Statistics 1.

Statistics

1.

Thefirst capability is the company’s commitment to analytics. Thelevel of commitment defines the ability to combine skills andknowledge through training and promotional campaigns of theorganization. The combination of expertise and existing businessknowledge creates a competitive advantage. Second is the movementfrom technology to talent. The current flow of data is notcommensurate with the talent available to handle data. Consequently,organizations have more information that lacks insight. Enterprisesshould create a preference for analytical skills by creatingsystematic departments and roles such as data scientists and chiefanalytical officers. The practice of creating analytical employeesshould extend to the hiring processes. The relevance of analyticalpersonnel lay in their ability to provide critical insights into thebusiness (Elder, 2015).

2.

Thesize of the sample defines the reliability of the test outcome sincesamples represent the population. The accuracy of the test resultsincreases as the sample gets bigger. However, a larger sample size isassociated with an increased cost of administering research.Consequently, it is imperative to use an optimal sample size thatrepresents the population of the study. For example, asking the pollopinion from two voters from a population of two thousand does notprovide adequate representation of the population. In contrast,asking the view of 1200 individuals is substantial and a goodrepresentation of the population. The opinion of 1200 represents a60% of the population and may as well be a good indicator of thepopulation (Button et al., 2013).

Planningthe sample size before collecting data enables the investigator toorganize the study process. The researcher can accommodate variousdivisions of a heterogeneous population. Besides, it saves on costsand time as the canvasser can allocate various resources such asfinances and human capital. Besides, prior arrangements help inidentifying approaches that are more feasible to reach the chosensample (Fugarda, &amp Pottsb, 2015).

References

Button,K.S., Ioannidis, J.P, Mokrysz, C., Nosek, B.A., Flint, J., Robinson,E.S. &amp Munafò, M.R. (2013). Power failure: Why small sample sizeundermines the reliability of neuroscience. NatureReviews Neuroscience 14, 365-376

Elder,J.H. (2015). Big data and information analytics (BDIA): A newtraining program in data analytics &amp visualization. AmericanInstitute of Mathematical Sciences, 1(1).

Fugarda,A.J. &amp Pottsb, H. (2015). Supporting thinking on sample sizes forthematic analyses: A quantitative tool. InternationalJournal of Social Research Methodology, 18(6),669-684.

References

Andrejevic,M. &ampGates.(2014). Big Data Surveillance: Introduction.Surveillance &ampSociety12(2): 185-196.http://www.surveillance-and-society.org

Barnhill, M.,Godin, J., Madison, M. &amp Napier, C. (2015).NoSQL DatabaseTechnologies.Journal ofInternational Technology and Information Management,24(1)1-13 http://scholarworks.lib.csusb.edu/jitim/vol24/iss1/1

Fan, W., &ampGordon, M.D. (2014). The power of social media analytics.Communications of the ACM, 57 (6), 74-81.

Halper, F.(2011). The top 5 trends in predictive analytics: Information&ampmanagement, 2(16), 16-18

Olszak C.&ampBartuś, T. (2013).Multi-Agent Framework for Social CustomerRelationship Management Systems. Issues in Informing Science andInformation Technology, 10(1) 368-387http://iisit.org/Vol10/IISITv10p367-387Olszak0055.pdf

Raghupathi&ampRaghupathi(2014) Big Data Analytics in Healthcare: Promise and Potentialhttp://www.hissjournal.com/content/2/1/3