Data Analysis: Humidity Level of London, UK

This report provides an analysis of the humidity level in London, UK. It includes the arrangement of data, presentation in different chart formats, calculation of statistical elements, and the use of a linear forecasting model. The report concludes with the importance of data analysis in decision making.

Data Analysis

Table of Contents
INTRODUCTION...........................................................................................................................3
MAIN BODY...................................................................................................................................3
1. Arrangement of Data................................................................................................................3
2. Present the data in different chart format.................................................................................3
3. Calculate and discuss the following elements such as.............................................................4
4. Use the linear forecasting model..............................................................................................6
CONCLUSION ...............................................................................................................................8
REFERENCES ...............................................................................................................................9

INTRODUCTION
Data analysis is the process of collecting information, analysing data and present as per
the convince of the company (Blei, 2014). It further help the management to formulate their
strategies and policies accordingly. This report based on the humidity level of level of the
London city of UK. Further this report include the statistical analysis by using mean, media,
mode, linear forecasting etc.
MAIN BODY
1. Arrangement of Data
Below mention table arrange the data of humidity level of 10 constitutive days of London
city (Humidity Level of London, UK, 2020). Available data is from 31st December 2019 to 9th of
January 2020.
Days Humidity Level
1 86
2 91
3 89
4 82
5 82
6 81
7 93
8 90
9 90
10 86
2. Present the data in different chart format
Line Chart:

1 2 3 4 5 6 7 8 9 10
74
76
78
80
82
84
86
88
90
92
94
86
91
89
82 82 81
93
90 90
86
Days
H u m a d i ty L e v e l
Above mention graph represent the trend of the humidity level in the UK and it clearly
shops in the graph that data has huge fluctuation.
Column Chart:
1 2 3 4 5 6 7 8 9 10
74
76
78
80
82
84
86
88
90
92
94
86
91
89
82 82 81
93
90 90
86
Days
Humadity Level
With the help of above mention column chart, individual able to understand the highest
as well as lowest humidity level from the past ten days. Lowest is on 6th day and it was 81% and
the highest was 93% on 7th day.
3. Calculate and discuss the following elements such as
Days Humidity Level
1 86

2 91
3 89
4 82
5 82
6 81
7 93
8 90
9 90
10 86
Total 870
Mean 87
Mode 82
Median 87.5
Range 12
Maximu
m 93
Minimu
m 81
Standard
deviation 4
Mean: In statistics, it is the average value of the given series and it calculated through
dividing whole value of the sample from the number of observation (Finotello and Di Camillo,
2015).
Formula: Mean = ∑X/N
= 870 / 10
= 87
Median: It is the middle value of the series which is used to evaluate the centre value
from the huge number of data and further calculation mention below:
Formula:

If sample size are even = (N+1) / 2
If sample size are odd = N / 2
From the available information, given data even observation than middle value is 5.5th
observation that is 87.5.
Mode: It is the value which most of time repeat in the series called mode and it is used
for the statistical analysis.
In the above mention table, mode of the series are 82 because it often repeat in the series.
Range: It is the difference among the maximum or minimum value of the observation
(O'Mahony and Shmoys, 2015). By using formula, individual able to calculate the range of the
sample which mentioned below:
Formula:
Range = Maximum value – minimum value
= 93 – 81
= 12
Standard deviation: It is the measurement of variance or dispersion which is required to
re-evaluate and make other understand that how these data beneficial for the organizations.
Days
Humidity
(x) x- mean (x-m)2
1 86 -1 1
2 91 4 16
3 89 2 4
4 82 -5 25
5 82 -5 25
6 81 -6 36
7 93 6 36
8 90 3 9
9 90 3 9
10 86 -1 1
Formula:
Variance = [ ∑ (x–mean)2/N ]

= 162 / 10
= 16.2
Standard deviation = √16.2
= 4.02
4. Use the linear forecasting model
With the help of linear forecasting model, people able to forecast the future humidity
level on 15th or 20th day (Tang and et.al., 2017).
Step: 1 Formulate table
Days (X) Humidity (Y) X2 XY
1 86 1 86
2 91 4 182
3 89 9 267
4 82 16 328
5 82 25 410
6 81 36 486
7 93 49 651
8 90 64 720
9 90 81 810
10 86 100 860
∑X = 55 ∑Y = 784 ∑X2 = 385 ∑XY = 4800
Step 2: Calculation of the value of M:
Formula:
M = [N ∑XY - ∑x ∑y]/ [N ∑X2 - (∑x)2]
= [ 10 * 4800 – (55 * 784) ] / [10*385- (55)2 ]
= [48000 – 43120] / [3850 – 3025]
= 4880 / 825
= 5.91
Step 3: Calculation of value of C:
Formula:
C = ∑y - m ∑x / N

= (784 – 5.91 * 55) / 10)
= (784 - 325.05) / 10
= 458.95 / 10
= 45.89
Step 4: Humidity on 15th day:
Formula:
Y = mx + c
= 5.91 * 15 + 45.89
= 88.65 + 45.89
= 134.54
The level of humidity on 15th day will be 134.54.
Step 5: Humidity on 20th Day:
Formula:
Y = mx + c
= 5.91 * 20 + 45.89
= 118.2 + 45.89
= 164.09
The humidity level on 20th day will be 164.09.
CONCLUSION
From the above discussion it has been concluded that, by using data analysis method
business able to evaluate understand the data and further it is utilize by the managers in their
decision making process.

REFERENCES
Books & Journals
Blei, D. M., 2014. Build, compute, critique, repeat: Data analysis with latent variable
models. Annual Review of Statistics and Its Application, 1, pp.203-232.
Finotello, F. and Di Camillo, B., 2015. Measuring differential gene expression with RNA-seq:
challenges and strategies for data analysis. Briefings in functional genomics. 14(2).
pp.130-142.
O'Mahony, E. and Shmoys, D. B., 2015, February. Data analysis and optimization for (citi) bike
sharing. In Twenty-ninth AAAI conference on artificial intelligence.
Tang, B. and et.al., 2017. Incorporating intelligence in fog computing for big data analysis in
smart cities. IEEE Transactions on Industrial informatics. 13(5). pp.2140-2150.
Online
Humidity Level of London, UK. 2020. [Online]. Available Through:
<https://www.timeanddate.com/weather/uk/london/historic>