2.1 Visualising the data and assumptions of the test

As usual, it is a good idea to visualise the data as this can give us a good overview and help us understand what we are analysing. The below stacked bar chart can help us to understand visually how different or similar the set of observed and expected proportions are with regard to Facebook usage among social media users.

For the chi-squared goodness of fit test, the following assumptions apply:

Chi-squared goodness of fit assumptions:

1. No more than 20% of categories have an expected count of less than 5
2. There are no expected counts of zero

We will be able to check these assumptions after carrying out the test.

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These notes have been prepared by Amanda J. Shaker. The copyright for the material in these notes resides with the author named above, with the Department of Mathematics and Statistics and with La Trobe University. Copyright in this work is vested in La Trobe University including all La Trobe University branding and naming. Unless otherwise stated, material within this work is licensed under a Creative Commons Attribution-Non Commercial-Non Derivatives License CC BY-NC-ND