Understanding and Managing Data: A Statistical Analysis Report

This report provides a comprehensive analysis of data management and statistical techniques. It covers key concepts such as mean, standard deviation, frequency distribution, and cumulative frequency. The report also delves into project management using critical path analysis, probability calculations using contingency tables, and break-even point analysis. Furthermore, it includes a detailed exploration of correlation and regression analysis, including the construction of a correlation matrix, identification of the best predictor of sales, creation of scatter graphs, and interpretation of correlation coefficients. The report concludes with a short discussion on the importance of statistical tools in business decision-making, highlighting the use of regression equations for predicting future sales and the correlation between average order value and sales revenue. Desklib provides access to this and many other solved assignments for students.

Understanding and Managing
the Data

TABLE OF CONTENTS
PART 1............................................................................................................................................4
TASK 1............................................................................................................................................4
1...................................................................................................................................................4
2...................................................................................................................................................4
TASK 2............................................................................................................................................5
1...................................................................................................................................................5
2...................................................................................................................................................5
3...................................................................................................................................................6
TASK 3............................................................................................................................................6
1...................................................................................................................................................6
2...................................................................................................................................................6
3...................................................................................................................................................7
TASK 4............................................................................................................................................7
1...................................................................................................................................................7
2...................................................................................................................................................7
TASK 5............................................................................................................................................7
1...................................................................................................................................................7
2...................................................................................................................................................8
3...................................................................................................................................................8
PART 2............................................................................................................................................9
1........................................................................................................................................................9
Constructing correlation matrix..................................................................................................9
2........................................................................................................................................................9
Identifying best predictor of sale.................................................................................................9
3......................................................................................................................................................10
Creating scatter graph...............................................................................................................10
4......................................................................................................................................................10
Interpreting relevant correlation coefficient..............................................................................10

5......................................................................................................................................................10
Stating the regression equation.................................................................................................10
6......................................................................................................................................................10
Predicting value of quarterly sales when x= 100......................................................................10
7......................................................................................................................................................11
Short report...............................................................................................................................11
REFERENCES..............................................................................................................................13

PART 1
TASK 1
1
2
With the help of the above table it is clear that mean of the data set is 35 and this implies
that average number of people expending over selfie ring light is £35. Moreover, the standard
deviation is 7.35 and this states that the dispersion of the data from the mean value is 7.35. This
value of standard deviation outlines that the data set is more spread from the mean value.

TASK 2
1
2
Report 1

The added advantage of Table 2A is that it provides the percentage information relating
to the frequency of the different level of expenditure. This assist in analysing the specific
percentage of people from the total.
Report 2
The table 2B outlines the cumulative frequency and this adds the advantage of analysing
the data on the basis of less than frequency. This concept of cumulative frequency is essential in
order to determine the number of observation which lies above or below the particular value
provided in data set.
Report 3
The table 2C further outlines the calculation of mean and other tools for analysing the
data in proper manner. The mean value found within both the task are different because of
grouped and ungrouped data (Cheng, 2020). The mean of grouped data is preferred because of
the reason that it provides more precise and accurate estimation of the data set.
3
By referring to the ogive chart it is clear the estimated minimum amount which the
customer need to spend over selfie ring light is 35 as per the mean calculated. By evaluating the
graph, it is clear the 35 is coming central to the expenditure.
TASK 3
1
2
By evaluating the network diagram, it is clear that the critical path involves the activity C, D, F,
G, J and K. The total time of the critical path is 17 weeks that is 3 + 4+ 1 + 6 + 2 + 1 = 17

3
There is a distinction being present within the critical and non- critical activities. the
critical activity is the one whose starting and ending point is strictly defined. On the other hand,
the non- critical activity is the one which has no specified starting or ending points (Petroutsatou,
2019). Moreover, another difference is that any delay in critical activity will affect the
completion of project. But in case of delay in non- critical activity creates no impact over
completion of project.
TASK 4
1
2
With the help of the contingency table prepared, the probability that random male is selected
who are full time workers
40/ 100 * 34/ 40
= 34/100
= 0.34
By evaluating the above probability, it is clear that there is chance of 0.34 times that the selected
person will be male working full time.
TASK 5
1
Variable cost per mirror

2
Fixed cost
3
BEP
With the help of the above break even calculation it is clear that the BEP production
wherein the company will be in no profit no loss situation is 22000 units. This simply implies

that in case company will start producing beyond this level then the company will start earning
profit which is beneficial for the company.
PART 2
1
Constructing correlation matrix
2
Identifying best predictor of sale
With the evaluation of the above correlation matrix it is clear that the best predictor of sale
as per the correlation matrix is the average order value. The reason behind this fact is that in case
the average order value will reduce then the total sales revenue will also be reducing (Kiregyera,
2020). Hence, as a result of this the total revenue will reduce.

3
Creating scatter graph
4
Interpreting relevant correlation coefficient
With the above graph it is clear that correlation coefficient is 0.0641 or 6.41 % that is the
correlation is 6.41 which is very low. This simply means that the change in independent
variables will cause a 6.41 % change in the dependent variable.
5
Stating the regression equation
The regression equation being highlighted or created with help of the graph is y= 6.4292x
+ 498.92. with the help of this equation it is clear that the gradient is 6.492 and intercept involves
498.92.
6
Predicting value of quarterly sales when x= 100
In case the value of x = 100 then the total quarterly sales will be
Y= 6.4292 (100) + 498.92
Y= 642.92 + 498.92
Y= 1141.84

7
Short report
Introduction
the business statistics involves the use of various data analysis tool in order to evaluate
the data and have some generalised conclusion applicable to common public. The present report
will lay a focus on development of knowledge relating to correlation and regression. Further the
analysis of scatter graph and calculating the value with help of regression equation will take
place.
Main body
With the help of the above discussion it was analysed that the correlation assists the
person in analysing the relation between two or more variables. With the correlation matrix it
was evident that the correlation is present between all the variables (Naveed and et.al., 2018).
Though the least correlated variable were total cost and the net profit. This is pertaining to the
fact that in case the total cost increases then the net profit reduces in case there is not any
simultaneous increase in sales revenue.
Further it was evaluated that the best predictor of the sales revenue involves the average
order value as they are highly correlated to one another. Moreover, the regression equation was
also generated using the scatter graph (Zhong and Kolassa, 2021). The tool of regression is being
used in order to identify the relation between the two different variables where one is dependent
and another is independent.
Moreover, the regression equation was also used in order to predict the future quarterly
sales. In this it was assumed that x is 100 and in accordance to that the sales predicted was
1141.84. Along with this, it was evaluated that the use of regression is very important in order to
predict the data and make the strategies accordingly.
Conclusion
In the end the above report concluded that using the different statistical tool is important
in order to take proper decision. The reason underlying this fact is that these tools assist in
evaluating data and drawing conclusion from it. The above analysis concluded that sales best
predictor of average order value. Further with help of regression the amount of sales predicted
for future period was 1141.84.