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Statistical Methods (DSC-670)

Semester: Summer 2021
Number: 0207-670-001
Instructor: Susan Li
Days: Tuesday Thursday 6:00 pm - 9:00 pm
Note: Online, Synchronous
Location: Online
Credits: 3
Course Meets: July 6 - August 9

Class Will Be Livestreamed Online On Tr From 6-9pm.

Course Materials: View Text Books
Related Syllabi: Adam Wittenstein for Summer 2013*
Jiang Zhang for Summer 2013*
Darko Skorin-Kapov for Spring 2020*

*Attention Students: Please note that the syllabi available for your view on these pages are for example only. The instructors and requirements for each course are subject to change each semester. If you enroll in a particular course, your instructor and course outline may differ from what is presented here.


Organizations confront a myriad of problems characterized by uncertainty in the environment. The decision making process requires models, analysis, and solutions that account for this uncertainty, and statistics provides a methodology that assists in the resolution of those issues. Topics include probability, sampling distributions, parameter estimation, hypothesis testing, regression/correlation.

Learning Goals:   After completing this course, you should be able to:1. Use measures of central tendency (mean, median, and mode) and dispersion (variance and standard deviation) to summarize numeric data2. Describe the distributions of many economic variables - both discrete and continuous3. Show how probability distributions are used to characterize the nature of random variables (such as revenues, earnings, interest rates, and customer demographic data)4. Describe the properties of a good estimator, and be able to identify the appropriate probability distribution for each estimator5. Calculate both point and interval estimates for each population parameter introduced in class6. Carry out a test of hypothesis for a single population.7. Explain the concept of regression, and then show how a regression model could be used to determine relationships between variables in simple linear forms8. Explain the difference between a simple linear model and a multiple regression model

*The learning goals displayed here are those for one section of this course as offered in a recent semester, and are provided for the purpose of information only. The exact learning goals for each course section in a specific semester will be stated on the syllabus distributed at the start of the semester, and may differ in wording and emphasis from those shown here.


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