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 IEEEEEEEEEE z eeeee EEEE==========EEEEEEEEE======= Dep. Variable: Quality R-squared: 0.978 Model: OLS Adj. R-squared: 0.975 Method: Least Squares F-statistic: 332.2 Date: Fri, 16 Aug 2019 Prob (F-statistic): 3.80-13 Time: 12:49:37 Log-Likelihood: -21.142 No. Observations: AI: 48.28 Df Residuals: BIC: 50.95 Df Model: Covariance Type: nonrobust =================RELLEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEE=========EEEEEEE coef std err P>tl [0.025 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 EEEEEEEE=========EEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEE============ 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 = 0.473 +0.176 Speed + 0.178 Angle coefficient of determination=332.2 Quality = 0.473 +0.176 Speed +0.178 Angle coefficient of determination = 0.978 Quality=0.5382 -1.9046 Speed +4.0280 Angle coefficient of determination = 0.978 C) Quality = 0.5382 -1.9046 Speed + 4.0280 Angle coefficient of determination = 332.2 Suppose a multiple regression model is fitted into a variable called model. Which Python method below returns residuals for a data set based on a multiple regression model? Select one. model.residvalues model.residualsvalues model residuals model.resid Which Python module and method are used to create a multiple regression model for a given data set? Select one. ols method from scipy module ols method from statsmodel module linregress method from statsmodel module linregress method from scipy module Suppose 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. model.values fittedvalues.model values.model model.fittedvalues What is a residual for a multiple regression model and the data that is used to create it? Select one. A statistic that is used to evaluate the significance of the multiple regression model The predicted value of the response variable using the multiple regression model A statistic that explains the relationship between response and predictor variables The difference between the actual value of the response variable and the corresponding predicted value (regression error) using the multiple regression model 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 IEEEEEEEEEE z eeeee EEEE==========EEEEEEEEE======= Dep. Variable: Quality R-squared: 0.978 Model: OLS Adj. R-squared: 0.975 Method: Least Squares F-statistic: 332.2 Date: Fri, 16 Aug 2019 Prob (F-statistic): 3.80-13 Time: 12:49:37 Log-Likelihood: -21.142 No. Observations: AI: 48.28 Df Residuals: BIC: 50.95 Df Model: Covariance Type: nonrobust =================RELLEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEE=========EEEEEEE coef std err P>tl [0.025 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 EEEEEEEE=========EEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEE============ 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 = 0.473 +0.176 Speed + 0.178 Angle coefficient of determination=332.2 Quality = 0.473 +0.176 Speed +0.178 Angle coefficient of determination = 0.978 Quality=0.5382 -1.9046 Speed +4.0280 Angle coefficient of determination = 0.978 C) Quality = 0.5382 -1.9046 Speed + 4.0280 Angle coefficient of determination = 332.2 Suppose a multiple regression model is fitted into a variable called model. Which Python method below returns residuals for a data set based on a multiple regression model? Select one. model.residvalues model.residualsvalues model residuals model.resid Which Python module and method are used to create a multiple regression model for a given data set? Select one. ols method from scipy module ols method from statsmodel module linregress method from statsmodel module linregress method from scipy module Suppose 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. model.values fittedvalues.model values.model model.fittedvalues What is a residual for a multiple regression model and the data that is used to create it? Select one. A statistic that is used to evaluate the significance of the multiple regression model The predicted value of the response variable using the multiple regression model A statistic that explains the relationship between response and predictor variables The difference between the actual value of the response variable and the corresponding predicted value (regression error) using the multiple regression model