# Correlation Exercises

Correlations are used to describe the strength and direction of a relationship between two variables. A correlation between two variables is known as a bivariate correlation. In this module, the Pearson Product-Moment Correlation will be used when running a correlation matrix. The Pearson correlation coefficient ranges from a value of –1.0 to 1.0. A correlation coefficient is never above 1.0 or below –1.0. A perfect positive correlation is 1.0, and a perfect negative correlation is –1.0. The size of the coefficient determines the strength of the relationship and the sign (i.e., + or –) determines the direction of the relationship. The closer the value is to zero, the weaker the relationship, and the closer the value is to 1.0 or –1.0, the stronger the relationship. A correlation coefficient of zero indicates no relationship between the variables.
A scatterplot is used to depict the relationship between two variables. The general shape of the collection of points indicates whether the correlation is positive or negative. A positive relationship will have the data points group into a cluster from the lower left-hand corner to the upper right-hand corner of the graph. A negative relationship will be depicted by points clustering in the lower right-hand corner to the upper left-hand corner of the graph. When the two variables are not related, the points on the scatterplot will be scattered in a random fashion.
Part I
Using Polit2SetB data set, create a correlation matrix using the following variables: Number of visits to the doctor in the past 12 months (docvisit), body mass index (bmi), Physical Health component subscale (sf12phys), and Mental Health component subscale (sf12ment). Run means and descriptives for each variable, as well as the correlation matrix.