## Linear-by-linear association test :

The linear-by-linear association test is a statistical method used to assess the relationship between two categorical variables. It is a type of chi-squared test that examines whether the observed frequency distribution of the two variables differs significantly from the expected frequency distribution under the assumption of independence.

One example of when this test may be used is in a study examining the relationship between gender and voting behavior in a presidential election. The two variables in this case are gender (male or female) and voting behavior (voted for candidate A or candidate B). The null hypothesis for this test would be that there is no association between gender and voting behavior, meaning that the probability of voting for candidate A or B is the same for males and females. The alternative hypothesis would be that there is an association between the two variables, indicating that the probability of voting for one candidate or the other is different for males and females.

To conduct this test, the observed frequency distribution of gender and voting behavior would be calculated and compared to the expected frequency distribution under the assumption of independence. For example, if the observed frequency distribution of gender and voting behavior showed that 60% of males voted for candidate A and 40% voted for candidate B, while 70% of females voted for candidate A and 30% voted for candidate B, the linear-by-linear association test would be used to determine whether these observed frequencies differ significantly from the expected frequencies under the assumption of independence.

Another example of when the linear-by-linear association test may be used is in a study examining the relationship between income level and exercise behavior. The two variables in this case are income level (low, medium, or high) and exercise behavior (regularly exercises or does not regularly exercise). The null hypothesis for this test would be that there is no association between income level and exercise behavior, meaning that the probability of regularly exercising is the same for individuals in different income levels. The alternative hypothesis would be that there is an association between the two variables, indicating that the probability of regularly exercising is different for individuals in different income levels.

To conduct this test, the observed frequency distribution of income level and exercise behavior would be calculated and compared to the expected frequency distribution under the assumption of independence. For example, if the observed frequency distribution of income level and exercise behavior showed that 60% of individuals with low income levels regularly exercise, while 70% of individuals with medium income levels regularly exercise and 80% of individuals with high income levels regularly exercise, the linear-by-linear association test would be used to determine whether these observed frequencies differ significantly from the expected frequencies under the assumption of independence.

In both of these examples, the linear-by-linear association test would be used to determine whether the observed frequency distribution of the two variables differs significantly from the expected frequency distribution under the assumption of independence. If the test indicates a significant difference, this would suggest that there is an association between the two variables, while a non-significant result would indicate that there is no association. This test can be useful in identifying potential relationships between categorical variables and providing evidence for further research and analysis.