CCSS.Math.Content.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.

 The numbers of years of work experience of 14 employees makes up a set of numerical data. The hourly wages of those 14 employees also make up a set of numerical data. If the years of experience and hourly wage of each employee are linked, those ordered pairs of data are called bivariate data. Bivariate data is often displayed in a scatter plot. A scatter plot can help you determine if there is a relationship, or association, between two pairs of data. (example)

 Data for two variables (usually two types of related data). Example: Ice cream sales versus the temperature on that day. The two variables are Ice Cream Sales and Temperature. (If you have only one set of data, such as just Temperature, it is called "Univariate Data")

 A graph of plotted points that show the relationship between two sets of data. In this example, each dot represents one person's weight versus their height. plot.html

 When data seems to be "gathered" around a particular value. For example: for the values 12, 19, 23, 23, 24, 24, 25, 30, 35, 39, there is a cluster around the value 24.

 A value that "lies outside" (is much smaller or larger than) most of the other values in a set of data. For example in the scores 3,25,27,28,29,32,33,85, both 3 and 85 are "outliers".

 Data increases together  As one variable becomes large, the other also becomes large, and vice versa.  The direct relationship between two Variables, the values of which fluctuate together, in the same direction. Read more: opic/positive-correlation- 1#ixzz2ervpP57m opic/positive-correlation- 1#ixzz2ervpP57m

 When there is no relationship between two data sets  The points are scattered in the scatter plot

 a correlation in which large values of one variable are associated with small values of the other;  As one value increases the other decreases

 An attempt to model the relationship between two variables by fitting a linear equation to observed data. One variable is considered as the independent variable, and the other is considered as the dependent variable.

 An association that doesn’t form a line  It may form a curve or another shape

 Bivariate Data  Scatter Plot  Clustering  Outliers  Positive Association  Negative Association  No Association  Linear Association  Non Linear Association