Free IIA IIA-CIA-Part3 Exam Braindumps (page: 55)

Based upon the data derived from the regression analysis, 420 maintenance hours in a month would mean the maintenance costs rounded to the nearest U dollar) would be budgeted at

  1. U $3,780
  2. U $3,600
  3. U $3,790
  4. U $3,746

Answer(s): D

Explanation:

Substituting the given data into the regression equation results in a budgeted cost o US $3,746 rounded to the nearest US dollar).

y = a + bx y = 684.65 + 7.2884 420
y = us $3,746



What is the percentage of the total variance that can be explained by the regression?

  1. 99.724%
  2. 69.613%
  3. 80.982%
  4. 99.862%

Answer(s): A

Explanation:

The coefficient of determination r) measures the percentage of the total variance in cost that can be explained by the regression equation. If the coefficient of determination is .99724, 99.724% of the variance is explained by the regression equation. Thus, the values in the regression equation explain virtually the entire amount of total cost.



An internal auditor for a large automotive parts retailer wishes to perform a risk analysis and wants to use an appropriate statistical tool to help identify stores that would be out of line compared to the majority of stores. The most appropriate statistical tool to use is

  1. Linear time series analysis.
  2. Cross-sectional regression analysis.
  3. Cross tabulations with chi-square analysis of significance.
  4. Time series multiple regression analysis to identify changes in individual stores overtime.

Answer(s): B

Explanation:

Time series data pertain to a given entity over a number of prior time periods. Cross sectional data, however, pertain to different entities for a given time period or at a given time.
Thus, cross-sectional regression analysis is the most appropriate statistical tool because it compares attributes of all stores' operating statistics at one moment in time.



A division uses a regression in which monthly advertising expenditures are used to predict monthly product sales both in millions of US dollars). The results show a regression coefficient for the independent variable equal to 0.8. This coefficient value indicates that:

  1. The average monthly advertising expenditure in the sample is US $800,000.
  2. When monthly advertising is at its average level, product sales will be US $800,000.
  3. On average, every additional dollar of advertising results in US $.80 of additional sales.
  4. Advertising is not a good predictor of sales because the coefficient is so small.

Answer(s): C

Explanation:

The regression coefficient represents the change in the dependent variable corresponding to a unit change in the independent variable. Thus, it is the slope of the regression line.



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