We estimated the relationship between housing prices, age of the house, owner occupancy status and the presence of hazardous waste in the neighborhood. Suppose our estimates were given by: log(hhvalue)N=560,R2=0.113N=11.45(3.4)0.175(0.34)+waste0.00532(0.00356)+nbyrs+2.68(1.435)+owneroccupied where standard errors are given in parentheses. As a reminder: hhvalue is the housing price measured in dollars and waste is the quantity in kgs of hazardous waste in the house's census tract, nbyrs is the number of years since the house was built, and owner_occupied is a dummy variable for whether the house is occupied by its owner (vs rented). 1. Is either the age of house or owner occupancy significant at the 5% level against a 2-sided alternative? Show your work. Double-click this text to type your answer here 2. Dropping nbyrs and owneroccupied from the equation gives the equation below. Are nbyrs or owneroccupied jointly significant at the 5% level? Justify your answer. log( hhvalue )=11.45(2.5)0.178(0.36) waste N=560,R2=0.103 where standard errors are given in parentheses. Double-click this text to type your answer here 3. Does including nbyrs and owneroccupied in the model greatly affect the estimated tradeoff between environmental value and housing prices? Double-click this text to type your answer here 4. Carefully explain the difference between the null hypothesis in your tests in part 1 and part 2. What could you learn by rejecting the null hypothesis in each of them? Double-click this text to type your answer here We estimated the relationship between housing prices, age of the house, owner occupancy status and the presence of hazardous waste in the neighborhood. Suppose our estimates were given by: log(hhvalue)N=560,R2=0.113N=11.45(3.4)0.175(0.34)+waste0.00532(0.00356)+nbyrs+2.68(1.435)+owneroccupied where standard errors are given in parentheses. As a reminder: hhvalue is the housing price measured in dollars and waste is the quantity in kgs of hazardous waste in the house's census tract, nbyrs is the number of years since the house was built, and owner_occupied is a dummy variable for whether the house is occupied by its owner (vs rented). 1. Is either the age of house or owner occupancy significant at the 5% level against a 2-sided alternative? Show your work. Double-click this text to type your answer here 2. Dropping nbyrs and owneroccupied from the equation gives the equation below. Are nbyrs or owneroccupied jointly significant at the 5% level? Justify your answer. log( hhvalue )=11.45(2.5)0.178(0.36) waste N=560,R2=0.103 where standard errors are given in parentheses. Double-click this text to type your answer here 3. Does including nbyrs and owneroccupied in the model greatly affect the estimated tradeoff between environmental value and housing prices? Double-click this text to type your answer here 4. Carefully explain the difference between the null hypothesis in your tests in part 1 and part 2. What could you learn by rejecting the null hypothesis in each of them? Double-click this text to type your answer here
