This preview shows pages 1–7. Sign up to view the full content.
This preview has intentionally blurred sections. Sign up to view the full version.
View Full DocumentThis preview has intentionally blurred sections. Sign up to view the full version.
View Full DocumentThis preview has intentionally blurred sections. Sign up to view the full version.
View Full Document
Unformatted text preview: STA 3024: Regression Douglas Whitaker Statistics Department 24 February 2012 Douglas Whitaker (Statistics Department) STA 3024: Regression 24 February 2012 1 / 18 Regression We’re now turning our attention to regression analysis . Regression is a term for a set of tools for analyzing a quantitative response variable and quantitative explanatory variables. We’re going to spend a good deal of time on regression this semester. Douglas Whitaker (Statistics Department) STA 3024: Regression 24 February 2012 2 / 18 Simple Linear Regression We’ll begin by talking about simple linear regression (or SLR). This means we’ll have one response variable and one explanatory variable. As we talk about this more, we’ll add more explanatory variables. Basically, this boils down to having data points ( X ’s and Y ’s) and fitting a line to them. Douglas Whitaker (Statistics Department) STA 3024: Regression 24 February 2012 3 / 18 Douglas Whitaker (Statistics Department) STA 3024: Regression 24 February 2012 4 / 18 Douglas Whitaker (Statistics Department) STA 3024: Regression 24 February 2012 5 / 18 Measuring Association Before we get down to the nittygritty about finding the best line to fit the data , let’s talk about something you learned in your previous statistics course: correlation ....
View
Full
Document
This note was uploaded on 03/01/2012 for the course STA 3024 taught by Professor Ta during the Spring '08 term at University of Florida.
 Spring '08
 TA
 Statistics, Regression Analysis

Click to edit the document details