Verdelingen beschrijven met kurtosis

Een andere manier om de vorm van een verdeling te beschrijven is via de kurtosis, die iets zegt over de hoogte van de centrale piek en hoe uitgespreid de staarten zijn.

Met kurtosis kun je samenvatten of waarden dicht rond het gemiddelde clusteren en hoe ver eventuele uitschieters van het gemiddelde afliggen.

Er zijn drie definities gegeven. Jij kiest welke definitie een normale verdeling met negatieve kurtosis correct weergeeft.

Deze oefening maakt deel uit van de cursus

Inleiding tot statistiek

Interactieve oefening met praktijkervaring

Zet theorie om in actie met een van onze interactieve oefeningen

Deze oefening maakt deel uit van de cursus

Inleiding tot statistiek

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Begin gratis met de cursus

Summary statistics gives you the tools you need to describe your data. 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: Using statistics in the real-world Exercise 3: Identifying data types Exercise 4: Descriptive vs. Inferential statistics Exercise 5: Measures of center Exercise 6: Typical number of robberies per London Borough Exercise 7: Choosing a measure Exercise 8: London Boroughs with most frequent crimes Exercise 9: Measures of spread Exercise 10: Defining measures of spread Exercise 11: Box plots for measuring spread Exercise 12: Which crime has the larger standard deviation

Probability underpins a large part of statistics, where it is used to calculate the chance of events occurring. You'll work with real-world sales data and learn how data with different values can be interpreted as a probability distribution. You'll find out about discrete and continuous probability distributions, including the discovery of the normal distribution and how it occurs frequently in natural events!

Exercise 1: What are the chances?Exercise 2: What is more likely?Exercise 3: Chances of the next sale being more than the mean Exercise 4: Conditional probability Exercise 5: Dependent vs. Independent events Exercise 6: Orders of more than 10 basket products Exercise 7: Discrete distributions Exercise 8: Identifying distributions Exercise 9: Sample mean vs. Theoretical mean Exercise 10: Continuous distributions Exercise 11: Discrete vs. Continuous distributions Exercise 12: Finding the normal distribution Exercise 13: Probability with a uniform distribution

It's time to explore more probability distributions. You'll learn about the binomial distribution for visualizing the probability of binary outcomes, and one of the most important distributions in statistics, the normal distribution. You'll see how distributions can be described by their shape, along with discovering the Poisson distribution and its role in calculating the probabilities of events occuring over time. You'll also gain an understanding of the central limit theorem!

Exercise 1: De binomiale verdeling Exercise 2: Een binomiale verdeling herkennen Exercise 3: Hoe kans de binomiale verdeling beïnvloedt Exercise 4: n en p bepalen Exercise 5: De normale verdeling Exercise 6: De normale verdeling herkennen Exercise 7: Wat maakt de normale verdeling bijzonder?Exercise 8: Scheefheid herkennen Exercise 9: Verdelingen beschrijven met kurtosis

Huidige oefening

Exercise 10: De centrale limietstelling Exercise 11: Steekproefverdelingen visualiseren Exercise 12: De CLT vs. de wet van de grote aantallen Exercise 13: Wanneer gebruik je de centrale limietstelling Exercise 14: De Poissonverdeling Exercise 15: Poisson-processen herkennen Exercise 16: Lambda herkennen in de Poisson-verdeling

In the final chapter, you'll be introduced to hypothesis testing and how it can be used to accurately draw conclusions about a population. You'll discover correlation and how it can be used to quantify a linear relationship between two variables. You'll find out about experimental design techniques such as randomization and blinding. You'll also learn about concepts used to minimize the risk of drawing the wrong conclusion about the results of hypothesis tests!

Exercise 1: Hypothesis testing Exercise 2: Sunshine and sleep Exercise 3: The hypothesis testing workflow Exercise 4: Independent and dependent variables Exercise 5: Experiments Exercise 6: Recognizing controlled trials Exercise 7: Why use randomization?Exercise 8: Correlation Exercise 9: Identifying correlation between variables Exercise 10: What can correlation tell you?Exercise 11: Confounding variables Exercise 12: Interpreting hypothesis test results Exercise 13: Significance levels vs. p-values Exercise 14: Type I and type II errors Exercise 15: Congratulations!