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.
ofrecido por
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 stars64,21 %
- 4 stars23,07 %
- 3 stars7,58 %
- 2 stars2,99 %
- 1 star2,14 %
Principales reseñas sobre MODELOS DE REGRESIÓN
por AK9 de dic. de 2016
I was hoping to learn about PROBIT models. I know they are very similar to LOGIT ones, but still... the pace is a little bit too fast and I think it requires more time than what it says.
por AC10 de ago. de 2017
Regression analysis is something that is kind of easy for people to understand (outcome and predictor - people get that!). It's easy to explain to people. So much practice using the lm function!
por SC14 de mar. de 2017
Good course on the theories behind regression, followed by significant applications and how to use them in R. Lectures are very dry, but the information within them is very useful.
por DC3 de may. de 2019
Very good course. Though basic, it provides you with the first tools and knowledge. The forums aren't what they used to be it seems, but you can find almost any answer there from past courses.
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