Consider this regression analysis output table. SUMMARY OUTPUT Dependent Variable: Number of Wins Regression Statistics Multiple R 0.3273 R square 0.1071 Adjusted R Square 0.0752 Standard Error 11.78 Observations 30 ANOVA df SS MS F Significance f Regression 1 4.66E+02 4.66E+02 3.36 0.0775 Residual 28 3.89E+03 1.39E+02 Total 29 4.35E+03 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Intercept 72.08 5.34 13.50 0.0000 61.14 83.01 Payroll ($ millions) 0.09 0.05 1.83 0.0775 -0.01 0.18 A baseball fan wants to forecast the number of wins per season of a Major League Baseball (MLB) team based on the annual payroll of the team. Assuming a 95% confidence level, what can be said about the relationship between number of wins and annual payroll? The fan should fail to reject the null hypothesis; there is sufficient evidence to conclude that there is a significant linear relationship between number of wins and annual payroll. The fan should fail to reject the null hypothesis; there is insufficient evidence to conclude that there is a significant linear relationship between number of wins and annual payroll. The fan should reject the null hypothesis; there is sufficient evidence to conclude that there is a significant linear relationship