Simulazione dei tempi di attesa

Per dare ad Amir un'idea più precisa di quanto tempo dovrà aspettare, simulerai un'attesa di 1000 volte e creerai un istogramma per mostrargli cosa deve aspettarsi. Ricordiamo dall'ultimo esercizio che il tempo di attesa minimo è di 0 minuti e quello massimo di 30 minuti.

È disponibile un data frame chiamato wait_times e vengono caricate le librerie dplyr e ggplot2.

Questo esercizio fa parte del corso

Introduzione alle statistiche in R

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Questo esercizio fa parte del corso

Introduzione alle statistiche in R

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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: Mean and median Exercise 6: Mean vs. median 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 possibilità?Exercise 2: Con o senza sostituzione?Exercise 3: Calcolo delle probabilità Exercise 4: Offerte di campionamento Exercise 5: Distribuzioni discrete Exercise 6: Creare una distribuzione di probabilità Exercise 7: Identificazione delle distribuzioni Exercise 8: Valore atteso vs. media del campione Exercise 9: Distribuzioni continue Exercise 10: Quale distribuzione?Exercise 11: Backup dei dati Exercise 12: Simulazione dei tempi di attesa

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Exercise 13: La distribuzione binomiale Exercise 14: Simulazione di accordi di vendita Exercise 15: Calcolo delle probabilità binomiali Exercise 16: Quante vendite saranno realizzate?

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: Too many distributions 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!