TABLE 15-1 A certain type of rare gem serves as a status symbol for many of its owners.In theory,for low prices,the demand increases and it decreases as the price of the gem increases.However,experts hypothesize that when the gem is valued at very high prices,the demand increases with price due to the status owners believe they gain in obtaining the gem.Thus,the model proposed to best explain the demand for the gem by its price is the quadratic model: Y = βS1U1B10S1U1B0 + βS1U1B11S1U1B0X + βS1U1B12S1U1B0XS1U1P12S1S1P0 + ε where Y = demand (in thousands)and X = retail price per carat. This model was fit to data collected for a sample of 12 rare gems of this type.A portion of the computer analysis obtained from Microsoft Excel is shown below: -Referring to Table 15-1,does there appear to be significant upward curvature in the response curve relating the demand (Y)and the price (X)at 10% level of significance?

A) Yes,since the p-value for the test is less than 0.10. B) No,since the value of β2 is near 0. C) No,since the p-value for the test is greater than 0.10. D) Yes,since the value of β2 is positive. A) Yes,since the p-value for the test is less than 0.10. B) No,since the value of β2 is near 0. C) No,since the p-value for the test is greater than 0.10. D) Yes,since the value of β2 is positive. C

TABLE 15-4 The superintendent of a school district wanted to predict the percentage of students passing a sixth-grade proficiency test.She obtained the data on percentage of students passing the proficiency test (% Passing),daily mean of the percentage of students attending class (% Attendance),mean teacher salary in dollars (Salaries),and instructional spending per pupil in dollars (Spending)of 47 schools in the state. Let Y = % Passing as the dependent variable,XS1U1B11S1U1B0 = % Attendance,XS1U1B12S1U1B0 = Salaries and XS1U1B13S1U1B0 = Spending. The coefficient of multiple determination ( )of each of the 3 predictors with all the other remaining predictors are,respectively,0.0338,0.4669,and 0.4743. The output from the best-subset regressions is given below: Following is the residual plot for % Attendance: Following is the output of several multiple regression models: Model (I): Model (II): Model (III): -Referring to Table 15-4,what are,respectively,the values of the variance inflationary factor of the 3 predictors?

The answer of TABLE 15-4 The superintendent of a school district...

As a project for his business statistics class,a student examined the factors that determined parking meter rates throughout the campus area.Data were collected for the price per hour of parking,blocks to the quadrangle,and one of the three jurisdictions: on campus,in downtown and off campus,or outside of downtown and off campus.The population regression model hypothesized is Y = β0 + β1X1i + β2X2i + β3X3i + ε Where Y is the meter price X1 is the number of blocks to the quad X2 is a dummy variable that takes the value 1 if the meter is located in downtown and off campus and the value 0 otherwise X3 is a dummy variable that takes the value 1 if the meter is located outside of downtown and off campus,and the value 0 otherwise Suppose that whether the meter is located on campus is an important explanatory factor.Why should the variable that depicts this attribute not be included in the model?

A) Its inclusion will introduce autocorrelation. B) Its inclusion will introduce collinearity. C) Its inclusion will inflate the standard errors of the estimated coefficients. D) Both B and C A) Its inclusion will introduce autocorrelation. B) Its inclusion will introduce collinearity. C) Its inclusion will inflate the standard errors of the estimated coefficients. D) Both B and C