## Entry Ticket: Estimating Population Percentages - Section 1: Entry Ticket

*Entry Ticket: Estimating Population Percentages*

# Estimating Population Percentages - It is all normal

Lesson 3 of 10

## Objective: SWBAT apply normal distributions to estimate population percentages using the mean and standard deviation of such distributions. SWBAT synthesize their ideas through recaps and other activities to show their understanding of the lesson objectives.

## Big Idea: Students will apply their knowledge of measures of center and spread to estimate population percentages in different contexts!

*90 minutes*

#### Entry Ticket

*15 min*

Student will complete an entry ticket (**Entry Ticket: Estimating Population Percentages**) where they have to activate prior knowledge about measures of center and spread. I ask students to write, discuss and revise their writing to engage in different domains of language and also to develop their own arguments and be able to navigate and reconcile those arguments with those of their peers (for more information see my video on **MP.3**). The entry ticket should get students interested in what the lesson for the day will be about.

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After the entry ticket, I will review a Powerpoint presentation with the class (**PowerPoint Slides: Estimating Population Percentages**) providing information and application regarding estimating population percentages. As part of the lesson students complete different **Turn and Talks **(see strategy folder for more information) that specifically address standard **ID-A.4** as students are engaged in the productive struggle of estimating population percentages using the mean and standard deviation for normal distributions.

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After completing the section on active note-taking, students complete practice problems to apply and demonstrate their knowledge about the day’s topic.

I have students complete the exit ticket (**Exit Ticket: Estimating Population Percentages**)during the middle section of the lesson in order to assess their learning of the topic and because the class then shifts to further work on their Our City Statistics project and I want students to synthesize and recap their ideas about estimating population percentages before moving on to a new activity.

I may have students work together on the two scenarios, or they can also work independently, as they complete the **Exit Ticket.**

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

In this part of the lesson, students can focus on developing a good research question, hypothesis and method. For more details on the expectations and steps for the project see the **Project: Our City Statistics Assignment Sheet. **I would not have students try to estimate population percentages because a number of research questions that students design tend to not follow a normal distribution, which often can make the task require mathematical understanding beyond the scope of the Algebra I course.

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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