Kwartielen, kwantielen en kwintielen

Kwantielen zijn een handige manier om numerieke data samen te vatten. Ze kunnen zowel centrum als spreiding meten en laten zien waar een datapunt staat ten opzichte van de rest van de gegevensset. Zo kun je bijvoorbeeld korting geven aan de 10% meest actieve gebruikers op een website.

In deze oefening bereken je kwartielen, kwintielen en decielen, die een gegevensset respectievelijk in 4, 5 en 10 stukken verdelen.

Het pakket dplyr is geladen en food_consumption is beschikbaar.

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Inleiding tot statistiek in R

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# Calculate the quartiles of co2_emission
___(___$___)

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Deze oefening maakt deel uit van de cursus

Inleiding tot statistiek 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: Wat is statistiek?Exercise 2: Beschrijvende en inferentiële statistiek Exercise 3: Classificatie van datatypes Exercise 4: Maten van centrum Exercise 5: Gemiddelde en mediaan Exercise 6: Gemiddelde vs. mediaan Exercise 7: Spreidingsmaten Exercise 8: Variantie en standaardafwijking Exercise 9: Kwartielen, kwantielen en kwintielen

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Exercise 10: Uitschieters vinden met 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: 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!