Numeracy and Data Analysis: Representing data, Descriptive statistics, Linear forecasting model

This report portrays the information regarding the measure of cash that is being spend on electricity bill for the 10 successive months. In addition to this, it features the application of the descriptive analysis for viably investigating the information.

NUMERACY AND DATA
ANALYSIS

TABLE OF CONTENTS
INTRODUCTION...........................................................................................................................3
MAIN BODY..................................................................................................................................3
1. Representing the data set in form of table...............................................................................3
2. Plotting the data on graph........................................................................................................3
3. Presenting descriptive statistics table......................................................................................4
4. Estimation of the amount for 14thand 16thmonth by utilising linear forecasting model.........7
CONCLUSION................................................................................................................................9
REFERENCES..............................................................................................................................10

INTRODUCTION
Data analysis is defined as analysing the data and information related to the different
areas of market research. The role of data analysis is very significant in nature as it ensure the
proper knowledge regarding the data and information over the respective topic. This report
portrays the information regarding the measure of cash that is being spend on electricity bill for
the 10 successive months. In addition to this, it features the application of the descriptive
analysis for viably investigating the information.
MAIN BODY
1. Representing the data set in form of table
Serial.
No. Date Amount of money spend on electricity
bill (in pounds £)
1 28-Feb-20 20
2 31-Mar-20 15
3 30-Apr-20 35
4 31-May-20 55
5 30-Jun-20 61
6 31-Jul-20 34
7 31-Aug-20 24
8 30-Sep-20 24
9 31-Oct-20 24
10 30-Nov-20 35
2. Plotting the data on graph
Column chart

Line chart
3. Presenting descriptive statistics table
i. Mean value
Serial. No. Date Amount of money spend on electricity
bill (in pounds £)
1 20 20
2 15 15
3 35 35
4 55 55
5 61 61

6 34 34
7 24 24
8 24 24
9 24 24
10 35 35
Sum of amount spend on
transport 327
Number of observations 10
Mean value 32.7
Interpretation: Mean value calculated in the above table is £32.7 which means that denote the
average money spent on the transportation is £32.7. This is calculated by dividing the total
amount of money spent by the number of observations (Landtblom, 2018).
ii. Median value
Step 1- Arranging the data in the ascending order
Serial.
No. Date Amount of money spend on electricity
bill (in pounds)
1 31-Mar-20 15
2 28-Feb-20 20
3 31-Aug-20 24
4 30-Sep-20 24
5 31-Oct-20 24
6 31-Jul-20 34
7 30-Apr-20 35
8 30-Nov-20 35
9 31-May-20 55
10 30-Jun-20 61
Step 2- Determining value by applying the formula (n+1)/2
Number of
observations 10
Median
(M) (10+1)/2 5.5
M= (24+34)/2 £29
Interpretation: The value £29 is the middle term that is being calculated from the given data set.
This is the average figure of the fifth and the sixth number of the table.

iii. Mode value
Date Amount of money spend on electricity
bill (in pounds£)
28-Feb-20 20
31-Mar-20 15
30-Apr-20 35
31-May-20 55
30-Jun-20 61
31-Jul-20 34
31-Aug-20 24
30-Sep-20 24
31-Oct-20 24
30-Nov-20 35
Mode 24
Interpretation: On the basis of the above table, the mode value is £24 as this is the most used
value.
iv. Range
Particulars Formula Amount
Maximum £61
Minimum £15
Range
Higher value-Smaller
value £46
Interpretation: Range is the difference in between the higher value and the lower value. The
high worth is £61 while the lower esteem is £15 That indicate about the range which is 46
(Zheng and et.al., 2017).

