Unit 8

Common Core Standards:

• 8.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.
• 8.SP.A.2 Know that straight lines are widely used to model relationships between two quantitative variables. For scatter plots that suggest a line
• 8.SP.A.3 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.
• 8.SP.A.4 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 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?

Vocabulary

*Bivariate Data: A set of data that is made of two paired variables.

*Clustering: A condition that occurs when data points in a scatter plot are grouped more in one part of the graph than another.

*Dependent Variable: The output of a function; a variable whose value depends on the value of the input, or independent variable.

Frequencies: The number of times the value appears in the data set.

*Independent Variable: The input of a function; a variable whose value determines the value of the output, or dependent variable.

*Intercept:

*Linear Association: The data on a scatter plot can best be described with a line.

*Maximum: the highest value in a data set

*Minimum: the lowest value in a data set

*Negative Association: In a data set, as one variable increases, the other tends to decrease.

*Nonlinear Association: The data on a scatterplot can be best described by something other than a line.

*Outliers: values much greater or much less than the others in a data set.

*Positive Association: In a data set, as one variable increases, the other does as well.

*Relationship:

*Relative Frequencies: The frequency of a specific data value divided by the total number of data values in the set.

*Scatter Plot: A graph with points plotted to show a possible relationship between two sets of data.

Slope: A measure of the steepness of a line on a graph; the rise divided by the run.

*Trend: A line on a scatter plot that helps show the correlation between data sets more clearly.

*Two-way Table: A table that displays two-variable data by organizing it into rows and columns.