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In this course, you will learn :
- The fundamentals of this well-known statistical model, as well as what regression is and how linear and logistic regressions differ.
- How to fit simple linear regression models with numeric and categorical explanatory variables, as well as how to use model coefficients to describe the relationship between the response and the explanatory variables.
- How to use linear regression models to forecast Taiwanese house prices and Facebook ad clicks.
- You'll also improve your regression skills by working with model objects, understanding the concept of "regression to the mean," and learning how to transform variables in a dataset.
- Learn how to ask questions of your model in order to determine fit.
- How to quantify the fit of a linear regression model, diagnose model issues with visualisations, and comprehend the leverage and influence of each observation used to create the model.
- Understand how to fit logistic regression models.
Syllabus :
- Simple Linear Regression
- Predictions and model objects
- Assessing model fit
- Simple logistic regression
