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Interpretation of Regression Results: Multiple Choice The Business School at Eastern College is collecting data as a first step in the preparation of next year’s budget. One cost that is being looked at closely is administrative staff as a function of student credit hours. Data on administrative costs and credit hours for the most recent 13 months follow

The controller’s office has analysed the data and given you the results from the regression analysis:

Required

a.    In the standard regression equation y = a + bx , the letter  b  is best described as the

(1)    Independent variable.   (4)   Correlation coefficient.  (2)   Dependent variable.   (5)   Variable cost coefficient.   (3)   Constant coefficient.   

 b.   In the standard regression equation y = a +bx , the letter  y  is best described as the

(1)    Independent variable.   (4)   Variable cost coefficient.   (2)   Correlation coefficient.   (5)   Dependent variable.   (3)   Constant coefficient.    

c.   In the standard regression equation y = a + bx , the letter  x  is best described as the (1)    Independent variable.   (4)   Variable cost coefficient.   (2)   Dependent variable.   (5)   Correlation coefficient.   (3)   Constant coefficient.   

 d.   If the controller uses the high-low method to estimate costs, the cost equation for administrative costs is (numbers are rounded to the nearest dollar) (1)    Cost  $291,637  229  Credit hours.   (4)   Cost  $404,874.   (2)   Cost  $233,571  101  Credit hours.   (5)   Some other equation.   (3)   Cost  $229.50  Credit hours.  

  e. Based on the results of the controller’s regression analysis, the estimate of administrative costs in a month with 2,100 credit hours would be (1)    $834,993.   (4)   $839,575.   (2)   $844,200.   (5)   Some other amount.   (3)   $404,917.   

 f.   The correlation coefficient (rounded) for the regression equation for administrative costs is (1)    0.871.   (4)     ______ 0.933.     (2)   0.933.   (5)   Some other amount.   (3)   0.859.    

g.   The per cent of the total variance (rounded) that can be explained by the regression is (1)    85.9.   (4)   96.6.   (2)   87.1.   (5)   Some other amount.   (3)   93.3.