Creating surface plots

May 28th, 2010

A 3d wireframe plot is a type of graph that is used to display a surface – geographic data is an example of where this type of graph would be used or it could be used to display a fitted model with more than one explanatory variable. These plots are related to contour plots which are the two dimensional equivalent. Read the rest of this entry »

Displaying data using level plots

May 3rd, 2010

A level plot is a type of graph that is used to display a surface in two rather than three dimensions – the surface is viewed from above as if we were looking straight down and is an alternative to a contour plot – geographic data is an example of where this type of graph would be used. A contour plot uses lines to identify regions of different heights and the level plot uses coloured regions to produce a similar effect. Read the rest of this entry »

Analysis of Covariance – Extending Simple Linear Regression

April 28th, 2010

The simple linear regression model considers the relationship between two variables and in many cases more information will be available that can be used to extend the model. For example, there might be a categorical variable (sometimes known as a covariate) that can be used to divide the data set to fit a separate linear regression to each of the subsets. We will consider how to handle this extension using one of the data sets available within the R software package. Read the rest of this entry »

Summarising data using box and whisker plots

April 25th, 2010

A box and whisker plot is a type of graphical display that can be used to summarise a set of data based on the five number summary of this data. The summary statistics used to create a box and whisker plot are the median of the data, the lower and upper quartiles (25% and 75%) and the minimum and maximum values. Read the rest of this entry »

Simple Linear Regression

April 23rd, 2010

One of the most frequent used techniques in statistics is linear regression where we investigate the potential relationship between a variable of interest (often called the response variable but there are many other names in use) and a set of one of more variables (known as the independent variables or some other term). Unsurprisingly there are flexible facilities in R for fitting a range of linear models from the simple case of a single variable to more complex relationships. Read the rest of this entry »