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Most of the times, we use datasets that contain multiple quantitative variables, and the goal of an analysis is often to relate those variables to each other. This can be done through the regression lines.

While building the regression models, we often check for **multicollinearity,** where we had to see the correlation between all the combinations of continuous variables and will take necessary action to remove multicollinearity if exists. In such cases, the following techniques helps.

There are two main functions in Seaborn to visualize a linear relationship determined through regression. These functions are **regplot()** and **lmplot()**.

regplot | lmplot |
---|---|

accepts the x and y variables in a variety of formats including simple numpy arrays, pandas Series objects, or as references to variables in a pandas DataFrame | has data as a required parameter and the x and y variables must be specified as strings. This data format is called “long-form” data |

Let us now draw the plots.

Plotting the regplot and then lmplot with the same data in this example

import pandas as pd import seaborn as sb from matplotlib import pyplot as plt df = sb.load_dataset('tips') sb.regplot(x = "total_bill", y = "tip", data = df) sb.lmplot(x = "total_bill", y = "tip", data = df) plt.show()

You can see the difference in the size between two plots.

We can also fit a linear regression when one of the variables takes discrete values

import pandas as pd import seaborn as sb from matplotlib import pyplot as plt df = sb.load_dataset('tips') sb.lmplot(x = "size", y = "tip", data = df) plt.show()

The simple linear regression model used above is very simple to fit, but in most of the cases, the data is non-linear and the above methods cannot generalize the regression line.

Let us use Anscombe’s dataset with the regression plots −

import pandas as pd import seaborn as sb from matplotlib import pyplot as plt df = sb.load_dataset('anscombe') sb.lmplot(x="x", y="y", data=df.query("dataset == 'I'")) plt.show()

In this case, the data is good fit for linear regression model with less variance.

Let us see another example where the data takes high deviation which shows the line of best fit is not good.

import pandas as pd import seaborn as sb from matplotlib import pyplot as plt df = sb.load_dataset('anscombe') sb.lmplot(x = "x", y = "y", data = df.query("dataset == 'II'")) plt.show()

The plot shows the high deviation of data points from the regression line. Such non-linear, higher order can be visualized using the **lmplot()** and **regplot()**.These can fit a polynomial regression model to explore simple kinds of nonlinear trends in the dataset −

import pandas as pd import seaborn as sb from matplotlib import pyplot as plt df = sb.load_dataset('anscombe') sb.lmplot(x = "x", y = "y", data = df.query("dataset == 'II'"),order = 2) plt.show()

Video

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