Correlation and regression & examples of correlation and regression

Week 5 Discussions and Required Resources

Part 1 and Part 2 must be at least 150 – 200 words unless otherwise

Part 1: Correlation and Regression 

There are strengths and weaknesses associated with correlation and regression. For this discussion, begin by reading about correlation and regression in your textbook. Then, keeping these techniques in mind, read the following quotes:

· “One of the first things taught in introductory statistics textbooks is that correlation is not causation. It is also one of the first things forgotten.”—Thomas Sowell, The Vision of the Anointed: Self-Congratulation as a Basis for Social Policy(1995)

· “Statistics were magic like this: they could tell you with near-certainty that a thing would occur, without a hint of when or where.”—Hugh Howey, Shift (2013)

· “If the statistics are boring, then you’ve got the wrong numbers.”—Edward R. Tufte

· “Those who ignore statistics are condemned to reinvent it. Statistics is the science of learning from experience.”—Bradley Efron (2006)

· “Nature is written in mathematical language.”—Galileo Galilei

· “Statistics is the grammar of science.”—Karl Pearson

https://www.goodreads.com/quotes/search?utf8=%E2%9C%93&q=statistics&commit=Search

Based on the above quotes, along with this week’s assigned readings and Instructor Guidance, discuss the importance of analyzing correlation and regression in research.

Part 2: Examples of Correlation and Regression in Research

Locate an example of a research study that uses correlation and regression techniques. Explain what it has allowed the researchers to accomplish and/or conclude in the study.

Required Resource

Text

Lind, D. A., Marchal, W. G., & Wathen, S. A. (2017). Statistical techniques in business and economics. (17th ed.). Retrieved from http://connect.mheducation.com/class/

Link to text: https://drive.google.com/file/d/0B2z1XRpzFSVfZ3ZkbWdsUk1lMzg/view?usp=sharing

· Chapter 13: Correlation and Linear Regression

· Chapter 14: Multiple Regression Analysis