3. A real estate industry in Boston (US) conduct a survey to estimate the price of house in $1000 using predictor: LSTAT (% lower status of the population). Using smf.ols function from the Python library statsmodels.formula.api we get the following results: OLS Regression Results Dep. Variable: price R-squared : 0. 544 Model : OLS Adj . R-squared: 0.543 Method: Least Squares F-statistic: 601.6 Date: Thu, 16 Sep 2021 Prob (F-statistic) : 5.08e-88 Time : 20 :15:19 Log-Likelihood: -1641. 5 No. Observations: 506 AIC: 3287. Of Residuals: 504 BIC: 3295. Of Model : Covariance Type: nonrobust coef std err t. P>ItI CO . 025 0. 975] Intercept 34.5538 0. 563 61 . 415 0.000 33. 448 35. 659 LSTAT -0. 9500 0. 039 -24. 528 0.000 -1. 026 -0.874 Omnibus : 137 .043 Durbin-Watson : 0. 892 Prob (Omnibus) : 0. 000 Jarque-Bera (JB) : 291.373 Skew : 1. 453 Prob ( JB) : 5.36e-64 Kurtosis: 5.319 Cond. No. 29.7 (i). Is the model adequate for predicting the price of the house? Comment. (ii).Express the fitted regression equation. Hence, interpret the estimated coefficients. (iii). Find out the significant variables (with justification) to estimate the price of the house. (iv). Calculate the coefficient of determination. Hence, interpret the results. (v). Estimate the price of house when LSTAT=20