### Prerequisites

HS 851, or equivalent, instructor may review syllabi of
previously taken courses (past 5 years) and/or require a test to
assess the equivalence of the student background, as necessary.

### Course Description

HS 852 is a general linear modeling course, building on HS 851,
focusing on commonly employed scientific computing techniques used in
health sciences. The primary aim of the course is to provide students
with the necessary skills to determine appropriate use, carry out, and
interpret general linear modeling. Statistical software will be used to
manipulate data, fit models and perform model diagnostics. Introduces students
to applied inference methods in studies.
HS852 is a 4 credit hour course (3 lectures + 1 lab/discussion).

### Objectives

Students will learn how to:

- Compare and contrast advanced statistical concepts, grasp model assumptions/limitations
and apply them for quantitative analyses in healthcare research
- Apply multivariate statistical modeling enabling consistency between research questions and selected advanced statistical analyses
- Critique and select appropriate advanced statistical linear models for defined healthcare issues
- Conduct multivariate statistical analyses, such as multidimensional chi squares,
logistic regression, principal components analysis, survival analysis,
repeated measures ANOVA, MANOVA, MANCOVA, linear mixed models, hierarchical linear models

### Examples of Topics Covered

- MLR Regression
- GLM
- ANOVA
- ANCOVA
- MANOVA
- MANCOVA
- Repeated measures ANOVA
- (partial) correlation
- Time series analysis
- Fixed, randomized and mixed models
- Hierarchical Linear Models
- Mixture modeling
- Surveys
- Longitudinal data
- Generalized Estimating Equations (GEE) models
- Model Fitting and Model Quality (KS-test)
- Common mistakes and misconceptions in using probability
and statistics, identifying potential assumption violations, and
avoiding them

### Teaching and Learning Methods

This course meets four times on campus and will use blended
instructional techniques to deliver learning materials, provide
instructional resources and assess student progress. Synchronous
web-streaming of lectures/labs and asynchronous virtual office
hour forums will be supported. Assignments will be announced on
the web and will be electronically collected, graded and recorded.
A variety of teaching methods will be used including lecture,
Journal Club, discussion, small group work, and guest
presentation.

### Textbooks

- SMHS EBook
and additional resources will be made available through the SOCR Wiki and may include
chapters, websites for review, references, reports posted online,
ebooks and learning modules.
- Julian Faraway, Linear Models with R,
Second Edition, 2004 (Chapman & Hall/CRC Texts in Statistical Science)
- Julian Faraway, Extending the Linear Model
with R: Generalized Linear, Mixed Effects and Nonparametric Regression Models,
2005 (Chapman & Hall/CRC Texts in Statistical Science)

### Assignments and Evaluation Methods

- 40% Homework Projects
- 30% Midterm Exam
- 30% Final Paper

Standard letter-grading distribution will be used:

- A: 90%+
- B: 80-90%
- C: 70-80%
- D: 60-70%
- ...
- Plus and minus grads will also be used (e.g., "B-":
80-83%; "B+": 87-90%)

### Grading Policy

The lowest graded Homework assignment will be dropped. All
Homework assignments must be completed by the corresponding
deadline, however. No late assignments will be accepted. For
students with genuine documented reasons for missing the midterm
arrangements will be made. If after receiving the graded exams or
HW/projects back you believe a grading error has occurred please
see the Instructor or your TA, within one week. Late regrade requests
may not be accommodated. Reading assignments will be given. You
will be responsible for the information covered in these
assignments. Lecture and discussion attendance will be recorded
from time to time.

### Office Hours

- Instructor:
TBD
- TA: TBD
- Forum/Chat: TBD