BUS 308 Discussion Questions Week 1 – 5
BUS 308 Week 1 Post Your Introduction – Discussion
On the first day of class, introduce yourself to your instructor and classmates by sharing a little about yourself. In addition, include in your introduction one of the options below:
- Your goals and experience with statistics and how it has affected some of the decisions you have made.
- A “startling” statistic that you find interesting and what the effect of that statistic might indicate. Be sure to cite the source of your statistic.
BUS.308 Discussion 1-1/ Language
Numbers and measurements are the language of business. Organizations look at results in many ways: expenses, quality levels, efficiencies, time, costs, etc. What measures does your department keep track of? Are they descriptive or inferential data, and what is the difference between these? (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, or use personal measures.)
BUS 308 Discussion 1-2/ Probability
What are some examples of probability outcomes in your work or life? How would looking at them in terms of probabilities help us understand what is going on? How does the normal curve relate to activities/things you are associated with?
BUS-308 Discussion 2-1/ Hypotheses
What is a hypothesis test? Why do we need to use them to make decisions about relating sample results to the population; why can’t we just make our decisions by the sample value?
BUS 308 Discussion 2-2/ 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 observed differences over time are important? How could using a mean difference test help?
BUS.308 Discussion 3-1/ ANOVA
In many ways, comparing multiple sample means is simply an extension of what we covered last week. Just as we had 3 versions of the t-test (1 sample, 2 sample (with and without equal variance), and paired; we have several versions of ANOVA – single factor, factorial (called 2-factor with replication in Excel), and within-subjects (2-factor without replication in Excel). What examples (professional, personal, social) can you provide on when we might use each type? What would be the appropriate hypotheses statements for each example?
BUS 308 Discussion 3-2/ 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?
BUS-308 Discussion 4-1/ Confidence Intervals
Many people do not “like” or “trust” single point estimates for things they need measured. Looking back at the data examples you have provided in the previous discussion questions on this issue, how might adding confidence intervals help managers accept the results better? Why? Ask a manger in your organization if they would prefer a single point estimate or a range for important measures, and why? Please share what they say.
BUS 308 Discussion 4-2/ 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?
BUS.308 Discussion 5-1/ 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?
BUS-308 Discussion 5-2/ 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?