v. Standard deviation
Months
Amount of
money spend
on electricity
bill (in pounds)
(X) U X-U (X-U)^2
1 20 32.7 -12.7 161.29
2 15 32.7 -17.7 313.29
3 35 32.7 2.3 5.29
4 55 32.7 22.3 497.29
5 61 32.7 28.3 800.89
6 34 32.7 1.3 1.69
7 24 32.7 -8.7 75.69
8 24 32.7 -8.7 75.69
9 24 32.7 -8.7 75.69
10 35 32.7 2.3 5.29
Total 327 2012.1
For purpose of derivation of the standard deviation value use of the given below formula is there.
Standard deviation (S.D.) = Square root of ∑(X-U)^2 / N
= SQRT of ((2012.1) / 10)
= SQRT of 201.2
= £44.85
Interpretation: The standard deviation figure calculated above is £44.85, that is moderate in
amount which gives an indication that there is variation which is moderate between the numbers
with its value of mean indication of a risk factor which is moderate (Kapoor and et.al., 2018).
4. Estimation of the amount for 14thand 16thmonth by utilising linear forecasting model
Date X
Amount of
money
spend on
electricity
bill (in
pounds) (Y)
X*Y X^2
28-Feb-20 1 20 20 1
31-Mar-20 2 15 30 4

30-Apr-20 3 35 105 9
31-May-20 4 55 220 16
30-Jun-20 5 61 305 25
31-Jul-20 6 34 204 36
31-Aug-20 7 24 168 49
30-Sep-20 8 24 192 64
31-Oct-20 9 24 216 81
30-Nov-20 10 35 350 100
Total 55 327 1,810 385
i. Calculation of the figure of ‘m’
Formula being applied is:
m = NΣxy – Σx Σy / NΣ x^2 – (Σx)^2
Y = mX + c
m = 10(1810) - (55*343) / (10*385) – (55)^2
m = (18100 – 17985) / (3850-3025)
m = 115 / 825 = 0.14
ii. Determination of the value of ‘c’
c = Σy – m Σx / N
c = 327 – (0.14 * 55) / 10
c = (327+ 7.7) / 10
c = 334.7 / 10
c = 33.47
iii. Forecasting of 14th and 16th month
Calculation of the figure of Y by using value of m and c
For 14th day-
Y = mX + c
= 0.14*14 + 33.47
= 1.96 + 33.47
= £35.43

For 16th day-
Y = mX + c
= 0.14*16 + 33.47
= 2.24 + 33.47
= £35.71
Interpretation: It can be expressed from the above calculation that the value of 'm' is 0.14 which
is interpreted using the condition and by a worth which is similar of 'c' is accordingly decided
(Kapoor and et.al., 2018). By using the values, the estimation is done for upcoming months. On
these lines, it is said that in the month of fourteenth and sixteenth, cost of transport shall be of
£35.43 and £35.71 accordingly.
CONCLUSION
It can be summed up that the data which is identified by using the techniques of
statistics assisted in adequate examination of the effect due to it over the use and pertinence of
the information, in this view, prominent business-related option can be followed. Along with
this, information investigation assisted in adequate monitoring and anticipation of the
information for the upcoming months also which helped in administration proper of money so
the future requirements of it could be met. As a consequence, analysis of data have become an
indispensable piece of every association that bring about better endeavor and business choices
which are educated.

REFERENCES
Books and Journals
Harrisson, S., 2018. The downside of dispersity: why the standard deviation is a better measure
of dispersion in precision polymerization. Polymer Chemistry. 9(12). pp.1366-1370.
Kapoor, R., Gupta, R., Jha, S. and Kumar, R., 2018. Boosting performance of power quality
event identification with KL Divergence measure and standard
deviation. Measurement. 126. pp.134-142.
Landtblom, K. K., 2018. Prospective teachers’ conceptions of the concepts mean, median and
mode. In Students' and Teachers' Values, Attitudes, Feelings and Beliefs in Mathematics
Classrooms (pp. 43-52). Springer, Cham.
Zheng, S. and et.al., 2017. The relationship between the mean, median, and mode with grouped
data. Communications in Statistics-Theory and Methods. 46(9). pp.4285-4295.

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