Econ 3818 is an introductory course in the theory and methods of statistics. Statistics allows datasets to be transformed into usable information, analyzed for patterns and trends, which improve decision-making.
Upon completion of the course, students should be able to
- Be prepared for a future course in Econometrics { the data driven side of economics.
- Will be able to load datasets into R and perform statistical methods to gather information about the data.
- Understand the probability theory behind basic statistical tests and implement the methods.
We will study basic probability, probability distributions (especially the normal distribution), and descriptive and inferential statistics, including estimation and hypothesis testing. Emphasis is on both theory and applications. Weekly problem sets will explore issues in statistical theory and practice. The course will use the programming language R to do data analysis on simulated and real datasets
The Basic Practice of Statistics. David Moore, William Notz, and Michael A Fligner.
Lecture 01: Introduction and Intro to Statistics
Readings: Chapter 1
Lecture 02: Describing Distributions
Readings: Chapter 2
Lecture 03: Introducing Probability
Readings: Chapter 12
Lecture 04: General Rules of Probability
Readings: Chapter 13
Lecture 05: The Bernoulli and Binomial Distributions
Readings: Chapter 14
Lecture 06: The Normal Distribution
Readings: Chapter 3
Lecture 07: Distributions
Lecture 08: Expectations
Lecture 09: Producing Data - Sampling
Readings: Chapter 8
Lecture 10: Producing Data - Experiments
Readings: Chapter 9
Lecture 11: Parameters and Statistics
Readings: Chapter 15
Lecture 12: Confidence Intervals
Readings: Chapter 16
Lecture 13: Tests for Significance
Readings: Chapter 17
Lecture 14: Inference in Practice
Readings: Chapter 18
Lecture 15: Inference about a Population Mean
Readings: Chapter 20
Lecture 16: Comparing Two Means
Readings: Chapter 21
Lecture 17: Inference about a Population Proportion
Readings: Chapter 22
Lecture 18: Comparing Two Proportions
Readings: Chapter 23
Lecture 19: Correlation
Readings: Chapter 4
Lecture 20: Regression
Readings: Chapter 5
Lecture 21: Two-Way Tables
Readings: Chapter 6
Lecture 22: Regression Inference
Readings: Chapter 26