please help!

CHAPTER 5.2 ASSESSMENT INFORMATION Use the information beta if to answer [no questions {or the 5.2 Assessment. II. Now we will add the following variables. X6 = fielding position 2 = catcher 3 = first baseman 4 = second baseman 5 = third baseman o = shortstop 7, B. 9 = outelder X? =1 if a right handed batter and 0 if a left handed batter X3 = errors made in the eld (E) X9 = number of stolen basese (SB) The table below has the data for these new variables Y (BA) )(g. {position} )(7 (hand) X3 (errors) X9 {SB} 0.293 3 0 10 Cn A CO 0.269 0.259 Section 5.1 Multiple Regression 0.219 https:/dsu.gricontent....sanalysis/page/chapter5# 0.270 1 5 0 0.291 7 3 0.280 5 0.316 19 0.261 2 - 0 0.258 No 0 0.268 8 18 0.236 15 0.296 6 1 6 27 0.269 0 5 0.235 O 0.225 4 10 The regression model now is y = Bo + B1X1 + B2X2 + ByX3 + BAX4 + 85X5 + BoXe + ByXr + BXs + By X, te The SAS output for this model is now given here. Linear Regression Results: BASEBALL STATS Second Model The REG Procedure Model: Linear_Regression_Model Dependent Variable: Y BA Number of Observations Read 16 Number of Observations Used 16 Analysis of Variance Source DF Sum of Squares Mean Square F Value Pr > F Mode 9 0.01052 0.00117 19.82 0.0008 Error 6 0.00035366 0.00005894 Corrected Total 15 0.01087Root MSE 0.00768 R-Square 0.9675 Dependent Mean 0.26531 Adj R-Sq 0.9187 Coeff Var 2.89375 Parameter Estimates Variable Label DF Parameter Standard t Value Pr > It| 95% Confidence Limits Estimate Error Intercept Intercept 1 0.26737 0.02359 11.33 <.0001 x1 g x2 ab x3 h xa k x5 team x6 position x7 hand x8 errors x9 sb y x4 xg predicted iclm uclm icl ucl residual x we are now going to test if these variables just added significant the model. ho : bo="By" bg="Bg" ha at least one b will be conducting partial f test. what is value of statistic o none save view correct answer2. decision reject or do not reject. answer conclusion terms when model as a group neither choice using best ba for player with following values. xs="10" and corre t answer5. refer question cl e pl three selection procedures discussed which combination other two forward stepwise regression backward elimination>