## Spreadsheet Lesson Framework

This lesson is an exploration of scatterplots and lines of best fit. In this activity, high school algebra students will enter given data of average points per basketball game vs. player height into a table. Using the chart feature of Google Sheets, students will create a scatterplot of the data with the line of best fit superimposed on the graph. They will use the formulas to calculate slope and y-intercept from their table. They will use the forecast formula to predict both height and points scored for people of their choice. Discussion will take place regarding the validity of the predicted data and outliers will be introduced.

## Goals

After this lesson, students will be able to

- enter data into a Google Sheet in table form, adding borders to increase readability.
- choose an appropriate chart to showcase data, add labels, and insert it into a spreadsheet
- add a line of best fit to the scatterplot
- use the =Slope formula to calculate the rate of change for a table of data
- use the =Intercept formula to calculate the y-intercept for a table of data
- use the =Forecast formula to predict data for either the x- or the y-axis
- explain the definition of an outlier and give one example

## Complex Concepts

- Why precision when entering data is important
- Why sample size is important
- The relationship between tables and graphs
- What outliers are and the effect they have on data
- What the line of best fit is, its importance and application
- What correlation is and how it applies to data

## The Table

The data used in the table will be given to students by the teacher and is a random sampling of basketball player's heights vs. average points scored per game. The column heading for the x-axis will be player's heights in centimeters. The column heading for the y-axis will be the average points scored per basketball game. Students will use the internet to find six more data points: three player heights and three game points averages. Students will then use the =Forecast formula in Google Sheets to predict the missing information.