Numeracy and Data Analysis: Humidity Levels in Bodmin, UK Report

This report presents a comprehensive analysis of humidity data collected from Bodmin, England, UK, from December 26, 2019, to January 4, 2020. The report begins with data arrangement in a tabular format and then proceeds to present the data using various chart formats, including column and line charts, to visualize humidity level fluctuations. Furthermore, the report delves into the calculation of essential statistical aspects such as mean, median, mode, range, and standard deviation, providing a detailed understanding of the data's characteristics. The report also implements a linear forecasting method to predict future humidity levels, calculating expected humidity levels for the 15th and 20th days. The conclusion emphasizes the importance of data analysis for informed decision-making and strategic planning, for both organizations and individuals. The report is well-structured, includes appropriate references, and provides a solid foundation for understanding data analysis techniques and their practical applications.

Numeracy and
Data Analysis

Table of Contents
INTRODUCTION...........................................................................................................................1
MAIN BODY...................................................................................................................................1
1. Data arrangement.....................................................................................................................1
2. Presenting data in different chart format.................................................................................1
3. Calculation of different statistical aspects...............................................................................3
4. Linear forecasting method.......................................................................................................4
CONCLUSION................................................................................................................................6
REFERENCES................................................................................................................................7

INTRODUCTION
Numeracy and data analysis are the process of collecting, organization and managing
information which help the people to understand the data and make further strategies
accordingly. Statistical analysis include the various aspects which helps in analysing data and
present in well manner which can be understand by others (Dumuid and et.al., 2018). This report
based on the humidity level of Bodmin City of UK. This assessment include the arrangement of
data which further represent in different chart forms. In addition, it contain the calculation of
mean, median, mode, range etc. Along with linear forecasting model for future analysis.
MAIN BODY
1. Data arrangement
Data arranged in the below mention table which is about Humidity level of Bodmin,
England, UK. Collected information is from 26th of December 2019 to 4th of January 2020
(Statistical data of Humidity, 2020). There is 10 days consecutive data placed in tabular format
which mentioned below:
Days Humidity Level
1 93
2 100
3 99
4 94
5 99
6 95
7 97
8 91
9 99
10 92
2. Presenting data in different chart format
Data presentation in column chart format and it mentioned below:
1

1 2 3 4 5 6 7 8 9 10
86
88
90
92
94
96
98
100
102
93
100
99
94
99
95
97
91
99
92
Days
Humidity Level
From the above column chart, it has been observed that humidity level of Bodmin city
has huge fluctuation which clearly mentioned in the chart. On 2nd day, humidity level was 100%
which is the highest and 91% on 8th day that is the lowest one.
Data presented in line chart format:
1 2 3 4 5 6 7 8 9 10
86
88
90
92
94
96
98
100
102
93
100
99
94
99
95
97
91
99
92
Days
Humadity Level
With the help of line chart, people able to understand the trend of data such as humidity
level increases or decreases. These charts used by the organizations in order to represent their
data which can be understood by everyone.
2

3. Calculation of different statistical aspects
Days Humidity Level
1 93
2 100
3 99
4 94
5 99
6 95
7 97
8 91
9 99
10 92
Total 959
Mean 95.9
Mode 99
Median 96
Range 9
Maximum 100
Minimum 91
Standard
deviation 3.3149493041
Mean: In statistics, it is the average value of the entire sample which selected for the
analysis (Harding, 2018). It is called mean which calculated through dividing total value of the
sample with the number of observations. Further calculation mention below:
Formula: Mean = ∑X/N
= 959 / 10
= 95.9
Median: It is the middle value of the observations which evaluated for the further
analysis. There are two condition to identify middle value of the serious such as:
If values are even then N + 1 / 2
3

If values are odd then N / 2.
Formula: Median = [N+1] / 2
= [ 10 + 1 ] / 2
= 5.5th observation is 96
Mode: In context of statistics, mode is the value which frequently appear in the series. In
the available data of humidity level, 99 is the mode for this observation.
Range: It is the value which find after deducting maximum or minimum value of the
series. Further calculation of range is mention below:
Formula:
Range = Maximum Value – Minimum Value
= 100 - 91
= 9
Standard deviation: This term refer to the measurement of variation or dispersion of the
mean value. Lower the standard deviation is closed to the mean value and higher one is far from
the set of mean value (Mihas, 2019). Further calculation based on the collected data of Humidity
level of Bodmin city UK and it mention below:
Days Humidity Level (x) X-Mean (X-M)2
1 93 -2.9 8.41
2 100 4.1 16.81
3 99 3.1 9.61
4 94 -1.9 3.61
5 99 3.1 9.61
6 95 -0.9 0.81
7 97 1.1 1.21
8 91 -4.9 24.01
9 99 3.1 9.61
10 92 -3.9 15.21
Formula: Variance = [ ∑ (x–mean)2/N ]
= 98.9 / 10
= 9.89
4

Formula: Standard deviation = √Variance
= √ 9.89
= 3.14
4. Linear forecasting method
In order order to implement linear forecasting method, days will be consider as X and
humidity level consider as Y (Wasserman, 2018). It helps in future forecasting regarding weather
condition in the UK. Further calculation mention in the below table:
Step 1: Table formulation
Days (X) Humidity Level (Y) X2 XY
1 93 1 93
2 100 4 200
3 99 9 297
4 94 16 376
5 99 25 495
6 95 36 570
7 97 49 679
8 91 64 728
9 99 81 891
10 92 100 920
∑x= 55 ∑y= 959 ∑X2= 385 ∑XY= 5156
Step 2: Calculation of the value of M:
Formula:
M = [ N ∑XY - ∑x ∑y ] / [ N ∑X2 - (∑x)2 ]
= [ 10 * 5156 – (55 * 959) ] / [10*385- (55)2 ]
= [51560 – 52745] / [3850 – 3025]
= −1185 / 825
= -1.43
Step 3: Calculation of value of C:
Formula: C = {∑y - m ∑x} / N
5

= (959 – {-1.43 * 55}) / 10
= 1037.65 / 10
= 103.76
Step 4: Humidity on 15th day:
Formula: Y = mx + c
= -1.43 * 15 + 103.76
= −21.45+ 103.76
= 82.31
The level of humidity on 15th day will be 82.31.
Step 5: Humidity on 20th Day:
Formula: Y = mx + c
= -1.43 * 20 + 103.76
= −28.6 + 103.76
= 75.16
The humidity level on 20th day will be 75.16.
CONCLUSION
From the above observation it has been concluded that data analysis help the
organisations as well as individual to organize their data and present in appropriate way which
help the managers to take business regarding decisions. With the help of available information,
individual able to formulate strategy and policies regarding business.
6

REFERENCES
Books and Journals:
Dumuid, D. and et.al., 2018. Compositional data analysis for physical activity, sedentary time
and sleep research. Statistical methods in medical research. 27(12). pp.3726-3738.
Harding, J., 2018. Qualitative data analysis: From start to finish. SAGE Publications Limited.
Mihas, P., 2019. Qualitative data analysis. In Oxford Research Encyclopedia of Education.
Wasserman, L., 2018. Topological data analysis. Annual Review of Statistics and Its Application.
5. pp.501-532.
Online
Statistical data of Humidity. 2020. [Online]. Available through:
<https://www.timeanddate.com/weather/uk/bodmin/historic>
7