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Exam 23: Bivariate Statistical Analysis: Measures of Association

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Question 1

True/False

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The symbol for the Pearson product-moment correlation coefficient is b.

A) True
B) False

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False

Question 2

Multiple Choice

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Which of the following provides a common metric allowing regression results to be compared to one another no matter what the original scale range may have been?

A) standardized regression coefficient (b)
B) c²
C) coefficient of determination (R²)
D) raw parameter estimates

E) B) and D)
F) B) and C)

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Question 3

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A correlation coefficient indicates both the ______ of the relationship between two variables and the ______ of this relationship.

The _____ is a measure obtained by squaring the correlation coefficient.

In a correlation matrix, the correlations in the main diagonal are all equal to _____.

In a regression equation, the slope of the regression line is denoted by the symbol ______.

The Pearson product-moment correlation coefficient ranges between ______ and ______.

Which of the following is the standard form for reporting observed correlations among multiple variables?

The coefficient of determination reflects the proportion of variance that can be explained by the regression line.

In regression analysis, the symbol X is commonly used for the ______ variable, and the symbol Y is commonly used for the ______ variable.

In a correlation matrix, the main diagonal contains correlations of 1.00 because it represents a variable's correlation with itself.

In the regression equation, Y = a + b X , b is the _____.

In regression analysis, the equation of a straight line is Y = a + b X.

A bivariate statistical technique that is used to measure the strength of the relationship between two variables is called a _____.

Discuss the pros and cons of raw regression estimates and standardized regression estimates and discuss when each is appropriate.

If the purpose of the regression analysis is forecasting, then standardized regression estimates must be used.

If the regression equation is: Y = -4.2 + 3.6 X , then the expected score for Y when X is 4 would be _____.

Explain the ordinary least-squares (OLS) method of regression analysis and the logic behind it.

In most business research, the estimate of b in a regression equation is most important.

The regression parameter that represents the height of the regression line relative to horizontal is _____.