Numeracy and Data Analysis: Interpreting Humidity Data of Durham

This report highlights how numeracy and different measures in statistics are used to interpret the humidity data of Durham, a city in the North East England. It includes the presentation of data in table format, two appropriate charts, calculation of different measures of central tendency and measures of dispersion, and calculation of different elements of linear forecasting model.

and Data Analysis
(Pass Criteria)

Contents
INTRODUCTION...........................................................................................................................3
TASK...............................................................................................................................................3
I. Presentation of data in table format..........................................................................................3
II. Presentation of data in two appropriate charts........................................................................4
III. Calculation of different measures of central tendency and measures of dispersion..............4
IV. Calculation of different elements of linear forecasting model..............................................7
.........................................................................................................................................................7
CONCLUSION................................................................................................................................8
REFERENCES................................................................................................................................9

INTRODUCTION
Numeracy refers to the knowledge, skill set that is required by individuals and/or group of
individuals to use the tools of mathematics in different situations. Data analysis is a process by
which the users of numeracy can interpret and analyse the results that have been calculated by
using different tools in numeracy (Relander, 2021). The following report highlights how
numeracy and different measures in statistics are used to interpret the humidity data of Durham,
a city in the North East England.
TASK
I. Presentation of data in table format.
Days
Humidity
level
1 85
2 83
3 83
4 99
5 96
6 82
7 97
8 80
9 77
10 88
870
Table 1: Presentation of the humidity data of the last 10 days of Durham

II. Presentation of data in two appropriate charts.
0 2 4 6 8 10 12
0
20
40
60
80
100
120
Humidity level
Graph 1: Presentation of the humidity data of the last 10 days of Durham using a line graph
1 2 3 4 5 6 7 8 9 10
0
20
40
60
80
100
120
Humidity Level
Graph 2: Presentation of the humidity data of the last 10 days of Durham using a column chart
III. Calculation of different measures of central tendency and measures of dispersion.
A. Mean
Mean of a data set is the value which is the average of all the observations present in the
data set. Following are the steps to calculate the mean of a data set:

Step 1: Identify all the observations in a data set.
Step 2: Addition of all the observations is done here.
Step 3: Determine the number of observation in the data set.
Step 4: Put the values determined in the formula and calculate the mean.
Mean = Sum of all the Observations / Number of Observations
Mean of the Humidity data of Durham = 85 + 83 + 83 + 99 + 96 + 82 + 97 + 80 + 77 + 88 / 10
= 870 / 10
= 87
B. Median
Median of a data set is the value which is coming in the exact middle of the data set. It is
usually determined after arrangement of data set into ascending or descending order (Goos, and
et.al., 2020). Following are the steps which are used in calculation of median:
Step 1: The first step in the calculation of median is to arrange the observations in data set in
ascending order.
Step 2: Calculate the number of observations either in odd or even.
Step 3: If the number of observation is even, the formula which will be used to calculate the
median is (N/2)
Step 4: If the number of observations is odd, the formula which will be used in calculation of
median is ( ( N + 1 ) / 2)
Step 5: After putting the observations, the resulted figure is the term which is the median of the
Observations.
85, 83, 83, 99, 96, 82, 97, 80, 77, 88
77, 80, 82, 83, 83, 85, 88, 96, 97, 99
Median of the Humidity data of Durham = 10 / 2
Median of the Humidity data of Durham = 5th Term
That is 83.
C. Mode
This measure of central tendency refers to the value which has been most occurred in a
data set. The steps to calculate mode of a data set is:
Step 1: Determine the data set and organise it.

Step 2: Check out all the distinct values.
Step 3: count all the values and their frequencies occurred in the data.
Step 4: The value with the most occurred value will be considered as the Mode of the given data
set.
77, 80, 82, 83, 83, 85, 88, 96, 97, 99
Mode of the Humidity data of Durham = 83 as it is occurring two times in the data set.
D. Range
Range refers to one of the measure of dispersion which is ascertained by the highest and
the lowest value of the data set (Grunau, 2020). Assuming the reach is little, it addresses the
central tendency and in the event that the reach is enormous then it doesn't address the central
tendency .
Following are the steps to calculate the Range:
Step 1: Arrange every one of the information accessible.
Step 2: Then identify the highest and the lowest value.
Step 3: Subtract the lowest value from the highest.
Step 4: The value we get after the third step is the Range.
Range = Maximum observation – Minimum Observation.
Range of the Humidity data of Durham = 99 – 77
= 22
E. Standard Deviation
It is the distance of the variable from the focal propensity, which is for the most part
mean (Kelleher, and Tierney, 2018). Following are the steps to calculate Standard deviation.
Step 1: Firstly we have to find the mean of the data given.
Step 2: For each observation find out the difference between the value and the mode of the data.
Step 3: Sum of all the values of step 2.
Step 4: Divide by number of terms (n).
Step 5: Finally, Square root the result of step 4.
Standard Deviation of the Humidity data of Durham= √ (xi – μ) 2 / N
= √ (568.4) / 10
= √ 56.84

= 7.53
IV. Calculation of different elements of linear forecasting model.
Linear Forecasting Model predicts the Future Values based on the past values in a linear
y = mx + c
where, 'y' is the dependent variable
'mx' is the independent variable
'c' is the constant
Following are the steps to calculate m:
Step 1: Multiply both the factors X and Y which are named as days and moistness
Step 2: Do the addition of the above computation.
Step 3: Sum of the x variable and y factor individually.
Step 4: Then multiply both the factors.
Step 5: Calculate ( x)⅀ 2 at the end put the values in the formula.
Step 6: The resultant value is the value of 'm'.
m = (10 * 4760) – (55 * 870) / (10 * 385) – (385)
m= 47600 – 47850 / 3850 – 385
m = −250 / 3465
m= −0.072
Following are the steps to calculate C:
Step 1: First of all, calculate the sum of 'y' variable.
Step 2: Then figure out the sum of 'x' variable.
Step 3: Finally divide it with the sum of 'N'.
Step 4: The value derived from Step 3 is the value of 'c'.

C= 870 - (−0.072 * 55) / 10
C= 870 - (−3.96) / 10
C= 873.96 / 10
C= 87.396
Humidity on Day 11th :
m= −0.072, c= 87.396, x= 11
y= mx + c
y = -0.072 * 11 + 87.396
y= −0.792 + 87.396
y= 86.604
Humidity on 11th Day = 86.604 %
Humidity on Day 13th :
m= −0.072, c= 87.396, x= 13
y= mx + c
y = -0.072 * 13 + 87.396
y= −0.936+ 87.396
y= 86.46
Humidity on 11th Day = 86.46 %
CONCLUSION
From the above calculation, it can be concluded that the numeracy and data analysis is a major
aspect in the field of study. Individuals need to maintain full knowledge about the tools of
statistics as these helps in the analysing and interpretation of the data when presented. The above

report has taken the humidity data of Durham to use different tools and interpret how these are
used to analyse the data.

REFERENCES
Books and Journals
Relander, A., 2021. Reliability and validity evidence of numeracy measures used in elementary
school for children aged 9 to 12 Years: A Systematic Review.
Goos, M., and et.al., 2020. Numeracy across the curriculum: Research-based strategies for
enhancing teaching and learning. Routledge.
Grunau, P., 2020. Literacy and numeracy of overeducated and undereducated workers: revisiting
the allocation process in the labour market. Education Economics. 28(4). pp.403-417.
Kelleher, J.D. and Tierney, B., 2018. Data science. MIT Press.