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# Numeracy and Data Analysis (Coursework Sample)

Instructions:

In this report, the data is analyzed in Excel. Initially, the wind speed data for ten consecutive days in Manchester, UK, is considered. Subsequently, it is visualized in chart format, followed by the calculation of descriptive statistics. Finally, the Linear Forecasting model is employed to predict the data for the 11th and 12th days. source..

Content:

Numeracy and Data Analysis
Table of Contents TOC \o "1-3" \h \z \u Introduction PAGEREF _Toc123825462 \h 3Arrangement of data PAGEREF _Toc123825463 \h 4Visualisation of data PAGEREF _Toc123825464 \h 5Calculation PAGEREF _Toc123825465 \h 6Linear Forecasting model PAGEREF _Toc123825466 \h 10Conclusion PAGEREF _Toc123825467 \h 12References PAGEREF _Toc123825468 \h 13
Introduction
In this report, the data will be analysed in excel. At first, the data of the wind speed will be considered for ten consecutive days in Manchester, UK. Then it will be visualised in chart format. Then the descriptive statistics will be calculated. Then, using the Linear Forecasting model the data for the 11th day and the 12th will be predicted.
Arrangement of data
Date
25/12/2022
26/12/2022
27/12/2022
28/12/2022
29/12/2022
30/12/2022
31/12/2022
01/01/2023
02/01/2023
03/01/2023
Wind speed (“Km/hr”)
11
19
11
13
24
17
4
9
11
11
Table 1: Arrangement of data in tabular form
(Source: Created by the learner)
For the data, the wind speed for Manchester, England, United Kingdom was considered from the website https://www.timeanddate.com. From, table 1, the wind speed can be observed for ten consecutive days, from 25th December 2022 to 3rd January 2023. For this case, the wind speed is taken at 11:50 am for the respective days.
Visualisation of data
Chart 1: Line chart for wind speed
(Source: Created by the learner)
Chart 2: Bar chart for wind speed
(Source: Created by the learner)
Calculation
Mean
In the mean, the summation of all the data is divided with the number of items in the set (Cooksey, 2020). With the help of the mean, the central tendency of the set of data is observed.
Formula:
Here, µ = “Mean”, ∑ = “Sum of / Total”, x = “Individual data value”, N = “Number of items”
∑x = 11+19+11+13+24+17+4+9+11+11
Total terms = 10,
Thus, N = 10
Thus, mean = 130 / 10 = 13 “Km/hr”
Median
In the median, the central value of the sorted data is observed. Through this, the value in the middle of the database is observed (Cooksey, 2020).
Serial number
Wind speed (Km/hr)
1
4
2
9
3
11
4
11
5
11
6
11
7
13
8
17
9
19
10
24
Table 2: Arrangement of wind speed in ascending order
(Source: Created by the learner)
Formula:
Here, N = “Number of items”
Thus, Median = [5th term + 6th term]/2
= (11+11)/2
= 22/2
= 11
Thus, the median windspeed is 11 “Km/hr”
Mode
In the mode, the value which has the highest number of occurrence in the dataset is observed (Cleff, 2019).
Wind speed (Km/hr)
Frequency
4
1
9
1
11
4
13
1
17
1
19
1
24
1
Table 3: Wind speed frequency table
(Source: Created by the learner)
From table 3, 11 has the highest frequency. Thus, the mode is 11 “Km/hr”.
Range
In the range, the variation of the data between the highest point and lowest point is observed (Cleff, 2019).
The highest windspeed is 24 “Km/hr”,
and the lowest windspeed is 4 “Km/hr”.
Highest windspeed - Lowest windspeed = 24 “Km/hr” - 4 “Km/hr”
Thus, the range is 20 “Km/hr”.
Standard Deviation
In the standard deviation, the square root of the variance is calculated. It provides the dispersion of the values from the mean.
Formula:
Here, σ = “Standard deviation”, µ = “Mean”, ∑ = “Sum of / Total”, x = “Individual data value’, N = “Number of items”.
x
11
19
11
13
24
17
4
9
11
11
x-µ
-2
6
-2
0
11
4
-9
-4
-2
-2
(x-µ)^2
4
36
4
0
121
16
81
16
4
4
∑(x-µ)^2
286
Table 4: Standard deviation calculation
(Source: Created by the learner)
From table 4,
∑(x-µ)^2 = 286
Thus, ∑(x-µ)^2/ 10 = 286 /10 = 28.6
Then, √(∑(x-µ)^2/ 10) = 5.35
Hence, the standard deviation is 5.35.
Linear Forecasting model
Linear forecasting helps in determining the future values from the present values. It uses the linear function (Oluwa, 2019). In this model, the value of an unknown variable is calculated with the help of a known variable. With the help of this forecasting, the demand can be predicted (Oluwa, 2019).
Assumption: For the calculation, it has been assumed that is a dependence of wind speed on temperature. So, the wind speed in “Km/hr” is taken as the “y” variable, the dependent variable. And the temperature in Celsius is taken as the “x” variable, the independent variable
Wind speed (Km/hr)
11
19
11
13
24
17
4
9
11
11
∑y
130
Temperature (Celsius)
9
6
6
10
7
9
9
8
5
9
∑x
78
x^2
81
36
36
100
49
81
81
64
25
81
∑(x^2)
634
xy
99
114
66
130
168
153
36
72
55
99
∑xy
992
Table 5: Forecasting model calculation
(Source: Created by the learner)
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