1. A low R2 squared means:

a) there is no causal relationship between the dependent and independent variables

b) the regression suffers from omitted variable bias

c) both (a) and (b)

d) none of the above.

2. Suppose the error terms are correlated across observations and you ignore this correlation when calculating standard errors. This:

a) means the study is internally invalid because the coefficient estimator will be biased.

b) means the study is internally invalid because the confidence intervals will be inconsistent.

c) does not effect whether the study is internally valid, but it does mean the study is externally invalid.

d) none of the above.

3. Suppose you run a regression of ln GDP per capita (the dependent variable) on a measure of

institutional quality (the explanatory variable). You estimate the value of the coefficient () on the institutions variable to be 0.54 with a standard error of 0.04. Your classmate suggests that institutional quality is positively correlated with having British legal origins. That is, countries with British legal origins have higher quality institutions. Your classmate also thinks that British legal origins has a direct positive effect on ln GDP per capita. You gather data on whether a country's legal system originated from Britain and add this as a control variable to the regression. If your classmate is correct,then it is likely that in the new regression the coefficient estimate will:

A) be greater than 0.54

B) be less than 0.54

C) remain unchanged, but the R2 will increase

D) will remain unchanged and the R2 will not change.