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Data X:
1 132 132 15 10 10 77 77 5 5 4 4 15 15 11 11 12 12 13 13 6 6 0 24 0 12 20 0 63 0 6 0 4 0 9 0 12 0 7 0 11 0 4 0 0 135 0 15 16 0 73 0 4 0 10 0 12 0 12 0 13 0 14 0 6 0 0 95 0 12 10 0 76 0 6 0 6 0 15 0 11 0 11 0 12 0 5 0 0 122 0 14 8 0 90 0 3 0 5 0 17 0 11 0 16 0 12 0 5 0 0 144 0 8 14 0 67 0 10 0 8 0 14 0 10 0 10 0 6 0 4 0 1 23 23 11 19 19 69 69 8 8 9 9 9 9 11 11 15 15 10 10 5 5 1 42 42 15 15 15 70 70 3 3 6 6 12 12 9 9 5 5 11 11 3 3 0 105 0 4 23 0 54 0 4 0 8 0 11 0 10 0 4 0 10 0 2 0 0 68 0 13 9 0 54 0 3 0 11 0 13 0 12 0 7 0 12 0 5 0 1 139 139 19 12 12 76 76 5 5 6 6 16 16 12 12 15 15 15 15 6 6 1 51 51 10 14 14 75 75 5 5 8 8 16 16 12 12 5 5 13 13 6 6 1 7 7 15 13 13 76 76 6 6 11 11 15 15 13 13 16 16 18 18 8 8 0 52 0 6 11 0 80 0 5 0 5 0 10 0 9 0 15 0 11 0 6 0 1 138 138 7 11 11 89 89 3 3 10 10 16 16 12 12 13 13 12 12 3 3 0 41 0 14 10 0 73 0 4 0 7 0 12 0 12 0 13 0 13 0 6 0 0 125 0 16 12 0 74 0 8 0 7 0 15 0 12 0 15 0 14 0 6 0 1 152 152 16 18 18 78 78 8 8 13 13 13 13 12 12 15 15 16 16 7 7 1 106 106 14 12 12 76 76 8 8 10 10 18 18 13 13 10 10 16 16 8 8 0 26 0 15 10 0 69 0 5 0 8 0 13 0 11 0 17 0 16 0 6 0 1 33 33 14 15 15 74 74 8 8 6 6 17 17 12 12 14 14 15 15 7 7 1 136 136 12 15 15 82 82 2 2 8 8 14 14 12 12 9 9 13 13 4 4 0 48 0 9 12 0 77 0 0 0 7 0 13 0 15 0 6 0 8 0 4 0 1 156 156 12 9 9 84 84 5 5 5 5 13 13 11 11 11 11 14 14 2 2 1 114 114 14 11 11 75 75 2 2 9 9 15 15 12 12 13 13 15 15 6 6 1 75 75 12 15 15 54 54 7 7 9 9 13 13 10 10 12 12 13 13 6 6 1 91 91 14 16 16 79 79 5 5 11 11 15 15 11 11 10 10 16 16 6 6 1 146 146 10 17 17 79 79 2 2 11 11 13 13 13 13 4 4 13 13 6 6 1 82 82 14 12 12 69 69 12 12 11 11 14 14 6 6 13 13 12 12 6 6 1 102 102 16 11 11 88 88 7 7 9 9 13 13 12 12 15 15 15 15 7 7 1 96 96 10 13 13 57 57 0 0 7 7 16 16 12 12 8 8 11 11 4 4 1 109 109 8 9 9 69 69 2 2 6 6 14 14 10 10 10 10 14 14 3 3 1 2 2 12 11 11 86 86 3 3 6 6 18 18 12 12 8 8 13 13 5 5 1 113 113 11 9 9 65 65 0 0 6 6 15 15 12 12 7 7 13 13 6 6 0 123 0 8 20 0 66 0 9 0 5 0 9 0 11 0 9 0 12 0 4 0 0 29 0 13 8 0 54 0 2 0 4 0 16 0 9 0 14 0 14 0 6 0 1 104 104 11 12 12 85 85 3 3 10 10 16 16 10 10 5 5 13 13 3 3 0 6 0 12 10 0 79 0 1 0 8 0 17 0 12 0 7 0 12 0 3 0 0 62 0 16 11 0 84 0 10 0 6 0 13 0 12 0 16 0 14 0 6 0 1 64 64 16 13 13 70 70 1 1 5 5 17 17 11 11 14 14 15 15 6 6 1 50 50 13 