Factoren die de statistische power beïnvloeden

In deze oefening fris je een paar basisbegrippen op om je begrip van statistische power te testen. Het is heel belangrijk om statistische power te begrijpen, zeker als je een A/B-test ontwerpt.

Bekijk de vier opties hieronder en kies de factor die geen invloed heeft op de statistische power van een experiment:

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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: 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: Simulatie voor bedrijfsplanning Exercise 2: Maïsproductie modelleren Exercise 3: Winst modelleren Exercise 4: Kosten optimaliseren Exercise 5: Monte Carlo-integratie Exercise 6: Een eenvoudige functie integreren Exercise 7: De waarde van pi berekenen Exercise 8: Simulatie voor poweranalyse Exercise 9: Factoren die de statistische power beïnvloeden

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Exercise 10: Poweranalyse - Deel I Exercise 11: Poweranalyse - deel II Exercise 12: Toepassingen in de financiële sector Exercise 13: Portefeuillesimulatie - Deel I Exercise 14: Portefeuillesimulatie - Deel II Exercise 15: Portefeuillesimulatie - Deel III Exercise 16: Afronding