# eco stat ch 17

### 26 terms by ccparker17

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-5

all of these

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 .

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.

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

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

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 interaction.

\$27,000

true

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.

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

true

false

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.

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.

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 - 1) qualitative variables

2

gender

college major

true

true

true

true

independent

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