Statistics for Economics 2026

Logistics

Instructor: Srikanth B. Pai
Schedule: Tuesdays and Thursdays, 9:30–11:00 AM
Venue: New BA Building, first floor
Teaching Assistants: Saisree S, Aarti Balu, Shreya Shatarupa, and Sai Vibhuti Srivastava

This page contains the public course outline. The weekly plan is tentative and may be modified depending on pace, holidays, and internal examination dates.

Course Description

This course introduces the probability and statistical inference needed for economics and data analysis. The first part develops probability theory, random variables, standard distributions, expectation, variance, covariance, and joint distributions. The second part studies random sampling, the Law of Large Numbers, the Central Limit Theorem, estimation, confidence intervals, and hypothesis testing.

The emphasis will be on conceptual clarity, mathematical calculation, proving claims and problem-solving. Examples will be drawn from economics, finance, data analysis, and standard probability models.

Textbook

Richard J. Larsen and Morris L. Marx, An Introduction to Mathematical Statistics and Its Applications, Fifth Edition, Prentice Hall, 2012.

The textbook is the main reference. Additional problems, notes, or practice sheets may be provided during the course.

Tentative Class Plan

Week	Topics
1	Basic probability: sample spaces, probability axioms, conditional probability, Bayes’ rule
2	Independence, counting methods, random variables, distributions of random variables (pmf and cdf)
3	Standard discrete distributions: Bernoulli, binomial, hypergeometric, Poisson
4	Continuous random variables, densities, cumulative distribution functions ; TEST
5	Uniform, exponential, and normal distributions; standardization and approximations
6	Expectation, variance, functions of random variables, moment-generating functions
7	Joint distributions, marginal distributions, multivariate normal
8	First Assessment, Covariance, correlation.
9	Conditional expectation, random samples, sample mean, sample variance,
10	Order statistics, Modes of convergence: almost sure convergence
11	Convergence in probability and L1, Markov/Chebyshev, Law of Large Numbers
12	Convergence in distribution, Central Limit Theorem, standard errors, normal approximation; TEST
13	Point estimation: method of moments, maximum likelihood estimation, bias, variance, consistency
14	Confidence intervals: means, proportions, variances, and interpretation
15	Second Assessment Examinations; introduction to hypothesis testing
16	Hypothesis testing, p-values, errors, power, common tests, likelihood ratio tests; Neyman-Pearson lemma, or Bayesian inference depending on time

Assignments

Tests

Remarks

The course plan is tentative. Some topics may take more or less time depending on the class pace. The instructor may modify the order of topics while preserving the broad structure of the course.