Numeracy and Data Analysis: Forecasting London Humidity - CCCU Report

This assignment provides a detailed analysis of London's humidity levels using various statistical methods. It includes the presentation of humidity data in tabular and chart formats, followed by the calculation of measures of central tendency (mean, median, mode), range, and standard deviation, with a step-by-step explanation of each calculation. The assignment further applies a linear forecasting model to predict humidity levels for future days, demonstrating the calculation of 'm' and 'c' values and their use in forecasting. The report concludes by summarizing the findings and emphasizing the role of data analysis tools in decision-making. Desklib offers a wide array of study resources, including similar solved assignments and past papers, to support students in their academic endeavors.

Numeracy and data
Analysis-Individual
assignment

Contents
INTRODUCTION...........................................................................................................................1
MAIN BODY...................................................................................................................................1
1. Presentation of data related to Humidity of London in table format..................................1
2. Representation of data into appropriate charts...................................................................1
3. Determining the measures of central tendency, range and standard deviation while
mentioning the steps of same.................................................................................................2
4. Answering the following questions using the Linear Forecasting Model..........................4
a) Calculate the value of m by writing down all the steps......................................................4
b.) Value of C with the following steps..................................................................................4
c.) Finding value of Day 11 and Day 13................................................................................5
CONCLUSION................................................................................................................................5
REFERENCES................................................................................................................................7

INTRODUCTION
Numeracy and data analysis includes interpretation of the statistics which help to make
further decisions. This report contains the humidity level of past few days of London, city in UK.
In this there are four sections. First section includes the presentation of humidity level in the
table format and section two includes the representation of data in chart style. Measures of
central tendency, range and standard deviation is calculated and its step by step procedure is
shown in section three. Values of C and M is computed in the section four.
MAIN BODY
1. Presentation of data related to Humidity of London in table format
DAY Humidity level
1 88
2 91
3 91
4 95
5 92
6 94
7 92
8 87
9 93
10 90
913
2. Representation of data into appropriate charts
1 2 3 4 5 6 7 8 9 10
0
10
20
30
40
50
60
70
80
90
100
DAY
Humidity level
1

1
2
3
4
5
6
7
8
9
10
3. Determining the measures of central tendency, range and standard deviation while mentioning
the steps of same
 Mean- It is a measure of central tendency used in finding average of the given set of
numbers. This considers all the data and affected by the extreme values. It is also used in
further algebraic treatment (Avvisati and Givord, 2021)
Steps for calculating mean can be discussed as given below-
Step-1: Take all the given numbers and add the same
Step-2: Find out the average of data by dividing with n numbers.
Formula for finding mean-
=Sum of observations/ total number of observations
=913/10
=91.3
 Median- It can be described as the middle value of all the given set of data which can be
arranged either in ascending or descending order. It is not necessary that the value
computed must present in the data set.
Procedure for calculating median are as follows-
Step-1: Sort all the data in ascending or descending order
Step-2: From the arranged data if number of observations are odd then the mid value will be
median (McFadden, Viskupic and Egger, 2021).
Step-3: In case of even observations apply the formula n+1/2
computation for finding Median-
Median=n+1/2
Data in ascending order- 87,88,90,91,91,92,92,93,94,95
= taking 5th and 6th term (as data is even number of observations)
=91+92/2
=91.5
 Mode- It is the most frequently occurring value in the data and also known as fashionable
value. Data can have unimodal and bimodal or ill mode. Unimodal refers to data having
one mode but if in a given series there are more than one mode it is known as ill mode.
Steps for calculating mode-
Series- 87,88,90,91,91,92,92,93,94,95
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step 1: By using inspection method observe the given series
Step 2: choose the number which is having highest frequency of occurrence.
Step 3: From the above data it can be analysed that it is having bimodal because 91 and 92 is
appearing two times.
Hence, mode will be 91 and 92.
 Range- In statistics range is the difference between highest value and lowest value in a
given series. It is a measure of dispersion used to find out the class interval between the
given data.
Steps for calculating range-
Step-1: Arrange the given input in from low to high order.
Step-2: Deduct lowest value in a range from the highest value.
Range = highest value - lowest value
=94-87
= 7
 Standard deviation- It is a measure of dispersion used to find out the how much mean
is dispersed from the actual data. It is an absolute measure of dispersion and calculated by
taking mean (Ramli and Zulminiati 2021).
Steps for calculating standard deviation-
Step 1: Firstly, calculate mean of the given data
step 2: Subtract mean from humidity of different cities.
Step 3: square the difference of mean and humidity level (Tverdostup and Paas, 2019)
Formula for calculating SD-
Standard Deviation = √ (xi – μ) 2 / N
= √56.1/10
= 7.48/10
= 0.7489
4. Answering the following questions using the Linear Forecasting Model.
a) Calculate the value of m by writing down all the steps.
Steps for calculating m value-
Step-1: find product of x and y
Step-2: sum the values of x and y
Step-3: multiply n with submission of x and y
step-4: subtract sum of product x and y from step-3
step-5: multiply n with sum of squares of x
step-6: deduct sum of twice x from step-5
n=Number of days
y=humidity of London city
x=day
= 10(5024)-55*913/10*385-385
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=50240-50215/3850-385
=25/3465
= 0.00721
b.) Value of C with the following steps
Steps for calculating c value
step-1: Find the sum of y
Step-2: Multiply value of m with sum of x
Step-3: Deduct step2 from step1
step-4: Divide step 3 by value of n
=913-0.00721*55/10
=913-0.39655/10
=912.6035/10
=91.26035
c.) Finding value of Day 11 and Day 13
finding values of day 11 and 13 -
Equation-
Day 11-
y=mx+c
y=0.00721*11+91.26035
=91.33966
Day 13-
y=mx+c
y=0.00721*13+91.26035
=91.35408
CONCLUSION
The above report shows the humidity level of London and its analysis by calculating
measures of central tendency, range and standard deviation. Data analysis tools helps to
make interpretations for reaching to a particular point. Hence, these tools assist in decision
making process.
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REFERENCES
Books and Journals
Avvisati, F. and Givord, P., 2021. The learning gain over one school year among 15-year-olds:
An analysis of PISA data for Austria and Scotland (United Kingdom).
McFadden, R.R., Viskupic, K. and Egger, A.E., 2021. Faculty self-reported use of quantitative
and data analysis skills in undergraduate geoscience courses. Journal of Geoscience
Education, 69(4), pp.373-386.
Ramli, C.P. and Zulminiati, Z., 2021. Pengaruh video animasi terhadap kemampuan berhitung
anak di tk pertiwi iv talawi. Edukids: Jurnal Pertumbuhan, Perkembangan, dan
Pendidikan Anak Usia Dini, 18(2), pp.117-123.
Tverdostup, M. and Paas, T., 2019. Immigrant–native wage gap in Europe: the role of cognitive
skills and their use at work. International Journal of Manpower.
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