Team :•Bhavya Roongta (U111013)•Manoj Prabhakaran (U111031)•Mayank Patnaik (U111032)•Moonis Raza (U111134)•Prateek Swain(U111040)•Shrusti Mohanty(U111051)•Sushant Mishra (U111056)•Tarunkanti Nayak (U111059)

SRM Presentation: Effect on Sales of Two-Wheelers with the Advent

of Tata Nano

Topics for DiscussionObjectives

Research Problem

Source of Data

Tools used for Analysis

1

2

5

3

4

A Priori Reasoning and Hypothesis

6 Data Analysis

7 Findings and Conclusion

• To understand and analyze the effect on sales of two-wheelers with the advent of Nano using data analysis tools.

• To identify the level of dependence of buying habits on age, sex, type of city and annual income.

Objectives

“Has the advent of Tata Nano influenced the sales of two wheelers”

Research Problem

A Priori Reasoning and Hypothesis Of Research Problem

A Priori Reasoni

ng

• Having more cash in hand will make the workers more complacent towards their daily work. It will also effect their labor supply at a constant daily wage.

Hypothesis

• HO: There is no relationship between Labor supply and Cash in Hand.

• H1: There is relationship between Labor supply and Cash in Hand

Type and Source of Data

Sources of Data : Survey done through questionnaire

Primary Data : Collected through interview by filling up of a questionnaire by residents of Bhubaneswar

Univariate

Bivariate

Multivariate

Factor

Cluster

Tools Used for Data Analysis

DEMOGRAPHIC VARIABLESTools and Analysis

83%

11%4%2%

Age18-30 years

31-45 years

46-60 years

>60 years

69%

31%

Gender Distribution

MaleFemale

52%

38%

10%Type of city

Tier 1Tier 2Tier 3

UNIVARIATE ANALYSISTools and Analysis

< 250000

250000-400000

400000-550000

550000-700000

700000-1000000

> 1000000

0

5

10

15

20

25

30

35

Annual Income in Rupees

Number of re-spondents

BIVARIATE ANALYSISTools and Analysis

Equation R Square F Constant B1 & Sign B2 & Sign B3 & Sign

Linear .005 .121 1.790 .000(.731)

Logarithmic

.006 .156 1.669 .030(.696)

Quadratic .010 .122 1.730 .001(.886) -9.644E-7(.724)

Cubic .023 .191 1.832 -.001(.902) 9.223E-6(.609)

-1.151E-8(.568)

Independent Variable: Distance TravelledDependant Variable: Nano as an alternative to Two-wheelers

MULTIVARIATE ANALYSISTools and Analysis

Dependent Variable

R Square Constant

Number of persons in a family Family Income Distance travelled

Nano Alternative

.097 1.215(.008) .311(.150) -.020(.921) -.007(.973)

Nano Alternative

.096 1.197(.004).313(.139) Removed -.054(.979)

Nano Alternative

.014 1.766(.000)Removed -.040(.844) .105(.604)

Independent Variable: Distance Travelled, Family Income, Number of persons in a family

Dependant Variable: Nano as an alternative to Two-wheelers

FACTOR ANALYSISTools and Analysis

Variables UsedStyle, Fuel, Safety, Speed, Comfort, Space, Value

Rotated Component Matrix

1 2 3 4Style .820Fuel .815Safety

.663 .547Speed .877ComfortSpace .761Value

.858

FACTOR ANALYSIS-FINDINGSTools and Analysis

1 The component matrix shows that the 7 variables analyzed are divided into four factors. Although, component matrix is not giving a clear picture, but the rotated component matrix classifies the factors into common themes.

2Thus, we get four factors, Factor 1 includes space and value. Hence it can be Practical customer. Similarly, Factor 2 includes fuel and safety. Hence, it can be Cautious customer. Factor 3 includes style and safety, can be Balanced customers and Factor 4 includes only speed, can be speed loving customer.

CLUSTER ANALYSISTools and Analysis

Age

Daily Wage

Number of family

members Optimum

wage

Educational

background Variables

considered for

Clustering

CLUSTER ANALYSIS RESULTSTools and Analysis

Mean values of the variables(rounded off)Variables Cluster 1 Cluster 2 Cluster 3Age 18-30 >60 18-30Gender Male Male Male

Family Income 250000<Income<4000000 >1000000 lac550000<Income<100000

0Type of City Tier 1 Tier 1 Tier 1

• Cluster 1 formed 23% of the entire respondents.

• Cluster 2 formed 5% of the entire respondents.

• Cluster 3 formed 72% of the entire respondents.

Findings From Cluster Analysis

Predominantly males who live in Tier 1 city form the clusters.

The annual family income is the centre differentiating point along clusters.

Thank YouAny Questions?