AY2023
Biostatistics Practicum 2 (advanced programming using Stata and R)
(Adjunct lecturer at Graduate School of Public Health, St. Luke's International University)
Course Information
Course number: 5002040
Meeting time: 18:00-20:15 every Thursday
Room: None - online
Office hours: by email appointment
Teaching assistant: Khin Maung Soe July
Zoom link and password: will be provided via the manaba system
Syllabus: can be downloaded here.
Course Materials
Class 1 (Sep 8): Review of Biostatistics I: with Stata
Class 2 (Sep 15): Review of Biostatistics I: with Stata (cont.)
Class 3 (Sep 22): Analysis of Variance (ANOVA) in Stata
Class 4 (Sep 29): Introduction to R software
Class 5 (Oct 6): Introduction to R software (cont.)
Class 6 (Oct 13): Analysis of Variance (ANOVA) in R Assignment 1: ANOVA exercise (R or Stata at students’ choice)
Class 7 (Oct 20): Simple and Multiple Linear Regression Models: assumptions, model fit in Stata
Class 8 (Oct 27): Simple and Multiple Linear Regression Models: model fit, confounding, mediation and interaction in R Assignment 2: Linear regression (R or Stata at students’ choice)
Class 9 (Nov 10): Logistic Regression Models: Single and multiple predictor models; assumptions, model fits and interpreting the parameters’ estimates in Stata
Class 10 (Nov 17): Logistic Regression Models: Single and multiple predictor models; assumptions, model fits and interpreting the parameters’ estimates in R
Class 11 (Nov 24): Loglinear Regression Models (Poisson Model) in Stata
Class 12 (Dec 1): Loglinear Regression Models (Poisson Model) in R Assignment 3: Logistic regression and Poisson model (R or Stata at students’ choice)
Class 13 (Dec 8): Survival analysis in R: Survival time, censoring, survival functions, Kaplan-Meier estimate and Log-rank Test
Class 14 (Dec 15): Survival analysis in R: Hazard functions, Cox’s proportional hazards model, interpretation of the model parameters Assignment 4: Survival analysis (R or Stata at students’ choice)
Class 15 (Dec 22): Survival analysis in Stata
Economic Statistics (Statistics and programming using R)
(Adjunct lecturer at Faculty of Liberal Arts, Sophia University)
Course Information
Course number: IBE340
Meeting time: 10:55-12:35 every Monday and Thursday
Room: 2-310
Office hours: by email appointment
Teaching assistant: None
Syllabus: can be downloaded here
Course Materials
Class 1: Introduction
Class 2: Review of Probability and Statistics
Class 3: Review of Probability and Statistics (cont.)
Class 4: Review of Probability and Statistics (cont.)
Class 5: Simple regression
Class 6: Simple regression (cont.)
Class 7: Simple regression (cont.)
Class 8: Multiple Regression: Estimation
Class 9: Multiple Regression: Estimation (cont)
Class 10: Multiple Regression: Estimation (cont.)
Class 11: Multiple Regression: Inference
Class 12: Multiple Regression: Inference (cont.)
Class 13: Multiple Regression: Inference (cont.)
Class 14: Catch-up & Review
Class 15: Mid-term Exam
Class 16: Multiple Regression: Specification
Class 17: Multiple Regression: Specification (cont.)
Class 18: Multiple Regression: Specification (cont.)
Class 19: Multiple Regression: Assessing Empirical Studies
Class 20: Multiple Regression: Assessing Empirical Studies (cont.)
Class 21: Panel Data
Class 22: Panel Data (cont.)
Class 23: Instrument Variable Regression
Class 24: Instrument Variable Regression (cont.)
Class 25: Presentation
Class 26: Presentation (cont.)
Class 27: Catch-up & Review
Class 28: Final Exam