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Exam 10: Regression Analysis: Estimating Relationships

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Question 41

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In a nonlinear transformation of data,the Y variable or the X variables may be transformed,but not both.

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Question 42

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In the multiple regression model we interpret X₁ as follows: holding X₂ constant,if X₁ increases by 1 unit,then the expected value of Y will increase by 9 units.

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Question 43

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In linear regression,a dummy variable is used to

If a scatterplot of residuals shows a parabola shape,then a logarithmic transformation may be useful in obtaining a better fit.

The coefficients for logarithmically transformed explanatory variables should be interpreted as the percent change in the dependent variable for a 1% percent change in the explanatory variable.

The regression line has been fitted to the data points (28,60) ,(20,50) ,(10,18) ,and (25,55) .The sum of the squared residuals will be

The adjusted R² adjusts R² for

A logarithmic transformation of the response variable Y is often useful when the distribution of Y is symmetric.

When the scatterplot appears as a shapeless swarm of points,this can indicate that there is no relationship between the response variable Y and the explanatory variable X,or at least none worth pursuing.

A correlation value of zero indicates _____ relationship.

A regression analysis between X = sales (in $1000s)and Y = advertising ($)resulted in the following least squares line: = 84 +7X.This implies that if there is no advertising,then the predicted amount of sales (in dollars)is $84,000.

Correlation is measured on a scale from 0 to 1,where 0 indicates no linear relationship between two variables,and 1 indicates a perfect linear relationship.

The percentage of variation (R²) ranges from

In regression analysis,if there are several explanatory variables,it is called _____ regression.

Which of the following is an example of a nonlinear regression model?

To help explain or predict the response variable in every regression study,we use one or more explanatory variables.These variables are also called response variables or independent variables.

The standard error of the estimate ( ) is essentially the

Regression analysis asks

The covariance is not used as much as the correlation because

In a simple linear regression problem,suppose that = 12.48 and = 124.8.Then = 0.90.