Numeracy and Data Analysis: A Report on Bristol Temperature Trends

This report presents a numeracy-based analysis of Bristol's temperature data over a 10-day period. The data is displayed in tabular and graphical forms, including bar and line charts. The report calculates measures of central tendency such as mean, median, mode, and range, along with standard deviation, to understand the distribution and variability of temperatures. Furthermore, linear forecasting is applied to predict temperatures for subsequent days, critically analyzing the method and its assumptions. The analysis provides a systematic approach to understanding temperature trends in Bristol, utilizing various descriptive statistical measurements. Desklib provides access to similar solved assignments and past papers for students.

Numeracy Data
Table of Contents

INTRODUCTION...........................................................................................................................3
MAIN BODY...................................................................................................................................3
1. Data of Bristol Country temperature presented in the table ...................................................3
2. Data is represented through graphs in the below: ..................................................................3
3. Calculate the measure of central tendency by using the above information and explain with
the steps ......................................................................................................................................5
4. Critically analyse the terms of the M and C and note down the steps to be considered for
calculating the day of 11 and 14 temperature.............................................................................8
CONCLUSION................................................................................................................................9
REFERENCES..............................................................................................................................11

INTRODUCTION
Numeracy is the capacity to know the the numerical concepts or to apply the skills in
practical way. It is the composition of the basic arithmetical operations and the analysis of the
data. It is the research of the collection of analysis, presentment, interpretation and the
arrangement of data. It is the part of mathematics to concise the information. IN this there are
two main concepts included in the statistics which are variation and uncertainty. The data has
been chosen for the Bristol city of the United Kingdom (Hilton and Hilton, 2021). The
information is considered of the 10 days temperature of the city. There are several measures that
is computed from the table such as the standard deviation and the central tendency tools.
MAIN BODY
1. Data of Bristol Country temperature presented in the table
Date Temperature
1 18
2 19
3 16
4 20
5 22
6 16
7 15
8 14
9 20
10 19
Total 179

2. Data is represented through graphs in the below:
Bar chart:
Line chart:

3. Calculate the measure of central tendency by using the above information and explain with the
steps
1. Mean: It is the most common method used to estimate the data. It is generally means the
sum of all the numbers and it is divided by the total number of items. It is the average of
the acknowledged number of values. It showcase the distribution of numbers for the
predetermined data in the equal proportion (Kaye, 2018). It is key indicator that is
utilized to criterion the performance in the time period. It is very simple method to
compute the amount. It is widely used in the algebra numericals
= 179/10
= 17.9

2. Median: It is the central value in the given number of observations. First of all the data is
arranged in the increasing or the decreasing order. It is related to segregate the half lower
number of data and the half increasing number of data. It is generally used as a standard
of central tendency. It is not inclined by minor proportion of the larger or smaller values.
It is used to represent the complex data in the simplest form. It is measured differently
for the even or the odd numbers (Papic, Mulligan, and Highfield, 2020) . This
information is used for skewed distribution for analysis of the values.
M = ( n+1)/2th term
If the data is given in the even form used this formula:
M= [(n/2 + 1) th term + (n/2) th term]/ 2
= 10/2
= 5
= [ 5th + 6th ] term/ 2
= [ 22+16] / 2
= 38/2
= 19
3. Mode : It is the value that is repeated many times in the given data. It consist of all the
observations that incurs at the maximum time. It is highest number of frequency from the
particulars of the data. There are more than one modals value in the data. It is bifurcated
in to the grouped or ungrouped data. For calculating the values in the ungrouped data the
terms is arranged in descending or ascending order(Sikand, 2018). Through in this the
highest number of repeated frequency is choose from the input of the table. The list can
be single mode, bimodal, trimodal or multiple models.

In the given table the mode is = 16 & 20
4. Range: The range is measured by the difference between the highest and the lowest figure
during a point period of time such as the day, year and the month. It is denoted as the
identical units in the data. The descriptive statistics used range as the smallest quantity
which consist all the information and gives the sign of the statistical dispersion. It define
the relation between the two variables (Tanner, Jones, and Davies, 2020). It is used to
present the scattering of small set of data. It is used to know the pattern of changes in the
price and the variations in the risk level of figures.
= 22 – 14
= 8
5. Standard deviation: It is used to ascertain the changes in the standard or the actual
results. It refers to find out the variations in the mean value. It is used to find out the
severe figures from the dataset that has varied from the average value. It is determined to
spreading the figures in the number set. It is the difference of the single figures from the
deviated amount. It has the two affect low standard deviation show the figures is close to
value of the mean where as the high deviation denotes that the figures will be spread on
the broader range.

Standard deviation = √∑ (xi – μ) ^ 2 / N
= √17.9/10
= √1.79
= 1.337
4. Critically analyse the terms of the M and C and note down the steps to be considered for
calculating the day of 11 and 14 temperature.
Linear forecasting: It defines the relationship between the two variables. It is related to use the
historic data to present in the time series. It helps to predict the future and good method for
forecasting the data. It is used to estimate the projected values from the historical information. It
is the type of quantitative measurement to understand the changes in trend when cost are
exaggerated. It helps to reduces the length among the prices and it strategise the trend line of
every data.
Y = MX + C
= [( 10*977) - (55*175)] / (10*385) - (55*)^2
= [ 9770- 9625] / 3850- 3025
= 145/ 825
= 0.175

= {175- 0.175( 55)}/ 10
= [ 175 – 9.625} / 10
= 165.375/10
= 16.53
Temperature of day 11
M = 0.175
X = 11
C = 16.53
= 0.175*11+16.53
= 18.45
Temperature of day 14:
X = 14
= 0.175*14+16.53
= 18.93
CONCLUSION
It has been found from the above report that the data of 10 days temperature of Bristol
city of the UK is taken. The input of data is presented in the tabular form along with the days and
the temperature values. The charts and bar graphs is used to state the Bristol temperature. In the
next there is the calculation of the mean, mode, median and various tools of the descriptive
statistical measurement to furnish the information in the proper systematic way. The company ha
s made several assumptions to utilize the additional days temperature.

REFERENCES
Books and Journals
Hilton, A. and Hilton, G., 2021. Making Sense of Number: Improving Personal Numeracy.
Cambridge University Press.
Kaye, D., 2018. Defining adult and numeracy: An academic and political investigation.
In Contemporary Research in Adult and Lifelong Learning of Mathematics (pp. 11-37).
Springer, Cham.
Papic, M., Mulligan, J. and Highfield, K., 2020. Numeracy. In Teaching Early Years (pp. 86-
106). Routledge.
Sikand, M.V., 2018, December. Numeracy among Under Represented Minorities in Community
College Education. In AGU Fall Meeting Abstracts (Vol. 2018, pp. ED41C-1122).
Tanner, H., Jones, S. and Davies, A., 2020. Developing numeracy in the secondary school: a
practical guide for students and teachers. Routledge.
(Hilton and Hilton, 2021) (Kaye, 2018) (Papic, Mulligan, and Highfield, 2020) (Sikand, 2018)
(Tanner, Jones, and Davies, 2020)