136966150 False rldbqn=1 77121361 3 False MultipleSections NavigateFreely False /main/CourseMod An official of a major racetrack would like to develop a model to estimate the amount of money wagered (Y, WAGERED, in millions of dollars) based on attendance (X1, ATTENDANCE, in $1,000s) and whether it rains (X2, RAIN, with 0=no rain, 1=rain). A random sample of 10 days was selected with the results found below. ATTENDANCE RAIN WAGERED 14.5 1 7 21.2 1 8.3 11.6 1 6.2 31.7 0 11 46.6 0 12.7 31.4 0 10.2 40 0 11.5 21 1 8 16.3 1 7.1 32.1 0 10.4 Correlations: ATTENDANCE, RAIN, WAGERED ATTENDANCE RAIN RAIN 0.888 0.001 WAGERED 0.989 0.918 0.000 0.000 Cell Contents: Pearson correlation PValue Regression Analysis: WAGERED versus ATTENDANCE, RAIN The regression equation is WAGERED = 5.45 + 0.157 ATTENDANCE 0.787 RAIN. Predictor Coef SE Coef T P Constant 5.4495 0.6957 7.83 0.000 ATTENDANCE 0.15705 0.01878 8.36 0.000 RAIN 0.7869 0.4109 1.92 0.097 S = 0.298151 RSq = 98.6% RSq(adj) = 98.2% Analysis of Variance Source DF SS MS F P Regression 2 43.082 21.541 242.32 0.000 Residual Error 7 0.622 0.089 Total 9 43.704 Predicted Values for New Observations New Obs Fit SE Fit 95% CI 95% PI 1 10.1611 0.1790 (9.7379, 10.5844) (9.3388, 10.9835) Values of Predictors for New Observations New Obs ATTENDANCE RAIN 1 30.0 0.000000 a. Analyze the above output to determine the multiple regression equation. b. Find and interpret the multiple index of determination (R-Sq). c. Perform the multiple regression t-tests on 1 , 2 (use two tailed test with (= .10). Interpret your results. d. Predict the amount wagered for a single day where the attendance is 30,000 and it does not rain. Use both a point estimate and the appropriate interval estimate