Humidity Data of City Leeds, UK

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 20^th and 15^th day.

TASK

1. Representation of selected data in tabulated form:

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

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).

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

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 = ∑x / N (Sarkar and Rashid, 2016). Here N is total number of data and ∑x is aggregate sum of data.

N= 10

∑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/2^th item + N/2^th item + 1]/2 th value

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

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

= (5^th item + 6^th 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 – 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.

Variance= [ ∑(x – mean) ² / N ]

= 245.6 / 10

= 24.56

Standard deviation: √ ( variance )

= √24.56

= 4.9558

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

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

1. Compute value of M:

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

= 10 * 4674 – 55 * 858 / 10 * 385- (55) ²

= 46740 – 47190 / 3850 - 3025

= 450 / 825

= 0.5454

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

= 858 – 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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