In Part 9, let’s look at sub-setting in R. Let’s provide summary tables on the following data set of tourists from different countries, the numbers of their children, and the amount of money they spent while on vacation. Copy and paste the following array into…

In Part 8, let’s look at some basic commands in R. Set up the following vectors by cutting and pasting from this document: a <- c(3,-7,-3,-9,3,-1,2,-12, -14) b <- c(3,7,-5, 1, 5,-6,-9,16, -8) d <- c(1,2,3,4,5,6,7,8,9) Now figure out what each of the following commands…

In Part 7, let’s look at further plotting in R. Try entering the following three commands to create three variables. X <- c(3, 4, 6, 6, 7, 8, 9, 12) B1 <- c(4, 5, 6, 7, 17, 18, 19, 22) B2 <- c(3, 5, 8,…

In Part 6, let’s look at basic plotting in R. Try entering the following three commands together (the semi-colon allows you to place several commands on the same line). x <- seq(-4, 4, 0.2) ;  y <- 2*x^2 + 4*x – 7 plot(x, y) This…

In Parts 3 and 4 we used the lm() command to perform least squares regressions. We saw how to check for non-linearity in our data by fitting polynomial models and checking whether they fit the data better than a linear model. Now let’s see how…

In Part 3 we used the lm() command to perform least squares regressions. In Part 4 we will look at more advanced aspects of regression models and see what R has to offer. One way of checking for non-linearity in your data is to fit…

In Part 2 we created two variables and used the lm() command to perform a least squares regression on them, treating one of them as the dependent variable and the other as the independent variable. Here they are again. height = c(186, 165, 149, 206,…