Exam 13: Relationships Between Variables

The R-Square value is very important because it tells us how well our regression line fits the scatter of data points. It may range from 0 to +1.00 because it is the square of the correlation coefficient, which may range from -1.00 to +1.00.

In bivariate regression analysis, the higher the R-square value, the:

Covariation is defined as the amount of change in one variable systematically associated with a change in another variable.

A standardized beta coefficient is defined as:

Bivariate regression analysis is defined as a predictive analysis technique in which:

When the calculated chi-square value exceeds the critical chi-square table value:

It is more difficult for a chi-square value with high degrees of freedom (i.e., there are more cells) to achieve "significance" than for a chi-square value with fewer degrees of freedom (i.e., there are fewer cells).

The Pearson Product Moment correlation measures the linear relationship between two categorical variables.

A correlation coefficient is an index number constrained to fall between the range of -1.0 and +1.0 that communicates both the strength and the direction of association between three or more variables.

To run chi-square analysis in XL Data Analyst, you should go to the "Relate," then "Crosstabs," select the column variable and the categorical variable(s), then "OK."

The multiple R, also called the coefficient of determination, in multiple regression ranges from 0 to +1.00 and represents the amount of the dependent variable "explained" by the combined independent variables.

In bivariate regression analysis, t tests are used to test the significance of the slope and the intercept of the multiple dependent variables.

In chi-square, the null hypothesis states that there is no association. When the calculated chi-square value exceeds the critical chi-square table value, the null hypothesis is not supported.

A form of regression analysis where more than one independent variable is used in the regression equation is known as:

The intersection of a row and column in a cross-tabulation table is called a:

When it comes to determining the statistical significance of the correlation coefficient, there are rules of thumb. For example, 0.81 to 1.00 is considered to be "strong."

A cross-tabulation table is sometimes referred to as a:

What contains the raw counts and totals pertaining to all of the relevant Boolean relationships for the two categorical variables being analyzed?

If we were to graph two variables, let's say, height (in centimetres) and GPA, and the graph showed points scattered about in a formless shape, we could say there is:

What is the name of the analysis that determines whether the two categorical variables in a cross-tabulation table have a significant relationship?