AGSA R Workshop - Linear Regression and Plateau

Check out Dr. Bastos website!
agronomy.netlify.app/
In this workshop, Dr. Bastos covered:
• Linear, quadratic, and linear-plateau regression
• Regression analysis workflow including
- data import
- data wrangling
- exploratory data analysis
- formal statistical analysis
- model assumption checking with residual diagnostics
- model summary
- final plot
• Difference between ANOVA and regression
• Model selection
• Explore some cool applications of regression like
- finding an optimum input rate that maximizes yield
- prediction
Please follow us: linktr.ee/agsaksu

[@rishikulshrestha3128]

I have done a regression analysis on a dataset, but due to the way the data is, one of the variable is getting a negative coefficient in the final equation. Eg. Y = aX1 - bX2. But I know for sure that the variable X2 has a positive effect on Y. So, my question is: What to do when your regression or correlation analysis gives you a coefficient which is either of opposite sign or of low magnitude but you theoretically know that for sure the variable has a positive and a strong influence.please help