Numeracy and Data Analysis

This report covers data analysis techniques and calculations based on humidity data of Cardiff city. It includes the arrangement of data, presentation in charts, calculation of mean, median, mode, range, and standard deviation, and forecasting of humidity for the 15th and 20th day.

NUMERACY AND
DATA ANALYSIS

Contents
INTRODUCTION.......................................................................................................................................3
MAIN BODY..............................................................................................................................................3
1. Arrangement of data in table format................................................................................................3
2. Presentation of data in any two types of charts................................................................................3
3. Calculation of below mentioned terms:...........................................................................................5
4. Calculating values of m, c and wind forecast of day 15 and 20...........................................................8
CONCLUSION.........................................................................................................................................10
REFERENCES..........................................................................................................................................11

INTRODUCTION
Data analysis is a method of reviewing, processing, converting and analyzing data with
the goal of finding valuable information, influencing the inference and facilitating judgment-
making. Basically, it contributes in a significant manner in order to take crucial decision making
(Landtblom, 2018). The report covers about humidity data of Cardiff city, United Kingdom.
Under the project a vital range of techniques have been used in order to make proper analysis of
data. As well as some calculation is also done such as mean mode, median etc. In the end part of
report forecasting of humidity for 15th day and 20th days has been done (Humidity data of Cardiff,
2019).
MAIN BODY
1. Arrangement of data in table format.
Serial Number Date Humidity (In terms of %)
1 1st January, 2020 91
2 2nd January, 2020 94
3 3rd January, 2020 100
4 4th January, 2020 90
5 5th January, 2020 98
6 6th January, 2020 83
7 7th January, 2020 92
8 8th January, 2020 98
9 9th January, 2020 88
10 10th January, 2020 90
2. Presentation of data in any two types of charts.
Column chart- The main graph is a column chart with rectangular shapes shown in the dataset.
Column chart is a decent place to display shift over time since column distances can be
easily compared. This help to show a variety of information like temperature of city of
humidity in present time.

1st January, 2020
2nd January, 2020
3rd January, 2020
4th January, 2020
5th January, 2020
6th January, 2020
7th January, 2020
8th January, 2020
9th January, 2020
10th January, 2020
0
20
40
60
80
100 91 94 100 90 98
83 92 98 88 90
Humidity (In terms of %)
Humidity (In terms of %)
Bar chart- A bar graph is the visualization which shows the total amount of information observed
throughout the data of this classification that use rectangular bars called bins.
1st January, 2020
2nd January, 2020
3rd January, 2020
4th January, 2020
5th January, 2020
6th January, 2020
7th January, 2020
8th January, 2020
9th January, 2020
10th January, 2020
0 20 40 60 80 100 120
91
94
100
90
98
83
92
98
88
90
Humidity (In terms of %)
Humidity (In terms of %)

3. Calculation of below mentioned terms:
Date Humidity (In
terms of %)
1st January, 2020 91
2nd January,
2020
94
3rd January,
2020
100
4th January,
2020
90
5th January,
2020
98
6th January,
2020
83
7th January,
2020
92
8th January,
2020
98
9th January,
2020
88
10th January,
2020
90
(I) Mean- The mathematical aggregation of all words is perhaps the most commonly
used term for just a mean of such a probability distribution with such a separate
spontaneous data set. By incorporating that item of the variable because of its
possibility as described by the allocation the mean of a normal distribution with such
a continuous random factor, also known as the estimated value, is achieved.

