Spring 2019 - STAT 285 D100
Intermediate Probability and Statistics (3)
Class Number: 3410
Delivery Method: In Person
This course is a continuation of STAT 270. Review of probability models. Procedures for statistical inference using survey results and experimental data. Statistical model building. Elementary design of experiments. Regression methods. Introduction to categorical data analysis. Quantitative.
- Review of STAT 270 material and relationship to this course
- Statistical Models
- Parameter estimation - least squares and likelihood methods
- Hypothesis tests
- Introduction to Regression Analysis - simple and multiple
- Analysis of Variance
- Categorical Data Analysis
- Homework Assignments 10%
- Midterm 1 20%
- Midterm 2 20%
- Final Exam 50%
Above grading is subject to change.
Probability and Statistics for Engineering and the Sciences (9th ed.) by Jay L. Devore. Publisher: Duxbury Press
Department Undergraduate Notes:
Students with Disabilites:
Students requiring accommodations as a result of disability must contact the Centre for Accessible Learning 778-782-3112 or firstname.lastname@example.org
Students looking for a Tutor should visit http://www.stat.sfu.ca/teaching/need-a-tutor-.html. We accept no responsibility for the consequences of any actions taken related to tutors.
SFU’s Academic Integrity web site http://students.sfu.ca/academicintegrity.html is filled with information on what is meant by academic dishonesty, where you can find resources to help with your studies and the consequences of cheating. Check out the site for more information and videos that help explain the issues in plain English.
Each student is responsible for his or her conduct as it affects the University community. Academic dishonesty, in whatever form, is ultimately destructive of the values of the University. Furthermore, it is unfair and discouraging to the majority of students who pursue their studies honestly. Scholarly integrity is required of all members of the University. http://www.sfu.ca/policies/gazette/student/s10-01.html
ACADEMIC INTEGRITY: YOUR WORK, YOUR SUCCESS