Lecture 18: Hypothesis Testing and Correlation

Where the last lecture was about summary statistics for a single set of data, we now address multi-dimensional data with several linked sets of values among which we might look for correlations. This leads into several more sophisticated questions which are key to the effective application of statistics: how do we identify potential correlations; how do we know when a result is significant; and what might this tell us about any causal connections?

The slides include formulae for computing the correlation coefficient for two datasets, for estimating the correlation coefficient of population data from a random sample, and how to identify when this is statistically significant.

I also included a variety of material on the importance of visualisation, correlations which may or may not indicate causation, and the classic application of statistics to counting tanks.


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