Send output to:
Browser Blue - Charts White
Browser Black/White
CSV
Data X:
26.0 50.0 4.0 1.0 21.0 21.0 7.5 57.0 62.0 4.0 0.0 22.0 22.0 6.0 37.0 54.0 5.0 1.0 21.0 22.0 6.5 67.0 71.0 4.0 0.0 21.0 18.0 1.0 43.0 54.0 4.0 1.0 21.0 23.0 1.0 52.0 65.0 9.0 1.0 21.0 12.0 5.5 52.0 73.0 8.0 1.0 21.0 20.0 8.5 43.0 52.0 11.0 0.0 23.0 22.0 6.5 84.0 84.0 4.0 1.0 22.0 21.0 4.5 67.0 42.0 4.0 1.0 25.0 19.0 2.0 49.0 66.0 6.0 1.0 21.0 22.0 5.0 70.0 65.0 4.0 1.0 23.0 15.0 0.5 52.0 78.0 8.0 1.0 22.0 20.0 5.0 58.0 73.0 4.0 1.0 21.0 19.0 5.0 68.0 75.0 4.0 0.0 21.0 18.0 2.5 62.0 72.0 11.0 0.0 25.0 15.0 5.0 43.0 66.0 4.0 0.0 21.0 20.0 5.5 56.0 70.0 4.0 1.0 21.0 21.0 3.5 56.0 61.0 6.0 0.0 20.0 21.0 3.0 74.0 81.0 6.0 1.0 24.0 15.0 4.0 65.0 71.0 4.0 0.0 23.0 16.0 0.5 63.0 69.0 8.0 1.0 21.0 23.0 6.5 58.0 71.0 5.0 1.0 24.0 21.0 4.5 57.0 72.0 4.0 0.0 23.0 18.0 7.5 63.0 68.0 9.0 1.0 21.0 25.0 5.5 53.0 70.0 4.0 1.0 22.0 9.0 4.0 57.0 68.0 7.0 1.0 20.0 30.0 7.5 51.0 61.0 10.0 1.0 18.0 20.0 7.0 64.0 67.0 4.0 0.0 21.0 23.0 4.0 53.0 76.0 4.0 1.0 22.0 16.0 5.5 29.0 70.0 7.0 0.0 22.0 16.0 2.5 54.0 60.0 12.0 0.0 21.0 19.0 5.5 58.0 72.0 7.0 1.0 21.0 25.0 3.5 43.0 69.0 5.0 1.0 25.0 18.0 2.5 51.0 71.0 8.0 1.0 22.0 23.0 4.5 53.0 62.0 5.0 1.0 22.0 21.0 4.5 54.0 70.0 4.0 1.0 20.0 10.0 4.5 56.0 64.0 9.0 0.0 21.0 14.0 6.0 61.0 58.0 7.0 1.0 21.0 22.0 2.5 47.0 76.0 4.0 1.0 21.0 26.0 5.0 39.0 52.0 4.0 0.0 22.0 23.0 0.0 48.0 59.0 4.0 1.0 21.0 23.0 5.0 50.0 68.0 4.0 1.0 24.0 24.0 6.5 35.0 76.0 4.0 1.0 22.0 24.0 5.0 30.0 65.0 7.0 1.0 22.0 18.0 6.0 68.0 67.0 4.0 1.0 21.0 23.0 4.5 49.0 59.0 7.0 0.0 22.0 15.0 5.5 61.0 69.0 4.0 1.0 19.0 19.0 1.0 67.0 76.0 4.0 1.0 22.0 16.0 7.5 47.0 63.0 4.0 0.0 23.0 25.0 6.0 56.0 75.0 4.0 1.0 20.0 23.0 5.0 50.0 63.0 8.0 1.0 20.0 17.0 1.0 43.0 60.0 4.0 1.0 23.0 19.0 5.0 67.0 73.0 4.0 1.0 20.0 21.0 6.5 62.0 63.0 4.0 1.0 23.0 18.0 7.0 57.0 