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Data X:
1.00 0.50 0.67 0.67 0.00 0.50 7.5 1.00 0.60 1.00 0.67 0.50 1.00 2.5 0.89 0.50 0.83 0.33 0.50 1.00 6.0 0.89 0.40 1.00 0.67 0.00 1.00 6.5 0.89 0.50 0.83 0.00 0.00 0.00 1.0 0.89 0.70 0.67 0.00 1.00 1.00 1.0 0.78 0.30 0.00 0.00 0.50 0.50 5.5 0.89 0.40 0.83 0.67 0.50 0.00 8.5 1.00 0.40 0.50 0.67 1.00 1.00 6.5 0.89 0.70 0.83 0.00 0.50 0.00 4.5 0.78 0.60 0.33 0.67 0.50 0.50 2.0 1.00 0.60 0.50 1.00 0.00 0.50 5.0 0.78 0.20 0.67 0.00 0.50 0.50 0.5 0.89 0.40 1.00 0.00 0.50 0.50 5.0 0.89 0.40 0.50 0.67 0.00 1.00 5.0 0.89 0.50 0.67 0.33 0.00 0.00 2.5 0.89 0.30 0.17 0.67 0.00 0.50 5.0 0.89 0.40 0.83 0.33 0.50 0.50 5.5 0.67 0.70 0.67 0.33 0.50 1.00 3.5 1.00 0.50 0.67 0.33 0.00 1.00 3.0 0.78 0.20 0.67 0.00 0.00 1.00 4.0 0.78 0.30 0.50 0.67 0.00 0.50 0.5 0.89 0.60 1.00 0.33 0.00 1.00 6.5 0.78 0.60 0.83 0.33 0.00 1.00 4.5 0.89 0.20 0.83 0.33 0.00 1.00 7.5 0.89 0.70 1.00 0.67 1.00 0.00 5.5 0.33 0.20 0.67 0.00 0.00 0.00 4.0 1.00 1.00 1.00 0.33 1.00 1.00 7.5 0.89 0.40 0.83 0.67 0.00 0.50 7.0 0.89 0.40 1.00 1.00 0.00 1.00 4.0 0.67 0.20 0.83 0.67 0.00 0.50 5.5 0.56 0.40 0.67 0.33 0.00 1.00 2.5 0.89 0.40 0.67 0.00 0.50 1.00 5.5 0.89 0.60 1.00 0.67 0.50 1.00 0.5 0.89 0.70 1.00 0.67 0.50 0.50 3.5 1.00 0.20 0.67 0.67 0.00 0.50 2.5 0.78 0.60 1.00 1.00 0.00 0.50 4.5 0.78 0.30 1.00 1.00 0.50 0.50 4.5 0.33 0.30 0.50 0.33 0.00 0.00 4.5 0.78 0.20 0.67 0.00 0.50 0.00 6.0 0.89 0.50 0.83 0.67 0.50 0.50 2.5 0.89 0.70 1.00 0.67 0.50 1.00 5.0 0.78 0.60 1.00 0.67 0.50 0.50 0.0 0.89 0.40 1.00 0.67 0.50 1.00 5.0 0.89 0.60 1.00 0.33 0.50 1.00 6.5 1.00 0.40 1.00 1.00 0.00 1.00 5.0 0.67 0.30 0.83 0.67 0.00 1.00 6.0 1.00 0.50 0.83 0.67 0.50 0.50 4.5 0.89 0.20 0.50 0.00 0.00 1.00 5.5 0.89 0.30 0.83 0.00 0.50 1.00 1.0 0.89 0.50 0.17 0.00 0.00 1.00 7.5 0.78 0.70 0.83 1.00 0.50 1.00 6.0 0.89 0.40 1.00 0.67 1.00 0.50 5.0 0.78 0.30 1.00 0.00 0.00 0.50 1.0 0.78 0.20 0.67 0.67 1.00 1.00 5.0 1.00 0.50 1.00 0.00 0.00 0.50 6.5 0.78 0.40 1.00 0.00 0.50 0.00 7.0 1.00 0.60 1.00 0.67 1.00 1.00 4.5 