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Addressing Issues with Regression Assumptions

by David Spade, PhD
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    About the Lecture

    The lecture Addressing Issues with Regression Assumptions by David Spade, PhD is from the course Statistics Part 1. It contains the following chapters:

    • Addressing Issues with Regression Assumptions
    • Outliers, Leverage and Influential Points
    • Lurking Variables and Causation
    • The Logarithmic Transformation

    Included Quiz Questions

    1. We expect to see random scatter about 0.
    2. We expect to see a curved pattern in the plot.
    3. We expect to see outliers in the plot.
    4. We expect to see parts of the plot where the spread is larger in some parts than it is in others.
    1. Performing one linear regression for each subgroup is the best way to handle the presence of multiple groups in our data.
    2. Performing one linear regression with all the data points is the best way to handle the presence of multiple groups in our data.
    3. Linear regression cannot be used to deal with this type of data.
    4. If there are multiple groups, there is no way to analyze the data set.
    1. This point is said to have high leverage.
    2. This point is said to have high value.
    3. This point is said to have low leverage.
    4. This point is said to have low value.
    1. The best way to handle influential points is to perform two separate regressions. One of these regressions should be with the influential point included, and the other should be performed without the influential point.
    2. The best way to handle influential points is to perform a single linear regression, but mention that there is an influential point present.
    3. The best way to handle influential points is to find another method besides linear regression to analyze the data.
    4. The best way to handle influential points is to discard them and perform the regression as though they never existed.
    1. Squaring the response values is used to make unimodal, left-skewed distributions more symmetric.
    2. The log transformation is used to make unimodal, left-skewed distributions more symmetric.
    3. The negative reciprocal of the response values is used to make unimodal, left-skewed distributions more symmetric.
    4. The square root transformation is used to make unimodal, left-skewed distributions more symmetric.

    Author of lecture Addressing Issues with Regression Assumptions

     David Spade, PhD

    David Spade, PhD


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