Select any one of the following starter bullet point sections. Review the important themes within the sub questions of each bullet point. The sub questions are designed to get you thinking about some of the important issues. Your response should provide a succinct synthesis of the key themes in a way that articulates a clear point, position, or conclusion supported by research. Select a different bullet point section than what your classmates have already posted so that we can engage several discussions on relevant topics. If all of the bullet points have been addressed, then you may begin to re-use the bullet points with the expectation that varied responses continue. •You are a marketing manager for a company that makes ready-to-eat breakfast cereals. Your company recently initiated a loyalty program for consumers, which resulted in a large purchaser database. The brand managers are eager to mine the available data, which they can use to design more effective promotional programs. The management of your organization believes that to be effective, these programs have to take into account significant cross-region (i.e., the East Coast, the West Coast, the Midwest, and the South) purchase differences. Your task is to test the hypothesis that there are significant cross-region differences in purchasing patterns. Management has suggested that you use six different two-way comparisons (directly comparing each region with every other region), with each two-way comparison being suggested at the 5% level. ◦Assess how appropriate management’s proposed use of hypothesis testing would be to validate management’s belief in cross-region purchase differences. ◦Explain the goal of this hypothesis testing experiment. ◦Describe the mechanics of this hypothesis testing process. ◦Explain why the organization would go through the trouble of hypothesis testing in this situation. Support your discussion with relevant examples, research, and rationale. •You are a manager working for an insurance company. Your job entails processing individual claims filed by policyholders. In general, most claims are relatively minor, costwise, but a few are quite expensive. Each quarter, you compile a report summarizing key claims statistics that includes the number of claims submitted, the mean cost per claim, the median cost per claim, the proportion of claims being litigated, the number of emergency procedures, the proportion of men versus women, and the average age of claimants. Your measures are computed separately for the southern and northern regions, and you are interested in determining whether or not there are statistically significant differences between the two regions on each of the aforementioned measures. ◦Evaluate which comparisons would require the use of the t-test and which would use the chi-squared test. ◦Explain your answers. Support your discussion with relevant examples, research, and rationale. •In the past several weeks, you have been introduced to a range of statistical data analysis tools. Consider what you have learned in the context of progression of data, information, and knowledge. What are the specific techniques you would consider most helpful in transforming information into knowledge (as opposed to just translating data into information)? Support your discussion with relevant examples, research, and rationale. The final paragraph (three or four sentences) of your initial post should summarize the one or two key points that you are making in your initial response. Your posting should be the equivalent of 1 to 2 single-spaced pages (500–1000 words) in length.

In Ch. 10 we're learning how to conduct a t to compare the means of two independent samples to judge whether the populations the samples came from are different from each other both in a statistical sense and in a practical sense. The term for this kind of t test is independent measures t test or independent samples t test, because it uses data from two independent samples.Example of an independent measures t testFirst, as an example of how to conduct an independent measures t test, we'll use the data from Example 10.2. You can see the raw data on p. 334. These data are in the Example10-2.csv file.(1) Download Example10-2.csv and open it in Jamovi.(2) Open the Data tab and click Setup. Make sure the measure type and data type for the variables are correct.(3) Give names to the two levels of the Grp variable ( Well-Lit and Dimly Lit).(4) Open the Analyses tab and from the T-Tests menu select Independent Samples T-Test.(5) Move solved into the Dependent Variables box and move Grp into the Grouping Variable box.(6) From the Tests menu select Student's.(7) From the Hypothesis menu select Group 1 ≠ Group 2.(8) From the Additional Statistics menu select Mean difference and, just beneath it, Confidence interval (be sure it's 95%).(9) Select Effect size.(10) Select Descriptives.(11) From the Assumption Checks menu select Homogeneity test.