Suppose that a researcher collects data on houses that have sold in a particular neighborhood over the past year and obtains the regression results in the table shown below. Dependent variable: In(Price) Regressor (1) (2) (3) (4) (5) 0.00044 Size (0.000042) 0.78 0.76 0.57 0.716 In(Size) (0.057) (0.088) (2.05) (0.061) In(Size) 0.0086 (0.16) 0.0039 Bedrooms (0.041) 0.086 0.073 0.089 0.085 0.091 Pool (0.036) (0.035) (0.036) (0.034) (0.039) 0.044 0.028 0.028 0.028 0.028 View (0.032) (0.029) (0.031) (0.033) (0.032) 0.0022 Pool View (0.12) 0.16 0.16 0.21 0.14 0.12 Condition (0.046) (0.035) (0.037) (0.039) (0.038) 12.02 6.69 6.65 7.09 6.67 Intercept (0.069) (0.42) (0.56) (7.53) (0.41) Summary Statistics SER 0.112 0.105 0.107 0.101 0.108 0.72 0.76 0.73 0.74 0.75 Variable definitions: Price = sale price ($); Size = house size (in square feet); Bedrooms = number of bedrooms; Pool = binary variable (1 if house has a swimming pool, 0 otherwise); View = binary variable (1 if house has a nice view, 0 otherwise); Condition = binary variable (1 if real estate agent reports house is in excellent condition, 0 otherwise). Using the results in column (1), what is the expected increase in price of building a 500-square-foot addition to a house, holding everything else in the model constant? The expected increase in price of building a 500-square-foot addition to a house is 22.00% (Express your response as a percentage and round to two decimal places) Construct a 95% confidence interval for the percentage change in price. The 95% confidence interval for the percentage change in price is [ ] % (Express your response as a percentage and round to two decimal places)