Probabilités de la distribution normale

Étant donné que chaque transaction sur laquelle Amir a travaillé (conclue ou non) était différente, chacune était d’un montant différent. Ces valeurs sont stockées dans la colonne amount de amir_deals et suivent une distribution normale avec une moyenne de 5000 dollars et un écart-type de 2000 dollars. Dans le cadre de ses mesures de performance, vous souhaitez calculer la probabilité qu’Amir conclue une transaction pour différents montants.

norm est importé à partir de scipy.stats, et pandas est chargé en tant que pd. Le DataFrame amir_deals est chargé.

Cet exercice fait partie du cours

Introduction aux statistiques en Python

Afficher le cours

Exercice interactif pratique

Essayez cet exercice en complétant cet exemple de code.

# Probability of deal < 7500
prob_less_7500 = ____

print(prob_less_7500)

Modifier et exécuter le code

Cet exercice fait partie du cours

Introduction aux statistiques en Python

IntermédiaireNiveau de compétence

4.8+

Commencer le cours gratuitement

Summary statistics gives you the tools you need to boil down massive datasets to reveal the highlights. In this chapter, you'll explore summary statistics including mean, median, and standard deviation, and learn how to accurately interpret them. You'll also develop your critical thinking skills, allowing you to choose the best summary statistics for your data.

Exercise 1: What is statistics?Exercise 2: Descriptive and inferential statistics Exercise 3: Data type classification Exercise 4: Measures of center Exercise 5: Calculating mean and median Exercise 6: Mean, median, and distribution shape Exercise 7: Measures of spread Exercise 8: Variance and standard deviation Exercise 9: Quartiles, quantiles, and quintiles Exercise 10: Finding outliers using IQR

In this chapter, you'll learn how to generate random samples and measure chance using probability. You'll work with real-world sales data to calculate the probability of a salesperson being successful. Finally, you’ll use the binomial distribution to model events with binary outcomes.

Exercise 1: What are the chances?Exercise 2: With or without replacement?Exercise 3: Calculating probabilities Exercise 4: Sampling deals Exercise 5: Discrete distributions Exercise 6: Creating a probability distribution Exercise 7: Identifying distributions Exercise 8: Expected value vs. sample mean Exercise 9: Continuous distributions Exercise 10: Which distribution?Exercise 11: Data back-ups Exercise 12: Simulating wait times Exercise 13: The binomial distribution Exercise 14: Simulating sales deals Exercise 15: Calculating binomial probabilities Exercise 16: How many sales will be won?

It’s time to explore one of the most important probability distributions in statistics, normal distribution. You’ll create histograms to plot normal distributions and gain an understanding of the central limit theorem, before expanding your knowledge of statistical functions by adding the Poisson, exponential, and t-distributions to your repertoire.

Exercise 1: La distribution normale Exercise 2: Distribution des ventes d’Amir Exercise 3: Probabilités de la distribution normale

Exercice en cours

Exercise 4: Simuler des ventes dans de nouvelles conditions de marché Exercise 5: Quel est le meilleur marché ?Exercise 6: Le théorème de la limite centrale (CLT)Exercise 7: Visualisation des distributions d’échantillonnage Exercise 8: Le théorème de la limite centrale en action Exercise 9: La moyenne des moyennes Exercise 10: La distribution de Poisson Exercise 11: Identifier lambda Exercise 12: Suivi des réponses aux prospects Exercise 13: Plus de distributions de probabilités Exercise 14: Identifier les distributions Exercise 15: Modélisation du temps entre les prospects Exercise 16: La distribution t

In this chapter, you'll learn how to quantify the strength of a linear relationship between two variables, and explore how confounding variables can affect the relationship between two other variables. You'll also see how a study’s design can influence its results, change how the data should be analyzed, and potentially affect the reliability of your conclusions.

Exercise 1: Correlation Exercise 2: Guess the correlation Exercise 3: Relationships between variables Exercise 4: Correlation caveats Exercise 5: What can't correlation measure?Exercise 6: Transforming variables Exercise 7: Does sugar improve happiness?Exercise 8: Confounders Exercise 9: Design of experiments Exercise 10: Study types Exercise 11: Longitudinal vs. cross-sectional studies Exercise 12: Congratulations!