Determiningwhether the 70-degree temperature is a significant departure from theaverage temperature (60 degrees) involves calculating a standardtwo-tailed test. The additional data required is the standarddeviations associated with the 70 and 60-degree temperatures, as wellas the number of observations for each. It is possible to form thenull and alternative hypothesis at the 0.05 level of significanceusing the data (Jabber, 2014). The theory states that the differencebetween 60 and 70 is equal to zero in case there is no greatdeviation between the two figures. The second premise (alternativehypothesis) provides that the difference between the two numbers isgreater than zero in case there is a great deviation between the twofigures. Besides, the data will enable the calculation of the degreesof freedom. The results will lead to the generation of P values thatfurther helps in selecting the appropriate hypothesis (Neuhauser,2015).
Gandomiand Haider (2015) observe that descriptive analysis is the mostcommon type of business analytics. During big data analyses,descriptive analytics are conducted to summarize what transpiredwithin the business environment (Bayrack, 2015). For example, in thesocial media platforms, descriptive statistics are used to countsimple events such as the number of fans, followers, posts,check-ins, page views as well as mentions. The predictive analysisuses probabilities to forecast what might happen and adds to thecompetitive advantage of the firm by helping it to take action inadvance (Gleicher, 2016).
Datavisualization enhances decision-making during analysis of largeinformation volume (McGinn et al., 2016). The grouping of largevolumes of figures into data points enables business executives andfunctional heads to understand the relationship of the numbers.Consequently, they can enumerate meaningful discussions and makeobjective decisions (Samer, 2013). For example, a recruitment teamcan pinpoint at target sales and provide a significant justificationfor the organization to hire specific sales people based on theirperformance and position in the industry. Similarly, the humanresource department can move more purposefully to hiring the bestsales individuals (Xu, 2011).
Bayrack, T.(2015). A review of business analytics: A business enabler or anotherpassing fad. Journal ofsocial and behavioral sciences, 195,230-239
Gandomi, A. &Haider, M. (2015). Beyond the hype: Big data concepts, methods, andanalytics.InternationalJournal of Information Management,35(2),137-144.
Gleicher, M.(2016). A framework for considering comprehensibility in modeling.International Journal of, 4(2)75-88
Jabber, M.(2014). Managerialperceptual traits and competitive advantage representation:antecedents and consequences.Academy of Marketing Studies Journal, 18(2),44-88.
McGinn, D.,Birch, D., Akroyd, D., Molina-Solana, M., Guo, Y., & Knottenbelt,W.J. (2016).Visualizingdynamic bitcoin transaction patterns. InternationalJournal of , 4(2),109-119.
Neuhauser, M.(2015). Combining the t-test and Wilcoxon’s rank-sum tests. Journalof Applied Statistics,12 (42), 2769-2775.
Samer, B.(2013). Business intelligence in the mobile era. AmericanAcademic & Scholarly Research Journal,5(3).
Xu, Y.(2011). Competitive network and competitive behavior: A study of theU.S. airline industry. Academyof Marketing Studies Journal 10(1),12-26.