# Week 4 - Linear Models

**Linear Correlation**

**Why**

Many studies often espouse to have evidence that one variable is causing another. This is often based on the grounds that changes in the two quantities are correlated or related through an approximately linear mathematical relationship. While jumping from correlation to causation is a fallacy of logic we can often learn a lot from correlation and should know when and how it is justified. Given paired data sets we can try to find a pattern and use technology to pinpoint that relationship. From these relationships we can better understand the world around us.

**Learning Objectives**

- Learn how scatter and regression plots are used to establish correlation
- Know how to use and interpret regression equations

**Performance Criteria**

- The learner will create scatter plots with linear regression equations using technology
- The learner will quantify correlation with the correlation coefficient and coefficient of determination
- The learner will interpret the slope and intercept from linear regression equations

**Videos**

Chapter 12 (This is a video by the authors covering chapter 12. Just watch the parts relating to sections 12.1-12.3)

Scatter plots and correlation (Google Sheets)

Scatter plots and correlation (StatKey)

Click on "Textbook" to view the reading assignment for this experience or read sections 12.1-12.3 from your print or digital copy.

Use this site to explore cutting edge correlation studies.

Video "How To" for this app.

**Plan**

- Review - Read the above components and post any questions in the forum below.
- Practice - Complete the practice exercises that follow.
- Think - Answer the Critical Thinking questions in the "Critical Thinking" forum.
- Apply - Complete the Application Problem and upload your completed file.
- Assess - Complete the Self-Reflection for this Class.

If you have any questions about the content (readings, problems, etc.) then post in the "Questions about Week 4" forum.

Just for fun: A spreadsheet of data on flu cases vs deaths with sample charts.

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