0.78 0.40 0.83 1.00 0.00 1.00 0.0 0.67 0.40 0.33 0.00 0.00 0.50 8.5 0.33 0.20 0.33 0.33 0.00 0.00 3.5 1.00 0.90 1.00 0.67 0.50 1.00 7.5 1.00 0.80 1.00 0.67 1.00 0.50 3.5 0.78 0.80 0.83 0.00 0.50 1.00 6.0 0.67 0.30 1.00 1.00 0.50 1.00 1.5 1.00 0.20 0.83 0.67 0.00 0.50 9.0 0.89 0.40 0.67 0.00 0.50 1.00 3.5 0.89 0.20 0.83 1.00 0.00 1.00 3.5 0.78 0.20 0.67 0.67 0.50 1.00 4.0 1.00 0.10 0.83 0.67 0.00 1.00 6.5 0.56 0.40 0.67 1.00 0.50 0.00 7.5 0.67 0.50 1.00 0.00 0.50 0.50 6.0 0.89 0.80 0.83 0.33 0.50 1.00 5.0 0.89 0.40 0.67 0.67 0.00 0.50 5.5 0.89 0.60 0.83 0.33 0.50 0.50 3.5 0.89 0.50 0.83 0.67 0.50 1.00 7.5 1.00 0.60 1.00 0.67 0.50 1.00 1.0 0.78 0.30 0.67 0.00 0.00 0.00 6.5 0.89 0.80 1.00 1.00 0.50 1.00 NA 1.00 0.40 0.33 0.00 0.50 0.00 6.5 1.00 0.60 0.83 0.67 0.50 0.50 6.5 0.89 0.40 1.00 0.33 0.00 0.50 7.0 0.44 0.30 0.83 0.00 0.00 0.00 3.5 0.78 0.80 0.83 0.00 1.00 1.00 1.5 0.89 0.60 0.50 0.33 1.00 1.00 4.0 0.67 0.30 0.50 0.00 0.00 0.00 7.5 0.78 0.50 0.83 0.67 0.50 1.00 4.5 0.78 0.40 1.00 0.33 0.00 1.00 0.0 0.33 0.30 0.33 0.67 0.00 0.00 3.5 0.89 0.70 1.00 0.33 0.00 0.50 5.5 0.89 0.20 0.67 0.33 0.50 0.50 5.0 0.89 0.40 0.83 1.00 0.00 1.00 4.5 0.89 0.60 1.00 0.67 0.50 0.50 2.5 0.56 0.60 0.83 0.00 0.00 1.00 7.5 0.67 0.60 0.83 0.67 0.50 0.50 7.0 0.67 0.40 1.00 0.33 0.50 1.00 0.0 0.78 0.60 0.83 0.00 0.00 1.00 4.5 0.78 0.50 1.00 0.33 0.50 1.00 3.0 0.78 0.50 0.83 0.00 0.00 1.00 1.5 0.89 0.60 0.67 0.00 0.00 1.00 3.5 1.00 0.80 0.83 0.33 0.50 1.00 2.5 0.89 0.50 0.83 0.67 1.00 0.50 5.5 0.89 0.60 0.83 0.67 0.50 1.00 8.0 0.78 0.40 0.83 0.67 0.50 1.00 1.0 1.00 0.30 0.67 0.67 0.50 1.00 5.0 0.78 0.30 0.83 1.00 0.00 0.50 4.5 0.67 0.20 0.00 0.00 0.00 0.00 3.0 0.78 0.40 0.83 0.00 0.00 0.50 3.0 0.89 0.50 1.00 0.00 0.00 0.50 8.0 0.67 0.30 0.17 0.00 0.50 0.00 2.5 0.22 0.40 0.17 0.00 0.50 0.00 7.0 0.44 0.50 0.50 1.00 0.00 0.00 0.0 0.89 0.30 0.50 0.67 0.00 1.00 1.0 0.67 0.50 1.00 0.00 0.00 0.50 3.5 0.89 0.40 0.67 0.67 0.00 0.50 5.5 0.67 0.40 0.83 0.67 0.00 1.00 5.5 0.78 0.60 1.00 0.00 1.00 1.00 0.5 0.78 0.30 1.00 0.67 1.00 1.00 