Econometrics. a Regression Analysis

Question 1:
Run the regression Report your answer in the format of equation 5.8 (Chapter 5, p. 152) in the textbook including and the standard error of the regression (SER). Interpret the estimated slope parameter for LOT. In the interpretation, please note that PRICE is measured in thousands of dollars and LOT is measured in acres.

Model 1: OLS estimates using the 832 observations 1-832
Dependent variable: price

VARIABLE COEFFICIENT STDERROR T STAT P-VALUE

const 119.575 1.54566 77.362 <0.00001 *** lot 1.38850 0.209083 6.641 <0.00001 ***

Mean of dependent variable = 122.076 Standard deviation of dep. var. = 44.3478 Sum of squared residuals = 1.55189e+006 Standard error of residuals = 43.2406 Unadjusted R-squared = 0.05045 Adjusted R-squared = 0.04931 Degrees of freedom = 830 Log-likelihood = -4313.52 Akaike information criterion (AIC) = 8631.03 Schwarz Bayesian criterion (BIC) = 8640.48 Hannan-Quinn criterion (HQC) = 8634.66

PRICEi=119.575+1.3885LOTi, 1.54566 (0.209083)
R2=0.05045, SER=43.2406

Slope coefficient = 1.3885

* After analyzing the slope coefficient, one can assume that a one unit (one acre) change in lot would lead to an overall predicted change in price of $1388.50.

Question 2:
Now run the regression
Report your answer using the format of equation 5.8 (Chapter 5) in the textbook, including and SER. Interpret the estimated slope parameter for LOT. Also report the ANOVA table provided by GRETL (for use in answering question (9) below). In the interpretation, please note that PRICE is measured in thousands of dollars and LOT is measured in acres

Model 2: OLS estimates using the 832 observations 1-832
Dependent variable: price

VARIABLE COEFFICIENT STDERROR T STAT P-VALUE

const