In this topic, students will:
construct a scatter plot graph to model paired data
utliize a scatter plot to identify and interpret the relationship between paired data
recognize whether the paired data has linear association, a nonlinear association, or no association
draw a trend line to determine whether a linear association is positive or negative and strong or weak
use the slope and y-intercept of a trend line to make a prediction
make a prediction when no equation is given by drawing trend lines and writing the equation of the linear model
organize paired categorical data into two-way frequency table
compare and make conjectures about data displayed in a two-way frequency table
construct two-way frequency tables and two-way relative frequency tables
compare and make conjectures about data displayed in a two-way relative frequency table
Vocabulary:
Cluster - a group of points that lie close together
Gap - an area on a graph that contains no data points
Measurement data - number of visits, age, months, and time are examples of measurement data
Negative association - a relationship between two sets of data where when one variable decreases the other variable increases.
Outlier - a piece of data that does not seem to fit the rest of the data set
Positive association - a relationship between two points of data where one variable increases while the other increases as well
Scatter plot - a graph that uses points to display the relationship between two different sets of data. Each point can be represented by an ordered pair
Trend line - a line on a scatter plot, drawn near the points, that approximates the association between paired data
Categorical data - colors, gender, and nationality are examples of categorical data
Relative frequency table - this table shows the ratio of the number of data in each category to the total number of data items. The ratio can be expressed as a fraction, decimal, or percent