70.0 4.0 1.0 21.0 27.0 4.5 41.0 75.0 7.0 1.0 22.0 21.0 0.0 54.0 66.0 12.0 0.0 21.0 13.0 8.5 45.0 63.0 4.0 1.0 21.0 8.0 3.5 48.0 63.0 4.0 0.0 19.0 29.0 7.5 61.0 64.0 4.0 1.0 22.0 28.0 3.5 56.0 70.0 5.0 1.0 21.0 23.0 6.0 41.0 75.0 15.0 0.0 21.0 21.0 1.5 43.0 61.0 5.0 0.0 21.0 19.0 9.0 53.0 60.0 10.0 1.0 21.0 19.0 3.5 44.0 62.0 9.0 0.0 21.0 20.0 3.5 66.0 73.0 8.0 1.0 21.0 18.0 4.0 58.0 61.0 4.0 0.0 22.0 19.0 6.5 46.0 66.0 5.0 1.0 22.0 17.0 7.5 37.0 64.0 4.0 1.0 18.0 19.0 6.0 51.0 59.0 9.0 0.0 21.0 25.0 5.0 51.0 64.0 4.0 0.0 23.0 19.0 5.5 56.0 60.0 10.0 0.0 19.0 22.0 3.5 66.0 56.0 4.0 0.0 19.0 23.0 7.5 37.0 78.0 4.0 1.0 21.0 14.0 6.5 42.0 67.0 7.0 1.0 21.0 16.0 6.5 38.0 59.0 5.0 0.0 21.0 24.0 6.5 66.0 66.0 4.0 1.0 20.0 20.0 7.0 34.0 68.0 4.0 0.0 19.0 12.0 3.5 53.0 71.0 4.0 0.0 21.0 24.0 1.5 49.0 66.0 4.0 1.0 19.0 22.0 4.0 55.0 73.0 4.0 0.0 19.0 12.0 7.5 49.0 72.0 4.0 0.0 19.0 22.0 4.5 59.0 71.0 6.0 0.0 20.0 20.0 0.0 40.0 59.0 10.0 1.0 19.0 10.0 3.5 58.0 64.0 7.0 0.0 19.0 23.0 5.5 60.0 66.0 4.0 1.0 19.0 17.0 5.0 63.0 78.0 4.0 1.0 20.0 22.0 4.5 56.0 68.0 7.0 0.0 19.0 24.0 2.5 54.0 73.0 4.0 0.0 18.0 18.0 7.5 52.0 62.0 8.0 0.0 19.0 21.0 7.0 34.0 65.0 11.0 1.0 21.0 20.0 0.0 69.0 68.0 6.0 1.0 18.0 20.0 4.5 32.0 65.0 14.0 1.0 18.0 22.0 3.0 48.0 60.0 5.0 0.0 19.0 19.0 1.5 67.0 71.0 4.0 1.0 21.0 20.0 3.5 58.0 65.0 8.0 0.0 20.0 26.0 2.5 57.0 68.0 9.0 1.0 24.0 23.0 5.5 42.0 64.0 4.0 1.0 22.0 24.0 8.0 64.0 74.0 4.0 1.0 21.0 21.0 1.0 58.0 69.0 5.0 1.0 21.0 21.0 5.0 66.0 76.0 4.0 1.0 19.0 19.0 4.5 26.0 68.0 5.0 0.0 19.0 8.0 3.0 61.0 72.0 4.0 1.0 20.0 17.0 3.0 52.0 67.0 4.0 1.0 18.0 20.0 8.0 51.0 63.0 7.0 1.0 19.0 11.0 2.5 55.0 59.0 10.0 0.0 19.0 8.0 7.0 50.0 73.0 4.0 0.0 20.0 15.0 0.0 60.0 66.0 5.0 0.0 21.0 18.0 1.0 56.0 62.0 4.0 0.0 18.0 18.0 3.5 63.0 69.0 4.0 0.0 19.0 19.0 5.5 61.0 66.0 4.0 0.0 19.0 19.0 5.5 52.0 51.0 6.0 1.0 22.0 