Quartile, Quantile und Quintile

Quantile sind eine gute Möglichkeit, numerische Daten zusammenzufassen, da sie zur Messung der zentralen Tendenz und der Streuung verwendet werden können und ein Gefühl dafür vermitteln, wo ein Datenpunkt im Verhältnis zum Rest des Datensatzes steht. Das könntest du zum Beispiel nutzen, wenn du 10% der aktivsten Nutzenden deiner Website einen Rabatt gewähren willst.

In dieser Übung berechnest du Quartile, Quintile und Dezile, die einen Datensatz in 4, 5 bzw. 10 Teile unterteilen.

Sowohl pandas als pd als auch numpy als np sind geladen, und food_consumption ist verfügbar.

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<Kurs>Einführung in die Statistik in Python</Kurs>

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# Calculate the quartiles of co2_emission
print(____)

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Diese Übung ist Teil des Kurses

<Kurs>Einführung in die Statistik in Python</Kurs>

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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: Was ist Statistik?Exercise 2: Deskriptive und Inferenzstatistik Exercise 3: Klassifizierung von Datentypen Exercise 4: Maße der zentralen Tendenz Exercise 5: Berechnung von Mittelwert und Median Exercise 6: Mittelwert, Median und Form der Verteilung Exercise 7: Maße der Streuung Exercise 8: Varianz und Standardabweichung Exercise 9: Quartile, Quantile und Quintile

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Exercise 10: Ausreißer mit IQR finden

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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?

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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

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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!