Data Analysis Report: Humidity in Salford City and Linear Equations

This report provides a comprehensive analysis of humidity data in Salford City over a ten-day period. It begins with a table presenting the humidity readings and then visualizes the data using line and bar charts. The report proceeds to calculate key statistical measures including mean, mode, median, range, and standard deviation. Furthermore, the linear equation method is applied to forecast humidity levels for the eleventh and twelfth days. The methodology involves calculating the values of 'm' and 'c' using historical data and then using these values to predict future humidity levels. The report concludes with a summary of the findings and a list of references.

Numeracy and Data
Analysis

Table of Contents
INTRODUCTION...........................................................................................................................3
TASK...............................................................................................................................................3
The humidity of Salford city arranged in the table format..........................................................3
Representation of humidity by different types of charts.............................................................3
Determine the value of mean, mode, median, range and standard deviation..............................5
Linear equation method is followed to calculate the value of m and c.......................................7
CONCLUSION ...............................................................................................................................9
REFERENCES..............................................................................................................................10

INTRODUCTION
Every enterprise, company and expert are used data analysis in different style. It collects,
process and analyses information and used for making important business decision. It is needed
in every business because it helps to make better consumer targeting, decrease administrative
cost and to solve the business problem (Cappelli and Baten, 2021). The report includes the
humidity of Salford city which is located in United Kingdom of the last ten days. This report also
includes the analysis of statics data and find the value of M and C by using linear forecasting
model.
TASK
The humidity of Salford city arranged in the table format
Days Humidity(%)
1st 92
2nd 67
3rd 68
4th 53
5th 46
6th 51
7th 76
8th 73
9th 73
10th 63
Representation of humidity by different types of charts
Line chart- it is very simple method to represent the data. It shows the information as a
ordination data points. It can be show by curve and straight line (Luo and et.al, 2018).

In the above chart shows the humidity of first five days are very high but further five
days humidity remain low.
Bar chart- It is used to comparative between the discrete categories. It may used where the
difficult comparison of the data (Muir and et.al, 2018).
Day1 Day2 Day3 Day4 Day5 Day6 Day7 Day8 Day9 Day10
0
10
20
30
40
50
60
70
80
90
100
Hummidity(%)
Column 1
Day1 Day2 Day3 Day4 Day5 Day6 Day7 Day8 Day9 Day10
0
10
20
30
40
50
60
70
80
90
100
Column 1

Determine the value of mean, mode, median, range and standard deviation
Average-This shows the actual picture of an enterprise. It is most commonly used in the
finance departments and businesses. Through its value the whole distribution and data can be
recognised. It is also called as average (Tout, 2020). When the organisations determine the value
of mean, the followings points should consider.
1. To gather all the values of data
2. Sum of all the given number
3. count all the observation
4. Sum of all the data is divided by observation
Mean= ∑X / N
=(92+67+68+53+46+51+76+73+73+73) / 10
=672 / 10
=67.2
Humidity mean is 67.2
Mode-It has no effect on the extreme values. It is used in the organisations to observe a
categorical data. Like the most used products, trending items, etc. The number whose frequency
appears most of the time, it is called as mode. Steps are considered when calculating the mode:
1. Count all the number
2. choose those number whose frequency appears more times
The mode of the given data set is 73 because it shows in the table of 3 times.
So that, the mode is 73.
Median- When the values are classified, the middle number is called as median. The number
classifies the lowest to highest and highest to lowest.
Table should arranged in descending to ascending order:
Days Humidity(%)
1st 46
2nd 51
3rd 53
4th 67

5th 68
6th 73
7th 73
8th 73
9th 76
10th 92
Total value is 10
Median =( (N/ 2)+(n/2)+1) / 2
=((10/2)+ (10/2) /+1)/2
=(5+6) term/2
=(68+73)/2
=141/2
=70.5
The median of the given data set is 70.5
Range- Range can be describe as a difference between the upper value and lower value. The data
should be set in the lowest to highest and vice versa form. The necessary steps are very important
to calculate the range.
1. Select the upper and lower value.
2. Subtract the lower value from the upper value
Range = upper value- lower value
=92-46
=46
So the range is 46.
Standard deviation- It is very important method in statics (Traczyk and et.al, 2020). The
organisation should follow the steps that are given in the statics they are as follow:
 Firstly, calculation of mean
 Each value should be subtract form the average
 Square of the subtracted number

 The sum of all the square number
 The total number of given set is divided by the summation
 The square of the given number
The formula of Standard deviation = √∑ (xi – μ) ^ 2 / N
Days Humidity (%) Mean (μ) (x-μ) (x-μ)^2
1st 92 67.2 (92-67.2)=24.8 615.04
2nd 67 67.2 (67-67.2)=-0.2 0.04
3rd 68 67.2 (68-67.2)=0.8 0.64
4th 53 67.2 (53-67.2)=-14.2 201.64
5th 46 67.2 (46-67.2)=-21.2 449.44
6th 51 67.2 (51-67.2)=-16.2 262.44
7th 76 67.2 (76-67.2)=8.8 77.44
8th 73 67.2 (73-67.2)=5.8 33.64
9th 73 67.2 (73-67.2)=5.8 33.64
10th 73 67.2 (73-67.2)=5.8 33.64
Total Gross 672 0 1707.6
=√1707.6 / 10
=4.13
The SD is 4.13
Linear equation method is followed to calculate the value of m and c
Days(X) Humidity(%)(Y) (XY) X square
1st 92 92 1
2nd 67 134 4
3rd 68 204 9
4th 53 212 16
5th 46 230 25

6th 51 306 36
7th 76 532 49
8th 73 584 64
9th 73 657 81
10th 73 730 100
55 672 3681
The value of m and c by using linear forecasting theory:
(y = mx + c)
Linear forecasting rule- Historical data is used to find the value of future.
=(10*3681)-(55*672)/(10*385-3025)
=-150/825
=0.18
Calculation the value of c:
=(672-0.18*55)/10
=66.21

The 11th day Humidity level : m = 0.18, x= 11, c = 66.21
y = mx + c
y = 0.18*11 + 66.21
y = 68.19
Humidity level of day 12: m = 0.18, x= 11, c = 66.21
y= mx + c
= 0.18*12 + 66.21
y = 68.37
The humidity levels for Day 11 will be 97.16 and Day 12 will be 99.48 by linear equation model.
CONCLUSION
As the above report, it is analysed it there are different types of methods which can be
used by an organisation and calculated the value of mean, mode and other statics term. In every
organisation the data analysis is very important term. The company is used various charts such as
line chart and bar chart to show the humidity level of the city.

REFERENCES
Books and Journals
Cappelli, G. and Baten, J., 2021. Numeracy development in Africa: new evidence from a long-
term perspective (1730–1970). Journal of Development Economics, 150. p.102630.
Luo and et.al, 2018. Validation of a combined health literacy and numeracy instrument for
patients with type 2 diabetes. Patient education and counseling, 101(10). pp.1846-1851.
Muir and et.al, 2018. School leaders’ identification of school level and teacher practices that
influence school improvement in national numeracy testing outcomes. The Australian
Educational Researcher, 45(3). pp.297-313.
Tout, D., 2020. Evolution of adult numeracy from quantitative literacy to numeracy: Lessons
learned from international assessments. International Review of Education, 66(2). pp.183-
209.
Traczyk and et.al, 2020. The experience‐based format of probability improves probability
estimates: The moderating role of individual differences in numeracy. International Journal
of Psychology, 55(2). pp.273-281.