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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이 강좌에 대하여
배울 내용
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
귀하가 습득할 기술
- Model Selection
- Generalized Linear Model
- Linear Regression
- Regression Analysis
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강의 계획표 - 이 강좌에서 배울 내용
Week 1: Least Squares and Linear Regression
This week, we focus on least squares and linear regression.
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.
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.
Week 4: Logistic Regression and Poisson Regression
This week, we will work on generalized linear models, including binary outcomes and Poisson regression.
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- 5 stars64.27%
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회귀 모델의 최상위 리뷰
The best course in my mind, but I am chocked about how Data Science people approach regression type of problems, it is almost 100% data mining and no theory!! I wonder where it will take us..
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!
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.
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!
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