Title: Introduction to P-value
Today’s class was all about important things like p-values and heteroskedasticity. P-values help us figure out how important our findings are in statistics, while heteroskedasticity is a problem when the spread of our data isn’t consistent in regression analysis.
To spot heteroskedasticity, we square the leftover errors (residuals). This makes it easier to see if the spread of errors changes as we look at different parts of our data. We do this because spotting these changes is vital, and squared residuals help us do that.
When we find heteroskedasticity, we have tools like weighted least squares to fix it. These tools use squared residuals to help make our regression analysis more accurate.