CLT iş başında

Merkezi limit teoremi, bir örnek istatistiğinin örnekleme dağılımının, hangi orijinal dağılımdan örneklediğine bakılmaksızın, daha fazla örnek aldıkça normal dağılıma yaklaştığını söyler.

Bu egzersizde, örnek ortalamasına odaklanacak ve Amir'in sattığı ürünü kullanmayı planlayan kişi sayısını içeren amir_deals veri kümesindeki num_users sütununu daha yakından incelerken merkezi limit teoremini iş başında göreceksin.

pandas pd olarak, numpy np olarak ve matplotlib.pyplot plt olarak yüklendi ve amir_deals kullanılabilir durumda.

Bu egzersiz

Python ile İstatistiğe Giriş

kursunun bir parçasıdır

Kursu Görüntüle

Uygulamalı interaktif egzersiz

Bu örnek kodu tamamlayarak bu egzersizi bitirin.

# Create a histogram of num_users and show
____
____

Kodu Düzenle ve Çalıştır

Bu egzersiz

Python ile İstatistiğe Giriş

kursunun bir parçasıdır

IntermediárioNível de habilidade

4.8+

Kursa Ücretsiz Başlayın

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: Normal dağılım Exercise 2: Amir'in satışlarının dağılımı Exercise 3: Normal dağılımdan olasılıklar Exercise 4: Yeni pazar koşullarında satışları simüle etme Exercise 5: Hangi pazar daha iyi?Exercise 6: Merkezi limit teoremi Exercise 7: Örnekleme dağılımlarını görselleştirme Exercise 8: CLT iş başında

Geçerli Egzersiz

Exercise 9: Ortamaların ortalaması Exercise 10: Poisson dağılımı Exercise 11: Lambda'yı belirleme Exercise 12: Potansiyel müşteri yanıtlarını izleme Exercise 13: Daha fazla olasılık dağılımı Exercise 14: Dağılımları sürükle-bırak Exercise 15: Aday müşteri talepleri arasındaki zamanı modelleme Exercise 16: t-dağılımı

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!