Formula : ∑x/N
= 924/10
= 92.4
(ii) Median: The median of an allocation with such a discrete generic factor depend heavily on if
there is even or unusual percentage of definitions throughout the distribution (Beyer, 2019). If
there is a strange number of a context, the median is really the middle value of the data series.
The aggregate of the two aspects in the middle is the midpoint so as to comparable or higher the
number of characters. It is computed by below mentioned formula:
When data series is odd: (N+1)/2th Item
When data is even: (N/2th item+ N/2th item + 1) / 2
Arrangement of data is ascending order:
Serial number Humidity
1 83
2 88
3 90
4 90
5 91
6 92
7 94
8 98
9 98
10 100
Median = (10/2th item + 10/2th item + 1)/2
= (5th+6th item)/2
= (91+92)/2
=91.5
(iii) Mode: The distribution style with such a discrete randomized factor is the frequently
occurring meaning of the word. It's not unusual to get more than one variable, particularly if
there are few words in an allocation with a distinct random factor (Leech, Barrett and Morgan,
2013). A two-mode allocation is termed a statistically significant correlation. Tri-modal is termed
a ratio with 3 modes. The allocation method with a constant allocation factor is the function's
final value. Such as in the above data of humidity this can be find out that mode is of 90.

(iv) Range: Discrepancy among maximum and minimum sum is the size of the variance with a
distinct random factor. The range is really the distinction among two extreme examples
on the normal distribution, in which the valuation of the feature falls into zero, for a
distribution with the continual allocation factor.
Range = Higher – lower value
= 100-83
= 17
(v) Standard deviation: Is a statistic which measures under which data set distribution is being
compared to its mean and therefore is measured as the square root of the deviation (Sarkar and
Rashid, 2016). This is computed by assessing the change across each data set in relation to the
mean as the square root of variability.
Date Humidity
(In terms
of %)
(x-m) (x-m)2
1st
January
, 2020
91 -1.4 1.96
2nd
January
, 2020
94 1.6 2.56
3rd
January
, 2020
100 7.6 57.76
4th
January
, 2020
90 -2.4 5.76
5th
January
, 2020
98 5.6 31.36
6th
January
, 2020
83 -9.4 88.36
7th
January
, 2020
92 -0.4 0.16

8th
January
, 2020
98 5.6 31.36
9th
January
, 2020
88 -4.4 19.36
10th
January
, 2020
90 -2.4 5.76
Total=
244.4
Variance = [ ∑(x – mean) 2 / N ]
= (244.4/10)
= 24.44
Standard deviation: √ ( variance )
= √ 24.44
= 4.94
4. Calculating values of m, c and wind forecast of day 15 and 20.
(i) Calculation of value c
c= n (∑xy)- (∑x) (∑y)/ n(∑x2)-( ∑x)2
Days
(Date)
Humidity
(values in
%) x2 (xy)
Days
(Date)
Humidity
(values in
percentage-
form)
1 91 1 91
2 94 4 188
3 100 9 300

4 90 16 360
5 98 25 490
6 83 36 498
7 92 49 644
8 98 64 784
9 88 81 792
10 90 100 900
55 924 385 5047
c= 10*5047-55*924/10*385-(55) 2
= 50470-50820 / 3850-3025
= -350/825
= -0.42
(ii) Calculation of value of m:
m= [(∑y) / n]-c(∑x/n)
= (924/10)- (-0.42*55/10)
= 92.4+2.31
= 94.35
(iii) Forecasting of humidity of 15th and 20th day
For 15th day-
Y= m+cx
= 94.35+ (-0.42*15)
= 94.35-6.3
= 88.05%
For 20th day-
= 94.35+ (-0.42*20)

= 94.5-8.4
= 86.1%
CONCLUSION
On the basis of above project report, this can be found out that data analysis technique is so
important in order to take crucial decision. Report concludes information regards calculation of mean
mode median etc. on the basis of humidity data of ten days. In the end part of linear regression model has
been applied in order to forecast humidity data.

REFERENCES
Books and Journals:
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.
Beyer, W. H., 2019. Handbook of tables for probability and statistics. Crc Press.
Sarkar, J. and Rashid, M., 2016. Visualizing mean, median, mean deviation, and standard deviation of a
set of numbers. The American Statistician. 70(3). pp.304-312.
Leech, N., Barrett, K. and Morgan, G. A., 2013. SPSS for intermediate statistics: Use and interpretation.
Routledge.
Online
Humidity data of Cardiff. 2019. [Online]. Available through:
<https://www.timeanddate.com/weather/uk/london/historic>