Statistical tests

IBM® Cognos Analytics uses many statistical tests to perform analyses of your data. It is important to understand the definitions of these tests as they apply to Cognos Analytics.

Analysis of variance (ANOVA)
Analysis of variance, or ANOVA, is a linear modeling method for evaluating the relationship among fields. For key drivers and for insights that are related to a number of charts, ANOVA tests whether the mean target value varies across categories of one input or combinations of categories of two inputs.
Classification tree
A classification tree is a type of decision tree. It uses Gini impurity measure to classify records in the target field’s categories. The predictions are based on combinations of values in the input fields.
Regression tree
A regression tree is a type of decision tree. It uses sum of squares and regression analysis to predict values of the target field. The predictions are based on combinations of values in the input fields.
Chi-square test of equal frequencies
The chi-square test of equal frequencies checks whether the frequencies (number of values) in each category or group are statistically different from each other.
Chi-square test of independence
The chi-square test of independence determines whether two categorical fields are independent. If the fields are not independent, they are associated.
Influence test
The influence test is a chi-square test that tests whether the number of records in a group is significantly different from the expected frequency. The group might be a category or combination of categories. Considering the significance value and effect size, the test identifies influential groups.
Influence t test
The influence t test compares mean value of a measure in a group with overall mean value for the measure.
Multiple linear regression
Multiple linear regression model is a versatile statistical model for evaluating relationships between a continuous target and predictors.
Studentized residual test
Studentized residual is computed as regression model residual divided by its adjusted standard error.
Sum comparison test
The sum comparison test is a chi-square test that tests whether the sum of a specified measure is equal across all categories of the explanatory field.
Sum influence test
The sum influence test compares sum of a measure in a category with the average sum value for the measure across all categories of explanatory field.

Parent topic: Dashboards