Question

Someone has a hypothesis that the Gender can be used to explain the number of Piercings that students in the StudySurvey have. Fit the model and save it. Then, create a function that takes the model as an input. Finally, use your function to make a prediction for males. (Note that males are coded as "M")

What does the output tell you?
A) The mean number of piercings for males is 0.717
B) The mean number of piercings for males is 2.98 lower than piercings for female
C) The error for male is 0.17
D) You should expect the next randomly chosen male to have 1.71 piercings

Answer 1

D) You should expect the next randomly chosen male to have 1.71 piercings

Here's how we arrive at this answer:

1. First, we need to understand the hypothesis. It is suggesting that there might be a relationship between Gender (the independent variable) and Piercings (the dependent variable) among students in this study.

2. To test this hypothesis, we need to fit a Regression model to our data. The formula 'Piercings ~ Gender' is used for this purpose. What this formula does is to model the number of Piercings as a function of Gender.

3. After fitting the model, we create a function that would take this model and a gender (in our case 'M' for males) as inputs. This function is used for making predictions about the number of piercings a student of that gender would have.

4. Once we pass our model and 'M' to our function, it returns a prediction. According to the given information, the output is 1.71.

5. What this output actually means is that if we randomly select a male student from our data, we can expect him to have around 1.71 piercings. Of course, this is a statistical expectation and actual number of piercings can vary.