Pregunta
When making predictions based on regression lines, which of the following is not listed as a consideration?
Choose the correct answer below.
A. Use the regression line for predictions only if the data go far beyond the scope of the available sample data.
B. If the regression equation does not appear to be useful for making predictions, the best predicted value of a variable is its point estimate.
C. Use the regression equation for predictions only if the linear correlation coefficient r indicates that there is a linear correlation between the two
variables.
D. Use the regression equation for predictions only if the graph of the regression line on the scatterplot confirms that the regression line fits the
points reasonably well.
Choose the correct answer below.
A. Use the regression line for predictions only if the data go far beyond the scope of the available sample data.
B. If the regression equation does not appear to be useful for making predictions, the best predicted value of a variable is its point estimate.
C. Use the regression equation for predictions only if the linear correlation coefficient r indicates that there is a linear correlation between the two
variables.
D. Use the regression equation for predictions only if the graph of the regression line on the scatterplot confirms that the regression line fits the
points reasonably well.
Ask by Henry Valdez. in the United States
Mar 24,2025
Solución de inteligencia artificial de Upstudy
Respuesta verificada por el tutor
Responder
A. Use the regression line for predictions only if the data go far beyond the scope of the available sample data.
Solución
To determine which option is not a consideration when making predictions based on regression lines, let’s analyze each option:
A. Use the regression line for predictions only if the data go far beyond the scope of the available sample data.
- This statement is incorrect because predictions should be made within the range of the data used to create the regression model. Extrapolating beyond the sample data can lead to unreliable predictions.
B. If the regression equation does not appear to be useful for making predictions, the best predicted value of a variable is its point estimate.
- This is a valid consideration. If the regression model is not useful, using the mean (point estimate) of the dependent variable is a reasonable approach.
C. Use the regression equation for predictions only if the linear correlation coefficient r indicates that there is a linear correlation between the two variables.
- This is a valid consideration. A strong correlation is necessary for the regression model to be useful for predictions.
D. Use the regression equation for predictions only if the graph of the regression line on the scatterplot confirms that the regression line fits the points reasonably well.
- This is also a valid consideration. A good fit is essential for making reliable predictions.
Based on this analysis, the correct answer is:
A. Use the regression line for predictions only if the data go far beyond the scope of the available sample data.
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Simplifique esta solución Beyond the Answer
A. Use the regression line for predictions only if the data go far beyond the scope of the available sample data. This is a crucial tip! Making predictions based on a regression line should ideally be done within the range of the data used to create it. Extending beyond this range, known as extrapolation, can lead to wildly inaccurate predictions because the relationship may not hold outside the observed data.
C. Use the regression equation for predictions only if the linear correlation coefficient r indicates that there is a linear correlation between the two variables. This is fundamental to ensure the reliability of your predictions! A strong correlation (close to -1 or 1) confirms that the linear relationship is robust, providing more confidence that the regression equation can be used for accurate predictions. If r is close to zero, it suggests a weak or no linear relationship, making predictions suspect.
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