IQR kullanarak aykırı değerleri bulma

Aykırı değerler, ortalama gibi istatistikleri ve ortalamaya dayanan varyans ile standart sapma gibi ölçümleri ciddi şekilde etkileyebilir. Çeyrekler arası aralık, yani IQR, dağılımı ölçmenin aykırı değerlerden daha az etkilenen başka bir yoludur. IQR genellikle aykırı değerleri bulmak için de kullanılır. Bir değer \(\text{Q1} - 1.5 \times \text{IQR}\)'den küçük veya \(\text{Q3} + 1.5 \times \text{IQR}\)'den büyükse aykırı kabul edilir. Aslında matplotlib kutu grafiğinde bıyıkların uzunluğu bu şekilde hesaplanır.

Orta değer, çeyrekler ve aykırı değerleri gösteren bir kutu grafiği diyagramı

Bu egzersizde IQR'ı hesaplayacak ve bunu kullanarak bazı aykırı değerleri bulacaksın. pandas pd olarak ve numpy np olarak yüklendi ve food_consumption hazır.

Bu egzersiz

Python ile İstatistiğe Giriş

kursunun bir parçasıdır

Kursu Görüntüle

Uygulamalı interaktif egzersiz

Bu örnek kodu tamamlayarak bu egzersizi bitirin.

# Calculate total co2_emission per country: emissions_by_country
emissions_by_country = ____

print(emissions_by_country)

Kodu Düzenle ve Çalıştır

Bu egzersiz

Python ile İstatistiğe Giriş

kursunun bir parçasıdır

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Kursa Ücretsiz Başlayın

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: İstatistik nedir?Exercise 2: Betimsel ve çıkarımsal istatistik Exercise 3: Veri türü sınıflandırması Exercise 4: Merkez ölçüleri Exercise 5: Ortalama ve medyanı hesaplama Exercise 6: Ortalama, medyan ve dağılım şekli Exercise 7: Yayılım ölçüleri Exercise 8: Varyans ve standart sapma Exercise 9: Çeyrekler, kantiller ve beşlikler Exercise 10: IQR kullanarak aykırı değerleri bulma

Geçerli Egzersiz

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!