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.
Об этом курсе
- 5 stars64,19 %
- 4 stars23,07 %
- 3 stars7,57 %
- 2 stars2,98 %
- 1 star2,17 %
Лучшие отзывы о курсе РЕГРЕССИОННЫЕ МОДЕЛИ
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.
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.
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
Great subject, was a bit frustrated with some of the material (seemed rushed and not well prepared). Great assignment, but too restrictive on the max number of pages allowed. Wasted a lot of time.
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Когда я получу доступ к лекциям и заданиям?
Что я получу, оформив подписку на специализацию?
Можно ли получить финансовую помощь?
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