plzzz, answer the 3 questions thanks

if you are doing this regression and P is price of apartment , Age: is the Age of apartment and Size is the size of apartment Dependent Variable: PRICE Method: Least Squares Date: 05/05/20 Time: 18:25 Sample: 1990 1995 Included observations: 60 Variable Coefficient Std. Error Prob. 50.19505 4.529372 0.0004 C AGE -0.812927 0.170910 0.0089 Mean dependentvar 35.83333 R-squared Adjusted R-squared 0.849760 0.812200 S.D. dependentvar 19.08315 7.324313 8.269856 Akaike info criterion 273.5621 Schwarz criterion 7.254899 S.E. of regression Sum squared resid Log likelihood F-statistic Prob(F-statistic) 7.046445 -19.97294 Hannan-Quinn criter. 22.62406 Durbin-Watson stat 0.008928 3.548697 1 The lower value for Durbin-Watson test for *this regression will equal t-Statistic 11.08212 -4.756476 (1 ) 2 Find the following value based on the above (model: (round to the nearest integer L (e; - i-1)2 (1 ) based on the above test (from question 1) we *: conclude Linear model is correct log-linear model is correct old model is correct add new variable to old model no colinearity colinearity exist model is homoschedastic model is hetroschedastic no autocorrelation positive autocorrelation exist negative autocorrelation exist can,t decide (1 ) if you are doing this regression and P is price of apartment , Age: is the Age of apartment and Size is the size of apartment Dependent Variable: PRICE Method: Least Squares Date: 05/05/20 Time: 18:25 Sample: 1990 1995 Included observations: 60 Variable Coefficient Std. Error Prob. 50.19505 4.529372 0.0004 C AGE -0.812927 0.170910 0.0089 Mean dependentvar 35.83333 R-squared Adjusted R-squared 0.849760 0.812200 S.D. dependentvar 19.08315 7.324313 8.269856 Akaike info criterion 273.5621 Schwarz criterion 7.254899 S.E. of regression Sum squared resid Log likelihood F-statistic Prob(F-statistic) 7.046445 -19.97294 Hannan-Quinn criter. 22.62406 Durbin-Watson stat 0.008928 3.548697 1 The lower value for Durbin-Watson test for *this regression will equal t-Statistic 11.08212 -4.756476 (1 ) 2 Find the following value based on the above (model: (round to the nearest integer L (e; - i-1)2 (1 ) based on the above test (from question 1) we *: conclude Linear model is correct log-linear model is correct old model is correct add new variable to old model no colinearity colinearity exist model is homoschedastic model is hetroschedastic no autocorrelation positive autocorrelation exist negative autocorrelation exist can,t decide (1 )