Chi-squared test is typically used for evaluating strength of association in scenarios where the measurement variables are categorical (example: vehicles classified as two-wheelers, four-wheelers etc.). It's also a nonparametric test - in other words, it is "distribution-free". Similar to how correlation coefficient helps interpret strength of association between real-valued variables, Cramer's V-score from Chi-squared test can be used with categorical variables. Degrees of freedom (df) for a chi-aquare distribution is 1 less than number of categories (df = number of categories - 1). Here's a handy table for how to interpret Cramer's V-score :
There are two popular types of correlation coefficients (Pearson and Spearman). See the table below for how to interpret these cofficients.
In our group, we have been interested in the question of how the structure of a webpage influences its performance on the web. It is (in my opinion) one of the key questions at the heart of distributed web application delivery. Thanks to amazing resources like HTTP Archive and BigQueri.es, it is pretty straight forward to access and play with large scale web performance data (measured twice a month across 400,000+ websites and made available for free!).