A.   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.

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

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

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.   there is a weak association.

B.   there is a moderate relationship.

C.   there is no relationship.

D.   an invisible relationship exists.

E.   a strong association is evident.

A.   The non-parametric hypothesis coefficient

B.   Covariation

C.   Beta coefficient analysis

D.   Bivariate regression analysis

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

C.   It is an estimated correlation coefficient.

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

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

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

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

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

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

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

A.   type

B.   strength of association

C.   dispersion

D.   direction

E.   presence

A.   FALSE

B.   TRUE

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

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

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

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

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

A.   simple regression.

B.   multiple regression analysis.

C.   bivariate regression analysis.

D.   the Pearson correlation coefficient.

E.   the Spearman correlation coefficient.

A.   ranges from .00 to 1.0.

B.   ranges from -1.0 to +1.0.

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

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

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