![]() It shows data of infertility among women with the education they received (in years) against their number of induced abortions. In this tutorial, I’ll be using a built-in data set of R, “infert” for its structural simplicity. I’ll begin by showing you a contingency table. This is easier than trying to perform those calculations with a different R object such as a data frame that has a different column variable set up. You can find conditional probability, relative frequency, expected value, the chi squared statistic, and other similar statistic measures from the character vector contingency table object. Tests with a null hypothesis such as the Chi Square test and Fisher’s Exact test can be conducted with a breeze if you have a flat contingency table for the data set instead of a data frame. When working with big data, as statisticians normally do, a contingency table condenses a large number of observations and neatly displays them in a table that makes readability and further calculations particularly easier. You may be able to crunch the numbers for a small data set on paper, but when working with larger data, you need more sophisticated tools and a contingency table is one of them. ![]() As a first step you would want to see the frequency count of each variable against a condition. Imagine yourself in a position where you want to determine a relationship between two variables. The Purpose and Uses of a Contingency Table In this tutorial, I will be explaining how to generate a contingency This can work with a categorical variable because it is only counting the odds ratio and marginal totals of occurrences of the categorical variable, and can do any calculations like a chi square test that you may see with a data frame instead of a flat contingency table. What’s important in a contingency table is that it presents the frequency count, or in layman terms, the occurrences of a variable in a convenient manner. A contingency table is something that statisticians often use to determine a relationship between two or more categorical variables.
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