Data Analysis and Forecasting: Kuala Lumpur Wind Speed Project

This project analyzes wind speed data from Kuala Lumpur over a 10-day period. The analysis begins with presenting the data in tabular format and is followed by various chart presentations, including column, line, and bar charts for visual representation. Descriptive statistics such as mean, median, mode, range, and standard deviation are calculated to provide insights into the data. The project then employs a linear forecasting model (Y=mX+c) to forecast wind speeds for days 14 and 21. The steps for calculating the values of 'm' and 'c' are detailed, and the predicted wind speeds for the specified days are provided. The report concludes that data analysis and numeracy are crucial for analyzing and forecasting data, as demonstrated by the wind speed analysis project, offering visual and tabular representations. The project provides a comprehensive approach to data analysis, incorporating various statistical techniques and forecasting models to predict future wind speeds.

DATA ANALYSIS AND
FORECASTING

Declaration form
I declare that this project is all my own work and that I have acknowledged all materials used
from the published or unpublished works of other people. Al references have been duly cited.

TABLE OF CONTENTS
INTRODUCTION...........................................................................................................................1
MAIN BODY..................................................................................................................................1
1) Presentation of data in table format.........................................................................................1
2) Chart presentation....................................................................................................................1
3) Calculation of values...............................................................................................................3
4) Use linear forecasting model...................................................................................................4
a) Steps for calculating value of m..............................................................................................5
b) Steps for calculating value of c...............................................................................................5
c) Forecasting m and c value for day 14 and 21..........................................................................5
CONCLUSION................................................................................................................................6
REFERENCES................................................................................................................................7

INTRODUCTION
Numeracy and data analysis is used as an important tool for practically using mathematical
equations and calculating them to obtain solutions which can be further used for numerical
analysis and forecasting (Rousselet and Wilcox, 2019). This report will present a brief analysis
of wind speed data for 10 days of Kuala Lumpur which is gathered through online source and
analysis will be presented in tubular form following line chart and pie chart for visual
presentation. Mathematical tools like mean, median, mode, range and standard deviation will be
devised to gain descriptive information for further forecasting and analysis. Followed by linear
forecasting model which is Y=mX+c for calculation and further forecasting for day 14 and 21.
MAIN BODY
1) Presentation of data in table format
Day Wind speed (in miles/hour)
1 2 m/h
2 7 m/h
3 8 m/h
4 7 m/h
5 6 m/h
6 7 m/h
7 6 m/h
8 5 m/h
9 5 m/h
10 6 m/h
2) Chart presentation
(a) Column and line chart
1
0 DayWi nd speed( in m il es/hou r)

Figure 1Column chart
Figure 2line chart
(b) Bar chart
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3) Calculation of values
There are certain formulas and steps that can be used to calculate values in spreadsheet,
these are as follows:
Mean=5.9
Mean can be defined as statistical average of all numbers. It is generally calculated by
adding all number sand dividing it with total number (Kosmidis, Pagui and Sartori, 2018).
Mean= sum of all numbers/ total numbers
=2+7+8+7+6+7+6+5+5+6=59
=59/10
=5.9
Median=3.5
Median can be defined as middle value of existing data set (Dubey, Singh and Singh,
2019).
(N+1)/2 th item
3

2,5,5,6,6,6,7,7,7,8
Median=6+1/2
Median=7/2
=3.5
Mode = 6
It is the value which appears in data set most often (Moran and et.al., 2019).
Mode = l + h [(f1-f0)/(2f1-f0-f2)]
Range = 6
It is the data set which shows difference between largest value of available data set and
small value in this set (Rousselet and Wilcox, 2019). When maximum value is subtract from
minimum value then individual can get range value.
Range= max (value) – min (value)
8-2
=6
Standard deviation=1.663329993
Standard deviation is the most commonly used statistical tool which is used by person to
show the vary things in group or the extent person vary in group (Kosmidis, Pagui and Sartori,
2018). If there is high vary level then value of standard deviation go high whereas if it is small
them SD get low. There is need to have clear difference between SD of population and sample.
Here X shows population mean
Xi shows population’s element
N= number of element
It can be analysed from statistics that mean value of wind speed is 5.9 m/h. The median
value is 6 m/h. However, mode of data is 6 (Dubey, Singh and Singh, 2019). Range is 6 and
standard deviation is 1.663329993.
4) Use linear forecasting model
Days Days (x)
Wind speed (in
miles/h) XY X^2
04/10/19 1 2 2 1
05/10/19 2 7 14 4
4

06/10/19 3 8 24 9
07/10/19 4 7 28 16
08/10/19 5 6 30 25
09/10/19 6 7 42 36
10/10/19 7 6 42 49
11/10/19 8 5 40 64
12/10/19 9 5 45 81
13/10/19 10 6 60 100
Total 55 59 327 385
a) Steps for calculating value of m
Particulars Details
m NΣxy – Σx Σy / NΣ x^2 – (Σx)^2
(10 * 327) – (55 * 59) / (10 * 385) – (55)^2
(3270-3245) / (3850-3025)
0.03
b) Steps for calculating value of c
Particulars Details
c Σy - m Σx / N
(59 – (0.03 * 55))/10
5.74
c) Forecasting m and c value for day 14 and 21
Forecast of 14th day
Y = mX + C
Y 0.03 (X) + 5.74
X 14
5

Y 0.03 (14) +5.74
6.16
Forecast of 21th day
Y = mX + C
Y 0.03(X) + 5.74
X 21
Y 0.03 (21) + 5.74
6.37
CONCLUSION
From this report, it can be concluded that numeracy and data analysis are very significant for
analysing any details as it has shown with regard to wind speed using wind speed data of Kuala
Lumpur. This report displays wind speed and for avoiding any argument there is representation
of visual and tabular format. Moreover, it has provided descriptive statistics of data set and
reflected application of linear forecasting model where on day 14th wind speed will be 6.16 m/h
and on 21st it will be 6.37 m/h.
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REFERENCES
Dubey, S., Singh, A.K. and Singh, M.K., 2019, July. Speech Enhancement using Adaptive Mean
Median Deviation and EMD Technique. In 2019 IEEE International Conference on
Signals and Systems (ICSigSys) (pp. 34-39). IEEE.
Kosmidis, I., Pagui, E.C.K. and Sartori, N., 2018. Mean and median bias reduction in
generalized linear models. Statistics and Computing. pp.1-17.
Moran, L.and et.al., 2019. Carpal Tunnel Syndrome: Diagnostic Usefulness of Ultrasound
Measurement of the Median Nerve Area and Quantitative Elastographic Measurement of
the Median Nerve Stiffness. Journal of Ultrasound in Medicine.
Rousselet, G.A. and Wilcox, R.R., 2019. Reaction times and other skewed distributions:
problems with the mean and the median. Meta-Psychology.
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