Introduction

[Audio] Hello in today's presentation I will be walking you through the results of my analysis on hot dog eating contest data. This dataset provides performance data from various contests capturing the number of hot dogs eaten by different participants over time. By applying statistical methods I sought to uncover insights that could answer key questions about performance trends and variations among contestants. Specifically my goal was to determine if any meaningful differences could be identified based on this dataset..

[Audio] The core question I aimed to explore was whether there was a statistically significant difference in the number of hot dogs eaten by participants across different years of contests. To investigate this I formulated two hypotheses: the null hypothesis which stated that there is no significant difference in hot dogs eaten over time and the alternative hypothesis which proposed that such a difference does indeed exist..

[Audio] To explore this research question I followed a structured process of data analysis. This included data wrangling to clean and filter the dataset ensuring that only relevant information was considered. I then conducted hypothesis testing using resampling methods which involved generating null distributions to simulate the expected outcomes under the null hypothesis. Visualization played a key role in this process and I used the R programming language along with the ggplot2 package to create graphs and plots that provided a clear visual representation of the data..

[Audio] The results of the analysis are presented here. As you can see from the graph on the slide this is a visualization of the null distribution with the observed statistic clearly marked. The observed statistic falls outside the bulk of the null distribution indicating that the results are statistically significant. Specifically the calculated p-value was below the common significance threshold of 0.05 allowing us to confidently reject the null hypothesis. This means that there is indeed a statistically significant difference in the number of hot dogs eaten by participants across different contest years..

[Audio] In conclusion the data analysis confirms that the number of hot dogs eaten by contestants has significantly changed over time. This could be due to several factors such as improved training techniques changes in competition strategy or even differences in the physical capabilities of contestants. The null hypothesis was rejected based on a p-value below 0.05 indicating that observed differences are not due to random chance. Performance improvements in the contest could be attributed to factors like enhanced training techniques strategy evolution or physical conditioning of participants Understanding these trends could provide valuable insights for future participants and researchers interested in the dynamics of competitive eating. It opens the door for further study on what factors might be driving these performance improvements..