6 4 5 1 0 1 43 57 4 19 12 14.05 0 1 21 119 51 24 102 8 5 5 1 1 0 41 60 14 13 13 16.1 0 0 20 126 2 61 221 8 5 6 0 1 2 63 68 5 14 11 13.35 0 1 20 132 24 23 128 7 3 6 1 0 2 63 68 5 15 11 11.85 0 1 21 142 40 14 91 9 6 6 0 1 2 56 73 5 15 13 11.95 0 0 21 83 20 54 198 9 3 4 2 0 1 51 69 5 14 12 14.75 1 1 19 94 19 51 204 7 6 5 3 1 1 50 67 7 16 12 15.15 1 0 19 81 16 62 158 7 3 2 1 0 2 22 60 19 17 10 13.2 0 1 21 166 20 36 138 8 7 6 2 2 2 41 65 16 12 9 16.85 1 0 19 110 40 59 226 8 7 6 3 0 2 59 66 4 15 10 7.85 1 1 19 64 27 24 44 6 6 4 3 1 2 56 74 4 17 13 7.7 0 0 24 93 25 26 196 9 5 6 1 1 0 66 81 7 15 13 12.6 1 0 19 104 49 54 83 6 5 5 1 0 1 53 72 9 10 9 7.85 1 1 19 105 39 39 79 5 4 4 0 0 2 42 55 5 16 11 10.95 1 1 20 49 61 16 52 7 4 6 1 2 2 52 49 14 15 12 12.35 1 0 19 88 19 36 105 9 7 6 3 0 2 54 74 4 11 8 9.95 1 1 19 95 67 31 116 6 2 1 0 1 0 44 53 16 16 12 14.9 1 1 19 102 45 31 83 7 5 5 2 0 1 62 64 10 16 12 16.65 1 0 19 99 30 42 196 5 4 5 2 1 0 53 65 5 16 12 13.4 1 1 19 63 8 39 153 9 2 6 2 2 2 50 57 6 14 9 13.95 1 0 19 76 19 25 157 8 5 4 2 0 0 36 51 4 14 12 15.7 1 0 20 109 52 31 75 4 4 3 0 0 2 76 80 4 16 12 16.85 1 1 20 117 22 38 106 9 7 4 3 2 2 66 67 4 16 11 10.95 1 1 19 57 17 31 58 8 6 5 2 2 0 62 70 5 18 12 15.35 1 0 21 120 33 17 75 7 4 5 0 0 0 59 74 4 14 6 12.2 1 1 19 73 34 22 74 8 5 6 2 2 2 47 75 4 20 7 15.1 1 0 19 91 22 55 185 1 0 1 0 0 0 55 70 5 15 10 17.75 1 0 19 108 30 62 265 8 7 6 2 1 2 58 69 4 16 12 15.2 1 1 21 105 25 51 131 8 4 4 2 0 2 60 65 4 16 10 14.6 0 0 22 117 38 30 139 9 5 4 3 0 2 44 55 5 16 12 16.65 1 0 19 119 26 49 196 8 6 5 2 0 1 57 71 8 12 9 8.1 1 1 19 31 13 16 78 9 8 3 2 1 1 45 65 15 8 3
Names of X columns:
TOT programma gender age LFM PRH CH Blogs Calculation Algebraic_Reasoning Graphical_Interpretation Proportionality_and_Ratio Probability_and_Sampling Estimation AMS.I AMS.E AMS.A CONFSTATTOT CONFSOFTTOT
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$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '') myeq <- paste(myeq, signif(mysum$coefficients[i,1],6), sep=' ') if (rownames(mysum$coefficients)[i] != '(Intercept)') { myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='') if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='') } } myeq <- paste(myeq, ' + e[t]') a<-table.row.start(a) a<-table.element(a, myeq) a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable1.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Variable',header=TRUE) a<-table.element(a,'Parameter',header=TRUE) a<-table.element(a,'S.D.',header=TRUE) a<-table.element(a,'T-STAT<br />H0: parameter = 0',header=TRUE) a<-table.element(a,'2-tail p-value',header=TRUE) a<-table.element(a,'1-tail p-value',header=TRUE) a<-table.row.end(a) for (i in 1:k){ a<-table.row.start(a) a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE) a<-table.element(a,signif(mysum$coefficients[i,1],6)) a<-table.element(a, signif(mysum$coefficients[i,2],6)) a<-table.element(a, signif(mysum$coefficients[i,3],4)) a<-table.element(a, signif(mysum$coefficients[i,4],6)) a<-table.element(a, signif(mysum$coefficients[i,4]/2,6)) a<-table.row.end(a) } a<-table.end(a) table.save(a,file='mytable2.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Multiple R',1,TRUE) a<-table.element(a, signif(sqrt(mysum$r.squared),6)) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'R-squared',1,TRUE) a<-table.element(a, signif(mysum$r.squared,6)) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Adjusted R-squared',1,TRUE) a<-table.element(a, signif(mysum$adj.r.squared,6)) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'F-TEST (value)',1,TRUE) a<-table.element(a, signif(mysum$fstatistic[1],6)) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE) a<-table.element(a, signif(mysum$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$fstatistic[3],6)) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'p-value',1,TRUE) a<-table.element(a, signif(1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]),6)) 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, signif(mysum$sigma,6)) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Sum Squared Residuals',1,TRUE) a<-table.element(a, signif(sum(myerror*myerror),6)) a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable3.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Time or Index', 1, TRUE) a<-table.element(a, 'Actuals', 1, TRUE) a<-table.element(a, 'Interpolation<br />Forecast', 1, TRUE) a<-table.element(a, 'Residuals<br />Prediction Error', 1, TRUE) a<-table.row.end(a) for (i in 1:n) { a<-table.row.start(a) a<-table.element(a,i, 1, TRUE) a<-table.element(a,signif(x[i],6)) a<-table.element(a,signif(x[i]-mysum$resid[i],6)) a<-table.element(a,signif(mysum$resid[i],6)) 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,signif(gqarr[mypoint-kp3+1,1],6)) a<-table.element(a,signif(gqarr[mypoint-kp3+1,2],6)) a<-table.element(a,signif(gqarr[mypoint-kp3+1,3],6)) 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,signif(numsignificant1/numgqtests,6)) 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') }
Compute
Summary of computational transaction
Raw Input
view raw input (R code)
Raw Output
view raw output of R engine
Computing time
1 seconds
R Server
Big Analytics Cloud Computing Center
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