(12) Save your analysis as Example10-2.omv, to be submitted as part of this assignment.If you did everything right, you should see that your results match those reported in the textbook for sample mean, df, estimated standard error, t, and estimated Cohen's d. The textbook authors used Hartley's F-Max Test to evaluate equality of variances and concluded that the difference in variances was not large enough to be significant. Jamovi evaluates equality of variances by using Levene's test. The way it works is pretty simple. Each of the solved scores is replaced by (a) the absolute value of its deviation from its sample mean, or (b) its squared deviation from the mean. Either way will work. Then an independent samples t test is conducted on these transformed scores. If there is greater variability in one group, the absolute/squared values of the deviations will be greater than in the other group. So if the calculated t value is significant (p < .05), then the variances of the two groups differ, which means the independent measures t test is not appropriate. Both Hartley's F-Max test and Levene's test have p values that are not significant, that is, they're greater than .05. So the independent measures t test is appropriate for these data.As we discussed for the Ch09 homework, Jamovi calculates confidence intervals (CIs) for the mean difference. For the single-sample t test this meant we had to do an additional calculation to construct a CI to estimate the population mean. But for an independent measures t test, that's not necessary because the CI around the mean difference is exactly what we're looking for.Homophobia and homosexual arousalAdams, Wright, & Lohr (1996) were interested in psychoanalytic theories that homophobia may be unconsciously related to anxiety about being or becoming homosexual. They administered the Index of Homophobia to 64 heterosexual males and categorized them as homophobic or nonhomophobic on the basis of their scores. Then they exposed all of the men to videotapes of sexually explicit erotic stimuli portraying heterosexual and homosexual behavior, and recorded their level of sexual arousal. They reasoned that if homophobia were unconsciously related to anxiety about one's own sexuality, individuals rated homophobic based on their Index of Homophobia scores would show greater arousal to the homosexual videos than would nonhomophobic individuals.There were no group differences when the two groups viewed the heterosexual and lesbian videos. So in this example we examine only the data from the homosexual video. The data in this data set were created to have the same means and pooled variance as the data that Adams et al. collected, so our conclusions will be the same as theirs. The dependent variable is the degree of arousal at the end of the four-minute video, with larger values indicating greater arousal. The file, Homophobia.csv, attached to this assignment, contains the arousal scores for the 64 men in the study. We will use these data to illustrate the independent measures t test by comparing the mean arousal score of the homophobic and nonhomophobic men.Adams, H.E., Wright, L.W., & Lohr, B.A. (1996). Is homophobia associated with homosexual arousal? Journal of Abnormal Psychology, 105, 440-445.(1) Download Homophobia.csv and open it in Jamovi.(2) Make sure the measure type and variable type are correct for the variables.(3) Change the label for Group 1 to Homophobic and the label for Group 2 to Nonhomophobic.(4) Conduct an independent measures t test. All settings should be the same as for Example10-2.(5) Save the Jamovi file as Ch10_YourLastName.omv and use it to answer the questions on the worksheet.Instructions for submitting this assignment.(1) Locate Example10-2.omv, Ch10_YourLastName.omv, and Ch10 Homework.docx where you saved them on your computer.(2) Click the title of this assignment ( Ch10 Homework) to open the Upload Assignment page.(3) In the ASSIGNMENT SUBMISSION section, attach Example10-2.omv, Ch10_YourLastName.omv, and the Ch10 Homework file. Click Submit.(4) If you submit this assignment early enough, I might have time to provide feedback that you can use to improve it and possibly improve your grade. So soon after you submit it, return to this assignment to see if I've given you any feedback. Click the assignment title and on the Review Submission History page you'll see the details of your prior submissions. To revise a prior submission, click Start New, download and revise, be sure to save it, and upload it as previously.

Time Series Prediction Project Two choices Option 1: Use one of the US Census time series linked. here (note that the Census will allow you to check “seasonally adjusted” or “not seasonally adjusted.” You’ll almost certainly want “not seasonally adjusted” since it will help you to show that you can deal with seasonality). Option 2: Find another time series of interest to you on the internet (any time series OK, but be sure to include a link to the data.) Question: Perform a time series analysis. Consider is it horizontal? Does it show a trend? Seasonality?Consider smoothing (moving average or exponential smoothing). Include graphs of both. Do you believe they improve the model? Why or why not? Rubric: 5 points, subjective: was this interesting? (interesting data, interesting conclusion, interesting method) 5: very 4: largely 3: somewhat 2: a little 1: no 0: no data 5 points, graphs: 5 attractive 4 good 3 passable 2 OK 1 cringeworthy 0 no graph 20 points, Time series tools considered (horizontal; seasonality; trend; cycle) For each of the four tools above: 5 - considered and clearly explained 4 - considered if somewhat unclear 3 - considered with no explanation 2 - possibly considered 1 - no mention of that tool 0 - none considered 10 points: Smoothing and moving average 5 - judgment given, graphs support 4 - judgment given but graphs don’t support, or good graphs but no clear judgment 3 - no clear judgment and graphs don’t make the argument clearly on their own 2 - no graphs 1 - no clear judgment and no graphs 0 - smoothing not considered.