Tijd tussen leads modelleren

Om Amir's prestaties beter te beoordelen, wil je weten hoe lang het duurt voordat hij op een lead reageert nadat hij die heeft geopend. Gemiddeld reageert hij op 1 verzoek per 2,5 uur. In deze oefening bereken je kansen voor verschillende tijden tussen het ontvangen van een lead en het versturen van een reactie door Amir.

Deze oefening maakt deel uit van de cursus

Inleiding tot statistiek in Python

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# Import expon from scipy.stats
____

# Print probability response takes < 1 hour
print(____(____, scale=____))

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Deze oefening maakt deel uit van de cursus

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: De normale verdeling Exercise 2: Verdeling van Amirs verkopen Exercise 3: Kansen uit de normale verdeling Exercise 4: Verkoop simuleren onder nieuwe marktomstandigheden Exercise 5: Welke markt is beter?Exercise 6: De centrale limietstelling Exercise 7: Steekproefverdelingen visualiseren Exercise 8: De CLT in de praktijk Exercise 9: Het gemiddelde van gemiddelden Exercise 10: De Poisson-verdeling Exercise 11: Lambda herkennen Exercise 12: Respons op leads bijhouden Exercise 13: Meer kansverdelingen Exercise 14: Verdelingen slepen en neerzetten Exercise 15: Tijd tussen leads modelleren

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Exercise 16: De t-verdeling

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!