CUSTOMER SATISFACTION<br />PRESENTED BY-<br />DEEPAKKHANDELWAL<br />
INTRODUCTION<br />Customer satisfaction is a measure of how products and services supplied by a company meet customer expe...
REGRESSION ANALYSIS<br />The statistical tool with the help of which we are in a position to estimate (or predict) the unk...
TYPES OF VARIABLE UNDER REGRESSION<br />Dependent variable:- The variable whose value is estimated using the algebraic equ...
ANALYSIS OF VARIANCE<br />Bivariate analysis<br />Multivariate analysis <br />
BIVARIATE REGRESSION ANALYSIS<br />Y=predicted variable<br />X=variable used to predicted y<br />a=intercept<br />b=slope<...
EXAMPLE<br />INCOME AND EXPENDITURE <br /><ul><li>In this case there are two variable income (Y) and expenditure (E) its s...
Y increases than E increases
Y decreases than E decreases
Y is independent
E is dependent
Hence direct relationship</li></ul>PRICE AND DEMAND <br />In this case of law of demand  says there are two variable price...
ADVANTAGES OF REGRESSION ANALYSIS<br />Regression analysis helps in developing a regression equation by which the value of...
MULTIPLE REGRESSION ANALYSIS<br />Multiple regression analysis is a method for explanation of phenomena and prediction of ...
MODEL OF MULTIPLE REGRESSION ANALYSIS<br />
MULTIPLE REGRESSION EQUATION<br />Y= dependent variable<br />X= independent variable<br />a = intersect<br />b1= slope of ...
Case study<br />
Case study<br />Novartis Pharmaceutical company sales territory and number of sales person are given below.<br />To find r...
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4. a customer satisfaction

  1. 1. CUSTOMER SATISFACTION<br />PRESENTED BY-<br />DEEPAKKHANDELWAL<br />
  2. 2. INTRODUCTION<br />Customer satisfaction is a measure of how products and services supplied by a company meet customer expectation<br />It is meeting the customers expectations with a organization and/or department’s efforts.<br /> It is seen as a key business performance indicator.<br />
  3. 3. REGRESSION ANALYSIS<br />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.<br />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.<br />
  4. 4. TYPES OF VARIABLE UNDER REGRESSION<br />Dependent variable:- The variable whose value is estimated using the algebraic equation is called dependent or response variable.<br />Independent variable:- The variable whose value is used to estimate this value is called independent or predictor variable.<br />The linear algebraic equation used for expressing dependent variable in terms of independent variable is called linear regression equation. <br />
  5. 5. ANALYSIS OF VARIANCE<br />Bivariate analysis<br />Multivariate analysis <br />
  6. 6. BIVARIATE REGRESSION ANALYSIS<br />Y=predicted variable<br />X=variable used to predicted y<br />a=intercept<br />b=slope<br />
  7. 7. EXAMPLE<br />INCOME AND EXPENDITURE <br /><ul><li>In this case there are two variable income (Y) and expenditure (E) its says-
  8. 8. Y increases than E increases
  9. 9. Y decreases than E decreases
  10. 10. Y is independent
  11. 11. E is dependent
  12. 12. Hence direct relationship</li></ul>PRICE AND DEMAND <br />In this case of law of demand says there are two variable price (P) and demand (D) according to this law –<br />P increases than D decreases , P decreases than D increases.<br />P is independent <br />D is dependent <br />Hence indirect relationship.<br />
  13. 13. ADVANTAGES OF REGRESSION ANALYSIS<br />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.<br />Regression analysis help to determine standard error of estimate to measure the variability with respect to the regression line.<br />By help of various variable to find out the customer satisfaction.<br />
  14. 14. MULTIPLE REGRESSION ANALYSIS<br />Multiple regression analysis is a method for explanation of phenomena and prediction of future events.<br />Multiple regression involve a single dependent variable and two or more independent variable.<br />Regression analysis is widely used for prediction and forecasting, where its use has substantial overlap with the field of machine learning.<br />
  15. 15. MODEL OF MULTIPLE REGRESSION ANALYSIS<br />
  16. 16. MULTIPLE REGRESSION EQUATION<br />Y= dependent variable<br />X= independent variable<br />a = intersect<br />b1= slope of independent variable<br />m= no of independent variable<br />
  17. 17. Case study<br />
  18. 18. Case study<br />Novartis Pharmaceutical company sales territory and number of sales person are given below.<br />To find regression equation and analysis it. <br />
  19. 19.
  20. 20.
  21. 21. Formula for b, the slope,inBivariate regression-<br />Xi=An x variable value<br />Yi=y value paired with each x value<br />n=the number of pairs <br />

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