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Business Statistics

University of Chicago Booth School of Business

Business Statistics 41000 - Autumn 2022

David K.A. Mordecai, Adjunct Professor of Econometrics and Statistics

Course Description
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 in the 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 times series analysis: autocorrelation, autoregression, the random walk, the z-transform and its role in time series analysis. What is prediction (vs estimation, classification, regression)?
  • Autumn Schedule: September 30, 2022 – December 9, 2022
  • Section 41000-04: Friday 8:30AM-11:30AM, Harper Center C01
  • Section: 41000-05: Friday 1:30PM-4:30PM, Harper Center C01
  • In-Person Only
  • Final Project due: Friday December 9, 2022
  • Download Syllabus
  • Teaching Assistant(s): Franco Calle Falcon
Recommended Textbook(s)
  • Principal reference text: OpenIntro Statistics, Dietz, D. et al (2019)
  • Supplemental reference text: Statistical Techniques in Business Economics, Lind, D.A., W.G. Marchal and S.A. Wathen (2019)
Supplemental Reading
  • The Lady Tasting Tea: How Statistics Revolutionized Science in the Twentieth Century, Salsburg, D. (2002)
  • Errors Blunders & Lies: How to Tell the Difference, Salsburg, D. (2017)
  • Common Errors in Statistics (and How to Avoid Them), Good, P.I., J.W. Hardin (2012) 
  • Mostly Harmless Econometrics, Angrist, J.D. and J-S. Pischke (2008)
  • Data Science for Business: What You Need to Know About Data-Mining and Data-Analytic Thinking, Provost, F. and T. Fawcett (2013)
  • Recent Advances and Future Directions in Causality, Prediction, and Specification Analysis: Essays in Honor of Halbert L. White Jr., Chen, X. and N.R. Swanson eds. (1989)
Class Notes
  • Section 1: Introduction and Probability
  • Section 2: Estimation and AB Testing
  • Section 3: Linear Regression
  • Section 4: Multiple Regression, Trees, Forests, Bootstrap, Generalized Linear Models (e.g., Logistic Regression)
  • Section 5: Advanced Topics: Machine Learning Applications
Homework/Problem Sets
  • See Canvas
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