Numeracy and Data Analysis

This report explores the concept of data analysis and its application in decision-making. It includes calculations of mean, mode, median, range, and standard deviation for humidity data in Manchester. The report also uses a linear regression model to forecast future humidity levels.

NUMERACY AND
DATA ANALYSIS

Table of Contents
INTRODUCTION...........................................................................................................................3
MAIN BODY...................................................................................................................................3
1. Arrangement of data in table format........................................................................................3
2. Presentation of data in two charts............................................................................................3
3. Calculation of below mentioned items:...................................................................................5
4. Calculating values of m, c and humidity forecast of day 15 and 20........................................7
CONCLUSION................................................................................................................................9
REFERENCES..............................................................................................................................10

INTRODUCTION
The term data analysis is a comprehensive tool of gathering and analysing monetary by
help of different kinds of techniques (Laracy, Hojnoski and Dever, 2016). By help of this
analysis, it becomes easier for managerial aspect of companies to take corrective actions. The
report consists calculation of mean-mode-median as per the chosen data of humidity of
Manchester city, United Kingdom (Humidity data of London, 2019.). In the further part of report
projection of futuristic humidity percentage is done by applying linear regression model.
MAIN BODY
1. Arrangement of data in table format.
In accordance of requirement of brief under this task, humidity data of 10 days of London
city has been shown in table format:
Date S. No. Humidity (in terms of %)
1st of October, 2019 1 94
2nd of October, 2019 2 84
3rd of October, 2019 3 96
4th of October, 2019 4 91
5th of October, 2019 5 95
6th of October, 2019 6 97
7th of October, 2019 7 95
8th of October, 2019 8 93
9th of October, 2019 9 83
10th of October, 2019 10 93

2. Presentation of data in two charts.
Bar chart- This can be defined as a type of diagram that presents quantitative data in the form of
horizontal bars. Underneath, presentation of humidity data has been done in the form of bar
chart:
1st of October, 2019
2nd of October, 2019
3rd of October, 2019
4th of October, 2019
5th of October, 2019
6th of October, 2019
7th of October, 2019
8th of October, 2019
9th of October, 2019
10th of October, 2019
75 80 85 90 95 100
94
84
96
91
95
97
95
93
83
93
Humidity (in terms of %)
Column chart- This can be defined as a type of diagram that presents quantitative data in the
form of vertical heights (Geiger, Goos and Forgasz, 2015). Underneath, presentation of humidity
data has been done in the form of column chart:

1st of October, 2019
4th of October, 2019
7th of October, 2019
10th of October, 2019
75
80
85
90
95
100
94
84
96
91
95
97
95
93
83
93
Humidity (in terms of %)
3. Calculation of below mentioned items:
Date Humidity (in terms of %)
1st of October, 2019 94
2nd of October, 2019 84
3rd of October, 2019 96
4th of October, 2019 91
5th of October, 2019 95
6th of October, 2019 97
7th of October, 2019 95
8th of October, 2019 93
9th of October, 2019 83
10th of October, 2019 93
Total 921
Mean 92.1
Mode 93
Median 93.5
Range 14 (97-83)
Standard deviation 4.84

(I) Mean- The value of mean is calculated by dividing total of data values from number of
values. Underneath, mean is computed by applying formula that is as: Mean = ΣX/N
ΣX= 921
N = 10
Mean = 921/10
= 92.1
(ii) Mode- In simple terms, mode is a kinds of number whose frequency is higher in a particular
data set. This is presented by Z. In the above data set of humidity, value of Z is 93 because this
value has maximum frequency.
(iii) Median- This is defined as mid value among different range of number of a data set (Shalley
and Stewart, 2017). This is denoted by M. Herein, below formula to calculate median is
mentioned in such manner:
If data set is odd:
M = (N+1)/2
If data set is even:
M= (N/2th item + N/2th item + 1) / 2
Calculation of median as accordance of humidity data of 10 days-
Arrangement of data in ascending order:-
S. No. Humidity (In %)
1 83
2 84
3 91
4 93
5 93
6 94
7 95

