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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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Description
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.
Course content
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- Lecture 1.1: Introduction to Regression 00:55:00
- Lecture 1.2: Basic Notation and Background 01:00:00
- Lecture 1.3: Linear Least Squares 01:00:00
- Lecture 1.4: Regression to the Mean 01:00:00
- Lecture 1.5: Statistical Linear Regression Models 01:06:00
- Lecture 1.7: Inference in Regression 00:55:00
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- Lecture 2.1: Multivariate Regression 00:55:00
- Lecture 2.2: Multivariable Regression Example 01:00:00
- Lecture 2.3: Multivariable Simulation Exercises 00:55:00
- Lecture 2.4: Residuals 00:55:00
- Lecture 2.5: Some thoughts on model selection 00:55:00
- Lecture 3.1: Generalized Linear Models 00:55:00
- Lecture 3.2: Binary Data GLMs 00:55:00
- Lecture 3.3: Poisson Regression 01:00:00
- Lecture 3.4: Fitting Functions 01:00:00
Instructor
OpenCoursa
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