L/508/0442 - Quantitative Methods Of Calculation Represent Mathematical Data

University: University of Chester

  • Unit No: 6
  • Level: Undergraduate/College
  • Pages: 10
  • Words: 2500
  • Paper Type: PPT
  • Course Code:

    L/508/0442

  • Country: AU
  • Downloads: 0
Question :

The purpose of this report is to conduct quantitative analysis through different and effective methods that can assist in gathering more reliable information for business purpose. Quantitative methods of calculation represent mathematical data for which individual needs to have specific knowledge in such areas.

  • Determine an application of different quantitative methods and gather authenticate information from its effective uses.
Answer :
Organization Selected : -

INTRODUCTION

Quantitative assessment implies for the evaluation of data set through the means of statistical tools as well techniques. In the recent times, business unit laid high level of emphasis on undertaking quantitative tools and techniques for the purpose of decision making. Statistical tools and techniques are highly significant which in turn helps in drawing suitable conclusion from data set. The present report is based on different case situations which will provide deeper insight about the manner in which tools such as descriptive statistics aid in decision making. Further, report also presents how regression analysis helps in determining the impact of one variable on another.  It also depicts the use of probability evaluation or assessment in decision making.

QUESTION 1

a. Stating the Frequency, Relative Frequency, Cumulative Relative Frequency and Class Midpoint

Measures of Variability and Association

Class interval

Number of passengers at each train station in Melbourne

Cumulative  frequency

Relative frequency

Cumulative relative frequency

mid point

169-468

19

19

0.32

0.32

403

469-768

12

31

0.20

0.52

853

769-1068

9

40

0.15

0.67

1303

1069-1368

8

48

0.13

0.80

1753

1369-1668

5

53

0.08

0.88

2203

1669-1968

2

55

0.03

0.92

2653

1969-2268

1

56

0.02

0.93

3103

2569-2868

2

58

0.03

0.97

4003

2869-3168

1

59

0.02

0.98

4453

7669-7968

1

60

0.02

1.00

11653

Grand Total

60

 

 

 

 

 

Histogram

Bin

Frequency

169

1

1249

45

2329

10

3409

3

4489

0

5569

0

6649

0

More

1

3. Descriptive statistics

Computation of mean, mode and median

Number of passengers at each train station in Melbourne

Particulars

Outcome

Mean

1033.43

Standard Error

141.11

Median

715

Mode

401

Standard Deviation

1093.04

Sample Variance

1194731

Kurtosis

23.78

Skewness

4.21

Range

7560

Minimum

169

Maximum

7729

Sum

62006

Count

60

 

The above depicted table of descriptive statistics show that average number of passengers at each train station in Melbourne accounts for 1033 respectively. Further, outcome of descriptive statistics present that 50% value of data set and median value implies for 715 significantly. Along with this, it has assessed from the evaluation that mode is 401. On the basis of this, it can be presented that 401 passengers repeatedly arrived at each train station in Melbourne for the weekday peak time such as 7 am to 9.29 am.         

QUESTION 2

a. Assessing whether above given data comes under population or sample

            From research, it has found that data set given in relation to the weekly attendance in Holmes and number of chocolate bars sold. Referring given case, it can be presented that manager of supermarket wants to assess whether there is any relationship takes place between the students who attending class and sales of chocolate bars. Hence, in this, whole students of Holmes have been considered. Thus, by taking into such aspects it can be presented that given data set implies for population rather than sample. Moreover, sample is the small part of population, whereas population accounts for the whole target. Hence, it can be mentioned that given data set includes population. Through assessment, it has identified that significant difference takes place between sample and population (Cressie, 2015). Moreover, population implies for the collection of all elements who possess common characteristics that comprise universe.  

b. Calculating standard deviation of the weekly attendance

Computation of standard deviation

S. No.

Weekly attendance

1

472

2

413

3

503

4

612

5

399

6

538

7

455

 

 

Standard deviation

74.1

 

            Findings of statistical assessment presents that in the near future mean value will deviate from the figure of 74.1 respectively.

3. Inter-quartile range assessment

Interquartile range may be swerved as a viability measure which divides or distinguishes whole data set into quartiles. By subtracting 1st quartile from the 3rd one interquartile range can be assessed.

Calculation of inter-quartile range

Particulars

Number of chocolate bars sold

 

6916

 

5884

 

7223

 

8158

 

6014

 

7209

 

6214

1st quartile

6114

3rd quartile

7216

Inter-quartile range (3rd quartile – 1st quartile)

1102

 

4. Assessing correlation co-efficient

Correlation is the most effectual statistical tool which helps in assessing the extent to which two variables are associated with each other. In other words, it helps in assessing the level to which one variable will move when changes take place in another. 

Correlation co-efficient

Weekly attendance

Number of chocolate bars sold

472

6916

413

5884

503

7223

612

8158

399

6014

538

7209

455

6214

 

 

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