Wind Speed Data Analysis, Linear Forecasting Model & Regression

This report provides a detailed analysis of wind speed data for a specific country, focusing on numeracy and data analysis techniques to support future decision-making. The analysis includes calculating the mean, median, mode, range, and standard deviation of the wind speed data. Furthermore, the report employs a linear forecasting model to estimate future wind speed values based on historical data, utilizing regression analysis to determine the constants 'c' and 'm' in the linear equation. The study concludes by forecasting wind speeds for specific future days, demonstrating the practical application of the data analysis and forecasting methods. The report uses statistical tools to formulate the values in the given set of data.

And data analysis

Table of Contents
INTRODUCTION...........................................................................................................................1
MAIN BODY...................................................................................................................................1
1. Formulate the Data of the country:.........................................................................................1
2. Information of Wind Speed data of the nation in a Chart Format:.........................................1
3.There are several types of data analysis are as follows:...........................................................2
4. Use Linear forecasting model do regression analyse and find the value of c and m..............4
CONCLUSION................................................................................................................................6
REFERENCES ...............................................................................................................................7

INTRODUCTION
The above report discuss about the country wind speed which helps in determining
numeracy and analysis of data of the country to identify various needful details for future
decision making. In which there are many ways to determine the data in numeracy and the
formulation of mean, median, mode, standard deviation and range are take place(Dong and et.al.,
2019). It also involve linear forecasting theory which helps to find out the future estimated
values of past years. The components of data analysis are equipments of statistics which assist to
formulate the values in given set of data.
MAIN BODY
1. Formulate the Data of the country:
2. Information of Wind Speed data of the nation in a Chart Format:
1

3.There are several types of data analysis are as follows:
Mean: This value is very helpful for determining the average amount from a given set of
a particular data information(Fischer and Scholz-Böttcher, 2019). This is formulated by dividing
the overall set of all the data given by the number of information.
There are some following points which are take place for measuring the mean:
Point 1: Gather the given set of data.
Point 2: Formulate the overall set of data.
Point 3: Formulate the total value of data.
Point 4: divide the sum of all data with total number of information.
Mean of Wind Speed = Sum of data set / Total number of data set
Mean = 190 / 10
Mean = 19
Median: The amount of median identify by sorting all the given set of data in ascending
or descending orders. It is also known as the mid-value of the data set(Lin and Tsai, 2020). There
are tow different ways to calculate the median. Which is based on the amount of median.
Following points to formulate the amount of median:
Point 1: Manage the data in ascending or descending order.
Point 2: Count the value in given set of data.
Point 3: Identifying the value of median whether it was even or odd.
2
1 2 3 4 5 6 7 8 9 10
0
5
10
15
20
25
30
35
Wind Speed

Point 4: If the stage of the median is odd then the calculation is (N+1)/2.
Point 5: If the “n” vale of median is even then the formula put in median is normal.
Median Value (even) = N/2
Median Value (odd)= (N+1)/2
Median of wind speed are as follows:
17,31,34,32,21,13,15,11,7,9
7,9,11,13,15,17,21,31,32,34
Median = 5.5th position
Median = 16
Mode = The amount of mode is determining by observing the increasing outputs of a
individual number(Mavi, Saen and Goh, 2019).
Here are some following steps of mode:
Point 1: Give position to the values in a form of ascending or descending order.
Point 2: Determine the given data.
Point 3: Find the value which comes from the data sheet.
Point 4: After that select the higher outputs value.
7,9,11,13,15,17,21,31,32,34
Mode = There is no repetitive number in the above series so, hence there is no mode.
Range: It is the amount of range which is derived from deducting the less value of data
from the higher value of data.
There are some following ways to formulate the value of range:
Point 1: Manage the information in appropriate way.
Point 2: Choose the higher and Lower amount.
Point 3: Formulate the distinguish between the higher and lower value.
Point 4: At the last. The amount of rage is formulated.
Measure the range of wind speed:
Range = maximum value – Minimum value
= 34 -7
Range = 27
3

