Data Analysis and Calculations of Mean, Mode, Median

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1. Representation of data in tabular form:

Herein, below presentation of gathered data regarding humidity percentage in London, UK is done. This data is gathered from the 22^nd of December to the 31^st of December (Humidity data of London, 2019).

2. Data representation in charts:

Bar Graph: A bar graph is being used to clearly show data using bars of different heights or distances. It contributes in a significant manner in order to understand analysed data in the most effective way. Herein, below a presentation of the above humidity data is done in such a manner:

Column Chart: A column diagram is a chart in which data shows vertical bars horizontally around the chart, with the value axis shown on the graph's left side (Leech, Barrett and Morgan, 2013). Same as the above graph, this is also prepared in the Excel sheet. Herein, below presentation of the above humidity data is done in a column chart which is as follows:

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

Mean- The term mean can be defined as an average of gathered monetary data (Beyer, 2019). This is calculated by a particular formula which is as: Mean = ∑N/ N. Herein, the below calculation of mean value is done in such manner:

N= 10

∑N = 931

Mean = 931 / 10

= 93.1

Also Read:- Data Analysis and Decision Making

Median = As the name assists, it can be defined as a mid-value of the number of data series. This is calculated by a formula which is applied in accordance with number data.

If the number of data is odd then, M= (N+1 / 2)th item

If number of data is even then, M= {N/2^th item+ N/2^th item + 1}2

Herein, below calculation of the median of humidity data is done below in such a manner:

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

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

= (92+96)/2

= 94

Mode- The term mode can be defined as a set of data values which appear most of the time from a group of numbers. Such as in the aspect of the above data of humidity this can be found that humidity of 100% has been incurred with the higher frequency of 2. Thus, the value of the mode is 100.

Range- This can be defined as the difference between a higher value and a minimum value from a group of numbers. Herein, below in the context of the above set of data on humidity, this can be found that the higher value is 100 and the lower is 80. Hence, the range is (100-80)= 20.

Standard deviation- Standard Deviation is a quantitative term used to assess the average number of variations or diffusion (Sarkar and Rashid, 2016). Functionally, it's a kind of volatility measure. Herein, the below calculation of standard deviation is done in such manner:

Variance= [ ∑(x – mean) ² / N ]

= 406.9/10

= 40.69

Standard deviation: √ ( variance )

= √40.69

= 6.38

4. Calculating values of m, c and wind forecast of days 15 and 20.

The form above computations are summarised in the table, following are the steps to find out the value of “m” in the equation which is y = mx + c, as follows:

1. Compute value of M:

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

= 10*5179-55*931/10*385-(55) ²

= 51790- 51205/3850-3025

= 585/825

= 0.71

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

= 931- 0.71 * 55*10

= 540.5

3. By the above-calculated data, forecasting of humidity is done below in such a manner:

Forecast humidity for 15 days Y= mx+c

Y= 0.71*15+540.5

= 551.15

= 55%

Forecast humidity for 20 days Y= mx+c

= 0.71*20+540.5

= 554.7 or 55.4%

CONCLUSION

On the basis of the above project report, it has been concluded that data analysis is too crucial in order to compute any particular result from the collection of a wide range of data series. The report concludes with the calculation of mean-mode-median as per the given data set. As well as standard-deviation is also computed. In the end part of the report, forecasting of humidity percentage is done on the 15 and 20^th day.

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