Data Analysis and Forecasting Assignment - LSC University of Suffolk

This assignment analyzes humidity data for Leeds, UK, from November 1st to 10th, 2019. It presents the data in tabular and graphical formats, including bar and column charts. The core of the analysis involves calculating mean, median, mode, range, and standard deviation. The document demonstrates the application of these statistical tools to understand the data's central tendencies and variability. Furthermore, it uses the data to forecast humidity levels for the 14th and 21st days of November, employing calculations of 'm' and 'c' within a linear equation (y = mx + c). The conclusion emphasizes the value of data analysis in recognizing trends and evaluating interpersonal criteria. Desklib offers a platform for students to access past papers and solved assignments like this one.

Numeracy
&
Data Analysis

Table of Contents
INTRODUCTION...........................................................................................................................1
MAIN BODY...................................................................................................................................1
1. Representation of data in tabular form:..............................................................................1
2. Dara representation in charts:.............................................................................................1
3. Calculations of mean, median, mode, standard deviation and range:................................3
4. Calculating values of m, c and wind forecast of day 14 and 21:........................................5
CONCLUSION ...............................................................................................................................6
REFERENCES................................................................................................................................7

INTRODUCTION
Data analysis implies to processes are related to set of numerous theoretical and practical
methods that relate to enhancing data reliability and subjectivity. This also includes visualization
of business data and information in charts, graphs and other visual content with aim to effective
analysis (Chen and Yang, 2015). The assessment study provides explanation about various
aspects of data analysis by taking data of humidity for 10 consecutive days of Leeds, UK
(Humidity Data of Leeds. 2019).
Also some key topics like mean-mode-median, standard deviation, ranges etc. are applied
to critically analysis of respective data. Further though computation of m and c, a forecast about
humidity on 14th and 21st day has been made.
MAIN BODY
1. Representation of data in tabular form:
Here below table contains data of Humidity Percentage of period of 1st Nov.2019 to 10th
Nov.2019 (10consecutive days) of Leeds (Humidity Data of Leeds. 2019), as follows:
Days (Date) Humidity Percentage
1 Nov 2019 100
2 Nov 2019 91
3 Nov 2019 100
4 Nov 2019 99
5 Nov 2019 97
6 Nov 2019 92
7 Nov 2019 98
8 Nov 2019 83
9 Nov 2019 93
10 Nov 2019 76
1

2. Dara representation in charts:
Bar Graph: It is simple graph which present data is generally presented in horizontal bars to
define the increase in decrease in levels or percentage change.
2

Column Chart: This is common chart in which standard columns are used to represent the
levels and percentage changes in data and each column exhibits trend during specific period
(Marks, 2015).
3
1 Nov 19
2 Nov 19
3 Nov 19
4 Nov 19
5 Nov 19
6 Nov 19
7 Nov 19
8 Nov 19
9 Nov 19
10 Nov 19
0
10
20
30
40
50
60
70
80
90
100
Humidity percentage
01 November 2019
02 November 2019
03 November 2019
04 November 2019
05 November 2019
06 November 2019
07 November 2019
08 November 2019
09 November 2019
10 November 2019
0 10 20 30 40 50 60 70 80 90 100
Humidity percentage

Analysis: As per above graphical representation of humidity level of Leeds during 10 days it has
been analysed that at 1st and 3rd day humidity percentage reached to 100% which is maximum
while on 10th day humidity level was minimum i.e. 76%.
3. Calculations of mean, median, mode, standard deviation and range:
Days (Date) Humidity Percentage
1 Nov 2019 100
2 Nov 2019 91
3 Nov 2019 100
4 Nov 2019 99
5 Nov 2019 97
6 Nov 2019 92
7 Nov 2019 98
8 Nov 2019 83
9 Nov 2019 93
10 Nov 2019 76
∑ X 929
Mean 92.9
Median 94.5
Mode 100
Range 24
Maximum range 100
Minimum 76
4

