Voorbeeld geldautomaat

Als je weet hoeveel specifieke gebeurtenissen er per eenheid van maat plaatsvinden, kun je ervan uitgaan dat de verdeling van de toevalsvariabele een Poisson-verdeling volgt om het verschijnsel te bestuderen.

Stel je een geldautomaat (ATM) voor in een erg druk winkelcentrum. De bank wil voorkomen dat klanten in de rij moeten staan om de automaat te gebruiken. Er is vastgesteld dat het gemiddelde aantal klanten dat tussen 10.00 en 10.05 uur geld opneemt op een willekeurige dag 1 is.

Als data-analist bij de bank wordt je gevraagd wat de kans is dat de bank een extra geldautomaat moet plaatsen om de drukte aan te kunnen.

Om de vraag te beantwoorden, moet je de kans berekenen dat er in die periode meer dan één klant komt.

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Basis van kansrekening in Python

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Importeer poisson uit scipy.stats.
Bereken de kans dat er in deze periode van 5 minuten meer dan één klant de geldautomaat bezoekt.

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Probeer deze oefening eens door deze voorbeeldcode in te vullen.

# Import poisson from scipy.stats
from ____

# Probability of more than 1 customer
probability = ____.____(k=____, mu=____)

# Print the result
print(probability)

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Deze oefening maakt deel uit van de cursus

Basis van kansrekening in Python

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A coin flip is the classic example of a random experiment. The possible outcomes are heads or tails. This type of experiment, known as a Bernoulli or binomial trial, allows us to study problems with two possible outcomes, like “yes” or “no” and “vote” or “no vote.” This chapter introduces Bernoulli experiments, binomial distributions to model multiple Bernoulli trials, and probability simulations with the scipy library.

Exercise 1: Let’s flip a coin in Python Exercise 2: Flipping coins Exercise 3: Using binom to flip even more coins Exercise 4: Probability mass and distribution functions Exercise 5: Predicting the probability of defects Exercise 6: Predicting employment status Exercise 7: Predicting burglary conviction rate Exercise 8: Expected value, mean, and variance Exercise 9: Calculating the expected value and variance Exercise 10: Calculating the sample mean Exercise 11: Checking the result Exercise 12: Calculating the mean and variance of a sample

In this chapter you'll learn to calculate various kinds of probabilities, such as the probability of the intersection of two events and the sum of probabilities of two events, and to simulate those situations. You'll also learn about conditional probability and how to apply Bayes' rule.

Exercise 1: Calculating probabilities of two events Exercise 2: Any overlap?Exercise 3: Measuring a sample Exercise 4: Joint probabilities Exercise 5: Deck of cards Exercise 6: Conditional probabilities Exercise 7: Delayed flights Exercise 8: Contingency table Exercise 9: More cards Exercise 10: Total probability law Exercise 11: Formula 1 engines Exercise 12: Voters Exercise 13: Bayes' rule Exercise 14: Conditioning Exercise 15: Factories and parts Exercise 16: Swine flu blood test

Until now we've been working with binomial distributions, but there are many probability distributions a random variable can take. In this chapter we'll introduce three more that are related to the binomial distribution: the normal, Poisson, and geometric distributions.

Exercise 1: Normale verdelingen Exercise 2: Bereik van waarden Exercise 3: Normale verdelingen plotten Exercise 4: Binnen drie standaarddeviaties Exercise 5: Normale kansen Exercise 6: Voorbeeld: restaurantbestedingen Exercise 7: Voorbeeld: batterij van een smartphone Exercise 8: Voorbeeld: lengtes van volwassenen Exercise 9: Poissonverdelingen Exercise 10: Voorbeeld geldautomaat

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Exercise 11: Voorbeeld: ongelukken op de snelweg Exercise 12: Poisson-verdelingen genereren en plotten Exercise 13: Geometrische verdelingen Exercise 14: Voorbeeld: zalm vangen Exercise 15: Voorbeeld met vrije worpen Exercise 16: Geometrische verdelingen genereren en plotten

No that you know how to calculate probabilities and important properties of probability distributions, we'll introduce two important results: the law of large numbers and the central limit theorem. This will expand your understanding on how the sample mean converges to the population mean as more data is available and how the sum of random variables behaves under certain conditions. We will also explore connections between linear and logistic regressions as applications of probability and statistics in data science.

Exercise 1: From sample mean to population mean Exercise 2: Generating a sample Exercise 3: Calculating the sample mean Exercise 4: Plotting the sample mean Exercise 5: Adding random variables Exercise 6: Sample means Exercise 7: Sample means follow a normal distribution Exercise 8: Adding dice rolls Exercise 9: Linear regression Exercise 10: Fitting a model Exercise 11: Predicting test scores Exercise 12: Studying residuals Exercise 13: Logistic regression Exercise 14: Fitting a logistic model Exercise 15: Predicting if students will pass Exercise 16: Passing two tests Exercise 17: Wrapping up