What interests you? Your data set and hypotheses do not have to have obvious Economics overtones

| August 30, 2017

Question

1. Pose a question. What interests you? Your data set and hypotheses do not have to have obvious Economics overtones, so if you want to study sports or entertainment, that’s okay. Just make sure you can find data on the topic of interest. For example:

• Your friend says that “free throw shooting percentage isn’t important to winning in the NBA playoffs.” What is the causal relationship of interest? Winning playoff games is caused by making a larger proportion of your free throws, ceteris paribus. You should be able to find data on the FT% of teams that win a lot of NBA playoff games and compare them to the FT% of teams that don’t. If you find a significantly higher % for teams that go deep in the playoffs, you can go back to your friend and say, “Aha! You don’t know diddily about basketball, and I’ve got the data to prove it!”

Think of some claim that has been made in one of your other classes or by a friend/co-worker/family member that you want to test with data. Then try to find a sample that contains observations you can use to test the claim.

Remember: a good question is specific, capable of being answered empirically, and interesting (non-obvious, non-trivial, original).

Need to do

1 page typed summary of their topic and responses to the first 2 FAQs .

The summary must include:

• A testable causal relationship between observable variables,

• A compelling explanation why this relationship interests the student,

• The unit of observation, e.g., individual, country, football team, that will be used in the test.

Keep in mind your first 2 FAQs. How will you operationalize them into a regression? Where can you go to observe the “x” and “y” variables in the causal relationship of interest?

2. Data collection. Go find data! Data are all around you, waiting to be organized and analyzed. All one has to do is observe the phenomenon of interest and systematically record observations.

Constraints:

• Data consist of observations (rows) and variables (columns) and should have a “spreadsheet” layout. A data set must observe multiple variables for multiple (n) elements.

• I’m not asking you to formulate your own survey or anything like that; if you’re really ambitious, you can certainly do it, but there are plenty of suitable sample data sets already collected that you can use (see below).

• You need at least two (ratio level) variables, and it is strongly preferred that you have a ratio level dependent variable like wage, price, population, et al., because regression is better suited to analyzing these.

• You need enough information to make meaningful statistical inferences, i.e., large enough sample size and variation in your variables. E.g., it would be hard to infer much about a small Indiana town that enacts a zoning regulation, based on a comparison with 5 neighboring towns that didn’t (?????=6and????=1for only 1

observation!).

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