Q.E.-2012

SFA - 4053 MASTER OF BUSINESS ECONOMICS - MBE SEMESTER IIIrd EXAMINATION 2012 QUANTITATIVE ECONOMICS  –  II  II  –  MBE  MBE -302 Time: Three hrs 70

Max Marks :

NOTE- Attempt Five questions in all. Question No. 1 is compulsory. Attempt one question from each unit. Marks are indicated against each question.

Q1. Read the case given below, and answer the questions given in the end of the case“I‟d trade in my Corvette convertible in a minute to buy this car.” Exclaimed an excited observer at an advance showing of the then Chrysler Motors Corporation‟s (now DaimlerChrysler, www.daimlerchrysler.com) design ideas for the 1990s. Since battling back from the brink of the  bankruptcy in the late 1970s, Chrysler continued to run run a distant third to GM and Ford in the American American Mobile Market, and even that position was challenged by Toyota in 2004. Chrysler dramatically rebounded in the early 1980s and gained almost two percentage points over the first five years of 1980s by adding more economical, middle-class cars to its line of luxury sedans. However, increased competition from Japanese imports, poor product quality, and unimaginative design led to falling market share in the latter half of the decade. Chrysler did, however, succeed with its minivan. Because of their triumph with minivan, Chrysler was even more determined to succeed in the car market, so engineers and managers tried to design the automobile that fit the stylish, high-quality image Chrysler needed. Chrysler continued to maintain its business strategy of focusing on the profit instead of market share, avoiding global alliances, and thriving on a shortage of capital. In 1989, Chrysler held an advance showing of concept cars for the 1990s that included a V-10 engine for both trucks and cars. Two stylish, yet pragmatic concepts were released, including the Chrysler Millennium and the tiny Plymouth Speedster. Both cars featured eye catching design but failed to deliver performance because underneath they based on traditional Chrysler platform and power train. The reviewers, however, did take note of the rear-drive two-seat sports car, made available in 1992, which incorporated the V-10 engine. Code named the Dodge TBD (To Be Determined) and later the named the Dodge Viper, it looked like a Chevrolet Corvette- but carried a price tag of $ 55,000. Since the introduction of the Viper, Chrysler raised the starting price several times. At the beginning of 2002, Chrysler added a four figure price hike bringing the price to a starting value of $75,000 for the RT/10 Roadster model and $76,000 for the GTS Coupe model. The Viper was positioned to restore Chrysler‟s reputation for designing exciting cars. Even though some call the Dodge Viper the “sexiest yet silliest” car around, it appears that the introduction of the Dodge Viper was a success. Recently Chrysler Corporation President John Lutz stated that the company will keep Viper production lower than the numbers of Vipers demanded, estimated as approximately 2000 cars per year. Chrysler also revealed that it would offer the Viper in two new colours, emerald green and yellow. Previously, the first 250 cars were red, and the res were  painted black. Improvements are also planned for the interior of the Viper. Chrysler also introduced a coupe version of the Viper, the Viper GTS, which featured a roof instead of a soft convertible top. In April 2002, Dodge planned to end the production of the GTS coupe with a limited Final Edition  production run. The Final Edition GTS will be painted as eye-catching red and have white racing stripes. It features other unique touches such as black leather steering wheel and shift knob embellished with red stitching. Only 360 of the Final Edition GTS models will be produced. IN May

2002, Dodge planned to begin production on the 2003 Dodge Viper SRT-10, which will be available exclusively in convertible form. For continued success the Viper must attract the yuppie crowd-the highly educated, affluent baby  boomers- that tend to prefer imported vehicles. Because hi s group would be the prime target group for such a high performance car, Chrysler needed to ensure that it could compete in a market traditionally dominated by Corvette, Mazda Miata, Porsche Boxster, Porsche 911/96, and Mitsubishi 3000GT. Primary concerns for Chrysler were overcoming its boxcar image with this group, determining if they should offer incentives on the Dodge Viper, the importance of styling and prestige when promoting to this market, and how to exploit its merger with Daimler-Benz to the advantage of the Viper. To address these concerns, 10 statements were constructed to measure attitudes towards these factors and to examine their relationship with intention to buy Viper. The respondents used a nine  point Likert scale (1=definitely disagree, 9=definitely agree). The respondents were obtained from mailing lists of Car and Drivers, Business Week and Inc.  magazines and they were telephoned at their homes by an independent surveying company. The statements used in the survey of 400 respondents are listed below: 1. 2. 3. 4. 5. 6. 7. 8. 9. 10.

