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OLS Regression Results Dep. Variable: sale_price R-squared : 0. 515 Model: OLS Adj . R-squared: 0. 514 Method : Least Squares F-statistic: 766.7 Date: Fri, 05 Jul 2024 Prob (F-statistic) : 0.00 Time : 12:47:03 Log-Likelihood: -28257. No. Observations: 2174 AIC: 5. 652e+04 Of Residuals: 2170 BIC: 5. 654e+04 Of Model: 3 Covariance Type: nonrobust coef std err t P It| [0. 025 0.975] Intercept 7. 965e+06 1.57e+06 5. 078 0.000 4. 89e+06 1. 1e+07 area 119. 7316 5.273 22. 706 0. 000 109.391 130. 073 fixtures 7183. 6622 1509. 354 4. 759 0. 000 4223. 732 1. 01e+04 zipcode -93. 6665 18. 419 -5. 085 0. 000 -129. 787 -57.545 Omnibus : 409. 555 Durbin-Watson: 1. 977 Prob (Omnibus ) : 0. 000 Jarque-Bera (JB) : 1505. 866 Skew: 0. 896 Prob (JB) : 0.00 Kurtosis : 6. 662 Cond. No. 5. 83e+07 Suppose you ran the following regression using statsmodels and print the results table as shown. results = smf.ols('sale_price ~ area + fixtures + zipcode', data=house_df).fit() Zip code is numeric variable. How is statsmodels treating zip code when you run the regression? O Zip code has been converted to a categorical variable. The results suggest some zip codes are more valuable than others. O The regression results suggest that an increase in the numeric value of the zip code is associated with a decrease in the sales price. This is a meaningful result and suggests that low zip code numbers are more valuable. O The regression results suggest that an increase in the numeric value of the zip code is associated with a decrease in the sales price. This result is by chance and should not be interpreted as significant