13 13 54 54 4 4 9 9 15 15 12 12 16 16 16 16 6 6 1 108 108 14 13 13 70 70 6 6 10 10 14 14 11 11 15 15 15 15 8 8 0 70 0 5 15 0 54 0 6 0 6 0 10 0 14 0 4 0 5 0 2 0 0 154 0 14 12 0 69 0 4 0 9 0 13 0 10 0 12 0 15 0 6 0 1 31 31 13 13 13 68 68 4 4 10 10 11 11 10 10 8 8 8 8 4 4 1 101 101 16 13 13 68 68 7 7 6 6 11 11 11 11 17 17 16 16 7 7 0 149 0 14 9 0 71 0 7 0 6 0 16 0 11 0 15 0 16 0 6 0 0 149 0 15 9 0 71 0 7 0 6 0 16 0 11 0 16 0 14 0 6 0 1 3 3 15 14 14 66 66 0 0 13 13 11 11 10 10 12 12 16 16 6 6 1 111 111 11 9 9 67 67 3 3 8 8 15 15 10 10 12 12 14 14 5 5 1 69 69 15 9 9 71 71 8 8 10 10 15 15 12 12 13 13 13 13 6 6 1 116 116 16 15 15 54 54 8 8 5 5 12 12 11 11 14 14 14 14 6 6 1 28 28 13 10 10 76 76 10 10 8 8 17 17 8 8 14 14 14 14 5 5 0 67 0 11 13 0 77 0 11 0 6 0 15 0 12 0 15 0 12 0 6 0 0 32 0 12 8 0 71 0 6 0 9 0 16 0 10 0 14 0 13 0 7 0 1 88 88 12 15 15 69 69 2 2 9 9 14 14 7 7 11 11 15 15 5 5 1 92 92 10 13 13 73 73 6 6 7 7 17 17 11 11 13 13 15 15 6 6 1 97 97 8 24 24 46 46 1 1 20 20 10 10 7 7 4 4 13 13 6 6 0 87 0 9 11 0 66 0 5 0 8 0 11 0 11 0 8 0 10 0 4 0 1 78 78 12 13 13 77 77 4 4 8 8 15 15 8 8 13 13 13 13 5 5 0 137 0 14 12 0 77 0 6 0 7 0 15 0 11 0 15 0 14 0 6 0 1 76 76 12 22 22 70 70 6 6 7 7 7 7 12 12 15 15 13 13 6 6 0 34 0 11 11 0 86 0 4 0 10 0 17 0 8 0 8 0 13 0 4 0 0 103 0 14 15 0 38 0 1 0 5 0 14 0 14 0 17 0 18 0 6 0 0 14 0 7 7 0 66 0 6 0 8 0 18 0 14 0 12 0 12 0 4 0 0 46 0 16 14 0 75 0 7 0 9 0 14 0 11 0 13 0 14 0 7 0 1 127 127 16 19 19 80 80 7 7 9 9 12 12 12 12 14 14 16 16 8 8 0 15 0 11 10 0 64 0 2 0 20 0 14 0 14 0 7 0 13 0 6 0 1 58 58 16 9 9 80 80 7 7 6 6 9 9 9 9 16 16 16 16 6 6 1 134 134 13 12 12 86 86 8 8 10 10 14 14 13 13 11 11 15 15 6 6 1 129 129 11 16 16 54 54 5 5 11 11 11 11 8 8 10 10 14 14 5 5 1 39 39 13 13 13 74 74 4 4 7 7 16 16 11 11 14 14 13 13 6 6 1 63 63 14 11 11 88 88 2 2 12 12 17 17 9 9 19 19 12 12 6 6 1 143 143 15 12 12 85 85 0 0 12 12 16 16 12 12 14 14 16 16 4 4 0 38 0 10 11 0 63 0 7 0 8 0 12 0 7 0 8 0 9 0 5 0 1 60 60 15 13 13 81 81 0 0 6 6 15 15 11 11 15 15 15 15 8 8 0 118 0 11 13 0 81 0 5 0 6 0 15 0 12 0 8 0 16 0 6 0 1 45 45 11 10 10 74 74 3 3 9 9 15 15 11 11 8 8 12 12 6 6 1 80 80 6 11 11 80 80 3 3 5 5 16 16 12 12 6 6 11 11 2 2 1 21 21 11 9 9 80 80 3 3 11 11 16 16 9 9 7 7 13 13 2 2 0 141 0 12 13 0 60 0 3 0 6 0 11 0 11 0 16 0 13 0 4 0 0 124 0 13 15 0 65 0 7 0 6 0 15 0 13 