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Linear Maximum Likelihood Regression Analysis for Untransformed Log Normally Distributed Data

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Figure

Figure 1. A linear regression where Ytransformation stabilizes the variance but distort the linear relation (middle) and the estimation based on log(e|X follows a log-normal distribution
Table 1. The expected value (Epected value and standard errors for the estimate of [–]), standard errors (SE[–], E[se(–)]) and skewness (γ[–]) for the estimates of β, and the ex- σZa
Figure 2. The SEμˆ X=x0 and ˆEse μX=x0 for
Table 4. The power for tests with alternative h pothesyis H1:β  > 0 whe risk of type I ert a1re theror is se  to 0.05
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