1. Correlation By Dr.Muthupandi,

2. Correlation Correlation is a statistical technique which can show whether and how strongly pairs of variables are related. For example, height and weight are related - taller people tend to be heavier than shorter people.

3. Correlation Correlation is used to measure and describe a relationship between two variables. Usually these two variables are simply observed as they exist in the environment; there is no attempt to control or manipulate the variables.

4. Correlation The correlation coefficient measures two characteristics of the relationship between X and Y: The direction of the relationship. The degree of the relationship. Product Moment Correlation was developed by Karl Pearson. ( Pearson’s r)

5. Direction of Relationship A scatter plot shows at a glance the direction of the relationship. A positive correlation appears as a cluster of data points that slopes from the lower left to the upper right.

6. Positive Correlation If the higher scores on X are generally paired with the higher scores on Y, and the lower scores on X are generally paired with the lower scores on Y, then the direction of the correlation between two variables is positive. As the value of one variable increases (Degreases) the value of the other variable increase (Degreases) is called passitive Correlation.

7. Positive Correlation 123456789 1 2 3 4 5 6 Age Intelligence

8. Positive Correlation 123456789 1 2 3 4 5 6

9. Direction of Relationship A scatter plot shows at a glance the direction of the relationship. A negative correlation appears as a cluster of data points that slopes from the upper left to the lower right.

10. Negative Correlation If the higher scores on X are generally paired with the lower scores on Y, and the lower scores on X are generally paired with the higher scores on Y, then the direction of the correlation between two variables is negative. As the value of one variable degrease (increase) the value of the other variable increase (Degreases) is called negative Correlation.

11. Nagative Correlation 123456789 1 2 3 4 5 6 Age Innocence

12. Negative Correlation 123456789 1 2 3 4 5 6

13. No Correlation (Spurious Correlation) In cases where there is no correlation between two variables (both high and low values of X are equally paired with both high and low values of Y), there is no direction in the pattern of the dots. They are scattered about the plot in an irregular pattern.

14. Perfect Correlation When there is a perfect linear relationship, every change in the X variable is accompanied by a corresponding change in the Y variable.

15. Form of Relationship Pearson’s r assumes an underlying linear relationship (a relationship that can be best represented by a straight line). Not all relationships are linear.

16. Strength of Relationship How can we describe the strength of the relationship in a scatter plot? A number between -1 and +1 that indicates the relationship between two variables.  The sign (- or +) indicates the direction of the relationship.  The number indicates the strength of the relationship. -1------------ 0 ------------ +1 Perfect Relationship No Relationship Perfect Relationship The closer to –1 or +1, the stronger the relationship.

17. Correlation Coefficient

18. Pearson’s r Definitional formula: Computational formula:

19. An Example: Correlation What is the relationship between level of education and lifetime earnings?

20. An Example: Correlation

23. Interpreting Pearson’s r Correlation does not equal causation. Can tell you the strength and direction of a relationship between two variables but not the nature of the relationship.  The third variable problem.  The directionality problem.