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[su_tab title=”Description”]
Linear models, as their name implies, relates an outcome to a set of predictors of interest using linear assumptions. Regression models, a subset of linear models, are the most important statistical analysis tool in a data scientist’s toolkit. This course covers regression analysis, least squares and inference using regression models. Special cases of the regression model, ANOVA and ANCOVA will be covered as well. Analysis of residuals and variability will be investigated. The course will cover modern thinking on model selection and novel uses of regression models including scatter plot smoothing.
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[su_tab title=”Program structure”]
In this course students will learn
- how to fit regression models,
- how to interpret coefficients,
- how to investigate residuals and variability.
Students will further learn special cases of regression models including use of dummy variables and multivariable adjustment. Extensions to generalized linear models, especially considering Poisson and logistic regression will be reviewed.
Duration:
4 weeks
3-5 hours per week
Part time/ Full time: – Part time
Fees: – Join for Free
Important Date:
Course Start Date: 31-Oct-2016
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[su_tab title=”Eligibility”]
R programming, mathematical aptitude.
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[su_tab title=”Faculty”]
- Brian Caffo: – Johns Hopkins University
- Jeff Leek: – Johns Hopkins University
- Roger D. Peng: – Johns Hopkins University
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[su_tab title=”Contact”]
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