B. Interpret the Rsquared value. Does the multiple regression equation help us predict the total golf score much better than we could without knowing that equation? A. No, because Rsquared is close to 1. B. Yes, because Rsquared is not close to 1. C. No, because Rsquared is not close to 1. D. Yes, because Rsquared is close to 1.

Depending upon the golf score predictors we can conclude the answer from given Conditional statements

If Someone want Total golf score better then, following .condition

Yes, Because R squared is close to 1 .  (If Regression values are prefect to fit with model then only)

(But the data is not sufficient to say 100%, it depends on score given)

Step-by-step explanation:

Given :

Explanatory Question on r-squared in regression.

To Find :

Does regression equation help us to know total golf score much better.

Solution;

As  in Question there is no golf score given so we cant see "yes" or "no".

But we can be conditional here ,

1) If golf score to be accurate then , multiple regression predict points should fit the model prefect ,hence the R squared value "must be close to 1" .

2) If gold score is not accurate then multiple regression   suggest that model does  not explain any variables, no linear relationship.

then r squared value "must not be close to 1".

Depending upon the golf score predictors we can conclude the answer from given Conditional statements.

that  Yes, Because R squared is close to 1 .

b

step-by-step explanation:

given

- 2 (3x + 12y - 5 - 17x - 16y + 4 )

simplify the parenthesis by collecting like terms

= - 2 (- 14x - 4y - 1) ← distribute the parenthesis by - 2

= 28x + 8y + 2 → b

function 2 has a greater rate of change than function 1

function 2 has a greater y-intercept than function 1

step-by-step explanation: