Unit2, Section1: What is Your True Color?

Instructional Days: 7

Enduring Understandings

Statistics enable us to make sense of large amounts of data. Numerical summaries capture important elements of a distribution. Measures of center, also known as measures of central tendency, show the tendency of quantitative data to gather around a central value. Measures of spread, also known as measures of variability, show how much the quantitative data is spread out. Measurements of the propensity for the data to cluster on a central location and the range of variability within the data can provide insightful indicators about the data.

Engagement

Students will complete the True Colors Personality Test to discover the qualities and characteristics of their personality styles. Students will use the results from the personality color test to learn about subsetting data and finding measures of center and spread. The data from their personality test will be collected in a survey using the IDS UCLA App or via web browser at https://tools.idsucla.org

Learning Objectives

Statistical/Mathematical:

S-ID 2: Use statistics appropriate to the shape of the data distribution to compare center (median, mean) and spread (interquartile range, standard deviation) of two or more different data sets.

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-IC 6: Evaluate reports based on data.

Focus Standards for Mathematical Practice for All of Unit 2:

SMP-4: Model with mathematics.

SMP-5: Use appropriate tools strategically.

Data Science:

Understand the information that numerical summaries provide about the data. Understand that a boxplot is a graphical representation of a numerical summary.

Applied Computational Thinking using RStudio:

• Calculate numerical summaries (mean, median, Sum of Absolute Deviations (SAD), and Mean of Absolute Deviations (MAD)).

• Create graphical representations to compare two or more data sets, including boxplots.

Real-World Connections:

We must be able to synthesize vast amounts of data into coherent, comprehensible measures. Today’s media is continuously publishing articles that include statistical references. Critical consumerism requires that we understand the information provided in summaries of data.

Language Objectives

  1. Students will use complex sentences to construct summary statements about their understanding of data, how it is collected, how it used and how to work with it.

  2. Students will engage in partner and whole group discussions and presentations to express their understanding of data science concepts.

Data File or Data Collection Method

Data Collection Method:

  1. True Colors Personality Test: Students will complete the Personality Color survey that will collect their data about their personality styles.

Data Files:

  1. Students’ Personality Color survey data (colors)

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