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Nova Southeastern University
HPH 7310 CRN 32104
BIOSTATISTICS II
Winter 2009
SYLLABUS
I. DESCRIPTION: Second of a two-course sequence focusing on inferential statistics for
students interested in conducting quantitative research in the health
professions. It is designed to enable students to gather data and apply
experimental-design models toward solving practical problems and
improving the efficiency of formulating and providing healthcare
services.
II. GOAL: Educate students to generate, interpret, and evaluate clinical, biomedical,
and healthcare-services regression models.
III. PREREQUISITE: Successful completion of Biostatistics I (HPD 7300).
IV. OBJECTIVES: After successful completion of this course, students will be able to:
1. conduct empirical research using statistical methods.
2. apply bivariate and multivariate regression hypothesis-testing models
to experimental and quasi-experimental research questions.
3. evaluate the assumptions of regression models.
4. estimate and assess the impact of regressors in functional
relationships.
5. estimate parameters with adequate confidence intervals.
6. transform variables in ordinary least squares from linear to quadratic,
cubic, logarithmic, and other expressions.
7. measure the effect of non-quantitative variables.
8. work with time-series and truncated data.
9. apply different operations research models in search of optimal
solutions.
V. INSTRUCTOR: Sarah Ransdell, PhD
Tel. (800)356-0026, ext. 1208
e-mail: ransdell@nova.edu
VI. MEETINGS: Email, Discussion, and Tegrity will be utilized to facilitate learning
within WebCT. Students are responsible for keeping up with these
communications.
VII. ASSIGNMENTS: Five problem sets will be distributed throughout the class. Deadlines are

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posted on the course schedule and within the Assignment Dropbox.
VIII. CREDIT: Three credit hours.
IX. TEXTBOOK: Wayne W. Daniel, Biostatistics: A Foundation for Analysis in the Health
Sciences (New York: John Wiley & Sons, Inc., Eighth Edition, 2005). A
license for SPSS 15.0 or 16.0 should be available through the end of the
term.
X. POLICIES: a. On-line attendance and civility requirements as specified in the
Student's Manual.
b. A grade of incomplete is available at the instructor’s discretion.
Students are expected to remove the incomplete within two
semesters or by the end of the next semester in which the course is
offered again.
c. Students who fail to complete the final exam will be given a grade of
incomplete and will be able to complete the exam at the instructor’s
discretion.
d. Academic dishonesty in the form of cheating, plagiarism, etc.
constitute transgressions against the honor code and may bring
penalties ranging from severe reprimand to recommendation for
expulsion from the program, including failing the entire course or
part of it.
XI. GRADING: Midterm exam (15pts), Problem sets (30pts, 5 @ 6pts each)
Final exam (15pts)
100 – 90 A
89 – 80 B
79 – 0 F
XIII. SCHEDULE:
Week Date Chapter Topic Assignment
1 1/5 Course organization
Review of the previous course in the sequence
2 1/12 9/443-456 Nature of functional relationships
The parametric correlation coefficient
3 1/20 13/730-740 Non-parametric Spearman rank correlation Problem Set 1
Due 1/25,
Sunday, 9pm
4 1/26 9/410-423; Simple linear (bivariate) regression model and

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426-427 assumptions; R-squared
5 2/2 9/424-426; Analysis of variance in bivariate regression Problem Set 2
426-440 (F-statistic) Due 2/8,
Regression coefficients and standard errors Sunday, 9pm
Tests of hypotheses concerning regression
coefficients (t-statistic)
6 2/9 10/all Generalized linear model (multivariate
regress.) and assumptions
Estimation of dependent variable
7 2/16 9/456-457 Checking assumptions of regression Problem Set 3
--Homoskedasticity, multicollinearity, etc Due 2/22
8 2/23 review Midterm exam Due 3/1,
Sunday, 9pm
9 3/2 11/537-555 Dummy variables
Adjusted R-squared
10 3/9 11/556-559 Problem Set 4
Regression Model Building
Ordinary Least squares (OLS) vs. other types Due 3/15
11 3/16 13/740-742 Nonlinear regression
Linear transformations
Limited data / Cross-validation techniques
Week Date Chapter Topic Assignment
12 3/23 11/566-573 Logistic regression Problem Set 5
Log-linear regression Due 3/29
13 3/30 12/593-597 The Chi-square analysis
14 4/6 12/597-620 Goodness of fit and tests of independence
15 4/13 13/456-459
Precautions about regression and correlation
16 4/19 Final exam Due 4/19,
Sunday 9pm

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