Het gemiddelde van gemiddelden

Je wilt weten wat het gemiddelde aantal gebruikers (num_users) per deal is, maar je wilt dit weten voor het hele bedrijf zodat je kunt zien of Amirs deals meer of minder gebruikers hebben dan de gemiddelde deal van het bedrijf. Het probleem is dat het bedrijf het afgelopen jaar aan meer dan tienduizend deals heeft gewerkt, dus het is niet realistisch om alle data te verzamelen. In plaats daarvan schat je het gemiddelde door meerdere willekeurige steekproeven van deals te nemen, omdat dit veel makkelijker is dan gegevens van iedereen in het bedrijf verzamelen.

De gebruikersdata voor alle deals van het bedrijf is beschikbaar in all_deals.

Deze oefening maakt deel uit van de cursus

Inleiding tot statistiek in R

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Oefeninstructies

Zet de random seed op 321.
Neem 30 steekproeven van grootte 20 uit all_deals$num_users en bereken het gemiddelde van elke steekproef. Sla de steekproefgemiddelden op in sample_means.
Neem het gemiddelde van sample_means.
Neem het gemiddelde van de kolom num_users van amir_deals.

Praktische interactieve oefening

Probeer deze oefening eens door deze voorbeeldcode in te vullen.

# Set seed to 321
___

# Take 30 samples of 20 values of num_users, take mean of each sample
sample_means <- ___(___, ___(___, ___) %>% mean())

# Calculate mean of sample_means
___

# Calculate mean of num_users in amir_deals
___

Code bewerken en uitvoeren

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: 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

Huidige oefening

Exercise 10: De Poissonverdeling Exercise 11: Lambda herkennen Exercise 12: Reacties op leads bijhouden Exercise 13: Meer kansverdelingen Exercise 14: Te veel verdelingen Exercise 15: Tijd tussen leads modelleren 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!