Discussion 1: In looking at your business, when and why would you want to use a one-sample mean test (either z or t) or a two-sample t-test? Create a null and alternate hypothesis for one of these issues. How would you use the results?

Discussion 2: 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?

Discussion 3: 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: Situations could 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?

Discussion 4: 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?

Discussion 5: 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?

Discussion 6: 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?

Discussion 7: 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?

Discussion 8: 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?