statistics unit 6 discussion board 1

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Please follow these steps:

  1. 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?
  2. 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).
  3. 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.
  4. 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.

  1. Review the student’s 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.
  2. 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?

Discussion Requirements

Rubrics

Reading and Resources

  • Read the assigned chapters from the following textbooks:

Bennett, J., Briggs, W.L. & Triola, M.F. (2013) Statistical Reasoning for Everyday Life (4th ed.). Upper Saddle, NJ: Pearson.

  • Chapter 7: “Correlation and Causality”

Reading the textbook and reviewing the textbook examples are excellent methods for starting each unit. Reading the textbook offers context and explanations for new concepts and methods. Completing the textbook examples on paper (and with Excel) is a great way to practice and learn the new methods and concepts introduced. Student feedback has suggested that reading the textbook and practicing the textbook examples has been particularly helpful if completed before the unit Seminar. Some students have reported that keeping a notebook handy, and recording new definitions or concepts encountered while reading is helpful, more organized, and stress reducing.

This chapter includes a section that offers examples using technologies such as Excel. In addition, at the end of each chapter section, or at the end of the chapter, are review exercises that are very helpful for practicing and preparing.

In this course, students may use Excel for any statistical calculations. Excel can be used to evaluate data in many ways. Excel can be used to calculate numerical measures, such as measures of center (such as mean and median) and measures of variation (variance, standard deviation, and range), as well as many other measures such as min, max, and correlation (r-value). Excel can also be used to create visualizations, such as histograms, bar graphs, pie graphs, scatterplots, and others. Excel may also be used to create linear regression equations. Because Excel is a very common tool, the Internet and YouTube both contain considerable support resources and tutorials.

TEXTBOOKS

Bennett, J., Briggs, W.L. & Triola, M.F. (2013) Statistical Reasoning for Everyday Life (4th ed.). Upper Saddle, NJ: Pearson.

 
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