Advertisement

26 terms

Consider the first-order model = 70 + 35x1 - 10x2 + 5x1x2. A unit increase in x2, while holding x1 constant at 1, changes the value of E(y) by:

-10.

-5.

10.

5.

-10.

-5.

10.

5.

-5

Which of the following statements are important steps in selecting a model?

Specify the dependent variable and determine the goal for the analysis.

Specify possible predictor variables and collect the data.

Select a model, evaluate its suitability and test its reliability.

All of these.

Specify the candidate models, and for each candidate model decide which of the available predictor variables will be included in the model.

Specify the dependent variable and determine the goal for the analysis.

Specify possible predictor variables and collect the data.

Select a model, evaluate its suitability and test its reliability.

All of these.

Specify the candidate models, and for each candidate model decide which of the available predictor variables will be included in the model.

all of these

In regression analysis, qualitative variables are sometimes referred to as:

response variables.

nonsense variables.

dummy variables.

dependent variables.

response variables.

nonsense variables.

dummy variables.

dependent variables.

dummy variables

The shape of the model described by the equation E(y)= Bo + B1x + B2x^2

a straight line with an intercept and slope .

parabolic resembling an upright or upside-down "U", depending on the signs of the coefficients.

parabolic.

a straight line with an intercept and slope .

a straight line with an intercept and slope .

parabolic resembling an upright or upside-down "U", depending on the signs of the coefficients.

parabolic.

a straight line with an intercept and slope .

parabolic resembling an upright or upside-down "U", depending on the signs of the coefficients.

A term used to describe the case when the independent variables in a multiple regression model are correlated is:

regression

correlation

multicollinearity

None of the above answers is correct.

regression

correlation

multicollinearity

None of the above answers is correct.

multicollinearity

In explaining students' test scores, which of the following independent variables would not best be represented by a dummy variable?

Marital status

Number of hours studying for the test

Gender

Race

Marital status

Number of hours studying for the test

Gender

Race

number of hours studying for the test

In multiple regression analysis, the correlation among the independent variables is termed

adjusted coefficient of determination

homoscedasticity

linearity

multicollinearity

adjusted coefficient of determination

homoscedasticity

linearity

multicollinearity

multicollinearity

The model = b0 + b1x1 + b2x2 + b3x1x2 is referred to as the:

first order model with two predictor variables with no interaction.

second order model with three predictor variables with interaction.

first order model with two predictor variables with interaction.

second order model with three predictor variables with no interaction.

first order model with two predictor variables with no interaction.

second order model with three predictor variables with interaction.

first order model with two predictor variables with interaction.

second order model with three predictor variables with no interaction.

first order model with two predictor variables with interaction.

Suppose that the estimated regression equation of a College of Business graduates is given by: = 20,000 + 2000x + 1500D, where y is the starting salary, x is the grade point average and D is a dummy variable which takes the value of 1 if the student is a finance major and 0 if not. A hotel management major graduate with a 3.5 grade point average would have an average starting salary of:

$28,500.

$27,000.

$22,000.

$20,000.

$28,500.

$27,000.

$22,000.

$20,000.

$27,000

True or False

A qualitative variable such as gender can be included in regression analysis and is referred to as dummy variable.

A qualitative variable such as gender can be included in regression analysis and is referred to as dummy variable.

true

True or False

If the goal for the regression analysis is to interpret partial regression coefficients in an attempt to better understand the relationship between the dependent variable and one or more of the predictor variables, it will be desirable to build a smaller model with predictor variables that are more independent from each other.

If the goal for the regression analysis is to interpret partial regression coefficients in an attempt to better understand the relationship between the dependent variable and one or more of the predictor variables, it will be desirable to build a smaller model with predictor variables that are more independent from each other.

true

True or False

Multicollinearity is a situation in which the dependent variable is highly correlated with two or more of the independent variables in a multiple regression.

Multicollinearity is a situation in which the dependent variable is highly correlated with two or more of the independent variables in a multiple regression.

false (it has to be the independents that are correlated with each other)

True or False

The first-order polynomial model is the same as the simple linear regression model.

The first-order polynomial model is the same as the simple linear regression model.

true

True or False

If level of education has four possible categories, four qualitative variables would be needed to represent education in the regression model.

If level of education has four possible categories, four qualitative variables would be needed to represent education in the regression model.

false

True or False

Data transformation may include converting x to its square root or inverse.

Data transformation may include converting x to its square root or inverse.

true

Consider the first-order regression model = 15 + 6x1 + 5x2 + 4x1x2. A unit increase in x1 increases the value of y on average by:

30.

an amount that depends on the value of x2.

26.

5.

30.

an amount that depends on the value of x2.

26.

5.

an amount that depends on the value of x2

Multicollinearity in a regression model can be detected when:

when an independent variable is added or removed , the partial regression coefficients for the other independent variables change drastically.

a partial regression coefficient that should be positive turns out to be negative, or vise versa.

All of the above could be evidence that multicollinearity is present in the model.

an independent variable known to be an important predictor ends up having a partial regression coefficient that is not significant.

two or more independent variables are highly correlated with each other.

when an independent variable is added or removed , the partial regression coefficients for the other independent variables change drastically.

a partial regression coefficient that should be positive turns out to be negative, or vise versa.

All of the above could be evidence that multicollinearity is present in the model.

an independent variable known to be an important predictor ends up having a partial regression coefficient that is not significant.

two or more independent variables are highly correlated with each other.

all of the above could be evidence that multicollinearity is present in the model

In general, to represent a qualitative predictor variable that has n possible categories we must create:

(n + 2) qualitative variables.

(n + 1) qualitative variables.

(n - 1) qualitative variables.

n qualitative variables.

(n + 2) qualitative variables.

(n + 1) qualitative variables.

(n - 1) qualitative variables.

n qualitative variables.

(n - 1) qualitative variables

the sum of the exponents of the cross product will always be what?

2

In explaining the amount of money spent on children's toys each year, which of the following independent variables is best represented with a dummy variable?

Weight

Gender 100%

Height

Age

Weight

Gender 100%

Height

Age

gender

In explaining the income earned by college graduates, which of the following independent variables is best represented by a dummy variable?

Number of years since graduating from high school

Grade point average

College major

Age

Number of years since graduating from high school

Grade point average

College major

Age

college major

True or False

In general, we should attempt to use the simplest possible model that satisfies the goal for which the model is being developed.

In general, we should attempt to use the simplest possible model that satisfies the goal for which the model is being developed.

true

True or False

If two or more independent variables are highly correlated with each other, multicollinearity is present, and the partial regression coefficients will be unreliable.

If two or more independent variables are highly correlated with each other, multicollinearity is present, and the partial regression coefficients will be unreliable.

true

True or False

The exponential model = b0b1x can be converted to a linear model by taking the logarithms of both sides of the equation (either natural or common logarithms can be used as long as we are consistent).

The exponential model = b0b1x can be converted to a linear model by taking the logarithms of both sides of the equation (either natural or common logarithms can be used as long as we are consistent).

true

True or False

The first-order polynomial model is the same as the simple linear regression model.

The first-order polynomial model is the same as the simple linear regression model.

true

dummy variables are (independent/dependent) variables

independent