1. Introduction to Correlation Analysis

2. Objectives Correlation Types of Correlation Karl Pearson’s coefficient of correlation Correlation in case of bivariate frequency distribution Coefficient of determination Spearman’s Rank Correlation coefficient

3. Correlation The concept of measuring the degree of association between the variables is called correlation analysis. Correlation analysis deals with the association between two or more variables Eg. Price & demand, amount of rainfall and yield of rice, height & weight of an individual.

4. Methods Scatter Diagram Karl Pearson’s coefficient of correlation Rank Correlation

5. Scatter Plots and Correlation A scatter plot (or scatter diagram) is used to show the relationship between two variables Correlation analysis is used to measure strength of the association (linear relationship) between two variables –Only concerned with strength of the relationship –No causal effect is implied

6. Scatter Plot Examples y x y x y y x x Linear relationshipsCurvilinear relationships

7. Scatter Plot Examples y x y x y y x x Strong relationshipsWeak relationships (continued)

8. Scatter Plot Examples y x y x No relationship (continued)

9. Correlation Coefficient The correlation coefficient r is used to measure the strength of the linear relationship in the observations Unit free Range between -1 and 1 The closer to -1, the stronger the negative linear relationship The closer to 1, the stronger the positive linear relationship The closer to 0, the weaker the linear relationship (continued)

10. r = +.3r = +1 Examples of Approximate r Values y x y x y x y x y x r = -1 r = -.6r = 0

11. Calculating the Correlation Coefficient where: r = Sample correlation coefficient n = Sample size x = Value of the independent variable y = Value of the dependent variable Actual Mean Method or the algebraic equivalent:

12. Calculation Example Tree Height Trunk Diameter yxxyy2y2 x2x2 358280122564 499441240181 27718972949 336198108936 60137803600169 21714744149 45114952025121 51126122601144  =321  =73  =3142  =14111  =713

13. Trunk Diameter, x Tree Height, y Calculation Example (continued) r = 0.886 → relatively strong positive linear association between x and y

14. Excel Output Excel Correlation Output Tools / data analysis / correlation… Correlation between Tree Height and Trunk Diameter

15. Actual Mean Method Assumed Mean Method Direct Method Bivariate frequency table

16. D= difference of rank between the paired items in two series N= No. of paired items in the two series m= No. of items whose ranks are common

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