Confidence Interval Estimating the Mean of Normal Distribution and Skewed Distribution
DOI:
https://doi.org/10.11113/mjfas.v20n5.3435Keywords:
Confidence intervals, bootstrapping, bootstrap-t, parameter estimations.Abstract
The confidence interval is an important statistical estimator of population location and dispersion parameters. The purpose of this paper is to comprehend CI utilising various techniques. This includes classical CI, percentile bootstrap method, bootstrap-t and proposed bootstrap-t decile mean method. Distributions that are skewed and normal are used to generate data. The efficiency of the proposed method is evaluated on the basis of an extensive simulation study. The simulation findings show that the performance of the Student-t and three bootstrap approaches varies dramatically depending on sample size and skewness type. The coverage probability and length of the proposed confidence interval are compared with certain existing and widely used confidence intervals. For illustrative purposes, two real-life data sets are analysed, which, to some extent, support the simulation study conclusions. This paper's findings will be useful to a variety of researchers with practical experience in the fields of science and social sciences.
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