Too Many Emails 1 Copyright © 2026 Pearson Education, Inc. Too Many Emails (50 – 60 minutes) Learning Objective(s): Students will describe and represent univariate numerical data. Students will make inferences about the population based on the mean and margin of error in the data. Students will compare the mean and median of two data sets and make inferences based on the comparisons. Material needed: Student pages: Too Many Emails Calculator or online calculator Lesson Procedure: Warm–Up 10 minutes Prompt: How do you communicate with friends? What forms of communication do you use most often? Discuss: Have students share with the group how they like to communicate. Guide students to think about the number of emails they receive daily. Guided Instruction 15 minutes Present: scenario for Too Many Emails Example: Organizations often collect data to help improve their processes. Suppose that a company collects data about employee emailing. What types of questions could they ask? Sample answers: number of emails they receive, time they spend on emails, size of attachments What statistics can be used to summarize data? Sample answers: mean, median, mode, range What can the shape of a distribution of data tell us? Sample answers: whether most values are in the middle or to the sides, whether there are multiple modes or only one Review: key terms – histogram, univariate data, bivariate data, categorical data, numerical data histogram: a graphical representation of numerical data univariate data: a data set with data for only one variable or characteristic bivariate data: data set with data for two variables or characteristics categorical data: type of statistical data that represents categories or groups numerical data: type of statistical data that represents values that can be measured or counted Independent Practice 20 minutes Distribute: student activity Too Many Emails Have students complete questions 1–4 individually and question 5 in pairs. Allow students to select procedures that allow them to solve the problem efficiently and accurately. After the task, ask students to share their methods with partners and compare the efficiency of their methods. Closure 10–15 minutes Review Answers: 1. a. numerical; b. univariate; c. Sample answer: histogram, because a histogram can show the shape of distribution of univariate numerical data 2. See sample histogram. 3. a. 39.825 emails; b. 37.83375 to 41.81625 emails; c. 9080.1 to 10035.9 emails; d. Sample answer: Yes, there are 240 employees in the department, so each receives on average 40 emails daily, and the range of expected emails should be in the stated range. 4. a. more; b. more 5. a. accounting; b. marketing; c. Sample answer: The emails in accounting are more spread out; they are not clustered. Discuss: What types of inferences can be drawn from univariate numerical data? How does this help you compare two sets of data?
RkJQdWJsaXNoZXIy NjM5ODQ=