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In this course, you will learn:
- Understanding the background behind simple linear regression.
- How to assess simple linear regression models.
- How a simple linear regression model is used to estimate and forecast probable values.
- Understanding the assumptions required for a simple linear regression model to be viable.
- How to add many predictors in a regression model.
- Understanding the assumptions that must be met when several predictors are included in a regression model for the model to be valid.
- How a multiple linear regression model is used to estimate and predict possible values.
- Understanding how categorical predictors can be included to a regression model.
- How to alter data to address concerns highlighted in the regression model.
- Strategies for creating regression models.
- Distinguishing between outliers and influential data points, and how to deal with them.
- Handling common regression-related issues.
- In addition to ordinary least squares, there are other approaches for calculating a regression line.
- Understanding regression models in time-dependent scenarios.
- Understanding regression models in nonlinear situations.
