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:

Time Duration : 06:00 — 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).

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

Time: 06:00 — 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
∑ 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 = ∑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:

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

= {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.

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= [ ∑(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:

Days	Humidity each day percentage	X2	∑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
∑x= 55	∑y= 858	∑X2=385	∑xy=4674

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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