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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Universidad Johns HopkinsAcerca de este Curso
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Aprox. 54 horas para completar
Inglés (English)
Subtítulos: Árabe (Arabic), Francés (French), Portugués (europeo), Italiano, Vietnamita, Coreano, Alemán (German), Ruso (Russian), Inglés (English), Español (Spanish)
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
Fechas límite flexibles
Restablece las fechas límite en función de tus horarios.
Certificado para compartir
Obtén un certificado al finalizar
100 % en línea
Comienza de inmediato y aprende a tu propio ritmo.
Aprox. 54 horas para completar
Inglés (English)
Subtítulos: Árabe (Arabic), Francés (French), Portugués (europeo), Italiano, Vietnamita, Coreano, Alemán (German), Ruso (Russian), Inglés (English), Español (Spanish)
ofrecido por
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
13 horas para completar
Week 1: Least Squares and Linear Regression
This week, we focus on least squares and linear regression.
13 horas para completar
9 videos (Total 74 minutos), 11 lecturas, 4 cuestionarios
11 horas para completar
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.
11 horas para completar
10 videos (Total 70 minutos), 5 lecturas, 4 cuestionarios
14 horas para completar
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.
14 horas para completar
14 videos (Total 168 minutos), 5 lecturas, 5 cuestionarios
16 horas para completar
Week 4: Logistic Regression and Poisson Regression
This week, we will work on generalized linear models, including binary outcomes and Poisson regression.
16 horas para completar
7 videos (Total 95 minutos), 6 lecturas, 6 cuestionarios
Reseñas
- 5 stars64,30 %
- 4 stars23 %
- 3 stars7,56 %
- 2 stars2,96 %
- 1 star2,14 %
Principales reseñas sobre MODELOS DE REGRESIÓN
por ST25 de may. de 2018
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
por JV15 de oct. de 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.
por KA16 de dic. de 2017
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.
por DA10 de mar. de 2019
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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