BMP4003 - Economic Concepts: Analyzing Trampoline Price Increase

This presentation provides a microeconomic analysis of an article discussing the potential surge in trampoline prices due to increased shipping costs and port congestion, exacerbated by factors like Brexit. It applies microeconomic concepts to explain the main arguments of the article, focusing on supply chain disruptions, increased transportation expenses, and the economic implications for businesses and consumers. The analysis uses relevant economic models to illustrate the relationship between these factors and the resulting price changes, offering a comprehensive understanding of the market dynamics at play. The presentation also touches upon potential long-term consequences for the shipping industry and the broader economic environment.

Numeracy and Data Analysis

Table of Contents
MAIN BODY..................................................................................................................................3
Arranging data in table format.....................................................................................................3
Presenting Data............................................................................................................................3
Calculation of Mean, Median, Mode, Range and Standard Deviation........................................4
REFERENCES................................................................................................................................1
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MAIN BODY
Arranging data in table format
Date Average Wind Speed
25 July 2022 14.9
26 July 2022 7.9
27 July 2022 9.6
28 July 2022 11.0
29 July 2022 5.9
30 July 2022 8.3
31 July 2022 11.3
1 August 2022 6.8
2 August 2022 11.1
3 August 2022 11.1
(London, England, United Kingdom Weather History, 2022)
Presenting Data
25-Jul-22
26-Jul-22
27-Jul-22
28-Jul-22
29-Jul-22
30-Jul-22
31-Jul-22
1-Aug-22
2-Aug-22
3-Aug-22
14.9
7.9
9.6
11
5.9
8.3
11.3
6.8
11.1 11.1
Average Wind Speed
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25-Jul-22
26-Jul-22
27-Jul-22
28-Jul-22
29-Jul-22
30-Jul-22
31-Jul-22
1-Aug-22
2-Aug-22
3-Aug-22
14.9
7.9
9.6
11
5.9
8.3
11.3
6.8
11.1 11.1
Average Wind Speed
Calculation of Mean, Median, Mode, Range and Standard Deviation
Mean
Definition: Mean or arithmetic average is defined as the sum of all the numerical
observations divided by the total number of observations (Kaur, Stoltzfus and Yellapu, 2018).
Formula: Σ x / N
Where, x = The value of term,
N = Total number of terms.
Calculation:
Date Average Wind Speed (x)
25- 07 - 2022 14.9
26 - 07 - 2022 7.9
27 - 07 - 2022 9.6
28 - 07 - 2022 11.0
29 - 07 - 2022 5.9
30 - 07 - 2022 8.3
31 - 07 - 2022 11.3
1 - 08 - 2022 6.8
2 - 08 - 2022 11.1
3 - 07 - 2022 11.1
N = 10 Sum = 97.9
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Mean (X̅) = Σ x / N = 97.9 / 10 = 9.79.
Steps:
 First all the value in the x column are summed.
 Then the number observations are counted.
 The value of sum is divided by the total number of observations.
Median
Definition: Median is that value which is the centre one in the data set that is arranged
either in ascending or descending order.
Formula:
 In case number of observations (N) is even, Median = [ (N / 2) th term + ((N / 2) +1) th
term] / 2.
 When N is odd, Median = ((N + 1) / 2) th term.
Calculation:
Average Wind Speed Arranged in Ascending Order
14.9 5.9
7.9 6.8
9.6 7.9
11.0 8.3
5.9 9.6
8.3 11.0
11.3 11.1
6.8 11.1
11.1 11.3
11.1 14.9
Median = [ (N/2) th term + ((N/2) +1) th term]/2.
= [ (10/2) th term+ ((10/2) +1) th term]/2.
= (9.6 + 11.0) / 2.
= 10.3.
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Steps: Firstly, the data is sorted in ascending order. Then the number of observation are counted to know the value for N. In this case N is 10 so the formula for median applicable in even case is used. The value for the observation 5th and 6th are added. The result is divided by two to reach the median value.
