Trascina e rilascia le distribuzioni

A questo punto hai visto così tante distribuzioni di probabilità che può essere difficile ricordare quale sia quale. In questo esercizio, allenerai la capacità di distinguerle e di identificare la distribuzione che meglio descrive diversi scenari.

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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: 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 distribuzione normale Exercise 2: Distribuzione delle vendite di Amir Exercise 3: Probabilità dalla distribuzione normale Exercise 4: Simulare le vendite in nuove condizioni di mercato Exercise 5: Quale mercato è migliore?Exercise 6: Il teorema centrale del limite Exercise 7: Visualizzare le distribuzioni campionarie Exercise 8: Il TCL in azione Exercise 9: La media delle medie Exercise 10: La distribuzione di Poisson Exercise 11: Identificare lambda Exercise 12: Monitorare le risposte ai lead Exercise 13: Altre distribuzioni di probabilità Exercise 14: Trascina e rilascia le distribuzioni

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Exercise 15: Modellare il tempo tra i lead Exercise 16: La distribuzione 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!