Final answer:
The question involves using chi-square tests for independence and hypothesis testing to determine if there is a statistically significant relationship between gender and education, which is a topic in Mathematics, particularly in college-level statistics courses.
Step-by-step explanation:
You are asking about a statistically significant relationship between gender and education level, which falls under statistical analysis in Mathematics, specifically the use of chi-square tests for independence and hypothesis testing for proportions. When looking at gender categorized as woman (1), man (2), and nonbinary/undisclosed (3), and comparing it with levels of education or other factors, this involves conducting tests to see if the observed distribution differs significantly from an expected distribution, or if two variables are associated in a meaningful way.
For example, in Exercise 73 mentioned, you're dealing with a situation where you're asked to calculate expected numbers of students enrolled in higher education based on national percentages. This is a chi-square goodness of fit test. Similarly, in Example 10.9 about gender differences in texting, and Example 11.8 about living arrangements of college students, you would use a chi-square test of independence to analyze the data. These procedures help determine if there's a statistically significant association between the categories of one variable and the categories of another variable, such as gender and educational attainment or lifestyle choices.
Concerning relationship investigation like the one in Exercise 108, where smartphone usage is compared between men and women, a two-proportion z-test might be used, since you're comparing the proportions from two independent samples. All these methods are part of inferential statistics, a core concept in higher-level Mathematics courses, often at the college level.