23.0 0.5 16.0 56.0 4.0 1.0 22.0 22.0 7.5 46.0 67.0 8.0 1.0 22.0 21.0 9.0 56.0 69.0 5.0 1.0 20.0 25.0 9.5 52.0 57.0 4.0 1.0 19.0 30.0 8.5 55.0 56.0 17.0 0.0 20.0 17.0 7.0 50.0 55.0 4.0 1.0 22.0 27.0 8.0 59.0 63.0 4.0 1.0 21.0 23.0 10.0 60.0 67.0 8.0 0.0 21.0 23.0 7.0 52.0 65.0 4.0 1.0 21.0 18.0 8.5 44.0 47.0 7.0 0.0 21.0 18.0 9.0 67.0 76.0 4.0 0.0 21.0 23.0 9.5 52.0 64.0 4.0 1.0 21.0 19.0 4.0 55.0 68.0 5.0 1.0 21.0 15.0 6.0 37.0 64.0 7.0 1.0 22.0 20.0 8.0 54.0 65.0 4.0 1.0 24.0 16.0 5.5 72.0 71.0 4.0 1.0 21.0 24.0 9.5 51.0 63.0 7.0 1.0 22.0 25.0 7.5 48.0 60.0 11.0 1.0 20.0 25.0 7.0 60.0 68.0 7.0 1.0 21.0 19.0 7.5 50.0 72.0 4.0 0.0 24.0 19.0 8.0 63.0 70.0 4.0 1.0 25.0 16.0 7.0 33.0 61.0 4.0 1.0 22.0 19.0 7.0 67.0 61.0 4.0 1.0 21.0 19.0 6.0 46.0 62.0 4.0 1.0 21.0 23.0 10.0 54.0 71.0 4.0 1.0 22.0 21.0 2.5 59.0 71.0 6.0 1.0 23.0 22.0 9.0 61.0 51.0 8.0 0.0 24.0 19.0 8.0 33.0 56.0 23.0 1.0 20.0 20.0 6.0 47.0 70.0 4.0 1.0 22.0 20.0 8.5 69.0 73.0 8.0 1.0 25.0 3.0 6.0 52.0 76.0 6.0 1.0 22.0 23.0 9.0 55.0 68.0 4.0 0.0 21.0 23.0 8.0 41.0 48.0 7.0 0.0 21.0 20.0 9.0 73.0 52.0 4.0 0.0 21.0 15.0 5.5 52.0 60.0 4.0 0.0 22.0 16.0 7.0 50.0 59.0 4.0 0.0 22.0 7.0 5.5 51.0 57.0 10.0 0.0 21.0 24.0 9.0 60.0 79.0 6.0 1.0 22.0 17.0 2.0 56.0 60.0 5.0 0.0 23.0 24.0 8.5 56.0 60.0 5.0 1.0 21.0 24.0 9.0 29.0 59.0 4.0 1.0 21.0 19.0 8.5 66.0 62.0 4.0 0.0 21.0 25.0 9.0 66.0 59.0 5.0 1.0 19.0 20.0 7.5 73.0 61.0 5.0 1.0 21.0 28.0 10.0 55.0 71.0 5.0 1.0 21.0 23.0 9.0 64.0 57.0 5.0 0.0 19.0 27.0 7.5 40.0 66.0 4.0 0.0 18.0 18.0 6.0 46.0 63.0 6.0 0.0 19.0 28.0 10.5 58.0 69.0 4.0 0.0 21.0 21.0 8.5 43.0 58.0 4.0 1.0 22.0 19.0 8.0 61.0 59.0 4.0 0.0 22.0 23.0 10.0 51.0 48.0 9.0 1.0 19.0 27.0 10.5 50.0 66.0 18.0 0.0 20.0 22.0 6.5 52.0 73.0 6.0 1.0 19.0 28.0 9.5 54.0 67.0 5.0 0.0 21.0 25.0 8.5 66.0 61.0 4.0 1.0 19.0 21.0 7.5 61.0 68.0 11.0 0.0 20.0 22.0 5.0 80.0 