Understand and apply various data collection methods, measures of central tendency and spread, and analyze the relationship between two variables using appropriate statistical techniques.
Calculate and interpret conditional, joint, and marginal probabilities, utilize probability distributions, and conduct statistical inference, including point estimates, confidence intervals, significance testing, and goodness-of-fit tests.
Perform various two-sample tests, understand and apply Bayesian statistics including Bayes theorem, conjugate priors, posterior distributions, and credible intervals, and conduct A/B testing.
Understand the principles of logistic regression, apply it to real-world data for classification and prediction, and interpret the results to make informed decisions based on statistical analysis.