Many questions in the world, including questions you will face in your future job, cannot be answered without analyzing some data.  In this class, you have learned something about how to answer questions using data.  This skill is widely applicable. 

At the very least, you can write "Proficient in Excel" on your resume.

The review sheet for the test is here.

Today (Wednesday), we will continue our work with scatterplots, regression lines and correlations.

In the first half of the class, we will do another application about scatterplots, regression lines, and correlations.  This application will be based on a dataset of used cars for sale in New Jersey right now.

During the second half of the class, you will take a test on these subjects. 

Good luck!

As you know, the semester is almost over.  We will only cover one more topic: using statistical inference with sample means and sample proportions. 

After covering this topic, we will review all of the work we have done so you can be better prepared for the final exam.

In the end, I would like all of you to be able to claim some level of expertise in using Excel.  You should be able to put the following statement on your resume: "Proficient in Excel." 

Here are your expectations.

Today, I will introduce you to a dataset that I have constructed which contains information about nations around the world.

We will first become familiar with the data by looking at how various variables are distributed.

Next, we will guess at how poor nations differ from rich nations.  Enter your expectations here.

Finally, we'll test out some relationships to see

Today we looked at data from RateMyProfessors.com about William Paterson faculty.  The names were removed from the data file to protect the innocent (and the guilty). 

We looked at the distributions of each of the variables.  The distribution of the hotness ratings were skewed to the right.  The distribution of overall ratings was skewed to the left. 

Then we looked at the data in order to answer this question:

Do easier professors get higher overall ratings? 

We looked at a crosstab of overall ratings by easiness.  We found that the answer appears to be yes.  An overwhelming majority (over 70%) of professors who were rated as very easy also got very high overall ratings.  Meanwhile, professors who were rated as very difficult were very unlikely to get a very high rating. 

OK - if people rate their professors partly based on how easy the class was, how do we know who is really a "good teacher"?  How do we separate out "good teaching" from simple easiness? 

Next week, we will look at a method that allows you to do this.

For homework, please read pp. 292-297 and pp. 300-305 about regression.  This is the technique we will be using with the RateMyProfessor data.

You should know the following for the test:

* percentiles

* frequencies

* cross-tab (how to obtain it and how to interpret it

* chi-square test statistic (how to obtain it and how to interpret it.)

In order to demonstrate statistical inference, we are going to take a brief, anonymous survey as a class. 

Click here to take the survey!