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

What you'll learn

1. 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
2. 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
3. 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)
4. 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
5. 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

Prerequisites

• Construct and interpret scatter plots for bivariate measurement data