Rajshahi Krishi Unnayan Bank is playing a vital role in the economic development of Bangladesh, especially in supporting farmers in sixteen districts of Rajshahi and Rangpur divisions. Agriculture is the foremost important part of the Bangladeshi economy.

1. Student ID:
Student Name:

2. Table of Contents
INTRODUCTION ..................................................................................................................................3
ARRANGING DATA IN A TABLE FORMAT....................................................................................4
COLUMN AND LINE CHART FOR WIND SPEED ...........................................................................4
MEAN.....................................................................................................................................................5
MEDIAN ................................................................................................................................................6
MODE.....................................................................................................................................................7
RANGE...................................................................................................................................................7
STANDARD DEVIATION....................................................................................................................7
FORECASTING.....................................................................................................................................8
CONCLUSIONS...................................................................................................................................11
Reference list ........................................................................................................................................12

3. INTRODUCTION
This report begins with a table that displays data on the speed of wind in London for ten
consecutive days. Two charts will be constructed to examine data from the table: a Column
Chart and a Line Chart. The data presented at the start of the article will be used to determine
the Mean, Median, Mode, and Range in the next. Variation will also be calculated
because based on this that the value of Standard Variation can be determined. The final section
of this report will forecast wind speed for days 12 and 14 by using the linear forecasting model
y = mx + c (Hogg, Mckean and Craig, 2019).

4. ARRANGING DATA IN A TABLE FORMAT
The data demonstrates how things vary and by how much (Garner and Collins, 2019). To
demonstrate how wind speeds fluctuate and by how much, a table was developed in which data
on wind speed was updated for 10 consecutive days, beginning March 31 and ending April 09,
as seen below:
Table 1.0: Wind Speed of Ten Days in London City
Days Average Wind speed (mph)
1 24
2 23
3 12
4 10
5 22
6 22
7 27
8 35
9 11
10 16
Table 1: data of wind speed for ten consecutive days (timeanddate.com, 2020).
Source: https://www.timeanddate.com/weather/uk/london/historic
COLUMN AND LINE CHART FOR WIND SPEED
Column Charts are most often defined as bar charts (S Sarantakos, 2016). In Microsoft Excel,
however, a column chart has vertical bars, whereas a bar chart has horizontal bars. They both
are used to compare variables in one or more data sets (Vaninsky, 2018). The Column chart
would be following created using the data from 10 consecutive days of wind speed.

5. Figure 1: Column chart for the wind speed for ten consecutive days.
Line chart is used when is needed to show or focus on data trends (Garner and Collins, 2019).
In order to show how data tends for wind speed from the last 10 days, the below Line Chart
was created:
Figure 2: Line chart for the wind speed for ten consecutive days.
MEAN
The mean is the often-used metric for determining central tendency. It can handle both
continuous and discrete data types. The mean is calculated by dividing the total amount of
values in a dataset set by the number of items in the dataset (Aliya Whiteley, 2018). The
following formula will be used to compute the Mean:
24 23
12
10
22 22
27
35
11
16
0
5
10
15
20
25
30
35
40
1 2 3 4 5 6 7 8 9 10
MPH
DAYS
Average Wind speed
0
5
10
15
20
25
30
35
40
1 2 3 4 5 6 7 8 9 10
MPH
Days
Average Wind…

6. MEAN :
𝜇 =
∑𝑥
𝑁
Where:
µ = Mean
∑ = Sum of / Total
x = Individual data value
N = Number of items
In this case, the formula will be:
𝜇 =
∑𝑥
𝑁
∑x = 24 + 23 + 12 + 10 + 22 + 22 + 27 + 35 + 11 + 16 = 202
N = 10
µ =
202
10
µ = 20.2
Based on the above, it was proved that Mean, µ= 20.2
MEDIAN
The median is the middle score for a set of data that has been arranged in order of magnitude
(Aliya Whiteley, 2018).
Median position: =
𝑁+1
2
=
10+1
2
= 11/2 = 5.5th position.
Median position = 5.5th
position.
These data will be organized from lowest to greatest value in order to get the median wind
speed from 10 consecutive days: 10, 11, 12, 16, 22, 22, 23, 24, 27, 35. From the list, median
values are 22 and 22.
Median =
22+22
2
= 22
 The Median is 22

