Multiple Choice Identify the
choice that best completes the statement or answers the question.


1.

In a statistics course, a linear regression
equation was computed to predict the final exam score from the score on the first test. The
equation was y = 10 + .9x where y is the final exam score and x is the score on the first
test. Carla scored 95 on the first test. On the final exam, Carla scored 98. What
is the value of her residual?
a.  98  b.  2.5  c.  –2.5  d.  0  e.  None of the
above 


2.

In the scatterplot below, if each xvalue were
decreased by one unit and the yvalues remained the same, then the correlation r
would
a.  Decrease by 1 unit  b.  Decease slightly  c.  Increase
slightly  d.  Stay the same  e.  Can’t tell without knowing the data
values 


3.

In regression, the residuals are which of the
following?
a.  Those factors unexplained by the
data  b.  The difference between the observed responses and the
values predicted by the regression line  c.  Those data points
which were recorded after the formal investigation was completed  d.  Possible models unexplored by the investigator  e.  None of the above 


4.

Which of the following statements are
true?
I. Correlation and regression require explanatory and response
variables. II. Scatterplots require
that both variables be quantitative.
III. Every leastsquare regression line passes through .
a.  I and II only  b.  I and III only  c.  II and III
only  d.  I, II, and III  e.  None of the above 


5.

Suppose the following information was collected,
where X = diameter of tree trunk in inches, and Y = tree height in feet.
If the LSRL equation is y = –3.6
+ 3.1x, what is your estimate of the average height of all trees having a trunk diameter of 7
inches?
a.  18.1  b.  19.1  c.  20.1  d.  21.1  e.  22.1 


6.

Suppose we fit the least squares regression line to
a set of data. What is true if a plot of the residuals shows a curved
pattern?
a.  A straight line is not a good model for the
data.  b.  The correlation must be 0.  c.  The correlation must be positive.  d.  Outliers must be present.  e.  The LSRL might or might not be a good model for the data, depending on the
extent of the curve. 


7.

Which of the following are
resistant?
a.  Least squares regression line  b.  Correlation coefficient  c.  Both the least
squares line and the correlation coefficient  d.  Neither the least
squares line nor the correlation coefficient  e.  It
depends 


8.

A copy machine dealer has data on the number x of
copy machines at each of 89 customer locations and the number y of service calls in a month at each
location. Summary calculations give = 8.4, S_{x} = 2.1, = 14.2,
S_{y} = 3.8, and r = 0.86. What is the slope of the least squares regression line of
number of service calls on number of copiers?
a.  0.86  b.  1.56  c.  0.48  d.  None of
these  e.  Can’t tell from the information
given 


9.

There is a linear relationship between the number
of chirps made by the striped ground cricket and the air temperature. A least squares fit of
some data collected by a biologist gives the model = 25.2 +
3.3x, 9 < x < 25, where x is the number of chirps per minute and
is the estimated
temperature in degrees Fahrenheit. What is the estimated increase in temperature that
corresponds to an increase in 5 chirps per minute?
a.  3.3°F  b.  16.5°F  c.  25.2°F  d.  28.5°F  e.  41.7°F 


10.

A set of data relates the amount of annual salary
raise and the performance rating. The least squares regression equation is = 1,400 +
2,000x where y is the estimated raise and x is the performance rating. Which
of the following statements is not correct?
a.  For each increase of one point in performance rating,
the raise will increase on average by $2,000.  b.  This equation
produces predicted raises with an average error of 0.  c.  A rating of 0 will yield a predicted raise of
$1,400.  d.  The correlation for the data is
positive.  e.  All of the above are
true. 
