Final answer:
The contingency coefficient is an alternative to regression when assumptions are severely violated. It measures the association between two categorical variables in a contingency table.
Step-by-step explanation:
The statement in the question is true. When assumptions are severely violated in a regression analysis, one alternative is to use the contingency coefficient. The contingency coefficient is a measure of the association between two categorical variables in a contingency table. It measures the strength and direction of the relationship between the variables.
Unlike regression analysis, which assumes a linear relationship between variables, the contingency coefficient is specifically designed for analyzing categorical data. It helps determine whether there is a statistically significant association between the variables.
For example, if we have data on the type of music people listen to (rock, pop, jazz) and their preferred mode of transportation (car, bike, public transport), we can use a contingency table and calculate the contingency coefficient to determine if there is a relationship between the two variables.