Numeracy and Data Analysis: A Report on Humidity Data Analysis for Carlisle City

This report includes data analysis of Carlisle city of UK which involves data collection on humidity of ten city that presents in the tables and charts format. Along with this report includes calculation of different measures of mean, median, mode, range and standard deviation as well as dispersion and central tendency for the gathered data. Lastly report includes linear forecasting model for the data humidity.

Numeracy and Data
Analysis

Table of Contents
INTRODUCTION ..........................................................................................................................3
TASK...............................................................................................................................................3
1 Arrange the collected data of humidity of ten consecutive days for the city Carlisle in the
format of table.............................................................................................................................3
2. Present data using chart Bar chart and Line Chart..................................................................4
3. Calculate Mean, Median, Mode, Range and standard Deviation............................................5
4. Linear forecasting Model (y=mx+c).......................................................................................8
CONCLUSION .............................................................................................................................10
REFERENCES..............................................................................................................................12
Books and Journals........................................................................................................................12

INTRODUCTION
Data Analysis is an effective and systematic process of transforming and modelling data
with the main of identifying useful information, which help to take effective decisions. It
includes the activities such as gathering information, receive sample from it, transforming or
modelling it, identify different trends and examples them with various charts and graphs. Main
aim of this report is gather humidity of city for ten consecutive days after that a report was
prepared on that collected humidity (Van Ooijen and Vrabec 2019). This report includes data
analysis of Carlisle city of UK which involves data collection on humidity of ten city that
presents in the tables and charts format. Along with this report includes calculation of different
measures of mean, median, mode, range and standard deviation as well as dispersion and central
tendency for the gathered data. Lastly report includes linear forecasting model for the data
humidity.
TASK
1 Arrange the collected data of humidity of ten consecutive days for the city Carlisle in the
format of table.
Day Humidity report
1 83.00%
2 91.00%
3 88
4 76
5 67
6 77
7 67
8 70
9 58

10 48
Total 723
2. Present data using chart Bar chart and Line Chart

3. Calculate Mean, Median, Mode, Range and standard Deviation.
Mean:
It refers to the one of the central tendency measure, it is the average of the given number
which is calculated by dividing sum of the given numbers by the numbers of all observation.
This is the most common measure which is used for central tendency. There are various step
which have to be considered while following mean such as firstly gathering of whole data, sum
of all the given data sets, calculate all data, Sum of all the given set of data is divided by count of
all numbers (Liao Jaehnig and Zhang 2019).
Mean= ∑X / N
= (83+91+88+76+67+77+65+70+58+48) / 10
= 723 / 10
= 72.3
The Humidity mean is 72.3
Mode:
It is also measure of central tendency which help to determine the value of data set. Value
which is repeating higher number of times known as mode. The observing frequency of this
value is most frequent. But there are also cases when there is zero repeated value, then it became
difficult to find mode. Calculation of mode became easy when data is in repeated number of
frequency. There are some essential step while doing mode is to gather and organize data from
data set, analyse all distinct value, Count occurrence frequency for every distinct value.
Mode of given data is 67
In above case mode of above mentioned data set is 67 because the number of frequency
shown many times which states that 67 shows 2 times while other number is represents only one
time.
Median:
It refers to the middle number in given data set. Number should be arranged in ascending
order from lowest to highest value. It divides the lowest number to highest numbers for data
distribution. Median is considered as simple metric to calculate. It is also known as middle value

of observation or Center (Botvinik-Nezer and Avesani 2020). Sometimes it became difficult to
calculate all the observation but with the median it became simple to analyse all observations.
From the above data for median data is considered from lowest to highest value
Days Humidity
1 48
2 58
3 67
4 67
5 70
6 76
7 77
8 83
9 88
10 91
Median =( (N/2)+(n/2)+1)/2
= (( 10/2)+(10/2)/+1)/2
= (5+6) term/2
= (70+76) / 2
= 73
Median for above data is 73
Range:

