Question

PREGUNTA 1

1. Number of hours per week on the treadmill and cholesterol level is an example of:

   	A.	Positive Correlation
   	B.	Negative Correlation
   	C.	No Correlation
   	D.	None of the above

PREGUNTA 2

1. Shoe size and IQ is an example of:

   	A.	Positive Correlation
   	B.	Negative Correlation
   	C.	No Correlation
   	D.	None of the above

PREGUNTA 3

1. Number of children in the household under the age of 3 and expenditures on diapers is an example of:

   	A.	Positive Correlation
   	B.	Negative Correlation
   	C.	No Correlation
   	D.	None of the above

PREGUNTA 4

1. A researcher determines that the linear correlation coefficient is 0.85 for a paired data set. This indicates that there is:

   	A.	No linear correlation but that there may be some other relationship
   	B.	A strong positive linear correlation
   	C.	Insufficient evidence to make any decision about the correlation of the data
   	D.	A strong negative linear correlation

PREGUNTA 5

1. An instructor wishes to determine if there is a relationship between the number of absences from his class and a studentʹs final grade in the course. What is the predictor variable?

   	A.	Absences
   	B.	The instructorʹs point scale for attendance
   	C.	Studentʹs performance on the final examination
   	D.	Final Grade

PREGUNTA 6

1. A medical researcher wishes to determine if there is a relationship between the number of prescriptions written by medical professionals, per 100, children and the childʹs age. She surveys all the pediatricianʹs in a geographical region to collect her data. What is the response variable?

   	A.	100 prescriptions
   	B.	Number of prescriptions written
   	C.	Pediatricians surveyed
   	D.	Age of the child

  

PREGUNTA 7

1. A residual is the difference between

   	A.	The observed value of x and the predicted value of x.
   	B.	The observed value of x and the predicted value of y.
   	C.	The observed value of y and the predicted value of y.
   	D.	The observed value of y and the predicted value of x.

PREGUNTA 8

1. The least squares regression line

   	A.	Minimizes the sum of the residuals squared
   	B.	Maximizes the mean difference between the residuals squared
   	C.	Maximizes the sum of the residuals squared
   	D.	Minimizes the mean difference between the residuals squared

  

PREGUNTA 9

1. If a hypothesis test of the linear relation between the explanatory and the response variable is of the type where

   H0 : β1 = 0

   H1 : β1 > 0

   , then we are testing the claim that

   	A.	The slope of the least square regression model is positive
   	B.	A relationship exists without regard to the sign of the slope
   	C.	The slope of the least squares regression model is negative
   	D.	No linear relationship exists

Answer 1

Please don't hesitate to give a "thumbs up" for the answer in case the answer has helped you

1. B. Negative correlation

The more you exercise on the treadmill the lesser in the cholestrol level in your body

2. C. No correlation
No relation between the two

3. A. Postive Correlation

The more the number of children below 3 years, the more diapers required

4. B. A strong positive linear correlation
0.85 is positive in sign, and also near 1 ,indicating a strong positive linear correlation

5. A. Absences
This is used to predict student' grade

6. D. Age of the child
As this is the variable we want to predict

7. C is correct
Residual is y-y (predicted)

8. A is correct
It minimizes the sum of squared residual of the regression line and then fits the appropriate line

9. A is correct. the slope....positive
The claim is the alternate hypothesis, B > 0, which is testing whether coefficient is more than 0 (ie. positive)