Assignment 1
Discussions
To participate in the following discussions, go to this week's Discussion link in the left navigation.
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?
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.
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.
Assignment 2
Discussions
To participate in the following discussions, go to this week's Discussion link in the left navigation.
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 manager in your organization if they would prefer a single point estimate or a range for important measures, and why? Please share what they say.
Guided Response: Review several of your classmates' posts. Respond to at least two classmates by commenting on whether or not you think adding or using confidence intervals would result in greater acceptance. Explain if you agree or disagree with the role of a confidence interval in the interpretation of the answer.
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..
Assignment 3
Discussions
To participate in the following discussions, go to this week's Discussion link in the left navigation.
1. Correlation
What results in your departments seem to be correlated or related (either causal or not) to other activities? How could you verify this? 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.
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.