Calcolo della dimensione del campione

Concludiamo l’esplorazione dei test statistici eseguendo un’analisi della potenza per determinare la dimensione del campione necessaria. L’analisi della potenza coinvolge quattro elementi:

Dimensione del campione
Dimensione dell’effetto
Effetto minimo
Potenza

In questo esercizio lavori con un sito web e vuoi testare una differenza nel tasso di conversione. Prima di iniziare l’esperimento, devi decidere quanti campioni ti servono per variante usando una significatività del 5% e una potenza del 95%.

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# Standardize the effect size
from statsmodels.stats.proportion import proportion_effectsize
std_effect = ____

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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: Descriptive statistics Exercise 2: Mean or median Exercise 3: Standard deviation by hand Exercise 4: Categorical data Exercise 5: Encoding techniques Exercise 6: Exploring laptop prices Exercise 7: Two or more variables Exercise 8: Types of relationships Exercise 9: Pearson correlation Exercise 10: Sensitivity to outliers

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: Intervalli di confidenza Exercise 2: Intervallo di confidenza a mano Exercise 3: Applicare gli intervalli di confidenza Exercise 4: Test d'ipotesi Exercise 5: Z-test a una coda Exercise 6: t-test a due code Exercise 7: Potenza e dimensione del campione Exercise 8: Effetto sull'errore di tipo II Exercise 9: Calcolo della dimensione del campione

Esercizio in corso

Exercise 10: Visualizzare la relazione Exercise 11: Test multipli Exercise 12: Calcolare i tassi di errore Exercise 13: Correzione di Bonferroni

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