Queen dan sekop

Dalam contoh ini, Anda akan menggunakan rumus probabilitas umum \(P(A \cup B) = P(A) + P(B) - P(A \cap B)\) untuk menghitung probabilitas dua kejadian. Pertimbangkan satu set kartu remi (13 kartu x 4 jenis = total 52 kartu). Satu kartu diambil secara acak. Berapa probabilitas mendapatkan queen atau sekop? Di sini kejadian A adalah kartunya queen dan kejadian B adalah kartunya sekop. Pertimbangkan dengan saksama apakah kedua kejadian tersebut memiliki irisan.

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Simulasi Statistik di Python

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Latihan ini adalah bagian dari kursus

Simulasi Statistik di Python

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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: Dasar-dasar probabilitas Exercise 2: Queen dan sekop

Latihan Saat Ini

Exercise 3: Sepasang (Two of a Kind)Exercise 4: Game of Thirteen Exercise 5: Konsep peluang lanjutan Exercise 6: Urna bersyarat Exercise 7: Masalah ulang tahun Exercise 8: Full house Exercise 9: Proses pembangkitan data Exercise 10: Ujian mengemudi Exercise 11: Pemilu nasional Exercise 12: Target kebugaran Exercise 13: Simulasi Iklan eCommerce Exercise 14: Alur Pendaftaran Exercise 15: Alur Pembelian Exercise 16: Probabilitas mengalami kerugian

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