7.5 0.78 0.40 1.00 0.33 1.00 0.50 9 1.00 0.30 1.00 1.00 1.00 1.00 9.5 0.78 1.00 1.00 1.00 1.00 1.00 8.5 0.67 0.40 1.00 0.00 0.00 0.50 7 0.89 0.80 0.83 1.00 0.50 1.00 8 0.89 0.30 1.00 0.67 1.00 1.00 10 1.00 0.50 0.83 0.67 0.00 1.00 7 0.78 0.40 1.00 0.00 0.00 0.50 8.5 0.67 0.30 0.83 0.67 0.00 1.00 9 0.89 0.50 0.83 1.00 0.00 1.00 9.5 0.67 0.30 1.00 0.67 0.00 1.00 4 0.67 0.30 0.67 0.00 0.00 1.00 6 1.00 0.40 0.83 0.00 0.00 1.00 8 0.67 0.30 1.00 0.00 0.00 0.50 5.5 1.00 0.60 1.00 0.33 0.50 0.50 9.5 0.89 0.60 0.83 0.67 1.00 1.00 7.5 0.89 0.40 1.00 1.00 1.00 1.00 7 1.00 0.40 1.00 0.00 0.00 0.00 7.5 0.67 0.40 1.00 0.67 0.00 0.50 8 0.44 0.30 0.67 0.67 0.50 1.00 7 0.89 0.20 1.00 0.33 1.00 0.00 7 0.56 0.50 0.83 0.67 0.00 1.00 6 0.78 0.40 1.00 0.67 1.00 1.00 10 1.00 0.40 1.00 0.67 0.00 0.00 2.5 1.00 0.40 0.83 0.67 0.00 1.00 9 0.89 0.30 0.67 0.67 0.50 0.50 8 0.67 0.40 0.83 0.67 1.00 0.50 6 0.89 0.20 1.00 0.33 0.50 1.00 8.5 0.33 0.00 0.00 0.00 0.00 0.00 6 0.89 0.40 1.00 0.67 0.50 1.00 9 0.78 0.30 0.67 0.00 0.00 0.00 8 0.78 0.60 1.00 0.00 1.00 1.00 8 1.00 0.40 0.67 0.67 0.00 0.50 9 0.44 0.40 1.00 0.00 0.00 0.50 5.5 0.78 0.20 0.67 0.00 0.00 0.00 5 0.67 0.40 0.83 0.00 0.50 0.00 7 0.33 0.20 0.17 0.00 0.50 0.00 5.5 0.89 0.40 0.83 1.00 1.00 1.00 9 0.89 0.30 0.83 0.00 0.00 0.50 2 1.00 0.60 0.83 0.67 1.00 0.00 8.5 0.89 0.60 0.83 1.00 0.00 1.00 9 0.89 0.40 0.83 0.00 0.00 1.00 8.5 1.00 0.50 1.00 0.67 1.00 0.50 9 0.89 0.40 0.83 0.00 0.50 1.00 7.5 1.00 0.60 1.00 1.00 1.00 1.00 10 0.78 0.60 0.83 0.67 0.50 1.00 9 0.78 0.90 1.00 0.67 0.50 1.00 7.5 0.67 0.40 0.83 0.67 0.50 0.00 6 0.89 0.80 1.00 1.00 0.50 1.00 10.5 0.67 0.50 0.83 1.00 0.00 1.00 8.5 0.78 0.40 0.83 1.00 0.00 0.00 8 0.89 0.40 1.00 0.67 1.00 0.50 10 0.89 0.70 1.00 1.00 1.00 0.50 10.5 0.78 0.40 1.00 0.33 1.00 1.00 6.5 1.00 0.80 1.00 0.67 0.50 1.00 9.5 1.00 0.40 1.00 1.00 1.00 0.50 8.5 1.00 0.30 1.00 0.67 0.00 0.50 7.5 0.67 0.50 1.00 0.67 0.50 1.00 5 0.89 0.80 1.00 0.67 1.00 1.00 8 1.00 0.40 0.83 0.33 0.00 0.50 10 1.00 1.00 1.00 1.00 0.50 0.00 7 0.89 0.50 1.00 0.67 1.00 