R. 215, College of Public Health, NTU
Speaker:
Hung Hung (National Taiwan University)
Organizers:
Weichung Wang (National Taiwan University)
I. Course Goal：
This course covers some of the statistical foundations for medical AI. In particular, the topics include linear regression, logistic regression, ROC curve analysis, Poisson regression, and survival analysis. The course will start from continuous data to illustrate the rationale of the linear regression model and its applications. The concept will be extended to different data structures, including binary data, count data, and survival data. Another topic of this course will focus on the implementation of the R software and the ability to analyze the real data sets. We expect students can choose the appropriate methods (as well as understand the rationale of the methods) to conduct the statistical analysis for a given data set correctly.
II. Contents：

Review basic statistical analysis

Simple linear regression

Least squares estimate

Rsquare & adjusted Rsquare

mean response, prediction, residual

Multiple linear regression

Full & Reduced model, ANOVA

Introduction of generalized linear model, MLE

Logistic regression

ROC curve analysis

Poisson regression

Introduction of survival analysis

KaplanMeier estimator

Log rank test

Cox PH model and its extensions
Contact:
murphyyu@ncts.ntu.edu.tw