Analytics and Sampling

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The emerging trends include new business analytics applications inprice optimization, analyzing personnel and supply chain visibility(Kohavi, Rothleder &amp Simoudis, 2002). New applications have beenintroduced aimed at better managing the supply chain oforganizations. They include applications that assess sales andanalyze data. Process mining is an illustration of an emerging modelthat is being used to manage supply chains (Chen, Chiang &ampStorey, 2012). The widespread use of supply chain management softwaretools has made it possible for businesses to completely put togethertheir supply as well as demand chains. As a result, it is nowpossible to collect up to date information concerning the demand ofspecific products. Bihani and Patil (2014) describe this asprescriptive analytics, which focuses on how a business can improveits supply chain to increase service levels and at the same timereduce expenses. Software tools that allow for the integration ofdemand and supply chains make it possible to optimize the price ofproducts. Emerging employee-centered analytics are making it possiblefor organizations to analyze information about their workers, likeperformance, benefits and compensation (Kohavi, Rothleder &ampSimoudis, 2002).

Sample size is related to statistical tests as well as outcomesbecause, when conducting a research, it is important to have a samplesize that will produce the required results. Also, it is impossibleto conduct a statistical test without a sample size. For instance,when testing the popularity of a product within a population of 100people, a sample size of the same number is required. Once the studyhas been conducted, and 90% of the sample population claims to knowabout the product, then the statistical outcome is achieved, which isthat more people are aware of the product. Sample size is plannedprior to data collection to ensure that the researcher plans inadvance for the instruments needed, study method, research designthat will be used and ensure that the sample size is not too low toconduct a study.

References

Bihani, P., &amp Patil, S. T. (2014). A comparative study of dataanalysis techniques. International Journal of Emerging Trends andTechnology in Computer Science, 3(2), 95-101.

Chen, H., Chiang, R., Storey, V. C. (). Business intelligence andanalytics: From big data to big impact. MIS Quarterly, 36(4),1165-1188.

Kohavi, R., Rothleder, N. J., Simoudis, E. (2002). Emerging trends inbusiness analytics. Communications of the ACM, 45(8), 45-48.