G651: Biostatistics I
Division of Biostatistics
Fall 2007
Course Description and Objectives
G651 is an introductory level biostatistics course designed for healthcare professionals. It is the first in the G651 and G652 series on biostatistics methodology. The course covers topics such as data description and presentation techniques, probability and probability distributions, sampling distributions, statistical inferences from small and large samples, analysis of categorical data, analysis of variance, correlation and simple linear regression analysis.
Upon completion of the course, students will achieve a basic understanding of the concepts and techniques of data description and statistical inferences. Students will also acquire a working knowledge of SPSS, a commonly used statistical computation program. Students will be able to understand and interpret the statistical analyses in research articles published in medical journals. Students that complete the course with grade B or better will have adequate preparation for G652.
Textbook
Principles of Biostatistics, Second Edition by Pagano and Gauvreau.
Prerequisites
One year undergraduate mathematics is required. Working knowledge on linear algebra and elementary calculus is expected. Students with insufficient mathematics preparation are expected to remedy the deficiency on their own.
Instructors
This course is taught by two instructors. Each instructor is responsible for answering questions that arise in his section. For general course related questions, please contact Dr. Yunlong Liu, the course coordinator.
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Yunlong Liu, Ph.D., Course Coordinator
Assistant Professor
Center for Computational Biology and Bioinformatics
HITS, HS5015
Tel: 278-9222
Email: yunliu@iupui.edu
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Wanzhu Tu, Ph.D
Associate Professor
Division of Biostatistics
HITS, HS3017
Tel: 278-6451
Email: wtu1@iupui.edu |
Meeting Time and Place
From August 22nd to December 10th, 2007, the class meets every Tuesday and Thursday, 02:15 – 03:45 PM in Daly 185 (No class on November 22nd due to Thanksgiving Recess). Exam 1 is scheduled to be held at regular class time on October 9th, 2007. Exam 2 will be held in Daly 185 on December 13, 2007, (see Fall 2007 Final Exam Schedule posted by the IUPUI Office of the Registrar). Location of lab sessions will be announced separately.
Office Hours
To be announced by individual instructors
Assessment and Grading
Student’s performance will be assessed by grades of homework assignments, and two exams. SPSS skills will be evaluated via homework assignments. Students are encouraged to participate in discussions on course material but they are expected to work independently on all homework assignments.
Class materials and home work assignments will be posted on Oncourse and home works will be collected in class the following week. No late home works will be accepted. Make-up examinations will be given only in extraordinary situations (such as serious illness) and can be arranged after receiving prior consent from the instructor. The class has two equal weight, non-cumulative exams. The final course grade will be determined using the following weighting scheme:
Homework 30%
Exam 1 35%
Exam 2 35%
Major Topics
Part 1:
Dr. Tu will teach the first half of the course. Part 1 of this course will cover the following chapters of the textbook. In addition, Dr. Tu will supplement the textbook material with his own lecture notes. All materials presented in class are required and are subject to test unless otherwise stated.
Chapter 2. Data Presentation
2.1 Types of data
2.2 Frequency Tables
2.3 Graphs
Chapter 3. Numerical Summary Measures
3.1 Measures of central tendency
3.2 Measures of dispersion
3.3 Grouped data
Chapter 6. Probability
6.1 Basic concept of probability
6.2 Conditional probability
6.3 Diagnostic tests
Chapter 7. Probability Distributions
7.1 Probability distributions
7.2 Binomial distribution
7.3 Poisson distribution
7.4 Normal distribution
7.5 Applications
Chapter 8. Sampling Distributions
8.1 Normal distribution of the sample mean
8.2 Central limit theorem
8.3 Applications of the central limit theorem
Chapter 9. Confidence Intervals of a Single Mean
9.1 Two-sided confidence intervals
9.2 One-sided confidence intervals
9.3 Applications
Chapter 10. Hypothesis Testing
10.1 General concepts
10.2 Two-sided tests of hypotheses
10.3 One-sided and two-sided tests of hypotheses
10.4 Types of error
10.5 Power
Chapter 10. Hypothesis Testing
- Student’s t distribution
10.2 One-sample t-test
10.6 Applications of One-sample test
Exam 1 will be on October 09, 2007.
Part 2:
Dr. Liu will teach rest of the materials. Part 2 of this course will cover the following chapters of the textbook. All materials presented in class are required and are subject to test unless otherwise stated.
Chapter 11&9. Comparison of Two Means
11.1 Paired samples
11.2 Independent samples
9.1 Confidence intervals on difference of two means
Chapter 12. Analysis of Variance
12.1 One-way analysis of variance
12.2 Multiple comparisons procedures
Chapter 14. Inference on Proportions
14.1 Normal approximation to the Binomial distribution
14.2 Sampling distribution of a proportion
14.3 Confidence intervals
14.4 Hypothesis testing
14.5 Sample size estimation
14.6 Comparison of two proportions
Chapter 15. Contingency Tables
15.1 Chi-Square test for 2 × 2 tables
15.2 Chi-Square test for r × c tables
15.3. Odds ratio
Chapter 17. Correlation
17.1 The two-way scatter plot
17.2 Pearson’s correlation coefficient
Chapter 18. Simple Linear Regression
18.1 Linear regression concepts
18.2 Fittings of a regression line by the method of least squares
18.4 Some applications
Chapter 18. Simple Linear Regression
18.2 Inference for regression concepts
18.3 Evaluation of the model
Final review
Exam 2 will be on December 13, 2007.
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