BUS308 Week 4 Discussion 2 Example of Statistical Outcomes

Lecture 2 has been like the other lectures we have been learning about during the few past weeks. What I mean is the bread down of the information that we are learning and simplifying the material to where it is relatable in our everyday life. Whereas, the first discussion gives us somewhat of a brief overview of what we are about to take a deep dive into for the lecture. Linear correlation talks about two things which could be interval or ratio level measures that move in a somewhat predictable way. Whether that way be a direct or an inverse direction there is said to be a correlation between the two things. The Pearson Correlation Coefficient is the most commonly method of measuring the strength of the relationship of the two objects being measured. In which the measurement is from -1.0 to 0 to 1.0. A negative or inverse correlation is represented by -1.0 and 0 has no correlation, while 1.0 has a positive or direct correlation. 

Regression was next in the lecture to be explained and that is uses interval data. I mentioned in first discussion this week that it was an equation and based off the information we receive from the correlation we will use this data in trying to get more clarification on the data we have been researching since we began the course. Also, multiple regression was stated as being the most powerful tool we would learn about in the course because of its ability to observe multiple inputs on a single output. This is not like the regular regression as it is only one input and one output. In an article it talked about regression being the way you can prove an impact has been made with this mathematical equation (Gallo, Davenport, & Kim, 2017). I think this should be interesting in see what we will find when using this week’s material in our assignment.

Gallo, A., Davenport, T. H., & Kim, J. (2017, November 30). A Refresher on Regression Analysis. Retrieved from https://hbr.org/2015/11/a-refresher-on-regression-analysis .