0 15 0 14 0 6 0 1 37 37 12 14 14 62 62 6 6 10 10 12 12 12 12 10 10 15 15 6 6 0 147 0 8 14 0 63 0 3 0 8 0 14 0 12 0 8 0 14 0 5 0 1 153 153 9 11 11 89 89 0 0 7 7 15 15 11 11 9 9 12 12 4 4 1 133 133 10 10 10 76 76 2 2 8 8 17 17 12 12 8 8 16 16 4 4 1 117 117 16 11 11 81 81 0 0 9 9 19 19 12 12 14 14 14 14 6 6 1 71 71 15 12 12 72 72 9 9 8 8 15 15 11 11 14 14 13 13 5 5 0 155 0 14 14 0 84 0 10 0 10 0 16 0 11 0 14 0 12 0 6 0 1 112 112 12 14 14 76 76 3 3 13 13 14 14 8 8 15 15 13 13 7 7 1 4 4 12 21 21 76 76 7 7 7 7 16 16 9 9 7 7 12 12 6 6 1 19 19 10 14 14 78 78 3 3 7 7 15 15 11 11 7 7 9 9 4 4 1 121 121 12 13 13 72 72 6 6 7 7 15 15 12 12 12 12 13 13 4 4 0 98 0 8 11 0 81 0 5 0 8 0 17 0 13 0 7 0 10 0 3 0 1 150 150 16 12 12 72 72 0 0 9 9 12 12 12 12 12 12 15 15 8 8 1 10 10 11 12 12 78 78 0 0 9 9 18 18 6 6 6 6 9 9 4 4 1 145 145 12 11 11 79 79 4 4 8 8 13 13 12 12 10 10 13 13 4 4 1 49 49 9 14 14 52 52 0 0 7 7 14 14 11 11 12 12 13 13 5 5 0 55 0 14 13 0 67 0 0 0 6 0 14 0 13 0 13 0 13 0 5 0 0 22 0 15 13 0 74 0 7 0 8 0 14 0 11 0 14 0 15 0 7 0 0 54 0 8 12 0 73 0 3 0 8 0 12 0 12 0 8 0 13 0 4 0 1 140 140 12 14 14 69 69 9 9 4 4 14 14 10 10 14 14 14 14 5 5 0 5 0 10 12 0 67 0 4 0 8 0 12 0 10 0 10 0 11 0 5 0 1 89 89 16 12 12 76 76 4 4 10 10 15 15 11 11 14 14 15 15 8 8 1 47 47 17 12 12 77 77 15 15 7 7 11 11 11 11 15 15 14 14 5 5 0 59 0 8 18 0 63 0 7 0 8 0 11 0 11 0 10 0 15 0 2 0 1 65 65 9 11 11 84 84 8 8 7 7 15 15 9 9 6 6 12 12 5 5 1 110 110 8 15 15 90 90 2 2 10 10 14 14 7 7 9 9 15 15 4 4 0 73 0 11 13 0 75 0 8 0 9 0 15 0 11 0 11 0 14 0 5 0 1 93 93 16 11 11 76 76 7 7 8 8 16 16 12 12 16 16 16 16 7 7 0 142 0 13 11 0 75 0 3 0 8 0 12 0 12 0 14 0 14 0 6 0 1 43 43 5 22 22 53 53 3 3 5 5 14 14 15 15 8 8 12 12 3 3 1 13 13 15 10 10 87 87 6 6 8 8 18 18 11 11 16 16 11 11 5 5 1 94 94 15 11 11 78 78 8 8 9 9 14 14 10 10 16 16 13 13 6 6 1 77 77 12 15 15 54 54 5 5 11 11 13 13 13 13 14 14 12 12 5 5 0 86 0 12 14 0 58 0 6 0 7 0 14 0 13 0 12 0 12 0 6 0 1 40 40 16 11 11 80 80 10 10 8 8 14 14 11 11 16 16 16 16 7 7 1 128 128 12 10 10 74 74 0 0 4 4 17 17 12 12 15 15 13 13 6 6 1 17 17 10 14 14 56 56 5 5 16 16 12 12 12 12 11 11 12 12 6 6 1 126 126 12 14 14 82 82 0 0 9 9 16 16 12 12 6 6 14 14 5 5 1 130 130 4 11 11 64 64 0 0 16 16 15 15 8 8 6 6 4 4 4 4 0 11 0 11 15 0 67 0 5 0 12 0 10 0 5 0 16 0 14 0 6 0 0 36 0 16 11 0 75 0 10 0 8 0 13 