Sprint Challenge

Sprint Challenge Overview

The Sprint Challenge is your opportunity to demonstrate the statistical and linear algebra concepts you've learned throughout this sprint. You'll apply linear regression inference, multiple regression, linear algebra, and the bias-variance tradeoff to real-world datasets.

Challenge Setup

To get started with the Sprint Challenge, follow these steps:

1. Access the Jupyter notebook using the link below.
2. Complete all tasks in the notebook, demonstrating your understanding of the sprint concepts.
3. Submit your completed challenge according to the provided instructions.

Challenge Expectations

The Sprint Challenge is designed to test your mastery of the following key concepts:

• Linear regression inference: Testing hypotheses for statistical significance between quantitative variables
• Multiple regression: Modeling relationships with multiple predictor variables and comparing model fit
• Linear algebra: Applying vector and matrix operations to solve linear algebra problems
• Bias-variance tradeoff: Understanding the tradeoff in model building and evaluation
• Statistical interpretation: Drawing statistically sound conclusions from data analysis

What to Expect

The Sprint Challenge will involve working with real-world datasets and demonstrating your ability to:

1. Test hypotheses for statistical significance between quantitative variables
2. Conduct t-tests for slope parameters and interpret results
3. Build confidence intervals for slope terms
4. Model relationships with multiple predictor variables
5. Compare model fit using adjusted R-squared
6. Apply vector and matrix operations to solve linear algebra problems
7. Understand the bias-variance tradeoff in model building

Sprint Challenge 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