Data And Statistical Management Assignment Sample

Statistical Wizardry: Sculpting Data into Strategic Assets

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Introduction of The Data Analysis

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Part 1

Task 1: Podcast on “The value of data and statistical management”

Welcome to a new episode of the series "Business Breakfast" today a vital discussion on the "value of data and statistical management" within the business competency will endeavour in influencing the financial capabilities of working professionals. In this episode, a detailed discussion will cover the meaning of data and highlight its importance thereby evaluating the relationship between data, information and knowledge. This will be followed by evaluating a few live examples for a better understanding of the concept. The critical evaluation concerning multiple methods of data analysis like "descriptive, exploratory and confirmatory methods" will be discussed highlighting their pros and cons for better business performance evaluation.

Data is a source of fact which can either be derived as a numeric or be subjective depending on the topic it is based on. As per the suggestions of Lobe et al. (2020), data allows businesses or any organisation in measuring the "effectiveness of a provided strategy". At times when these strategies are concerned with overcoming critical challenges, the action of data collection helps allow and determine how well the results are performing. Furthermore, analysis of data helps determine "whether or not" the organisational approach is required to be tweaked and can be changed over the long term. Data facilitates the firm in predicting and forecasting key business trends as well as identifying key opportunities to stay ahead of the other competitive firms. Therefore, data provides companies with in-depth insights towards consumer behaviour along with market conditions before they can take place which makes data essential for companies to "grow and prosper".

Data is considered to be the simplest form thereby consisting of "raw alphanumeric values". On the contrary, as mentioned by Faccia (2019), information can be created when raw data get processed and structured or organised in providing context. Therefore, information is derived as processed data and knowledge is the learning derived from the information. Concerning the company's annual report of Tesla for the third quarter in 2022, raw data presented in the company's financial statements had been justified and summarised as key highlights, printed at the start of the report. Here, financial statements present the detailed financials under individual heads which had been processed in creating highlights, financial summaries and key metrics. For example, "$3.3B GAAP net income" and "27.9% GAAP Automotive gross margin in Q3" provide information about the net profits generated by Tesla in the third quarter and the percentage of profit based on sales (Tesla-cdn.thron, 2023). Thus this information held in gaining knowledge regarding the profitability of Tesla in Q3.

Similarly reflecting critical published data sources on the financial performance of one of the major competitors of Tesla in the world automotive market is Nissan. Deriving financial data from their annual report also helps protect the relationship established between data, information and knowledge gained. Data concerned for "Consolidated Financial Results for FY2021" in Nissan highlights that the net sales in FY2020 were 7,862,572 million Yen a -20.4% whereas in FY2021 it was 8,424,585 million Yen, a 7.1% increase (Nissan-global, 2022). Following this, Nissan generated a -448,697 million Yen loss in 2020 which got better in 2021 to profits of 215,533 million Yen. Therefore, these data provide information on the company's profitability and sales thereby helping to gain knowledge about the financial condition of Nissan which was poor in 2020, due to the global pandemic but critically recovered in 2021. Thus, overall knowledge of Tesla's and its competitor Nissan's last financial performance was achieved by evaluating the published sources.

Data analysis can be identified as the process following inspection, cleansing, transforming as well as modelling the data derived with an object to discover useful information and inform conclusions. In the opinions of Kuckartz (2019), data analysis can be identified as a systematic process applicable to logical and statistical techniques for describing and illustrating data followed by condensing, recapping and evaluating the same. Different methods of data analysis using statistical methods outline the tools like descriptive, exploratory and confirmatory methods. Descriptive methods of adat analysis involve using a "table of means and quantiles", and "measures of dispersions" like variance and standard deviations which are critically used to examine the disparate hypothesis.

It is useful because it requires basic mathematic skills to present complex data in easily digestible formats however, is limited only to making summations. The exploratory method helps in understanding in-depth data to learn different characteristics of data however is limited in providing inconclusive results. The confirmatory method uses traditional statistical tools like inference, significance and confidence which are deprived to involve modern lookouts.

Task 2: Production of raw data and analysis of findings

  • Production of analysis of raw data highlights the unformatted and unstructured data coming directly from the source.
  • This task evaluates differences between descriptive and inferential data with multiple statistical tests.
  • Data analysis of the findings helps understand the evaluation criteria for conducting the tests on data sets.

Data analysis can be highlighted as a process where the systematic process applies logical and statistical techniques for describing and illustrating data followed by condensing, recapping and evaluating the same. The task had derived data sets from two different sources on which descriptive statistical methods and deployed to derive results from the t-test, ANOVA test and Chi-square test.

