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# Correlation and Causation

Lesson 2 of 10

## Objective: SWBAT differentiate the concepts of correlation and causation and use them to interpret relationship between variables of interest on a scatterplot.

## Big Idea: Students will distinguish between correlation and causation by analyzing relevant real life examples!

*90 minutes*

#### Entry Ticket

*15 min*

Student will complete the **Entry Ticket: Correlation and Causation** where they have to interpret a cartoon and explain the joke. The joke focuses on the distinction between causation and correlation (**ID.C-9) **but also allows every student to give an answer in explaining the joke without necessarily knowing mathematical terminology or concepts.

Students will also be asked to generate their own joke that utilizes a similar underlying humor. The entry ticket should get students interested in what the lesson for the day will be about.

**Interdisciplinary content area(s): **This lesson taps into content in science through examples of daily temperature and a research summary. The lesson also integrates aspects of ELA, primarily asking students to write a complex and complete paragraph recapping the important aspects of the lesson.

**Academic Vocabulary:**

**Correlation – **when two variables are related, but don’t necessarily cause the change.

**Causation – **when one variable causes a change in another variable

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

#### Resources

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After the entry ticket, the teacher will review a Powerpoint presentation (**PowerPoint Slides: Correlation and Causation**)comparing and contrasting the concepts of correlation and causation. As part of the lesson students complete two different **Turn and Talks **(see strategy folder for more information). One prompt asks students to interpret a graph on daily temperatures to identify correlations and causations in the data. The second turn and talk has students translate a table of values into a scatterplot and analyze the relationship between the two variables, which is highly associated with standard **ID-B.6**.

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After the lecture and turn and talks, the class turns to a small group activity – students read a brief article that summarizes research and complete the **Class Activity: Correlation and Causation** activity.

In groups they identify different correlations and causations that are implied in the article. Each group will write down the correlations and causations they identified along with a justification/explanation of their thinking and put their findings on the whiteboards in class. For more information on the math practice standard **MP.3** about creating arguments and critiquing those of other see my strategy folder. Each group will then present their findings to the class.

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At this point in the lesson I assign a short writing prompt for independent work(**Independent Work: Writing Activity on Correlation and Causation**). Students complete a written recap in a complete paragraph answering the following prompt:

How would you explain the relationship between correlation and causation to a friend? Include at least one example of a correlation and one example of a causation.

I use the **Short Response Rubric for Correlation and Causation Writing Activity** to assess student writing and content understanding for this assignment.

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#### Our City Statistics Project

*20 min*

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

For this section, I suggest having students discuss whether their results of their research question will be a correlation or a causal one for their group project. The project assignment sheet is here: **Project: Our City Statistics Assignment Sheet. **Students can still write about this distinction if they have not completed the data collection or analysis section of the project.

As long as they know the methods they did/will do and the research question, they should be able to identify whether the results will suggest a correlation or a causal relationship.

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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