## WEEK 8 DISCUSSION

Think back to week 1 of the course when you joined the fictional committee to assist prospective students interested in your program. One student had asked how statistics was used in your field, and you did an online search to identify 3 examples. Now that we have wrapped up the course, consider how you would change your response to the student. Reflecting on the content topics that you have learned, what are 3 additional ways in which statistics is used in your field?

After posting your initial reply (due by 11:59 pm EST on Saturday), make at least one *substantive* peer reply post by 11:59 pm EST on Tuesday.

## WEEK 7 DISCUSSION

**Choose either topic #1 or topic #2 for your initial post. Respond to either for your peer reply post.**

**Topic 1:**

One goal of statistics is to identify relations among variables. What happens to one variable as another variable changes? Does a change in one variable cause a change in another variable? These questions can lead to powerful methods of predicting future values through linear regression.

It is important to note the true meaning and scope of correlation, which is the nature of the relation between two variables. Correlation does not allow to say that there is any causal link between the two variables. In other words, we cannot say that one variable *causes* another; however, it is not uncommon to see such use in the news media. An example is shown below.

## WEEK 6 DISCUSSION

**Choose either topic #1 or topic #2 for your initial post. Respond to either for your peer reply post.**** **

**Topic 1:**

Results from surveys or opinion polls often report a range of values—the sample statistic plus or minus a margin of error (the resulting range is called a confidence interval). This tells us that the range is likely to contain the population parameter. How much wiggle room we provide is based on how much confidence we wish to have that the range contains the actual population mean. That confidence level is directly related to the middle “truth” area we will accept versus the dubious tail area we will reject–also known as alpha (α). The more confidence we wish to have—the more middle ground we will need to accept (more wiggle room) thus a smaller tail area. If we insist on a larger alpha (more dubious tail area) we narrow the middle ground we will accept and thus provide less wiggle room—so the more likely it is that we will miss the true average (and thus we have a lower confidence level). A 95% confidence level leaves 5% alpha. A 99% confidence level leaves 1% alpha.

Now, without calculating a mean or margin of error or a confidence level, provide an example from your current (or your future) professional or personal life that describes a measurement that is normal—and how much wiggle room on either side would be appropriate. When would you want a 95% confidence interval and when would you be interested in a 99% confidence level (a little more wiggle room—so a wider range)? This serves as your initial post to the discussion (if you choose topic #1) and is due by 11:59 pm EST on Saturday.

-OR-

**Topic 2:**

Two or more samples are often compared when we suspect that there are differences between the groups—for example, are cancer rates higher in one town than another, or are test scores higher in one class than another? In your chosen field, when might you want to know the mean differences between two or more groups? Please describe the situation (what groups, what measurements) including how and why it would be used. This serves as your initial post to the discussion (if you choose topic #2) and is due by 11:59 pm EST on Saturday.

At least one *substantive* peer reply post is due by 11:59 pm EST on Tuesday.