Échantillonnage avec remise

Dans cet exemple, vous allez revoir la fonction np.random.choice() que vous avez déjà rencontrée dans les chapitres précédents. On vous propose plusieurs variantes de np.random.choice() pour échantillonner des tableaux. Examinez chaque variante attentivement et utilisez la console pour tester les options. Sélectionnez l’option qui pourrait produire ['a', 'c', 'c'] en sortie.

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<cours>Simulation statistique en Python</cours>

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This chapter gives you the tools required to run a simulation. We'll start with a review of random variables and probability distributions. We will then learn how to run a simulation by first looking at a simulation workflow and then recreating it in the context of a game of dice. Finally, we will learn how to use simulations for making decisions.

Exercise 1: Introduction to random variables Exercise 2: np.random.choice()Exercise 3: Poisson random variable Exercise 4: Shuffling a deck of cards Exercise 5: Simulation basics Exercise 6: Throwing a fair die Exercise 7: Throwing two fair dice Exercise 8: Simulating the dice game Exercise 9: Using simulation for decision-making Exercise 10: Simulating one lottery drawing Exercise 11: Should we buy?Exercise 12: Calculating a break-even lottery price

This chapter provides a basic introduction to probability concepts and a hands-on understanding of the data generating process. We'll look at a number of examples of modeling the data generating process and will conclude with modeling an eCommerce advertising simulation.

Exercise 1: Probability basics Exercise 2: Queen and spade Exercise 3: Two of a kind Exercise 4: Game of thirteen Exercise 5: More probability concepts Exercise 6: The conditional urn Exercise 7: Birthday problem Exercise 8: Full house Exercise 9: Data generating process Exercise 10: Driving test Exercise 11: National elections Exercise 12: Fitness goals Exercise 13: eCommerce Ad Simulation Exercise 14: Sign up Flow Exercise 15: Purchase Flow Exercise 16: Probability of losing money

In this chapter, we will get a brief introduction to resampling methods and their applications. We will get a taste of bootstrap resampling, jackknife resampling, and permutation testing. After completing this chapter, students will be able to start applying simple resampling methods for data analysis.

Exercise 1: Introduction aux méthodes de rééchantillonnage Exercise 2: Échantillonnage avec remise

Exercice actuel

Exercise 3: Exemple de probabilité Exercise 4: Bootstrap Exercise 5: Exécuter un bootstrap simple Exercise 6: Estimateurs non standards Exercise 7: Bootstrap en régression Exercise 8: Rééchantillonnage jackknife Exercise 9: Estimation jackknife de base - moyenne Exercise 10: Intervalle de confiance jackknife pour la médiane Exercise 11: Test par permutation Exercise 12: Générer une permutation unique Exercise 13: Test d’hypothèse - Différence de moyennes Exercise 14: Tests d’hypothèses - Statistiques non standard

In this chapter, students will be introduced to some basic and advanced applications of simulation to solve real-world problems. We'll work through a business planning problem, learn about Monte Carlo Integration, Power Analysis with simulation and conclude with a financial portfolio simulation. After completing this chapter, students will be ready to apply simulation to solve everyday problems.

Exercise 1: Simulation for Business Planning Exercise 2: Modeling Corn Production Exercise 3: Modeling Profits Exercise 4: Optimizing Costs Exercise 5: Monte Carlo Integration Exercise 6: Integrating a Simple Function Exercise 7: Calculating the value of pi Exercise 8: Simulation for Power Analysis Exercise 9: Factors influencing Statistical Power Exercise 10: Power Analysis - Part I Exercise 11: Power Analysis - Part II Exercise 12: Applications in Finance Exercise 13: Portfolio Simulation - Part I Exercise 14: Portfolio Simulation - Part II Exercise 15: Portfolio Simulation - Part III Exercise 16: Wrap Up