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Data Analysis and Decision Making

University: LONDON SCHOOL OF COMMERCE

  • Unit No:
  • Level: High school
  • Pages: 12 / Words 3075
  • Paper Type: Assignment
  • Course Code:
  • Downloads: 693
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 with the help of a vital range of techniques. This analysed data helps business entities in order to make accurate decisions (Tsilimigras and Fodor, 2016). The project report is based on an analysis of data regards to humidity percentage in London city of ten days (Humidity Data of London, 2019). The report covers detailed calculations of different terms such as mean-mode-median etc. In the end part of the report, forecasting of humidity percentage is done with the help of a linear model.

MAIN BODY

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:

Days (Date)

Humidity (values in %)

1st of November, 2019

98

2nd of November, 2019

89

3rd of November, 2019

89

4th of November, 2019

96

5th of November, 2019

98

6th of November, 2019

95

7th of November, 2019

94

8th of November, 2019

95

9th of November, 2019

98

10th of November, 2019

91

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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:

Days (Date)

Humidity (values in %)

1st of November, 2019

98

2nd of November, 2019

89

3rd of November, 2019

89

4th of November, 2019

96

5th of November, 2019

98

6th of November, 2019

95

7th of November, 2019

94

8th of November, 2019

95

9th of November, 2019

98

10th 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 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.

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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+1/2th item. While if the number of data is even then M = (N/2th item+N/2th 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:

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

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

= (5th item+6th 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:

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

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

 

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 15th

Y= mx+c

= 0.13*15+942.28

= 944.23 or 94.44%

For 20th 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 15th and 20th day.

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