Exploratory Data Analysis: AJ Davis Department Stores
AJ Davis Department Stores Course Project
Part A: Exploratory Data Analysis
Tiffahanie Seger
GM533 Applied Managerial Statistics
Professor Keyhani
Project Part A: Exploratory Data Analysis:
A. Brief Introduction
AJ Davis, a popular department store chain, retains many credit customers.
Therefore, they completed an in-depth study to find out more about these
customers who are essential to their future success as a company. A sample of fifty
customers were selected to conduct this study. Next data was collected in relation
to the following five variables:
1. Location
2. Income
3. Size
4. Years
5. Credit Balance
The following report was generated to represent a statistical analysis of …show more content…
The three subcategories of location include:
urban, rural, and suburban.
Frequency Distribution: Location
Frequency Distribution: | Location | Frequency | Urban | 21 | Suburban | 15 | Rural | 14 |
Pie Chart: Location
Analysis:
Looking at the statistical analysis of both the frequency distribution and the
pie chart for the location variable, the majority of the credit customers live in urban
areas. Urban areas, which are 21 and 42% respectively, are followed by suburban
and rural areas for AJ Davis’ credit customers.
C. Second Individual Variable
The second individual variable is the customer’s household size, which is a
quantitative variable. Through the quantitative variable of household size, the
number of people living in each of the fifty customer’s households can be physically
counted.
Frequency Distribution: Size
Frequency Distribution: | Size | Frequency | 1 | 5 | 2 | 15 | 3 | 8 | 4 | 9 | 5 | 5 | 6 | 5 | 7 | 3 |
Bar Graph: Size
Descriptive Statistics Summary: Size
Descriptive Statistics Summary: Size | Minimum | 1 | Maximum | 7 | Range | 6 | Sum | 171 | Count | 50 …show more content…
There is a pattern and significant clustering
recognized above. Since the points cluster in a band running from lower left to
upper right, there is a positive correlation. If x increases, then y increases also. So
as income increases, the credit balance also increases. In conclusion, there is a
correlation between the income and credit balance variables.
G. Third Pairing of Variables
The relationship between the two variables the income and size is
represented in the scatter plot below.
Scatter Plot: Location vs. Income
According to the scatter plot, there is no direct connection or relationship
between location and income. There is no pattern or significant clustering
recognized above. In conclusion, there is no correlation between the location and
income variables. H. Conclusion
In conclusion, some variables, like income vs. credit balance do impact AJ
Davis’ sales. However, other variables, like years vs. credit balance and location vs.
income do not show a direct correlation to each other, thus not showing a
purchasing pattern of AJ Davis’ credit customers’ spending
Part A: Exploratory Data Analysis
Tiffahanie Seger
GM533 Applied Managerial Statistics
Professor Keyhani
Project Part A: Exploratory Data Analysis:
A. Brief Introduction
AJ Davis, a popular department store chain, retains many credit customers.
Therefore, they completed an in-depth study to find out more about these
customers who are essential to their future success as a company. A sample of fifty
customers were selected to conduct this study. Next data was collected in relation
to the following five variables:
1. Location
2. Income
3. Size
4. Years
5. Credit Balance
The following report was generated to represent a statistical analysis of …show more content…
The three subcategories of location include:
urban, rural, and suburban.
Frequency Distribution: Location
Frequency Distribution: | Location | Frequency | Urban | 21 | Suburban | 15 | Rural | 14 |
Pie Chart: Location
Analysis:
Looking at the statistical analysis of both the frequency distribution and the
pie chart for the location variable, the majority of the credit customers live in urban
areas. Urban areas, which are 21 and 42% respectively, are followed by suburban
and rural areas for AJ Davis’ credit customers.
C. Second Individual Variable
The second individual variable is the customer’s household size, which is a
quantitative variable. Through the quantitative variable of household size, the
number of people living in each of the fifty customer’s households can be physically
counted.
Frequency Distribution: Size
Frequency Distribution: | Size | Frequency | 1 | 5 | 2 | 15 | 3 | 8 | 4 | 9 | 5 | 5 | 6 | 5 | 7 | 3 |
Bar Graph: Size
Descriptive Statistics Summary: Size
Descriptive Statistics Summary: Size | Minimum | 1 | Maximum | 7 | Range | 6 | Sum | 171 | Count | 50 …show more content…
There is a pattern and significant clustering
recognized above. Since the points cluster in a band running from lower left to
upper right, there is a positive correlation. If x increases, then y increases also. So
as income increases, the credit balance also increases. In conclusion, there is a
correlation between the income and credit balance variables.
G. Third Pairing of Variables
The relationship between the two variables the income and size is
represented in the scatter plot below.
Scatter Plot: Location vs. Income
According to the scatter plot, there is no direct connection or relationship
between location and income. There is no pattern or significant clustering
recognized above. In conclusion, there is no correlation between the location and
income variables. H. Conclusion
In conclusion, some variables, like income vs. credit balance do impact AJ
Davis’ sales. However, other variables, like years vs. credit balance and location vs.
income do not show a direct correlation to each other, thus not showing a
purchasing pattern of AJ Davis’ credit customers’ spending