In 2015 college students at a large state university completed a survey about their academic and personal life. Questions ranged from "How many credits are you registered for this semester?" to "Would you define yourself as a vegetarian?"The researcher randomly selected 325 students from the university; 312 responded to the survey.PromptThe cell-phone datafile is available in the Data section below.Once again, here is the research question for this lab.Based on a recent study, roughly 80% of college students in the U.S. own a smartphone. Is the proportion of smartphone owners lower at this university?Respond to each of the following in your initial post.State your hypotheses in symbolic form and in words. (The following should be clear in your answer: the population of interest and the meaning of the proportion p in terms of the variable Cell.)StatCrunch uses a normal model to estimate the P-value probability. Verify that normality conditions are metGive your P-value and interpret its meaning as a conditional probability.State a conclusion that answers the research question. Use a significance level of 5%. (Your answer should include the P-value and reference the population and the variable Cell.)

Per the textbook, trying to understand factors that impact the outcomes of business process is an important aspect of improving business operations. Conventional wisdom plans experiment one-factor-at-a-time (OFAAT). Compare and contrast the main advantages and disadvantages of OFAAT and DOE and select the approach (e.g., OFAAT or DOE) that you would use in order to obtain effective business process. Provide a rationale for your response.Please respond to peer post:Experimental DesignOne-factor-at-a-time (OFAT) an experiment has only one variable at a time with the rest kept constant as compared to the design experimentation (DOE) that has several factors varied when the study involves more than consideration (Miriam et al., 2015). Using OFAT, therefore, implies that fewer assets for data measuring is required. Moreover, the impacts of every composition of the data are accurately achieved with decreased variability in the process of analysis when using DOE. However, both the OFAAT and DOE are data analysis tools, though with varied applications.The resources required for a designed experiment are less in terms of time, operations, and materials (Treglia, 2015). The benefit of this is the relevance in the industry where trials involved are many, expensive, and require a lot of time to conduct. Notably, when using design experiments, it is possible to estimate the interaction existing between the variables systematically. While using the OFAAT experiment, it is quite hard to determine the communications, and those who do not apply designed experiments use the hit-and-miss shots scattered over a large area. The scattershot cannot estimate the interactions. Contrarily, the advantage of OFAAT over DOE is that it requires few resources to complete the analysis. In approximation, six runs are necessary for a double scheme design (Miriam et al., 2015). However, the long-run cost of the experimental analysis is expensive in terms of resources such as time as compared to the requirements by DOE for an equal number of experiments. In DOE, the preliminary information in a more significant factor space region can be deduced. The benefit of this is the improvement of the predictability in response since it reduces the variability of the estimates. Therefore, with DOE, the optimization of the process thus becomes efficient.In my experimental data analysis, I would prefer DOE over OFAAT due to its preciseness. The DOE can handle many variables at a go, thus allowing convenient and dependable business data.ReferencesMiriam, K. R., Márcia, F. S., Alexandre, J. d., André, L. D., Adrícia, F. M., & Messias, B. S. (2015). The Design of Experiment Application (DOE) in the Beneficiation of Cashew Chestnut in Northeastern Brazi. American Journal of Theoretical and Applied Statistics., 4(1), 6-14. doi:10.11648/j.ajtas.20150401.12 Treglia, M. (2015, March 12). Understanding the Design of Experiments. Retrieved from Quality Digest Website: https://www.qualitydigest.com/inside/quality-insid...

For this Assignment, you will examine statistical significance and meaningfulness based on sample statements.To prepare for this Assignment:Review the Week 5 Scenarios found in this week’s Learning Resources and select two of the four scenarios for this Assignment. For additional support, review the Skill Builder: Evaluating P Values and the Skill Builder: Statistical Power, which you can find by navigating back to your Blackboard Course Home Page. From there, locate the Skill Builder link in the left navigation pane.For this Assignment:Critically evaluate the two scenarios you selected based upon the following points:Critically evaluate the sample size.Critically evaluate the statements for meaningfulness.Critically evaluate the statements for statistical significance.Based on your evaluation, provide an explanation of the implications for social change.