This question requires us to calculate a relative probability from a two-way frequency chart. Before we can calculate this probability, we need to consider the composition of these tables. The numbers located within the table are not quantitative (i.e. they simply represent the number of times a particular phenomenon occurs). These tables are made up of marginal and joint frequencies. Marginal frequencies occur in the columns and rows labeled as “titles,’” while the joint frequencies are embedded in the table. Let’s observe the frequency table given in the question:

Relative probabilities are calculated by dividing the marginal and joint frequencies by the total in the table. If we divide each frequency by the total frequency, then we can create the following table:

In this table, we can see that the relative probability of men who like dramas is:

This can also be solved by graphing the relative probabilities on a stacked bar chart such as the one shown below:

In order to properly solve this question, we need to understand the differences between what is meant by correlation and causation. A correlation refers to the strength of the linear association between two quantitative variables. On the other hand, causation indicates that the change in one variable is the cause of change in another.

Correlation can be used as an indicator of causal relationships; however, experimentation is needed to properly identify which variable is actually causing the observed change. Scientific experimentation identifies causality through he implementation of laboratory procedures in a controlled setting. When variables are controlled, causation can be determined through observation and repeated tests.

Several logical fallacies explain why correlation does not directly imply causation. First, cause-and-effect is not determined by two events occurring simultaneously. In other words, events that occur together do not necessarily cause one another. Second, causality is not determined by an event preceding another temporally. In other words, this means that event B is not always a consequence of event A simply because event A occurs before event B.

Lurking or linking variables can cause events that are highly correlated to one another appear to have a casual relationship. This is because a third separate factor may be inducing change in the two variables. 

Now, let's solve this problem. It asks us to describe the relationship in the scatterplot. We know that there is a positive relationship between the two variables; however, if we think critically we know that shark attacks and ice cream sales are independent of one another. The answers that suggest causality are incorrect. A linking or lurking variable—in this case warm temperatures—is causing change in both of the variables. In other words, warmer temperatures cause individuals to purchase ice cream and frequent the beach. Greater populations of beach goers increase the probability of shark attacks.