Sprint Challenge

Overview

In this sprint challenge, you will demonstrate your understanding of linear regression inference, multiple regression, linear algebra, and the bias-variance tradeoff. You'll apply these concepts to solve real-world data problems.

This sprint challenge will test your ability to:

  • Test hypotheses for statistical significance between quantitative variables
  • Conduct t-tests for slope parameters and interpret results
  • Build confidence intervals for slope terms
  • Identify violations of linear regression assumptions
  • Model relationships with multiple predictor variables
  • Compare model fit using adjusted R-squared
  • Apply vector and matrix operations to solve linear algebra problems
  • Understand the bias-variance tradeoff in model building

Challenge Materials

GitHub Repository

Access the Challenge Materials

Study Resources

Correlation and Multiple Linear Regression

Yale.edu Multiple Linear Regression JMP: What is Multiple Regression

Bias and Variance

Statistics How To: What is Bias? Explaining Linear Regression with Hypothesis Testing

Preparation Tips

To best prepare for this sprint challenge:

  1. Review all module materials - Make sure you understand the key concepts from each module
  2. Practice with the code-alongs - The techniques covered in the code-alongs are directly applicable
  3. Focus on interpretation - Be able to explain what your results mean, not just how to calculate them
  4. Understand the assumptions - Know when regression models are appropriate and when they might fail
  5. Work through practice problems - Apply your knowledge to different datasets before the challenge