The Experts below are selected from a list of 39666 Experts worldwide ranked by ideXlab platform

Simon Harrisson - One of the best experts on this subject based on the ideXlab platform.

Rose Gaines Das - One of the best experts on this subject based on the ideXlab platform.

  • comments on the interpretation of data from log normal distributions and definition of geometric standard deviation
    Drug Development and Industrial Pharmacy, 1992
    Co-Authors: Rose Gaines Das
    Abstract:

    AbstractEstimates of relative potency (commonly obtained as antilogs of log relative potencies) and the distribution of many pharmaceutical Measurements are positively skewed and appear to follow a log normal distribution. The geometric mean arises naturally as a Measure of location in these circumstances. There is not, however, a Measure of Dispersion which corresponds to the geometric mean and conflicting definitions have been used for the term geometric standard deviation. Examples of data for which the use of geometric means may be appropriate are given, together with comments on inferences which may be drawn regarding the population from which the data arise. It is suggested that the term geometric standard deviation leads to confusion and inappropriate inferences, and that it should therefore be avoided.

Laurent Licata - One of the best experts on this subject based on the ideXlab platform.

  • detecting outliers do not use standard deviation around the mean use absolute deviation around the median
    Journal of Experimental Social Psychology, 2013
    Co-Authors: Christophe Leys, Christophe Ley, Olivier Klein, Philippe Bernard, Laurent Licata
    Abstract:

    A survey revealed that researchers still seem to encounter difficulties to cope with outliers. Detecting outliers by determining an interval spanning over the mean plus/minus three standard deviations remains a common practice. However, since both the mean and the standard deviation are particularly sensitive to outliers, this method is problematic. We highlight the disadvantages of this method and present the median absolute deviation, an alternative and more robust Measure of Dispersion that is easy to implement. We also explain the procedures for calculating this indicator in SPSS and R software.

Kalpesh S Tailor - One of the best experts on this subject based on the ideXlab platform.

  • sample standard deviation s chart under the assumption of moderateness and its performance analysis
    International Journal of Research, 2017
    Co-Authors: Kalpesh S Tailor
    Abstract:

    Moderate distribution proposed by Naik V.D and Desai J.M., is a sound alternative of normal distribution, which has mean and mean deviation as pivotal parameters and which has properties similar to normal distribution. Mean deviation (δ) is a very good alternative of standard deviation (σ) as mean deviation is considered to be the most intuitively and rationally defined Measure of Dispersion. This fact can be very useful in the field of quality control to construct the control limits of the control charts. On the basis of this fact Naik V.D. and Tailor K.S. have proposed 3δ control limits. In 3δ control limits, the upper and lower control limits are set at 3δ distance from the central line where δ is the mean deviation of sampling distribution of the statistic being used for constructing the control chart. In this paper assuming that the underlying distribution of the variable of interest follows moderate distribution proposed by Naik V.D and Desai J.M, 3δ control limits of sample standard deviation(s) chart are derived. Also the performance analysis of the control chart is carried out with the help of OC curve analysis and ARL curve analysis.

Mark J. Wierman - One of the best experts on this subject based on the ideXlab platform.

  • consensus and dissention a Measure of ordinal Dispersion
    North American Fuzzy Information Processing Society, 2007
    Co-Authors: William J Tastle, Mark J. Wierman
    Abstract:

    A new Measure of Dispersion is introduced as a representation of consensus (agreement) and dissention (disagreement). Building on the generally accepted Shannon entropy, this Measure utilizes a probability distribution and the distance between categories to produce a value spanning the unit interval. The Measure is applied to the Likert scale (or any ordinal scale) to determine degrees of consensus or agreement. Using this Measure, data on ordinal scales can be given a value of Dispersion that is both logically and theoretically sound.

  • an information theoretic Measure for the evaluation of ordinal scale data
    Behavior Research Methods, 2006
    Co-Authors: William J Tastle, Mark J. Wierman
    Abstract:

    This article describes a new Measure of Dispersion as an indication of consensus and dissention. Building on the generally accepted Shannon entropy, this Measure utilizes a probability distribution and the ordered ranking of categories in an ordinal scale distribution to yield a value confined to the unit interval. Unlike other Measures that need to be normalized, this Measure is always in the interval 0 to 1. The Measure is typically applied to the Likert scale to determine degrees of agreement among ordinal-ranked categories when one is dealing with data collection and analysis, although other scales are possible. Using this Measure, investigators can easily determine the proximity of ordinal data to consensus (agreement) or dissention. Consensus and dissention are defined relative to the degree of proximity of values constituting a frequency distribution on the ordinal scale Measure. The authors identify a set of criteria that a Measure must satisfy in order to be an acceptable indicator of consensus and show how the consensus Measure satisfies all the criteria.