Small Sample Confidence Intervals in Log Space Back-Transformed From Normal Space

<p>The logarithmic transformation is commonly applied to a lognormal data set to improve symmetry homoscedasticity and linearity. Simple to implement and easy to understand the logarithm function transforms the original data to closely resemble a normal distribution. Analysis in the normal space provides point estimates and confidence intervals but transformation back to the original space using the naive approach yields confidence intervals of impractical width. The naive approach applies the exponential function e to the parameter of interest in normal space to obtain the corresponding parameter of interest in the original space. The naive approach offers results that are often inadequate for practical purposes. We present an alternative approach that provides improved results in the form of decreased interval width increased confidence level or both. Our alternative approach yields dramatically improved results at small sample sizes drawn from the right tail of the lognormal distribution.</p><p>This work has been selected by scholars as being culturally important and is part of the knowledge base of civilization as we know it. This work was reproduced from the original artifact and remains as true to the original work as possible. Therefore you will see the original copyright references library stamps (as most of these works have been housed in our most important libraries around the world) and other notations in the work.</p><p>This work is in the public domain in the United States of America and possibly other nations. Within the United States you may freely copy and distribute this work as no entity (individual or corporate) has a copyright on the body of the work.</p><p>As a reproduction of a historical artifact this work may contain missing or blurred pages poor pictures errant marks etc. Scholars believe and we concur that this work is important enough to be preserved reproduced and made generally available to the public. We appreciate your support of the preservation process and thank you for being an important part of keeping this knowledge alive and relevant.</p>