Concept C1.2

Measures of spread

Examine dataset variability by applying measures of spread to identify and quantify outliers.

K–2 Competencies

Describe the upper and lower bounds of a set of objects. e.g., tallest and shortest, biggest and smallest

K-2.C.1.2a

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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

Calculate the range for numerical data.

3-5.C.1.2a

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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

Categorically identify the presence of potential outliers in a dataset.

6-8.C.1.2a

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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.

9–10 Competencies

Calculate standard deviation from mean or interquartile range.

9-10.C.1.2a

Use standard deviation as a measure of variability and a modified boxplot for identifying outliers.

9-10.C.1.2b

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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

Numerically operationalize the meaning of an "outlier" using standard deviation as a measure of variability and a modified boxplot.

11-12.C.1.2a

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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.

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