When I must choose between the two, I usually dress for fashion, not comfort. I want to look little different from others. Life is too short not to take some gambles. Our family is not too heavily in debt today. I like to pay cash for everything I buy. I use credit cards because I can pay the bill off slowly. Interest rates are low enough to allow me to buy what I want. American made cars can‟t compare with foreign made cars. The government should restrict imports of products from Japan. I am usually among the first to try new products.

In addition, the criterion variable, attitude towards Dodge Viper, was measured by asking each  person to respond to the statement, “I would consider buying the Dodge Viper made by Daimler Chrysler” on a seven point Likert scale (1=definitely no, 7=definitely yes).Further information regarding various demographic characteristics of the respondents was also noted down. The director of marketing for Chrysler is interested in knowing the psychological-demographic characteristics of the respondents to configure the Dodge Viper program. You have been presented with the responses from the survey outlined above. Answer the following questions: (a). What are the management problems/ issues confronting Director of Marketing for Chrysler vis-à-

vis Dodge Viper program? (5 marks) (b). The data obtained from the survey has been analyzed, using the software package SPSS and the output is presented in Exhibit-I. Interpret the results from the survey and make appropriate

recommendations to the director (marketing). The analysis should attempt to answer the following questions: (i) Can the „intention to buy Dodge Viper‟ (purchase decision) be explained in terms of consumers‟ attitudinal response expressed in terms of 10 statements (considered variables)? Comment upon the (15 marks) findings. (ii) Does the „gender‟ of a buyer have an impact on the purchase decision for Dodge Viper? Comment upon the findings. (5 marks) (iii) Based on the analysis, prepare a report for the management explaining the types of customers and

offering recommendations on the design-modifications suggested. Your recommendations should aid DaimlerChrysler in achieving a new attractive image and greater market appeal. (5 marks)

EXHIBIT- I Regression Model Summary Model 1

R

R Square Adjusted R Square Std. Error of the Estimate Durbin-Watson .751 .564 .529 1.989 2.041 a. Predictors: (Constant), Try New Products, Look Different, Like to Pay Cash, Restrict Imports From Japan, Take Some Gambles, Interest Rates are Low, Have Stylish Clothes, Use Credit Cards, Foreign Cars are Better, Not Heavily in Debt  b. Dependent Variable: Consider Buying Dodge Viper

ANOVA Model 1

Regression

Sum of Squares 1132.568

df 10

Mean Square 113.257

Residual

1538.230

389

3.954

Total

2670.797

399

F

Sig. 28.641

.000

Coefficients Unstandardized Coefficients B Std. Error -3.290 .709

(Constant)

Standardize d Coeff Beta

t

Sig.

Collinearity Statistics Tolerance VIF

-4.638

.000

Have Stylish Clothes

.199

.075

.123

2.652

.008

.687

1.456

Look Different Take Some Gambles

.191

.092

.097

2.081

.038

.687

1.456

.818

.065

.507

12.675

.000

.925

1.081

 Not Heavily in Debt

.183

.176

.123

1.040

.299

.105

9.495

Like to Pay Cash

-.193

.179

-.129

-1.082

.280

.104

9.643

Use Credit Cards

.072

.117

.045

.614

.539

.279

3.578

Interest Rates are Low

-.001

.089

-.001

-.013

.990

.564

1.775

Foreign Cars are Better

.062

.163

.039

.377

.706

.142

7.059

Restrict Imports From Japan

.105

.155

.069

.677

.499

.142

7.051

Try New Products

.319

.072

.174

4.436

.000

.961

1.041

Collinearity Diagnostics Model 1

Dimension 1 2 3 4 5 6 7 8 9 10

Eigen value 10.729 .065 .039 .033 .028 .025 .021 .019 .016 .011

Condition Index 1.000 12.881 16.583 17.933 19.551 20.524 22.564 23.613 25.750 31.529

