I found an answer from reference.wolfram.com

Chapter **4 Fitting Data** to Linear **Models** by Least-Squares Techniques

**It** means that the **model** being **fit** is linear in the parameters to which we are **fitting**
... The **best**-known and most widely used method is least-squares regression,
which ... **it** to give a totally misleading **summary** of the relationship between y and
x . ... Then for each **data** point the **residual** is defined as the **difference** between
the ...

For more information, see Chapter **4 Fitting Data** to Linear **Models** by Least-Squares Techniques

I found an answer from www.britannica.com

Statistics - **Residual** analysis | Britannica

If the error term in the regression **model** satisfies the **four** assumptions noted ...
**These residuals**, computed from the available **data**, are treated as estimates. ... **it**
often suggests ways in which the **model** can be modified to obtain **better** results.
... how much or how many; qualitative variables **represent types** or categories.

For more information, see Statistics - **Residual** analysis | Britannica

I found an answer from stanford.edu

Visualizing regression **models** — seaborn 0.11.1 documentation

Many **datasets** contain multiple quantitative variables, and the goal of an analysis
is ... draw a **scatterplot** of two variables, x and y , and then **fit** the regression **model**
y ~ x ... **dataset** is the **same**, but the **plot** clearly shows that this is not a **good**
**model**: ... **It fits** and removes a simple linear regression and then **plots** the
**residual** ...

For more information, see Visualizing regression **models** — seaborn 0.11.1 documentation

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