# Lesson 10

Fossils and Flags

### Lesson Narrative

This lesson is optional. It gives students an opportunity to put into practice what they have learned from this unit, but may be safely skipped if there is a shortage of time.

The mathematical purpose of this lesson is for students to collect, summarize, interpret, and draw conclusions from bivariate data using scatter plots, best fit lines, residuals, and correlation coefficients. This connects to previous work because students summarized, represented, interpreted, and drew conclusions from bivariate data using scatter plots, best fit lines, residuals, and correlation coefficients. This connects to upcoming work because students will continue to investigate data using conditional probability and statistical experiments.

Technology isn’t required for this lesson, but there are opportunities for students to choose to use appropriate technology to solve problems (MP5). We recommend making technology available. When students collect and analyze data to answer a question, they are making sense of problems and persevering in solving them (MP1).

### Learning Goals

Teacher Facing

• Collect data to create a linear model to predict information, answer questions, and draw conclusions.
• Interpret bivariate data using scatter plots, lines of best fit, residuals, and correlation coefficients.

### Student Facing

• Let’s collect some data and analyze it.

### Required Preparation

Technology is not required for this lesson, but there are opportunities for students to research their own data about sports teams and penalties if they have access to devices that can access the internet. If students do not have access to technology, copies of the blackline master are needed.

When students can research their own data, provide materials for them to create a visual display containing their information and any conclusions they make to share with the class.

### Student Facing

• I can collect data, create a linear model to fit the data, determine if the linear model is a good fit, and use the information from my linear model to answer questions.