Answer:

So the best option is b. .42
Step-by-step explanation:
Previous concepts
Analysis of variance (ANOVA) "is used to analyze the differences among group means in a sample".
The sum of squares "is the sum of the square of variation, where variation is defined as the spread between each individual value and the grand mean"
The correlation coefficient is a "statistical measure that calculates the strength of the relationship between the relative movements of two variables". It's denoted by r and its always between -1 and 1. And is defined as:
When we conduct a multiple regression we want to know about the relationship between several independent or predictor variables and a dependent or criterion variable.
Solution to the problem
If we assume that we have
independent variables and we have
individuals, we can define the following formulas of variation:
And we have this property
We can find 
The degrees of freedom for the model on this case is given by
where k =2 represent the number of variables.
The degrees of freedom for the error on this case is given by
.
The determination coefficient when we conduct a multiple regression is defined as:

So the best option is b. .42