75.0 4.0 0.0 21.0 28.0 8.0 51.0 62.0 10.0 1.0 19.0 20.0 10.0 56.0 69.0 6.0 0.0 21.0 29.0 7.0 56.0 58.0 8.0 1.0 21.0 25.0 7.5 56.0 60.0 8.0 1.0 21.0 25.0 7.5 53.0 74.0 6.0 1.0 19.0 20.0 9.5 47.0 55.0 8.0 1.0 25.0 20.0 6.0 25.0 62.0 4.0 1.0 21.0 16.0 10.0 47.0 63.0 4.0 0.0 20.0 20.0 7.0 46.0 69.0 9.0 1.0 25.0 20.0 3.0 50.0 58.0 9.0 0.0 19.0 23.0 6.0 39.0 58.0 5.0 0.0 20.0 18.0 7.0 51.0 68.0 4.0 0.0 22.0 25.0 10.0 58.0 72.0 4.0 1.0 19.0 18.0 7.0 35.0 62.0 15.0 0.0 20.0 19.0 3.5 58.0 62.0 10.0 1.0 19.0 25.0 8.0 60.0 65.0 9.0 0.0 19.0 25.0 10.0 62.0 69.0 7.0 0.0 18.0 25.0 5.5 63.0 66.0 9.0 0.0 19.0 24.0 6.0 53.0 72.0 6.0 0.0 21.0 19.0 6.5 46.0 62.0 4.0 1.0 19.0 26.0 6.5 67.0 75.0 7.0 1.0 20.0 10.0 8.5 59.0 58.0 4.0 1.0 20.0 17.0 4.0 64.0 66.0 7.0 1.0 19.0 13.0 9.5 38.0 55.0 4.0 0.0 19.0 17.0 8.0 50.0 47.0 15.0 0.0 22.0 30.0 8.5 48.0 72.0 4.0 1.0 21.0 25.0 5.5 48.0 62.0 9.0 0.0 19.0 4.0 7.0 47.0 64.0 4.0 0.0 19.0 16.0 9.0 66.0 64.0 4.0 0.0 19.0 21.0 8.0 47.0 19.0 28.0 0.0 23.0 23.0 10.0 63.0 50.0 4.0 1.0 19.0 22.0 8.0 58.0 68.0 4.0 1.0 20.0 17.0 6.0 44.0 70.0 4.0 0.0 19.0 20.0 8.0 51.0 79.0 5.0 0.0 22.0 20.0 5.0 43.0 69.0 4.0 1.0 19.0 22.0 9.0 55.0 71.0 4.0 0.0 25.0 16.0 4.5 38.0 48.0 12.0 1.0 19.0 23.0 8.5 45.0 73.0 4.0 1.0 19.0 0.0 9.5 50.0 74.0 6.0 0.0 19.0 18.0 8.5 54.0 66.0 6.0 1.0 20.0 25.0 7.5 57.0 71.0 5.0 1.0 20.0 23.0 7.5 60.0 74.0 4.0 1.0 21.0 12.0 5.0 55.0 78.0 4.0 0.0 19.0 18.0 7.0 56.0 75.0 4.0 0.0 21.0 24.0 8.0 49.0 53.0 10.0 0.0 23.0 11.0 5.5 37.0 60.0 7.0 1.0 19.0 18.0 8.5 59.0 70.0 4.0 0.0 22.0 23.0 9.5 46.0 69.0 7.0 1.0 20.0 24.0 7.0 51.0 65.0 4.0 1.0 18.0 29.0 8.0 58.0 78.0 4.0 0.0 21.0 18.0 8.5 64.0 78.0 12.0 0.0 20.0 15.0 3.5 53.0 59.0 5.0 0.0 21.0 29.0 6.5 48.0 72.0 8.0 1.0 21.0 16.0 6.5 51.0 70.0 6.0 1.0 21.0 19.0 10.5 47.0 63.0 17.0 0.0 19.0 22.0 8.5 59.0 63.0 4.0 0.0 21.0 16.0 8.0 62.0 71.0 5.0 