NTHU STAT 5410 - Linear Models
Sep 2019 ~ Jan 2020
Institute of Statistics,
National Tsing Hua University
This course is designed to provide students with a solid overview of basic and advanced topics in linear model. Linear model is one of the most fundamental and powerful model used in Statistics. This course will include various topics about linear models from an applied viewpoint, such as definition, fitting, inference, interpretation of results, meaning of regression coefficients, identifiablity, Gauss-Markov theorem, lack of fit, multi-collinearity, transformations of response and predictors, variable selection, ridge regression, principal components regression, partial least squares, regression splines, diagnostics, influential observations, robust procedures, ANOVA and analysis of covariance, randomized block, factorial designs, missing data.
The course will include LECTURE and LAB. The lecture will be given on the basis of 3 hours a week (Wednesday, 2:20PM~5:20PM) in 綜合三館 room 837. The purposes of lab are for students to reinforce ideas learned in the lecture and to practice the skills of real-data analysis. Video files will be used to teach and demonstrate the labs. Success in both lecture and lab are required to pass the course. The materials for teaching will be regularly posted on the course website (http://www.stat.nthu.edu.tw/~swcheng/Teaching/stat5410/index.php).
The software I will be using for the course is R (S-plus is similar and will also work but is not free). R is free with Windows, MacOS, and Unix versions. You can download your own copy from its website and use it wherever you find convenient.
Faraway, J.J. (2015), Linear Models with R, 2nd edition, Chapman & Hall/CRC.
Faraway, J.J. (2005), Linear Models with R, 1st edition, Chapman & Hall/CRC.
Draper N.R., and Smith, H. (1998), Applied Regression Analysis, 3rd edition, Wiley.
Faraway, J.J. (2016), Extending the Linear Model with R, 2nd edition, Chapman & Hall/CRC.
There will be weekly or bi-weekly assignments. You are free to discuss the assignment questions with your classmates, but you must write up your answers individually. You CANNOT turn answers in as a group or simply reproduce a classmate's write up. Please DO NOT hand in your assignments in the format of including all details of R outputs. You should present your results in summary format only with R outputs that are required to support your answers. All homework must be handed in before the end of class on the day it is due.
My office is in 綜合三館 room 818 (phone extension: 33162). My office hour is scheduled on Wednesday, 1:00~2:00PM. Questions by e-mail are also welcome: my e-mail address email@example.com.
The information of teaching assistants:
|邱奕豪firstname.lastname@example.org||Monday, 11AM~12PM||綜合三館 839 室|
|蔡明諺email@example.com||Tuesday, 2:20-3:10 PM||綜合三館 839 室|
|陳則諭firstname.lastname@example.org||Thursday, 1-2 PM||綜合三館 839 室|
|黃俊閔email@example.com||Friday, 4-5 PM||綜合三館 839 室|
Your grade will be determined by homework (30 %), a midterm (30 %), and a final exam (40 %).
Knowledge of matrix algebra. Knowledge of basic probability and statistics. Computing will be required, but no specific prior experience is necessary.