Consider the output here from a regression in R. What is B₂? Coefficients: Estimate (Intercept) 1.708 5.404 -1.478 9.531 X1 X2 X3 Std. Error 0.555 2.792 0.6 2.758

A regression analysis of company profits and the amount of money the company spent on advertising produced a R² = 0.72. Which of these is TRUE? 1. This model can correctly predict the profit for 72% of companies. II. 72% of the variance in company profit can be accounted for by the model. III. On average, companies spend about 72% of their profits on advertising. OA. None OB. I and III OC. II only OD. III only OE. I only

You estimated a regression with the following output.         Source |       SS           df       MS      Number of obs   =       289 -------------+----------------------------------   F(1, 287)       =  41986.64        Model |   664544048         1   664544048   Prob > F        =    0.0000     Residual |  4542496.25       287  15827.5131   R-squared       =    0.9932 -------------+----------------------------------   Adj R-squared   =    0.9932        Total |   669086544       288  2323217.17   Root MSE        =    125.81   ------------------------------------------------------------------------------            Y |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval] -------------+----------------------------------------------------------------            X |   43.81013   .2138056   204.91   0.000     43.38931    44.23096        _cons |   49.31707   16.96222     2.91   0.004     15.93094    82.70319…

In an OLS regression, which value represents the "best" R2 in terms of explained variance in the dependent variable?  A. 2.53 B. 16.22 C. .001 D. 0.53