Lecture 2 Linear Regression: A Model for the Mean
Lecture 2 Linear Regression: A Model for the Mean
Sharyn O'Halloran
Closer Look at:
Linear Regression Model
Least squares procedure Inferential tools Confidence and Prediction Intervals
Assumptions Robustness Model checking Log transformation (of Y, X, or
both)
U9611
Spring 2005
2
Linear Regression: Introduction
Data: (Yi, Xi) for i = 1,...,n
Interest is in the probability distribution of Y as a function of X
Linear Regression model:
Mean of Y is a straight line function of X, plus an error term or residual
Goal is to find the best fit line that minimizes the sum of the error terms
U9611
Spring 2005
3
Estimated regression line
Steer example (see Display 7.3, p. 177)
Equation for estimated regression line:
7
Intercept=6.98
.73
6.5
Fitted line
1
Y^ = 6.98-.73X
PH
6
0 U9611
5.5
Error term
1
2
ltime
Fitted v alues
PH
Spring 2005
4
Create a new variable ltime=log(time)
Regression analysis
U9611
Spring 2005
5
................
................
In order to avoid copyright disputes, this page is only a partial summary.
To fulfill the demand for quickly locating and searching documents.
It is intelligent file search solution for home and business.
Related download
- 1 karnaugh map express the following function in a
- 1 the probability distribution of a discrete random
- week 5 simple linear regression princeton
- solution edu
- circular convolution university of pennsylvania
- 3 summary statistics notation san jose state university
- the accuracy of the gaussian approximation to the sum of
- math 0015 final exam review revised fall 2020 multiple
- complex numbers chino valley unified school district
- name unit3 2 writing expressions and equations