- DQ 1 Consider the following news headline, "Cigarette Smokers Make Lower College Grades Than Nonsmokers." The article goes on to say that researchers at a university collected information on the rate of cigarette smoking (total number of cigarettes smoked in a one-month period) and the college student's current GPA for completed college courses. They found that those with higher levels of cigarette use had lower GPAs, and vice versa. What can you rightfully conclude from this? Why? Be specific about what this kind of information may indicate and mean.DQ 2A pharmaceutical company conducts a study to evaluate the relationship between length of time taking drug X and size of tumor. They enlist 2,400 patients with the same type of benign tumor who have been on drug X for varying lengths of time (measured in days). They find that the longer the patient is on the drug, the smaller the patient’s tumor size (measured in volume). In fact, they report the results as r(2398) = -.21, with significance of p < .0001. What is your initial perception of this information? What are other ways, beyond looking at the p-value alone, you could use to try to interpret what this outcome of r(2398) = -.21 tells us about the relationship between length of time taking drug X and the size of the tumor? Support your responses.

This week’s Learning Resources include several different ways to identify and assess the issue of missing data in your database. From your assessment and knowledge of possible ways to manipulate data, and to overcome potential challenges presented by the missing data, you will be able to analyze and reach conclusions about your analysis. It is important to recognize when and how to apply the most viable solution to the missing data. In addition, it is important to learn to recognize when your data results could be compromised by missing data.For this, you will analyze the impact of missing data on data analysis.To prepare:Review this week’s Learning Resources.Analysis of missing data. Include the following in your post:An explanation of the importance of handling missing data and skipping patterns and how missing data can affect data analysisA technique on how you may handle missing data appropriately with an explanation of why this technique worksChoose one of the variables you selected for your Scholar-Practitioner Project (select one with missing values) and report proportion of missing for that variableIndicate the possible reason for the missing values in that variable.ReferencesWang, R., Sedransk, J., & Jinn, J. H. (1992). Secondary data analysis when there are missing observations. Journal of the American Statistical Association, 7(420), 952–961. Long, J. A., Bamba, M. I., Ling, B., & Shea, J. A. (2006). Missing race/ethnicity data in Veterans Health Administration based disparities research: A systematic review. Journal of Health Care for the Poor and Underserved, 17(1), 128–140.Langkamp, D. L., Lehman, A., & Lemeshow, S. (2010). Techniques for handling missing data in secondary analyses of large surveys. Academic Pediatrics, 10(3), 205–210.

Analyze a provided data file for a scenario and write a 2–4 page management report detailing your findings and recommendations based on the results.Note: The assessments in this course build upon each other, so you are strongly encouraged to complete them in sequence.