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Introduction to Data Science Curriculum
Lesson 11: What Shape Are You In?
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    • Table of Contents
    • Overview & Philosophy
    • Scope and Sequence
      • Daily Overview
      • Essential Concepts
        • Data Are All Around
        • Lesson 1: Data Trails
        • Lesson 2: Stick Figures
        • Lesson 3: Data Structures
        • Lesson 4: The Data Cycle
        • Lesson 5: So Many Questions
        • Lesson 6: What Do I Eat?
        • Lesson 7: Setting the Stage
        • Campaign Guidelines – Food Habits
        • Visualizing Data
        • Lesson 8: Tangible Plots
        • Lesson 9: What is Typical?
        • Lesson 10: Making Histograms
        • Lesson 11: What Shape Are You In?
          • Lesson 11: What Shape Are You In?
            • Objective:
            • Materials:
            • Vocabulary:
            • Essential Concepts:
            • Lesson:
            • Class Scribes:
            • Homework
        • Lesson 12: Exploring Food Habits
        • Lesson 13: RStudio Basics
        • Lab 1A - Data, Code & RStudio
        • Lab 1B: Get the Picture?
        • Lab 1C: Export, Upload, Import
        • Lesson 14: Variables, Variables, Variables
        • Lab 1D: Zooming Through Data
        • Lab 1E: What’s the Relationship?
        • Practicum: The Data Cycle & My Food Habits
        • Would You Look at the Time!
        • Lesson 15: Americans’ Time on Task
        • Campaign Guidelines – Time Use
        • Lab 1F: A Diamond in the Rough
        • Lesson 16: Categorical Associations
        • Lesson 17: Interpreting Two-Way Tables
        • Lab 1G: What’s the FREQ?
        • Practicum: Teen Depression
        • Lab 1H: Our Time
        • End of Unit Project and Oral Presentation: Analyzing Data to Evaluate Claims
      • Daily Overview
      • Essential Concepts
        • What is Your True Color?
        • Lesson 1: What is Your True Color?
        • Lesson 2: What Does Mean Mean?
        • Lesson 3: Median in the Middle
        • Lesson 4: How Far is it from Typical?
        • Lab 2A - All About Distributions
        • Lesson 5: Human Boxplots
        • Lesson 6: Face Off
        • Lesson 7: Plot Match
        • Lab 2B - Oh the Summaries ...
        • Practicum: The Summaries
        • How Likely is it?
        • Lesson 8: How Likely Is It?
        • Lesson 9: Dice Detective
        • Lesson 10: Marbles, Marbles…
        • Lab 2C - Which Song Plays Next?
        • Lesson 11: This AND/OR That
        • Lab 2D - Queue It Up!
        • Practicum: Win, Win, Win
        • What Are the Chances That You Are Stressing or Chilling?
        • Lesson 12: Don’t Take My Stress Away!
        • Campaign Guidelines – Stress/Chill
        • Lesson 13: The Horror Movie Shuffle
        • Lab 2E - The Horror Movie Shuffle
        • Lesson 14: The Titanic Shuffle
        • Lab 2F - The Titanic Shuffle
        • Lesson 15: Tangible Data Merging
        • Lab 2G - Getting It Together
        • Practicum: What Stresses Us?
        • What’s Normal?
        • Lesson 16: What Is Normal?
        • Lesson 17: A Normal Measure of Spread
        • Lesson 18: What’s Your Z-Score?
        • Lab 2H - Eyeballing Normal
        • Lab 2I - R’s Normal Distribution Alphabet
        • End of Unit Project: Asking and Answering Statistical Questions of Our Own Data
      • Daily Overview
      • Essential Concepts
        • Testing, Testing…1, 2, 3…
        • Lesson 1: Anecdotes vs. Data
        • Lesson 2: What Is an Experiment?
        • Lesson 3: Let’s Try an Experiment!
        • Lesson 4: Predictions, Predictions
        • Lesson 5: Time Perception Experiment
        • Lab 3A: The Results Are In!
        • Practicum: Music to my Ears
        • Would You Look at That?
        • Lesson 6: Observational Studies
        • Lesson 7: Observational Studies vs. Experiments
        • Lesson 8: Monsters That Hide in Observational Studies
        • Lab 3B: Confound It All!
        • Are You Asking Me?
        • Lesson 9: Survey Says…
        • Lesson 10: We’re So Random
        • Lesson 11: The Gettysburg Address
        • Lab 3C: Random Sampling
        • Lesson 12: Bias in Survey Sampling
        • Lesson 13: The Confidence Game
        • Lesson 14: How Confident Are You?
        • Lab 3D: Are You Sure about That?
        • Practicum: Let’s Build a Survey!
        • What’s the Trigger?
        • Lesson 15: Ready, Sense, Go!
        • Lesson 16: Does It Have a Trigger?
        • Lesson 17: Creating Our Own Participatory Sensing Campaign
        • Lesson 18: Evaluating Our Own Participatory Sensing Campaign
        • Lesson 19: Implementing Our Own Participatory Sensing Campaign
        • Webpages
        • Lesson 20: Online Data-ing
        • Lab 3E: Scraping Web Data
        • Lab 3F: Maps
        • Lesson 21: Learning to Love XML
        • Lesson 22: Changing Orientation
        • Practicum: What Does Our Campaign Data Say?
        • End of Unit Project: TB or Not TB
      • Daily Overview
      • Essential Concepts
        • Campaigns and Community
        • Lesson 1: Trash
        • Lesson 2: Drought
        • Lesson 3: Community Connection
        • Lesson 4: Evaluate and Implement the Campaign
        • Lesson 5: Refine and Create the Campaign
        • Predictions and Models
        • Lesson 6: Statistical Predictions Using One Variable
        • Lesson 7: Statistical Predictions by Applying the Rule
        • Lesson 8: Statistical Predictions Using Two Variables
        • Lesson 9: Spaghetti Line
        • LAB 4A: If the Line Fits…
        • Lesson 10: What’s the Best Line?
        • LAB 4B: What’s the Score?
        • LAB 4C: Cross-Validation
        • Lesson 11: What’s the Trend?
        • Lesson 12: How Strong Is It?
        • LAB 4D: Interpreting Correlations
        • Lesson 13: Improving your Model
        • LAB 4E: Some Models Have Curves
        • Piecing it Together
        • Lesson 14: More Variables to Make Better Predictions
        • Lesson 15: Combination of Variables
        • LAB 4F: This Model Is Big Enough for All of Us
        • Practicum: Predictions
        • Decisions, Decisions!
        • Lesson 16: Football or Futbol?
        • Lesson 17: Grow Your Own Decision Tree
        • LAB 4G: Growing Trees
        • Ties That Bind
        • Lesson 18: Where Do I Belong?
        • LAB 4H: Finding Clusters
        • Lesson 19: Our Class Network
        • End of Unit 4 Modeling Activity Project and Presentation
      • Unit 1 Vocabulary
      • Unit 2 Vocabulary
      • Unit 3 Vocabulary
      • Unit 4 Vocabulary
      • Unit 1 Lab Code
      • Unit 2 Lab Code
      • Unit 3 Lab Code
      • Unit 4 Lab Code
      • IDS_Curriculum
      • IDS_LMRs
      • IDS_Lab Response Sheets
      • IDS_Teacher Resources
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    • Lesson 11: What Shape Are You In?
      • Objective:
      • Materials:
      • Vocabulary:
      • Essential Concepts:
      • Lesson:
      • Class Scribes:
      • Homework

