Displaying and Describing Two-Variable Data: DUFS!
Alright, so far in Unit 1, we've been looking at data for *one* variable. Like, what's the average t
Correlation: Putting a Number on the Relationship!
Okay, so we've got our scatterplot, and we can describe it using DUFS. But what if we want to put an
Modeling Linear Relationships: The LSRL!
Alright, so we've established that we have a strong, positive, linear relationship. Excellent! Now,
Residuals: The Leftovers Tell All!
So, we've got our fancy LSRL, our 'best-fit' line. But how good is this line, really? Is it actually
Calculating LSRL, s, and r-squared: The Metrics of Fit!
Alright, so we've talked about the LSRL, and how it minimizes the sum of squared residuals. But how
Influential Points, Outliers, and Leverage: The Data Troublemakers!
Even the best models can get messed up by a few bad apples, right? In data, these 'bad apples' are u
Transformations to Achieve Linearity: Straightening Out the Curves!
What if you make a scatterplot and it's clearly, undeniably curved? What do you do? Throw up your ha