Differences between “descriptive and inferential data”

  • Descriptive statistical data are concerned to describe a target population thereby highlighting data already known (Mishra et al. 2020).
  • Comparatively, inferential statistical data makes inferences from samples to generalise into population making conclusions about data which is beyond available.
  • Descriptive statistics "organise, analyse and present data" meaningfully, whereas inferential statistics "compares, tests and predicts" future results.

Both the statistical data help describe, characterise, test and predicting on future outcomes thereby drawing information from a large population and creating samples to conduct tests. Descriptive focuses mainly to describe and characterise the population with sampling methods and inferential is focused on drawing inferences concerning the population using the sampling methods.

  • Descriptive statistics project its final results in form of charts. graphs and tables whereas, inferential statistics project final results as probability scores.
  • Descriptive statistics uses tools like "measures of central tendency" covering "mean, median and mode"; "spread of data" (standard deviation, variance and range").
  • Comparatively, the tools used in inferential statistics are various hypothesis tests and "analysis of variance" (Sanders, 2019).

Descriptive research helps to "classify, describe, compare and measure data", which in comparison to inferential statistics derives the inferences from a wide range of populations. Thereafter the statics critically constructs smaller samples which are thereby helpful in conducting tests to examine hypotheses.

Calculation of data sets using statistical methods

T-test

  • The t-test conducted for "Clothing Retail Ltd." highlights that all p-values derived as answers are more than the "level of significance 5%" i.e. more than 0.005.
  • Along with this, the one-sample t-test conducted on "Football club points" also highlights that p-values for all clubs are more than 0.005.
  • Therefore, it can be commented that the results generated in the one-sample t-test for both data sets were not significant as the values derived for answers were much higher than 0.005 (Kim and Park, 2019).

Conduction of "one sample t-test" in statistical tests helps in testing hypotheses when it is required to learn whether samples are derived from an unknown range of "population mean is different" from any specific values. In Figure 3, the results of P-values of the club points have been depicted in a graph. The figure depicts the chart prepared for the estimation of “P-values” and “T-statistic”.

ANOVA test

  • In this testing, the results are significant if the p-value is below 0.005 ("confidence level 95 %") and F-ratio is greater than 1 (Elmushyakhi, 2021). The ANOVA test of the club points has been performed and relevant data analysis has been prepared according to the derived data. s
  • Conducting ANOVA testing on data sets of "Clothing Retail Ltd." highlights the p-value as 0.00421687 and F-ratio as 4.36039975.
  • The ANOVA testing on "Football club points" highlights that the p-value is 0.976484717 and the F-ratio is 0.156159069.

An ANOVA testing allows researchers for comparing "more than two groups" at the same time for determining the existence of a relationship between the groups. ANOVA test of clothing retail depicted that results are significant and the model is fit, whereas, football club points depicted that the results are not significant and the model is not efficient and not fit.

Chi-square test

Figure 6: “Chi-square test” graph of club points

  • The chi-square statistics is a method projecting a relationship between multiple categorical values where low chi-square values reflect higher correlation and vice versa for the sets of data (Sari et al. 2019). The figure Represents the data of the football club points along with their expected values for January to June. The respective chi-square values of several football clubs have been calculated based on the data provided.
  • Conducting the chi-square test for "Clothing Retail Ltd." the result generated is 0.000235469
  • Similarly, conducting the chi-square test for "Football club points" the results generated is 0.488866551

The Chi-square testing in statistics is used for data which consists of different variables that are distributed across multiple categories and are critically denoted as χ2, and the formula of chi-square is "χ2 = ∑(Oi – Ei)2/Ei" where "Oi = observed value (actual value) and Ei = expected value". The figure describes the final results derived after performing “chi-square test” on a bar graph on the basis of the values of the club point data provided.

Data analysis and findings

  • The process of data analysis projects activities like "inspecting, cleaning, transforming and modelling" the data sets collected having the objective to discover useful facts (Lee et al. 2020).

  • The t-test conducted for the two types of data sets provided critically highlighted that the results were not significant for the data provided as clothing details of different categories and different club points.
  • The p-value derived from the t-statistics for both the data sets was more than 0.05 in the "level of confidence of 5%" thereby suggesting the results not being significant.

The formula of "one-sample t-test" is "T = (X?The formula of "one-sample t-test" is "T = (X? – μ) / S/√n" where "X? is the sample mean", "μ is considered the hypothesized population mean", "S is the standard deviation" and "n is the number of sample observations".

