Lectures

Linear Regression

by David Spade, PhD
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    About the Lecture

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

    • Linear Regression
    • The Y-Intercept
    • Making Predictions
    • The R-squared Quantity
    • After Regression

    Included Quiz Questions

    1. The line represented by the linear model goes through every data point.
    2. The linear model is the equation of a straight line that goes through our data.
    3. The linear model can be used to model the relationship between two quantitative variables.
    4. None of the above is correct.
    1. The slope of the regression line is equal to the correlation.
    2. Correlation can, in many cases, give insight into how well the linear model will fit our data.
    3. We must be careful in using correlation to describe how well the linear model will fit our data.
    4. Correlation tells us whether the slope of the regression line is positive or negative.
    1. For a one-unit increase in the value of X, we expect a decrease of 0.275 units in the value of Y.
    2. For a one-unit increase in the value of X, we expect an increase of 0.275 in the value of Y.
    3. For a one-unit increase in the value of X , we expect a 12.5 unit increase in the value of Y .
    4. If Y = 0, then X = 12.5.
    1. High values of R² mean that the changes in the value of X cause the changes in the value of Y.
    2. The R² quantity can be used to assess how well the line fits the data in many cases.
    3. The R² quantity provides the percentage of variation in the response variable that is explained by the regression on the explanatory variable.
    4. The R² quantity is calculated by squaring the correlation.
    1. A linear pattern indicates with no outliers and even spread indicates a lack of violations of the regression assumptions.
    2. The presence of outliers indicates a lack of violation of assumptions.
    3. Patterns in the residuals indicate a lack of violations of the regression assumptions.
    4. Places where the residuals are more spread out than in others indicate a lack of violations of the regression assumptions.

    Author of lecture Linear Regression

     David Spade, PhD

    David Spade, PhD


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