## Entry Ticket: Outliers and Outsiders - Section 1: Entry Ticket: Outliers and Outsiders

# Outliers and Outsiders: The Impact on Data

Lesson 10 of 10

## Objective: SWBAT interpret differences in shape, center and spread between data sets. SWBAT explain the possible effects of outliers.

## Big Idea: Students real world relationships to gain understanding of the power of individual data points.

*90 minutes*

Student will complete an entry ticket (**Entry Ticket: Outliers and Outsiders**) where they have to respond to a quote from the book *Outliers *by Malcolm Gladwell and then complete a **Turn and Talk **to share their ideas and listen to the ideas of a classmate. This entry ticket provides a good opportunity for all students to engage and participate in the conversation as it asks for their perspective on a vocabulary term and they can bring in different representations of the word that may help their peers better understand the term and main idea of the lesson.

**Academic Vocabulary:**

**Outlier**

**Correlation Coefficient**

*Note: place academic vocabulary on word wall as a strategy to assist students in learning academic vocabulary.

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#### Resources

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After the entry ticket, we will discuss the topic of outliers using the **PowerPoint Slides: Outliers and Outsiders: The Effect on Data**. The presentation includes an example of the effect of outliers looking at a data set comparing the population and crime index (note that a crime index of 100 is the safest possible score – the higher the score, the safer the city). I ask students to complete a number of **Turn and Talks ** (see strategy folder for more information) as part of this lesson.

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After reviewing examples, I have students complete the independently to assess their understanding of the day’s lesson. This particular ticket to leave asks students to interpret the impact of outliers on the data set about crime and population. This formative assessment ties directly to math practice standard **MP.8** as students can rely on regularity and repeated reasoning to help distinguish between the correlation coefficient and data sets with and without the outliers (NY, LA and Atlanta).

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To conclude today's lesson I have students work in groups on their collaborative project: **Our City Statistics Project Overview**

For more details on the expectations and steps for the project see the **Project: Our City Statistics Assignment Sheet.**

For this particular working session of the project, I recommend that students work on analyzing their group’s data to 1. Identify possible outliers and 2. Assess the impact of these extreme data points.

To facilitate this process, I have included some possible prompts in the last slide of the powerpoint slides on outliers. Having each group share focuses on **MP.3** as students are asked to navigate a group setting and work collaboratively to create and critique mathematical decisions and arguments.

It is helpful to let each group use one computer to assess the impact of possible outliers. I use the statistical software program Inspire but students could also use another program (like Excel, for example) to run scatterplots (and calculate correlation coefficients of linear fit) with and without outliers. In this way, students are offered an opportunity to independently practice the day’s concepts in a meaningful context (group project).

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- 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