The application of multiple regression analysis to a data set yields an f statistic that is highly significant and t ratios that are not significant. this is an indication that

(a) autocorrelation is present.
(b) multicollinearity is present.
(c) homoscedasticity is present.
(d) heteroscedasticity is present.

(B) multicollinearity is present.

Explanation:

Multicollinearity -

It is the process where , one of the predictor variable in the multiple regression model can be linearly predicted from the others with  the substantial degree of accuracy , is known as multicollinearity or  collinearity .

In this case , the coefficient estimated of the multiple regression can change erratically for even a small change in the model .

hence , from the question , the indication is of (B) multicollinearity is present .

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explanation:

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