This course introduces fundamental statistical concepts and basic computational methods for data analysis, in order to perform descriptive and predictive data analysis based on real datasets as a quantitative foundation for Chicago Booth elective courses in marketing, finance and economics as well as for advanced courses in data science. The primary emphasis will be practical application of critical statistical thinking and problem solving to analyze complex empirical examples from economics, finance and marketing with a primary emphasis on conceptual intuition for critical analysis of data, as well as fundamental intuition for statistical science, foundational to critical analysis and validation of models and evaluate workproduct in order to manage quants and data science teams.
Topics to be addressed include: (i) descriptive statistics and data visualization; (ii) random variables and expectations; (iii) statistical model specification and inference; (iv) analysis of variance, goodness-of-fit and error diagnostics, population versus sample statistics, confidence intervals, hypothesis tests, t-statistics and p-values; (iv) linear and logistic regression; (v) introduction to multiple regression; (vi) basic time series analysis: autocorrelation, autoregression, the random walk process. What is prediction (vs estimation, classification, regression)?