Formule 1-motoren

Stel dat twee fabrikanten, A en B, de motoren leveren voor Formule 1-wagens, met de volgende kenmerken:

99% van de motoren van fabriek A haalt meer dan 5.000 km.
Fabriek B produceert motoren die met 95% kans meer dan 5.000 km halen.
70% van de motoren is van fabrikant A, de rest wordt geproduceerd door fabrikant B.

Wat is de kans dat een motor meer dan 5.000 km meegaat?

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Basis van kansrekening in Python

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Bereken de volgende kansen:
- De fabrikant is A (P_A).
- De motor gaat meer dan 5.000 km mee, gegeven dat de fabrikant A is (P_last5000_g_A).
- De fabrikant is B (P_B).
- De motor gaat meer dan 5.000 km mee, gegeven dat de fabrikant B is (P_last5000_g_B).
Gebruik de wet van de totale kans om de kans te berekenen dat de motor meer dan 5.000 km meegaat en sla het resultaat op in P_last_5000.

Praktische interactieve oefening

Probeer deze oefening eens door deze voorbeeldcode in te vullen.

# Needed probabilities
P_A = ____
P_last5000_g_A = ____
P_B = ____
P_last5000_g_B = ____

# Total probability calculation
P_last_5000 = ____

print(P_last_5000)

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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: Kansen van twee gebeurtenissen berekenen Exercise 2: Enige overlap?Exercise 3: Een steekproef meten Exercise 4: Gezamenlijke kansen Exercise 5: Kaartspel Exercise 6: Voorwaardelijke kansen Exercise 7: Vertraagde vluchten Exercise 8: Contingentietabel Exercise 9: Meer kaarten Exercise 10: Wet van de totale waarschijnlijkheid Exercise 11: Formule 1-motoren

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Exercise 12: Kiezers Exercise 13: Regel van Bayes Exercise 14: Conditioneren Exercise 15: Fabrieken en onderdelen Exercise 16: Bloedtest op Mexicaanse griep

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: Normal distributions Exercise 2: Range of values Exercise 3: Plotting normal distributions Exercise 4: Within three standard deviations Exercise 5: Normal probabilities Exercise 6: Restaurant spending example Exercise 7: Smartphone battery example Exercise 8: Adults' heights example Exercise 9: Poisson distributions Exercise 10: ATM example Exercise 11: Highway accidents example Exercise 12: Generating and plotting Poisson distributions Exercise 13: Geometric distributions Exercise 14: Catching salmon example Exercise 15: Free throws example Exercise 16: Generating and plotting geometric distributions

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