This preview shows pages 1–2. Sign up to view the full content.
This preview has intentionally blurred sections. Sign up to view the full version.View Full Document
Unformatted text preview: Bivariate correlation and regression are used to investigate how a continuous (interval or ratio level) independent variable (X) and continuous dependent variable (Y) are linearly related. With two variables, we can graph their relationship using a scatterplot. We can draw a line that goes through the middle of the points in the scatterplot. If the line goes up, there is a positive relationship and thus a positive correlation between X and Y; if the line goes down, there is a negative relationship and thus a negative correlation between X and Y; if the line is flat (horizontal), X and Y are unrelated and not correlated. Pearsons correlation measures how tightly clustered around the line the scatterplot points are. It varies from -1 (a perfect negative relationship), to +1 (a perfect positive relationship). A correlation of 0 means the two variables are not correlated. Its formula is: There is always one line that best fits a scatterplot; by fit, I mean that this line minimizes the...
View Full Document
This note was uploaded on 08/30/2010 for the course SOCI 301 taught by Professor Kupchick during the Spring '09 term at University of Delaware.
- Spring '09