课程号 
01132676 
学分 
3 
英文名称 
Biostatistics 
先修课程 
Math courses at undergraduate level for nonmath majors 
中文简介 
This course provides some of the most important topics in analyzing data often seen in biological research. Specific topics are  Random events and probability distributions  Confidence interval estimations  Hypothesis testing and its application to group comparisons  Issues of power and sample size  Study design and analysis of variance  Regression methods and correlation analysis  Categorical data analysis  Nonparametric statistical methods  Survival analysis This course introduces a statistical software (R), with plenty of examples and lab sessions for analyzing data and implementing the topics of this course. This course also encourages active participations from the students with sessions of problem solving, research paper discussion, and group projects. 
英文简介 
This course provides some of the most important topics in analyzing data often seen in biological research. Specific topics are  Random events and probability distributions  Confidence interval estimations  Hypothesis testing and its application to group comparisons  Issues of power and sample size  Study design and analysis of variance  Regression methods and correlation analysis  Categorical data analysis  Nonparametric statistical methods  Survival analysis This course introduces a statistical software (R), with plenty of examples and lab sessions for analyzing data and implementing the topics of this course. This course also encourages active participations from the students with sessions of problem solving, research paper discussion, and group projects. 
开课院系 
生命科学学院 
通选课领域 

是否属于艺术与美育 
否 
平台课性质 

平台课类型 

授课语言 
中英双语 
教材 
新世纪高等学校教材《生物统计》第二版,李春喜,科学出版社,2013, 
参考书 
5,9787030375025；

教学大纲 
This course provides some of the most important topics in analyzing data often seen in biological research. Specific topics are  Random events and probability distributions  Confidence interval estimations  Hypothesis testing and its application to group comparisons  Issues of power and sample size  Study design and analysis of variance  Regression methods and correlation analysis  Categorical data analysis  Nonparametric statistical methods  Survival analysis This course introduces a statistical software (R), with plenty of examples and lab sessions for analyzing data and implementing the topics of this course. This course also encourages active participations from the students with sessions of problem solving, research paper discussion, and group projects.
Session 1： Random Event and Probability Date: 7/1 【Description of the Session】(purpose, requirements, class and presentations scheduling, etc.) The 3 lectures will first describe the motivation of learning biostatistics and introduce several running examples of problems. Then we will learn 1) random events and probability; 2) random variables and probability distribution. We will start to discuss random variable and its probability distribution. Time preferred: Period 2, 3, and 4 (from 9 am to 12 noon).
【Questions】 1) What are random events and how to describe them with probability? 2) Conditional probability and independent events 3) Random events 【Readings, Websites or Video Clips】Chapter 1 and Chapter 2 of the text book 【Assignments for this session (if any)】Problems 1, 7, 12 for Chapter 1 Session 2：Random Variable and R Tutorial (#1) Date:7/1 【Description of the Session】(purpose, requirements, class and presentations scheduling, etc.) We will continue Chapter 2 for random variable and probability distribution. Next the first R software tutorial will be presented. Lastly, we will have a lab session for R. Time preferred: Periods 6, 7, 8 (from 2 pm to 5 pm).
【Questions】 1) Discrete random variables and probability mass functions 2) Continuous random variables and probability density functions 3) Distribution functions 4) Introduction to R 【Readings, Websites or Video Clips】 Chapter 2 of the text book R tutorials (which will be distributed to the students beforehand) 【Assignments for this session (if any)】Problem 3, 18 of Chapter 2; 3 R problems