Mode
Definition: It is defined as that value in a data set that occurs maximum number of times.
Formula: The easiest way is to observe the value that occurs the most. It is possible is individual
series by seeing the frequency of each event that happens (Kaliyadan and Kulkarni, 2019). The
continuous series the formula is Mode = l + [((f1 – f0) / (2 f1 – f0 – f2))] * h.
Calculation:
Average Wind Speed (x) Frequency (f)
14.9 1
7.9 1
9.6 1
11.0 1
5.9 1
8.3 1
11.3 1
6.8 1
11.1 2
Mode = 11.1.
Steps All the observations are allotted frequency; it is the number of time it occurs. The observation with maximum occurrence is the value for mode, 11.1.
Range
Definition: It is the largest value of the data minus the smallest one.
Formula: Largest \ Maximum – smallest \ Minimum.
Calculation: 14.9 – 5.9.
Steps:
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 The day with maximum wind speed is selected. The day on which the average wind speed was least is taken. Minimum value is deducted from the maximum value.
Standard Deviation
Definition: It the measure of dispersion of data on the basis of mean value (Rojas-Martínez and
et.al., 2018).
Formula: √ Σ (x - X̅)2 / N.
Calculation:
Date Average Wind Speed
(x)
(x - X̅) (x - X̅)2
25- 07 - 2022 14.9 5.11 26.11
26 - 07 - 2022 7.9 -1.89 3.57
27 - 07 - 2022 9.6 -0.19 0.04
28 - 07 - 2022 11.0 1.21 1.46
29 - 07 - 2022 5.9 -3.89 15.13
30 - 07 - 2022 8.3 -1.49 2.22
31 - 07 - 2022 11.3 1.51 2.28
1 - 08 - 2022 6.8 -2.99 8.94
2 - 08 - 2022 11.1 1.31 1.72
3 - 07 - 2022 11.1 1.31 1.72
N = 10 Total = 63.19
Mean (X̅)= 9.7.
Standard Deviation = √ Σ (x - X̅)2 / N
= √ (63.19 / 10)
= 2.5.
Steps:
 The mean value is subtracted from the values in the x column and noted in (x - X̅)
column. All the values in (x - X̅) column are squared and written in another column.
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 All the values in (x - X̅)2 column are added. The result is divided by N value. The outcome is square root and final result is known as standard deviation.
Linear Forecasting Model
x y xy x2 y2
1 14.9 14.9 1 222.01
2 7.9 15.8 4 62.41
3 9.6 28.8 9 92.16
4 11.0 44 16 121
5 5.9 29.5 25 34.81
6 8.3 49.8 36 68.89
7 11.3 79.1 49 127.69
8 6.8 54.4 64 46.24
9 11.1 99.9 81 123.21
10 11.1 111 100 123.21
55 97.9 527.2 385 1021.63
I. c = [(Σ y) (Σ x2) - (Σ x) (Σ xy)] / [n (Σ x2) – (Σ x)2]
= [97.9 * 385 – 55 * 527.2] / [10 (385) – (55)2]
= (37,691.5 – 28996) / (3850 – 3025)
= 8695.5 / 825 = 10.54
II. m = [ n (Σ xy) - (Σ x) - (Σ y)] / [n (Σ x2) – (Σ x)2]
= [ 10 (527.2) – 55 – 97.9] / [10 * 385 – (55)2]
= [5272 – 55 – 97.9] / [ 3850 – 3025]
= 5119.1 / 825 = 6.20.
The relationship between two variables is calculated using this equation y = mx + c. m
value is the slope of the variable and c value indicates the y intercept (Gupta and Kapoor, 2020).
I. Forecast for day 12 = y = mx + c
y = 6.20 * 12 + 10.54 = 84.94.
II. Forecast for say 14 = y = mx + c
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y = 6.20 * 14 + 10.54 = 97.34.
REFERENCES
Books and Journals
Gupta, S. C. and Kapoor, V. K., 2020. Fundamentals of mathematical statistics. Sultan Chand &
Sons.
Kaliyadan, F. and Kulkarni, V., 2019. Types of variables, descriptive statistics, and sample
size. Indian dermatology online journal. 10(1). p.82.
Kaur, P., Stoltzfus, J. and Yellapu, V., 2018. Descriptive statistics. International Journal of
Academic Medicine. 4(1). p.60.
Rojas-Martínez, R. and et.al., 2018. Prevalence of previously diagnosed diabetes mellitus in
Mexico. Salud publica de Mexico. 60(3). pp.224-232.
Online
London, England, United Kingdom Weather History. 2022. [Online]. Available through: <
https://www.wunderground.com/history/monthly/gb/london/EGLC/date/2022-8>
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