Consider the following regression model: Y = Bo + B1X1i + B2X2i + B3X3i + 1. = 1. If the independent variables X1 and X2 are known to be highly positively correlated, will our OLS estimates of $1 tend to be too high or too low? Explain. 2. If, on the other hand, the independent variables X1 and X2 are known to be highly negatively correlated, will our OLS estimates of B1 tend to be too high or too low? Explain. 3. Why might we observe unexpected signs for , and 2 from OLS when X, and X2 are highly correlated? Would observing unexpected signs for B, and B2 indicate biased estimates? Explain. Consider the following regression model: Y = Bo + B1X1i + B2X2i + B3X3i + 1. = 1. If the independent variables X1 and X2 are known to be highly positively correlated, will our OLS estimates of $1 tend to be too high or too low? Explain. 2. If, on the other hand, the independent variables X1 and X2 are known to be highly negatively correlated, will our OLS estimates of B1 tend to be too high or too low? Explain. 3. Why might we observe unexpected signs for , and 2 from OLS when X, and X2 are highly correlated? Would observing unexpected signs for B, and B2 indicate biased estimates? Explain