A.   estimated regression coefficient that has been recalculated to have a mean of zero and a standard deviation of 1.

B.   statistical procedure that estimates regression equation coefficients that produce the lowest sum of squared differences between the actual and predicted values of a dependent variable.

C.   statistical technique that analyzes the linear relationship between a dependent variable and multiple independent variables by estimating coefficients for the equation for a straight line.

D.   situation in which several independent variables are highly correlated with each other.

E.   statistic that compares the amount of variation in the dependent measure "explained" or associated with the independent variables to the "unexplained" or error variance.

A.   an invisible relationship exists.

B.   there is a moderate relationship.

C.   there is a weak association.

D.   a strong association is evident.

E.   there is no relationship.

A.   Multiple regression analysis

B.   The non-parametric hypothesis coefficient

C.   Covariation

D.   Bivariate regression analysis

E.   Beta coefficient analysis

A.   It is an F-ratio that has been recalculated to have a mean of 1 and a standard deviation of 0.

B.   It is an estimated correlation coefficient.

C.   A positive beta indicates that as the size of an independent variable decreases, the size of the dependent variable increases.

D.   It ranges from 1.00 to 3.00 and is a positive correlation coefficient.

E.   It shows the change in the dependent variable for each unit change in the independent variable.

A.   the variables have been measured using interval- or ratio-scaled measures.

B.   the relationship we are trying to measure is curvilinear.

C.   the variables we want to analyze have a binomially distributed population.

D.   when the correlation coefficient is strong and significant, the two variables of interest are associated in a curvilinear fashion.

E.   when the correlation coefficient is weak, there is a consistent, systematic relationship between the two variables of interest.

A.   strength of association

B.   dispersion

C.   presence

D.   type

E.   direction

A.   TRUE

B.   FALSE

A.   The Pearson correlation coefficient measures the degree of linear association between three variables.

B.   The null hypothesis for the Pearson correlation coefficient states that there is always a strong association between two variables.

C.   The larger the correlation coefficient, the weaker the association between two variables.

D.   The Pearson correlation coefficient measures the degree of linear association that ranges from 1.0 to 10.0.

E.   The null hypothesis for the Pearson correlation coefficient states that the correlation coefficient is zero.

A.   multiple regression analysis.

B.   bivariate regression analysis.

C.   the Spearman correlation coefficient.

D.   simple regression.

E.   the Pearson correlation coefficient.

A.   is a stronger measure than the Pearson correlation coefficient.

B.   describes the variation in the dependent variable caused by the control variable.

C.   ranges from .00 to 1.0.

D.   tells you the percentage of the total variation in the independent variable caused by the dependent variable.

E.   ranges from -1.0 to +1.0.