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Exam 10: Regression Analysis: Estimating Relationships

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Question 41

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

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Question 42

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The term autocorrelation refers to:

A) the analyzed data refers to itself
B) the sample is related too closely to the population
C) the data are in a loop (values repeat themselves)
D) time series variables are usually related to their own past values

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Question 43

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The effect of a logarithmic transformation on a variable that is skewed to the right by a few large values is to "squeeze" the values together and make the distribution more symmetric

The multiple standard error of estimate will be:

The correlation value ranges from

Outliers are observations that

Scatterplots are used for identifying outliers and quantifying relationships between variables.

In multiple regression,the constant :

A correlation value of zero indicates.

In regression analysis,which of the following causal relationships are possible?

A single variable X can explain a large percentage of the variation in some other variable Y when the two variables are:

(A)Use the information above to estimate the linear regression model. (B)Interpret each of the estimated regression coefficients of the regression model in (A). (C)Would any of the variables in this model be considered a dummy variable? Explain your answer. (D)Identify and interpret the coefficient of determination ( )and the standard error of the estimate (s_e)for the model in (A).

A regression analysis between sales (in $1000)and advertising (in $)resulted in the following least squares line: = 32 + 8X.This implies that an increase of $1 in advertising is expected to result in an increase of $40 in sales.

The residual is defined as the difference between the actual and predicted,or fitted values of the response variable.

For the multiple regression model ,if were to increase by 5 units,holding and constant,the value of Y would be expected to decrease by 50 units.

The multiple R for a regression is the correlation between the observed Y values and the fitted Y values. .

In reference to the equation, ,the value 0.10 is the expected change in Y per unit change in .

In linear regression,we can have an interaction variable.Algebraically,the interaction variable is the other variables in the regression equation.

Use what you have learned about transformations to fit an alternative model to the one in Question 135.

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