Identificação de distribuições

Qual amostra é mais provável de ter sido retirada de uma distribuição uniforme?

A: distribuição em forma de sino, B: distribuição relativamente plana, C: muitos valores mais baixos, menos valores altos

Este exercicio faz parte do curso

Introdução à estatística em Python

exercicio interativo prático

Transforme teoria em prática com um dos nossos exercicio interativos

Iniciar exercicio

Este exercicio faz parte do curso

Introdução à estatística em Python

IntermediárioNível de habilidade

4.8+

Comece o curso gratuitamente

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: Quais são as chances?Exercise 2: Com ou sem reposição?Exercise 3: Cálculo de probabilidades Exercise 4: Amostragem de negócios Exercise 5: Distribuições discretas Exercise 6: Criação de uma distribuição de probabilidade Exercise 7: Identificação de distribuições

Exercicio Atual

Exercise 8: Valor esperado vs. média da amostra Exercise 9: Distribuições contínuas Exercise 10: Qual distribuição?Exercise 11: Backups de dados Exercise 12: Simulação de tempos de espera Exercise 13: A distribuição binomial Exercise 14: Simulação de negócios de vendas Exercise 15: Cálculo de probabilidades binomiais Exercise 16: Quantas vendas serão realizadas?

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!