What is the percentage of the total variance that can be explained by the regression
Answer(s): A
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
Answer(s): B
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
Answer(s): C
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.
Quality control programs employ many tools for problem definition and analysis. A scatter diagram is one of these tools. The objective of a scatter diagram is to
The objective of a scatter diagram is to demonstrate correlations. Each observation is represented by a dot on a graph corresponding to a particular value of X the independent variable) and Y the dependent variable).
Violation of which assumption underlying regression analysis is prevalent in time series analysis?
Time series analysis is a regression model in which the independent variable is time. In time series analysis, the value of the next time period is frequently dependent on the value of the time period before that. Hence, the error terms are usually correlated or dependent on the prior period; i.e., they are characterized by autocorrelation serial correlation).
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