1. CUSTOMER SATISFACTION PRESENTED BY- DEEPAKKHANDELWAL

2. INTRODUCTION Customer satisfaction is a measure of how products and services supplied by a company meet customer expectation It is meeting the customers expectations with a organization and/or department’s efforts. It is seen as a key business performance indicator.

3. REGRESSION ANALYSIS The statistical tool with the help of which we are in a position to estimate (or predict) the unknown values of one variable from known values of another variable is called regression. With the help of regression analysis, we are in a position to find out the average probable change in one variable given a certain amount of change in another.

4. TYPES OF VARIABLE UNDER REGRESSION Dependent variable:- The variable whose value is estimated using the algebraic equation is called dependent or response variable. Independent variable:- The variable whose value is used to estimate this value is called independent or predictor variable. The linear algebraic equation used for expressing dependent variable in terms of independent variable is called linear regression equation.

5. ANALYSIS OF VARIANCE Bivariate analysis Multivariate analysis

6. BIVARIATE REGRESSION ANALYSIS Y=predicted variable X=variable used to predicted y a=intercept b=slope

8. Y increases than E increases

9. Y decreases than E decreases

10. Y is independent

11. E is dependent

12. Hence direct relationshipPRICE AND DEMAND In this case of law of demand says there are two variable price (P) and demand (D) according to this law – P increases than D decreases , P decreases than D increases. P is independent D is dependent Hence indirect relationship.

13. ADVANTAGES OF REGRESSION ANALYSIS Regression analysis helps in developing a regression equation by which the value of dependent variable can be estimated given a value of an independent variable. Regression analysis help to determine standard error of estimate to measure the variability with respect to the regression line. By help of various variable to find out the customer satisfaction.

14. MULTIPLE REGRESSION ANALYSIS Multiple regression analysis is a method for explanation of phenomena and prediction of future events. Multiple regression involve a single dependent variable and two or more independent variable. Regression analysis is widely used for prediction and forecasting, where its use has substantial overlap with the field of machine learning.

15. MODEL OF MULTIPLE REGRESSION ANALYSIS

16. MULTIPLE REGRESSION EQUATION Y= dependent variable X= independent variable a = intersect b1= slope of independent variable m= no of independent variable

17. Case study

18. Case study Novartis Pharmaceutical company sales territory and number of sales person are given below. To find regression equation and analysis it.

21. Formula for b, the slope,inBivariate regression- Xi=An x variable value Yi=y value paired with each x value n=the number of pairs