COMM5011 Data Analysis For Business

Start-up Muster

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Table of Contents

Introduction. 3

Discussion. 3

Conclusion. 5

Reference list 6

Introduction

In this assignment, the Start-up Muster report will be analysed based on its statistical parts of data analysis. The main key essences of the report are based on the technological and science-oriented balances. So, there are so many parts which can be changed to develop the quality. The process of deriving the statistical metrics will be analysed based on its quotes in the paper of the report. An alternative presentation will be suggested to the report which can increase the quality of the project. A few numbers of comments on the data collection and management also have been given to improve the quality.

Discussion

The statistical metric is the part in which the entire database has been judged based on the rules of statistics. The entire metric has some parts which are standard deviation, range, variance, absolute deviation, and many more. In the scenario of the report of Start-up Muster, statistical metrics have been given which will be analysed in this part. The most significant five statistical measures are extreme values which are minimum and maximum values, lower and upper quartiles, median (Calle, 2019). So, based on these different parts, the entire calculation has been done which is the most important part in the chapter of data analysis. As an example, a statistical metric of this report paper will be derived below:

The ratio of the Means (ROM) = ((Summation of maximum values from 1 to N)/ N)/ ((Summation of minimum values from 1 to N)

Unpaired Peak Accuracy (UPA) = ((Maximum value – Minimum value) / Minimum value) * 100%

So, these formulas help to know about the stages of the mean value and the medium which has been already present in the report paper. The Mean value helps to know about the variation of the maximum and minimum value which helps to derive the range on a later basis. If someone does not calculate the mean in statistical analysis, he or she can not calculate the median which is the most important part of the next step to derive the unpaired peak accuracy. The sum of the maximum and the minimum value has to be subtracted which helps to retain the difference between them in the numeric format. On the basis of the report, the number of the most purchasing customers is the maximum value and the customers who are buying at the lower rate are the minimum value. Then to calculate the percentage of the unpaired peak accuracy the entire difference has to be divided by the sum or the minimum value and then multiplied by the 100 (Poppick et al., 2020). If a company can follow the peak accuracy in the highest value, then it is clear that their sales rate is high. Based on this rate, it can be also stated that the company has the most number of customers who are regular buyers. So, trust and loyalty with the company have been made in the mind of those customers. This helps to grow the profit level which helps the company to secure a permanent place in the global market.

Figure 1: Graphical presentation of statistical analysis

Source: (Zhao et al., 2020)

The entire presentation of the part of data analysis can also be derived in the graphical format. It helps to enhance the visual presentation level of the report paper (Zhao et al., 2020). Also, the viewer of the report can easily understand the variation of the minimum and maximum values. Even with the help of the excel sheet, the visibility of the graph can also be increased. The calculator of the mean value and the medium with the help of graphical representation, the ups and downs of the values can also be presented in an efficient way. Also, it is true that a perfect and significant graph can help to enhance the quality of the writing. So, if they maintain the graphical presentation then it may increase the reach of the report. Also, there is another option which is that they can use statistical and graphical presentations at the same time (Ghazaryan, Alecian & Hakobyan, 2018). After calculating all the values from the statistical analysis, they can represent those in the graphical format. It helps to prove the calculation and authenticity along with the visibility. In this way, the entire project can gain a hike in the eyes of the viewers and the reach of the site can also be increased.

Data collection is the process in which a kind of data has been collected from any primary source or any secondary source. Primary sources are those where the researcher has collected the data from the person through an interview or survey. In the process of the interview, researchers have asked a few numbers of questions that the candidates have to answer in their quotes. On the other hand, in the process of the survey, the researchers have asked questions but here they have given the options to the candidates from which they have to select. So, the primary data collection helps to enhance the authenticity and the genuine reaction of the candidates who have been suffering from the particular situation which has been provided in the job title (Mishra et al., 2019). However, in the secondary sources, the data which have been already presented in the articles and journals have been studied to gather the information. These are the different processes of data collection that have been managed to prepare the analysis.

Conclusion

After analysing the entire assignment, it can be concluded that statistical analysis helps to get the authentic data calculator based on the given scenario. It helps to gather the exact value of mean and medium which helps to know the perfect scenario of the given report. Also, on the alternative, the researcher can present the graphical representation which helps to enhance the quality of visualisation of the paper. On the other hand, the comment on the data collection and management has been given to suggest some improvement in the paper. Altogether, it is the entire representation of the given report based on the data analysis.

Reference list

Calle, M. L. (2019). Statistical analysis of metagenomics data. Genomics & informatics, 17(1). DOI: https://dx.doi.org/10.5808%2FGI.2019.17.1.e6

Ghazaryan, S., Alecian, G., & Hakobyan, A. A. (2018). New catalogue of chemically peculiar stars, and statistical analysis. Monthly Notices of the Royal Astronomical Society, 480(3), 2953-2962. DOI: https://doi.org/10.1093/mnras/sty1912

Mishra, P., Pandey, C. M., Singh, U., Keshri, A., & Sabaretnam, M. (2019). Selection of appropriate statistical methods for data analysis. Annals of cardiac anaesthesia, 22(3), 297. DOI: https://dx.doi.org/10.4103%2Faca.ACA_248_18

Poppick, A., Nardi, J., Feldman, N., Baker, A. H., Pinard, A., & Hammerling, D. M. (2020). A statistical analysis of lossily compressed climate model data. Computers & Geosciences, 145, 104599. DOI:https://doi.org/10.1016/j.cageo.2020.104599

Zhao, S., Gao, D., Zhuang, Z., Chong, M. K., Cai, Y., Ran, J., … & Wang, M. H. (2020). Estimating the serial interval of the novel coronavirus disease (COVID-19): a statistical analysis using the public data in Hong Kong from January 16 to February 15, 2020. Frontiers in Physics, 8, 347. DOI: https://doi.org/10.3389/fphy.2020.00347