# 播放清單 (請按影片右上角選取影片觀看)
source: NCTU OCW 2016年10月27日
The goals of this course are to introduce regression analysis for continuous and discrete data. Topics include simple and multiple linear regressions, inferences for regression coefficients, confounding and interaction, regression diagnostics, logistic regressions, Poisson regressions, and generalized linear models.
The course consists of lectures and laboratory sessions. The lectures are given on Tuesday 9:00-11:00. The lectures will primarily review and reinforce major issues. There is a laboratory session on Tuesday 11:10-12:00. The laboratory exercise will be distributed prior to each class, and students are expected to read each lab exercise at home. Each student will be assigned to a lab group and discuss the exercise with group members in the lab. At the end of the lab, there will be a seminar-type discussion. Each group is required to hand in a write-up of laboratory problems.
The course uses the R software for statistical computing. Students are expected to be familiar with the usage of the software.
本課程是由交通大學統計學研究所提供。
課程資訊：http://ocw.nctu.edu.tw/course_detail....
更多課程歡迎瀏覽交大開放式課程網站：http://ocw.nctu.edu.tw/
Lec 01 Introduction 10:10
Lec 02 A review of basic statistical concepts 57:23
Lec 03 Measures of association with emphasis on the difference of means 25:24
Lec 04 Basics of linear regression analysis 2:02:21
Lec 05 同學報告 10:13
Lec 06 Correlation 1:04:49
Lec 07 同學報告 14:05
Lec 08 Analysis of variance (ANOVA) table and prediction of y 48:40
Lec 09 Basics of multiple linear regression 45:40
Lec 10 同學報告 12:16
Lec 11 Hypothesis testing in multiple regression 50:06
Lec 12 Polynomial terms and dummy variables 46:43
Lec 13 同學報告 16:47
Lec 14 Interaction and confounding 54:10
Lec 15 補充: Confounding and interaction in epidemiology 45:14
Lec 16 同學報告 18:29
Lec 17 Regression diagnosis 1:23:04
Lec 18 Variable selection and model building 54:31
Lec 19 同學報告 7:00
Lec 20 同學報告 13:43
Lec 21 Relative risk, odds ratio and significance testing for 2*2 tables 1:43:34
Lec 22 Introduction to logistic regression 1:05:54
Lec 23 同學報告 22:51
Lec 24 Logistic regression for contingency tables 47:54
Lec 25 Goodness-of-t for logistic regression 45:55
Lec 26 同學報告 12:01
Lec 27 Logistic regression for case-control data and conditional logistic regression 1:10:09
Lec 28 同學報告 8:55
Lec 29 Analysis of polytomous data 1:06:00
Lec 30 同學報告 5:18
Lec 31 Poisson regression and log-linear model 1:13:54
Lec 32 同學報告 17:08
Lec 33 Generalized linear models 42:05
Lec 34 同學報告 10:54
No comments:
Post a Comment