Data Analysis Report: Investment in UK Food Manufacturing Sector

This report provides an in-depth analysis of the UK Food Manufacturing dataset, encompassing 410 companies, to provide investment insights. It uses descriptive analysis to examine profit margin distributions for both SMEs and large businesses, including mean, median, and skewness. Cross-tabulation and chi-square tests assess activity differences between company sizes. Correlation analysis explores relationships between variables like employee count, production costs, and profit margins, highlighting the impact of credit limits. Regression analysis models the effect of credit limits on profit margins and salary turnover, offering predictions and evaluating model fit using R-squared values. The report concludes with key findings to guide potential investors in the UK food manufacturing sector. Desklib provides access to similar reports and solved assignments for students.

Business Analysis 1
Name of Student
Institution Affiliation

Business Analysis 2
Table of Content
Introduction………………………………………………………………………………….…...3
Descriptive Analysis…………………………………….………………………………………..5
Correlation Analysis…………………………………………………………………...………...6
Regression Analysis………………………………………………………………………………7
Summary and Conclusion………………………………………………………………………..9
References………………………………………………………………………………………....11

Business Analysis 3
Introduction
We have been provided with UK Food Manufacturing dataset which contains information for
410 manufacturing companies in the United Kingdom. A client wants to invest in the UK and we are
supposed to analyze the data in depth in order to come up with relevant insight that will help the client
make the decisions that he or she requires. By doing this, we will be able to make the client understand
the Food Manufacturing Sector. We will analyze the performance of both the SME's and large
companies. We are to provide analysis according to the client's requirements. The requirements were to
analyze the distribution of the profit rates across the companies in the data set, compare the profit rates
between large and SME food manufacturers, to analyze the distribution of Shareholder's return to large
and small Manufacturing companies, to compare profit rates of the Manufacturing companies, to
analyze the main activities of the companies against their size, to analyze the extent of the profit
compared to other variables in the companies and to analyze the extent of credit limit in relation to the
quantitate variables.
To achieve these objectives, we will use descriptive analysis in which we will create tables, create
histograms and charts. The correlation analysis which will show the relationship between the chosen
variables and regression analysis which will give us predictions of a chosen dependent variable using
one or several independent variables.

Business Analysis 4
Descriptive Analysis
To discuss the importance of the data in terms of investments, we will first focus on the profit margin
analysis under which we will discuss the distribution of the profit margin. We will come up with
histograms to display the information graphically. From the data set, we have profit margins for both
the SME's and large businesses. We will subset the data and describe the distribution of each, the mean,
standard deviation, the median, and their importance.
i) Large Business
From the table on Frequency of large business, we have the display of the profit margins in terms of
frequencies and percentages. It can be noticed that the majority of the large business has a profit margin
of between 0-5. This means that in case of an investment from a variety of large companies, one should
expect a profit variation of between 0-5 obtained from those large companies.
The mean profit of large companies is about 6.0 meaning, the average return one should expect after
investing in large companies is 6.0. This is important for planning and it will give you a clue about
what you should expect after investing in big companies. The median Profit Margin for large
companies is 4.36, the minimum Profit Margin is -3.4 and the maximum Profit Margin is 39.23. From
these outputs, one can deduce that half of the large companies get a Profit Margin of between -3.4 to
4.36 and the other half gets a Profit Margin of about 4.36 to 39.23. Less than half of the large
companies get above the average Profit Margin. To check the distribution of the Profit Margin, we will
consider the Value of Skewness, it shows whether your distribution is symmetric or not. When the
value of Skewness is between -0.5 to 0.5, then the data is fairly skewed. When the value of Skewness is
between -1 to -0.5 or between 0.5 to 1, then the data is moderately skewed. When the value of
Skewness is above 1 or below -1, then the data is highly skewed Kim (2013 pp.52-55). In our situation,
the value of Skewness is 2.34 and this means that the Profit Margin is highly skewed. Investing in
many companies will result in getting a wide margin of Profit Margin. The standard deviation is 6.8
which are very close to the mean profit. This means that there is a high probability of the investor
getting Return which is closer to the mean.
The same information can be displayed using Histogram, the highest Profit Margin obtained to be from
0 to 5.
Table 6 shows the descriptive Analysis of the Profit Margin for SME's. The average Return an investor
can obtain after investing in SME's is 1.49. The median Return and the mean return from SME's is
almost similar. The Median Profit Margin is 1.4 which is almost equal to the mean of 1.49. The
standard deviation of the Profit Margin is 4.3 which is not close to the mean. This means that an
investor has a high chance of getting Return which is not close to the mean. To check the distribution of
the Profit Margin, we will focus on the value of Skewness. The Skewness Value is -0.27 meaning that
the distribution is slightly skewed to the left and this means that if one invests in the majority of SME's,
there are higher chances of getting Returns lower than the mean.
The same descriptive analysis can be displayed using a histogram, from the histogram diagram shown
below shows the frequencies of the SME's. Most of the SME's obtain Profit Margin of between 0 to 5.
Cross-tabulation
We conducted a chi-square of the activity of the manufacturing food company against their size. We
came up with a null hypothesis:
i) There is no difference between the large company and SME's in the food manufacturing
After conducting the analysis, we obtained the following output. We will use results from table 8
To test whether our hypothesis is true or not, we focused on the value of chi-square. When the value of
chi-square is less than 0.05, then we will have enough statistical evidence to reject the null hypothesis
otherwise, when the value of the chi-square is greater than 0.05, then we don’t have enough statistical

