Numeracy and Data Analysis

This study material provides an in-depth understanding of numeracy and data analysis. It covers topics such as representing data in tables, plotting graphs, calculating descriptive statistics, and using linear forecasting models. The material also includes examples and interpretations to enhance learning.

Numeracy and data analysis
Student name: Muraru Maria Lavinia
Student id: S221096

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. Estimating the amount for 14th and 16th month by making use of linear forecasting
model......................................................................................................................................6
CONCLUSION..........................................................................................................................8
REFERENCES...........................................................................................................................9

INTRODUCTION
The data analysis is fundamentally the cycle through which the significant data
relating to the informational collections can be achieved. It fundamentally includes the use of
the measurable instruments to appropriately dissect the information and present the
information in numeric structure. This report portrays the information in regard to the
measure of cash which is being spend on transport for the 10 successive months. Along with
that, 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 transport
(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

1-Feb-20 1-Mar-20 1-Apr-20 1-May-20 1-Jun-20 1-Jul-20 1-Aug-20 1-Sep-20 1-Oct-20 1-Nov-20
0
10
20
30
40
50
60
70
Am ount of money spend on
transport (in pounds)
Line chart
20-Jan-20 10-Mar-20 29-Apr-20 18-Jun-20 7-Aug-20 26-Sep-20 15-Nov-20 4-Jan-21
0
10
20
30
40
50
60
70
Amount of money spend
on transport (in pounds)
3. Presenting descriptive statistics table
i. Mean value
Serial. No. Date Amount of money spend on transport
(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: The mean value which is being derived from the above table is £32.7 which
means that on an average the money spent on the transportation is £32.7. It is derived by
dividing the total amount of money spent by the number of observations (Munch, 2017).
ii. Median value
Step 1- Arranging the data in the ascending order
Serial.
No. Date Amount of money spend on transport
(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 value which is being derived from the given data
set. It is the average of the fifth and the sixth number of the table.
iii. Mode value
Date Amount of money spend on transport
(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: Based on the above table, the mode value is £24 as it is the most frequently
occurring number in the given table.
iv. Range
Particulars Formula Amount
Maximum £61
Minimum £15
Range
Higher value - Smaller
value £46
Interpretation: It tends to be expressed from the above that the range basically accounts for
the contrast between the higher and the lower esteem in the given informational index.
Relating to the abovementioned, the high worth is £61 while the lower esteem is £15 which
plainly expresses that the range lies between the two which is 46 (Kamilaris, Kartakoullis and
Prenafeta-Boldú, 2017).
v. Standard deviation
Months
Amount of
money spend
on transport (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 the purpose of deriving the value of standard deviation the below given formula is used.
Standard deviation (S.D.) = Square root of ∑(X - U) ^2 / N
= SQRT of ((2012.1) / 10)
= SQRT of 201.2
= £44.85
Interpretation: The figure of standard deviation derived above is £44.85, which is moderate
in number that indicates that there is moderate variation among the numbers with its means
value indicating a moderate risk factor.
4. Estimating the amount for 14th and 16th month by making use of linear forecasting model
Date X
Amount of
money
spend on
transport (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. Calculating the figure of ‘m’
Formula to be used 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. Determining 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. Forecast for 14th and 16th month
Calculating the figure of Y by making use of m and c value
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 tends to be expressed from the above computation that the value of 'm' is
0.14 which is inferred utilizing the condition and through a similar worth of 'c' is likewise
decided. By utilizing these values, the estimation is accomplished for the coming months
(Loeb and et.al., 2017). Along these lines, it is determined that in the fourteenth and sixteenth
month, transport cost will be of £35.43 and £35.71 respectively.
CONCLUSION
It can be concluded that the figures which are identified by utilizing the statistical
techniques helped in adequately examining the effect of it over the pertinence and utilization
of the information, in view of this, significant business-related choice can be embraced.
Along with that, information investigated helped in adequately overseeing and anticipating
the information for the coming up months also which assisted in proper administration of
money so that it can meet with the future requirements. Consequently, data analysis has
become an indispensable piece of each association which brings about endeavor better and
educated business choices.

REFERENCES
Books and Journals
Kamilaris, A., Kartakoullis, A. and Prenafeta-Boldú, F. X., 2017. A review on the practice of
big data analysis in agriculture. Computers and Electronics in Agriculture. 143.
pp.23-37.
Loeb, S., and et.al., 2017. Descriptive Analysis in Education: A Guide for Researchers.
NCEE 2017-4023. National Center for Education Evaluation and Regional
Assistance.
Munch, E., 2017. A user’s guide to topological data analysis. Journal of Learning
Analytics. 4(2). pp.47-61.