Fall, 2012
Wednesday, Sept. 19
Stat 324 Day 2
Correlation (p. 64-65)
Last Time: Think about meaningful variables and types of variables. Look at the data!
Use scatterplots to explore the relationship between two quantitative variables
Look for direction,
Fall, 2012
Monday, Oct. 1
Stat 324 Day 8
Inference for Regression, cont. (2.1)
Recap: The basic regression model tells us conditions (LINE) under which the least squares
estimators have nice properties. If these conditions are not met, then we should not
Fall, 2012
Reading: Section 1.5
Friday, Sept. 28
Stat 324 Day 7
Logic of Inference for Regression (2.1)
Example 1: Suppose we want to know whether students GPAs are related to how much they study. A
student project group surveyed a random sample of 80 stu
Fall, 2012
Tuesday, Sept. 25
Stat 324 Day 5
Assessing Conditions (1.3)
Last Time: The basic regression model has the following conditions:
L: There is a linear relationship between the means at each x and x:
E(Y|X=x) = 0 + x
I: The observations (errors) a
Fall, 2012
Monday, Sept. 24
Stat 324 Day 4
The Basic Regression Model
Last Time:
Simple probabilistic linear model: Y = 0 + 1X +
Least squares estimates for 0 and 1 minimize the residual sum of squares: ( yi )2
o
= y - x ; = r(sy/sx)
hats indicate thes
Fall, 2012
Friday, Sept. 21
Stat 324 Day 3
Least Squares Regression (1.1)
Last Time: Pearsons correlation coefficient
Use r to measure the strength of the linear association between two quantitative variables
Can test the statistical significance of a c