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

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

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

E.   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.

A.   a strong association is evident.

B.   there is no relationship.

C.   there is a moderate relationship.

D.   an invisible relationship exists.

E.   there is a weak association.

A.   Covariation

B.   Bivariate regression analysis

C.   Beta coefficient analysis

D.   The non-parametric hypothesis coefficient

E.   Multiple regression 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.   It ranges from 1.00 to 3.00 and is a positive correlation coefficient.

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

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

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

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

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

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

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

A.   direction

B.   strength of association

C.   dispersion

D.   type

E.   presence

A.   TRUE

B.   FALSE

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

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

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

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

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

A.   bivariate regression analysis.

B.   the Pearson correlation coefficient.

C.   multiple regression analysis.

D.   simple regression.

E.   the Spearman correlation coefficient.

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

B.   ranges from -1.0 to +1.0.

C.   ranges from .00 to 1.0.

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

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