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My teaching materials for biostatistics
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Here, I attach my presentation documents for the course of biostatistics that I am teaching in the Graduate University of Chinese Academy of Sciences. There are 13 pdf files in total, each for a three-hour-long lecture. I thank Dianmo Li, David Schneider, Matt Litvak, who taught me biostatistics, and allowed me to use their cases and materials for teaching. I also cited some contents from Zar's book (Zar 1999), and others.
Since 2011, I use R instead of SAS as the analysis tool for this course. I provided R code for all case studies.
The syllabus and the links for downloading are:
Download: 1_Introduction to biological statistics.pdf
Lecture 1 Introduction to biological statistics
l Brief history of biostatistics
l Key persons
l Major contents of biostatistics
l Basic concepts
² Data types
² Descriptive statistics
Download: 2_Probability distribution.pdf
Lecture 2 Probability distribution
l Probability theory
l Common distributions of random variables
² Binomial distribution
² Poisson distribution
² Negative binomial distribution
² Normal distribution
² Chi square distribution
Download: 3_Hypothesis Testing 1.pdf
Lecture 3 Hypothesis testing 1
l What is hypothesis testing?
l Standard procedures
l Case studies
² T test and Z test
² Situations of one tail and two tails
² One sample hypothesis tests and two samples hypothesis tests
² Paired test
Download: 4_Hypothesis Testing 2.pdf
Lecture 4 Hypothesis testing 2
l Philosophy of hypothesis testing
l Type I and Type II Errors
l Chi-square test
l Power of test
Download: 5_ANOVA_1.pdf
Lecture 5 Analysis of variance (ANOVA) 1
l Rationale of ANOVA
l Compared with T test, Chi-square test
l Generic Recipe of general linear model
l One-way ANOVA
l Random blocked design
l Two-way ANOVA
Download: 6_ANOVA_2.pdf
Lecture 6 Analysis of variance (ANOVA) 2
l Three-way ANOVA
l Latin Square Design
l Repeated measures ANOVA
l Hierarchical ANOVA
l Split Plot Design
l Mixed-effects models
Download: 7_Simple Linear regression and correlation.pdf
Lecture 7 Simple linear regression and correlation
l Rationale of simple linear regression
l Least square
l Regression coefficient (slope) and intercept
l Significance of a regression
l Assumptions of regression analysis
l Applications of simple linear regression
l Rationale of simple linear correlation
l Coefficient of Correlation
l Power and sample size in correlation
Download: 8_ANCOVA.pdf
Lecture 8 Analysis of covariance (ANCOVA)
l Rationale of Analysis of covariance
l Assumptions of Analysis of covariance
l Compared with ANOVA and regression
l Case studies
l Coding convention of R
Download: 9_Data transformation and Nonparametric Statistics.pdf
Lecture 9 Data transformation and Nonparametric statistics
l Data transformation
² Logarithmic transformation
² Square root transformation
² Arcsine transformation
² Reciprocal transformation
² Square transformation
² Box-Cox transformation
l Rationale of nonparametric statistics
l Sign test
l Wilcoxon signed rank test
l Wilcoxon rank sum test
l Kruskal-Wallis test
l Friedman’s test
l Bootstraping
Download: 10_Multivariate analysis 1.pdf
Lecture 10 Multivariate analysis 1
l Multiple regression
² Linear regression
² Non-linear regression
² Evaluating multiple regression model
l Multiple correlation
l Partial correlation
Download: 11_Multivariate analysis 2.pdf
Lecture 11 Multivariate analysis 2
l Cluster analysis
l Discriminant analysis
l Principal component analysis
l Factor analysis
l Correspondence analysis
Download: 12_Generalized linear models.pdf
Lecture 12 Generalized linear model
l Rationale of Generalized linear model
l Logistic Regression
² Assumptions
² Biological means of coefficients
² Maximum likelihood estimation
² Goodness of fit
l Structure of generalized linear model
² Random component
² Systematic component
² Link function
l Compared with general linear model
l Case studies
Download: 13_Common errors and advanced models.pdf
Lecture 13 Common errors, sample survey and advanced models
Common errors
l Normality
l Homogeneity
l Interaction
l Multicollinearity
l Autocorrelation
l Outliers
l Zero trouble
Sample survey
•Simple Random Sampling (SRS)
•Stratified Sampling
•Systematic Sampling
•Cluster Sampling
•Multistage Sampling
Advanced models
https://blog.sciencenet.cn/blog-661364-559901.html
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