Math 1281 Statistical Inference

MATH 1281: STATISTICAL INFERENCE

Course Description:

This course covers inferential statistics, estimation, and hypothesis testing. The emphasis in the course is on the presentation of statistical methods and on the interpretation of the outcome. This course considers inferential statistics: point estimation, confidence intervals, and hypothesis testing. The inference is applied to single measurements and is used to investigate the relations between measurements. The R system for data analysis is used as part of the teaching. The philosophy and practice of statistics, and not its mathematics, is at the center; needed mathematical computations are demonstrated via simulations rather than by abstract proofs.

Required Textbook and Materials:

The main required textbooks for this course are listed below and can be readily accessed using the provided links. There may be additional required/recommended readings, supplemental materials, or other resources and websites necessary for lessons; these will be provided for you in the course’s General Information and Forums area, and throughout the term via the weekly course Unit areas and the Learning Guides.

Software Requirements/Installation:

Students are required to download and install the R program on their own computer or on a USB portable storage device. Complete instructions are provided as an integral part of the course content and learning objectives regarding how to download, install and test the application.

Learning Objectives and Outcomes:

By the end of this course students will be able to:

1. Define different methods for statistical inference: point estimation, confidence intervals, and hypothesis testing.
2. Recognize and use models for describing relations between measurements.
3. Apply and interpret the outcomes of statistical inference.
4. Explain the assumptions behind various procedures for inference.
5. Use simulations in order to validate probabilistic properties of procedures for inference.

Course Schedule and Topics:

This course will cover the following topics in eight learning sessions, with one Unit per week. The Final Exam will take place during Week/Unit 9 .

Week 1: Unit 1 – Introduction

Week 2: Unit 2 – Point Estimation

Week 3: Unit 3 – Confidence Intervals

Week 4: Unit 4 – Testing Hypothesis

Week 5: Unit 5 – Comparing Two Samples

Week 6: Unit 6 – Linear Regression

Week 7: Unit 7 – R-squared in Linear Regression and Bernoulli Response

Week 8: Unit 8 – Case Studies

Week 9: Unit 9 – Course Overview and Final Exam