Sampling methods is one of the most important topics in Statistics which developed in recent years. Since, all traditional methods use equations that estimate the sampling distribution for a specific sample statistic when the data follow a particular distribution. Unfortunately, formulas for all combinations of sample statistics and data distributions do not exist! For example, there is no known sampling distribution for medians, which makes bootstrapping the perfect analyses for it. This paper aims to introduce the concept and Methodology of bootstrap methods in statistics and focuses on applications in regression analysis.These applications contrast two forms of bootstrap re-sampling in regression; these techniques require fewer assumptions and offer greater accuracy and insight than do standard methods in many problems.Inflation rate is one of the most important topics raised in the present time, specially after the crises faced in the world (i.e. COVID-19 pandemic, the war of Russian-Ukraine and the crises of food and Energy around world), so inflation rate was selected to apply the bootstrapping techniques using regression method and compare the results with the original sample data using R-Package.