7. MODE
The mode is the most often occurring number, i.e. the number that appears the most frequently
(Aliya Whiteley, 2018). In this example, the most often used wind speed number is 10, 11, 12,
16, 22, 22, 23, 24, 27, 35.
We've seen the figure 22 for wind speed twice in the previous ten days.
Hence, it can be said that the Mode is 22 for this case.
RANGE
The range indicates the difference in between greatest and lowest values in the data, however,
the range is 0 when there is no fluctuation in the variable (Garner and Collins, 2019).
From the following list 10, 11, 12, 16, 22, 22, 23, 24, 27, 35 can be seen that the lowest value
is 10, and the highest value is 35
Range = highest value – lowest value
= 35- 10
= 25
From the above, it was demonstrated that Range = 25
STANDARD DEVIATION
The standard deviation is an essential measure of variance that is usually unknown (Vaninsky,
2018).
It was established in this paper that the mean is 20.2. This value has been added to the table
below.
The deviation will be calculated by subtracting the mean from every value (x- µ). The results
were entered into the table below. In addition, the squared difference (x – µ)2
was computed,
and the total was determined.
Days Average Wind speed (mph) µ x- µ (x – µ)2
4 10 20.2 -10.2 104.04
9 11 20.2 -9.2 84.64
3 12 20.2 -8.2 67.24
10 16 20.2 -4.2 17.64
5 22 20.2 1.8 3.24
6 22 20.2 1.8 3.24
2 23 20.2 2.8 7.84
1 24 20.2 3.8 14.44
7 27 20.2 6.8 46.24
8 35 20.2 14.8 219.04
Total 567.6
Table 2: Value of mean, (x- μ) and (x – μ)2

8. In order to find out the variance, the below formula will be used:
σ2

∑(x− μ)
𝑁
σ2

567.6
10
σ2
 56.76
Standard deviation, which is the square root of the variance, may be determined based on the
variation.
σ = √
∑(x− μ)
𝑁
σ = √56.76
σ = 7.53
The standard deviation is 7.53
FORECASTING
A forecast is a prediction of an upcoming event or series of events (Vaninsky, 2018). Making
good predictions, as Neil Bohr suggests, is not always simple. Forecasting is crucial because it
allows for the prediction of future occurrences in a variety of planning and decision-making
processes.
In order to forecast, Linear Model is going to be used and it is represented using the below
formula:
y = mx + c
where,
y = dependant variable
x = independant veriable
m = slope
c = intercept
x y xy x²
1 24 24 1
2 23 46 4
3 12 36 9
4 10 40 16
5 22 110 25
6 22 132 36
7 27 189 49
8 35 280 64
9 11 99 81
10 16 160 100

9. ∑x= 55 ∑y= 202 ∑xy= 1116 ∑x²= 1373
Table 3: Data used to find out values of x, y, xy and x²
Let recall,
∑xy = 1116 (From the table)
∑x = 55
∑y = 202
∑x² = 1373 (From the table)
N = 10
Therefore:
m=
10×1116−55×202
10×1373– (55)2
=
11160−11110
13730−3025
=
50
10705
=0.0046
Interpretation: if the day extends by one, the average wind speed is expected to
drop by 0.0046.
Let's insert the value of m= 0.0046 into the formula c
c =
202−0.0046×55
10
=
202−0.253
10
=
201.747
10
= 20.1747

10. Interpretation: If the day is zero, the model forecasts that costs will be about
20.1747.
After calculating the values of m and c, the following method may be used to anticipate the
wind speed for days 12 and 14:
Forecasting wind speed for day 12
y = mx + c,
where:
m = 0.0046
c = 20.1747
x = 12
y = 0.0046 × 12 + 20.1747
= 20.23
Forcasting wind speed of 12th day is = 20.23
Forecasting wind speed for day 14
y = mx + c,
where:
m = 0.0046
c = 20.1747
x = 14
y = 0.0046×14+20.1747
= 20.23
Forcasting wind speed of 14th day is = 20.23

11. CONCLUSIONS
In this report, the wind speed of 10 consecutive days was gathered and a table with all of the
data was constructed. Two charts were constructed using these data: a Column Chart and a
Line Chart. The data from the table was used to compute the mean, median, mode, and range.
Furthermore, Variation was estimated since Standard deviation may be calculated using it.
The relevance of forecasting was outlined and addressed at the end of this report. Furthermore,
forecasting for the 12th and 14th days was completed.

12. Reference list
Aliya Whiteley (2018). Mean mode median. 12th ed. Portishead: Bluechrome.
Garner, J. and Collins, J. (2019). Numeracy for work: handling data. 5th ed. Clayton South,
Vic.: Blake Education.
Hogg, R.V., Mckean, J.W. and Craig, A.T. (2019). Introduction to mathematical statistics.
8th ed. Boston: Pearson.
S Sarantakos (2016). Data analysis. 12th ed. London: Sage.
timeanddate.com (2020). Past Weather in London, England, United Kingdom — Yesterday or
Further Back. [online] Timeanddate.com. Available at:
https://www.timeanddate.com/weather/uk/london/historic.
Vaninsky, A. (2018). Simplified Data Envelopment Analysis: What Country Won the
Olympics, and How about our CO2 Emissions? Numeracy, 6(2).