T-Test

Group Statistics Gender Male

Consider Buying Dodge Viper

Female

N

Mean

Std. Deviation

Std. Error Mean

226

4.4694

1.32001

.28143

174

3.7825

1.26803

.28354

Independent Samples Test

Consider Buying Dodge Viper

Equal variances assumed Equal variances not assumed

Levene's Test for t-test for Equality of Equality of Variances Means F Sig. t df Sig 3.069 .012 1.732 398 .052 2.974 396.84 .033

UNIT-I Q.2. . In the increasingly competitive diaper market, Procter & Gamble‟s marketing department

wanted to formulate new approaches to the construction and marketing of its diapers. They surveyed 300 mothers of infants. Each was given a randomly selected brand of diaper and asked to rate that diaper on nine attributes and to give her overall preference for the brand. Preferences were obtained as Preferred or Not Preferred. Diaper ratings on nine attributes were obtained on 7-point scale (1= Very Unfavorable; 7= Very Favorable). The goal of the study was to learn which attributes of diapers were most important in influencing purchase preference (Y) i.e. in discriminating between Preferred/NotPreferred. The data obtained from the survey has been analyzed, using the software package SPSS and the output are presented in Exhibit-II. Interpret the results from the survey and make appropriate recommendations to the Procter & Gamble‟s marketing department.

EXHIBIT- II Logistic Regression Case Processing Summary

Unweighted Cases(a) Selected Cases Included in Analysis

N

Missing Cases Total Unselected Cases Total

300

Percent 100.0

0 300

.0 100.0

0

.0

300

100.0

Dependent Variable Encoding

Original Value  Not Preferred

Internal Value 0

Preferred

1

Omnibus Tests of Model Coefficients

Step 1

Step

Chi-square 233.491

Block Model

Df

233.491 233.491

9

Sig. .000

9 9

.000 .000

Model Summary

-2 Log Cox & Snell  Nagelkerke R Step likelihood R Square Square 1 175.316(a) .541 .727 a Estimation terminated at iteration number 7 because parameter estimates changed by less than .001. Hosmer and Lemeshow Test

Step 1

Chi-square 4.413

df 8

Sig. .818

Classification Table(a)

Predicted Brand Preference Group Step 1

Observed Brand Preference

Not Preferred

 Not Preferred 108

Preferred 19

Percentage Correct Not Preferred 85.0

Group

Preferred

20

153

88.4

Overall Percentage

87.0

a The cut value is .500 Variables in the Equation

B Step 1(a)

count

S.E.

Wald

df

Sig.

Exp(B)

1.023

.415

6.070

1

.014

2.782

 price

.053

.421

.016

1

.901

1.054

value

.318

.240

1.754

1

.185

1.374

unisex

1.414

.328

18.618

1

.000

4.112

style

-.430

.326

1.745

1

.187

.650

absorbency

1.078

.548

3.867

1

.049

2.938

leakage

.053

.525

.010

1

.919

1.055

comfort

.279

.270

1.061

1

.303

1.321

-.159

.216

.538

1

.463

.853

taping Constant

-14.914 1.921 60.255 1 .000 .000 a . Variable(s) entered on step 1: count, price, value, unisex, style, absorbency, leakage, comfort, taping.

(10 marks) 3.  How Regression Analysis is important in Economic decision making. What do you mean by

Classical Linear Regression Model (CLRM)? Mention and discuss the assumptions underlying the Method of Least Squares used for estimating CLRM and the impact of t heir violation. (10 marks)

UNIT-II Q4. What do you mean by the term „multicollinearity‟? What are its sources? What are the practical consequences of the presence of „multicollinearity‟? What reme dial steps should be taken if „multicollinearity‟ is present in the data? To assess the feasibility of guaranteed annual wage, the Rand Corporation conducted a study to assess the response of labor supply (average hours of work) on the basis of certain predictors. Comment on the presence of „multicollinearity‟ in the following analysis (Exhibit III). (10 marks) EXHIBIT-III Model Summary Model R 1 .891(a)

R Square .794

Adjusted R Square .703

Std. Error of the Estimate 1.432

Coefficients

(Constant) Average hourly wage Average yearly earnings of spouse Average yearly earnings of other family members Average yearly non-earned

Unstandardized Coefficients B Std. Error 2.017 .251 .105 .037

Standardized Coefficients Beta

t

Sig.

