Types de relations

Comment identifier efficacement ces relations en pratique ? La première étape, souvent suffisante à elle seule, consiste à visualiser les données avec un nuage de points. Dans cet exercice, vous allez examiner plusieurs types de relations, tracer le nuage de points, puis analyser ce que chaque graphique révèle sur la relation.

Vous utiliserez différentes variables du jeu de données Australian weather déjà vu, importé sous le nom weather. Pour les besoins de cet exercice, veillez à passer les variables dans l’ordre indiqué !

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<cours>S’entraîner aux questions d’entretien en statistiques avec Python</cours>

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Exercice interactif pratique

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# Display a scatter plot and examine the relationship
plt.scatter(____, ____)
plt.show()

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This chapter kicks the course off by reviewing conditional probabilities, Bayes' theorem, and central limit theorem. Along the way, you will learn how to handle questions that work with commonly referenced probability distributions.

Exercise 1: Conditional probabilities Exercise 2: Setting up problems Exercise 3: Bayes' theorem applied Exercise 4: Central limit theorem Exercise 5: Samples from a rolled die Exercise 6: Simulating central limit theorem Exercise 7: Probability distributions Exercise 8: Bernoulli distribution Exercise 9: Binomial distribution Exercise 10: Normal distribution

In this chapter, you will prepare for statistical concepts related to exploratory data analysis. The topics include descriptive statistics, dealing with categorical variables, and relationships between variables. The exercises will prepare you for an analytical assessment or stats-based coding question.

Exercise 1: Statistiques descriptives Exercise 2: Moyenne ou médiane Exercise 3: Écart type à la main Exercise 4: Données catégorielles Exercise 5: Techniques d’encodage Exercise 6: Explorer les prix des ordinateurs portables Exercise 7: Deux variables ou plus Exercise 8: Types de relations

Exercice actuel

Exercise 9: Corrélation de Pearson Exercise 10: Sensibilité aux valeurs aberrantes

Prepare to dive deeper into crucial concepts regarding experiments and testing by reviewing confidence intervals, hypothesis testing, multiple tests, and the role that power and sample size play. We'll also discuss types of errors, and what they mean in practice.

Exercise 1: Confidence intervals Exercise 2: Confidence interval by hand Exercise 3: Applying confidence intervals Exercise 4: Hypothesis testing Exercise 5: One tailed z-test Exercise 6: Two tailed t-test Exercise 7: Power and sample size Exercise 8: Effect on type II error Exercise 9: Calculating sample size Exercise 10: Visualizing the relationship Exercise 11: Multiple testing Exercise 12: Calculating error rates Exercise 13: Bonferroni correction

Wrapping up, we'll address concepts related closely to regression and classification models. The chapter begins by reviewing fundamental machine learning algorithms and quickly ramps up to model evaluation, dealing with special cases, and the bias-variance tradeoff.

Exercise 1: Regression models Exercise 2: Linear regression Exercise 3: Logistic regression Exercise 4: Evaluating models Exercise 5: Regression evaluation Exercise 6: Classification evaluation Exercise 7: Missing data and outliers Exercise 8: Handling null values Exercise 9: Identifying outliers Exercise 10: Bias-variance tradeoff Exercise 11: Test and training error Exercise 12: Visualizing the tradeoff Exercise 13: Wrapping up