1.00 7.5 0.89 0.50 1.00 0.67 1.00 1.00 7.5 0.89 0.30 1.00 0.33 0.00 1.00 9.5 0.89 0.30 0.83 0.33 0.50 1.00 6 0.89 0.30 0.50 0.00 0.00 1.00 10 1.00 0.40 0.67 0.33 0.50 0.50 7 0.67 0.50 1.00 0.33 0.00 1.00 3 1.00 0.50 0.67 0.67 0.50 1.00 6 0.89 0.40 1.00 0.00 0.00 0.00 7 0.89 0.70 1.00 1.00 0.50 0.00 10 0.89 0.50 0.50 0.33 0.00 0.50 7 0.89 0.40 0.67 0.33 1.00 0.00 3.5 1.00 0.70 0.67 1.00 0.00 1.00 8 1.00 0.70 0.67 1.00 0.00 1.00 10 1.00 0.70 0.67 1.00 0.00 1.00 5.5 0.89 0.70 0.67 1.00 0.00 1.00 6 0.89 0.70 0.67 0.00 0.00 0.00 6.5 0.89 0.70 1.00 0.67 0.50 1.00 6.5 0.33 0.10 0.67 0.33 0.50 0.00 8.5 0.67 0.20 0.67 0.67 0.50 1.00 4 0.56 0.30 0.33 0.33 0.00 1.00 9.5 0.44 0.60 0.83 0.33 0.00 0.50 8 1.00 0.80 1.00 1.00 1.00 1.00 8.5 0.89 0.80 1.00 0.33 0.50 0.50 5.5 0.33 0.00 0.17 0.00 0.00 0.00 7 0.67 0.30 0.67 0.33 0.00 1.00 9 0.67 0.60 0.83 0.33 0.50 1.00 8 1.00 0.50 0.83 0.67 0.00 1.00 10 0.78 0.70 1.00 0.33 0.00 0.50 8 0.67 0.30 0.83 0.00 0.50 1.00 6 1.00 0.30 1.00 0.67 0.00 0.00 8 0.78 0.40 1.00 0.67 0.00 0.50 5 0.89 0.40 0.83 1.00 0.00 1.00 9 0.89 0.10 0.83 0.00 0.00 1.00 4.5 0.89 0.50 1.00 0.67 0.00 1.00 8.5 0.78 0.30 0.67 0.33 0.00 0.50 7 0.00 0.00 0.00 0.00 0.00 0.00 9.5 0.67 0.40 1.00 0.33 0.50 0.00 8.5 1.00 0.60 0.83 0.67 1.00 0.50 7.5 1.00 0.40 1.00 0.33 0.50 1.00 7.5 0.67 0.10 0.33 0.00 0.50 1.00 5 0.89 0.30 0.83 0.00 0.00 1.00 7 0.89 0.70 0.83 0.67 0.00 1.00 8 0.56 0.30 0.17 0.00 0.00 1.00 5.5 0.67 0.50 0.83 0.33 0.50 0.00 8.5 0.67 0.30 0.67 0.33 0.00 0.00 7.5 1.00 0.30 0.83 0.67 1.00 1.00 9.5 1.00 0.60 0.67 0.67 0.50 1.00 7 1.00 0.90 1.00 1.00 0.00 1.00 8 0.67 0.40 0.83 0.00 0.50 1.00 8.5 0.44 0.30 1.00 0.00 0.50 0.50 3.5 0.89 0.90 1.00 0.67 1.00 1.00 6.5 0.44 0.50 1.00 0.00 0.50 0.00 6.5 0.56 0.30 1.00 1.00 0.50 0.50 10.5 0.89 0.60 0.83 0.67 0.00 0.50 8.5 0.67 0.20 1.00 0.33 0.00 0.50 8 0.89 0.40 0.83 1.00 0.50 1.00 10 1.00 0.50 0.83 0.67 0.50 0.50 10 0.78 0.40 0.83 0.67 0.00 0.50 9.5 0.44 0.00 0.00 0.00 0.00 0.00 9 0.89 0.20 1.00 0.33 0.50 1.00 10 0.89 0.50 1.00 0.67 0.50 1.00 7.5 0.89 