Final answer:
Relative frequencies in statistics refer to the ratio of occurrences in a dataset, and they come in three main types: joint, conditional, and marginal. They help in understanding the likelihood of various combinations of events or characteristics within a dataset.
Step-by-step explanation:
In statistics, when working with frequencies in a dataset, it is important to understand different types of frequencies: joint relative frequency, conditional relative frequency, and marginal relative frequency.
Joint relative frequency is calculated by dividing the frequency of the occurrence of a particular combination of outcomes by the total number of outcomes. For instance, if 30 out of 100 students are freshmen who take Spanish, the joint relative frequency for freshmen who take Spanish is 30/100, which simplifies to 0.30 or 30%.
Conditional relative frequency is found by dividing the joint frequency by the marginal frequency of the condition being observed. If out of those 30 freshmen taking Spanish, 10 are in the debate club, and there are 50 students in the debate club in total, the conditional relative frequency of freshmen who take Spanish given they are in the debate club is 10/50, or 20%.
Marginal relative frequency refers to the probability of a single event without consideration of another variable. Using the same example, if there are 100 students, and 30 are freshmen, the marginal relative frequency of being a freshman is 30/100, or 30%.
Using the criteria above, only the choices that comply with the definitions will be correct. For example, if 1160 is the total number of outcomes, and 93 is the frequency for a specific combination, then 93/1160 could be a joint relative frequency if it pertains to a specific combination. But if 1825 corresponds to the total occurrences of one of the variables across all combinations, then 1067/1825 might refer to the conditional relative frequency, assuming 1067 is the joint frequency for a subset of that variable.