Relations entre les variables

Dans ce chapitre, vous travaillerez avec un jeu de données world_happiness contenant les résultats du Rapport 2019 sur le bonheur mondial. Ce rapport évalue différents pays en fonction du degré de bonheur de leurs habitants. Il classe également chaque pays en fonction de divers aspects sociétaux tels que le système social, la liberté, la corruption, etc. La base de données comprend également le PIB par habitant et l’espérance de vie pour chaque pays.

Dans cet exercice, vous examinerez la relation entre l’espérance de vie (life_exp) et le score de bonheur (happiness_score) des pays, à la fois visuellement et quantitativement. seaborn est chargé en tant que sns, matplotlib.pyplot en tant que plt, et pandas en tant que pd, et world_happiness est disponible.

Cet exercice fait partie du cours

Introduction aux statistiques en Python

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Exercice interactif pratique

Essayez cet exercice en complétant cet exemple de code.

# Create a scatterplot of happiness_score vs. life_exp and show
____

# Show plot
____

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Cet exercice fait partie du cours

Introduction aux statistiques en Python

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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: The normal distribution Exercise 2: Distribution of Amir's sales Exercise 3: Probabilities from the normal distribution Exercise 4: Simulating sales under new market conditions Exercise 5: Which market is better?Exercise 6: The central limit theorem Exercise 7: Visualizing sampling distributions Exercise 8: The CLT in action Exercise 9: The mean of means Exercise 10: The Poisson distribution Exercise 11: Identifying lambda Exercise 12: Tracking lead responses Exercise 13: More probability distributions Exercise 14: Distribution dragging and dropping Exercise 15: Modeling time between leads Exercise 16: The t-distribution

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: Corrélation Exercise 2: Devinez la corrélation Exercise 3: Relations entre les variables

Exercice en cours

Exercise 4: Mises en garde sur les corrélations Exercise 5: Qu’est-ce que la corrélation ne peut pas mesurer ?Exercise 6: Transformer des variables Exercise 7: Le sucre améliore-t-il le bonheur ?Exercise 8: Facteurs de confusion Exercise 9: Plan d’expériences Exercise 10: Types d’études Exercise 11: Études longitudinales ou transversales ?Exercise 12: Félicitations !