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Unit 1, Section 2: Visualizing Data

Instructional Days: 14

Enduring Understandings

Data collection methods affect what we can know about the real world. Visual representations help tell stories with data. Distributions of numerical and categorical variables help describe variability in the data. Technology and computers allow us to visualize complex relationships in data.

Engagement

Students will view the video called The Value of Data Visualization to help them understand the importance of graphical representations of data. Discussion questions will allow students to begin to think about how they would want to see a data set visualized. The video can be found at: https://www.youtube.com/watch?v=xekEXM0Vonc

Learning Objectives

Statistical/Mathematical:

S-ID 1: Represent data with plots on the real number line (dotplots, histograms, bar plots, and boxplots).

S-ID 3: Interpret differences in shape, center, and spread in the context of the data sets, accounting for possible effects of extreme data points (outliers).

S-ID 6: Represent data on two quantitative variables on a scatterplot and describe how the variables are related.

Data Science:

Create visualizations with data. Learn the difference between plots used for categorical and numerical variables. Interpret and understand graphs of distributions for numerical and categorical variables.

Applied Computational Thinking Using RStudio:

• Learn to download, load, upload, and work with data using RStudio syntax and structure.

• Create appropriate graphical displays of data.

• Differentiate between observations and variables.

• Learn to use objects, functions, and assignments.

Real-World Connections:

Students will continue to understand that data on its own is just collected; but once interpreted, it can lead to discoveries or understandings.

Language Objectives

  1. Students will describe orally and in writing the center, shape, and spread of distributions.

  2. Students will discuss the appropriateness of dotplots, bargraphs, histograms, and scatterplots for various data analyses and the pros and cons of each.

  3. Students will compare and contrast their hand-drawn data visualizations to computer-generated RStudio/Posit Cloud data visualizations.

Data File or Data Collection Method

Data Files:

  1. Students’ Food Habits Campaign Data

  2. Centers for Disease Control and Prevention (CDC): data(cdc)

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