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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: 270
    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:
    1. Means
    2. Median
    3. Mode
    4. Range
    5. 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

    1. Analyse and use techniques for summarising and analysing data.
    2. Provide reasonableness in the calculation of answers.
    3. 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 %)

    1st of October, 2019

    1

    94

    2nd of October, 2019

    2

    84

    3rd of October, 2019

    3

    96

    4th of October, 2019

    4

    91

    5th of October, 2019

    5

    95

    6th of October, 2019

    6

    97

    7th of October, 2019

    7

    95

    8th of October, 2019

    8

    93

    9th of October, 2019

    9

    83

    10th 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:

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    3. Calculation of below mentioned items

    Date

    Humidity (in terms of %)

    1st of October, 2019

    94

    2nd of October, 2019

    84

    3rd of October, 2019

    96

    4th of October, 2019

    91

    5th of October, 2019

    95

    6th of October, 2019

    97

    7th of October, 2019

    95

    8th of October, 2019

    93

    9th of October, 2019

    83

    10th 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 = ΣX/N

    ΣX= 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/2th item + N/2th 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/2th item + N/2th item + 1)/2

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

    = (5th item + 6th 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= [∑(x – mean)2 / N]

    = (210.9/10)

    = 21.09

    Standard deviation = √variance

    = √21.09

    = 4.59

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

    Days (X)

    Humidity (Y)

    X2

    ∑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

    ∑X= 55

    ∑Y= 921

    ∑X2= 385

    ∑XY= 5057

    ∑Y2 = 85035

    (I) Calculation of value of m:

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

    = 10(5057)-(55)(931)/10(385)-(55)2

    = 50570-51205/3850-3025

    = -635/825

    = -0.77

    (iii) Forecasting of humidity:

    For 15th day-

    Y = m+cx

    = 92.67+(-0.77*15)

    = 92.67- 11.55

    = 81.12%

    For 20th 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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