1. Chapter 14 Inference for Regression
Objectives:
Review how to make a scatterplot to show relationship between explanatory and response variables.
Use a calculator to find the correlation and least-square regression line.
Recognize the regression setting for a line.
Recognize the type of inference needed in a particular regression setting.
Inspect data for non-linear relationship, influential observations, skewed data, or nonconstant variation.
Explain meaning of slope, intercept, and standard error.
Determine tests and confidence intervals for ß.

2. 14.1 Inference About the Model
A scatter plot shows a linear relationship between quantitative explanatory variable x and quantitative response variable y.
Use least square regression line to predict y.
Look at form, direction, and strength of relationship as well as outliers or other deviations.
Numerical summary – correlation describes strength and direction of relationship. It will explain how much of the variation is due to the explanatory value.
Mathematical model – LSRL ŷ = a + bx
Conditions for Regression Inference
For any fixed value of x, the response y varies according to a normal distribution. Repeated responses y are independent of each other.
The mean response μy has a straight line relationship with x:
True line regression: μy = α + βx where α and β are unknown parameters.
The standard deviation of y (call it σ) is the same for all values of x. The value of σ is unknown.

3. 14.1 Inference for Regression (cont.)
Standard Error about the line
S =
Confidence Interval for Regression Slope
Estimate + t*SEb
SEb=
t*is upper (1-c)/2 critical value with a n-2 degree of freedom
Statistic Test of H0: β=0 t statistic where t=
calculator linear regression t test is LinRegTTest
(Remember regression analysis usually give a two sided P value so if you need one sided just divide the P value by 2.)

4. 14.2 Predictions and Conditions
To estimate the mean response for a LSR we use a confidence interval.
μy = α + βx*..
A level of confidence interval for the mean response μy when x takes on a value of x* is
The standard error is
To estimate the individual response we use a prediction interval. A prediction interval estimates a single random response y rather than a parameter like μy . The response is not a fixed number
The level of confidence C prediction interval for a single observation on y when x takes on the value x* is

5. 14.2 Predictions and Conditions (cont.)
Observations are independent
The true relationship is linear.
The standard deviation of the response about the true line is the same everywhere.
The response varies normally about the true regression line.