Numeracy and Data Analysis: Tabular and Graphical Presentation of Data

This report presents statistical data set in pictorial & tabular manner regarding advertisement expenditure. It includes calculation and discussion regarding the mead, mode, median, standard deviation. It involves utilization of linear forecasting model to fulfill the requirement of report by calculating m & c value.

NUMERACY AND DATA
ANALYSIS

TABLE OF CONTENTS
INTRODUCTION.......................................................................................................................................3
MAIN BODY..............................................................................................................................................3
1. Arranging the data related to advertisement expenses in the tabular format....................................3
2. Graphical representation of selected expenditure data.....................................................................4
3. Reflecting the required calculations as below:.................................................................................4
4. Forecasting linear equation model is shown as below:....................................................................9
CONCLUSION.........................................................................................................................................10
REFERENCES..........................................................................................................................................11

INTRODUCTION
Numerical and Data Analysis (NDA) is the one of the important part to get accurate
information for the purpose of decision making. In the current scenario, scope of data analysis
has increased which improves efficiency and accuracy of strategic decision formulation practices
of organization. The present report is based on presenting statistical data set in pictorial & tabular
manner regarding advertisement expenditure. Current report will include calculation and
discussion regarding the mead, mode, median, standard deviation. It will involve utilization of
linear forecasting model to fulfill the requirement of report by calculating m & c value.
MAIN BODY
1. Arranging the data related to advertisement expenses in the tabular format
Serial No. Months Money incurred on
Advertisement expenses
1 Mar 25
2 April 19
3 May 30
4 June 34
5 July 19
6 Aug 45
7 Sept 50
8 Oct 19
9 Nov 42
10 Dec 65

2. Graphical representation of selected expenditure data
Mar
April
May
June
July
Aug
Sept
Oct
Nov
Dec
1 2 3 4 5 6 7 8 9 10
0
10
20
30
40
50
60
70
Money spent on Advertisement
expenses
Money spent on
Advertisement expenses
Mar
April
May
June
July
Aug
Sept
Oct
Nov
Dec
1 2 3 4 5 6 7 8 9 10
0
10
20
30
40
50
60
70
Money spent on Advertisement
expenses
Money spent on
Advertisement expenses
3. Reflecting the required calculations as below:
I. Mean Determination as follows:
Serial No. Months Money Spent on
Advertisement expenses

1 Mar 25
2 April 19
3 May 30
4 June 34
5 July 19
6 Aug 45
7 Sept 50
8 Oct 19
9 Nov 42
10 Dec 65
Total amount of money
incurred on transportation
cost 348
Total number of observation 10
Mean 34.8
Interpretation:
From the above calculation it can be stated that mean derived is 34.8 which is obtained
by dividing the total expense incurred for advertisement purpose by the number of observation
(Cao, 2021). Mean for the advertisement expenditure in this particular situation is 34.8as per the
above illustrated table.
II. Median calculation shown below:
In order to get the value of median there are two steps which need to emphasized which
are as below that are implemented in sequence manner (Mölder and et.al., 2021). These
both steps provides systematic procedure to get the value of median in accurate and
reliable manner.
Step 1: Arranging data in ascending order

Serial No. Months Money spent on
Advertisement expenses
1 April 19
2 July 19
3 Oct 19
4 Mar 25
5 May 30
6 June 34
7 Nov 42
8 Aug 45
9 Sept 50
10 Dec 65
Step 2: Executing formula
Median = (n+1)/2
Median (M) Number of observations 10
M (10+1)/2 5.5
(30+34)/2 47
Interpretation:
From the above table it can be analyzed that median is determined by giving conservation
on 5Th and 6Th cell. The value of these cells are 30 and 34 respectively and by dividing it from 2
the value achieved is 47 which is median value of advertisement expenses (Babak and et.al.,
2020).
III. Mode computation as follows:

