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.
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Acerca de este Curso
Qué aprenderás
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
Habilidades que obtendrás
- Model Selection
- Generalized Linear Model
- Linear Regression
- Regression Analysis
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Universidad Johns Hopkins
The mission of The Johns Hopkins University is to educate its students and cultivate their capacity for life-long learning, to foster independent and original research, and to bring the benefits of discovery to the world.
Programa - Qué aprenderás en este curso
Week 1: Least Squares and Linear Regression
This week, we focus on least squares and linear regression.
Week 2: Linear Regression & Multivariable Regression
This week, we will work through the remainder of linear regression and then turn to the first part of multivariable regression.
Week 3: Multivariable Regression, Residuals, & Diagnostics
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.
Week 4: Logistic Regression and Poisson Regression
This week, we will work on generalized linear models, including binary outcomes and Poisson regression.
Reseñas
- 5 stars64,30 %
- 4 stars23 %
- 3 stars7,56 %
- 2 stars2,96 %
- 1 star2,14 %
Principales reseñas sobre MODELOS DE REGRESIÓN
I appreciate coefficients interpretation and variance influence to choose among models. Running code takes a few seconds, understanding the model's outputs is a much hard
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.
Excellent course that is jam-packed with useful material! It is quite challenging and gives a thorough grounding in how to approach the process of selecting a linear regression model for a data set.
This module was the maximum. I learned how powerful the use of Regression Models techniques in Data Science analysis is. I thank Professor Brian Caffo for sharing his knowledge with us. Thank you!
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