Virtual Reality #2

1)

The least squares procedure minimizes the

sum of the residuals.
square of the maximum error.
sum of absolute errors.
sum of squared residuals.


2)

A residual is

the difference between the mean of Y conditional on X and the unconditional mean.
the difference between the mean of Y and its actual value.
the difference between the regression prediction of Y and its actual value.
the difference between the sum of squared errors before and after X is used to predict Y.


3)

The Y-intercept of the simple regression model

rarely has a useful interpretation.
almost always has a useful interpretation.
is always a positive number.
is always positive when the correlation between the dependent and independent variable is positive.t


4)

The Y-intercept of a regression line is -14 and the slope is 3.5. Which of the following statements is not correct?

When Y increases by one, X increases by 3.5.
When X increases by one, Y increases by 3.5.
The regression line crosses the Y-axis at -14.
X and Y are positively related.

5)

Income is used to predict savings. For the regression equation Y = 1,000 + .10X, which of the following is true?

Y is income, X is savings, and income is the independent variable.
Y is income, X is savings, and savings is the independent variable.
Y is savings, X is income, and savings is the independent variable.
Y is savings, X is income, and income is the independent variable.

6)

X-Y data have been collected in which X ranges between 50 and 100 and Y ranges between 1200 and 2000. It is not wise to use the resulting regression line equation to predict Y when X is equal to -10 because:

Q = 400 - 2.0P + .015I -.17Px.

(250) (1.0) (.010) (.10)

(standard errors are in parentheses)

a negative number cannot be used.
the predicted value for Y might turn out to be negative.
) the Y-intercept might be above zero.
the proposed X value is well beyond the range of observed data.

7)

The following regression equation was estimated: Y = -2.0 + 4.6X. This indicates that

there has been an error since "b" cannot be a negative number.
there is a negative relationship between the two variables.
Y equals 44 when X is 10.
the correlation coefficient for Y and X will be negative.

8)

Sample regression model forecast errors are called

disturbances.
residuals.
least-squares predictions.
outliers.

9)

Testing for the statistical significance of the relationship between the dependent and independent variables

does not depend on sample size.
is accomplished by application of the t-distribution.
is accomplished by testing the null hypothesis: H0: b &Mac173; 0.
is accomplished by application of the normal distribution.

10)

The autocorrelation parameter is used to measure

disturbances or independent random variates.
correlation between residuals.
the Durbin-Watson statistic.
error or difference between a data point and the regression line.

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