Échantillonnage de transactions

Dans l’exercice précédent, vous avez compté les transactions sur lesquelles Amir a travaillé. Il est maintenant temps de choisir au hasard cinq transactions afin de contacter chaque client et de leur demander s’ils ont été satisfaits du service reçu. Vous essaierez de le faire avec et sans remise.

En outre, vous voulez vous assurer que cette opération est effectuée de manière aléatoire et qu’elle peut être reproduite au cas où l’on vous demanderait comment vous avez choisi les transactions. Vous devrez donc définir la graine aléatoire avant de procéder à l’échantillonnage des transactions.

pandas est chargé en tant que pd et numpy en tant que np, et amir_deals est disponible.

Cet exercice fait partie du cours

Introduction aux statistiques en Python

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Exercice interactif pratique

Essayez cet exercice en complétant cet exemple de code.

# Set random seed
____

# Sample 5 deals without replacement
sample_without_replacement = ____
print(sample_without_replacement)

Modifier et exécuter le code

Cet exercice fait partie du cours

Introduction aux statistiques en Python

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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: What is statistics?Exercise 2: Descriptive and inferential statistics Exercise 3: Data type classification Exercise 4: Measures of center Exercise 5: Calculating mean and median Exercise 6: Mean, median, and distribution shape Exercise 7: Measures of spread Exercise 8: Variance and standard deviation Exercise 9: Quartiles, quantiles, and quintiles Exercise 10: Finding outliers using IQR

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: Quelles sont les chances ?Exercise 2: Avec ou sans remise ?Exercise 3: Calcul de probabilités Exercise 4: Échantillonnage de transactions

Exercice en cours

Exercise 5: Distributions discrètes Exercise 6: Créer une distribution de probabilités Exercise 7: Identifier des distributions Exercise 8: Valeur attendue par rapport à la moyenne de l’échantillon Exercise 9: Distributions continues Exercise 10: Quelle distribution ?Exercise 11: Sauvegardes de données Exercise 12: Simulation de temps d’attente Exercise 13: La distribution binomiale Exercise 14: Simulation de transactions de ventes Exercise 15: Calcul de probabilités binomiales Exercise 16: Combien de ventes seront réalisées ?

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!