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.
\(\)The significance level α is the probability of rejecting the null hypothesis when it is assumed to be true. Let us set α = 0.05, which indicates a 5% risk of concluding that a difference exists when there is no actual difference.(http://blog.minitab.com/blog/adventures-in-statistics-2/understanding-hypothesis-tests%3A-significance-levels-alpha-and-p-values-in-statistics)
Since we use the numeric output, i.e. Chi-square statistic, we compare the P-value to critical value determined by α to make conclusions. For the Chi-square distribution, the critical  value at α = 0.05 equals 3.84. (http://intersci.ss.uci.edu/wiki/images/3/3a/Normal01.jpg)
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 \(H_1.\)