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- Author:
-
Newton, Elizabeth
- Subject:
- Business, Mathematics and Statistics
- Institution Name:
- M.I.T.
- Collection:
-
MIT OpenCourseWare
- Grade Level:
- Post-secondary
- Abstract:
Introduces statistical data analysis, concentrating on techniques used in management science and finance. Topics chosen from: statistical graphics, basics of sampling, estimation, hypothesis testing, linear and logistic regression, analysis of variance, contingency tables, forecasting, statistical quality control, principal components, and factor analysis. SAS or similar package used for data analysis. 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.
- Languages:
- English
- Material Type:
- Assessments, Full Course, Homework and Assignments, Lecture Notes, Syllabi, Other
- Media Format:
- Text/HTML, Downloadable docs
- Conditions of Use:
-
Creative Commons Attribution-Noncommercial-Share Alike 3.0
No restrictions on your remixing, redistributing, or making derivative works.
Give credit to the author, as required.
Your remixing, redistributing, or making derivatives works comes with some
restrictions, including how it is shared.
Your redistributing comes with some restrictions. Do not remix or make
derivative works.
Copyrighted materials, available under Fair Use and the TEACH Act for US-based
educators, or other custom arrangements. Go to the resource provider to see
their individual restrictions.
Applied Statistics, Spring 2003
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