Stat 111 - Statistical Methods and Inference
Course Description
Definitions and properties of events; probability and random variables; joint,
conditional, and marginal distributions; some important probability distributions
Course Learning Outcomes
After completion of the course, the student should be able to:
- Demonstrate mastery of the basic concepts of classical statistical inference
- Analyze the data in terms of location, spread, and other descriptive measures
- Examine some distribution theoretical results that are engendered by sampling
- Construct estimators of parameters
- Examine optimal properties of estimators and tests of hypotheses
- Formulate steps in explaining phenomena and making decisions based on available data
Course Outline
UNIT 0. Preliminaries and Introduction
UNIT 1. Descriptive Statistics
- Basic Terms and Concepts
- Data Collection
- Data Presentation
- Data Descriptio1
UNIT 2. Sampling and Sampling Distribution
- Introduction to Sampling
- The Sample Mean and Sample Variance
- Sampling from the Normal Distribution
UNIT 3. Parametric Point Estimation
- Introduction
- Methods of Finding Point Estimators
- Properties of Point Estimators
UNIT 4. Parametric Interval Estimation
- Introduction and Basic Concepts
- Confidence Interval for the Mean when Sampling from a Normal Population
- Confidence Interval for the Variance when Sampling from a Normal Population
- Confidence Interval for the Difference in Means when Sampling from a Normal Population
UNIT 5. Parametric Tests of Statistical Hypotheses
- Introduction and Basic Concepts
- Tests of Hypotheses on the Mean of a Normal Population
- Tests of Hypotheses on the Difference in Means of Two Normal Populations