These are True False questions:

1. If you run a regression of wages on schooling and the regression R2 is approximately 0.41, this means that in your sample, variation in schooling predicts approximately 41% of variation in wages.

2. If you have a problem of simultaneous causality, you can address this by making sure that all possible omitted variables are added to your regression as control variables.

3. When you have data on the price and quantity sold of a good, an OLS regression of log quantity on log price will normally give you a consistent estimate of the price elasticity of demand for that good.

4. Classical measurement error in the dependent variable implies that the OLS estimator of the slope coefficient will be downward biased.