Below are two examples demonstrating the format that I would prefer for your Biometry portfolio proposal. Please note that you will have four (not two) sections in your actual proposal.
  • Analysis #1 (One-Way ANOVA)
    • Data: The completed season batting averages and position group (outfielder, “heavy” infielder, and “light” infielder) for players from the 2017 Major League Baseball seasons. I gathered these data for all players that played more than 100 games in 2017 from information on MLB.com. I categorized the infielders into a “heavy” group (catcher, first baseman, and third baseman) and “light” group (shortstop and second baseman) based on my belief that the “light” positions are often on teams for their fielding skills and ability to get on base, whereas the “heavy” positions are often there for their home run hitting ability.
    • Question/Hypothesis: I hypothesize that the batting averages will be higher for the “light” infielders than the other two groups based on their ability to “get on base” and not strikeout.
    • Response Variable: The response variable for this analysis is the season batting average of each player. This variable is quantitative continuous.
    • Explanatory Variale: The explanatory variable for this analysis is the position group (outfielder, “light” infielder, “heavy” infielder). This variable is categorical nominal.
    • Topic: I will use a one-way ANOVA for this analysis because the response variable is quantitative, but the explanatory variable is categorical.


  • Analysis #2 (Simple Linear Regression)
    • Data: The weight (grams) and fork length (mm) of Slimy Sculpin (Cottus cognatus) from Toolik Lake, Alaska. I gathered these data from the Arctic Long-Term Ecological Research (LTER) website (http://arc-lter.ecosystems.mbl.edu/arclterlakesfish1986-2009). I will restrict my analysis to just fish captured in 1987.
    • Question/Hypothesis: I wish to determine if the weight of a Slimy Sculpin can be adequately predicted from the fork length. This will be useful as it is much easier to measure the length of a fish than it is to weigh it, especially in the Arctic.
    • Response Variable: The response variable for this analysis is the weight of the Slimy Sculpin. This variable is quantitative continuous.
    • Explanatory Variale: The explanatory variable for this analysis is the fork length of the Slimy Sculpin. This variable is quantitative continuous.
    • Topic: I will use a simple linear regression for this analysis as both the response and explanatory variables are quantitative.