Applied Statistics ( ocw.mit.edu)
As taught in: Spring 2003
Diagram showing the difference between statistics and probability. (Image by MIT OpenCourseWare. Based on Gilbert, Norma. Statistics. W.B.
Saunders Co., 1976.)
Level:
Undergraduate
Instructors:
Dr. Elizabeth Newton
Course Features
Course Description
Course Features

Lecture notes
Lecture Notes
The lecture notes reference the 15.075 course textbook: Statistics and Data Analysis from Elementary to Intermediate
by Ajit C. Tamhane and Dorothy D. Dunlop, Prentice Hall, 2000. They also occasionally refer to: Casella, George, and
Roger L. Berger. Statistical Inference. Belmont, CA: Duxbury Press, 1990.
Some slides were prepared by or based on slides by: Prof. Roy Welsch (MIT), Prof. Gordon Kaufman (MIT), Prof.
Jacqueline Telford (Johns Hopkins University), and Prof. Ramón León (University of Tennessee). These contributions
have been acknowledged on the first slide of each lecture.
LEC #
TOPICS
1
Introduction and Collecting Data (PDF)
2
Summarizing and Exploring Data (PDF - 1.4 MB)
LEC #
TOPICS
3
Summarizing and Exploring Data (See PDF file in Lecture 2)
4
Review of Probability (PDF)
5
Sampling Distributions (PDF)
6
Basic Concepts of Inference (PDF)
7
Inference for Single Samples (PDF)
8
Inference for Two Samples (PDF)
9
Inference for Proportion and Count Data (PDF)
10
Review and Examples (PDF)
Inference in a Nutshell (PDF)
11
Midterm Exam
12
Simple Linear Regression and Correlation (PDF)
13
Simple Linear Regression and Correlation (See PDF file for
Lecture 12)
14
Multiple Linear Regression (PDF)
Multiple Linear Regression (See PDF file for Lecture 14)
15
Logistic Regression (PDF)
Regression Review and Robust Regression (PDF)
16
ANOVA - single factor (PDF)
17
ANOVA - single factor (See PDF file for Lecture 16)
18
ANOVA - multifactor (PDF)
LEC #
TOPICS
19
ANOVA - multifactor (See PDF file for Lecture 18)
20
Nonparametric Methods (PDF)
21
Nonparametric Methods (See PDF file for Lecture 20)
22
Special Topics
23
Special Topics
24
Review and Examples (PDF)
25
Final Exam
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.