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Confidence Intervals vs. Prediction Intervals Confidence Intervals – provide an interval estimate with a 100(1-) measure of reliability about the mean (or some parameter) of a population Prediction Intervals – provide an interval estimate with a 100(1-) measure of reliability about future

Confidence Intervals vs. Prediction Intervals Confidence Intervals – provide an interval estimate with a 100(1- ) measure of reliability about the mean

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Page 1: Confidence Intervals vs. Prediction Intervals Confidence Intervals – provide an interval estimate with a 100(1-  ) measure of reliability about the mean

Confidence Intervals vs. Prediction Intervals

Confidence Intervals – provide an interval estimate with a 100(1-) measure of reliability about the mean (or some parameter) of a population

Prediction Intervals – provide an interval estimate with a 100(1-) measure of reliability about future observations (or individual random variables) from a population

Page 2: Confidence Intervals vs. Prediction Intervals Confidence Intervals – provide an interval estimate with a 100(1-  ) measure of reliability about the mean

Inference on the Mean Response

Let x* be some specified value of the predictor variable x

The mean response at x* is

Hence, the point estimate of the mean at x* is

**)( 1* xxYE oxY

*ˆˆˆ 1* xoxY

Page 3: Confidence Intervals vs. Prediction Intervals Confidence Intervals – provide an interval estimate with a 100(1-  ) measure of reliability about the mean

Inference on the Mean Response Cont’d

Aside: The estimated mean is on the least squares line

Mean and Variance of *ˆ xY*]ˆ[ 1* xE oxY

2

22

* )(

)*(1]ˆ[

xx

xx

nVar

ixY

Page 4: Confidence Intervals vs. Prediction Intervals Confidence Intervals – provide an interval estimate with a 100(1-  ) measure of reliability about the mean

C.I. for the Mean Response at x*

A 100(1-)% confidence interval for is given by

*xy

2

2

2,21 )(

)*(1*

xx

xx

nstx

ino

Recall: 2

n

SSEs

Page 5: Confidence Intervals vs. Prediction Intervals Confidence Intervals – provide an interval estimate with a 100(1-  ) measure of reliability about the mean

Remarks about C.I.’s at x*

• The best estimation (i.e. tightest C.I.’s) of occurs at since the variance increases as x* moves away from

• The estimation of the mean response should only be used for x values within the range of the data (i.e., x*’s within the range of the x’s of the data). Extrapolation in very dangerous and should be used with extreme caution.

*xyxx *

x

Page 6: Confidence Intervals vs. Prediction Intervals Confidence Intervals – provide an interval estimate with a 100(1-  ) measure of reliability about the mean

Multiple C.I.’s

Consider a collection of k different 100(1-)% C.I.’s correspond the k different specifications of x*. The joint confidence level for this collection of confidence intervals is bounded below by Bonferroni’s inequality; as such the confidence level is guaranteed to no less than 100(1-k)%.

Page 7: Confidence Intervals vs. Prediction Intervals Confidence Intervals – provide an interval estimate with a 100(1-  ) measure of reliability about the mean

Example – Multiple C.I.’s

Consider the data of problem 12.4. Suppose we wish to construct 95% confidence intervals for the mean response when x* = 4, 10, 18. The joint confidence level for these 3 intervals is guaranteed to be at least 100(1-3(.05))% = 85%.

Page 8: Confidence Intervals vs. Prediction Intervals Confidence Intervals – provide an interval estimate with a 100(1-  ) measure of reliability about the mean

Prediction Intervals

Frequently, we would like to use our L.S. regression model to predict the response y when x = x*

• Our best guess would be the mean response at x*, i.e. *ˆˆˆ 1* xy oxY

Page 9: Confidence Intervals vs. Prediction Intervals Confidence Intervals – provide an interval estimate with a 100(1-  ) measure of reliability about the mean

Prediction Intervals Cont’d

• Computing the variance of this guess (or prediction) yields

2

22

1

)(

)*(11

*]ˆˆ[][]ˆ[

xx

xx

n

xVarYVaryYVar

i

o

Page 10: Confidence Intervals vs. Prediction Intervals Confidence Intervals – provide an interval estimate with a 100(1-  ) measure of reliability about the mean

Prediction Interval Cont’d

Hence, the 100(1- )% Prediction Interval for Y when x = x* is

2

2

2,21 )(

)*(11*ˆˆ

xx

xx

nstx

ino

Page 11: Confidence Intervals vs. Prediction Intervals Confidence Intervals – provide an interval estimate with a 100(1-  ) measure of reliability about the mean

Remarks about P.I.’s at x*

• The P.I.’s are wider than C.I.’s.

• As with C.I.’s the prediction interval is tightest at x = x* and as a rule, should not be used for extrapolated x* specifications

• Additionally, as with C.I.’s, the joint confidence level of Multiple P.I.’s is bounded by Bonferroni’s inequality

Page 12: Confidence Intervals vs. Prediction Intervals Confidence Intervals – provide an interval estimate with a 100(1-  ) measure of reliability about the mean

Example 12.4 Cont’d

For the data of example 12.4, calculate the confidence interval and prediction interval corresponding to x* = 30

Page 13: Confidence Intervals vs. Prediction Intervals Confidence Intervals – provide an interval estimate with a 100(1-  ) measure of reliability about the mean

Table of Confidence Intervals

Page 14: Confidence Intervals vs. Prediction Intervals Confidence Intervals – provide an interval estimate with a 100(1-  ) measure of reliability about the mean

Table of Prediction Intervals

Page 15: Confidence Intervals vs. Prediction Intervals Confidence Intervals – provide an interval estimate with a 100(1-  ) measure of reliability about the mean

Scatter Plot with Confidence Intervals and Prediction Intervals