1. The Y-intercept ( b 0 ) represents the A) variation around the sampleregressi

Question

1. TheY-intercept (b0) represents the

A) variation around the sampleregression line.
B) predicted value of Ywhen X = 0.
C) predicted value ofY.
D) change in estimated averageY per unit change in X.

2. The slope(b1) represents the

A) predicted value of Ywhen X = 0.
B) estimated average change inY per unit change in X.
C) variation around the line ofregression.
D) predicted value ofY.

3. The least squaresmethod minimizes which of the following?

A) SST
B) SSE
C) SSR
D) all of the above

4. The standard errorof the estimate is a measure of the

A) explained variation.
B) variation of the Xvariable.
C) total variation of theY variable.
D) variation around the sampleregression line.

5. TABLE13-1

A large national bank charges local companies for using theirservices. A bank official reported the results of a regressionanalysis designed to predict the bank's charges(Y)—measured in dollars per month—for servicesrendered to local companies. One independent variable used topredict the service charge to a company is the company's salesrevenue (X)—measured in millions of dollars. Datafor 21 companies who use the bank's services were used to fit themodel:

E(Y) = 0 +1X

The results of the simple linear regression are provided below.

Y= -2,700 + 20X, SYX = 65, two-tailedp value = 0.034 (for testing1)


Referring to Table 13-1, interpret the estimate of0, the Y-intercept of the line.

A) There is no practicalinterpretation since a sales revenue of $0 is a nonsensicalvalue.
B) All companies will be chargedat least $2,700 by the bank.
C) About 95% of the observedservice charges fall within $2,700 of the least squares line.
D) For every $1 million increasein sales revenue, we expect a service charge to decrease$2,700.

Explanation / Answer

1) B. The regression predicts the output for a giveninput. Plugging y = 0 into the regression, all that remains0. 2) B. The regression predicts the change in output for agiven change input as well. 3) B. SSE is the sum of squared error. Regressionsminimize this quantity. 4) D. SSE is the sum of the squared error between the actualand predicted output. 5) B. 0 is the fixed charge of adding another company,regardless of how much revenue they have. While revenueincreases the charge of their services, a firm with no revenuewould still result in charges of 2700.

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