Business Analysis 5
evidence to reject the null hypothesis and thus we accept the null hypothesis McHugh (2013 pp.143-149).
In our case, the Value of chi-square is 3.778E-46, therefore, we reject the null hypothesis. Thus we deduce
that there is a big difference between the large company and SME's in the food manufacturing. This means that
there is a big difference in Return when one invests a specific type of food manufacturing company considering
their sizes.
Correlation Analysis
We conduct a correlation analysis to determine the relationship between two variables. In our case, we
will conduct a correlation analysis to determine whether there was a relationship between the number
of employees in a company and the cost of production. We conduct the analysis and came up with the
following output:
From table 1, we can observe that there is a positive relationship between the number of employees and
the Cost of Production. The relationship is also strong and it means that a company that has many
employees experiences a higher cost of production and conversely, a company that has fewer
employees' experiences low cost of production. We will also investigate whether the number of
employees affects the profit margin. To achieve this, we will conduct a correlation analysis between the
profit margin and the number of employees. Below is the result of the result:
From table 2, we can observe that there is a positive relationship between the profit margin and the
number of employees. What differentiates it from table one is that the relationship is so low. The
relationship is about 3.4 %. This means that the there is no significant influence of the number of
employees to the profit margin in a company.
We can now investigate whether the cost of production affects the profit margin.
From table 3, we observe that there is a positive relationship between the profit margin and the Cost of
Production. The relationship is about 1.8 % which is really low. This means that the Cost of Production
has no significant influence on the profit margin.
Since both the number of employees and the Cost of Production doesn't affect the profit margin, we
will investigate whether the remaining variables affect the profit margin.
From table 4, we can notice that the credit limit has a weak and almost medium relationship to Profit
Margin. The relationship is about 25.8 % and these means that the higher the credit limit the more a
company gains more profits Cohen, West, and Aiken (2014). The reason behind this is that the company
gets higher gain from the interest paid when paying the loans.
The Regression Analysis
Effect of other variables on Profit
To understand the regression model, we need to come up with the right variables and the hypothesis.
Generally, regression analysis predicts the effect of independent variable towards the dependent
variable. The general equation of regression analysis is given by:
y = mx + c, where y is the dependent variable, m is the gradient (slope) of the equation, x is the
independent variable and c is the y-intercept.
i) Formulation of hypothesis testing
We will take Profit Margin as our dependent variable and Credit limit as our independent variable since
we have already seen that they are correlated from table 4.
 The credit limit of a company doesn't have any influence on the Profit Margin – Null
hypothesis

Business Analysis 6
ii) Hypothesis testing
We will conduct the regression analysis to test our assumption. From the regression analysis, we
obtained the equation of regression (linear model) to be:
Profit Margin = 3.81 + 3.02E-07 * Credit Limit
From the regression analysis, we can deduce the profit margin of about 3.81 (y-intercept) is not affected by
Credit Limit. 1 unit of Profit margin adds a total of 3.02E-07 Credit Limit. Profit margin increases by 3.02E-07 by
every Credit Limit. This means that an increase in Credit Limit causes an increase in Profit Margin i.e. the
predictor variable (Credit Limit) is directly proportional to the dependent variable (Profit Margin). To estimate
the goodness of our linear model, scatter plot was plotted to view the goodness of the model graphically
Faraway (2016).
From the scatter diagram below, it is observed that the goodness of the model is not that strong. This can also
be observed using the value of R2. From the regression analysis, the value of R-squared is 0.066. R-squared is a
statistical measure and it's used to measure how close the data is to the linear model. The variance of the R-
squared to test the goodness of the limit. In our case, the value of R-squared as only 6.66 % of the Credit Limit
affects the Profit Margin. This means that Credit Limit only has little effect on the Profit Margin and it is not
that a big factor when you want to determine the profit margin. To test our hypothesis, we will focus on the p-
value. When the value of p-value is less than 0.05 then we reject the null hypothesis otherwise we accept the
alternate hypothesis. In our case, the p-value obtained was 1.18596E-07, which is less than 0.05 and therefore
we reject the null hypothesis. We can conclude that Credit Limit has an effect on the Profit Margin.
Effect of other variables on Credit Limit
Company loan limits mean a lot to the company. The main aim of the taking Credit is to make a Profit. We
investigated whether Profit Margin and Salary Turnover had an effect on Credit Limit. Credit can sometimes be
used to pay salary and that is why Salary Turnover is very important in this analysis. We conducted a regression
analysis in order to test our results.
Formulation of hypothesis