  • Conduction of ANOVA testing for both the data sets had been illustrated with the help of estimating values of p-value and F-ratio which are required to remain below 0.05 and more than 1 respectively.
  • ANOVA tests are useful in comparing the projecting "differences of means" between two groups or more and estimating the amount of variation (de Figueiredo et al. 2020).
  • However, in both the data sets results were suitable as they were projected significantly being the test model efficiently fit.

The formula used in calculating ANOVA where "F = Anova Coefficient" (MSB/MSE) and where "MSB = Mean sum of squares between the groups" and "MSW = Mean sum of squares within the groups".

  • The "chi-square distribution" can be identified as the "continuous probability distribution" where the shape of this distribution is dependent on its "degree of freedom" (k) (Akinkunmi, 2019).
  • It can be seen from the chi-squared test conducted on "Clothing Retail Ltd." that the answer is low depicting the variable having a high correlation.
  • On the other hand, the Chi-square test on Football club points derived the result to be high depicting that a lower correlation is present between variables.

The "chi?square (χ 2) test" is utilised for evaluating the relationship preset between the 2 categorical variables where observed values get deducted from the expected values.

Conclusion

  • The task focused on analysing raw data based on the conduction of statistical methods by calculating a range of descriptive and inferential statistics.
  • Justification of methods like "T-Test, ANOVA testing, and chi-square testing" have helped to drive answers and frame conclusion
  • The findings of the test results are explained by communicating the process of data analysis.

A critical reflection of different formats and methods of statistical tools are presented with relevant calculations based on the provided data sets for "Clothing Retail Ltd." and for "Football club points". Thus, analysing raw data and deploying various statistical methods have helped find key insights from the results.

Part 2

Task 3: Discussion on “Continual Professional Development (CPD)”

Importance of statistical methods in business planning

In business planning, the incorporation of statistical methods aids various aspects as the producers and manufacturers who require estimating the demands for produced goods in recent contexts as well as in future. Based on the suggestions of Hoerl and Snee (2020), the projection of statistical analysis in business involves the procedure to collect and analyse data for identifying trends and patterns. This removes bias as well as informs the producers with critically sound decision-making. While planning business decisions statistical methods help to access data findings which helps to gain insight into "future opportunities" that might arise while conducting business operations. The statistical methods can be utilised in finding new markets thereby promoting better retention of customers resulting in increased sales which will help to identify sale opportunities. The test conducted for the statistical significance comments the manufacturers concerning the probability of the relationship that can be found due only to random chances.

Business owners use statistical methods for making decisions for financial planning as well as budgeting processes as the organisation are critically guided by the "statistics in financial policy decisions". Thus, these statistical measures are helpful in attaining lower risks while operating as analysing diverse activities in the financial markets worldwide along with predicting the impacts of the economic crisis (Keller, 2022). The major role played by statistical methods thereby implemented in business planning facilitates improving the key decisions made in the "face of uncertainties" which benefits attaining the decision-making objectives as well as data-driven rather than making decisions based on instincts. In economic planning, the statistical methods thereby deployed by the business managers have crucial purposes as the statistical data benefits in comparing the "rate of development" in different countries. Based on the results generated the business managers critically decide on the price mechanism in the commodity markets to set the prices of products.

Use of statistical process in operations

Significant use of the "statistical process control" along with appropriate techniques for application in operations are beneficial for monitoring as well as control processes. As mentioned by Shamsuzzaman et al. (2021), "statistical process control" SPC are applicable to use tools mentioning control charts, focusing on continuous operational improvement and designing experiments. Thus, various operations in business like inventory, quality, flowtime and capacity are monitored and controlled by employing SPC. Processes of monitoring along with controlling critically ensure that a business can operate at its full potential, therefore, benefitting the managers who apply the same when confirming products are made so that products can meet their specifications.

Distinctions between and evaluation of different probability distribution

A probability distribution can be described as mathematical functions illustrated as diverse possible values for the concerned variables within the data set. In binomial distribution, the "number of outcomes" is 2 denoting "success or failure" describing the distributions of "binary data from the finite sample". On the contrary, as opined by Oladipo (2021), a Poisson distribution is a probability distribution where there are unlimited possible outcomes. Comparatively, a "normal probability distribution" is continuous where most "data points cluster" in the "middle of the range" while the remaining are tapered symmetrically towards the extreme.

