1. Regression MBA/510 Week 5

2. Objectives Describe the use of correlation in making business decisions Apply linear regression and correlation analysis. Interpret the output produced by a multiple regression analysis.

3. Correlation in Business Decision-Making

4. Correlation Analysis Independent Variable The Independent Variable provides the basis for estimation. It is the predictor variable. Correlation Analysis Correlation Analysis is a group of statistical techniques to measure the association between two variables. Scatter Diagram A Scatter Diagram is a chart that portrays the relationship between two variables. Dependent Variable The Dependent Variable is the variable being predicted or estimated.

5. Negative values indicate an inverse relationship and positive values indicate a direct relationship. Coefficient of Correlation The Coefficient of Correlation (r) is a measure of the strength of the relationship between two variables. Also called Pearson’s r and Pearson’s product moment correlation coefficient. It requires interval or ratio- scaled data. It can range from -1.00 to 1.00. Values of -1.00 or 1.00 indicate perfect and strong correlation. Values close to 0.0 indicate weak correlation.

6. 0 1 2 3 4 5 6 7 8 9 10 10 9 8 7 6 5 4 3 2 1 0 X Y Perfect Negative Correlation

7. 0 1 2 3 4 5 6 7 8 9 10 10 9 8 7 6 5 4 3 2 1 0 X Y Perfect Positive Correlation

9. 0 1 2 3 4 5 6 7 8 9 10 10 9 8 7 6 5 4 3 2 1 0 X Y Zero Correlation

10. 0 1 2 3 4 5 6 7 8 9 10 10 9 8 7 6 5 4 3 2 1 0 X Y Strong Positive Correlation

11. Correlation “test” Is this a positive or negative correlation? Why might we find a relationship? The longer couples have been together the more similar they are in their attitudes and opinions. A researcher finds that students who attend fewer classes get poorer grades. Cities with more stores selling pornography have higher rates of violence. In each case above there was more than one explanation for why we might find the relationship between the variables. Since we cannot rule out these alternative explanations, we cannot conclude that changes in one variable "caused" changes in the other variable. Thus, CORRELATION does not equal CAUSATION.

12. Group Exercise 1

13. Group Exercise 1 cont

14. Situation 1: Goal next month 600 Situation 2: New model came out last month Situation 3: Goal next month 900 because new model is coming out

15. Group Exercise 2

16. Group Exercise 2 cont

17. Situation 1: Produced 125 units Situation 2: 90°F: How many units? Situation 3: How to control power used?