Practice Dealing with Outliers - 2.6 | 2. Data Wrangling and Feature Engineering | Data Science Advance
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Dealing with Outliers

2.6 - Dealing with Outliers

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Learning

Practice Questions

Test your understanding with targeted questions

Question 1 Easy

What is an outlier?

💡 Hint: Think about values that stand out in a dataset.

Question 2 Easy

Name one method for detecting outliers.

💡 Hint: Consider visual methods.

4 more questions available

Interactive Quizzes

Quick quizzes to reinforce your learning

Question 1

What is a possible sign of an outlier in a box plot?

A dot outside the whiskers
A large number
An exact mean

💡 Hint: Visualize a box plot.

Question 2

True or False: Using Z-scores requires knowledge of the mean and standard deviation.

True
False

💡 Hint: Remember what Z-scores quantify.

2 more questions available

Challenge Problems

Push your limits with advanced challenges

Challenge 1 Hard

Given a dataset of house prices with values ranging from $150,000 to $1,500,000, how would you detect and treat outliers? Discuss your approach.

💡 Hint: Consider both visual and statistical methods for identifying outliers.

Challenge 2 Hard

You are working with a dataset that has performance metrics for a sales team. If one representative has far higher sales than the others, how would you address this? Discuss the analysis and treatment options.

💡 Hint: Think about the implications of removing or treating an outlier in the analysis.

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Reference links

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