Question 1

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.

Question 1 options:

fittedvalues.model

model.values

model.fittedvalues

values.model

Question 2

Which of the following choices correctly identifies the following Q-Q plots for the normality of residuals assumption? Select one.

Question 2 options:

Both graphs show residuals with a distribution that is not Normal.

Graph A shows residuals with a distribution that more closely approximates a Normal distribution than Graph B.

Graphs A and B both show residuals with distributions that closely approximate a Normal distribution.

Graph B shows residuals with a distribution that more closely approximates a Normal distribution than Graph A.

Question 3

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.

Question 3 options:

model.residualsvalues

model.residuals

model.residvalues

model.resid

Question 4

Which of the following correctly shows the general form of a multiple regression model? Select one.

Question 4 options:

BX = Y C B,X1 = Y + Bot BzXz + ...+ BnXn Y = Bo+ B1X1+ B2X2 + ...+ BnXn C Y = Bo+ B1X1OLS Regression Results 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) : 3.80e-13 Time: 12: 49:37 Log-Likelihood: -21. 142 No. Observations : 18 AIC : 48. 28 Df Residuals: 15 BIC: 50.95 Df Model : Covariance Type: nonrobust coef std err t P>Itl [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 Omnibus : 4. 358 Durbin-Watson: 2. 121 Prob (Omni bus) : 0. 113 Jarque-Bera (JB) : 1.414 Skew: 0. 082 Prob (JB) : 0. 493 Kurtosis : 1.637 Cond. No. 14.4 Question 5 options: 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 = 332.2 Quality = 0.5382 - 1.9046 Speed + 4.0280 Angle coefficient of determination = 0.978