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# Humidity Data of City Leeds, UK

University: LSC London, University of Suffolk

• Unit No: 4
• Pages: 9 / Words 2343
• Paper Type: Minor Case Study
• Course Code: WUC116

## INTRODUCTION

In order to find the appropriate information, a data Analysis is a pathway of obtaining, translating, processing and modelling data. The outcomes obtained in this way are reported and are definitive. Data visualisation is occasionally used to display data for easy exploration of the relevant data trends. The data needed for the study is concentrated on a query or hypothesis (Landtblom, 2018). The data used as references to analysis is defined depending on the criteria of those leading the analysis. The study report covers the entire process of data analysis and related measures.

The study data analysis is conducted based on the humidity data of 10-consecutive days of city Leeds, United Kingdom. It also includes the forecast of humidity for 20th and 15th day.

## 1. Representation of selected data in tabulated form:

Below is the table containing data of humidity over the 10 consecutive days from 1st December, 2019 to 10th December, 2019 of City Leeds, UK (Humidity data of Leeds, 2019), as follows:

 Time Duration : 06:00 â€” 12:00 Days Humidity each day percentage 1st Day: 01, December, 2019 86 2nd Day: 02, December, 2019 84 3rd Day: 03, December, 2019 88 4th Day: 04, December, 2019 88 5th Day: 05, December, 2019 86 6th Day: 06, December, 2019 94 7th Day: 07, December, 2019 90 8th Day: 08, December, 2019 83 9th Day: 09, December, 2019 74 10th Day: 10, December, 2019 85

## 2. Presenting data in different charts:

Bar Graph: A bar graph is a graphic that depicts statistics for that classification using rectangle labels or columns showing the total number of findings in the sample. Bar charts could be shown with rectangular shapes, vertical rows, comparable bars (multiple bars displaying a value contrast), or clustered bars (bars containing multiple data types).

Column Chart: A column diagram is a principal form of Excel graph with the use of vertical columns to overarching plot-line data sequence. Column chart is good way to show changes over period since column sizes can be easily compared (Beyer, 2019).

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## 3. Computation of mean, median, mode, range and standard-deviation:

 Time: 06:00 â€” 12:00 Days Humidity each day percentage 01 December 2019 86 02 December 2019 84 03 December 2019 88 04 December 2019 88 05 December 2019 86 06 December 2019 94 07 December 2019 90 08 December 2019 83 09 December 2019 74 10 December 2019 85 âˆ‘ X 858 Mean 85.8 Median 90 Mode 86 Range 17 Maximum range 94 Minimum 74

Mean: It implies to simple average which termed as mean of figures or data selected. This is ascertained by a specific formula i.e. Mean = âˆ‘x / N (Sarkar and Rashid, 2016). Here N is total number of data and âˆ‘x is aggregate sum of data.

N= 10

âˆ‘N = 858

Mean = 858 / 10

= 85.8

Median: It refers to mid figure in selected data sample. Here are two kind of formula for ascertaining median value.

Where no. of data is odd, then:

• M= [(N + 1)/ 2]th value

Where no. of data is even, then:

• M= [ N/2th item + N/2th item + 1]/2 th value

Following is computation of Median value of selected data, as follows:

 Humidity each day percentage 86 84 88 88 86 94 90 83 74 85

= {10/2+ 10/2 +1} / 2

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

= (86 + 94) / 2

= 90

Mode: This indicates the value which is occurred most frequently in selected data-set. The whole measure focuses on frequency number (Leech, Barrett and Morgan, 2013). As in humidity data of 10 days 86 percent and 88 percentage humidity is occurred two times so Mode would be 86 and 88 but 86 is more closer to 86 hence Mode would be 86.

Range: This value simply defines the gape between higher and lowest value among the data set. Here in case of humidity data of 10 days, maximum humidity percentage is 97 and lowest percentage is 74. Thus range would be (97 percent â€“ 74 percent) = 23 percent.

Standard Deviation: This is a metric that calculates a data set's dispersion relative to its mean and is measured as the variance's square root.

 Dates Humidity percentages x- mean (x-m)2 01 December 2019 86 0.2 0.04 02 December 2019 84 -1.8 3.24 03 December 2019 88 2.2 4.84 04 December 2019 88 2.2 4.84 05 December 2019 86 0.2 0.04 06 December 2019 94 8.2 67.24 07 December 2019 90 4.2 17.64 08 December 2019 83 -2.8 7.84 09 December 2019 74 -11.8 139.24 10 December 2019 85 -0.8 0.64 245.6

Variance= [ âˆ‘(x â€“ mean) 2 / N ]

= 245.6 / 10

= 24.56

Standard deviation: âˆš ( variance )

= âˆš24.56

= 4.9558

## 4. Computing values of m, c and humidity forecast of 15th and 20th Day:

 Days Humidity each day percentage X2 âˆ‘xy 1 86 1 86 2 84 4 168 3 88 9 264 4 88 16 352 5 86 25 430 6 94 36 564 7 90 49 630 8 83 64 664 9 74 81 666 10 85 100 850 55 858 385 4674 âˆ‘x= 55 âˆ‘y= 858 âˆ‘X2=385 âˆ‘xy=4674

By above work out presented in the table, here following are key steps to assess actual value of â€œmâ€ in equation of y = mx + c , as follows:

1. Compute value of M:

M = N * âˆ‘xy - âˆ‘x * âˆ‘y / N*âˆ‘x2 - ( âˆ‘x )2

= 10 * 4674 â€“ 55 * 858 / 10 * 385- (55) 2

= 46740 â€“ 47190 / 3850 - 3025

= 450 / 825

= 0.5454

2. Assessment of value of c: âˆ‘y - m âˆ‘x/ N

= 858 â€“ 0.5454 * (55*10)

= 558.03

3. Through above computed/assessed data, prediction of humidity has been done here, as follows

Forecast humidity for 15 day Y= mx+c

Y= 0.5454 * 15 + 558.03

= 566.211

= 56.62%

Forecast humidity for 20 day Y= mx+c

= 0.5454 * 20 + 558.03

= 568.938

= 56.89%

## CONCLUSION

From above report it has been concluded that data analysis is wider aspect which provides comprehensive details about any selected data set. It also combines different techniques to evaluate the outcomes and findings. Moreover this supports forecasting model and help to make precise forecast.

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