This document discusses various measures of simple correlation. It begins by explaining how to create a scatter plot using Excel to graph the relationship between independent and dependent variables. It then provides an example dataset measuring temperature and water consumption over seven summer days. It proceeds to define the Pearson Product Moment Correlation Coefficient formula to measure the strength of linear relationships. It also mentions the Spearman Rank-Order Correlation Coefficient and notes there are other measures of relationship. Finally, it cautions that correlation only indicates a relationship exists and does not prove causation.

1. MEASURES OF SIMPLE CORRELATION Prepared by: Dr. Maria Theresa P. Pelones, DM Graduate School Faculty

10. Temperature (F) Water Consumption (ounces) 75 16 83 20 85 25 85 27 92 32 97 48 99 48 For seven random summer days, a person recorded the temperature and their water consumption, during a three-hour period spent outside.

11. Temperature (F) Water Consumption (ounces) 75 16 83 20 85 25 85 27 92 32 97 48 99 48 For seven random summer days, a person recorded the temperature and their water consumption, during a three-hour period spent outside.

12. PEARSON PRODUCT MOMENT CORRELATION COEFFICIENT(R) Prepared by: Dr. Maria Theresa P. Pelones, DM Graduate School Faculty

15. r= {(6*47634)-(345*819)}/{[(6*20399)-3452][(6*112443)-8192]}0.5. r= 0.897. The correlation coefficient suggests a strong positive relationship between age and blood pressure.

16. SPEARMAN RANK-ORDER CORREALATION COEFFICIENT Prepared by: Dr. Maria Theresa P. Pelones, DM Graduate School Faculty

19. OTHER MEASURES OF RELATIONSHIP Prepared by: Dr. Maria Theresa P. Pelones, DM Graduate School Faculty

26. CORRELATIONAL STUDIES Prepared by: Dr. Maria Theresa P. Pelones, DM Graduate School Faculty

27. A correlation tells you that a relationship exists between 2 variables (aside from the 3rd variable problem), but tell you absolutely nothing about cause and effect.

28. When variable A actually causes the change in B.

29. Variables A and B really do NOT have anything to do with each other but happen to go up or down simultaneously.

30. Variable A is correlated with variable B but there is a third factor C (the common underlying cause) that causes the changes in both A and B.

31. PROBLEM SETS Prepared by: Dr. Maria Theresa P. Pelones, DM Graduate School Faculty