Introduction
If you are applying statistics to practical problems in industry, you may find that much of your work is concerned with making decisions concerning probability distributions. Sometimes it is advantageous to be able to describe the approximate probability distribution followed by a data set obtained experimentally. For example you may be asked to decide whether a data set is approximately normal. In order to make such decisions, you will find that you may use the chi-squared test provided that certain conditions are satisfied. On other occasions you may be given data concerning non-numeric variables in the form of a contingency table. This is one of those occasions when hypothesis tests can be applied to non-numeric variables.
Prerequisites
- understand how to find probabilities for a chi-squared distribution ( HELM booklet 40)
- understand the principles of hypothesis testing ( HELM booklet 41)
Learning Outcomes
- explain the term goodness-of-fit
- perform hypothesis tests based on the chi-squared distribution