Week 12: Modeling the distribution of the spiny pocket mouse

Group project schedule

Lab outline

• Regression analysis

• Regression analysis in Excel

• Worksheet overview

• To-do for next week

Quick inferential statistics recap

“How likely is it that the differences between my results and those expected based on a hypothesis have been produced by chance alone?“

Regression analysis

• estimate the relationship between a continuous dependent variable and one or more continuous independent variables

Regression analysis is an inferential statistics method that traces the relationship between a continuous dependent variable (what you’re trying to predict) and one or more continuous independent variables (the predictors).

It quantifies this relationship through coefficients—numerical values that measure the strength and direction of these relationships. Each coefficient tells you the expected change in the dependent variable for a one-unit change in an independent variable, holding other variables constant.

R²- quantifying model accuracy

Two metrics in regression analysis that measure its predictive power are R² (R-squared) and the p-value. R² is a statistical measure that represents the proportion of variance for a dependent variable that’s explained by the independent variables in the model, essentially quantifying the model’s accuracy in explaining the observed outcomes. A higher R² indicates a model that closely fits your data.

Regression analysis in Excel

Live demo- detailed overview in the lab worksheet.

Worksheet Overview

To-do

• Upload Lab Worksheet and Excel spreadsheet to Blackboard by next lab (04/17)

• After today, should be able to start analyses!

• Weekly lab survey is your ticket out

• Speaker questions before the end of class on Monday