Wachttijden simuleren

Om Amir een beter idee te geven van hoe lang hij moet wachten, ga je 1000 keer een wachttijd voor Amir simuleren en een histogram maken zodat hij weet wat hij kan verwachten. Denk aan de vorige oefening: zijn minimale wachttijd is 0 minuten en zijn maximale wachttijd is 30 minuten.

Er is een data frame wait_times beschikbaar en de libraries dplyr en ggplot2 zijn geladen.

Deze oefening maakt deel uit van de cursus

Inleiding tot statistiek in R

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# Set random seed to 334
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Deze oefening maakt deel uit van de cursus

Inleiding tot statistiek in R

SkillTag.level.intermediateSkillTag.label

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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: Hoe groot is de kans?Exercise 2: Met of zonder terugleggen?Exercise 3: Kansen berekenen Exercise 4: Deals steekproeven Exercise 5: Discrete verdelingen Exercise 6: Een kansverdeling maken Exercise 7: Verdelingen herkennen Exercise 8: Verwachtingswaarde vs. steekproefgemiddelde Exercise 9: Continue verdelingen Exercise 10: Welke verdeling?Exercise 11: Back-ups van data Exercise 12: Wachttijden simuleren

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Exercise 13: De binomiale verdeling Exercise 14: Verkoopdeals simuleren Exercise 15: Binomiale kansen berekenen Exercise 16: Hoeveel sales worden er gewonnen?

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!