Session 3：Characteristics of Random Variables Date: 7/2 【Description of the Session】(purpose, requirements, class and presentations scheduling, etc.) We will learn the concepts and calculations of mathematical expectation and variance of a random variable. We will start to cover random samples. Time preferred: Period 2, 3, and 4 (from 9 am to 12 noon).
【Questions】 1) Mathematical expectation of a random variable 2) Variance of a random variable 3) Sample versus population 【Readings, Websites or Video Clips】Chapter 3 and Chapter 4 of the text book
【Assignments for this session (if any)】Problem 5, 8 and 17 of Chapter 3. Session 4：Law of Large Number and Central Limit Theorem Date: 7/2 【Description of the Session】(purpose, requirements, class and presentations scheduling, etc.). We will learn 1) the concept of “statistic”; 2) learn two fundamental theorems for this course: law of large number and central limit theorem; 3) presentations of data (summary statistic and graphs) Time preferred: Periods 6, 7, 8 (from 2 pm to 5 pm).
【Questions】 1) What is statistic? 2) Law of large number and central limit theorem 3) Why normal distribution is so important? 【Readings, Websites or Video Clips】Chapter 4 of the text book 【Assignments for this session (if any)】Problem 3 and 12 of Chapter 4 Session 5：Estimation methods (Part 1) Date: 7/3 【Description of the Session】(purpose, requirements, class and presentations scheduling, etc.) We will learn methods to estimate parameters. We will start with point estimating methods and discuss probability distributions of some important point estimators. General confidence interval estimation method is covered. Time preferred: Period 2, 3, and 4 (from 9 am to 12 noon).
【Questions】 1) Maximum likelihood estimators 2) Chisquared distribution, t distribution, F distribution 3) What is a confidence interval and general way to construct it? 【Readings, Websites or Video Clips】Chapter 5 of the text book. 【Assignments for this session (if any)】Problem 1, 6 of Chapter 5 Session 6：Estimation methods (Part 2) Date:7/3 【Description of the Session】(purpose, requirements, class and presentations scheduling, etc.) We will learn confidence interval estimation methods for some common scenarios. Time preferred: Periods 6, 7, 8 (from 2 pm to 5 pm).
【Questions】 1) Confidence interval for mean with known variance 2) Confidence interval for mean with unknown variance 3) Confidence interval for proportion 4) Confidence interval for difference between two population means 【Readings, Websites or Video Clips】Chapter 5 of the text book. 【Assignments for this session (if any)】Problem 17, 18 of Chapter 5 Session 7：Hypothesis Testing (Part 1) Date: 7/4 【Description of the Session】(purpose, requirements, class and presentations scheduling, etc.) We will learn hypothesis testing methods in common scenarios of onesample tests and twosample tests. We will discuss the interpretation of pvalue and power. Time preferred: Period 2, 3, and 4 (from 9 am to 12 noon).
【Questions】 1) One sample testing 2) Two sample testing 3) P value and power of testing 【Readings, Websites or Video Clips】Chapter 6 of the text book and lecture notes 【Assignments for this session (if any)】Problem 1, 14 of Chapter 6. Session 8：Hypothesis Testing (Part 2) Date: 7/4 【Description of the Session】(purpose, requirements, class and presentations scheduling, etc.) We will discuss the two types of errors in hypothesis testing, sample size estimation in study design, and multiple testing problems. Time preferred: Periods 6, 7, 8 (from 2 pm to 5 pm).
【Questions】 1) Type 1 and type 2 errors in hypothesis testing 2) Sample size estimation 3) The multiple testing problem 【Readings, Websites or Video Clips】Chapter 6 of the text book and lecture notes 【Assignments for this session (if any)】2 or 3 problems from lecture notes.
Session 9：Regression and Correlation Analysis Date: 7/5 【Description of the Session】(purpose, requirements, class and presentations scheduling, etc.) We will study regression models and correlation analysis methods. Time preferred: Period 2, 3, and 4 (from 9 am to 12 noon).
