Learning Objectives
After completing this pre-class module, you should be able to:
5) State various uses of or views on regression.
6) State the equation of a straight line.
7) State the goal of linear regression with respect to a straight line.
8) Define and calculate a residual in a regression analysis.
1) Define the correlation between two variables.
2) Describe the degree and type of correlation between two variables based on the value of r as well as the scatter plot.
3) Discuss why data should be plotted before calculating a correlation coefficient.
4) Discuss the reasons why a correlation coefficient could be zero or approximately zero.
5) Describe the impact of outlier data points on the correlation coefficient depending on the magnitude of the correlation coefficient.
6) Describe how a transformation can be useful in a linear regression analysis.
7) Identify and perform common useful transformations within linear regression analysis.
8) Discuss why and how a transformation tends to be a trial-and-error process.
Videos
Title: V36 – Simple Linear Regression (Introduction to Regression, Uses of, & Residuals)
Summary: This video serves as an introduction to regression in terms of its definition and key uses or applications of it. The equation for a straight line (with its slope and y-intercept) is reviewed. The definition and calculation of a residual used in a regression analysis is covered.
Learning Objectives:
5) State various uses of or views on regression.
6) State the equation of a straight line.
7) State the goal of linear regression with respect to a straight line.
8) Define and calculate a residual in a regression analysis.
Transcript: Read the transcript
Annotated Slides: See the slides
Slides without Annotation: See the slides
Title: V37 – Simple Linear Regression (Correlation)
Summary: This video covers the definition of correlation, properties of the correlation coefficient, the degree/amount of correlation, positive vs. negative correlation, reasons for zero correlation, and the impact of outliers on the correlation coefficient.
Learning Objectives:
1) Define the correlation between two variables.
2) Describe the degree and type of correlation between two variables based on the value of r as well as the scatter plot.
3) Discuss why data should be plotted before calculating a correlation coefficient.
4) Discuss the reasons why a correlation coefficient could be zero or approximately zero.
5) Describe the impact of outlier data points on the correlation coefficient depending on the magnitude of the correlation coefficient.
Transcript: Read the transcript
Annotated Slides: See the slides
Slides Without Annotation: See the slides
Summary: This video covers variable transformations, such as the natural log or reciprocal, to linearize the relationship between variables x & y so that a linear regression can be run.
Learning Objectives:
6) Describe how a transformation can be helpful in a linear regression analysis.
7) Identify and perform common functional transformations within linear regression analysis.
8) Discuss why and how a transformation tends to be a trial-and-error process.
Transcript: Read the transcript
Slides With Annotation: See the slides
Slides Without Annotation: See the slides