Mean: It denotes to simple average of data selected with aim to provide an average figure of
data chosen. In this measure all figures are totalled and divided by total number of figures
(Gatobu, Arocha and Hoffman-Goetz, 2016).
Formula of mean:
∑x / N
= 929 / 10
= 92.9
Mode: It simply indicates a figure which is frequently repeated among the data selected or most
repetitive figure. As in case of selected data of humidity for 10 days, 100 percent humidity has
been occurred maximum time i.e. 2 times on 1st and 3rd day thus Mode of selected data would be
100.
Median: It relates to determination of mid value or figure in aggregate selected data. Normally it
is figure which exist exactly in mid of arranged data in specific series.
So, formula of median is:
Where data series is even = ( N +1 ) / 2
Where data series is odd = ( N / 2)
Here 10 days are selected i.e. even number therefore median value would be 10 / 2 = 5.5
thus medium would be average of 97 and 92 i.e. 94.5
Range: It is regarded as specific boundaries of selected figures or data which represented as
difference between maximum and minimum value.
Formula of range:
Max – Min
= 100 - 76
= 24
Standard deviation: This is a statistical tool to assess and find out absolute variableness of
selected distribution. The higher the variability/dispersion, the higher is the standard-deviation
and higher would be deviation's magnitude of value though assessed mean (Figueres-Esteban,
Hughes and Van Gulijk, 2015).
Days (Date) Humidity Percentage x- mean (x-m)2
1 Nov 2019 100 7.1 50.41
5

2 Nov 2019 91 -1.9 3.61
3 Nov 2019 100 7.1 50.41
4 Nov 2019 99 6.1 37.21
5 Nov 2019 97 4.1 16.81
6 Nov 2019 92 -0.9 0.81
7 Nov 2019 98 5.1 26.01
8 Nov 2019 83 -9.9 98.01
9 Nov 2019 93 0.1 0.01
10 Nov 2019 76 -16.9 285.61
568.9
Mean 92.9
Variance 56.89
STDEV 7.5425459893
Formula of standard deviation: √ ( variance )
Variance = [ ∑(x – mean) 2 / N ]
= 568.9 / 10
Thus variance = 56.89
Standard deviation is √ 56.89
Standard deviation = 7.5425459893
4. Calculating values of m, c and wind forecast of day 14 and 21:
Days Humidity percentage X2 ∑xy
1 100 1 100
2 91 4 364
3 100 9 900
4 99 16 1584
6

5 97 25 2425
6 92 36 3312
7 98 49 4802
8 83 64 5312
9 93 81 7533
10 76 100 7600
∑x= 55 ∑y= 929 ∑X2=385 ∑xy=33932
Form above computations summarised in table, following are the steps to find out the value of
“m” in equation which is y = mx + c , as follows:
1. Compute value of M:
M = N * ∑xy - ∑x * ∑y / N*∑x2 - ( ∑x )2
= 10*33932 – 55*929 / 10*385 – (55)2
= 339320- 51095/ 3850- 3025
= 288225 / 825
= 349.3636 or 349
2. Computation of value of c: ∑y- m ∑x/ N
= 929 – 349.36*55 /10
= -992.5
3. With assistance of above assessed value of 'm' and 'c' values, humidity forecast would be, as
follows:
Forecast wind for 14 day Y= mx+c
Y= 349.36*14+(-992.5)= 3898.54
= So the forecasted figure of day 14 is 38.98 percent or 39 percent.
Forecast wind for 21: Y= mx+c
Y= 349.36*21+(-992.5)= 6344.06
= Thus the humidity for day 21st could be around 63.44 percent or 63 percent.
7

CONCLUSION
From above assessment it has been articulated that data is developed and defined to
recognize and evaluate functional characteristics and trends that help in the evaluation of simple
interpersonal criteria. Also application of different statistical approaches and concepts such-as
standard-deviation, mean average, mode value, ranges and median value provide an assistive
framework to achieve efficient analysis of classified or non classified data.
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REFERENCES
Books and Journals:
Chen, Y. and Yang, Z. J., 2015. Message formats, numeracy, risk perceptions of alcohol-
attributable cancer, and intentions for binge drinking among college students. Journal of
drug education. 45(1). pp.37-55.
Figueres-Esteban, M., Hughes, P. and Van Gulijk, C., 2015, September. The role of data
visualization in railway big data risk analysis. In Proceedings of the 25th European
Safety and Reliability Conference, ESREL 2015 (pp. 2877-2882). CRC Press/Balkema.
Gatobu, S. K., Arocha, J. F. and Hoffman-Goetz, L., 2016. Numeracy, health numeracy, and
older immigrants’ primary language: an observation-oriented exploration. Basic and
Applied Social Psychology. 38(4). pp.185-199.
Marks, G. N., 2015. School sector differences in student achievement in Australian primary and
secondary schools: A longitudinal analysis. Journal of School Choice. 9(2). pp.219-238.
Online
Humidity data of leeds. 2019. [Online]. Available through:
<https://www.timeanddate.com/weather/uk/leeds/historic?month=11&year=2019>
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