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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: ● ● ● ● ● ● 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