1) A group of students conducted a survey to determine if buying propensity of Indians towards smartphones depends on age, profession, and gender. They collected data through a Google form distributed to friends, family, and classmates from different age groups and professions. 2) Chi-square tests of independence were used to analyze the relationships between buying propensity and each of the three factors. The results showed age and profession were significantly related to buying propensity but gender was not significantly related. 3) In conclusion, the students found sufficient evidence that age and occupation impact smartphone buying behavior in India, but not gender based on the sample data collected.

1. 1
Business Statistics Project
Submitted by: -
Ameeya Kumar (UM19137)
Aneek Mandal (UM19147)
Biswadeep Ghosh Hazra (UM19148)
Devansh Jajodia (UM19151)
Rishab Dev Gupta(UM19174)
Riya Ghosh (UM19176)
Shashank Thakur (UM19180)
Submitted to:
Prof. Rahul Kumar

2. 2
Problem Statement: To determine whether the buying propensity of Indians towards
smartphones is dependent on Age, Profession and Gender
Objective:
To determine whether the buying propensity of Indians towards smartphones is dependent on
1. Age
2. Profession
3. Gender
To what extent these factors affect the willingness of the Indian people to purchase a
smartphone
Sources of data collection
We have collected data from primary sources by floating a Google Form which was filled by
our batchmates, friends and relatives, each belonging to different age groups, diverse
backgrounds and also working in varied domains.
The following is a sample of the Google form that we floated:

3. 3

4. 4
Overall approach:
Identifying the
problem statement
Collecting data from
the primary souces
Data Analysis
Hypotheseis testing by
Chi-square method to
check the significance
of the variable
Concluding from the
result of the statistic

5. 5
Statistical method followed
Chi-Square test of independence:
The Chi-Square test of independence is used to determine if there is a significant relationship
between two nominal (categorical) variables. The frequency of each category for one
nominal variable is compared across the categories of the second nominal variable. The data
is displayed in a contingency table where each row represents a category for one variable and
each column represents a category for the other variable. For example, we wanted to
examine the relationship between gender (male vs. female) and buying propensity (likely,
indifferent & unlikely). The chi-square test of independence is used to examine this
relationship. The null hypothesis for this test is that there is no relationship between gender
and buying propensity. The alternative hypothesis is that there is a relationship between
gender and buying propensity.
Results and findings
Calculations for Chi-Square Test
Parameter: Age
 H0 : Buying propensity and age of the sample population are independent
 H1 : Buying propensity and age of the sample population are dependent
Observed
Values Likely Unlikely Total
Under
30 15 28 43
Above
30 17 11 28
Total 32 39
Expected
values Likely Unlikely Total
Under
30 19.38028 23.61972 43
Above
30 12.61972 15.38028 28
Total 32 39 71
Observed-
Expected Likely Unlikely
Under
30 -4.38028 4.380282
Above
30 4.380282 -4.38028

6. 6
Observed-
Expected
Squared Likely Unlikely
Under
30 19.18687 19.18687
Above
30 19.18687 19.18687
(O-E)^2/E Likely Unlikely
Under
30 0.99002 0.812324
Above
30 1.520388 1.247498
Chi
square
Value 4.57023
Critical
Value 3.8414
Chi square value – 4.570
Critical Value- 3.8414

7. 7
Parameter: gender
 H0 : Buying propensity and gender of the sample population are independent
 H1 : Buying propensity and gender of the sample population are dependent
Observed
Values Likely Unlikely
Male 30 30 60
Female 2 9 11
32 39 71
Expected
values Likely Unlikely
Male 27.04225 32.95775 60
Female 4.957746 6.042254 11
32 39 71
Observed-
Expected Likely Unlikely
Under 30 2.957746 -2.95775
Above
30 -2.95775 2.957746
Observed-
Expected
squared Likely Unlikely
Under 30 8.748264 8.748264
Above
30 8.748264 8.748264
(O-E)^2/E Likely Unlikely
Under 30 0.323504 0.265439
Above
30 1.764565 1.447848
Chi
square
Value 3.801355
Critical
Value 3.8414

8. 8
Parameter: occupation
 H0 : Buying propensity and occupation of the sample population are independent
 H1 : Buying propensity and occupation of the sample population are dependent
Observed
Values Likely Unlikely
Student 8 22 30
Business 14 2 16
Services 10 15 25
32 39 71
Expected
values Likely Unlikely
Student 13.52113 16.47887 30
Business 7.211268 8.788732 16
Services 11.26761 13.73239 25
32 39 71
Chi square Chi square critical-3.8414
Value3.801

9. 9
Observed-
Expected Likely Unlikely
Student -5.52113 5.521127
Business 6.788732 -6.78873
Services -1.26761 1.267606
Observed-
Expected
Squared Likely Unlikely
Student 30.48284 30.48284
Business 46.08689 46.08689
Services 1.606824 1.606824
(O-E)^2/E Likely Unlikely
Student 2.25446 1.849813
Business 6.390955 5.243861
Services 0.142606 0.11701
Chi square
Value 15.9987
Critical value 5.9914
Critical value-
5.9914
Chi square value –
15.9987

10. 10
Conclusion
 Parameter: Age
We have sufficient evidence to prove that Age and Buying propensity are related to
each other
 Parameter: Gender
We do not have sufficient evidence to prove that gender and Buying propensity are
related to each other.
 Parameter: Occupation
We have sufficient evidence to prove that occupation and Buying propensity are
related to each other.