0.30 1.00 0.67 0.00 0.50 4.5 0.44 0.00 0.00 0.00 0.00 0.00 4.5 1.00 0.50 0.83 1.00 0.00 1.00 0.5 0.89 0.60 0.83 0.33 0.00 1.00 6.5 0.67 0.30 0.83 0.00 0.50 0.50 4.5 0.33 0.00 0.00 0.00 0.00 0.00 5.5 0.78 0.30 0.67 0.00 0.50 0.00 5 0.89 0.50 1.00 0.67 0.50 1.00 6 0.78 0.40 0.67 0.00 0.00 1.00 4 0.78 0.50 0.83 0.67 0.00 0.50 8 0.89 0.70 1.00 1.00 1.00 0.50 10.5 0.89 0.40 0.83 1.00 0.50 1.00 8.5 0.78 0.80 1.00 0.67 0.50 1.00 6.5 0.78 0.60 1.00 0.33 0.50 1.00 8 0.67 0.40 0.83 0.33 0.00 0.50 8.5 0.89 0.50 0.83 0.33 0.50 0.00 5.5 0.89 0.50 1.00 0.00 0.50 1.00 7 0.78 0.30 1.00 0.33 0.00 1.00 5 1.00 0.60 1.00 0.00 0.50 1.00 3.5 1.00 0.30 0.67 0.67 0.00 0.50 5 0.78 0.60 0.83 1.00 0.50 0.50 9 0.78 0.30 0.33 0.33 0.00 1.00 8.5 0.89 0.70 1.00 0.67 1.00 1.00 5 0.89 0.70 1.00 1.00 0.00 1.00 9.5 0.67 0.60 0.67 1.00 0.50 1.00 3 1.00 0.50 1.00 0.33 0.50 0.00 1.5 0.67 0.50 0.83 0.33 0.00 0.50 6 0.56 0.40 0.67 0.00 0.00 1.00 0.5 0.78 0.40 1.00 0.33 1.00 1.00 6.5 1.00 0.70 1.00 1.00 0.00 1.00 7.5 0.67 0.20 0.17 0.00 0.50 0.00 4.5 0.78 0.50 0.83 0.67 0.00 0.50 8 0.56 0.40 0.83 0.67 0.50 0.00 9 1.00 0.20 1.00 0.67 1.00 1.00 7.5 0.89 0.50 0.67 0.67 0.00 0.00 8.5 0.44 0.40 0.50 0.00 0.00 1.00 7 1.00 0.70 0.67 1.00 1.00 1.00 9.5 0.89 0.60 0.83 0.67 1.00 0.00 6.5 0.78 0.40 0.83 0.00 0.00 0.00 9.5 0.89 0.50 1.00 0.67 1.00 1.00 6 0.11 0.00 0.17 0.00 0.00 0.00 8 0.89 0.70 1.00 0.67 0.50 1.00 9.5 0.89 0.40 0.67 0.67 0.00 1.00 8 1.00 0.50 0.67 1.00 0.00 1.00 8 0.89 0.60 0.83 0.67 0.00 0.50 9 1.00 0.80 0.50 0.67 0.50 0.50 5
Names of X columns:
Calculation Algebraic_Reasoning Graphical_Interpretation Proportionality_and_Ratio Probability_and_Sampling Estimation Ex

Sample Range: (leave blank to include all observations)
From:
To:

Column Number of Endogenous Series (?)


Fixed Seasonal Effects


Type of Equation


Degree of Predetermination (lagged endogenous variables)


Degree of Seasonal Predetermination


Seasonality


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') }

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