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Questions and Answers
Residual plots are primarily used to assess the fit of a linear regression model.
Residual plots are primarily used to assess the fit of a linear regression model.
True
A random pattern in a residual plot suggests a poor fit for the model.
A random pattern in a residual plot suggests a poor fit for the model.
False
Residuals close to zero indicate a better fit between observed and predicted values.
Residuals close to zero indicate a better fit between observed and predicted values.
True
A curved pattern in a residual plot may indicate that a non-linear model would fit the data better.
A curved pattern in a residual plot may indicate that a non-linear model would fit the data better.
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When interpreting a residual plot, the x-axis shows the fitted values, and the y-axis shows residuals.
When interpreting a residual plot, the x-axis shows the fitted values, and the y-axis shows residuals.
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Stem-and-leaf plots are useful for displaying small datasets in a simple, compact form.
Stem-and-leaf plots are useful for displaying small datasets in a simple, compact form.
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The “stem” in a stem-and-leaf plot represents the significant digits of data values.
The “stem” in a stem-and-leaf plot represents the significant digits of data values.
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Stem-and-leaf plots are ideal for large datasets.
Stem-and-leaf plots are ideal for large datasets.
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A stem-and-leaf plot provides a similar view of data distribution as a histogram but in a textual format.
A stem-and-leaf plot provides a similar view of data distribution as a histogram but in a textual format.
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To interpret a stem-and-leaf plot, examine the shape of the data distribution along the “leaf” side
To interpret a stem-and-leaf plot, examine the shape of the data distribution along the “leaf” side
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