Recommendations on the operation of statistical process control in business

Suggestions as well as judgements based on the operations of "statistical process control" can get business performance improvements with strategic business planning. It is recommended that SPC must be used within the production and manufacturing processes which measure the consistency of product performance based on its specific design. This would help to achieve consistent "quality and performance" thereby facilitating the manufacturers to reduce scrap amounts, and rework as well as warranty claims. It is recommended to investigate regulators and instruments thereby identifying "improvements in control" and planning for implementing the same.

References

  • Akinkunmi, M., 2019. Other Continuous Probability Distributions. In Introduction to Statistics Using R (pp. 103-113). Springer, Cham.s
  • de Figueiredo, M., Giannoukos, S., Wüthrich, C., Zenobi, R. and Rutledge, D.N., 2022. A tutorial on the analysis of multifactorial designs from one or more data sources using AComDim. Journal of Chemometrics, p.e3384.
  • Elmushyakhi, A., 2021, February. Parametric characterization of nano-hybrid wood polymer composites using ANOVA and regression analysis. In Structures (Vol. 29, pp. 652-662). Elsevier.
  • Faccia, A., 2019, August. Data and Information Flows: Assessing Threads and Opportunities to Ensure Privacy and Investment Returns. In Proceedings of the 2019 3rd International Conference on Cloud and Big Data Computing (pp. 54-59).
  • Hoerl, R.W. and Snee, R.D., 2020. Statistical thinking: Improving business performance. John Wiley & Sons.
  • Keller, G., 2022. Statistics for management and economics. Cengage Learning.
  • Kim, T.K. and Park, J.H., 2019. More about the basic assumptions of t-test: normality and sample size. Korean journal of anesthesiology, 72(4), pp.331-335.
  • Kuckartz, U., 2019. Qualitative text analysis: A systematic approach. In Compendium for early career researchers in mathematics education (pp. 181-197). Springer, Cham.
  • Lee, J., Hyeon, D.Y. and Hwang, D., 2020. Single-cell multiomics: technologies and data analysis methods. Experimental & Molecular Medicine, 52(9), pp.1428-1442.
  • Lobe, B., Morgan, D. and Hoffman, K.A., 2020. Qualitative data collection in an era of social distancing. International journal of qualitative methods, 19, p.1609406920937875.
  • Mishra, P., Pandey, C.M., Singh, U., Gupta, A., Sahu, C. and Keshri, A., 2019. Descriptive statistics and normality tests for statistical data. Annals of cardiac anaesthesia, 22(1), p.67.
  • Nissan-global (2022), FY2021 Consolidated Financial Results (Japanese Accounting Standards). Available at: https://www.nissan-global.com/EN/IR/LIBRARY/ASSETS/DATA/2021/2021results_financialresult_375_e.pdf [Accessed on 27.12.22]
  • Oladipo, A.T., 2021. Bounds for Poisson and neutrosophic Poisson distributions associated with chebyshev polynomials. Infinite Study.
  • Sanders, K., Sheard, J., Becker, B.A., Eckerdal, A. and Hamouda, S., 2019, July. Inferential statistics in computing education research: A methodological review. In Proceedings of the 2019 ACM conference on international computing education research (pp. 177-185).
  • Sari, N.C., Kusumaningrum, R. and Suryono, S., 2019. Information System for Analysis of the Need of Doctors Using K-Means Clustering and Chi-Square Test. In E3S Web of Conferences (Vol. 125, p. 25001). EDP Sciences.
  • Shamsuzzaman, M., Shamsuzzoha, A., Maged, A., Haridy, S., Bashir, H. and Karim, A., 2021. Effective monitoring of carbon emissions from industrial sector using statistical process control. Applied Energy, 300, p.117352.
  • Tesla-cdn.thron (2023), Q3 2022 Update. Available at: https://tesla-cdn.thron.com/static/WTULXQ_TSLA_Q3_2022_Update_KPK2Y7.pdf?xseo=&response-content-disposition=inline%3Bfilename%3D%22tsla-q3-2022-update.pdf%22 [Accessed on 27.12.22]

Bibliography

  • ANDERSON, D. et al (2010). Statistics for Business and Economics. 2nd Ed. Cengage Learning. DAVIS, D. and PECAR, B. (2013) Business Statistics Using Excel. 2nd Ed. Oxford: Oxford University Press.
  • MORRIS, C. (2012) Quantitative Approaches in Business Studies. 8th Ed. Harlow: Pearson Prentice Hall.
  • SLACK, N. and BRANDON-JONES, A. (2008) Quantitative Analysis in Operations Management. Harlow: Pearson Prentice Hall.
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