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Regression Plots (lmplot, regplot)

Why regression plots?

Regression plots help you see:

  • Trend direction
  • Strength of relationship (visual)
  • Outliers that affect the trend

regplotregplot (axes-level)

regplot
import seaborn as sns
import matplotlib.pyplot as plt
 
tips = sns.load_dataset("tips")
 
plt.figure(figsize=(7, 4))
sns.regplot(data=tips, x="total_bill", y="tip")
plt.title("Tip vs total bill")
plt.tight_layout()
plt.show()
regplot
import seaborn as sns
import matplotlib.pyplot as plt
 
tips = sns.load_dataset("tips")
 
plt.figure(figsize=(7, 4))
sns.regplot(data=tips, x="total_bill", y="tip")
plt.title("Tip vs total bill")
plt.tight_layout()
plt.show()

lmplotlmplot (figure-level, great for facets)

lmplot
import seaborn as sns
 
tips = sns.load_dataset("tips")
 
sns.lmplot(data=tips, x="total_bill", y="tip", hue="sex")
lmplot
import seaborn as sns
 
tips = sns.load_dataset("tips")
 
sns.lmplot(data=tips, x="total_bill", y="tip", hue="sex")

Non-linear patterns

You can try polynomial fits.

Polynomial fit
import seaborn as sns
import matplotlib.pyplot as plt
 
tips = sns.load_dataset("tips")
 
plt.figure(figsize=(7, 4))
sns.regplot(data=tips, x="total_bill", y="tip", order=2)
plt.title("Quadratic trend")
plt.tight_layout()
plt.show()
Polynomial fit
import seaborn as sns
import matplotlib.pyplot as plt
 
tips = sns.load_dataset("tips")
 
plt.figure(figsize=(7, 4))
sns.regplot(data=tips, x="total_bill", y="tip", order=2)
plt.title("Quadratic trend")
plt.tight_layout()
plt.show()

Tips

  • Regression plots show association, not causation.
  • Outliers can heavily affect the fitted line.
  • Use transformations (log) if relationships are multiplicative.

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