The ols() method in statsmodels module is used to fit a multiple regression model using "Quality" as the response variable and "Speed" and "Angle" as the predictor variables. The output is shown below. A text version is available. What is the correct regression equation based on this output? What is the coefficient of determination? Select one. OLS Regression Results Dep. Variable: Quality R-squared: 0. 978 Mode | : OLS Adj. R-squared: 0. 975 Method : Least Squares F-statistic: 332.2 Date : Fri , 16 Aug 2019 Prob (F-statistic) : 3.80e-13 Time : 12:49:37 Log-Likelihood: -21. 142 No. Observations : 18 AIC : 48. 28 Df Residuals: 15 BIC: 50. 95 of Model: Covariance Type: nonrobust coef std err t P>It| [O. 025 0. 975] Intercept 0. 5382 0. 473 1.137 0. 273 0. 471 1. 547 Speed -1. 9046 0. 176 -10 .834 0 . 000 -2.279 -1. 530 Angle 4. 0280 0. 178 22.574 0. 000 3. 648 4. 408 Omnibus : 4. 358 Durbin-Watson: 2. 121 Prob (Omnibus) : 0. 113 Jarque-Bera (JB) : 1. 414 Skew: 0 . 082 Prob (JB) : 0. 493 Kurtosis : 1.637 Cond. No. 14.4 Quality = 0.5382 - 1.9046 Speed + 4.0280 Angle coefficient of determination = 0.978 Quality = 0.473 + 0.176 Speed + 0.178 Angle coefficient of determination = 0.978 Quality = 0.473 + 0.176 Speed + 0.178 Angle coefficient of determination = 332.2 Quality = 0.5382 -1.9046 Speed + 4.0280 Angle coefficient of determination = 332.2Which Python module and method are used to create a multiple regression model for a given data set? Select one. O ols method from scipy module O linregress method from scipy module O ols method from statsmodel module O linregress method from statsmodel moduleThe ols() method in statsmodels module is used to fit a multiple regression model using "Exam4" as the response variable and "Exam1", "Exam2", and "Exam3" as predictor variables. The output is shown below. A text version is available. What is the correct regression equation based on this output and what is the coefficient of determination? Select one. OLS Regression Results Dep. Variable: Exam4 R-squared: 0. 178 Mode | : OLS Adj. R-squared: 0. 125 Method: Least Squares F-statistic: 3. 329 Date: Fri, 16 Aug 2019 Prob (F-statistic) : 0. 0276 Time : 12 : 38: 46 Log-Likelihood: -169. 85 No. Observations : 50 AIC : 347.7 Of Residuals : 46 BIC: 355. 4 of Model : Covariance Type: nonrobust coef std err P>It| [0 . 025 0. 975] Intercept 46. 2612 10.969 4. 217 0. 000 24. 181 68. 341 Exam1 0. 1742 0. 120 1. 453 0. 153 -0. 067 0. 416 Exam2 0. 1462 0. 078 1.873 0. 067 -0. 011 0. 303 Exam3 0. 0575 0. 053 1. 085 0 . 284 -0. 049 0. 164 Omni bus : 0. 886 Durbin-Watson: 1. 530 Prob (Omni bus) : 0. 642 Jarque-Bera (JB) : 0. 738 Skew: 0. 290 Prob (JB) : 0. 691 Kurtosis : 2 . 868 Cond. No. 1.41e+03 Exam4 = 46.2612 + 0.1742 Exam1 + 0.1462 Exam2 + 0.0575 Exam3 coefficient of determination = 0.178 Exam4 = 46.2612 + 0.1742 Exam1 + 0.1462 Exam2 + 0.0575 Exam3 coefficient of determination = 3.329 Exam4 = 10.969 + 0.120 Exam1 + 0.078 Exam2 + 0.053 Exam3 coefficient of determination = 0.178 Exam4 = 10.969 + 0.120 Exam1 + 0.078 Exam2 + 0.053 Exam3 coefficient of determination = 3.329Suppose a multiple regression model is fitted into a variable called model. Which Python method below returns fitted values for a data set based on a multiple regression model? Select one. O model.values O fittedvalues.model O model.fittedvalues O values.modelWhich of the following choices correctly identifies the following QQ plots for the normality of residuals assumption? Select one. Sample In no ntic's harnple quantize. -3 -2 -1 D 1 2 3 Inmrelural quaTIIES Thmtemal qua-Miles A B c. Both graphs show residuals with a distribution that is not Normal. (W, Graph A shows residuals with a distribution that more closely approximates a \\_/ Normal distribution than Graph B. (W, Graph B shows residuals with a distribution that more closely approximates a K._./ Normal distribution than Graph A. F\". Graphs A and B both show residuals with distributions that closely approximate L] a Normal distribution