Valore atteso vs. media campionaria

L'app a destra estrarrà un campione da una distribuzione uniforme discreta, che include i numeri da 1 a 9, e calcolerà la media del campione. Puoi regolare la dimensione del campione usando lo slider. Nota che il valore atteso di questa distribuzione è 5.

Viene estratto un campione e vinci venti dollari se la media del campione è inferiore a 4. C'è un però: puoi scegliere la dimensione del campione.

Quale dimensione del campione è più probabile che ti faccia vincere i venti dollari?

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Introduzione alla statistica in Python

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Introduzione alla statistica in 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: Quali sono le probabilità?Exercise 2: Con o senza reimmissione?Exercise 3: Calcolare le probabilità Exercise 4: Campionare le trattative Exercise 5: Distribuzioni discrete Exercise 6: Creare una distribuzione di probabilità Exercise 7: Riconoscere le distribuzioni Exercise 8: Valore atteso vs. media campionaria

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Exercise 9: Distribuzioni continue Exercise 10: Quale distribuzione?Exercise 11: Backup dei dati Exercise 12: Simulare i tempi di attesa Exercise 13: La distribuzione binomiale Exercise 14: Simulare gli accordi di vendita Exercise 15: Calcolare probabilità binomiali Exercise 16: Quante vendite verranno vinte?

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: 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!