Mathematical Statistics II
Undergraduate course, ADA University, School of Business, 2026
Mathematical Statistics II
Advanced statistical inference and methods for Economics and Finance students. This course builds on probability foundations to cover estimation, hypothesis testing, regression analysis, and ANOVA with applications to economic and financial data analysis.
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Course Topics
Unit 1: Multivariate Distributions
Unit 2: Functions of Random Variables
Unit 3: Estimation Theory
Unit 4: Hypothesis Testing
Unit 5: Advanced Estimation
Unit 6: Linear Models
Course Information
Learning Objectives & Program Alignment
Acquire and organize economic data from multiple sources using statistical software and digital tools for advanced statistical analysis
Contributes to PLO 2: Acquire and organize information relevant to economics using various resources and digital technologiesEvaluate the appropriateness of statistical methods and techniques for analyzing economic data by assessing underlying assumptions, data characteristics, and research objectives
Contributes to PLO 3: Evaluate the applicability of empirical and theoretical methods to economic problemsInterpret the results of estimation, hypothesis testing, and inference procedures to draw objective conclusions about economic relationships
Contributes to PLO 4: Interpret the results of empirical and theoretical analyses to draw objective conclusionsIdentify, analyze, and solve economic problems by applying advanced concepts such as two-sample inference, goodness-of-fit tests, regression, analysis of variance, and nonparametric statistics
Contributes to PLO 5: Identify, analyze, and solve problems by applying theoretical knowledge and empirical toolsCourse Topics & Interactive Lectures
Unit 1: Multivariate Probability Distributions
Topic 1: Multivariate Probability Distributions
Topic 2: Expected Values, Covariance and Correlation
Unit 2: Functions of Random Variables & Sampling Distributions
Topic 3: Functions of Random Variables
Topic 4: Sampling Distributions
Unit 3: Estimation Theory
Topic 5: Point Estimation
Topic 6: Interval Estimation
Topic 7: Properties of Point Estimators and Methods of Estimation
Unit 4: Hypothesis Testing
Topic 8: Fundamentals of Hypothesis Testing
Topic 9: Tests for Means and Proportions
Topic 10: Two-Sample Tests
Unit 5: Advanced Estimation Methods
Topic 11: Method of Moments & Maximum Likelihood Estimation
Unit 6: Linear Models and Regression
Topic 12: Linear Models & Least Squares Estimation
Assessment Strategy
Quizzes
Homework
Midterm Exam
Final Exam
Grading Scale
Course Schedule
| Week | Topic | Chapter(s) |
|---|---|---|
| 1 | Multivariate Probability Distributions | 5.5 – 5.7 |
| 2 | Covariance and Correlation | 5.8 – 5.11 |
| 3 | Functions of Random Variables | 6.1, 6.2, 6.5, 6.7 |
| 4 | Sampling Distributions | 7 |
| 5 | Central Limit Theorem | 7 |
| 6 | Point Estimation | 8 |
| 7 | Interval Estimation | 8 |
| 8 | Midterm Exam (March 18) | — |
| 9 | Properties of Estimators | 9 |
| 10 | Fundamentals of Hypothesis Testing | 10 |
| 11 | Tests for Means and Proportions | 10 |
| 12 | Two-Sample Tests | 10 |
| 13 | Method of Moments & Maximum Likelihood | 9 |
| 14 | Linear Models & Least Squares | 11 |
| 15 | Review & Final Preparation | — |
| Final Exam — May 6, 2026 | — |
Student Evaluations
Student evaluation data will be available after the Spring 2026 semester.
Course Literature
Mathematical Statistics with Applications
Authors: Wackerly, D. D., Mendenhall, W., & Scheaffer, R. L.
Edition: 7th Edition (2008)
Publisher: Cengage Learning
Course Materials
Lecture Notes: Available at sorujov.net
Software: R and Estat programs for computational practice
Additional Materials: Dropbox folder
Technical Notes for Interactive Lectures
Optimal Experience
Use fullscreen mode (F11) for mathematical visualizations
Navigation
Arrow keys or on-screen buttons for slide progression
Requirements
Modern browser with JavaScript enabled
Statistical Software
R and Estat for data analysis, visualization, and computational verification
Academic Integrity
All coursework conducted in accordance with ADA University Honor Code standards