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Understanding KDE Plots: A Quiz

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1. What is the primary purpose of a Kernel Density Estimation (KDE) plot?

2. Which parameter in Kernel Density Estimation plays a critical role in determining the smoothness of the resulting curve?

3. How does Kernel Density Estimation differ from histogram-based density estimation?

4. When constructing a KDE plot, which of the following kernels is less likely to be used?

5. In the context of KDE, what does the term 'non-parametric' imply?

6. What is an essential consideration when choosing the bandwidth for a KDE plot?

7. Which of the following statements about KDE plots is true?

8. How can the selection of kernel function affect the KDE plot?

9. What advantage does KDE have over the standard histogram in visualizing data distributions?

10. In KDE, if the dataset contains outliers, how should the bandwidth be chosen?