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HTLH 4100 Walden University The BAET 3000 Discussion

HTLH 4100 Walden University The BAET 3000 Discussion
Ana was feeling back pain and went for a medical checkup. Instead of following the treatments and medications suggested by her doctor, Ana decided to trust and consult artificial intelligence (AI). Ana believed AI was not prone to bias, errors, emotions or attitudes, so she went home and started her conversation with her Amazon Alexa device. She asked Alexa, “Alexa, what is lumbar disk pain?” and “Alexa, how can I alleviate lumbar?disk?pain?” Alexa quickly responded by referring her to WebMD and suggesting some common treatments for muscle, bone, and back nerve pain. Ana decided to follow Alexa’s suggestions. Artificial intelligence (AI) seems to be gaining support and trust among people. People are relying on these technologies to help make decisions. We regularly use AI for information about the weather, the traffic, or a recipe. But, should we trust AI with decision-making issues regarding our health? In order to make good decisions, we should consider multiple alternatives. We should also acknowledge that biases and errors may influence decision-making (Robbins & Judge, 2018). Decision-making models are a valuable tool for arriving at solutions that require wise judgment. For this Discussion, you will reflect on bias, stereotyping, risk aversion, halo effect, and overconfidence. You will also apply and evaluate a decision-making model (rational, bounded rationality, or intuition). Reference Robbins, S. P., & Judge, T. A. (2018). Essentials of organizational behavior (14th ed.). Upper Saddle River, NJ: Pearson Education. To prepare?for this Discussion:  Review the Learning Resources for this week.  Consider a decision you had to make for an organization.   Consider what errors or biases may have been a part of your decision-making process.  Consider what emotions or attitudes may have played in your decision-making process.  Download the Case Study: BAET 3000 document found in this week’s Learning Resources.  Choose one of the following decision-making models to use during this Discussion:  Rational model  Bounded rationality  Intuition  By Day 4 Post a comprehensive response to the following: Identify which of the decision-making processes you have selected to apply to the BAET 3000 case study.  Explain where you can identify potential for anchoring bias, stereotyping, risk aversion, halo effect, confirmation bias, and/or overconfidence in the BAET 3000 case study.   Indicate whether or not you would purchase the BAET 3000, and explain your decision.   Explain which decision-making model you used (rational, bounded rationality, or intuition) and why you used this model to make the decision.  Name at least one benefit and one challenge to the decision-making model (rational, bounded rationality, or intuition) you selected.  As the director of Central Texas Dermatology Associates, with whom would you need to communicate with in order to implement your decision? What methods would you use to communicate your decision? Would the communication be formal or informal? 

WU SPSS Descriptive and Inferential Analyses Data Analysis Plan Paper

WU SPSS Descriptive and Inferential Analyses Data Analysis Plan Paper
Throughout this course, you have practiced various skills that will allow you to identify, procure, and manipulate biosurveillance and secondary data. As addressed in previous sections, public health information needs are constantly growing, and the statistical analysis of data is just one step in this process. Decisions based on this information would rely not only on the accuracy of your analysis but also on the organization of its presentation.This week for your Scholar-Practitioner Project you will conduct descriptive and inferential analyses using your selected data set, your prepared database from Week 8, and SPSS.To prepare:Review this week’s Learning Resources, submit interpretation for your statistical analysis based on your selected data set, your prepared database, and SPSS. Mark sure to perform the following tasks for each of your research questions separately:Provide interpretation for descriptive statistical analyses based on your SPSS output.Summarize the numerical results with descriptive analysis tables or graphs, including your interpretation.Provide interpretation of your inferential statistical analyses using SPSS outputs.Summarize the numerical results with inferential analysis tables or graphs, including your interpretation.Provide full answer and interpretation for each of your research question(s).Follow APA guidelines.Required ReadingsKamin, L. F. (2010). Using a five-step procedure for inferential statistical analyses. The American Biology Teacher, 72(3), 186–188.Maiti, T. (2005). Tutorials in biostatistics, vol. 1: Statistical methods in clinical studies / tutorials in biostatistics, vol. 2: Statistical modelling of complex medical data. Journal of the American Statistical Association, 100(472), 1468–1468.Marshall, G., & Jonker, L. (2010a). A concise guide to descriptive statistics. Synergy, 22–25. Marshall, G., & Jonker, L. (2010b). A concise guide to inferential statistics. Synergy, 20–24. McHugh, M. L. (2003a). Descriptive statistics, part I: Level of measurement. Journal for Specialists in Pediatric Nursing, 8(1), 35–37.McHugh, M. L. (2003b). Descriptive statistics, part II: Most commonly used descriptive statistics. Journal for Specialists in Pediatric Nursing, 8(3), 111–116.Silva-Ayçaguer, L. C., Suárez-Gil, P., & Fernández-Somoano, A. (2010). The null hypothesis significance test in health sciences research (1995–2006): Statistical analysis and interpretation. BMC Medical Research Methodology, 10(1), 44.Thebane, L., & Akhtar-Danesh, N. (2008). Guidelines for reporting descriptive statistics in health research. Nurse Researcher, 15(2), 72–81.Wolverton, M. L. (2009). Research design, hypothesis testing, and sampling. The Appraisal Journal, 77(4), 370–382.Optional ResourcesBingenheimer, J. B., & Raudenbush, S. W. (2004). Statistical and substantive inferences in public health: Issues in the application of multilevel models. Annual Review of Public Health, 25, 53–77.Diez-Roux, A. (2000). Multilevel analysis in public health research. Annual Review of Public Health, 21, 171–192. Note: Retrieved from the Walden Library databases.Forthofer, R. N., Lee, E. S., & Hernandez, M. (2007). Biostatistics: A guide to design, analysis, and discovery. Amsterdam, Netherlands: Elsevier Academic Press. Chapter 3, “Descriptive Methods”Review: Chapters 8–15Gruber, F. A. (1999). Tutorial: Survival analysis—A statistic for clinical, efficacy, and theoretical applications. Journal of Speech, Language, and Hearing Research, 42(2), 432–447. Note: Retrieved from the Walden Library databases.Lee, E. T., & Go, O. T. (1997). Survival analysis in public health research. Annual Review of Public Health, 18, 105–134.Levy, P. S., & Stolte, K. (2000). Statistical methods in public health and epidemiology: A look at the recent past and projections for the next decade. Statistical Methods in Medical Research, 9(1), 41–55.Peace, K. E., Parrillo, A. V., & Hardy, C. J. (2008). Assessing the validity of statistical inferences in public health research: An evidence-based ‘best practices’ approach. Journal of the George Public Health Association, 1(1),10–23. Retrieved from http://faculty.mercer.edu/thomas_bm/documents/jgph…Rutledge, T., & Loh, C. (2004). Effect sizes and statistical testing in the determination of clinical significance in behavioral medicine research. Annals of Behavioral Medicine, 27(2), 138–145.

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