Steekproeven met teruglegging

In dit voorbeeld kijk je nog eens naar de functie np.random.choice() die je al in eerdere hoofdstukken hebt gezien. Je krijgt meerdere varianten van np.random.choice() om uit arrays te steekproeven. Bekijk elke variant zorgvuldig en gebruik de console om de opties te testen. Selecteer de optie die ['a', 'c', 'c'] als output kan genereren.

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Statisticale simulatie in Python

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Statisticale simulatie in Python

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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: Introductie tot hersteekproefmethoden Exercise 2: Steekproeven met teruglegging

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Exercise 3: Voorbeeld kansberekening Exercise 4: Bootstrappen Exercise 5: Een eenvoudige bootstrap uitvoeren Exercise 6: Niet-standaard schatters Exercise 7: Bootstrapping bij regressie Exercise 8: Jackknife-resampling Exercise 9: Basis jackknife-schatting - gemiddelde Exercise 10: Jackknife-betrouwbaarheidsinterval voor de mediaan Exercise 11: Permutatietest Exercise 12: Eén permutatie genereren Exercise 13: Toetsen van hypothesen - Verschil in gemiddelden Exercise 14: Hypothesis testing - Niet-standaard statistieken

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