0 11 0 16 0 15 0 6 0 0 61 0 7 10 0 69 0 0 0 4 0 15 0 12 0 8 0 12 0 3 0 1 35 35 9 10 10 72 72 5 5 11 11 16 16 12 12 11 11 11 11 4 4 0 120 0 14 16 0 71 0 6 0 11 0 15 0 11 0 12 0 12 0 4 0 1 16 16 11 12 12 54 54 1 1 8 8 14 14 12 12 13 13 11 11 4 4 1 84 84 10 14 14 68 68 5 5 8 8 11 11 10 10 11 11 12 12 5 5 0 20 0 6 15 0 54 0 3 0 12 0 13 0 7 0 9 0 11 0 4 0 1 25 25 14 10 10 71 71 3 3 8 8 17 17 12 12 15 15 13 13 6 6 1 12 12 11 12 12 53 53 6 6 6 6 14 14 12 12 11 11 12 12 6 6 1 57 57 11 15 15 54 54 2 2 8 8 16 16 9 9 12 12 12 12 4 4 0 27 0 9 12 0 71 0 5 0 6 0 15 0 11 0 15 0 15 0 7 0 1 30 30 16 11 11 69 69 6 6 14 14 12 12 12 12 8 8 14 14 4 4 0 81 0 7 10 0 30 0 2 0 10 0 16 0 12 0 7 0 12 0 4 0 0 44 0 8 20 0 53 0 3 0 5 0 8 0 11 0 10 0 12 0 4 0 0 90 0 10 19 0 68 0 7 0 8 0 9 0 11 0 9 0 12 0 4 0 1 66 66 14 17 17 69 69 6 6 12 12 13 13 12 12 13 13 13 13 5 5 1 8 8 9 8 8 54 54 3 3 11 11 19 19 12 12 11 11 11 11 4 4 1 151 151 13 17 17 66 66 6 6 8 8 11 11 11 11 12 12 13 13 7 7 0 85 0 13 11 0 79 0 9 0 8 0 15 0 12 0 5 0 12 0 3 0 0 53 0 12 13 0 67 0 2 0 9 0 11 0 12 0 12 0 14 0 5 0 0 99 0 11 9 0 74 0 5 0 6 0 15 0 8 0 14 0 15 0 5 0 0 148 0 10 10 0 86 0 10 0 5 0 16 0 15 0 15 0 15 0 6 0 1 56 56 12 13 13 63 63 9 9 8 8 15 15 11 11 14 14 13 13 5 5 1 83 83 14 16 16 69 69 8 8 7 7 12 12 11 11 13 13 16 16 6 6 0 74 0 11 12 0 73 0 8 0 4 0 16 0 6 0 14 0 17 0 6 0 0 1 0 13 14 0 69 0 5 0 9 0 15 0 13 0 14 0 13 0 3 0 0 119 0 14 11 0 71 0 9 0 5 0 13 0 12 0 15 0 14 0 6 0 1 72 72 13 13 13 77 77 9 9 9 9 14 14 12 12 13 13 13 13 5 5 1 131 131 16 15 15 74 74 14 14 12 12 11 11 12 12 14 14 16 16 8 8 1 100 100 13 14 14 82 82 5 5 6 6 15 15 12 12 11 11 13 13 6 6 1 9 9 12 14 14 54 54 12 12 4 4 16 16 12 12 14 14 14 14 4 4 1 107 107 9 14 14 54 54 6 6 6 6 14 14 10 10 11 11 13 13 3 3 1 79 79 14 10 10 80 80 6 6 7 7 13 13 12 12 8 8 14 14 4 4 0 115 0 15 8 0 76 0 8 0 9 0 15 0 12 0 12 0 16 0 7 0
Names of X columns:
Gender trend trend_G Popularity Depression Depression_G Belonging Belonging_G WeightedPopularity WeightedPopularity_G ParentalCriticism ParentalCriticism_G Happiness Happiness_G FindingFriends FindingFriends_G KnowingPeople KnowingPeople_G Liked Liked_G Celebrity Celebrity_G
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') }
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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