Not so much to say about the statistics course that is earth-shattering. I continue to be grateful to have encountered various warnings about how not to present data in misleading ways. Pie charts and badly depicted axes can make small effects look big (which happens every day in the paper when the stock index changes are depicted). In general, making sure that the quantity of data points is prominently conveyed is good, so a reader can get a quick sense of how robust the data collection or the particular cross tab is. There was also a nice bit on how to hunt for interactions, mediating variables, or prior factors. As I’ve mentioned before, a good deal that is published not only in the popular press but often as big social science conclusions doesn’t pass this muster.
In economics lately — lots of graphs and curves. This time through I am trying to get a bead on what the definitions are of “short run” and “long run”. In the short run, I think, the cost of at least some inputs are given, so there is a way for a firm to figure out how to profit. In the long run, I think, there are no fixed costs (not sure why), so firms are entering and exiting based on costs, and in perfect competition all profits will converge on zero. I don’t get yet why profit maximizing is what firms do, as opposed to earning the profits they choose even if it’s not the maximum, or if profit maximizing is just a short term motivation. I realize it’s an idealized picture, put forth because it works analytically to describe the real world. Apparently? Perfect competition of small firms that produce the same thing and which take the price as given doesn’t exist, or rarely does. I haven’t concentrated enough this time around to put it all together. I did good on my first test, but it was softballs and basics of supply, demand, and the impact of taxes vs. other nonquantifiable individual and social benefits (e.g. in the case of government regulation of drugs).
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