chapter 14 inference for regression
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Chapter 14 Inference for Regression. AP Statistics 14.1 – Inference about the Model 14.2 – Predictions and Conditions. Two Quantitative Variables. Plot and Interpret Explanatory Variable and Response Variable FSDD Numerical Summary Correlation (r) – describes strength and direction - PowerPoint PPT PresentationTRANSCRIPT
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Chapter 14Inference for Regression
AP Statistics14.1 – Inference about the Model14.2 – Predictions and Conditions
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Two Quantitative Variables
• Plot and Interpret– Explanatory Variable and Response Variable– FSDD
• Numerical Summary– Correlation (r) – describes strength and direction
• Mathematical Model– LSRL for predicting bxay ˆ
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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
• Parameters of Interest: • The standard deviation of y (call it ) is
the same for all values of x. The value of is unknown t-procedures!
• Degrees of Freedom: n – 2
xy
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Conditions for Regression Inference (Cont’d)
• Look at residuals: • residual = Actual – Predicted
• The true relationship is linear• Response varies Normally about the True
regression line• To estimate , use standard error about
the line (s)
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Inference
• Unknown parameters: • a and b are unbiased estimators of the
least squares regression line for the true intercept and slope , respectively
• There are n residuals, one for each data point. The residuals from a LSRL always have mean zero. This simplifies their standard error.
,,
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Standard Error about the Line• Two variables gives: n – 2 df (not n – 1)
• Call the sample standard deviation (s) a standard error to emphasize that it is estimated from data
• Calculator will calculate s! Thank you TI!
2
21 residualn
s
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t-procedures (n - 2 df)
• CI’s for the regression slopestandard error of
the LSRL slope b is:
• Testing hypothesis of No linear relationship
• x does not predict y r = 0
bSEtb *
2)( xx
sSEb
0:0 HbSEbtstatistict :
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• What is the equation of the LSRL?• Estimate the parameters• In your opinion, is the LSRL an appropriate
model for the data? Would you be willing to predict a students height, if you knew that his arm span is 76 inches?
and
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•Construct a 95% CI for mean increase in IQ for each additional peak in crying
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Scatter Plot and LSRL?Perform a Test of Significance
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Checking the Regression Conditions
• All observations are Independent• There is a true LINEAR relationship• The Standard Deviation of the response
variable (y) about the true line is the Same everywhere
• The response (y) varies Normally about the true regression line
* Verifying Conditions uses the Residuals!