BUSINESS DATA ANALYSIS 2022

Running head: BUSINESS DATA ANALYSIS
Business Data Analysis
Name of the Student:
Name of the University:
Author note:

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BUSINESS DATA ANALYSIS
Table of Contents
Answer to the question 1............................................................................................................3
Part (a)....................................................................................................................................3
Part (b)....................................................................................................................................3
Part (c)....................................................................................................................................3
Part (d)....................................................................................................................................3
Answer to the question 2............................................................................................................3
Part (a)....................................................................................................................................3
Part (b)....................................................................................................................................4
Part (c)....................................................................................................................................6
Answer to the question 3............................................................................................................7
Part (a)....................................................................................................................................7
Part (b)....................................................................................................................................8
Part (c)....................................................................................................................................9
Answer to the question 4..........................................................................................................10
Part (a)..................................................................................................................................10
Part (b)..................................................................................................................................10
Part (c)..................................................................................................................................10
Answer to the question 5..........................................................................................................10
Part (a)..................................................................................................................................10
Part (b)..................................................................................................................................11
Bibliography.............................................................................................................................13

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BUSINESS DATA ANALYSIS

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BUSINESS DATA ANALYSIS
Answer to the question 1
Part (a)
The survey method in the study is socio economic survey. Because the study shows
the amount of violence on television contributes to violence in the society.
Part (b)
The mail questionnaire method, telephone and personal interview method has been
applied to collect the sample.
The mail questionnaire method, telephone and personal interview are the way to
collect the data. Depends upon the area and locality or time the three method are applied.
Part (c)
The variable or the parameter of the study is watching TV hours and amount of debt.
Moreover the family has a children or not should be firstly identified.
The data type for the study is quantitative.
Part (d)
In data collection the following issues are faced by the data collector. These are time,
cost, respondent, false information and non-response.
Answer to the question 2
Part (a)
The number of classes for TV Hours is 10 and for Debt ($) is also 10.

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BUSINESS DATA ANALYSIS
Part (b)
0 to 6 6 to 12 12 to 18 18 to 24 24 to 30 30 to 36 36 to 42 42 to 48 48 to 54 54 to 60
0
10
20
30
40
50
60
70
80
90
100
Histogram on TV Hours
Class
Frequency
Figure 1 Histogram on TV Hours
Figure 1 shows the histogram on TV hours. In the X-axis represent the class that is
time and the Y-axis represent the frequency that is the observations. It has been seen that the
observations are symmetrically distributed. Thus this means that the shape of the above
histogram is symmetric.

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BUSINESS DATA ANALYSIS
0 to 30000 30000 to
60000
60000 to
90000
90000 to
120000
120000 to
150000
150000 to
180000
180000 to
210000
210000 to
240000
240000 to
270000
270000 to
300000
0
20
40
60
80
100
120
140
Histogram on Debt ($)
Class
Frequency
Figure 2 Histogram on Debt ($)
Figure 2 shows the histogram on debt ($). In the X-axis represent the class that is the
amount of money and the Y-axis represent the frequency that is the observations. It has been
seen that the observations are symmetrically distributed. Thus this means that the shape of the
above histogram is symmetric.

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BUSINESS DATA ANALYSIS
Part (c)
0 10 20 30 40 50 60
0
50000
100000
150000
200000
250000
300000
f(x) = 2531.8614793307 x + 49430.0214173968
R² = 0.306567314256723
Scatter Plot on TV Hours versus Debt ($)
TV Hours
Debt ($)
Figure 3 Scatter plot on TV Hours versus Debt ($)
The scatter plot shows the relationship between TV hours and debt ($). In the X-axis
represent the TV hours and the Y-axis represent the debt ($). It has been seen that the
relationship between these two variable is positive. Here X-axis represent the independent
variable and the Y-axis represent the dependent variable. Thus the TV hours is independent
and debt ($) is dependent. Because the debt that is amount of money is depends on the time
of watching television.

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BUSINESS DATA ANALYSIS
Answer to the question 3
Part (a)
Table 1 Summary Statistics on TV Hours
From the table 1 it has been seen that the mean and median of the TV hours is 30.475
and 30. The range of the TV hours is 51. The standard deviation and variance of these
variable is 9.865 and 97.33. Similarly the smallest and largest value of TV hours is 6 and 57.
The three quartiles are 24, 30 and 38.