【Questions】 1) Simple linear regression model – interpretation and estimation of model parameters. 2) Multiple linear regression model 3) Model diagnosis 4) Correlation analysis 【Readings, Websites or Video Clips】Chapter 8 of the text book. 【Assignments for this session (if any)】Problem 1, 14 of Chapter 8. Session 10：Model Selection in Regression and Exam #1 Date: 7/5 【Description of the Session】(purpose, requirements, class and presentations scheduling, etc.) We will discuss methods in simplifying model specifications for clearer interpretation and better predictive ability. We will have the first exam (covering Sessions 1 to 6) in Period 7 and 8. Time preferred: Periods 6, 7, 8 (from 2 pm to 5 pm).
【Questions】 1) Criteria for model selections 2) Stepwise selection methods 3) Exam #1 (covering Session 1 through 6) 【Readings, Websites or Video Clips】Lecture notes 【Assignments for this session (if any)】None
Session 11：Categorical Data Analysis – Part 1 Date: 7/6 【Description of the Session】(purpose, requirements, class and presentations scheduling, etc.) We will discuss methods of analyzing categorical data which are quite common in biological research. Time preferred: Period 2, 3, and 4 (from 9 am to 12 noon).
【Questions】 1) Basic concepts 2) Contingency table analysis 3) Hypothesis testing for categorical data: chisquare test, test of independence, Fisher’s exact test
【Readings, Websites or Video Clips】Lecture notes
【Assignments for this session (if any)】3 problems
Session 12：Categorical Data Analysis – Part 2 Date: 7/6 【Description of the Session】(purpose, requirements, class and presentations scheduling, etc.) We will discuss regression analysis for categorical data – logistic regression model. Then we will have the second R tutorial, a lab session and problem discussion session. Time preferred: Periods 6, 7, 8 (from 2 pm to 5 pm).
【Questions】 1) Logistic regression model 2) R tutorial #2 3) Lab session and problem discussion 【Readings, Websites or Video Clips】Lecture notes 【Assignments for this session (if any)】2 problems for logistic regression, and 2 R problems Session 13：Survival Data Analysis – Part 1 Date: 7/7 【Description of the Session】(purpose, requirements, class and presentations scheduling, etc.) We will learn methods to analyze timetoevent data. Time preferred: Period 2, 3, and 4 (from 9 am to 12 noon).
【Questions】 1) Why regular regression models don’t work? 2) Concepts and notations in survival analysis 3) Nonparametric Kaplan Meir estimator 4) Parametric survival models 【Readings, Websites or Video Clips】Supplemental materials 【Assignments for this session (if any)】Two problems
Session 14：Survival Data Analysis – Part 2 Date: 7/7 【Description of the Session】(purpose, requirements, class and presentations scheduling, etc.) We will learn the semiparametric Cox regression model – the most widely used method in biostatistics. A review and problem discussion session will be conducted. Time preferred: Periods 6, 7, 8 (from 2 pm to 5 pm).
【Questions】 1) Cox’s Proportional hazard model 2) Review session 3) Problem discussion and R lab 【Readings, Websites or Video Clips】Supplemental materials
【Assignments for this session (if any)】Two problems
Session 15：Selected Topics in biostatistics Date:7/8 【Description of the Session】(purpose, requirements, class and presentations scheduling, etc.) We will discuss nonparametric methods for hypothesis testing, design of experiments, and analysis of variance. Time preferred: Period 2, 3, and 4 (from 9 am to 12 noon).
【Questions】 1) Nonparametric testing methods 2) Design of experiments/studies 3) Analysis of variance 【Readings, Websites or Video Clips】Lecture notes
【Assignments for this session (if any)】None
Session 16：Exam 2 Date: 7/8 【Description of the Session】(purpose, requirements, class and presentations scheduling, etc.) We will have a question and answer session before the second exam. Time preferred: Periods 6, 7, 8 (from 2 pm to 5 pm).
【Questions】 1) Question and answer session/problem discussion session 2) Exam #2 (covering Session 7 through 14) 【Readings, Websites or Video Clips】None
【Assignments for this session (if any)】None
课堂讲授，讨论等
Homework 35% Exam 1 30% Exam 2 30% Class Participation 5%

教学评估 
成勤和：
学年度学期：18193，课程班：生物统计1，课程推荐得分：0.0，教师推荐得分：8.44，课程得分分数段：9095；

