## Cinderella Slipper: Scatterplots, Residuals and Goodness of Fit - Section 2: Active Note Taking and Explicit Instruction

*Cinderella Slipper: Scatterplots, Residuals and Goodness of Fit*

*Cinderella Slipper: Scatterplots, Residuals and Goodness of Fit*

# Cinderella's Slipper: Scatterplots, Residuals and Goodness of Fit

Lesson 7 of 10

## Objective: SWBAT choose an appropriate function to model a bivariate data set. SWBAT fit a linear function to the data. SWBAT plot, analyze and interpret residuals for a data set.

## Big Idea: Students explore the idea of Goodness of Fit for different data sets and learn to fit data that can be modeled with linear associations!

*95 minutes*

#### Entry Ticket

*15 min*

At the start of class students will complete **Entry Ticket: Cinderella's Slipper** where each has to analyze a scatter plot to identify which type of function (linear, exponential or quadratic) would best fit the data. I will also ask students also complete a **Turn and Talk **to share their ideas and listen to the ideas of a classmate. As they complete the exit ticket I listen for and help students with the use of appropriate statistical vocabulary (**MP6: attention to precision**).

**Lesson Modification**: If computer technology is available, it is possible for students to complete tables and plots for this lesson using technology (Excel or Google Sheets) as an approach to **MP5: use appropriate tools**.

#### Resources

*expand content*

After the entry ticket, I review a Powerpoint presentation (**Cinderella Slipper: Scatterplots, Residuals and Goodness of Fit**) on fitting functions to data. I ask students to complete a number of **Turn and Talks ** (see strategy folder for more information) as part of this lesson. During this time students are also taking **Two-Column Notes **as a reference and resource.

This section continues to build off of the discussion from the entry ticket and focuses on having students think about the different types of functions we have learned about during the year and how to apply those functions to better explain and model data in contexts. I want students to be able to make connections from today's lessons to previous big ideas and concepts covered in other units.

*expand content*

In this section we learn about what a residual is and go through an example from the GPA and Attendance data from the entry ticket as a class. Students also learn about how to plot and analyze residuals as a way to assess the fit of a particular function to data.

I have students take the time to calculate all of the residuals for the entry ticket data for them to get a better understanding of how to calculate residuals but also think about what residuals are.

Please see the student worksheet (**Classwork: Residuals Data Table**) that teachers can print out and have students complete - this will save time so students do not have to copy the original data and can instead focus on the task at hand of analyzing and plotting residuals.

#### Resources

*expand content*

In this section I review different equations for determining a line of best fit for a data set. I pose the question, "Can you prove that the line of best fit provided is indeed THE line of best fit?" I judged this to be a manageable task for my students. During this section students work on the **Collaborative Work: Creating a Line of Best Fit** worksheet.

I want students to compare linear function as a way of engaging in mathematical practices related to modeling bi-variate data with functions. I will actively engage with them as they complete this work. Without this support, some students may not be clear on what they are being asked to accomplished.

***Note:** I have found that many times I do not have time to complete this section in one 90 minute block. If class time runs out, this assignment could be used for homework, extra credit, and/or worked on the next day in class as an entry ticket.

*expand content*

With the remaining time in class, I ask students to begin work on their City Statistics Project. This is the culminating project/assessment for the unit, where students complete a cycle of inquiry into a research question related to their community. For more information on the project please see my **Our City Statistics Lesson** page. The **Project: Our City Statistics Assignment Sheet** for the project is also included in this section as a resource.

If the timing works, students will create scatter plots using the data from their projects. The reason for this is creating and interpreting scatter plots is highly connected to the day's objective. Alternatively, groups can utilize the remaining time in class to work on another section of the project, or even complete a section of the project for homework.

There are a number of different ways technology can be incorporated into the lesson to support the scatterplot creations. Students can use a software like **InspireData** or Excel to easily create scatterplots. I have a class set of ipads and a **Math Warehouse Scatterplot Maker** website makes it easy for students to create and save scatterplots as a jpeg file.

*expand content*

##### Similar Lessons

###### Function Notation

*Favorites(23)*

*Resources(17)*

Environment: Urban

###### Predicting Water Park Attendance

*Favorites(6)*

*Resources(11)*

Environment: Urban

###### Expore Correlation on Gapminder

*Favorites(6)*

*Resources(20)*

Environment: Urban

- UNIT 1: Thinking Like a Mathematician: Modeling with Functions
- UNIT 2: Its Not Always a Straight Answer: Linear Equations and Inequalities in 1 Variable
- UNIT 3: Everything is Relative: Linear Functions
- UNIT 4: Making Informed Decisions with Systems of Equations
- UNIT 5: Exponential Functions
- UNIT 6: Operations on Polynomials
- UNIT 7: Interpret and Build Quadratic Functions and Equations
- UNIT 8: Our City Statistics: Who We Are and Where We are Going

- LESSON 1: Our City Statistics Project and Assessment
- LESSON 2: Correlation and Causation
- LESSON 3: Estimating Population Percentages - It is all normal
- LESSON 4: Summing it Up: Dot Plots, Histograms and Box Plots
- LESSON 5: Making Relevant Comparisons: Comparing Populations
- LESSON 6: What's the Frequency Kenneth? Summarizing Data with Frequency Tables
- LESSON 7: Cinderella's Slipper: Scatterplots, Residuals and Goodness of Fit
- LESSON 8: How does this fit? CalculatingCorrelation
- LESSON 9: What does it mean? Interpreting linear models
- LESSON 10: Outliers and Outsiders: The Impact on Data