Let's go through the questions and identify the explanatory and response variables for each scenario.

Part (a) Cell phone data plans for a person who wants a large file size: file size, cost

1. Explanatory variable: File size (it explains or influences the cost)
2. Response variable: Cost (depends on or is affected by file size)
3. Association: We would expect a positive and possibly linear association, as larger file sizes generally cost more.

Part (b) Lightning strikes by a person watching a lightning storm: distance from lightning, time delay of the thunder

1. Explanatory variable: Distance from lightning (it explains the time delay)
2. Response variable: Time delay of the thunder (affected by the distance from lightning)
3. Association: We would expect a positive and possibly linear association, as greater distances result in longer delays in thunder due to the speed of sound.

Part (c) A streetlight that you are approaching: its apparent brightness, your distance from it

1. Explanatory variable: Your distance from the streetlight (it affects the brightness you perceive)
2. Response variable: Apparent brightness (depends on how close you are)
3. Association: We would expect a negative and nonlinear association, as brightness decreases quickly with increasing distance (inverse-square law).

Part (d) Cars being catalogued at a dealership: weight of car, age of owner

1. Explanatory variable: Age of owner (trying to find if it relates to car weight choice)
2. Response variable: Weight of car (could vary based on the owner's age)
3. Association: Likely weak or no association, possibly nonlinear if any, as car weight and owner age may not have a strong relationship.

Would you like me to provide detailed explanations for each choice or answer any questions you have about this?

Here are some additional questions to deepen understanding:

1. What is the importance of choosing the correct explanatory and response variables in data analysis?
2. How does the direction of association affect the interpretation of a scatterplot?
3. What are some real-life applications of linear versus nonlinear associations?
4. Can there be cases where the explanatory variable might also be influenced by the response variable? Explain.
5. What are other factors that could affect the strength of association in a dataset?

Tip: When interpreting scatterplots, consider both the form (linear or nonlinear) and the strength of association, as they offer insights into how predictable one variable is given the other.