Learning Objectives

1.1. Learning Objectives#

1.1.1. Conceptual#

This week we have introduced regression analysis.

After this week, you should be able to:

  • Know how regression relates to the methods of correlation and covariance.

  • Understand the “Least Squares” concept.

  • Distinguish a dependent from an independent variable.

  • Understand the form and interpretation of the regression equation.

  • Recognise and understand the equations for calculating regression coefficients.

These points will be covered in the lecture.

1.1.2. Python Skills#

Starting this week, and for the rest of the course, we will be working with the statsmodels package in Python.

This week, you will learn to:

  • Run a simple regression model in Python.

  • Understand the model outputs including the intercept and slope coefficients, and the sum of residuals.

  • Identify potential outliers and exclude them from your analysis.

  • Plot a regression line over a scatterplot.

We will also be re-capping some of the valuable skills you learned for handling real-world data (data cleaning and finding outliers), as well as other skills relevant to regression modelling, namely making scatter plots, and finding correlations.

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