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

University: University of Chester

• Unit No: 6
• Pages: 12 / Words 3071
• Paper Type: PPT
• Course Code: L/508/0442
Question :
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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.
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## 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.

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## 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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