Uitschieters vinden met de IQR

Uitschieters kunnen veel invloed hebben op statistieken zoals het gemiddelde, en ook op statistieken die op het gemiddelde steunen, zoals variantie en standaarddeviatie. De interkwartielafstand, of IQR, is een andere manier om spreiding te meten die minder gevoelig is voor uitschieters. IQR wordt ook vaak gebruikt om uitschieters te vinden. Als een waarde kleiner is dan \(\text{Q1} - 1.5 \times \text{IQR}\) of groter dan \(\text{Q3} + 1.5 \times \text{IQR}\), geldt die als uitschieter. Dit is zelfs hoe de lengte van de snorharen in een matplotlib-boxplot wordt bepaald.

Diagram van een boxplot met mediaan, kwartielen en uitschieters

In deze oefening bereken je de IQR en gebruik je die om enkele uitschieters te vinden. pandas als pd en numpy als np zijn geladen en food_consumption is beschikbaar.

Deze oefening maakt deel uit van de cursus

Inleiding tot statistiek in Python

Cursus bekijken

Praktische interactieve oefening

Probeer deze oefening eens door deze voorbeeldcode in te vullen.

# Calculate total co2_emission per country: emissions_by_country
emissions_by_country = ____

print(emissions_by_country)

Code bewerken en uitvoeren

Deze oefening maakt deel uit van de cursus

Inleiding tot statistiek in Python

SkillTag.level.intermediateSkillTag.label

4.8+

Begin de cursus gratis

Summary statistics gives you the tools you need to boil down massive datasets to reveal the highlights. In this chapter, you'll explore summary statistics including mean, median, and standard deviation, and learn how to accurately interpret them. You'll also develop your critical thinking skills, allowing you to choose the best summary statistics for your data.

Exercise 1: Wat is statistiek?Exercise 2: Beschrijvende en inferentiële statistiek Exercise 3: Classificatie van gegevenstypen Exercise 4: Maten van centrale neiging Exercise 5: Gemiddelde en mediaan berekenen Exercise 6: Gemiddelde, mediaan en de vorm van de verdeling Exercise 7: Spreidingsmaten Exercise 8: Variantie en standaarddeviatie Exercise 9: Kwartielen, kwantielen en kwintielen Exercise 10: Uitschieters vinden met de IQR

Huidige oefening

In this chapter, you'll learn how to generate random samples and measure chance using probability. You'll work with real-world sales data to calculate the probability of a salesperson being successful. Finally, you’ll use the binomial distribution to model events with binary outcomes.

Exercise 1: What are the chances?Exercise 2: With or without replacement?Exercise 3: Calculating probabilities Exercise 4: Sampling deals Exercise 5: Discrete distributions Exercise 6: Creating a probability distribution Exercise 7: Identifying distributions Exercise 8: Expected value vs. sample mean Exercise 9: Continuous distributions Exercise 10: Which distribution?Exercise 11: Data back-ups Exercise 12: Simulating wait times Exercise 13: The binomial distribution Exercise 14: Simulating sales deals Exercise 15: Calculating binomial probabilities Exercise 16: How many sales will be won?

It’s time to explore one of the most important probability distributions in statistics, normal distribution. You’ll create histograms to plot normal distributions and gain an understanding of the central limit theorem, before expanding your knowledge of statistical functions by adding the Poisson, exponential, and t-distributions to your repertoire.

Exercise 1: The normal distribution Exercise 2: Distribution of Amir's sales Exercise 3: Probabilities from the normal distribution Exercise 4: Simulating sales under new market conditions Exercise 5: Which market is better?Exercise 6: The central limit theorem Exercise 7: Visualizing sampling distributions Exercise 8: The CLT in action Exercise 9: The mean of means Exercise 10: The Poisson distribution Exercise 11: Identifying lambda Exercise 12: Tracking lead responses Exercise 13: More probability distributions Exercise 14: Distribution dragging and dropping Exercise 15: Modeling time between leads Exercise 16: The t-distribution

In this chapter, you'll learn how to quantify the strength of a linear relationship between two variables, and explore how confounding variables can affect the relationship between two other variables. You'll also see how a study’s design can influence its results, change how the data should be analyzed, and potentially affect the reliability of your conclusions.

Exercise 1: Correlation Exercise 2: Guess the correlation Exercise 3: Relationships between variables Exercise 4: Correlation caveats Exercise 5: What can't correlation measure?Exercise 6: Transforming variables Exercise 7: Does sugar improve happiness?Exercise 8: Confounders Exercise 9: Design of experiments Exercise 10: Study types Exercise 11: Longitudinal vs. cross-sectional studies Exercise 12: Congratulations!