Collinearity Statistics Tolerance VIF

.095

8.037 2.874

.000 .004

.915

1.093

.018

.041

.016

.452

.651

.0817

10.223

.075

.042

.061

1.767

.078

.836

1.197

.079

.040

.072

1.991

.047

.0735

10.360

income Average family asset holdings Average age of respondent Average number of dependents Average highest grade of school completed

.006 .025 -.035

.036 .043 .037

.005 .021 -.033

.154 .571 -.937

.878 .568 .349

.0833 .0711 .0803

10.200 10.406 10.246

-.045

.042

-.041

-1.057

.031

0.666

10.501

Collinearity Diagnostics Model Dimension Eigen value 1 1 10.729 2 .033 3 .028 4 .025 5 .021 6 .019 7 .016 8 .014 9 .011

Condition Index 1.000 17.933 19.551 20.524 22.564 23.613 29.642 33.789 42.529

5 (a) What do you mean by the problem of „autocorrelation‟ in the classical regression model? Differentiate between „spatial autocorrelation‟, „serial correlation‟ and „autocorrelation‟. What are t he  practical consequences of the presence of „autocorrelation‟? What are the different methods for

detecting presence of autocorrelation in the data? Comment on the presence of „autocorrelation‟ according to the following analysis (Exhibit IV). (5 marks) EXHIBIT-IV Model Summary Model R 1 .803(a)

R Square .645

Adjusted R Square .618

Std. Error of the Estimate 1.548

Durbin-Watson 2.173

Runs Test

Test Value(a) Total Cases  Number of Runs Z Asymp. Sig. (2-tailed)

Unstandardized Residual 3.4113 834 498 1.441 .109

(b) Explain „Production Function‟ and „Investment Function‟. marks)

(5

UNIT-III Q6. (a) What do you mean by „Simultaneous Equation Model‟? Explain “Keynesian Model of Income Determination” and derive its reduced form. (5 marks) (b) Explain „heteroscedasticity‟. Comment on its consequences and how to detect it. marks)

(5

7. I. Sager, in his research “Shattering the Myths of High-Tech Success”, Business Week, June 26,

2007; has shown that in the new fast-paced world of computers, the key factor that separates the winners from the losers is actually how slow a firm is in making economic and business decisions: The most successful firms take longer to arrive at strategic decisions on economics of product development, adopting new technologies, developing new products, or reaction towards change in the

outward economic conditions. The following values are the number of months to arrive at a decision for firms ranked „High‟ (High performing firms), „Good‟ (Good performing firms), „Medium‟ (Medium performing firms) and „low‟ (Low performing firms) in terms of performance. Analyze the results from Exhibit-V and comment on the research of I.Sager. Also give appropriate recommendations. (10 marks) EXHIBIT-V Descriptives  Numbers of months to arrive at a strategic decision  N Mean Std. Deviation High 44 7.075 .811 Good 45 8.064 .677 Medium 33 10.274 .450 Low 37 13.380 .342 Test of Homogeneity of Variances

Levene Statistic 2.801

df1 3

df2 155

Sig. .138

ANOVA  Numbers of months to arrive at a strategic decision X Sum of Squares df Between Groups 118.769 3 Within Groups 570.260 155 Total 689.029 158

Mean Square 39.589 3.679

Multiple Comparisons Dependent Variable: Numbers of months to arrive at a strategic decision Performance of Performance of the Mean Difference (Ithe firm (I) firm (J) J) Tukey HSD High Good -.888 Medium .-989 Low -1.683 Good High .888 Medium -1.011 Low -.795 Medium High .989 Good 1.01 Low -.693 Low High 1.683 Good .795 Medium .693 Tamhane High Good -.888 Medium -.9897 Low -1.683 Good High .888 Medium .101 Low -.795 Medium High .989 Good 1.011 Low -.693 Low High 1.683 Good .795 Medium .693

UNIT-IV

F 10.761

Sig. .000

Std. Error .227 .222 .327 .227 .185 .303 .222 .185 .299 .327 .303 .299 .287 .262 .288 .287 .178 .215 .262 .178 .179 .288 .215 .179

