Description

This instructional video reviews how a scatterplot is used to determine a relationship between two variables. The video reviews how a two variable data set can also be displayed using circle graphs and double bar graphs. Three different scatterplots show the three kinds of relationships that potentially exist with two variables: positive, negative, and no relationship. The possibility of outliers is also addressed. Like my other instructional math videos, music and gifs help to make the material more engaging for students. An assignment is also included with this video.

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Distance Learning Math: Scatterplots (Two Variable Data Sets)

Name: Distance Learning Math: Scatterplots (Two Variable Data Sets)
Brand: Make it Middle School
SKU: 5829646
Price: 1.75 USD
Availability: InStock

Make it Middle School

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$1.75

Highlights

Digital downloads

Microsoft Word, Video

Grades

7th - 9th

Subjects

Graphing, Other (Math), Statistics

Standards

CCSS8.SP.A.1

CCSS8.SP.A.2

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Distance Learning Math: Data Management Resource Package

The follow group of videos include such lessons as two variable data sets (scatterplots), histograms, calculating averages, and how graphs can misrepresent data. There are a variety of assignments and assessment rubrics included with these instructional videos.

Price $4.00Original Price $5.75Save $1.75

Description

Report this resource to TPT

Reported resources will be reviewed by our team. Report this resource to let us know if this resource violates TPT's content guidelines.

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Questions & Answers

Standards

to see state-specific standards (only available in the US).

CCSS8.SP.A.1

Construct and interpret scatter plots for bivariate measurement data to investigate patterns of association between two quantities. Describe patterns such as clustering, outliers, positive or negative association, linear association, and nonlinear association.

CCSS8.SP.A.2

Know that straight lines are widely used to model relationships between two quantitative variables. For scatter plots that suggest a linear association, informally fit a straight line, and informally assess the model fit by judging the closeness of the data points to the line.