Please follow these steps:
- Choose any Excel Discussion dataset. Include the name of the dataset. From that dataset, select any two quantitative variables that you suspect will be related (such as age and height for example). What is the name of the dataset you have chosen? Which two variables did you choose?
- Next, using Excel, calculate the relationship (r value) between the two variables. Recall that the Excel formula for correlation is =CORREL. What is the r value for the two variables that you have chosen? Is it positive or negative? Is it strong, medium, or weak? Note that it is best to have an r value that is medium or strong. It is recommended that you try a few different variables until you find two variables with an r value between .5 and 1 (or between -.5 and -1).
- Next, use Excel to create a scatterplot for the two variables. You decide which variable will be dependent (y) and which will be independent (x). On the scatterplot, include the trendline and the equation for the line using Excel options. Attach your scatterplot to your post.
- Finally, using the equation of the line that you generated above, plug in any reasonable value for x (your chosen independent variable) and solve the equation for y (your chosen dependent variable). It is up to you to determine which of your two variables is x and which is y. What prediction do you get? Show all your work. In other words, type out the equation, plug in a value for x, and show your solution for y.
Please create personalized and substantive responses to at least two other student main posts. In your response, include the following:
Choose any two classmates and review their main posts.
- Review the students regression equation (include it in your post). What is the independent variable name? (The answer is not x
What is the dependent variable name? Choose any other value for the independent variable (represented by the letter x in the equation) and plug that value in to solve for an estimate of the dependent variable (y in the equation). Show all steps and work.
- Review the correlation (r value) that the student calculated between the two variables. Is this correlation strong, medium, or weak and why? Based on the correlation strength, do you think that the regression equation will offer a fair estimate? Why or why not?
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