Chapter 12 Connect Quiz

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

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

C  is a stronger measure than the Pearson correlation coefficient.

D  ranges from .00 to 1.0.

E  ranges from -1.0 to +1.0.

A  the Spearman correlation coefficient.

B  multiple regression analysis.

C  simple regression.

D  the Pearson correlation coefficient.

E  bivariate regression analysis.

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

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

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

A  FALSE

B  TRUE

A  direction

B  type

C  presence

D  dispersion

E  strength of association

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

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

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

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

E  the relationship we are trying to measure is curvilinear.

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

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

C  It is an estimated correlation coefficient.

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

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

A  Covariation

B  The non-parametric hypothesis coefficient

C  Beta coefficient analysis

D  Multiple regression analysis

E  Bivariate regression analysis

A  a strong association is evident.

B  there is no relationship.

C  an invisible relationship exists.

D  there is a weak association.

E  there is a moderate relationship.

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