This question tests understanding of conditional distributions when analyzing the relationship between day type and dessert ordering. To assess this relationship, we need to compare the proportion of customers who order dessert within each day type - specifically, the percent who order dessert on weekdays versus the percent who order dessert on weekends. Choice C correctly identifies this comparison. Options B and E look at marginal distributions, D compares raw counts, and A conditions on dessert ordering instead of day type. When examining associations between categorical variables, we typically condition on the explanatory variable (day type) and compare how the response variable (dessert ordering) is distributed across those conditions.

This question asks about comparing conditional distributions to assess the relationship between subscription type and viewing habits. To determine if weekly viewing time is associated with subscription type, we should compare the proportion of users who watch at least 10 hours per week within each subscription category - that is, the percent who watch heavily among Basic users versus the percent who watch heavily among Premium users. Choice A correctly identifies this comparison of conditional distributions. Options B and E examine marginal distributions, D compares raw counts, and C conditions on the wrong variable (viewing time instead of subscription type). The key insight is that we condition on the explanatory variable (subscription type) and examine how the response variable (viewing time) differs across those conditions.

This question assesses the skill of representing two categorical variables, specifically using conditional distributions to evaluate the association between sports participation and sleep amount. The appropriate comparison is to condition on sports participation and compare the proportions of students sleeping at least 8 hours across the two sports categories, as this reveals whether the distribution of sleep differs by sports group. For example, the conditional proportion of >=8 hours given plays a sport is 54/100 = 0.54, while given does not play is 38/100 = 0.38, indicating an association. A common distractor is choice B, which conditions on sleep instead and compares sports participation across sleep groups, but the question's setup with sports as rows suggests conditioning on sports for the comparison. Another distractor is choice A, which uses overall proportions without conditioning, failing to assess association properly. In a mini-lesson on conditional distributions: these are found by dividing cell counts by the relevant row or column total, allowing us to examine how one variable's outcomes vary depending on the category of the other variable, which is key for detecting associations in two-way tables.

The skill involved is representing two categorical variables with conditional distributions to assess association between living situation and meal plan status. The appropriate comparison conditions on living situation and compares meal plan proportions: 88/110 = 0.80 for on-campus versus 44/110 = 0.40 for off-campus, showing association. This aligns with row conditioning. Distractor choice B reverses by conditioning on meal plan and comparing living situation. Choice A uses overall meal plan proportions without conditioning. Mini-lesson: Conditional distributions are row or column percentages (cell divided by marginal), used to compare how one variable's distribution differs across categories of the other, with differences evidencing association.

This question examines the skill of representing two categorical variables, remote work status and job satisfaction level, through conditional distributions for association. The correct approach is comparing the percent with high satisfaction among remote employees versus non-remote, conditioning satisfaction on remote status as explanatory. This aligns with evaluating if job satisfaction is associated with remote work, implying remote status influences satisfaction. Choice A distracts by reversing the conditioning, comparing remote status given satisfaction levels. For a mini-lesson, conditional distributions are computed as percentages of the response within explanatory groups; high satisfaction rates in remote and non-remote categories. Differences indicate association. Phrasing helps identify the explanatory variable correctly.

This question evaluates the skill of representing two categorical variables, subscription type and weekly viewing level, via conditional distributions to check for association. The suitable comparison is the percent of 10+ hours viewers among premium users versus among standard users, conditioning viewing level on subscription type as the explanatory variable. This fits the question's inquiry into whether viewing level is associated with subscription type, suggesting we examine viewing distributions given subscription. Choice A is a typical distractor, reversing the conditioning to treat viewing as explanatory, which contradicts the question's phrasing. For a mini-lesson, conditional distributions compute the proportions of one variable's categories within levels of another; compare percent 10+ hours for premium and standard groups. Significant differences in these percentages signal an association. Proper identification of the explanatory variable is key to selecting the right conditional comparison.

This question requires identifying the appropriate conditional distribution to examine the association between work arrangement and job satisfaction. To determine if work arrangement affects satisfaction, we need to condition on work arrangement and compare satisfaction rates. Option C correctly compares the percent with high satisfaction among remote employees to the percent with high satisfaction among in-office employees - this conditional comparison directly reveals whether satisfaction differs by work arrangement. Options A and B examine marginal distributions that don't address the relationship, while option D reverses the conditioning by looking at work arrangement given satisfaction level. Option E uses counts rather than percentages, failing to account for potentially different group sizes. The principle remains consistent: condition on the explanatory variable (work arrangement) and compare the response variable (satisfaction) across its categories.

This question asks about comparing conditional distributions to assess the relationship between membership plan and gym attendance frequency. To determine if these variables are associated, we should compare the proportion of members who attend frequently within each membership plan - that is, the percent who attend at least 3 times per week among Monthly-plan members versus the percent who attend at least 3 times per week among Annual-plan members. Choice D correctly identifies this comparison of conditional distributions. Options A and E examine marginal distributions, C compares raw counts rather than proportions, and B conditions on attendance frequency instead of membership plan. The key is to condition on the explanatory variable (membership plan) and examine how the response variable (attendance frequency) differs across those conditions.

This question tests understanding of conditional distributions when analyzing the relationship between homework completion and test performance. To interpret this relationship, we need to compare the proportion of students who passed within each homework completion category - specifically, the percent who passed among students who usually complete homework versus the percent who passed among students who don't usually complete homework. Choice A correctly identifies this comparison. Options B and E look at marginal distributions, C compares raw counts, and D conditions on test outcome instead of homework completion. When examining associations between categorical variables, we typically condition on the explanatory variable (homework completion) and compare how the response variable (test performance) is distributed across those conditions.

This question asks about using conditional distributions to assess the relationship between neighborhood type and flu-shot status. To determine if these variables are associated, we should compare the proportion of residents who get flu shots within each neighborhood type - that is, the percent who get flu shots among Urban residents versus the percent who get flu shots among Suburban residents. Choice C correctly identifies this comparison of conditional distributions. Options A and E examine marginal distributions, B compares raw counts instead of proportions, and D conditions on flu-shot status rather than neighborhood type. The principle here is to condition on the explanatory variable (neighborhood type) and examine how the response variable (flu-shot status) varies across those conditions.