London School of Commerce: Data Analysis and Forecasting Project, BABS

This assignment analyzes a dataset of monthly expenses over ten months using various statistical approaches. It includes data arrangement in table and graph formats (scatter and line charts), calculations of mean, median, mode, standard deviation, and range. The core of the assignment involves...Read More

Data Analysis and Forecasting

Contents
INTRODUCTION.....................................................................................................................................3
MAIN BODY.............................................................................................................................................3
1. Arranging data in table format:........................................................................................................3
1. Showing data in graph:....................................................................................................................4
2. Calculation of followings:...............................................................................................................5
3. Linear forecasting model:................................................................................................................9
CONCLUSION........................................................................................................................................10
REFERENCES........................................................................................................................................11

INTRODUCTION
The term data analysis is considered as one of crucial approach in order to manage wide range of
financial and non-financial data in an effective manner (Trought, 2017). In order to do
appropriate data analysis, it is essential to use suitable technique among various kinds of
approaches. The report is based on a data set that is about total expenses per month for different
10 months. This data set has been analyzed under various kinds of statistical approaches like
descriptive statistics, linear forecasting approaches etc.
MAIN BODY
1. Arranging data in table format:
Month
Total expense per
month (Expenses in
Pounds)
January 250000
February 340000
March 360000
April 400000
May 520000
June 550000
July 600000
August 665000
Septembe
r 701000
October 735000

1. Showing data in graph:
Scatter chart: In order to forecast and prepare for possible expenses, the scatter graph (or
scatter chart) approach is a graphic technique used during accounting to distinguish the
constant and variable components of a semi-variable bill (also called a mixed cost). It is
more difficult to evaluate a semi-linear cost because it is consisting of both constant and
variable variables (Held, D O'Neill and Wallinga, 2019).
0 2 4 6 8 10 12
0
100000
200000
300000
400000
500000
600000
700000
800000
250000
340000360000400000
520000550000600000665000701000735000
Total expense per month
Line chart: - A line map is a graphical representation of the historical market behavior of
a commodity that links a sequence of knowledge points with a continuous line. This is the
most common type of graph used in finance which usually only represents the price
movement of a commodity over time. Line charts may be used for any period, but most
commonly for shifts in daily values.

0
100000
200000
300000
400000
500000
600000
700000
800000
250000
340000 360000 400000
520000 550000 600000
665000 701000 735000
Total expense per month
2. Calculation of followings:
(I) Mean- The basic statistical average of a series of multiple or more figures is a mean.
The mean can be determined in more than one form for a specified sequence of
figures, namely the arithmetical mean process that utilizes the amount of the number
in the sequence, and the expected value method that uses the total of the figures in the
sequence, which is the measure of the category of tasks (Chen, Ning and Shi, 2019).
Mean: Sum of total values/Number of values
Month
Total expense per
month (Expenses in
Pounds)
January 250000
February 340000
March 360000
April 400000
May 520000
June 550000
July 600000
August 665000

Septembe
r 701000
October 735000
5121000
Mean: 5121000/10
= 512100
(I) Median- In an ordered set of values, Median is the middle value. The figures should
first be filtered or ordered in accurately assess through bottom to top for determining
the median value in a set of numbers. The variable which is in the center of an
equivalent number on each sides of a median can be used to find the median value in
a list with an arbitrary amount count.
Formula-
When data set is odd= (N+1)/2th item.
When data set is even= {N/2th item+ N/2th item + 1}2
The data of total expense per month is even, hence this is calculated as:
Setting data in lower to higher form:
Month
Total expense per
month (Expenses in
Pounds)
January 250000
February 340000
March 360000
April 400000
May 520000
June 550000

July 600000
August 665000
Septembe
r 701000
October 735000
N= 10
M= (10/2th item + 10/2th item + 1)/2
= (5th item+ 6th item)/2
= (520000+550000)/2
= 1070000/2
= 535000
(II) Mode- The mode is the number that occurs in a set of data almost always. A data
collection can have one mode, upwards of once mode, or no mode whatsoever
(Chong, Wishart and Xia, 2019). The mean, or the sum of a group, and the average,
the interquartile range in a set, are other common indicators of central inclination. In
the aspect of above data set, this can be inferred that there is no value which has been
repeated so mode will be assumed as one.
(III) Standard deviation: This can be understood as a form of approach which is used to
find out difference of a data set which is relative to mean and computed as square root
of variance. In the context of above data set, below calculation of standard deviation
is done in such manner which is as:
Month
Total expense per
month x-m (x-m) 2
January 250000 -
2621
686964100

00 00
Februar
y 340000
-
1721
00
296184100
00
March 360000
-
1521
00
231344100
00
April 400000
-
1121
00
125664100
00
May 520000 7900 62410000
June 550000
3790
0
143641000
0
July 600000
8790
0
772641000
0
August 665000
1529
00
233784100
00
Septem
ber 701000
1889
00
356832100
00
October 735000
2229
00
496844100
00
2.51987E+
11
Variance = [∑ (x – m) 2 / N]
= 2.51987E+11/10
= 25198690000
= 158740.95

(IV) Range: It can be measured by making difference of higher and lower value of a
particular data set (Brown, Tauler and Walczak, 2020). In the aspect of above data
set, this has been measured below:
Higher value= 735000
Lower value= 250000
Range= (735000-250000)
= 485000
3. Linear forecasting model:
Calculation of value m:
Y= mx + c
m= n (∑x y) - (∑x) (∑y)/ n(∑x2) -( ∑x)2
= 10(246291000) -(10) *(5121000)/10(385) -(55) 2
= 2462910000-51210000/3850-3025
= 2411700000/825
m= 2923272.72
Calculation of c:
c=
[(∑y) / n]-m (∑x/n)
= [(5121000/10)]- 2923272.72(55/10)
= 512100-16077999.96
Month(x
)
Total expense per month Expenses in
Pounds
(y)
x2 x y
1 250000 1 250000
2 340000 4 1360000
3 360000 9 3240000
4 400000 16 6400000
5 520000 25 13000000
6 550000 36 19800000
7 600000 49 29400000
8 665000 64 42560000
9 701000 81 56781000
10 735000 100 73500000
55 5121000 385 246291000

= -15565900
Forecasting for month 11:
Y=mx + c
= 2923272.72*11 +(-15565900)
= 32156000-15565900
= 16590100
Forecasting for month 12
= 2923272.72*12 +(-15565900)
= 35079272-15565900
= 19513372
CONCLUSION
On the basis of above project report this can be concluded that data analysis is one of the crucial
approach which is used to make proper decision with support of computed outcome. In the above
report data about expenses of various 10 months has been analyzed through statistical techniques
like mean, mode, standard deviation etc. As well as to this, in the end of report projection of
expenses has been done for month 11 and 12 respectively, it was done by help of linear
forecasting model.

REFERENCES
Trought, F., 2017. Brilliant employability skills: How to stand out from the crowd in the
graduate job market. Pearson UK.
Held, L., Hens, N., D O'Neill, P. and Wallinga, J. eds., 2019. Handbook of infectious disease
data analysis. CRC Press.
Chen, G., Ning, B. and Shi, T., 2019. Single-cell RNA-Seq technologies and related
computational data analysis. Frontiers in genetics, 10, p.317.
Chong, J., Wishart, D.S. and Xia, J., 2019. Using MetaboAnalyst 4.0 for comprehensive and
integrative metabolomics data analysis. Current protocols in bioinformatics, 68(1), p.e86.
Brown, S., Tauler, R. and Walczak, B. eds., 2020. Comprehensive chemometrics: chemical and
biochemical data analysis. Elsevier.