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    Humidity Data of City Leeds, UK

    University: LSC London, University of Suffolk

    • Unit No: 4
    • Level: Undergraduate/College
    • Pages: 9 / Words 2343
    • Paper Type: Minor Case Study
    • Course Code: WUC116
    • Downloads: 284

    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.

    TASK

    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 — 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 — 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/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 – 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 15th and 20th 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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