Download 8310 syallabus

Instructor: Chong He
As your instructor, I’ll be available to answer your questions during class and office
hours. Also, you can email me your questions and I will get back to you ASAP.
● Office: 134B Middlebush Hall
● Office Hours: 3pm – 4pm Fridays
● Email: [email protected]
Textbook:
● Kutner, Nachtsheim, Neter, and Li (2004), Applied Linear Statistical Models,
5th Edition.
● Stat 8310 Class note Files and R Example Files
Prerequisites:
● Basic knowledge of linear algebra and Stat 7710 or 7760 or instructor's
consent.
Course work:
● The course will focus on basic data analysis and applied linear models. R will
be used for the numerical calculation.
● This course covers a large amount of material and is intensive. Students are
expected to read the textbook and class notes before and after class.
● Homework is critical in this course and the assignments will require a
significant amount of time to complete.
● Attendance: Students are expected to attend classes on time to ensure
satisfactory progress. There will be group activities and discussions in class.
Students may have two absences.
● Homework is critical in this course and the assignments will require a
significantamount of time to complete. Homework will be assigned weekly
on Mondays. Students will have one week to complete their homework.
● Students should submit homework to the course canvas site by 11pm on
Mondays. Late homework will not be accepted.
● Students are encouraged to discuss homework problems with each other, but
do not copy other’s work.
● Extra Credit: Students can earn up to 3% extra credits by asking questions
or answering questions in class.
Computing:
● Computations will be illustrated using R statistical software.
● TA:
● Computer Lab: 2-4pm, Middlebush Hall, Room 8
Exams:
● There are two midterm exams and one final exam.
● The two midterm exams are closed-book classroom exams, except that you
are allowed to bring two sheets (regular notebook paper) of notes to the 1st
midterm exam, four sheets to the 2nd midterm exam.
● Exams may be scheduled in the evening for longer than the given class time.
If you have a problem with this, you need to contact me immediately!
● The final exam is a comprehensive take-home exam. Students must
complete the exam independently.
Grading:
The plus/minus system of grading will be used for all students. The final grade is based
on the following:
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●
●
●
●
●
Homework: 30%
1st Mid-term Exam: 30%
2nd Mid-term Exam: 30%
Final Exam: 10%
Total: 100%
Extra Credit: 3%
Grading Scale:
A+
A
A-
B+
B
B-
C+
C
C-
F
97-100 93-97 90-93 87-90 83-87 80-83 77-80 73-77 70-73 0-70
Course Topics and Coverage:
● Basic Descriptive Statistics
Skewness; kurtosis; assessing normality (graphical, summary statistics)
● Basic Inference
One-sample inference for mean, variance (Type I, II errors, power, sample size
determination); two-sample inferences (large sample tests, small sample tests, check
model assumptions, nonparametric tests);
● Simple Linear Regression
basic regression model and assumptions; least squares estimation; normal error
regression model and inference; diagnostics for residuals; remedial measures;
simultaneous inferences; matrix approach;
● Multiple Linear Regression
general linear regression model in matrix terms; coefficient of multiple determination;
extra sums of squares; multicollineariry; interaction; quantitative predictor variables;
model selection; diagnostics; remedial measures; model validation;
● Design and Analysis of Single-Factor Studies
basic concepts of experiment design; one-factor ANOVA model; randomization tests;
planning of sample sizes; estimation and testing of factor level means; multiple
comparison(Turkey, Scheffe, Bonferroni); one-factor ANOVA random effects model;
ANOVA diagnostics; remedial measures;
● Multi-factor Studies
two-factor ANOVA fixed effects models, two-factor ANOVA random/mixed effects
models; randomized block designs; analysis of covariance; three-factor ANOVA models;
empty cells; planning of sample sizes; nonparametric methods for ANOVA