This course introduces probability theory and statistical inference, with applications in economic modeling and data analysis. The first part covers probability concepts, random variables, and probability distributions, while the second part focuses on sampling distributions, estimation, and hypothesis testing.
📌 Note: This course consists of two parts. I am responsible for teaching Part 1 (Probability Theory). Part 2 (Statistical Inference) will be covered by another instructor.
Introduction to Statistics (3 hr.)
Role of Statistics in Economic Analysis
Descriptive vs. Inferential Statistics
Probability Theory (6 hr.)
Basic Probability Axioms & Rules
Conditional Probability & Bayes’ Theorem
Discrete Random Variables and Their Probability Distributions (6 hr.)
Bernoulli, Binomial, Geometric, Poisson Distributions
Expected Value, Variance, and Moments
Continuous Random Variables and Their Probability Distributions (6 hr.)
Uniform, Normal, Exponential, Log-Normal Distributions
The Importance of the Normal Distribution in Economics
Multivariate Probability Distributions (6 hr.)
Joint, Marginal, and Conditional Distributions
Covariance, Correlation, and Independence
📌 Total: 27 Hours
Sampling Distributions and the Central Limit Theorem (6 hr.)
Properties of Point Estimators and Methods of Estimation (6 hr.)
Hypothesis Testing (4.5 hr.)
Group Work / Case Studies (1.5 hr.)
📌 Total: 18 Hours
Dennis D. Wackerly, William Mendenhall III, and Richard L. Scheaffer (2008) – Mathematical Statistics with Applications
Speegle, D., & Clair, B. (2022) – Probability, Statistics, and Data: A Fresh Approach Using R
Casella, G., & Berger, R. L. (2002) – Statistical Inference