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Bootstrap Methods in Statistical Analysis
Statistics is a widely popular field when it comes to organizations attempting to answer business questions and forecast performance in the near or long-term future. In order for this to be accomplished, data must be collected and utilized. Data is not always plentiful and also may be very complex to analyze. This is where the bootstrap method can be used as an alternative to estimating population parameters of interest when conventional methods would have difficulty in doing so. This thesis will cover bootstrap methods for the general case as well as extend its application to hypothesis testing and simple linear regression. Alternative methods of bootstrapping along with strengths and weaknesses will be covered briefly as well.