Menemukan pencilan menggunakan IQR

Pencilan dapat sangat memengaruhi statistik seperti mean, serta statistik yang bergantung pada mean seperti varians dan simpangan baku. Rentang antar kuartil, atau IQR, adalah cara lain untuk mengukur sebaran yang kurang dipengaruhi oleh pencilan. IQR juga sering digunakan untuk menemukan pencilan. Jika suatu nilai kurang dari \(\text{Q1} - 1.5 \times \text{IQR}\) atau lebih besar dari \(\text{Q3} + 1.5 \times \text{IQR}\), nilai tersebut dianggap pencilan. Faktanya, inilah cara panjang whisker pada box plot matplotlib dihitung.

Diagram box plot yang menunjukkan median, kuartil, dan pencilan

Dalam latihan ini, Anda akan menghitung IQR dan menggunakannya untuk menemukan beberapa pencilan. pandas sebagai pd dan numpy sebagai np sudah dimuat dan food_consumption tersedia.

Latihan ini adalah bagian dari kursus

Pengantar Statistika di Python

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

# Calculate total co2_emission per country: emissions_by_country
emissions_by_country = ____

print(emissions_by_country)

Edit dan Jalankan Kode

Latihan ini adalah bagian dari kursus

Pengantar Statistika di Python

SkillTag.level.intermediateSkillTag.label

4.8+

Mulai Kursus 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: Apa itu statistika?Exercise 2: Statistika deskriptif dan inferensial Exercise 3: Klasifikasi tipe data Exercise 4: Ukuran pemusatan Exercise 5: Menghitung mean dan median Exercise 6: Rata-rata, median, dan bentuk distribusi Exercise 7: Ukuran penyebaran Exercise 8: Varians dan simpangan baku Exercise 9: Kuartil, kuantil, dan kuintil Exercise 10: Menemukan pencilan menggunakan IQR

Latihan Saat Ini

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!