DS Unit 1 Sprint 3

Welcome to Sprint 3

In this sprint, you'll learn about linear regression inference, multiple regression, linear algebra, and the bias-variance tradeoff. These foundations will help you understand how models work and how to interpret their results effectively.

You'll conduct hypothesis testing, understand how to interpret model outputs, explore vectors and matrices, and comprehend the essential balance between bias and variance in your models.

Learning Objectives

By the end of this sprint, you should be able to:

  • Apply hypothesis testing to linear regression models
  • Interpret slope parameters and build confidence intervals
  • Model relationships with multiple predictor variables
  • Work with vectors and matrices for data analysis
  • Understand and manage the bias-variance tradeoff in your models

Modules

This sprint is structured to provide you with comprehensive knowledge of advanced regression techniques:

Module 1

Inference for Linear Regression

Learn how to identify appropriate hypotheses for testing statistical significance between quantitative variables, conduct t-tests for slope parameters, build confidence intervals, and identify assumption violations for linear regression.

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Module 2

Multiple Regression

Explore how to model relationships with multiple predictor variables, conduct t-tests to determine variable significance, and compare model fit using adjusted R-squared.

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Module 3

Linear Algebra

Define vectors and calculate length and dot products, understand cosine similarity between vectors, work with matrices, and use linear algebra to solve for linear regression coefficients.

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Module 4

Bias - Variance Tradeoff

Understand and define bias and variance, describe the tradeoff between them, and learn about different types of biases in modeling.

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Sprint Resources

Primary Resources

Code-Along Sessions Sprint Challenge

Additional Resources

Yale.edu: Multiple Linear Regression JMP: What is Multiple Regression Statistics How To: What is Bias? Explaining Linear Regression with Hypothesis Testing

How to Succeed in This Sprint

  1. Establish a strong conceptual foundation: Understanding the principles behind each technique is essential.
  2. Practice implementing models: Hands-on practice is key to mastering these statistical techniques.
  3. Interpret results thoughtfully: Focus on what model outputs mean in practical terms.
  4. Participate actively: Engage in Code-Alongs and ask questions when needed.
  5. Apply your knowledge: Use real-world examples to reinforce your understanding.