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Humidity Data of Manchester City

University: LONDON SCHOOL OF COMMERCE

Unit No: 3
Level: Undergraduate/College
Pages: 8 / Words 1919
Paper Type: Assignment
Course Code: WUC116
Downloads: 2138

Question :

Briefs:

You are needed to collect humidity data for ten consecutive days from any city of your choice. This data can be easily collected with the help of online sources. Also, prepare the report on the following areas:

Arrange the data in a table format.
Provide the data using any two types of charts of your choice. Example: Scatter plot, line chart, pictograms, histograms etc.
Calculate and demonstrate the following. Please produce steps for the calculation and highlight the final value:

Means
Median
Mode
Range
Standard Deviation

For your data, use the linear forecasting model which is y=mx+cto calculate and discuss the followings:
Highlight the step of calculation ofmvalue and explain the answer.
Show the steps of calculation of c value and discuss the answer.
Using the calculated 'm' and 'c' values, forecast the humidity for days 15 and day 20.

Learning Outcomes

Analyse and use techniques for summarising and analysing data.
Provide reasonableness in the calculation of answers.
Discus and evaluate techniques used for forecasting.

Answer :

INTRODUCTION

The term data analysis is a comprehensive tool of gathering and analysing monetary by help of different kinds of techniques (Laracy, Hojnoski and Dever, 2016). By help of this analysis, it becomes easier for managerial aspect of companies to take corrective actions. The report consists calculation of mean-mode-median as per the chosen data of humidity of Manchester city, United Kingdom (Humidity data of London, 2019.). In the further part of report projection of futuristic humidity percentage is done by applying linear regression model.

MAIN BODY

1. Arrangement of data in table format

In accordance of requirement of brief under this task, humidity data of 10 days of London city has been shown in table format:

Date	S. No.	Humidity (in terms of %)
1^st of October, 2019	1	94
2^nd of October, 2019	2	84
3^rd of October, 2019	3	96
4^th of October, 2019	4	91
5^th of October, 2019	5	95
6^th of October, 2019	6	97
7^th of October, 2019	7	95
8^th of October, 2019	8	93
9^th of October, 2019	9	83
10^th of October, 2019	10	93

2. Presentation of data in two charts

Bar chart- This can be defined as a type of diagram that presents free sample quantitative data in the form of horizontal bars. Underneath, presentation of humidity data has been done in the form of bar chart:

Column chart- This can be defined as a type of diagram that presents quantitative data in the form of vertical heights (Geiger, Goos and Forgasz, 2015). Underneath, presentation of humidity data has been done in the form of column chart:

3. Calculation of below mentioned items

Date	Humidity (in terms of %)
1^st of October, 2019	94
2^nd of October, 2019	84
3^rd of October, 2019	96
4^th of October, 2019	91
5^th of October, 2019	95
6^th of October, 2019	97
7^th of October, 2019	95
8^th of October, 2019	93
9^th of October, 2019	83
10^th of October, 2019	93
Total	921
Mean	92.1
Mode	93
Median	93.5
Range	14 (97-83)
Standard deviation	4.84

(I) Mean- The value of mean is calculated by dividing total of data values from number of values. Underneath, mean is computed by applying formula that is as: Mean = Î£X/N

Î£X= 921

N = 10

Mean = 921/10

= 92.1

(ii) Mode- In simple terms, mode is a kinds of number whose frequency is higher in a particular data set. This is presented by Z. In the above data set of humidity, value of Z is 93 because this value has maximum frequency.

(iii) Median- This is defined as mid value among different range of number of a data set (Shalley and Stewart, 2017). This is denoted by M. Herein, below formula to calculate median is mentioned in such manner:

If data set is odd:

M = (N+1)/2

If data set is even:

M= (N/2^th item + N/2^th item + 1) / 2

Calculation of median as accordance of humidity data of 10 days-

Arrangement of data in ascending order:-

S. No.	Humidity (In %)
1	83
2	84
3	91
4	93
5	93
6	94
7	95
8	95
9	96
10	97

N= 10

Median = (N/2^th item + N/2^th item + 1)/2

= (10/2^th item + 10/2^th item + 1)/2

= (5^th item + 6^th item)/2

= (93+94)/2

= 93.5

(iv) Range- It is calculated by making variation between higher and lower value of a data series (Cahoon, Cassidy and Simms, 2017). Such as per the above mentioned humidity data, this can be find out that value of range is of 14.

(v) Standard-deviation- It can be defined as calculation of value of variation from a data set. In accordance of above humidity data, standard-deviation is computed below in such manner:

Days (Date)	Humidity (values in %)	(x- mean)	(x-mean)²
1st of October, 2019	94	1.9	3.61
2nd of October, 2019	84	-8.1	65.61
3rd of October, 2019	96	3.9	15.21
4th of October, 2019	91	-1.1	1.21
5th of October, 2019	95	2.9	8.41
6th of October, 2019	97	4.9	24.01
7th of October, 2019	95	2.9	8.41
8th of October, 2019	93	0.9	0.81
9th of October, 2019	83	-9.1	82.81
10th of October, 2019	93	0.9	0.81
210.9

Variance= [âˆ‘(x â€“ mean)² / N]

= (210.9/10)

= 21.09

Standard deviation = âˆšvariance

= âˆš21.09

= 4.59

4. Calculating values of m, c and humidity forecast of day 15 and 20

Days (X)	Humidity (Y)	X²	âˆ‘XY	Y²
1	94	1	94	8836
2	84	4	168	7056
3	96	9	288	9216
4	91	16	364	8281
5	95	25	475	9025
6	97	36	582	9409
7	95	49	665	9025
8	93	64	744	8649
9	83	81	747	6889
10	93	100	930	8649
âˆ‘X= 55	âˆ‘Y= 921	âˆ‘X²= 385	âˆ‘XY= 5057	âˆ‘Y² = 85035

(I) Calculation of value of m:

m= (âˆ‘Y)(âˆ‘X²)- (âˆ‘X)(âˆ‘XY) / n(âˆ‘X²)-(âˆ‘X)²

= (921)(385)-(55)(5057)/10(385)-(55)²

= 354585-278135/ 3850-3025

= 76450/825

= 92.67

(ii) Calculation of value of c:

c= n(âˆ‘XY)- (âˆ‘X)(âˆ‘Y) / n(âˆ‘X²)-(âˆ‘X)²

= 10(5057)-(55)(931)/10(385)-(55)²

= 50570-51205/3850-3025

= -635/825

= -0.77

(iii) Forecasting of humidity:

For 15^th day-

Y = m+cx

= 92.67+(-0.77*15)

= 92.67- 11.55

= 81.12%

For 20^th day-

= 92.67+ (-0.77*20)

= 92.67- 15.4

= 77.27%

CONCLUSION

On the basis of above project report, this can be concluded that data analysis technique is not limited till any specific department for taking decisions. It is needed any kinds of business entity for better decision-making. The report concludes about calculation of mean-mode-median, range and standard-deviation of humidity data of Manchester city. In the end part of report, forecasting of humidity is done by help of linear regress

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Humidity Data of Manchester City

Briefs:

Learning Outcomes

INTRODUCTION

MAIN BODY

1. Arrangement of data in table format

2. Presentation of data in two charts

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3. Calculation of below mentioned items

4. Calculating values of m, c and humidity forecast of day 15 and 20

CONCLUSION

Cite This Work

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