15.075 Applied Statistics, Spring 2003
Name
15-075-spring-2003/contents/index.htm
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16.93 KB
Format
HTML
Checksum (MD5)
6eda41bf9e983b4b319a709d4f39cf7c
Author(s)
Newton, Elizabeth
Alternative Title
Applied Statistics
Date Issued
June 2003
Abstract
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.
Subjects
data analysis
multiple regression
analysis of variance
multivariate analysis
data mining
probability
collecting data
sampling distributions
inference
linear regression
ANOVA
nonparametric methods
polls
surveys
statistics
management science
finance
statistical graphics
estimation
hypothesis testing
logistic regression
contingency tables
forecasting
factor analysis
Statistics
MIT Department
Sloan School of Management
Terms of Use
Persistent DSpace Link
