1. Consider the model Yt = β0 + β1 Xt + εt, where t = 1,..., n. If the errors εt are not correlated, then the OLS estimates of β0 and β1 will be unbiased.
2. In the following regression model ln Yi = β0 + β1 X1i + β2 ln X2i + εi, all βk coefficients measure the elasticities of the Y variable with the respective X variables, because the Y variable appears in a logarithmic form.
3. If we want to capture a curvature in the relationship between Yi and Xi, we have to use a quadratic model, where the slope is not constant everywhere and changes according to the value of Xi at which it is being assessed.
4. If a hypothesis is rejected at the 0.10 level of significance, it may not be rejected at the 0.05 level of significance.
5. If β1 is positive in the equation
(1) Yi = β0 + β1 X1i + β2 X2i + εi, it can never be negative in the equation
(2) Yi = β0 + β1 X1i + β2 X2i + β3 X3i + εi,.