0.0 19.0 23.0 10.0 62.0 74.0 4.0 1.0 21.0 23.0 10.0 51.0 67.0 5.0 1.0 21.0 19.0 9.5 64.0 66.0 5.0 0.0 22.0 4.0 9.0 52.0 62.0 6.0 0.0 21.0 20.0 10.0 67.0 80.0 4.0 0.0 22.0 24.0 7.5 50.0 73.0 4.0 1.0 22.0 20.0 4.5 54.0 67.0 4.0 1.0 22.0 4.0 4.5 58.0 61.0 6.0 1.0 22.0 24.0 0.5 56.0 73.0 8.0 1.0 21.0 22.0 6.5 63.0 74.0 10.0 0.0 22.0 16.0 4.5 31.0 32.0 4.0 1.0 23.0 3.0 5.5 65.0 69.0 5.0 1.0 19.0 15.0 5.0 71.0 69.0 4.0 1.0 22.0 24.0 6.0 50.0 84.0 4.0 0.0 21.0 17.0 4.0 57.0 64.0 4.0 0.0 19.0 20.0 8.0 47.0 58.0 16.0 1.0 19.0 27.0 10.5 47.0 59.0 7.0 1.0 20.0 26.0 6.5 57.0 78.0 4.0 1.0 18.0 23.0 8.0 43.0 57.0 4.0 1.0 21.0 17.0 8.5 41.0 60.0 14.0 0.0 21.0 20.0 5.5 63.0 68.0 5.0 1.0 20.0 22.0 7.0 63.0 68.0 5.0 0.0 20.0 19.0 5.0 56.0 73.0 5.0 1.0 21.0 24.0 3.5 51.0 69.0 5.0 1.0 21.0 19.0 5.0 50.0 67.0 7.0 0.0 19.0 23.0 9.0 22.0 60.0 19.0 1.0 19.0 15.0 8.5 41.0 65.0 16.0 0.0 21.0 27.0 5.0 59.0 66.0 4.0 1.0 19.0 26.0 9.5 56.0 74.0 4.0 0.0 19.0 22.0 3.0 66.0 81.0 7.0 1.0 24.0 22.0 1.5 53.0 72.0 9.0 0.0 19.0 18.0 6.0 42.0 55.0 5.0 0.0 19.0 15.0 0.5 52.0 49.0 14.0 1.0 20.0 22.0 6.5 54.0 74.0 4.0 1.0 19.0 27.0 7.5 44.0 53.0 16.0 0.0 19.0 10.0 4.5 62.0 64.0 10.0 1.0 19.0 20.0 8.0 53.0 65.0 5.0 1.0 19.0 17.0 9.0 50.0 57.0 6.0 0.0 19.0 23.0 7.5 36.0 51.0 4.0 1.0 19.0 19.0 8.5 76.0 80.0 4.0 0.0 20.0 13.0 7.0 66.0 67.0 4.0 0.0 20.0 27.0 9.5 62.0 70.0 5.0 1.0 19.0 23.0 6.5 59.0 74.0 4.0 1.0 21.0 16.0 9.5 47.0 75.0 4.0 0.0 19.0 25.0 6.0 55.0 70.0 5.0 1.0 19.0 2.0 8.0 58.0 69.0 4.0 0.0 19.0 26.0 9.5 60.0 65.0 4.0 0.0 21.0 20.0 8.0 44.0 55.0 5.0 1.0 22.0 23.0 8.0 57.0 71.0 8.0 0.0 19.0 22.0 9.0 45.0 65.0 15.0 0.0 19.0 24.0 5.0
Names of X columns:
AMS.I AMS.E AMS.A gender age NUMERACYTOT Ex
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
0 seconds
R Server
Big Analytics Cloud Computing Center
Click here to blog (archive) this computation