Months Advertisement expenses
Mar 25
April 19
May 30
June 34
July 19
Aug 45
Sept 50
Oct 19
Nov 42
Dec 65
Interpretation
Mode can be assessed by evaluating the above illustrated table to get the value of expense
that has been repeated more than one time (Fabián, 2021). From the analysis of data it can be
interpreted that 19 has been observed thrice in table so it’s the mode for specified case.
IV. Range calculation is reflected as follows
Range is assessed by making analysis of the chosen data set to identify the highest and lowest
range expenses (Munch, 2017). With help of applying formula as mentioned below range can be
determined
Range = Higher expense- smaller value
= 65-19
= 46
Interpretation:

The determined range value through substituting value of highest and lowest expenses which are
65 and 19 respectively & determined value by applying formula is 46.
V. Standard Deviation is illustrated below
Month
s
Money
incurre
d
Mean
(U)
X- U
(X-
U)^2
Mar 25 34.8 -9.8 96.04
April 19 34.8 -15.8 249.64
May 30 34.8 -4.8 23.04
June 34 34.8 -0.8 0.64
July 19 34.8 -15.8 249.64
Aug 45 34.8 10.2 104.04
Sept 50 34.8 15.2 231.04
Oct 19 34.8 -15.8 249.64
Nov 42 34.8 7.2 51.84
Dec 65 34.8 30.2 912.04
Total
2167.6
SD= Square root of ∑(X-U) ^2/N
= Square root of 2167.6 / 10
= SQRT OF 216.76
= 14.72
Interpretation:
The standard deviation for advertisement expense is 14.72 which have been computed by
implementing mentioned formula.

4. Forecasting linear equation model is shown as below:
Months X Y X*Y X^2
Mar 1 25 25 1
April 2 19 38 4
May 3 30 90 9
June 4 34 136 16
July 5 19 95 25
Aug 6 45 270 36
Sept 7 50 350 49
Oct 8 19 152 64
Nov 9 42 378 81
Dec 10 65 650 100
Total
55 348 2184 385
i. Determination of m value:
m = Σxy – Σx Σy / Σ x^2 – (Σx)^2
= 2184- (55*348)/ 385- (55)^2
= (2184- 19140) / (385-3025)
= -16956/ -2640
= 6.42
ii. Computation of value c is illustrated below
C = Σy – m Σx / N
= 348 – 6.42 (55)/10
= 348- 353.1/ 10
= -0.51
iii. Showing calculations of 12th and 14th Month

Determination of 12th month
Y= mX +c
= 6.42(12) + (-0.51)
= 77.04- 0.51
= 76.53
Calculation of 14th month expense
Y= mX +c
= 6.42 (14) + (-0.51)
= 89.88 – 0.51
= 89.37
Interpretation:
With help of above shown computation it can be interpreted that m and c value has been
derived by following systematic formula given (Lester, Cho and Lochmiller, 2020). Values of m
and c are 6.42 & (-0.51) respectively. Specific expenditure for the month of 12th and 14th are
76.53 & 89.37 respectively.
CONCLUSION
From the above report it can be concluded that numeracy and data analysis are crucial for
the purpose formulating important decisions in respect to the growth of firm. The present report
has included the tabular and graphical presentation of data. It has include calculation of mean,
mode, median, range, standard deviation, etc. in addition to this , current case study has forecast
the linear equation for the purpose of calculating m and c values. Both the values has been
calculated by applying given formula to meet the requirement of present report.

REFERENCES
Books and Journals
Babak, V. and et.al., 2020. Methods and models for information data analysis. Diagnostic
Systems for Energy Equipments. Studies in Systems, Decision and Control.
281. pp.23-70.
Cao, W., 2021. Discussion on Mean, Median, Mode and its Validity and Table Number. Journal
of Contemporary Educational Research. 5(3).
Fabián, Z., 2021. Mean, mode or median? The score mean. Communications in Statistics-Theory
and Methods. 50(10). pp.2360-2370.
Lester, J. N., Cho, Y. and Lochmiller, C. R., 2020. Learning to do qualitative data analysis: A
starting point. Human Resource Development Review. 19(1). pp.94-106.
Mölder, F. and et.al., 2021. Sustainable data analysis with Snakemake. F1000Research. 10.
Munch, E., 2017. A user’s guide to topological data analysis. Journal of Learning Analytics.
4(2), pp.47-61.