# Summary: Modeling With Linear Functions

### Key Concepts

- Scatter plots show the relationship between two sets of data.
- Scatter plots may represent linear or non-linear models.
- The line of best fit may be estimated or calculated, using a calculator or statistical software.
- Interpolation can be used to predict values inside the domain and range of the data, whereas extrapolation can be used to predict values outside the domain and range of the data.
- The correlation coefficient,
*r*, indicates the degree of linear relationship between data. - A regression line best fits the data.
- The least squares regression line is found by minimizing the squares of the distances of points from a line passing through the data and may be used to make predictions regarding either of the variables.

### Glossary

**correlation coefficient**- a value,
*r*, between –1 and 1 that indicates the degree of linear correlation of variables, or how closely a regression line fits a data set.

**extrapolation**- predicting a value outside the domain and range of the data

**interpolation**- predicting a value inside the domain and range of the data

**least squares regression**- a statistical technique for fitting a line to data in a way that minimizes the differences between the line and data values

**model breakdown**- when a model no longer applies after a certain point

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- Revision and Adaptation.
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- College Algebra.
**Provided by:**OpenStax**Authored by:**Abramson, Jay et al..**Located at:**https://openstax.org/books/college-algebra/pages/1-introduction-to-prerequisites.**License:**CC BY: Attribution.**License terms:**Download for free at http://cnx.org/contents/[email protected].