Project Strength

Preregistration of analyses

Analysis #1

Research Question: Does the use of equipment (wraps vs. no wraps) have an impact on lifting performance in powerlifting competitions?

Hypothesis: Lifters who use wraps will generally be able to lift more weight due to increased stability and support, but this may also depend on the individual’s gender and chosen event.

Data Source: https://www.openpowerlifting.org/ (collected by downloading the csv file from (https://www.kaggle.com/datasets/open-powerlifting/powerlifting-database))

Analysis Plan:

  • data cleaning: we have already completed this first step using the select, filter, and na.omit functions to create a clean data set. we also loaded in needed packages (tidyverse & ggplot)

  • explore descriptive analysis: we will compute descriptive statistics to summarize the data, including mean and standard deviation of total weight lifted by those with and without wraps.

  • visual analysis: in addition we will provide visual models showing patterns and/or relationships between total wight lifted and whether or not wraps were used. will also show visual models of data to explore variables.

    1. scatter plot of Age vs. TotalKg with color differentiation for using wraps or raw
    2. histogram of TotalKg for wraps and no wraps.

  • inferential analysis: next we will generate a 95% confidence interval to determine if there is significant difference in total weight lifted between those who use wraps versus those that don’t.

  • final report: lastly, the results will be reported including the methods used, the results of the analysis, our conclusion, and any limitations found.

Analysis #2

Research Question: How does age affect total weight lifted in powerlifting competitions?

Hypothesis: Younger lifters will generally lift more weight than older lifters due to better physical condition, body weight, and lower risk of injury.

Data Source: https://www.openpowerlifting.org/ (collected by downloading the csv file from (https://www.kaggle.com/datasets/open-powerlifting/powerlifting-database))

Analysis Plan:

  • data cleaning: we have already completed this first step using the select, filter, and na.omit functions to create a clean data set. we also loaded in needed packages (tidyverse & ggplot)

  • explore descriptive analysis: we will compute descriptive statistics to summarize the data, including mean and standard deviation of total weight lifted by age. will also use skim() to explore data set.

  • visual analysis: in addition we will provide visual models showing patterns and/or relationships between total wight lifted and age. will also show visual models of data to explore variables.

    1. scatter plot of Age vs TotalKg, with regression line
    2. box plot of TotalKg by Age group

  • inferential analysis: next we will generate a 95% confidence interval to determine if there is significant difference in total weight lifted between younger and older powerlifters.

  • final report: lastly, the results will be reported including the methods used, the results of the analysis, our conclusion, and any limitations found.

Analysis #3

Research Question: Does the type of federation (lifting organization) have an impact on lifting performance in powerlifting competitions?

Hypothesis: Different lifting federations may have different rules, standards, and competition formats that could potentially impact lifting performance, but this may also depend on the individual lifter’s familiarity and comfort with the particular federation.

Data Source: https://www.openpowerlifting.org/ (collected by downloading the csv file from (https://www.kaggle.com/datasets/open-powerlifting/powerlifting-database))

Analysis Plan:

  • data cleaning: we have already completed this first step using the select, filter, and na.omit functions to create a clean data set. we also loaded in needed packages (tidyverse & ggplot)

  • explore descriptive analysis: we will compute descriptive statistics to summarize the data, including mean and standard deviation of total weight lifted by federation. We will also use skim() to explore data set.

  • visual analysis: in addition we will provide visual models showing patterns and/or relationships between total wight lifted and federation. will also show visual models of data to explore variables.

    1. scatter plot of Age vs TotalKg colored by Federation
    2. scatter plot of BodyweightKg vs TotalKg colored by Federation.

  • inferential analysis: next we will conduct an ANOVA test to determine whether a significant difference in total weight lifted between federations exists.

  • final report: lastly, the results will be reported including the methods used, the results of the analysis, our conclusion, and any limitations found.