Sig. .153 .000 .000 .153 .037 .014 .000 .037 .066 .000 .014 .066 .038 .014 .000 .038 .994 .016 .014 .994 .033 .000 .016 .033

Q8. The Happy-Holidays Company Ltd wants to determine salient characteristics of the families that have visited the foreign during the last year, so that it may identify the target customers and finally concentrate its advertising campaign. Data were obtained from a sample of 42 households. The households that visited a resort during the last two years are coded as 1; those that did not, as 2 (visited). Data were also obtained on annual family income (Income), attitude towards travel (attitude, measured on a nine-point scale), importance attached to family vacations (family vacation, measured on a nine-point scale), house hold size (size), and age of the household head (age) and amount spent on family vacations (amount). The data was analyzed by software package SPSS and out put is presented in Exhibit-III. Analyze the results from Exhibit-VI and comment on the findings and give appropriate suggestions to the owner of the RXN. (10 marks) EXHIBIT-VI Discriminant Analysis Tests of Equality of Group Means

Annual family income (in $000) Attitude towards Travel Importance attached towards family vacations House hold size Age of the head of household Amount spent on family vacations

Wilks' Lambda .471 .913 .787 .784 .915 .510

F 44.877 3.822 10.849 11.010 3.733 38.400

df1 1 1 1 1 1 1

Box's Test of Equality of Covariance Matrices: Test Results Box's M 36.420 F Approx. 1.452 df1 21 df2 5884.797 Sig. .083 Tests null hypothesis of equal population covariance matrices. Summary of Canonical Discriminant Functions Eigenvalues Function Eigenvalue % of Variance Cumulative % 1 1.925(a) 100.0 100.0 Wilks' Lambda Test of Function(s) 1

Wilks' Lambda .342

Chi-square 39.714

Canonical Correlation .811

df

Sig. 6

.000

Standardized Canonical Discriminant Function Coefficients

Function Annual family income (in $000) Attitude towards Travel Importance attached towards family vacations House hold size Age of the head of household Amount spent on family vacations

.422 -.011 .380 .354 .287 .482

Structure Matrix

Function Annual family income (in $000) Amount spent on family vacations House hold size Importance attached towards family vacations

.763 .706 .378 .375

df2 40 40 40 40 40 40

Sig. .000 .058 .002 .002 .060 .000

Attitude towards Travel Age of the head of household

.223 .220

Canonical Discriminant Function Coefficients

Function Annual family income (in $000) Attitude towards Travel Importance attached towards family vacations House hold size Age of the head of household Amount spent on family vacations (Constant)

.050 -.006 .214 .293 .036 .806 -8.073

Unstandardized coefficients

Classification Results Visit to the resort in last 2 years

Original

Count

visited resort

Predicted Group Membership visited resort not visited resort 18 3

not visited resort %

visited resort

Total 21

0

21

21

85.7

14.3

100.0

.0

100.0

100.0

not visited resort a 92.9% of original grouped cases correctly classified.

9.  To develop an in depth insight into the complaining behavior displayed by customers (vis-à-vis

 banking services), and categorize customers into homogenous groups, on the basis of similar complaining attitudes, Cluster analysis has been performed and results are presented in Exhibit-VII. This segmentation of customers into homogenous groups will help us to better understand the complaining attitudes of customers and formulate effective and more customized service recovery (10 marks) strategies.

EXHIBIT-VII Cluster: Quick Cluster Initial Cluster Centers

Cluster My bank has efficient Complaint Handling Mechanism

1 5.00

2 1.00

3 2.00

4 5.00

Complain promptly if not satisfied

5.00

5.00

5.00

2.00

Usually get quick & Satisfactory response to complaints

5.00

1.00

1.00

2.00

Switch to another bank after non-response

1.00

5.00

5.00

5.00

Banks must have efficient Complaint Handling Mechanism

5.00

1.00

1.00

5.00

Many times don‟t know where to complain

1.00

1.00

5.00

5.00

Shouldn‟t complain unnecessarily

1.00

1.00

1.00

5.00

Banks can make mistakes & will ratify (remedial measures)  Nothing comes off the feedback forms

