PRESENTED BY-DEEPAK KHANDELWAL

CUSTOMER SATISFACTION

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.

INTRODUCTION

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.

REGRESSION ANALYSIS

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.

TYPES OF VARIABLE UNDER REGRESSION

Bivariate analysis

Multivariate analysis

ANALYSIS OF VARIANCE

BIVARIATE REGRESSION ANALYSIS

bxaY Y=predicted variableX=variable used to predicted ya=interceptb=slope

EXAMPLE

INCOME AND EXPENDITURE

In this case there are two variable income (Y) and expenditure (E) its says-

Y increases than E increases Y decreases than E decreases Y is independent E is dependent Hence direct relationship

PRICE 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.

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.

ADVANTAGES OF REGRESSION ANALYSIS

MULTIPLE REGRESSION ANALYSISMultiple 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.

PurchaseIntention to

purchasePreferencesatisfaction

AttitudeOpinionFeeling

Media exposure

Word of mouth

Demographiclifestyle

Past behaviorKnowledgeExperience

MODEL OF MULTIPLE REGRESSION ANALYSIS

MULTIPLE REGRESSION EQUATION

nmxbxbxbxbaY .........332211

Y= dependent variableX= independent variablea = intersectb1= slope of independent variablem= no of independent variable

Case study

Novartis Pharmaceutical company sales territory and number of sales person are given below.

To find regression equation and analysis it.

Case study

Territory[I]

Sales(y)

No. of salesperson

(x)

xy x2

1 102 7 714 49

2 125 5 625 25

3 150 9 1350 81

4 155 9 1395 81

5 160 9 1440 81

6 168 8 1344 64

7 180 10 1800 100

8 220 10 2200 100

9 210 12 2520 144

10 205 12 2460 144

Territory[I]

Sales(y)

No. of salesperson

(x)

xy x2

11 230 12 2760 144

12 255 15 3825 225

13 250 14 3500 196

14 260 15 3900 225

15 250 16 4320 256

16 275 16 4400 256

17 280 17 4760 289

18 240 18 4320 324

19 300 18 5400 324

20 310 19 5890 361

Sum 4325(Avg=216.25)

251(Avg=12.55)

58603 3469

Formula for b, the slope,in Bivariate regression-

2

21

2

11

11

n

ii

n

ii

n

i

n

ii

n

iii

xxn

yxyxn

b

Xi=An x variable valueYi=y value paired with each x valuen=the number of pairs

56.13

251346920

432525158603202

xbya

10.46

15.17025.216

55.1256.1325.216

xy 56.1310.46