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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Université Johns-HopkinsÀ propos de ce cours
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Approx. 54 heures pour terminer
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Ce que vous allez apprendre
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
Compétences que vous acquerrez
- Model Selection
- Generalized Linear Model
- Linear Regression
- Regression Analysis
Dates limites flexibles
Réinitialisez les dates limites selon votre disponibilité.
Certificat partageable
Obtenez un Certificat lorsque vous terminez
100 % en ligne
Commencez dès maintenant et apprenez aux horaires qui vous conviennent.
Approx. 54 heures pour terminer
Anglais
Sous-titres : Arabe, Français, Portugais (européen), Italien, Vietnamien, Coréen, Allemand, Russe, Anglais, Espagnol
Offert par
Université 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.
Programme de cours : ce que vous apprendrez dans ce cours
13 heures pour terminer
Week 1: Least Squares and Linear Regression
This week, we focus on least squares and linear regression.
13 heures pour terminer
9 vidéos (Total 74 min), 11 lectures, 4 quiz
11 heures pour terminer
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 heures pour terminer
10 vidéos (Total 70 min), 5 lectures, 4 quiz
14 heures pour terminer
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 heures pour terminer
14 vidéos (Total 168 min), 5 lectures, 5 quiz
16 heures pour terminer
Week 4: Logistic Regression and Poisson Regression
This week, we will work on generalized linear models, including binary outcomes and Poisson regression.
16 heures pour terminer
7 vidéos (Total 95 min), 6 lectures, 6 quiz
Avis
- 5 stars64,27 %
- 4 stars23,03 %
- 3 stars7,57 %
- 2 stars2,96 %
- 1 star2,15 %
Meilleurs avis pour MODÈLES DE RÉGRESSION
par AA28 févr. 2017
This course has been the most difficult in the Dara Science track so far, but you get a more in depth knowledge in data analysis and interpretation based on statistical models.
par DC3 mai 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.
par DA10 mars 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!
par JV15 oct. 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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