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15.075 Applied Statistics, Spring 2003

Image comparing statistics and probability.
Diagram showing the difference between statistics and probability. (Image by MIT OpenCourseWare. Based on Gilbert, Norma. Statistics. W.B. Saunders Co., 1976.)

Highlights of this Course

Material in 15.075 is presented through its comprehensive set of lecture notes. Students receive hands-on experience with statistical software through the assignments and with examples in the lecture notes.

Course Description

This course is an introduction to applied statistics and data analysis.  Topics include collecting and exploring data, basic inference, simple and multiple linear regression, analysis of variance, nonparametric methods, and statistical computing. It is not a course in mathematical statistics, but provides a balance between statistical theory and application. Prerequisites are calculus, probability, and linear algebra.

We would like to acknowledge the contributions that Prof. Roy Welsch (MIT), Prof. Gordon Kaufman (MIT), Prof. Jacqueline Telford (Johns Hopkins University), and Prof. Ramón León (University of Tennessee) have made to the course material.

 

Staff

Instructor:
Dr. Elizabeth Newton

Course Meeting Times

Lectures:
Two sessions / week
1.5 hours / session

Recitations:
One session / week
1 hour / session

Level

Undergraduate

Additional Features

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Translations
   Chinese (Simplified)

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