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Free IIA IIA-CIA-Part3 Exam Braindumps (page: 68)

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Correlation is a term frequently used in conjunction with regression analysis and is measured by the value of the coefficient of correlation, r. The best explanation of the value r is that it:

  1. Is always positive.
  2. Interprets variances in terms of the independent variable.
  3. Ranges in size from negative infinity to positive infinity.
  4. Is a measure of the relative relationship between two variables.

Answer(s): D

Explanation:

The coefficient of correlation r) measures the strength of the linear relationship between the dependent and independent variables. The magnitude of r is independent of the scales of measurement of x and y. The coefficient lies between -1.0 and +1.0. A value of zero indicates no linear relationship between the x and y variables. A value of +1.0 indicates a perfectly direct relationship, and a value of-1.0 indicates a perfectly inverse relationship.



The four components of time series data are secular trend, cyclical variation, seasonality, and random variation. The seasonality in the data can be removed by:

  1. Multiplying the data by a seasonality factor.
  2. Ignoring it.
  3. Taking the weighted average over four time periods.
  4. Subtracting a seasonality factor from the data.

Answer(s): C

Explanation:

Time series analysis relies on past experience. Changes in the value of a variable may have several possible components including secular trends, cyclical variation, seasonality, and random variation. Seasonal variations are common in many businesses. A variety of methods exist for including seasonal variations in a forecasting model, but most methods use a seasonal index. Alternatively, seasonal variations can be removed from data by using a weighted average of several time periods instead of data from individual periods.



A widely used approach that managers use to recognize uncertainty about individual items and to obtain an immediate financial estimate of the consequences of possible prediction errors is:

  1. Expected value analysis.
  2. Learning curve analysis.
  3. Sensitivity analysis.
  4. Regression analysis.

Answer(s): C

Explanation:

Sensitivity analysis determines how a result varies with changes in a given variable or parameter in a mathematical decision model. For example, in a present value analysis, a manager might first calculate the net present value or internal rate of return assuming that a new asset has a 10-year life. The NPV or IRR can then be recalculated using a 5-year life to determine how sensitive the result is to the change in the assumption. An international not for- profit organization finances medical research. The majority of its revenue and support comes from fund-raising activities, investments, and specific grants from an initial sponsoring corporation. The organization has been in operation over 15 years and has a small internal audit department. The organization has just finished a major fundraising drive that raised US $500 million for the current fiscal period. The following are selected data from recent financial statements US dollar figures in millions):



The auditor wishes to determine if the change in investment income during the current year was due to a) changes in investment strategy, b) changes in portfolio mix, or c) other factors.
Which of the following analytical review procedures should the auditor use?

  1. Simple linear regression that compares investment income changes over the past 5 years to determine the nature of the changes.
  2. Ratio analysis that compares changes in the investment portfolio on a monthly basis.
  3. Trend analysis that compares the changes in investment income as a percentage of total assets and of investment assets over the past 5 years.
  4. Multiple regression analysis that includes independent variables related to the nature of the investment portfolio and market conditions.

Answer(s): D

Explanation:

Regression analysis develops an equation to explain the behavior of a dependent variable for example, investment income) in terms of one or more independent variables for example, market risk and the risks of particular investments). Multiple regression analysis is the best approach because it allows the auditor to regress the change in investment income on more than one independent variable.



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