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Introducing Statistics: Are Variables Related?

Jackson Hernandez

Jackson Hernandez

7 min read

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Study Guide Overview

This study guide covers categorical and quantitative data types, including how to distinguish between them. It explores bivariate data relationships, using visualizations like histograms, frequency charts, mosaic plots, and scatterplots. The guide emphasizes interpreting relationships (positive, negative, or none) within the data's context and understanding correlation vs. causation.

AP Statistics: Data Analysis - Your Night-Before Review 🚀

Hey! Let's get you feeling super confident for your AP Stats exam tomorrow. We're going to break down data types and relationships, making sure it all clicks. Let's do this!

Data Types: Categorical vs. Quantitative

First things first, let's talk data. It's either categorical or quantitative.

  • Categorical Data: Think categories or groups. It's all about attributes, described by percentages or proportions. Like, "What's your favorite color?" or "Do you prefer cats or dogs?"
  • Quantitative Data: This is numerical data where you can do math. If you can average it, it's quantitative. Think, "How tall are you?" or "How many hours did you study?" 📏
Key Concept

Remember: If you can take an average, it's quantitative. If it's a label or category, it's categorical.

Memory Aid

Categorical = Categories. Quantitative = Quantities (numbers).

Bivariate Data: Exploring Relationships

Now, let's dive into relationships between two variables. This is called bivariate data. We'll look at both categorical and quantitative pairings.

Bivariate Categorical Data

When both variables are categorical, we're looking at how categories relate. For example, "Does class year affect homework completion?" Here are some ways to visualize it:

Previous Topic - Exploring Two-Variable DataNext Topic - Representing Two Categorical Variables