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BUSINESS DATA ANALYSIS
Table 2 Summary Statistics on Debt ($)
From the table 2 it has been seen that the mean and median of the debt ($) is 126588.5
and 127332. The range of the debt ($) is 256718. The standard deviation and variance of
these variable is 45111.17 and 2035017277.43. Similarly the smallest and largest value of
debt ($) is 20516 and 277234. The three quartiles are 95880.25, 127332 and 154040.3.
Part (b)
0
5
10
15
20
25
30
35
40
Box Plot on TV Hours
Figure 4 Box Plot on TV Hours

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BUSINESS DATA ANALYSIS
0
20000
40000
60000
80000
100000
120000
140000
160000
180000
Box Plot on Debt ($)
Figure 5 Box Plot on Debt ($)
Part (c)
Table 3 Correlation Coefficient
It has been seen that the correlation between TV hours and debt ($) is positive. The
coefficient of the correlation is 0.55.

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BUSINESS DATA ANALYSIS
Answer to the question 4
Table 4 Regression Output
Part (a)
In the regression analysis debt ($) is the dependent variable and the TV hours is the
independent variable. Because the debt that is amount of money is depends on the time of
watching television.
Part (b)
The simple linear regression model is as below
Dependent variable = Intercept +slope * dependent variable
Debt ($) = 49430.02+2531.86 * TV hours
Part (c)
The coefficient of determination that is R-square for the model is 0.307.
Answer to the question 5
Part (a)
The compound bar chart has been applied to compare the number of new passenger
car sales among the various car models during the period of 2015 and 2016. The reason for

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BUSINESS DATA ANALYSIS
apply the chart is that it is a graphical representation method which is easy to compare their
frequency values by the figure or plot.
Toyota
Holden
Ford
Mazda
Hyundai
Nissan
Mitsubishi
Volkswagen
Subaru
Honda
0
50,000
100,000
150,000
200,000
250,000
Bar Chart on The Number of New Passenger Car Sales in
Queensland
2015 2016
Car Model
New Passenger
Figure 6 Bar Chart on car model during 2015-16
Figure 6 represent the compound bar chart on the number of new passenger car sales
in Queensland during 2015 and 2016. From the figure it is cleared that the Toyota model has
the highest car sales model in 2015 and 2016. In most of the model the sales of car in 2015 is
greater than the 2016. But in the Ford, Mazda, Hyundai and Volkswagen has a highest car
sales on 2016 as correspond to 2015.
Part (b)
The line chart has been applied to compare the number of new passenger car sales
among the various car models during the period of 2015 and 2016. The reason for apply the
chart is that it is a graphical representation method which is easy to describe about the sales
of fluctuations during the period among the various category.

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BUSINESS DATA ANALYSIS
Toyota
Holden
Ford
Mazda
Hyundai
Nissan
Mitsubishi
Volkswagen
Subaru
Honda
0
50,000
100,000
150,000
200,000
250,000
Line Chart on The Market Share of Passenger Car Models in
Queensland
2015 2016
Car Model
New Passenger
Figure 7 Line Chart on car model during 2015-16
Figure 7 represent the compound line chart on the number of new passenger car sales
in Queensland during 2015 and 2016. It has been seen that the Toyota model has the market
share on sales in 2015. As correspond to Toyota the sales of other car models has been
decreased during 2015 and 2016. The lowest car sales model during the two period is Honda.
Moreover in Honda the sales of 2016 lesser than the 2015. It is approximately less than
50,000. Also the Toyota in 2015 has the highest sales among all the model and during the two
period.

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BUSINESS DATA ANALYSIS
Bibliography
Cameron, A. C., & Trivedi, P. K. (2013). Regression analysis of count data (Vol. 53).
Cambridge university press.
Chatterjee, S., & Hadi, A. S. (2015). Regression analysis by example. John Wiley & Sons.
Ho, A. D., & Yu, C. C. (2015). Descriptive statistics for modern test score distributions:
Skewness, kurtosis, discreteness, and ceiling effects. Educational and Psychological
Measurement, 75(3), 365-388.
Kleinbaum, D. G., Kupper, L. L., Nizam, A., & Rosenberg, E. S. (2013). Applied regression
analysis and other multivariable methods. Nelson Education.
Plonsky, L. (2015). Statistical power, p values, descriptive statistics, and effect sizes: A
“back-to-basics” approach to advancing quantitative methods in L2 research. In Advancing
quantitative methods in second language research (pp. 23-45). Routledge.