The lecture Addressing Issues with Regression Assumptions by David Spade, PhD is from the course Statistics Part 1. It contains the following chapters:
If the regression assumptions are not violated, what do we expect to see in a scatterplot of the residuals?
What is an acceptable way to handle the presence of subsets or multiple groups in our data set?
If a data point has an x-value that is far away from the average x-value, what property does it possess?
What is the best way to handle influential points?
What is a common transformation to make a left-skewed unimodal distribution more symmetric?
What is NOT a goal of transforming data?
What transformation is good for measurements that cannot be negative and for wide-ranging sets of data?
What transformation is good for changing the direction of relationships?
What transformation is not a data transformation technique?
What is NOT a caution for dealing with regression assumptions?
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