Academic Year/Semester: 111-1
Course Number: 130066
Instructor: Ka-Leung Hung
Course Title: Statistics (1)
Grade: 2 Credits: 3.0
Class location: Room 226, Management Building
Class hours: 5bcd
This course aims to cultivate the following core competencies for students:
Managerial decision-making skills (undergraduate program)
Course objectives:
The purpose is to enhance the student’s capability of analyzing and interpreting data so as to make managerial decisions. This course crosses two semesters and designed for introduction to statistics and data analysis, including data description, sample distribution, regression, analysis of variance, and nonparametric methods. The participants had better take the course in calculus or linear algebra before. This course will give you three hours of lectures each week and necessary homework which may need the aids of computing and statistical software without difficulty. The teaching assistants could introduce the statistical tools and answer the problems. I am also available after class, but the best way to see me is to schedule some time by phone or email, or meet at the announced office hours.
Instructor office hours:Tuesday 09:00~11:00 Room 5062 (College of Management)
Teaching approach:
In-class presentations, lectures.
Grading criteria:
Midterm exam:25%, Final exam:30% (If quizzes become necessary, these percentage will be increased accordingly.)|
Homework: 30% (Homework could be conducted by two partners at most. However, those partners must commit credibly to cooperate within the entire semester each other. More importantly, late homework will not be accepted.)
Class participation: 15% (The percentage may be reduced if quizzes become necessary.)
Course schedule (week, topic, activities, evaluation/assignment, text, etc.):
Week 1: Introduction to the course
Week 2: Introduction to Probability and Statistics
Week 3: Treatment of Data
Week 4: Probability: Sample spaces and events
Week 5: Conditional probability & Bayes’ formula
Week 6: Probability distribution: Random variables
Week 7: Variance, moment generating functions and the law of large numbers problems
Week 8: Bernoulli, Binomial, Poisson random variables
Week 9: Midterm exam
Week 10: Hypergeometric, uniform, normal exponential, Gamma random variables
Week 11: Checking the normality and data transformation
Week 12: Distributions of sampling statistics: sample mean, sample variance and the central limit theorem
Week 13: Sampling distribution from a normal population
Week 14: Inferences concerning means with point estimation
Week 15: Confidence intervals
Week 16: Hypothesis testing: t-test the equality of means
Week 17: Paired t-test and testing for equality of variances
Week 18: FINAL EXAM
