What Are Python Libraries for Data Science?
April 10, 2024
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This course is part of multiple programs.
Instructors: Brian Caffo, PhD
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Use regression analysis, least squares and inference
Understand ANOVA and ANCOVA model cases
Investigate analysis of residuals and variability
Describe novel uses of regression models such as scatterplot smoothing
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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 scatterplot smoothing.
This week, we focus on least squares and linear regression.
9 videos11 readings1 assignment3 programming assignments
This week, we will work through the remainder of linear regression and then turn to the first part of multivariable regression.
10 videos5 readings1 assignment3 programming assignments
This week, we'll build on last week's introduction to multivariable regression with some examples and then cover residuals, diagnostics, variance inflation, and model comparison.
14 videos5 readings2 assignments3 programming assignments
This week, we will work on generalized linear models, including binary outcomes and Poisson regression.
7 videos6 readings1 assignment4 programming assignments1 peer review
We asked all learners to give feedback on our instructors based on the quality of their teaching style.
Instructor ratings
We asked all learners to give feedback on our instructors based on the quality of their teaching style.
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University of Colorado Boulder
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Duke University
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Johns Hopkins University
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Johns Hopkins University
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3,362 reviews
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Reviewed on Feb 9, 2016
This was a tough class covering a lot of material. The last week on logistic regression completely lost me. If you're new to stats like me you might want to take it more than once.
Reviewed on Aug 1, 2017
Great introductory course on Regression Models. Super practical and well explained. Definitely doing the exercises and final project is a must to get all the learnings!
Reviewed on Oct 15, 2017
It is very interesting, however is difficult to follow the math explanations, it could be more easy with practical examples.... like the final assignment, it was difficult to me.
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