Regression Models – Johns Hopkins University – Coursera

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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.

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

R programming, mathematical aptitude.

  • Brian Caffo: – Johns Hopkins University
  • Jeff Leek: – Johns Hopkins University
  • Roger D. Peng: – Johns Hopkins University
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