Exam 13: Relationships Between Variables

We can sometimes improve the explanatory power of a multiple regression analysis finding by trimming the nonsignificant independent variables and rerunning the regression analysis.

The Pearson Product Moment correlation measures the linear relationship between two:

In the following straight line formula, y = a + bx, the variable being predicted is "b".

When the XL Data Analyst finds a significant cross-tabulation relationship, it will:

What criterion is used to establish the best "fit" of a straight line through the points on a scatter diagram?

With regression analysis, when we make predictions and compare the differences between our predictions and the actual results, we are looking at residuals to determine the standard error of the estimate.

The chi-square test is useful for determining:

Which type of relationship is described by the formula: y = a + bx?

If you plotted data between two variables and the points all fell precisely in a straight line that was sloping upward to the right, your correlation coefficient would be equal to +1.0.

What analytical technique assesses the statistical significance of Boolean relationships?

We would use cross-tabulation if we wanted to visualize the Boolean relationship between two metric-scaled variables.

When a scale has "labels" as opposed to "levels," we can normally assume the level of measurement is:

Which of the following is NOT a caveat of correlation?

In multiple regression we make a prediction, but we cannot put confidence intervals around our prediction as we can in bivariate regression.

Coffee orders at a restaurant are present at breakfast and soft drink orders are present at lunch. This is an example of a Boolean relationship.

When a marketing researcher is faced with a large number and variety of prospective independent variables, multiple regression analysis can be used as a way of spotting the salient independent variables for the dependent variable at hand. What application of multiple regression analysis is this?

Let's assume we find in a study that the correlation coefficient between number of years of education and cigarette smoking is -0.89. This means that as education level increases:

The chi-square analysis would be appropriate to determine if there is an association between the number of dollars spent on books and the number of years of education.

In bivariate regression analysis, the dependent variable is one that is:

Generally speaking, the chi-square value is calculated by dividing the sum of the squared differences between observed and expected frequencies by the expected frequencies.