Business Analytics


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. &ampHaider, 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&nbspcompetitive&nbspadvantage representation:antecedents and consequences.Academy of Marketing Studies Journal, 18(2),44-88.

McGinn, D.,Birch, D., Akroyd, D., Molina-Solana, M., Guo, Y., &amp 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 &amp 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.

Business Analytics


Course Name

The growth of knowledge and advancement in technology continues toinfluence business managers to use approaches that are morescientific to identify better and accurate methods of makingdecisions and predicting outcomes. Therefore, this led to thedevelopment of more analytic approach within management theory theseareas include predictive modeling and data mining.

Predictive Modeling

The application of predictive modeling aims at coming up with aprecise estimate of what will happen in the future based on whatoccurred in the past (Chopra, 2014). Predictive modeling, also knownas predictive analytics, is currently a common technique in allorganizations across various disciplines. Usually, computers performthe analysis at very high levels of accuracies to yield reliableresults. According to Metzger et al. (2015), predictive analyticsrely on statistical analysis and derives its roots from statisticalscience, a scientific way of analyzing data to extract the bestmeaning.

Data Mining

Data mining concerns the design of algorithms that aim at extractinginsights from large and potentially amorphous data. The techniquesapplied include feature selection, pattern recognition, supervisedclassification, and clustering (Mircea, 2015). It also utilizesstatistical methods but has a limited relationship with mathematicalscience (Baliņa, Zuka &amp Krasts, 2016). Similarly, this alsorelies on the computer in the process of organizing this vastclustered data into a form that can deliver meaning.


Knowledge and technological advancements have given rise to theexpansion of management theory leading to the emergent of many areasthat can yield expedient business analytics. Among these sectors ispredictive modeling that helps in identifying future outcome based onpast data and Data mining that aims at drawing meaning frompast-unorganized data. All these new techniques depend on moderntools such as the computer.


Baliņa, S., Zuka, R., &amp Krasts, J. (2016). Opportunities for theUse of Business Data Analysis Technologies. Economics &ampBusiness, 28(1), 20-25.

Chopra, K. N. (2014). Modeling and Technical Analysis of ElectronicsCommerce and Predictive Analytics. Journal Of Internet Banking &ampCommerce, 19(2), 1-10.

Metzger, A., Leitner, P., Ivanovic, D., Schmieders, E., Franklin, R.,Carro, M. &amp Pohl, K. (2015). Comparing and combining predictivebusiness process monitoring techniques. IEEE Transactions onSystems, Man, and Cybernetics: Systems, 45(2), 276-290.

Mircea, M. (2015). Collaborative Networks – Premises for Exploitationof Inter-Organizational Knowledge Management. InformaticaEconomica, 19(2), 57-65.