1. Suppose the correlation, r, between two variables x and y is -0.44. What percentage of the variability in y cannot be explained by x?

2. 19%

   44%

   81%

   88%

   12%

3. Suppose the correlation, r, between two variables x and y is -0.44. What would you predict about a y value if the x value is 2 standard deviations above its mean?

   It will be .88 standard deviations below its mean.

   It will be .88 standard deviations above its mean.

   It will be 2 standard deviations below its mean.

   It will be .44 standard deviations below its mean.

   It will be .44 standard deviations above its mean.

4. Datawerecollectedonmonthlysalesrevenues(in$1,000s)andmonthlyadvertisingexpenditures($100s)forasampleofdrugstores.Theregressionlinerelatingrevenues(Y)toadvertisingexpenditure(X)isestimatedtobey=48.3+9.00x.Thepredictedsalesrevenueforamonthinwhich$1,000wasspentonadvertisingis

   $50,000

   $851.70

   $8,951.70

   $41,700

   $90,000

Acompanystudyingtheproductivityofitsemployeesonanewinformationsystemwasinterestedindeterminingiftheage(X)ofdataentryoperatorsinfluencedthenumberofcompletedentriesmadeperhour(Y).Theregressionequationisy=14.3740.145x.Supposetheactualcompletedentriesperhourforanoperatorwhois35yearsoldwas8.Theresidualis

-1.3

2.6

-3.5

1.3

-2.2

6.A company studying the productivity of its employees on a new information system was interested in determining if the age (X) of data entry operators influenced the number of completed entries made per hour (Y). The regression equation isy = 14.374 0.145x.

If sx.04 and sy.61, then the correlation coefficient between age and productivity is

0.779

-0.236

0.575

-0.929

-0.779

1. A supermarket chain gathers data on the amount they spend on promotional material (e.g., coupons, etc.) and sales revenue generated each quarter. The predictor variable is

sales revenue

amount spent on promotional material

number of coupons offered

supermarket chains

none of the above