Comprehensive Report: Numeracy and Data Analysis of London Humidity

This report delves into the realm of numeracy and data analysis, employing statistical techniques to examine humidity data from London, United Kingdom, spanning ten days from December 2019. The report begins by presenting the data in both table and chart formats, followed by the calculation of descriptive statistical tools such as mean, median, mode, range, and standard deviation to derive insights from the dataset. Furthermore, the report utilizes a linear forecasting model to predict humidity levels for the 15th and 20th days, demonstrating the application of data analysis for future predictions. The report concludes by summarizing the key findings and emphasizing the effectiveness of numeracy and data analysis in interpreting large datasets and supporting informed decision-making. References to relevant academic sources are also included.

Numeracy and Data
Analysis

Contents
INTRODUCTION...........................................................................................................................1
MAIN BODY..................................................................................................................................1
Data in a table format..................................................................................................................1
Data in a Chart format.................................................................................................................1
Calculation of descriptive statistical tools...................................................................................2
Calculation of humidity level for day 15 and day 20..................................................................4
CONCLUSION................................................................................................................................5
REFERENCES................................................................................................................................6

INTRODUCTION
Numeracy and data analysis is a technique of analysing the information in a numerical
format (Chambers, 2017). Data analysis can assist an investigator to analyse a data which can
help in decision making by getting desired outcomes. This report has aim main of building an
understanding about data analysis and forecasting tools. In this report, various statistical tools are
used such as mean, median, mode, range and standard deviation in order to analyse the daily
humidity data of London, United Kingdom for ten days. Along with this, linear forecasting
model is also used to forecasting humidity level of 15th and 20th day.
MAIN BODY
Data in a table format
Humidity data for London, United Kingdom is procured for ten days starting from 05
December 2019 to 14 December 2019. This data is noted at 06:00 which is the first hour of the
day. This data is first procured and presented in a two column table below:
Date Humidity level
05-12-19 92%
06-12-19 91%
07-12-19 87%
08-12-19 71%
09-12-19 77%
10-12-19 77%
11-12-19 79%
12-12-19 75%
13-12-19 70%
14-12-19 57%
(Source: Humidity level in London, United Kingdom, 2019)
Data in a Chart format
Charts and graphs are the easier way to present a data set. Numeracy information presented
above is shown using histogram and bar graph below:
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Histogram
Bar graph
Calculation of descriptive statistical tools
Mean – It is a tool of measuring the average value of the whole data set. It is calculated by
comparing the total of values in a data set by total number of values in a data set. Mean for the
above information of humidity of London is calculated as follows:
Mean = Sum of the all values / Total number of the values
M = Σx/n
M = 776%/10
2

M = 77.6% or 78%
For the data set of humidity level, it has been ascertained that the London region of
United Kingdom has average humidity of 78%. This value is the centre value of the discrete set
of values presented in the table.
Median- It is the measure which is used to calculate the middle value of the whole data
set. It is calculated by dividing the total values of a data set from two so that middle position can
be determined (Zheng and et.al., 2017). For London’s humidity level, median is calculated
below:
Median = (n + 1)/2
= 5.5 position
Median = 77%
From the above determination, it has been ascertained that the middle value of the present
data set is 5.5 which is between 5th and 6th position. Both 5th and 6th position has 77% of humidity
level which means, this is the mid value of the whole data set.
Mode – This is the most recurring value from the data set. Mode helps an investigator to
find out the value from their information which repeats maximum times.
Mode of humidity level of London is 77% as this has repeated 2 times. This means 77% of the
humid level is the most recurring level in London.
Range – This is the statistical tool which helps in ascertaining the variation between the
maximum and minimum value of the data set. It helps in calculating the value which represents
the range by which values of the data set differentiates. For the information of London’s
humidity level, the range is calculated below:
Range = Maximum band value – Minimum band value
= 92% - 57%
= 35%
Standard deviation – This is the margin of error by which the values of the data set are
spread out from their mean (McGrath and et.al., 2019). This helps an investigator to observe the
margin by which the values of data set are spread from its mean. Standard deviation of the
present information is determined below:
Standard Deviations =√ (variance)
Variance 2 = {∑ (x – mean) / N}2
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= {∑ (x2 / N – (mean)2}
= {612% / 10 – (78%) 2}
= {61.2% – 60%}
= 1.2
Std. Dev. = √ 1.2
Standard deviation = 1.095
It is usually considered that if the standard deviation of a data set is more than “1”, the
values of the data set are not close to its mean which increase the standard margin of error. In the
context of present case, it can be said that the values of humidity level are not close to its mean
of 78%.
Calculation of humidity level for day 15 and day 20
Using the forecasting linear equation of y=mx+c, the humidity level of day 15th and 20th is
determined as follows.
Calculation of m value
Particulars Details
m NΣxy – Σx Σy / NΣ x^2 – (Σx)^2
(10 * 4019) – (55 * 776) / (10 * 385) –
(55)^2
(40190 – 42680)/ (3850 – 3025)
-3.018
Calculation of c value
Particulars Details
c Σy - m Σx / N
(776 – (-3.018 * 55))/10
-94.19
Forecasting humidity level for 15th day
Forecast of 15th day
y = mx + c
y -3.018 (x) +(-94.19)
x 15
4

y -3.018 (15) +(-94.19)
48.92
Forecasting humidity level for 20th day
Forecast of 20th day
y = mx + c
y -3.018 (x) +(-94.19)
x 20
y -3.018 (20) +(-94.19)
33.83
Linear forecasting model is an equation which helps in predicting the future values for
the present values which are in linear proposition (Fang and Lahdelma, 2016). From the above
forecasting model, it has been observed that the humidity level of 15th day which is 19th
December, 2019 is 48.92 and for the 20th day is 33.83.
CONCLUSION
After completion of the above report, it has been analysed that numeracy and data analysis is
a technique by which a big data can be analysed effectively. Tools of this data analysis are mean,
mode, median etc. which can assist an investigator in the process of decision making.
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REFERENCES
Books and Journals
Chambers, J. M., 2017. Graphical methods for data analysis: 0. Chapman and Hall/CRC.
Fang, T. and Lahdelma, R., 2016. Evaluation of a multiple linear regression model and SARIMA
model in forecasting heat demand for district heating system. Applied energy, 179.
pp.544-552.
McGrath, S. and et.al., 2019. Estimating the sample mean and standard deviation from
commonly reported quantiles in meta-analysis. arXiv preprint arXiv:1903.10498.
Zheng, S. and et.al., 2017. The relationship between the mean, median, and mode with grouped
data. Communications in Statistics-Theory and Methods. 46(9). pp.4285-4295.
Online
Humidity level in London, United Kingdom. 2019. [Online]. Available through:
<https://www.worldweatheronline.com/london-weather-history/city-of-london-greater-
london/gb.aspx >
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