Applications of Linear Regression


For the discussion boards, two options are given for you to choose from for your initial post. In your response posts, you are required to respond at least once to each option.

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This week you should have a minimum of three posts in total:

  1. Your initial post to either Option One or Option Two
  2. A response to one of your peers’ responses to Option One
  3. A response to one of your peers’ responses to Option Two

In your title for your discussion post, please clearly include which option you are answering by typing “Option One: [your title]” or “Option Two: [your title].”

For your initial post, 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 that’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 classmate’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.

To complete this assignment, review the Discussion Rubric document.

I will be needing 2 responses done on classmates.

My 1st classmate response needed.

7-1 Discussion Option 1

Contains unread posts

Megan Rebele posted Oct 12, 2020 1:00 PM

Hello Everyone,

I cannot believe we are in week 7 already. It seems this term has flown by. I selected option 1 due to a situation that occurred over this past weekend. I noticed my kids were playing more video games than normal. That made me think if I was slacking a little bit with how busy I have been or if they are just getting more into electronics as they grow up.

It seems like the older children get; the more time they spend engaged in their electronics. This could create a problem as there are studies show that this can be detrimental to both physical and mental health.

An example of a problem in which a linear regression model would be appropriate would be analyzing the relationship between the Age of children(x) and The number of hours spent on electronics daily (y).

The value of (y) must be predicted to see if there is in fact a positive correlation. To collect the data, I will need to record the age and number of hours spent on electronics. As we are looking at number of hours on devices daily we would not be able to see beyond 24 hours as that is the maximum number of hours in each day.

I could use an app installed to the electronic devices that could track how much it is being used. As the child gets older, I would expect to see an increase in use. I would remove only the phone calls from the data gathered.

Statistical Questions Being Asked:

  • What apps are used mostly on the device?
  • Could the difficulty falling asleep be related to device usage?
  • Do the participants fall asleep easy or has it become more of a struggle to settle as they have been on their devices more?
  • Do the participants feel like they need to be available through social media through their electronic devices?

Reasons for predicting the value of Y:

Predicting the value of Y is helpful to potentially see the correlation in the age of the participant and the amount of time spend on the electronic devices.

It would be helpful to be able to predict the value of y to show at what point doctors should start the education process regarding screen time.

Another reason for predicting the value of Y is to see if more device use leads to higher other health issues regarding physical and mental health.



I will be sending 2nd response as soon as it is posted. If needed I will extend the time for the 2nd response.

Student has agreed that all tutoring, explanations, and answers provided by the tutor will be used to help in the learning process and in accordance with Studypool’s honor code & terms of service.

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