Business Analysis 7
i) Profit Margin has no effect on Credit Limit- Null hypothesis
ii) Salary Turnover has no effect on Credit Limit- Null hypothesis
Hypothesis testing
From the regression analysis, we obtained the linear model to be
Credit Limit = 950330.7 + 222095.2 * Profit Margin -11371.6 * Salaries as a % of turnover
From the above equation, we can deduce that 950330.7 Credit Limit is neither affected by the Profit Margin
and the Salaries. Profit Margin increases the Credit Limit by 22095.2 per unit Profit Margin and Salaries as % of
turnover reduces the Credit Limit by 11371.6 per unit of Salaries. In short the higher the Profit Margin the
higher the Credit Limit and the higher the Salaries as a % of turnover the lower the Credit Limit. To test the
hypothesis, we focus on the p-value for both the two independent variables. The p-value of Profit Margin
which is 1.11E-07 is less than 0.05 and therefore we reject the first hypothesis. The p-value of Salaries as a % of
turnover which is 0.65 is greater than 0.05 and therefore we accept the 2nd hypothesis. Therefore, we can
conclude from the two tests that Profit Margin has a significant effect on the Credit Limit while Salaries as % of
turnover has no effect on Credit Limit.
Conclusion
From the analysis done above, we conclude that the number of employees affects the cost of production of the
company. In other words, the higher the number of employees the higher the cost of Production, The profit
margin of larger companies were bigger than the SME's. There was a big difference between the type of the
Manufacturing Company and their size. In other words, each of them had a difference Profit Margin and Credit
Limits. Profit Margin was correlated to Credit Margin and you could predict the value of one using the other.
Salaries had no effect on the credit limit and also it had no effect of Profit Margin since Credit Limit and Profit
Margin was correlated. From all these statistics, I can state that whenever a client wants to invest and he or she
requires large return he should choose large Company since they have a huge Return compared to the SME's

Business Analysis 8
and also their Return can easily be predicted since the variation of their Return is next to the mean. With all the
above information, am confident that the client can make his or her decision according to what he requires.
Appendix
Table 1
Number of
employees
Cost of Production
(£)
Number of employees 1 0.679
Cost of Production (£) 0.679 1
Table 2
Profit Margin
(%)
Number of
employees
Profit Margin (%) 1 0.034
Number of employees 0.034 1
Table 4
Profit Margin
(%)
Credit limit
(£)
Profit Margin
(%) 1
Credit limit (£) 0.257579 1
Table 3
Profit Margin
(%)
Cost of Production
(£)
Profit Margin (%) 1 0.018
Cost of Production (£) 0.018 1

Business Analysis 9
0
20000000
40000000
60000000-20
0
20
40
60
Credit limit (£) Line Fit Plot
Profit Margin (%)
Predicted Profit
Margin (%)
Credit limit (£)
Profit Margin (%)
Table 8
Count of Size 1=Large
2=SME Column Labels
Row Labels 1 2
Grand
Total Total %
1 118 171 289
0.70316
3
2 55 67 122
0.29683
7
Grand Total 173 238 411
New Value for testing 121.6472019 167.3528
16.32603406
19.88807
8
chi-square 3.778E-46

Business Analysis 10
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21
0
10
20
30
40
50
60
70
80
90
histogram of large company
frequencies

Business Analysis 11
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21
0
20
40
60
80
100
120
140
histogram of SME's
frequencies
Frequency table for large company
margin
frequencie
s
Percentag
e
0 19 10.98
5 80 46.24
10 43 24.86
15 18 10.40
20 7 4.05
25 0 0.00

Business Analysis 12
30 3 1.73
35 1 0.58
40 2 1.16
45 0 0.00
50 0 0.00
55 0 0.00
60 0 0.00
65 0 0.00
70 0 0.00
75 0 0.00
80 0 0.00
85 0 0.00
90 0 0.00
95 0 0.00
100 0 0.00
frequenc
y table
for
SME's
margin
frequencie
s
percentag
e
0 43
18.067226
9
5 126
52.941176
5
10 47
19.747899
2
15 19
7.9831932
8
20 3 1.2605042
25 0 0
30 0 0
35 0 0
40 0 0
45 0 0
50 0 0
55 0 0
60 0 0
65 0 0
70 0 0
75 0 0
80 0 0
85 0 0
90 0 0