Concept C3.2

Comparing variability

Examine differences between groups by analyzing measures of spread, such as range and standard deviation. Utilize visualizations like box plots and apply statistical methods, including mean, median, and standard deviation, to compare datasets, assess variability, and uncover patterns in data distributions and models.

K–2 Competencies

Describe how two things or groups are different from one another (e.g., more or less, bigger or smaller).

K-2.C.3.2a

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Data Science Starter Kit Module 3: Making Sense of Data - Analysis and Modeling

Welcome to the exciting part of data science—making sense of the information you’ve collected! This module focuses on how to analyze data to find patterns, understand what the numbers really mean, and start drawing conclusions.🔗

Analysis and Modeling isn’t about complex mathematics or advanced statistics. It’s about developing the thinking skills to look at data and ask, “What story is this telling me? What patterns do I notice? What questions does this raise?” Whether students are working with simple tally marks or sophisticated datasets, the fundamental thinking is the same.

3–5 Competencies

Understand how data varies by exploring spread (e.g., range) and comparing qualities (e.g., brightness or temperature).

3-5.C.3.2a

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Data Science Starter Kit Module 3: Making Sense of Data - Analysis and Modeling

Welcome to the exciting part of data science—making sense of the information you’ve collected! This module focuses on how to analyze data to find patterns, understand what the numbers really mean, and start drawing conclusions.🔗

Analysis and Modeling isn’t about complex mathematics or advanced statistics. It’s about developing the thinking skills to look at data and ask, “What story is this telling me? What patterns do I notice? What questions does this raise?” Whether students are working with simple tally marks or sophisticated datasets, the fundamental thinking is the same.

6–8 Competencies

Use visualizations (e.g., box plots) to compare variability across datasets.

6-8.C.3.2a

Classroom resources

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Getting Started
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Data Science Starter Kit Module 3: Making Sense of Data - Analysis and Modeling

Welcome to the exciting part of data science—making sense of the information you’ve collected! This module focuses on how to analyze data to find patterns, understand what the numbers really mean, and start drawing conclusions.🔗

Analysis and Modeling isn’t about complex mathematics or advanced statistics. It’s about developing the thinking skills to look at data and ask, “What story is this telling me? What patterns do I notice? What questions does this raise?” Whether students are working with simple tally marks or sophisticated datasets, the fundamental thinking is the same.

9–10 Competencies

Use simple statistics including mean, median, range, standard deviation, etc. to compare data distributions.

9-10.C.3.2a

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Data Science Starter Kit Module 3: Making Sense of Data - Analysis and Modeling

Welcome to the exciting part of data science—making sense of the information you’ve collected! This module focuses on how to analyze data to find patterns, understand what the numbers really mean, and start drawing conclusions.🔗

Analysis and Modeling isn’t about complex mathematics or advanced statistics. It’s about developing the thinking skills to look at data and ask, “What story is this telling me? What patterns do I notice? What questions does this raise?” Whether students are working with simple tally marks or sophisticated datasets, the fundamental thinking is the same.

11–12 Competencies

Explore variability through statistical methods, such as analyzing residuals or variance in linear models.

11-12.C.3.2a

Classroom resources

Classroom Tip
Getting Started
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Data Science Starter Kit Module 3: Making Sense of Data - Analysis and Modeling

Welcome to the exciting part of data science—making sense of the information you’ve collected! This module focuses on how to analyze data to find patterns, understand what the numbers really mean, and start drawing conclusions.🔗

Analysis and Modeling isn’t about complex mathematics or advanced statistics. It’s about developing the thinking skills to look at data and ask, “What story is this telling me? What patterns do I notice? What questions does this raise?” Whether students are working with simple tally marks or sophisticated datasets, the fundamental thinking is the same.

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