Type onderzoeken

Hoewel gecontroleerde experimenten ideaal zijn, lenen veel situaties en onderzoeksvragen zich er niet voor. In een gecontroleerd experiment kun je vaak causaliteit afleiden als de controlegroep en de testgroep vergelijkbare kenmerken hebben en er geen systematische verschillen zijn. Daarentegen kun je uit observationele studies meestal geen causaliteit afleiden; de resultaten daarvan worden vaak verkeerd geïnterpreteerd.

In deze oefening ga je oefenen met het onderscheiden van gecontroleerde experimenten en observationele studies.

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Inleiding tot statistiek in Python

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Inleiding tot statistiek in Python

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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: 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: 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: 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: Correlatie Exercise 2: Raad de correlatie Exercise 3: Relaties tussen variabelen Exercise 4: Kanttekeningen bij correlatie Exercise 5: Wat kan correlatie niet meten?Exercise 6: Variabelen transformeren Exercise 7: Verbetert suiker je geluk?Exercise 8: Confounders Exercise 9: Ontwerp van experimenten Exercise 10: Type onderzoeken

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Exercise 11: Longitudinaal vs. dwarsdoorsnedeonderzoek Exercise 12: Gefeliciteerd!