Applied Statistics

Syllabus (Fall 2017, HKUST)

• Chapter 0. Introduction to Statistics [Lecture notes]
  • Suggested reading: The art of R programming: A Tour of Statistical Software Design, by Norman Matloff. 2011.
  • Suggested reading: ggplot2: Elegant Graphics for Data Analysis (Use R!) 2nd edition, by Hadley Wickham. 2016.
• Chapter 1. Descriptive Statistics [Lecture notes]. [Reviewed Questions]
• Chapter 2. Probability [Lecture notes]. [Reviewed Questions] [Discussion]
  • Suggested reading: Chapter 1 "A Murder Mystery" of The book "Model-based machine learning" by John Winn and Christopher Bishop.
  • Suggested reading: Book "Thinking, Fast and Slow" by Daniel Kahneman
• Chapter 3. Random Variable and Probability Distribution. [Lecture notes] [Reviewed Questions 1] [Reviewed Questions 2]
  • Suggested reading: Chapter 2 "Assessing people's skills" of The book "Model-based machine learning" by John Winn and Christopher Bishop.
  • One additional exercise for Normal distribution and Bayes Rule [ex_Normal Distribution][Solution]
• Chapter 4. Estimation. [Lecture notes] [Reviewed Questions]
  • R code for confidence interval visualised by ggplot2
  • More R code examples
  • [Exercises][Solution]
• Chapter 5. Testing Hypothesis. [Lecture notes][Reviewed Questions]
  • [Exercises][Solution]
• Chapter 6. Simple linear regression. [Lecture notes][Reviewed Questions]
  • [Exercises][Solution]
  • [Rcode]
  • [Rcode for reviewed questions]
• Chapter 7. Two-sample problems. [Lecture notes][Reviewed Question]
  • [Exercises][Solution]
• Chapter 8. ANOVA (Analysis of Variance). [Lecture notes]
• Chapter 9. Goodness-of-fit test. [Lecture notes][Reviewed Questions]
• Epilogue: where to go from here
  • Overview of Statistical learning [notes]
  • An Introduction to Statistical Learning [On-line course by Hasite and Tibshirani]
  • Learning from Data by Yaser S. Abu-Mostafa, Malik Magdon-Ismail and Hsuan-Tien Lin. [On-line course by Yaser S. Abu-Mostafa]
  • Doing Bayesian Data Analysis. By John K. Kruschke.
  • The book "Model-based machine learning" by John Winn and Christopher Bishop. [website]

Exam

• Mid-term Examination (LTG & LTJ, October 11, 7:30pm-9:30pm)
• Final Examination (TBA)

Grading

• Mid-term Examination 35% (0%)
• Final Examination 65% (100%)

Policy

• No make-up midterm examination will be given.
• The weight of missed midterm exam will be transferred to the final exam.
• This course is essentially graded by curve.
  • Supporting information here is from JBstatistics.

• The maximum of the total scores calculated by the above two weighting schemes will be taken to determine the student's grade.