In this course, you will :
- Focus on the parameters slope and intercept, how they define the model, and how to interpret them in a variety of applications.
- We investigate some of the most common prediction flaws and limitations, and we evaluate and compare models by quantifying and contrasting several measures of goodness-of-fit, such as RMSE and R-squared.
- introduce inferential statistics concepts and apply them to investigate how maximum likelihood estimation and bootstrap resampling can be used to estimate linear model parameters.
- Exploring Linear Trends
- Building Linear Models
- Making Model Predictions
- Estimating Model Parameters