(a) A lurking variable is A. and variable that produces a large residual. B. the
ID: 3276861 • Letter: #
Question
(a) A lurking variable is
A. and variable that produces a large residual.
B. the true cause of a response.
C. the true variable that is explained by the explanatory variable.
D. a variable that is not among the variables studied but that affects the response variable.
E. None of the above.
(b) Suppose a straight line is fit to data having response variable yy and explanatory variable xx. Predicting values of yy for values of xx outside the range of the observed data is called
A. contingency.
B. extrapolation.
C. causation.
D. correlation.
E. None of the above.
(c) A plot of the residuals will indicate if a line is a good fit to the data if the plot
A. shows large residuals in a symmetric pattern.
B. shows a curved pattern.
C. shows increasing or decreasing spread about a line.
D. has no systematic pattern.
E. None of the above.
Explanation / Answer
a. Lurking variables are one kind of extraneous variable. They are neither of the variables under investigation (explanatory and response variables), but some other random variables that influences one or both. These random third variables are called lurking variables because they go unnoticed by lurking beneath the surface of the variables of interest, making the relationship between them seem stronger or weaker than it actually is.
Based on this definition we get that answer is D. a variable that is not among the variables studied but that affects the response variable.
b. "Extrapolation" beyond the "scope of the model" occurs when one uses an estimated regression equation to estimate a mean µY or to predict a new response ynew for x values not in the range of the sample data used to determine the estimated regression equation.
Hence answer is B. extrapolation.
c. Line is a good fit only if maximum number of data fall on the line which we draw in increasing or decreasing order.
Hence answer is C. shows increasing or decreasing spread about a line.
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