Standard Deviation: In this method of standard deviation this value helps in measuring
the value of spread data in comparison to the amount of mean(Navarro-Reig, Jaumot and Tauler,
2018).
Following ways to formulate the value of mean ;
Point 1: Firstly, identify the amount of mean.
Point 2: To formulate every given amount deviation from mean.
Point 3: Then measure the overall sum of squares.
Point 4: Afterwards it divide by the overall amount of data.
Point 5: At the end, take square of the above formulated amount.
Standard Deviation= √∑ (xi – μ) 2 / N
= √906 / 10
Standard Deviation = 9.51
4. Use Linear forecasting model do regression analyse and find the value of c and m.
Linear forecasting theory: This model is basically prepared on the basis of historic
rotation of documents. It also useful for the future predictions.
There are some following steps of linear forecasting model:
Point 1: identify the problem
Point 2: Search and gather the data
Point 3: analyse and observe from the starting
Point 4: Pick the model from future prospects.
Point 5: Confirm all the information and results of theory.
y = mx + C
In which 'y' refers to the dependent factor.
'mx' refers to the independent factor and
'c' refers to constant Factor
Here are some following steps to formulate the amount of 'm'
Point 1: Multiply both the value
Point 2: formulate the overall sum of total number.
Point 3: Adding each term individually.
4

Point 4: Multiply both the variables.
Point 5: At the end, formulate the amount.
M = (10*838 – 55*190) / (10*385 – 55*55)
= (8380 -10450) / (3850 – 3025)
= -2070 / 825
m = -2.50
There are some following ways to calculate the value of 'c'
Point 1: To formulate the overall sum of 'y' variable
Point 2: Identify the amount of 'm'
Point 3: To formulate the overall sum of 'x' variable and then multiply both 'm' and overall sum
of 'x'
Point 4: Deduct the sum of 'mx' from overall sum of 'y'
Point 5: Then divide the amount of Step4 by 'n'
c = (190 + 2.50*55) / 10
c= 327.5 / 10
c = 32.75
5

Wind Speed on Day 11:
m = -2.50, c = 32.75, x = 11
y = mx + c
=-2.50*11 + (32.75)
= -27.5+32.75
y = 5.25
Wind Speed on Day 13:
m = -2.50, c = 32.75, x = 13
y = mx + c
= -2.50*13 + (32.75)
= -32.5-32.75
Y = -65.25
CONCLUSION
As it is concluded from the above report the performance of the wind speed of the city
Scotland which express the numeracy and analysis of data. In this report the formulation of
mean, median,mode, range, standard deviation and linear forecasting model of the specific nation
which take place and it is also useful for formulating the two days' future amount of the nation
wind speed.
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REFERENCES
Books and Journals
Dong, Y and et.al., 2019. Economic development and the nutritional status of Chinese school-
aged children and adolescents from 1995 to 2014: an analysis of five successive national
surveys. The lancet Diabetes & endocrinology. 7(4).pp.288-299.
Fischer, M. and Scholz-Böttcher, B.M., 2019. Microplastics analysis in environmental samples–
recent pyrolysis-gas chromatography-mass spectrometry method improvements to
increase the reliability of mass-related data. Analytical methods. 11(18). pp.2489-2497.
Lin, W.C. and Tsai, C.F., 2020. Missing value imputation: a review and analysis of the literature
(2006–2017). Artificial Intelligence Review. 53(2). pp.1487-1509.
Mavi, R.K., Saen, R.F. and Goh, M., 2019. Joint analysis of eco-efficiency and eco-innovation
with common weights in two-stage network DEA: A big data approach. Technological
Forecasting and Social Change. 144. pp.553-562.
Navarro-Reig, M., Jaumot, J. and Tauler, R., 2018. An untargeted lipidomic strategy combining
comprehensive two-dimensional liquid chromatography and chemometric
analysis. Journal of Chromatography A. 1568. pp.80-90.
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