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IntroductionWe can use regression to find
relationships between random variables
This does not necessarily imply causation
Correlation can be used to measure predictability
Regression with Constant VarianceLinear Regression: E(Y|
X=x)=+xIn general, variance is function of
xIf we assume the variance is a
constant, then the analysis is simplified
Define total absolute error as the sum of the squares of the errors
Linear Regression
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02
02
Variance in Regression Analysis
Relevant variance is conditional: Var(Y|X=x)
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Confidence IntervalsRegression coefficients are t-
distributed with n-2 dofStatistic below is thus t-
distributed with n-2 dof
And the confidence interval is
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ExampleExample 8.1Data for compressive strength (q)
of stiff clay as a function of “blow counts” (N)
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Correlation Estimate
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Regression with Non-Constant VarianceNow relax
assumption of constant variance
Assume regions with large conditional variance weighted less
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Example (8.2)Data for maximum settlement (x)
of storage tanks and maximum differential settlement (y)
From looking at data, assume g(x)=x (that is, standard deviation of y increases linearly with x
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