Question

T F 5. Coefficient of correlation r tends to understate the strength of the relationship between the “based on” variable X and the variable to be predicted Y.

T F 6. We develop a regression equation for predicting salary based on GPA. GPA’s in the sample ranged from 1.8 to 3.7. We can safely use this equation to predict a salary for a student earning a 3.8 GPA in college.

T F 7. A standard error of Sy.x = 1.71 gives us a more accurate regression equation than a regression equation that produces a Sy.x=1.93, given that the Y variables are expressed in the same units.

T F 8. A negative regression coefficient (b1) means that as you increase your x1 variable, the variable being predicted also increases. T F 9. I say “correlation”, you say “prediction”. T F 10. The statistical concept of parsimony tells us to add more X variables to our regression equation in order to maximize prediction accuracy.

Answer 1

ANSWER:

5. False, it measures actual linear relationship but yes will fail to represent non linear.

6. False, because 3.8 GPA lies out of range and model is defined for 1.7-3.7 GPA

7. True, lesser the standard error better the regression because our goal is to minimize error.

8. False, for an increase in X there will be a decrease innY.

9. True

10. False : it focus on less but explanatory variables

(OR)

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