Question

1.A real estate analyst has developed a multiple regression
line, *y* = 60 + 0.068 *x*_{1} – 2.5
*x*_{2}*,* to predict *y* = the market
price of a home (in $1,000s), using two independent variables,
*x*_{1} = the total number of square feet of living
space, and *x*_{2} = the age of the house in years.
With this regression model, the predicted price of a 10-year old
home with 2,500 square feet of living area is __________.

$205.00

$255,000.00

$200,000.00

$205,000.00

2.A cost accountant is developing a regression model to predict
the total cost of producing a batch of printed circuit boards as a
linear function of batch size (the number of boards produced in one
lot or batch), production plant (Kingsland, and Yorktown), and
production shift (day, and evening). In this model, "shift" is
______.

an independent variable

a constant

a response variable

a dependent variable

a quantitative variable

3.A market analyst is developing a regression model to predict
monthly household expenditures on groceries as a function of family
size, household income, and household neighborhood (urban,
suburban, and rural). The response variable in this model is
_____.

suburban

family size

household neighborhood

expenditures on groceries

household income

4.A market analyst is developing a regression model to predict
monthly household expenditures on groceries as a function of family
size, household income, and household neighborhood (urban,
suburban, and rural). The "income" variable in this model is
____.

an indicator variable

an independent variable

a qualitative variable

a dependent variable

a response variable

5.The multiple regression formulas used to estimate the
regression coefficients are designed to ________________.

minimize the sum of squares of error (SSE)

minimize the total sum of squares (SST)

minimize the mean error

maximize the *p-*value for the calculated *F*
value

maximize the standard error of the estimate

Answer #1

1. The predicted price of a 10-year old home with 2,500 square
feet of living area is *y* = 60 + 0.068*2500 – 2.5*10

= $ 205 thousand =$ 205000.

2. In this model, "shift" is an independent variable.

3. The response variable in this model is expenditures on groceries.

4. The "income" variable in this model is an independent variable.

5.The multiple regression formulas used to estimate the regression coefficients are designed to

minimize the sum of squares of error (SSE).

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