np.random.choice()

Bu egzersizde, np.random.choice() fonksiyonuyla tanışacaksın. Bu, simülasyonlar için son derece kullanışlı bir fonksiyondur ve kursun ilerleyen kısımlarında yoğun şekilde kullanacaksın. İlk adım olarak, bu fonksiyonun temelini anlamaya çalışalım. help() kullanarak, aşağıdaki giriş parametrelerinden hangilerinin np.random.choice() fonksiyonu için isteğe bağlı değil, yani zorunlu olduğunu bul:

Bu egzersiz

Python'da İstatistiksel Benzetim

kursunun bir parçasıdır

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Bu egzersiz

Python'da İstatistiksel Benzetim

kursunun bir parçasıdır

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Kursa Ücretsiz Başlayın

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: Rastgele değişkenlere giriş Exercise 2: np.random.choice()

Geçerli Egzersiz

Exercise 3: Poisson rassal değişkeni Exercise 4: Bir deste iskambil kâğıdını karıştırma Exercise 5: Simülasyonun temelleri Exercise 6: Adil bir zar atma Exercise 7: İki adil zar atmak Exercise 8: Zar oyununu simüle etme Exercise 9: Karar verme için simülasyon kullanımı Exercise 10: Tek bir piyango çekilişini simüle etme Exercise 11: Almalı mıyız?Exercise 12: Başa baş piyango bilet fiyatını hesaplama

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 to resampling methods Exercise 2: Sampling with replacement Exercise 3: Probability example Exercise 4: Bootstrapping Exercise 5: Running a simple bootstrap Exercise 6: Non-standard estimators Exercise 7: Bootstrapping regression Exercise 8: Jackknife resampling Exercise 9: Basic jackknife estimation - mean Exercise 10: Jackknife confidence interval for the median Exercise 11: Permutation testing Exercise 12: Generating a single permutation Exercise 13: Hypothesis testing - Difference of means Exercise 14: Hypothesis testing - Non-standard statistics

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