1)Consider the following multiple regression model. What are the correct null and alternative hypotheses to test whether the variablex₂is significant? Select one.

O Null Hypothesis Ho: pl: 0 Alternative Hypothesis H1: p2 q 0 P-value = 0.000 Since the Pvalue is less than the level of significance, alpha, do not reject the null hypothesis. Conclude that the estimate for the Angle parameter is zero. Therefore, Angle is not statistically significant in the multiple regression model. 0 Null Hypothesis Ho: pl: 0 Alternative Hypothesis H1: p, #9 0 P-value = 0.000 Since the Pvalue is less than the level of significance, alpha, reject the null hypothesis. Conclude that the estimate for the Angle parameter is non-zero. Therefore, Angle is statistically significant in the multiple regression model. O Null Hypothesis Ho: 5,2: 0 Alternative Hypothesis H1: [3, q 0 P-value = 4.0280 Since the Pvalue is greater than the level of significance, alpha, do not reject the null hypothesis. Conclude that the estimate for the Angle parameter is zero. Therefore, Angle is not statistically significant in the multiple regression model. 0 Null Hypothesis Ho: 5,2: 0 Alternative Hypothesis H1: [3, 94 0 P-value = 4.0280 Since the Pvalue is greater than the level of significance, alpha, reject the null hypothesis. Conclude that the estimate for the Angle parameter is nonzero. Therefore, Angle is statistically significant in the multiple regression model. Null Hypothesis H : Bo # 0 Alternative Hypothesis Ha: P = 0 Null Hypothesis Ho : B2 7 0 Alternative Hypothesis Ha: B, = 0 Null Hypothesis Ho : Bo = 0 Alternative Hypothesis Ha: Bo# 0 Null Hypothesis Ho : P2= 0 Alternative Hypothesis Ha: B2# 0Y = Bo+ BIX1+ B2X2 + B3X3= Po + B, Speed + ByAngle8.114 0.774 2.005 O 1.000OLS Regression Results Dep. Variable: Quality R-squared: 0.978 Model: OLS Adj. R-squared: 0. 975 Method : Least Squares F-statistic: 332.2 Date: Sun, 18 Aug 2019 Prob (F-statistic) : 3.80e-13 Time : 11: 39:31 Log-Likelihood : -21. 142 No. Observations : 18 AIC: 48 . 28 Df Residuals: 15 BIC: 50.95 Df Model: 2 Covariance Type: nonrobust coef std err t P>It| [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 (Omnibus) : 0. 113 Jarque-Bera (JB) : 1. 414 Skew: 0. 082 Prob (JB) : 0. 493 Kurtosis : 1. 637 Cond. No. 14.4