### Question Description

In Unit 5, there are three main topics (problem types): describing correlations, measuring correlations (with the r value), and creating and evaluating scatterplots. You will be exposed to all three topics, and will have the opportunity to discuss and compare these topics with your fellow learners.

Using any Excel dataset, choose two quantitative variables from the dataset. For example, you might choose age and weight. Next, do the following:

- What is the name of the dataset you have chosen? What are the names of the two quantitative variables you are investigating?
- Using Excel, calculate the relationship (correlation) between these two variables. Write down your calculated r value.
- Given the r value you calculated in number 1 above, explain what the r value tells you about the relationship between the two variables. For example, is the relationship positive or negative? Is the relationship strong, medium, or weak?
- Using the two variables you have chosen, create and attach to your post a scatterplot. Does the scatterplot have a linear appearance? What does the scatterplot tell you about the relationship between your two variables?

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.

- Find the dataset that each student used, and repeat their r value calculation in Excel for the variables they chose. Are they correct? Do you agree with how they described the relationship between the two variables? Explain in one paragraph and include both your results and the student results.
- For the same two classmates, also review their scatterplots. Discuss the shape of the scatterplot is it mostly linear or is it all scattered around? Does the scatterplot seem to have a positive (upwards from left to right) or negative slope? Does their scatterplot visually match what the r value is suggesting? What can you say about the relationship based on the scatterplot?

# 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., Triola, M. (2014) Statistical Reasoning: For Everyday Life.(4th ed)

<