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If we were to use Regression Analysis and make a scatter chart' examples are in your text on page 586) of the relationship between ice cream cone sales and swimming deaths, we would see that as the number of drowning deaths variable rises (number of days when swimming is possible) with the ice cream sales variable.

In this case it is very important to understand the difference between causation and correlation. According to the website" Towards data source" Correlationis a statistical technique which tells us how strongly the pair of variables is linearly related and change together. It does not tell us why and how behind the relationship but it just says the relationship exists. For example, increasing sales of ice cream and the increase of drowning death is a type of correlation, but it's not causation. We know that there is a relationship there, but it does not tell us how. When it comes to causation the website "Towards data source" states that ittakes a step further than correlation. It says any change in the value of one variable willcausea change in the value of another variable, which means one variable makes other to happen. It is also referred as cause and effect. In this case, we can see a relationship between the increasing sales of ice cream and the increase of drowning death people. For example, the higher the temperature (summertime), the more people go to the beach to buy ice cream, and swim on the ocean. As a result of that, the higher is the chance of people drowns and dies. As we can see, the ice cream is not the cause of drowning people, but the weather (summertime.)

Why correlation does not imply causation? Medium. (2021). Retrieved 17 October 2021, from https://towardsdatascience.com/why-correlation-does-not-imply-causation-5b99790df07e.