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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Compétences que vous acquerrez
- 5 stars64,19 %
- 4 stars23,07 %
- 3 stars7,57 %
- 2 stars2,98 %
- 1 star2,17 %
Meilleurs avis pour MODÈLES DE RÉGRESSION
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.
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!
I liked this because I have almost no background on this sort of thing and it forced me to go waaay back and revisit and deepen my knowledge of modeling and statistics as well. I loved it.
Excellent overview of a very broad and complex topic with plenty of useful applications within R. The course project does an outstanding job at teaching the pitfalls of omitted variable bias.
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