BUS 308 Updated Course All Discussion Questions http://www.seetutorials.com/products/bus-308-updated-course-alldiscussion-questions.html BUS 308 Week 1 DQ 1 Language. Numbers and measurements are the language of business.. Organizations look at results, expenses, quality levels, efficiencies, time, costs, etc. What measures does your department keep track of ? How are the measurescollected, and how are they summarized/described? How are they used in making decisions? (Note: If you do not have a job where measures are available to you, ask someone you know for some examples or conduct outside research on an interest of yours.) Guided Response: Review several of your classmatesâ€™ posts. Respond to at least two of your classmates by providing recommendations for the measures being discussed. DQ 2 Levels. Managers and professionals often pay more attention to the levels of their measures (means, sums, etc.) than to the variation in the data (the dispersion or the probability patterns/distributions that describe the data). For the measures you identified in Discussion 1, why must dispersion be considered to truly understand what the data is telling us about what we measure/track? How can we make decisions about outcomes and results if we do not understand the consistency (variation) of the data? Does looking at the variation in the data give us a different understanding of results? Guided Response: Review several of your classmatesâ€™ posts. Respond to at least two classmates by commenting on the situations that are being illustrated. Week 2 DQ 1 t-Tests.

In looking at your business, when and why would you want to use a one-sample mean test (either z or t) or a twosample t-test? Create a null and alternate hypothesis for one of these issues. How would you use the results? Guided Response: Review several of your classmates’ posts. Respond to at least two classmates by commenting on the potential differences in the results and how that might affect decision making. DQ 2 Variation. Variation exists in virtually all parts of our lives. We often see variation in results in what we spend (utility costs each month, food costs, business supplies, etc.). Consider the measures and data you use (in either your personal or job activities). When are differences (between one time period and another, between different production lines, etc.) between average or actual results important? How can you or your department decide whether or not the variation is important? How could using a mean difference test help? Guided Response: Review several of your classmates’ posts. Respond to at least two classmates and comment on the use of the test. Week 3 DQ 1 ANOVA. In many ways, comparing multiple sample means is simply an extension of what we covered last week. What situations exist where a multiple (more than two) group comparison would be appropriate? (Note: Situationscould relate to your work, home life, social groups, etc.). Create a null and alternate hypothesis for one of these issues. What would the results tell you? Guided Response: Review several of your classmates’ posts. Respond to at least two classmates by commenting on why you agree or disagree with the statistical test that your peers have described as appropriate in this scenario. DQ 2 Effect Size Several statistical tests have a way to measure effect size. What is this, and when might you want to use it in looking at results from these tests on job related data?

Guided Response: Review several of your classmates’ posts. Respond to at least two of your classmates and…

Week 4 DQ 1 Confidence Intervals. Earlier we discussed issues with looking at only a single measure to assess job-related results. Looking back at the data examples you have provided in the previous discussion questions on this issue, how might adding confidence intervals help managers understand results better? Guided Response: Review several of your classmates’ posts. Respond to at least two classmates by commenting on whether or not you think changing the confidence intervals will result in a different outcome. Explain if you agree or disagree with the role of a confidence interval in the interpretation of the answer. DQ 2 Chi-Square Tests. Chi-square tests are great to show if distributions differ or if two variables interact in producing outcomes. What are some examples of variables that you might want to check using the chi-square tests? What would these results tell you? Guided Response: Review several of your classmates’ posts. Respond to at least two classmates by commenting on how this information might be used to make business decisions.

Week 5 DQ 1 Correlation. What results in your departments seem to be correlated or related to other activities? How could you verify this? Create a null and alternate hypothesis for one of these issues. What are the managerial implications of a

correlation between these variables? Guided Response: Review several of your classmates’ posts. Respond to at least two classmates by explaining whether or not you think that there is a relationship between the variables discussed. DQ 2 Regression. At times we can generate a regression equation to explain outcomes. For example, an employee’s salary can often be explained by their pay grade, appraisal rating, education level, etc. What variables might explain or predict an outcome in your department or life? If you generated a regression equation, how would you interpret it and the residuals from it? Guided Response: Review several of your classmates’ posts. Respond to at least two classmates by commenting on how this information might be used to make business decisions.

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