Biostatistics for Laboratory Scientists (BIOS610)
ENDED
Distance Learning by
World Mentoring Academy
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On-Site
/ Short Course
Details
Introduces the basic concepts and methods of statistics with emphasis on applications in the experimental biological sciences. Emphasis is on mastery of basic statistical skills and familiarity with situations in which advanced analytical skills
may be needed.
Course objectives include learning to use statistical reasoning to formulate scientific questions in quantitative terms, learning to design and interpret graphical and tabular displays of statistical information, using basic probability models to describe trends and random variation in laboratory data, and using basic statistical models, including tests and confidence intervals, to draw inferences from data. Topics include point and interval estimation, testing, experimental design, linear regression, sample size and power calculations, measurement error, and a selection of the following: logistic regression, principal components analysis, survival analysis, and methods for correction for multiple testing. Introduces and employs the freely available statistical software, R, to explore and analyze data. Emphasis on mastery of basic statistical analysis skills, familiarity with situations in which advanced analytic skills may be needed, the ability to critically review statistical analysis presented in relevant manuscripts, and the ability to clearly describe the results of statistical calculations when writing papers and grant applications.
Course objectives include learning to use statistical reasoning to formulate scientific questions in quantitative terms, learning to design and interpret graphical and tabular displays of statistical information, using basic probability models to describe trends and random variation in laboratory data, and using basic statistical models, including tests and confidence intervals, to draw inferences from data. Topics include point and interval estimation, testing, experimental design, linear regression, sample size and power calculations, measurement error, and a selection of the following: logistic regression, principal components analysis, survival analysis, and methods for correction for multiple testing. Introduces and employs the freely available statistical software, R, to explore and analyze data. Emphasis on mastery of basic statistical analysis skills, familiarity with situations in which advanced analytic skills may be needed, the ability to critically review statistical analysis presented in relevant manuscripts, and the ability to clearly describe the results of statistical calculations when writing papers and grant applications.
Course Delivery
Outline
Tentative List of Course Topics:
- Statistical reasoning in the basic biological sciences
- Experimental design
- Sampling
- Graphical methods for displaying and summarizing data
- Measurement error/outliers
- Basic descriptive statistics
- Data transformation and outliers
- Normal distribution
- Sampling distributions/Central Limit Theorem
- Confidence intervals and hypothesis testing: introduction
- Confidence intervals and hypothesis testing: single sample means, differences between means
- Confidence intervals and hypothesis testing: estimating and comparing proportions
- Confidence intervals and hypothesis testing: permutation tests and bootstrapping
- Hypothesis testing: chi-square tests
- Power/sample size calculations
- Issues when performing multiple hypothesis tests
- Correlation and simple linear regression
- Simple linear regression: tests, confidence intervals, prediction, and calibration
- Multiple linear regression and regression diagnostics
- Penalized regression
- ANOVA
- Logistic regression
- Principal components analysis/factor analysis
- Survival analysis
- Cluster analysis
- Mixed effects models
Schedules
Feb 27, 2017 - Jan 31, 2018
ENDED
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