srm group5 sec_a
TRANSCRIPT
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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
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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
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• 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
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“Has the advent of Tata Nano influenced the sales of two wheelers”
Research Problem
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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
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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
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Univariate
Bivariate
Multivariate
Factor
Cluster
Tools Used for Data Analysis
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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
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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
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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
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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
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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
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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.
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CLUSTER ANALYSISTools and Analysis
Age
Daily Wage
Number of family
members Optimum
wage
Educational
background Variables
considered for
Clustering
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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.
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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.
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Thank YouAny Questions?