Lectures

Standardizing Data and the Normal Distribution Part 2

by David Spade, PhD
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    About the Lecture

    The lecture Standardizing Data and the Normal Distribution Part 2 by David Spade, PhD is from the course Statistics Part 1. It contains the following chapters:

    • Scatterplots and Correlation
    • Making a Scatterplot
    • Choosing X and Y
    • What is Correlation?
    • Finishing the Calculation
    • Correlation and Causation

    Included Quiz Questions

    1. We can determine the form of a relationship between two quantitative variables.
    2. We can determine the form of a relationship between two categorical variables.
    3. We can determine the correlation between two quantitative variables.
    4. We can determine the strength of a relationship between two categorical variables.
    1. This means that as the values of the X variable increase, the values of the Y variable increase.
    2. This means that as the value of the X variable increases, the value of the Y variable decreases.
    3. This means that as the value of the X variable decreases, the value of the Y variable increases.
    4. This means that the values of X and Y are all positive.
    1. Correlation measures the strength of a linear relationship between two categorical variables.
    2. Correlation measures the strength of a linear relationship between two quantitative variables.
    3. Correlation takes values between -1 and 1.
    4. Correlation indicates the direction of a linear relationship between two quantitative variables.
    1. Correlation does not have a unit of measure.
    2. Two quantitative variables with correlation 0.6 have a stronger linear relationship than two quantitative variables with correlation -0.6.
    3. If two quantitative variables are highly correlated, it can be concluded that changing the value of the explanatory variable causes the change in the response variable.
    4. Outliers have little effect on the correlation.
    1. Correlation is appropriate when measuring the strength of a relationship between two quantitative variables that appear to be linearly related and have no outliers present.
    2. Correlation is appropriate when measuring the strength of the relationship between two categorical variables.
    3. Correlation is appropriate when measuring the strength of a relationship between two quantitative variables that appear to be linearly related and have several outliers present.
    4. Correlation is appropriate for measuring the strength of the relationship between two quantitative variables when the relationship appears nonlinear.

    Author of lecture Standardizing Data and the Normal Distribution Part 2

     David Spade, PhD

    David Spade, PhD


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