The question discusses two parts related to scatterplots, correlations, and determining explanatory and response variables.

1. Part (a): The correct answer here is (A). The scatterplot is expected to have a positive, straight (linear), and strong correlation. This indicates that as one variable increases, the other variable is also likely to increase in a roughly linear pattern.

2. Part (b): For the pair of variables "ice cream cone sales" and "air-conditioner sales":

   • The best choice for the explanatory variable would be ice cream cone sales, with air-conditioner sales as the response variable. This is based on the reasoning that higher ice cream sales are likely indicative of warmer weather, which could also increase air conditioner usage.
   • Therefore, Answer A is correct: "Ice cream cone sales would best be used as the explanatory variable, and air-conditioner sales as the response variable, to predict air-conditioner sales based on ice cream cone sales."

Let me know if you need a detailed explanation or have any questions!

Here are some additional related questions for practice:

1. What is an explanatory variable, and how does it differ from a response variable?
2. How does correlation differ from causation in interpreting scatterplots?
3. Why might a curved relationship appear in some scatterplots instead of a straight line?
4. How could seasonal factors affect the relationship between ice cream sales and air conditioner sales?
5. In what situations might a scatterplot show a weak or no correlation?

Tip: Always look at scatterplots for patterns in direction, form, and strength to understand the relationship between variables.