Exam 23: Bivariate Statistical Analysis: Measures of Association

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

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

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

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

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

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

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

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

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

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

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

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?

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

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

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

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

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

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