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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Habilidades que obtendrás
- 5 stars64,19 %
- 4 stars23,07 %
- 3 stars7,57 %
- 2 stars2,98 %
- 1 star2,17 %
Principales reseñas sobre MODELOS DE REGRESIÓN
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.
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.
This was a tough class covering a lot of material. The last week on logistic regression completely lost me. If you're new to stats like me you might want to take it more than once.
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.
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