Predictive Business Analytics Samples †MyAssignmenthelp.com

Questions: 1.Why is predictive analytics analysis the next logical step in any business analytics (BA) process? 2.Why would one use logic-driven models to aid in developing data-driven models? 3.How are neural networks helpful in determining both associations and classification tasks required in some BA analyses? 4.Why is establishing clusters important in BA? 5.Why is establishing associations important in BA 6.How can F-tests from the ANOVA be useful in BA? Answers: 1. Business analytics are very essential in logical predictive analytics of an organisation because they give them a competitive advantage. Some of the reasons why predictive analytics analysis is essential are that it aids in discovering fraud. Combinations of multiple analytics methods are in a capacity of improving pattern detectionand prevention of criminal activities.It is a common threat in the world today that cyber security is increasing becoming a threat to security. It is therefore a toll order for behavioural analytics to examine any possibility of fraud in our networks in order to prevent certain attacks from cyber-crimes(Jones, 2008). Another important aspect of predictive analytics is that it helps in boosting marketing campaigns. It can be used to determine customer responses or purchases. Additionally, predictive analytics play a role in promoting cross-sell opportunities. The analysis will therefore, help businesses draw, retain and increase their customers(Min). Carrying out a predictive analytics will also help in improving operations. A lot of organizations companies use predictive models to manage their resources and estimate their inventories. For instance, an Airline business can use predictive analytics to determine the suitable ticket prices(Shmueli, 2013). Predictive analytics also help assessing of risk factor thus helping the organization in coming up with means of mitigation foreseeable risk. Credit scores carried out during the analysis process can be used to assess the buyers probability of default for purchased items. This credits score will therefore be used to determine whether a certain customer can be given items on credit or not. A credit score is a figure is produced by a predictive model that analyses all data relevant to an individuals credit worthy. Other risk that predictive analytics is able to foresee include matters of insurance claims and settlement(Min). 2. Logic driven models would be the best in developing data-driven models because they shows steps taken to reach a particular destination. Logic driven models are suitable in developing data-driven models because they clearly demonstrate exactly how every activity will produce the expected changes. The logic driven models provide the opportunity for organization to have a rough idea of the chosen initiatives and how far the plan can be in place .It is a sort of a road map that demonstrates what factors affect what matters and in what sequence. It is worth noting that based on these logic driven roadmaps on can stop and make an assessment of whether there is progress or not and what changes need to be made for efficient results(Kolovic, 2016). The logic model also has the ability to demonstrate the rationale behind an initiative's plan. It therefore elaborates why certain the program has to work, reasons why it will succeed and causes as to why other attempts have not yielded the best results. The program theory or the rationale facet of the logic model allows the user to have a bigger picture based on various important aspects. The logic model therefore makes the program planners assumption categorical through identifying the problem or opportunity and demonstrating how intervention activities will address it (Rajagapol, 2012). 3. Neural networks are vital because they offer many advantages to the organization. These include the use of traditional methods of statistic to improve accuracy levels, an integrated tactic to a large array of predictive analytics problems. Additionally, neutral networks require less statistical conventions and can manage difficult predictive analytics tasks in an automated way thus saving the time the time of analysts and programmers (Jones, 2008). Neural networks also helpful because they are keeping able of replacing all of these methods used in predictive analytics and give forecasts that are accurate or even better than those gotten from using other statistical methods. 4. Creating clusters in business analytics are important because they allow the development of innovation through comprehensive knowledge flows and spillovers. This in the end strengthens entrepreneurship though advancing new enterprise formation and kick off survival thus enhancing p income levels, productivity and employment growth in organizations. Additionally, innovation that came about because of the clusters really influences local and capital region economic performance(Provost, 2013). Organization cluster is also important to keep because it will allow them to know what they do excellently, their best areas of specialty, what they do perfect than others and what job opportunities and cluster progress the region ultimately provides to the community.(Shmueli, 2013). 5. Additionally, cluster creation is important in this age of unprecedented global competition for jobs and economic investment. Jones (2008) suggest that implementation of an operational cluster strategy that might involve education, workforce training, using advanced technology in companies to increase the performance level and investing back into the community makes them to be more successful. 6. Enova test can be used in carrying out the null hypothesis in ANOVA I since always there is no difference in means. The F test can along these lines be utilized when giving comparison to statistical models that have been settled to an informational collection in order to locate the model that best fits a specific populace from which the information were investigated. The real F-tests basically emerge when the models have been fitted to the information by utilizing the least squares (Shmueli, 2013). Moreover, the F-test in one-route analysis of difference is can be utilized to set up to whether the normal estimations of a specific quantitative variable inside a few pre-characterized group are not quite the same as each other. The ANOVA F-test can be used to assess whether any of the programmers in an organization are on superior, average or inferior, to the others compared to the null hypothesis that all of the implemented programmers yield a similar mean response(Provost, 2013). References Hai, J. (2011, November). Industry Clusters: Importance of Place still Relevant to Business Success. Area Development. Havenport, T. e. (2012). The Complete Guide to Business Analytics (Collection). Jones, E. (2008). Neural Networks' Role in Predictive Analytics. Kolovic, Z. e. (2016). The impact of clustering on the business performance of Croatian SMHEs. ?https://dx.doi.org/10.1080/1331677X.2016.1204101. Min, H. (n.d.). Global Business Analytics Models: Concepts and Applications in Predictive analytics. Provost, F. e. (2013). Data Science for Business: What You Need to Know about Data Mining and Data analytic thinking. Rajagapol, B. e. (n.d.). Business Analytics and Cyber Security Management in Organizations. 2016. Shmueli, G. (2013). Data Mining for Business Analytics: Concepts, Techniques, and Applications with XLminer.