    Lesson 11: What Shape Are You In?

    Lesson 11: What Shape Are You In?

    Objective:

    Students will learn to classify distributions in terms of shape, and can suggest theories for why a distribution might be one shape or another.

    Materials:

    1. Sorting Histograms handout (LMR_1.10_Sorting Histograms) - one copy per group of 4 students. (This activity comes from the AIMS project, University of Minnesota, J. Garfield.)
      Advanced preparation required (see step 1 below)

    Vocabulary:

    symmetric left-skewed right-skewed unimodal bimodal

    Essential Concepts:

    Essential Concepts:

    Identifying the shape of a histogram is part of the interpret step of the Data Cycle.

    Lesson:

    1. Distribute the cutouts from the Sorting Histograms handout (LMR_1.10). Give each student team all of the 24 histograms (can be paper clipped together or put in small zippered bags).

      Advanced preparation required: Print the Sorting Histograms file (LMR_1.10). Cut each histogram so that it is on its own piece of paper. Create enough sets for each team to have all 24 histograms. They can be paper clipped together, or put in small zippered bags.


      LMR_1.10

    2. Inform students that the type of data being measured is indicated on the horizontal axis, and the vertical axis represents how many observations are in each bar.

    3. The students will then sort their stack of plots into different piles according to their shapes. Histograms that have similar shapes should be sorted into the same stack.

    4. Once the student teams have agreed upon the histogram shape groupings, they should discuss and write down answers to the following in their DS journals:

      1. Describe what’s similar about the plots in each group. Answers will vary, but should be grouped by the overall shape of the distribution. For example, plots with a higher density of bars on the right side of the plot should all be in the same group.

      2. Pick one graph in each group that is the best example of that group. Answers will vary.

      3. Give the group a name that you think describes the general shape. Answers will vary.

      4. If there are graphs that do not fit into any group, try to determine why it was impossible to place them. What is different or confusing about them? Answers will vary.

    5. After each team has had time to discuss and write down their observations, have a class discussion about the histogram groupings. Do the students agree about the general shapes?

    6. In statistics, we use specific terminology when discussing the shapes of distributions, such as symmetric, right-skewed, left-skewed, unimodal, bimodal, etc. Did any of the teams use these terms? If not, introduce each one and ask which of the 24 histograms could be classified as that shape.

    7. Next, introduce the following scenarios and ask students to determine what a corresponding histogram might look like. They should use statistical terms to describe their answer.

      1. The grades on an easy test. Left-skewed, unimodal

      2. The grades on a difficult test. Right-skewed, unimodal

      3. The number of times IDS students study during the first week of class. Answers will vary.

      4. The age of cars on a used car lot. Right-skewed, probably unimodal

      5. The amount of time spent by students on a difficult test (max time allowed is 50 mins). Left-skewed, but may also just be one bar with all observations at 50 mins, unimodal

      6. The heights of students in your high school band. Symmetric, bimodal

      7. The salaries of all persons employed at the Los Angeles Unified School District. Right-skewed, potentially bimodal (teachers vs. LAUSD administrators)

    Class Scribes:

    One team of students will give a brief talk to discuss what they think the 3 most important topics of the day were.

    Homework

    Students should continue to collect nutritional facts data using the Food Habits Participatory Sensing campaign on their smart devices or via web browser.

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