5.00 1.00

1.00 1.00

1.00 5.00

5.00 3.00

Complaint responsive bank has greater credibility

5.00

1.00

5.00

5.00

Generally satisfied with my bank

5.00

5.00

1.00

4.00

Can claim damages for poor service delivery

5.00

1.00

5.00

1.00

Tend to judge service-quality of bank by its complaint handling

5.00

1.00

5.00

1.00

Iteration History (a)

Change in Cluster Centers Iteration 1

1 4.957

2 5.333

3 5.280

4 5.063

2 3

.289

1.619

.612

.439

.203

.763

.344

.380

4

.109

.334

.136

.141

5

.068

.195

.075

.118

6

.064

.198

.071

.088

7

.082

.193

.055

.114

8

.104

.103

.075

.082

9

.115

.076

.049

.078

10

.134

.095

.094

.089

11

.160

.031

.029

.108

12

.192

.093

.043

.101

13

.131

.053

.054

.068

14 15

.108 .055

.067 .045

.038 .021

.034 .024

16

.023

.042

.027

.020

17

.009

.015

.013

.010

18

.000 .000 .000 .000 a Convergence achieved due to no or small change in cluster centers. The maximum absolute coordinate change for any center is .000. The current iteration is 18. The minimum distance between initial centers is 9.849. Final Cluster Centers

Cluster My bank has efficient Complaint Handling Mechanism

1 3.74

2 1.49

3 2.03

4 4.55

Complain promptly if not satisfied

1.31

4.09

4.70

3.32

Usually get quick & Satisfactory response to complaints

3.92

1.12

3.42

4.42

Switch to another bank after non-response

2.68

4.37

4.15

1.25

Banks must have efficient Complaint Handling Mechanism

1.63

2.57

4.14

4.64

Many times don‟t know where to complain

4.75

3.98

2.14

3.67

Shouldn‟t complain unnecessarily

4.39

2.04

1.12

3.95

Banks can make mistakes & will ratify (remedial measures)

4.59

3.19

1.04

4.05

 Nothing comes off the feedback forms

2.48

4.55

3.83

1.44

Complaint responsive bank has greater credibility

2.34

3.08

3.69

4.43

Generally satisfied with my bank

4.05

1.37

2.41

4.51

Can claim damages for poor service delivery

2.30

3.63

4.65

2.76

Tend to judge service-quality of bank by its complaint handling

1.76

3.80

4.35

3.03

Distances between Final Cluster Centers

Cluster 1

1

2

3.082

3

3.514

2.917

4

2.584

3.785

ANOVA

2 3.082

3 3.514

4 2.584

2.917

3.785 3.074

3.074

df

Error Mean Square

df

F

Sig.

3

1.010

1096

138.867

.000

3

.806

1096

91.675

.000

3

.997

1096

173.413

.000

3

.700

1096

562.941

.000

3

.687

1096

75.219

.000

3

.953

1096

22.237

.000

3

1.212

1096

196.760

.000

Banks can make mistakes & will ratify (remedial measures)

21.193 238.46 3 47.320

3

1.047

1096

45.215

.000

 Nothing comes off the feedback forms

12.755

3

1.066

1096

11.966

.000

Complaint responsive bank has greater credibility

39.194

3

.719

1096

54.531

.000

Generally satisfied with my bank

70.622

3

.725

1096

97.389

.000

Can claim damages for poor service delivery

30.490

3

1.281

1096

23.807

.000

My bank has efficient Complaint Handling Mechanism Complain promptly if not satisfied Usually get quick & Satisfactory response to complaints Switch to another bank after non-response Banks must have efficient Complaint Handling Mechanism Many times don‟t know where to complain Shouldn‟t complain unnecessarily  

Tend to judge service-quality of bank by its complaint handling

Cluster Mean Square 140.29 2 73.906 172.83 4 393.81 2 51.693

43.148 3 .862 1096 50.052 .000 The F tests should be used only for descriptive purposes because the clusters have been chosen to maximize the differences among cases in different clusters. The observed significance levels are not corrected for this and thus cannot be interpreted as tests of the hypothesis that the cluster means are equal. Number of Cases in each Cluster Cluster 1 668.000

60.73 %

2

74.000

6.73 %

3

178.000

16.18 %

4

180.000

16.36 %

Valid

1100.000

100.00 %

Missing

.000