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.

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

1. 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.