Simple and Multiple Regressions

Simple and Multiple Regressions

1. Describe the difference between a simple and a multiple regression.  Then include an example of each from your own experiences.

2. Determine the Regression Equation…

2. Determine the regression equation using values you create for x and y for at least 10 pairs of data.  Show the regression equation, correlation coefficient, and coefficient of determination.  Then switch the x and y values for each data point.  Based on that, again show the regression equation, correlation coefficient, and coefficient of determination.  Discuss the similarities and differences between the results.

3. Causation versus Relationship

3. Describe the difference between causation between two variables and a relationship between two variables.

4. Hat, No Hat?

4. Explain the difference between y and ?.

5. Week 8 Preparation Question 1

5. If a teacher wanted to compare the shoe size of their 4th grade students with the heights of those same students, which type of graph do you think would be most effective and why?

6. Week 8 Preparation Question 2

6. What graphic depiction would most effectively help a researcher compare the distribution of the hours spent online among teenagers, young adults, and the elderly and why?

7. Week 8 Preparation Question 3

7. Of the statistical processes we have covered throughout this course, which would be most helpful to determine if study hours and sleep could help estimate a student’s test score?

8. Week 8 Preparation Question 4

8. Which statistical method would be most effective to determine if the average size of dogs in Europe was different than the average size of dogs in the U.S.?  Why?

9. Week 8 Preparation Question 5

9. A grocery store is looking to purchase new carts for customers to use as they shop.  Assuming the amounts purchased are normally distributed, what statistical method would help the store estimate the most likely range of purchases?  Why?

10. Ethics! (Strong, not strong?)

10. Ethics:  If the computer gives you the slope and intercept for a regression equation, does that mean that the two variables have a strong relationship?  Why or why not?