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Common Core Standards (8.SP)
Investigate patterns of association in bivariate data.
- 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. - 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. - Use the equation of a linear model to solve problems in the context
of bivariate measurement data, interpreting the slope and intercept.
For example, in a linear model for a biology experiment, interpret a slope
of 1.5 cm/hr as meaning that an additional hour of sunlight each day is
associated with an additional 1.5 cm in mature plant height. - Understand that patterns of association can also be seen in bivariate
categorical data by displaying frequencies and relative frequencies in
a two-way table. Construct and interpret a two-way table summarizing
data on two categorical variables collected from the same subjects.
Use relative frequencies calculated for rows or columns to describe
possible association between the two variables. For example, collect
data from students in your class on whether or not they have a curfew on
school nights and whether or not they have assigned chores at home. Is
there evidence that those who have a curfew also tend to have chores?
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