It refers to the difference between the smallest and largest values, it is the lowest interval
size which includes all the data and give indication of dispersion. There are some step includes to
identify range that is order data from low to high value in data set, secondly deduct lowest value
from the highest value (Kang and Zhang 2018).
Range = Lowest value – Highest value
= 48-91
= 43
Range of the above given data is 43
Standard Deviation:
It is an measure of amount of dispersion of value set. Standard deviation with low value
shown the value which is tend to relatively close to the mean (Lee Yoon and Van Der Schaa
2019). Standard deviation is also known as the degree of variation of data observation from the
mean. It is also the variance square root. There are various steps that followed while calculating
standard variation. First step is to calculate mean of the given observation data, secondly
calculation of squared difference from the average, Then calculated the sum of squared
difference calculated in step 2, then divide the total calculated in 3v step by the total number of
observation. Lastly take variance square root calculated in 4 step.
Formula of standard deviation =
Day Humidity Mean (xi-u)
1 83 72.3 (83-72.3)
2 91 72.3 (91-72.3)
3 88 72.3 (88-72.3)
4 76 72.3 (76-72.3)
5 67 72.3 (67-72.3)
6 77 72.3 (77-72.3)

7 67 72.3 (76-72.3)
8 70 72.3 70-72.3
9 58 72.3 58-72.3
10 48 72.3 48-72.3
Total 723
4. Linear forecasting Model (y=mx+c)
This model is an effective statistical tool that assist to forecast value of future from past
values. IT is mainly utilized for quantitative way to analyse the underlying trend. This model
predict future on the basis of ongoing linear trends. The x is the number that an 9individual
waant to predict as the new value. There is dependent array that is known y.
Calculation of value of m and c:
Day (X) Humidity (y) (XY) X
1 83 83 1
2 91 182 4
3 88 264 9
4 76 304 16
5 67 335 25
6 77 462 36
7 67 469 49
8 70 560 64

9 58 522 81
10 48 480 100
55 723 39765 385
Value of m and c by using linear forecasting
Y=mx+c
Y refers to the dependent variable
X refers to the independent variable
M refers to slope
C refers to constant
m= (39765-55*723) / (385-55*55)
= (39710-39765)/ (330-3025)
= 55/2965
= 49
Calculation of C value
= ( 723-49*55)/10
= (723-2695)/10
= (1972)/10
=197.2
Humidity level on 11 day:
m= 49, X= 11, C= 197.2

y = mx + c
y= 49*11+197.2
y= 539+197.2
= 736.2
Humidity level on day 12:
m= 49, X=12, C= 66.21
y= mx+c
y= 0.18*12+ 66.21
y= 2.16 + 66.21
y= 68.37
CONCLUSION
It is concluded from the above report that company can use different types of methods and can
calculate mean, mode, median value. After that linear forecasting method used by company for
the calculation of m and c value along with this humidity for the day 11 and 12 is also measured
with the help of linear equation.

REFERENCES
Books and Journals
Van Ooijen, I. and Vrabec, H.U., 2019. Does the GDPR enhance consumers’ control over
personal data? An analysis from a behavioural perspective. Journal of consumer
policy, 42(1), pp.91-107.
Liao, Y., Wang, J., Jaehnig, E.J., Shi, Z. and Zhang, B., 2019. WebGestalt 2019: gene set
analysis toolkit with revamped UIs and APIs. Nucleic acids research, 47(W1), pp.W199-
W205.
Botvinik-Nezer, R., Holzmeister, F., Camerer, C.F., Dreber, A., Huber, J., Johannesson, M.,
Kirchler, M., Iwanir, R., Mumford, J.A., Adcock, R.A. and Avesani, P., 2020. Variability
in the analysis of a single neuroimaging dataset by many teams. Nature, 582(7810),
pp.84-88.
Kang, J., Yu, R., Huang, X., Wu, M., Maharjan, S., Xie, S. and Zhang, Y., 2018. Blockchain for
secure and efficient data sharing in vehicular edge computing and networks. IEEE
Internet of Things Journal, 6(3), pp.4660-4670.
Lee, C., Yoon, J. and Van Der Schaar, M., 2019. Dynamic-deephit: A deep learning approach for
dynamic survival analysis with competing risks based on longitudinal data. IEEE
Transactions on Biomedical Engineering, 67(1), pp.122-133.