Choose one of the following two prompts to respond to. Then in your two follow up posts, respond at least once in each option. Use the discussion topic as a place to ask questions, speculate about answers, and share insights. Be sure to embed and cite your references for any supporting images.

Option 1:

Think of a problem dealing with two possibly related variables (Y and X) that you may be interested in. Share your problem and discuss why a regression analysis could be appropriate for this problem.

Specifically, what statistical questions are you asking? Why would you want to predict the value of Y? What if you wanted to predict a value of Y thatâ€™s beyond the highest value of X (for example if X is time and you want to forecast Y in the future)?

You should describe the data collection process that you are proposing but you do not need to collect any data.

Option 2:

Give an example of a problem dealing with two possibly related variables (Y and X) for which a linear regression model would not be appropriate. For example, the relationship could be curved instead of linear, or there may be no significant correlation at all.

What is the impact of using a linear regression model in this case? What options, other than linear regression, can you see? You do not need to collect any data.

For your response to a classmate (two responses required, one in each option), examine your classmateâ€™s problem to assess the appropriateness and accuracy of using a linear regression model. Discuss the meaning of the standard error of the estimate and how it affects the predicted values of Y for that analysis.

Option 1 classmate post:

Hello everyone,
While thinking about variables that are related this week, Iâ€™m in the middle of planning a trip to celebrate my Grandfatherâ€™s 97th birthday. He is the family patriarch but is much healthier than most of his descendants. I am curious about the relationship between age and health issues.
Statistically, I want to know how age (X) impacts the number of health issues a person has (Y). I would want to predict the number of health issues a person has (Y) so that myself and others can have a better idea of what to expect in the future. I would not want to predict a value of Y thatâ€™s beyond the highest value of X because that would be very unrealistic for a person to have that many health issues.
To collect data, you would have to perform random surveys all over the world and be sure to use a sample size that is appropriate for the worldâ€™s population. The sample should include people of all ages so that you can compare the data.

Option 2 classmate post:

Hello all,
For this weeks discussion, I chose option 2. Here is my example of a problem dealing with two possibly related variables: Is there a relationship between the color of your car and the gas mileage?
This example does not have a significant correlation. Since these two variables are not related, a linear regression line is not necessary. A regression line is used to find the best fit line and estimate one variable on the basis of another variable. In regression, a functional relationship between two variables is established to make future predictions.
I'm not sure if there is an alternate option for linear regression.

Choose one of the following two prompts to respond to. Then in your...