Know that straight lines are widely used to model relationships between two quantitative variables

Start lessonBegins with Fitting a line · Slides

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Lesson Plan →Objectives, pacing and practice, built from this lesson's brief.
Guided Notes →One page your students fill in and keep.
Exit Ticket →Three items at the end of class. No student accounts.
Re-teach →After an exit ticket: who missed what, and what to do tomorrow.
Homework →Assign practice; it grades itself.

8.SP.A.2

**8.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.

8.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.

What you'll learn

Explain why straight lines are useful models for bivariate data that show a linear association, and identify when a linear model is and is not appropriate
Informally fit a straight line to a scatter plot that suggests a linear association, using the criterion that the line should have roughly equal numbers of data points above and below it and should follow the general trend of the data
Assess the quality of a linear model by judging the closeness of the data points to the fitted line, distinguishing between data that cluster tightly around the line (good fit) and data that spread widely around the line (poor fit)
Recognize that a fitted line is a model that simplifies the actual data pattern, and that different people may draw slightly different lines for the same data set
Use a fitted line to make predictions for values within and near the range of the data, and describe the fitted line using an equation of the form y = mx + b

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