We are working with categorical data, i.e. subscribers and customers and looking at proportions of Citi Bike usage during weekend and weekdays. Therefore Chi-Square test of proportions is implemented in the statistical analysis.The chi square statistic \(\chi^{2\ }\)is given by:
\(\chi^{2\ }=\ \Sigma\frac{\left(f_{observed\ }-\ f_{expected}\right)^2}{f_{expected}}\)
where \(f_{observed\ },\ f_{expected\ }\)
are derived from contingency tables.
Calculated Chi-square statistics for two selected months (January and July) in 2016 is significantly higher than the critical value. Therefore we can reject \(H_0\) in favor of the \(H_1.\)