8 95
9 96
10 97
N= 10
Median = (N/2th item + N/2th item + 1)/2
= (10/2th item + 10/2th item + 1)/2
= (5th item + 6th item)/2
= (93+94)/2
= 93.5
(iv) Range- It is calculated by making variation between higher and lower value of a data series
(Cahoon, Cassidy and Simms, 2017). Such as per the above mentioned humidity data, this can be
find out that value of range is of 14.
(v) Standard-deviation- It can be defined as calculation of value of variation from a data set. In
accordance of above humidity data, standard-deviation is computed below in such manner:
Days (Date) Humidity (values in %) (x- mean) (x-mean)2
1st of October, 2019 94 1.9 3.61
2nd of October, 2019 84 -8.1 65.61
3rd of October, 2019 96 3.9 15.21
4th of October, 2019 91 -1.1 1.21
5th of October, 2019 95 2.9 8.41
6th of October, 2019 97 4.9 24.01
7th of October, 2019 95 2.9 8.41
8th of October, 2019 93 0.9 0.81
9th of October, 2019 83 -9.1 82.81
10th of October, 2019 93 0.9 0.81
210.9

Variance = [∑(x – mean)2 / N]
= (210.9/10)
= 21.09
Standard deviation = √variance
= √21.09
= 4.59
4. Calculating values of m, c and humidity forecast of day 15 and 20.
Days (X) Humidity (Y) X2 ∑XY Y2
1 94 1 94 8836
2 84 4 168 7056
3 96 9 288 9216
4 91 16 364 8281
5 95 25 475 9025
6 97 36 582 9409
7 95 49 665 9025
8 93 64 744 8649
9 83 81 747 6889
10 93 100 930 8649
∑X= 55 ∑Y= 921 ∑X2= 385 ∑XY= 5057 ∑Y2 = 85035
(I) Calculation of value of m:
m= (∑Y)(∑X2)- (∑X)(∑XY) / n(∑X2)-(∑X)2
= (921)(385)-(55)(5057)/10(385)-(55)2
= 354585-278135/ 3850-3025
= 76450/825
= 92.67
(ii) Calculation of value of c:
c= n(∑XY)- (∑X)(∑Y) / n(∑X2)-(∑X)2
= 10(5057)-(55)(931)/10(385)-(55)2

= 50570-51205/3850-3025
= -635/825
= -0.77
(iii) Forecasting of humidity:
For 15th day-
Y = m+cx
= 92.67+(-0.77*15)
= 92.67- 11.55
= 81.12%
For 20th day-
= 92.67+ (-0.77*20)
= 92.67- 15.4
= 77.27%
CONCLUSION
On the basis of above project report, this can be concluded that data analysis technique is
not limited till any specific department for taking decisions. It is needed any kinds of business
entity for better decision-making. The report concludes about calculation of mean-mode-median,
range and standard-deviation of humidity data of Manchester city. In the end part of report,
forecasting of humidity is done by help of linear regression model.

REFERENCES
Books and journal:
Laracy, S .D., Hojnoski, R. L. and Dever, B .V., 2016. Assessing the classification accuracy of
early numeracy curriculum-based measures using receiver operating characteristic curve
analysis. Assessment for Effective Intervention. 41(3). pp.172-183.
Geiger, V., Goos, M. and Forgasz, H., 2015. A rich interpretation of numeracy for the 21st
century: A survey of the state of the field. ZDM. 47(4). pp.531-548.
Shalley, F. and Stewart, A., 2017. Aboriginal adult English language literacy and numeracy in
the Northern Territory: A statistical overview. Charles Darwin University.
Cahoon, A., Cassidy, T. and Simms, V., 2017. Parents' views and experiences of the informal
and formal home numeracy environment. Learning, Culture and Social Interaction. 15.
pp.69-79.
Online
Humidity data of London. 2019. [Online]. Available through:
<https://www.timeanddate.com/weather/uk/london/historic>