When we use a least-squares line to predict y values for x values beyond the range of x values found in the data, are we extrapolating or interpolating? Are there any concerns about such predictions?
When we use a least-squares line to predict y values for x values beyond the range of x values found in the data, are we extrapolating or interpolating? Are there any concerns about such predictions?
Glencoe Algebra 1, Student Edition, 9780079039897, 0079039898, 2018
18th Edition
ISBN:9780079039897
Author:Carter
Publisher:Carter
Chapter4: Equations Of Linear Functions
Section: Chapter Questions
Problem 4SGR
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![**Understanding Extrapolation and Interpolation in Least-Squares Regression**
When we use a least-squares line to predict \( y \) values for \( x \) values beyond the range of \( x \) values found in the data, are we extrapolating or interpolating? Are there any concerns about such predictions?
*Explanation:*
- **Extrapolation:** This occurs when we use a model to predict \( y \) values for \( x \) values that are outside the range of the data we originally used to create the model.
- **Interpolation:** This refers to using the model to predict \( y \) values for \( x \) values that fall within the range of the original data.
**Concerns about Extrapolation:**
- Extrapolation can be problematic as it assumes that the pattern observed in the data continues beyond the observed range, which might not always be the case.
- Predicting outside the data range can lead to large errors if the true relationship deviates from the pattern detected within the original data range.
**Concerns about Interpolation:**
- Interpolation is generally more reliable as it operates within the range of the data used to fit the model, hence it is less prone to large prediction errors.](/v2/_next/image?url=https%3A%2F%2Fcontent.bartleby.com%2Fqna-images%2Fquestion%2Ff6805edd-9acc-4ccd-9c7a-22df1558ea0b%2F21d8e4ea-46c9-4eb4-9732-40fe17b7e9bd%2F9v1g5wj_processed.jpeg&w=3840&q=75)
Transcribed Image Text:**Understanding Extrapolation and Interpolation in Least-Squares Regression**
When we use a least-squares line to predict \( y \) values for \( x \) values beyond the range of \( x \) values found in the data, are we extrapolating or interpolating? Are there any concerns about such predictions?
*Explanation:*
- **Extrapolation:** This occurs when we use a model to predict \( y \) values for \( x \) values that are outside the range of the data we originally used to create the model.
- **Interpolation:** This refers to using the model to predict \( y \) values for \( x \) values that fall within the range of the original data.
**Concerns about Extrapolation:**
- Extrapolation can be problematic as it assumes that the pattern observed in the data continues beyond the observed range, which might not always be the case.
- Predicting outside the data range can lead to large errors if the true relationship deviates from the pattern detected within the original data range.
**Concerns about Interpolation:**
- Interpolation is generally more reliable as it operates within the range of the data used to fit the model, hence it is less prone to large prediction errors.
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