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# Stats Ii Syllabus (Hph 7310)Ransdell Winter2009rev

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### Stats Ii Syllabus (Hph 7310)Ransdell Winter2009rev

1. 1. 1 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
2. 2. 2 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
3. 3. 3 426-427 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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