Concept C2.1

Comparing variables

Identify similarities and differences between variables and explore potential associations. Use distributions, numerical summaries, and simulations to compare groups based on numerical or categorical data.

K–2 Competencies

Describe similarities or differences across two variables.

K-2.C.2.1a

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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

Observe whether or not there appears to be an association between two variables. e.g., student height compared to shoe size vs. student height compared to favorite color

3-5.C.2.1a

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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 reasoning about distributions to compare two groups based on quantitative variables.

6-8.C.2.1a

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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 numerical measures such as average, standard deviation and quartiles to compare two groups.

9-10.C.2.1a

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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

Use simulations to investigate associations between two categorical variables and to compare groups.

11-12.C.2.1a

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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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