Pengambilan sampel dengan pengembalian

Dalam contoh ini, Anda akan meninjau fungsi np.random.choice() yang sudah Anda lihat di bab sebelumnya. Anda diberikan beberapa variasi np.random.choice() untuk mengambil sampel dari array. Perhatikan setiap variasi dengan saksama dan gunakan konsol untuk mencoba opsi-opsinya. Pilih opsi yang dapat menghasilkan keluaran ['a', 'c', 'c'].

Latihan ini adalah bagian dari kursus

Simulasi Statistik di Python

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Latihan ini adalah bagian dari kursus

Simulasi Statistik di Python

SkillTag.level.intermediateSkillTag.label

4.9+

Mulai Kursus Gratis

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: Pengantar metode pengambilan sampel ulang Exercise 2: Pengambilan sampel dengan pengembalian

Latihan Saat Ini

Exercise 3: Contoh probabilitas Exercise 4: Bootstrapping Exercise 5: Menjalankan bootstrap sederhana Exercise 6: Estimator non-standar Exercise 7: Bootstrapping regresi Exercise 8: Pengambilan sampel jackknife Exercise 9: Estimasi jackknife dasar - mean Exercise 10: Interval kepercayaan jackknife untuk median Exercise 11: Uji permutasi Exercise 12: Membuat satu permutasi Exercise 13: Pengujian hipotesis - Selisih rata-rata Exercise 14: Pengujian hipotesis - Statistik non-standar

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