###### Tag Archives: Statistics

Last time we created two variables and used the lm() command to perform a least squares regression on them, and diagnosing our regression using the plot() command. Here are the data again. height = c(176, 154, 138, 196, 132, 176, 181, 169, 150, 175) bodymass = c(82, 49, 53, 112, 47, 69, 77, 71, 62, …

Last time 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(176, 154, 138, 196, 132, 176, 181, 169, 150, 175) bodymass = c(82, 49, 53, …

In my last blog 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(176, 154, 138, 196, 132, 176, 181, 169, 150, 175) bodymass = c(82, …

Today let’s re-create two variables and see how to plot them and include a regression line. We take height to be a variable that describes the heights (in cm) of ten people. Copy and paste the following code to the R command line to create this variable. height

## Quick start with R: Symbol colours and legend in qplot (Part 25)

In Blog 24 we saw how to use qplot to map symbol size to a categorical variable. Now we see how to control symbol colours and create legend titles. Copy in the same dataset of Blog 24 (a medical data set relating to patients in a randomised controlled trial: M

In Blog 24, let’s see how to use qplot to map symbol colour to a categorical variable. Copy in the following dataset (a medical dataset relating to patients in a randomised controlled trial): M

In Part 23, let’s see how to use qplot to create a simple scatterplot. The qplot (quick plot) system is a subset of the ggplot2 (grammar of graphics) package which you can use to create nice graphs. It is great for creating graphs of categorical data, because you can map symbol colour, size and shape …