Question 1. The strength of the linear relationship between two numerical variables may be measured by the
scatter diagram.
coefficient of correlation.
slope.
Y-intercept.
Question 2. If you wanted to analyze the correlation between the NUMBER OF HOURS PRACTICED and the NUMBER OF TARGETS HIT by a sharpshooter, which variable would be the dependent variable?
Number of hours practiced
Number of targets hit
Neither one is dependent
Either one could be dependent (impossible to determine)
Question 3. Before proceeding with a simple linear regression, you should first construct a scatter diagram in order that you can remove all outliers from the data.
True
False
Question 4. What does it mean to have a negative coefficient in the regression model?
That variable reduces the coefficient of determination.
The values for that variable are negative.
There is an inverse relationship with that variable.
The correlation is weak.
Question 5. Assuming a linear relationship between X and Y, if the coefficient of correlation (r) equals -0.30,
there is no correlation.
the slope (b1) is negative.
variable X is larger than variable Y.
the variance of X is negative.
Question 6. If the hypothesis test for correlation is found to be significant (i.e., we rejected the null hypothesis / accepted the alternate hypothesis), what can we automatically conclude about the strength of the correlation?
We can conclude that the correlation must be strong.
We can only conclude that the correlation is not weak
We cannot conclude anything about the strength of the correlation yet.
Question 7. A negative correlation coefficient implies that as the value of independent variable increases, the value of the dependent variable _________________.
Increases
Decreases
Cannot be determined from the information given
Question 8. There is a strong positive correlation between a baby's weight and the size of his/her vocabulary. From this we can conclude that overeating will improve one's vocabulary.
True
False
Question 9. If the correlation coefficient (r) = 1.00, then
all the data points must fall exactly on a straight line with a slope that equals 1.00.
all the data points must fall exactly on a straight line with a negative slope.
all the data points must fall exactly on a straight line with a positive slope.
all the data points must fall exactly on a horizontal straight line with a zero slope.
Question 10. Assume the regression model for predicting home prices by the square footage were: PRICE = 12510 + 83 (SQRFT) For every additional one square foot, how much does the price increase?
$12,593
$83
$166
Cannot be determined from the information given.