Logistic Regression

 

 


 

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E.g., 2016-12-09
E.g., 2016-12-09
E.g., 2016-12-09
Dec 16th 2016

This course focuses on one of the most important tools in your data analysis arsenal: regression analysis. Using either SAS or Python, you will begin with linear regression and then learn how to adapt when two variables do not present a clear linear relationship. You will examine multiple predictors of your outcome and be able to identify confounding variables, which can tell a more compelling story about your results. You will learn the assumptions underlying regression analysis, how to interpret regression coefficients, and how to use regression diagnostic plots and other tools to evaluate the quality of your regression model. Throughout the course, you will share with others the regression models you have developed and the stories they tell you.

Average: 6 (30 votes)
Nov 28th 2016

A practical and example filled tour of simple and multiple regression techniques (linear, logistic, and Cox PH) for estimation, adjustment and prediction.

Average: 9 (1 vote)
Aug 15th 2016

This course provides theoretical and practical training on the increasingly popular logistic regression model, which has become the standard analytical method for use with a binary response variable. This is a hands-on, applied course where students will become proficient at using computer software to analyze data drawn primarily from the fields of medicine, epidemiology, and public health.

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Apr 12th 2016

Through inspiring examples and stories, discover the power of data and use analytics to provide an edge to your career and your life. In the last decade, the amount of data available to organizations has reached unprecedented levels. Data is transforming business, social interactions, and the future of our society. In this course, you will learn how to use data and analytics to give an edge to your career and your life.

Average: 7.5 (2 votes)
Nov 25th 2015

Master the tools of predictive analytics in this statistics based analytics course.

Average: 1 (1 vote)