Data Analysis and Decision Making

Question :

This assessment will cover the following questions:

How application of summarising and analysing data helps in effective decision-making process.
Demonstrate and apply various techniques used for forecasting and making logical reasoning.
How collected data and forecasting techniques helps to deal with the real-life situations.

Answer :

INTRODUCTION

Data analysis is a systematic process of collecting data from various sources and after that making detailed analysis by help of vital range of techniques. This analysed data, helps to business entities in order to make accurate decisions (Tsilimigras and Fodor, 2016). The project report is based on analysing of data regards to humidity percentage of London city of ten days (Humidity data of London, 2019). Report covers detailed calculation of different terms such as mean-mode-median etc. In the end part of report, forecasting of humidity percentage is done by help of linear model.

MAIN BODY

1. Representation of data in tabular form:

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

Days (Date)	Humidity (values in %)
1^st of November, 2019	98
2^nd of November, 2019	89
3^rd of November, 2019	89
4^th of November, 2019	96
5^th of November, 2019	98
6^th of November, 2019	95
7^th of November, 2019	94
8^th of November, 2019	95
9^th of November, 2019	98
10^th of November, 2019	91

2. Dara representation in charts:

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

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

Days (Date)	Humidity (values in %)
1^st of November, 2019	98
2^nd of November, 2019	89
3^rd of November, 2019	89
4^th of November, 2019	96
5^th of November, 2019	98
6^th of November, 2019	95
7^th of November, 2019	94
8^th of November, 2019	95
9^th of November, 2019	98
10^th of November, 2019	91
∑X	943
Mean	94.3
Mode	98
Median	95
Range	9
Maximum	98
Minimum	89

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

Mean= ∑N/ N

N= 10

∑N= 943

Mean= 943/10

= 94.3

Mode- There is no any specific formula to compute mode in the case of individual data series (Greenacre, 2017). This can derived by checking 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, value of mode is 98 because its frequency is three that is higher among all numbers.

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Median- It is a mid value in a data series. This can be computed as per the nature of data like if number of data are odd then formula will be as: M = N+1/2^th item. While if number of data are 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 median of humidity data is done in such manner:

S. No.	Humidity (in terms of %)
1	89
2	89
3	91
4	94
5	95
6	95
7	96
8	98
9	98
10	98

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 group of numbers (Fisher, 2017). Such as in the above mentioned data of humidity, higher number is 98 and lower number is 89. So value of range is 9 (98-89).

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

Days (Date)	Humidity (values in %)	(x- mean)	(x-mean)²
1st of November, 2019	98	3.7	13.69
2nd of November, 2019	89	-5.3	28.09
3rd of November, 2019	89	-5.3	28.09
4th of November, 2019	96	1.7	2.89
5th of November, 2019	98	3.7	13.69
6th of November, 2019	95	0.7	0.49
7th of November, 2019	94	-0.3	0.09
8th of November, 2019	95	0.7	0.49
9th of November, 2019	98	3.7	13.69
10th of November, 2019	91	-3.3	10.89
			112.1

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 day 15 and 20.

Days (Date)	Humidity (values in %)	X²	∑XY
1	98	1	98
2	89	4	178
3	89	9	267
4	96	16	384
5	98	25	490
6	95	36	570
7	94	49	658
8	95	64	760
9	98	81	882
10	91	100	910
∑X= 55	∑Y= 943	∑X²= 385	∑XY= 5197

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

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