Data Analysis and Decision Making

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1. Representation of data in tabular form:

In this task of the report, humidity data of London city for ten days has been presented in the format of the table in below mentioned manner:

2. Data representation in charts:

Bar chart- It is a type of graph or chart that presents monetary data in the form of rectangular bars that contain heights proportional to values. Herein, below a bar chart is prepared that includes information about the humidity percentage for ten days:

Column chart- It is a type of graph or chart that presents monetary data in the form of vertical bars that contains values horizontally (Wang and Sun, 2015). Herein, below a bar chart is prepared that includes information about the humidity percentage for ten days:

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

Mean- The term mean can be defined as a type of value that is computed by dividing the addition of all numbers by the number of values. Herein, below formula to calculate the mean is mentioned in such manner:

Mean= ∑N/ N

N= 10

∑N= 943

Mean= 943/10

= 94.3

Mode- There is no specific formula to compute mode in the case of individual data series (Greenacre, 2017). This can derived by checking the frequency of numbers in a data series and if a number whose frequency is higher then it will be considered as mode. Such as in the above-mentioned data series, the value of the mode is 98 because its frequency is three which is higher among all numbers.

Median- It is a mid-value in a data series. This can be computed as per the nature of the data if the number of data is odd then the formula will be as: M = N+1/2^th item. While if the number of data is even then M = (N/2^th item+N/2^th item + 1)/2. Before applying this formula, it is necessary to arrange all data in ascending order. Herein, below calculation of the median of humidity data is done in such manner:

N = 10 (Even)

M = (N/2^th item+N/2^th item + 1)/2

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

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

= (95+95) / 2

= 95

Range- It is defined by making variation between maximum and minimum values among groups of numbers (Fisher, 2017). Such as in the above-mentioned data on humidity, the higher number is 98 and the lower number is 89. So the value of the range is 9 (98-89).

Standard deviation- This may be defined as the calculation of the value of variation in any particular data series. Herein, underneath calculation of standard deviation is done below in such a manner:

Variance = [ ∑(x – mean) ² / N ]

= 112.1/10

= 11.21

Standard deviation = √ ( variance )

= √ 11.21

= 3.35

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

Also Read:- Managing Innovation In Business 

1. Calculation of value of M-

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

= 10*5197-55*943/10*385-(55)²

= 51970- 51865/3850-3025

= 105/825

= 0.13

2. Calculation of value of c:

C = ∑y- m ∑x/ N

= 943- 0.13* 55/10

= 943-0.715

= 942.28

3. Forecasting for 15^th

Y= mx+c

= 0.13*15+942.28

= 944.23 or 94.44%

For 20^th day

= 0.13*20+942.28

= 2.6+942.28

= 944.28 or 94.44%

CONCLUSION

On the basis of the above project report, it has been concluded that data analysis contributes effectively in order to make accurate judgements. The report concludes with the computation of mean-mode-median as per the given data set. As well as in the further part of the report, forecasting of humidity is done for the 15^th and 20^th day.

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