1. Correlation Analysis

2. 2 Introduction Introduction  Correlation analysis is one of the most widely used statistical measures.  In all sciences, natural, social or biological as well as in business, we are largely concerned with the study of interrelationships among variables.  For example, we may be intersected to know the relationship between the age and weight of the students, between family income and expenditure, or the relationship between price of a commodity and amount demand.  Correlation analysis enables us to measure or quantify the relationship between the pairs of variables.

3. Correlation Analysis3 Definition  The statistical tool with the help of which the relationship between two or more than two variables is studied is called correlation.  Correlation shows whether and how strongly pairs of variables are related.  In other words, the appropriate statistical tool for discovering and measuring the relationship and expressing in a brief formula is known as correlation.

4. Correlation Analysis4 Measurement of relationship Measurement of relationship  The relationship between the variables is measured by the Coefficient of Correlation, is denoted by ‘r’.  The correlation coefficient, r is used for describing the degree and direction of relationship between two variables.  If we are interested to know the relationship between two variables x and y, r is defined as

5. Correlation Analysis5  For computation r can be written as  The coefficient of correlation r is also known as Pearson’s coefficient of correlation, after Karl Pearson, a British Mathematician suggested this formula, is most widely used in practice.

6. Correlation Analysis6 Interpretation of r Interpretation of r  The value of the correlation coefficient r lies between -1 and +1;  The closer the value of r to either +1 or -1, the more strongly the two variables is related.  When r is positive, one variable tends to increase as the other increases or vice versa;  When r is negative, one variable tends to decrease as the other increases or vice versa ;  When r = +1, it means there is perfect positive correlation between the variables;  When r = -1, it means there is perfect negative correlation between the variables;  When r = 0, the variables are said to be uncorrelated.

7. Correlation Analysis7 Practical Example Find the correlation between age and weight and comment Practical Example Find the correlation between age and weight and comment Age (years) Height (inches) 640 742 845 947 1049 1152 1255 1359 1462 1565

8. Correlation Analysis8 We calculate the correlation coefficient r as follows So there is strong positive correlation between advertising expenditure and annual turnover. It means that when advertising expenditure increases, yearly turnover also increases; and when advertising expenditure decreases, yearly turnover also decreases. So there is strong positive correlation between advertising expenditure and annual turnover. It means that when advertising expenditure increases, yearly turnover also increases; and when advertising expenditure decreases, yearly turnover also decreases.

9. Correlation Analysis9 Thank You