STA302/1001 week 1
1
Regression Models  Introduction
•
In regression models, two types of variables that are studied:
A dependent variable, Y, also called response variable. It is
modeled as
random.
An independent variable, X,
also called predictor variable or
explanatory variable. It is sometimes modeled as random and
sometimes it has fixed value for each observation.
•
In regression models we are fitting a statistical model to data.
•
We generally use regression to be able to predict the value of one
variable given the value of others.
This preview has intentionally blurred sections. Sign up to view the full version.
View Full Document
2
Simple Linear Regression  Introduction
•
Simple linear regression studies the relationship between a
quantitative response variable Y, and a single explanatory variable X.
•
Idea of statistical model: Actual observed value of Y = …
•
Box (a well know statistician) claim: “All models are wrong, some
are useful”. ‘Useful’ means that they describe the data well and can
be used for predictions and inferences.
•
Recall:
This is the end of the preview.
Sign up
to
access the rest of the document.
 Summer '09
 Regression Analysis, Linear Regression Models

Click to edit the document details