Memvisualisasikan sebaran penarikan contoh

Di sebelah kanan, coba buat sampling distributions dari berbagai statistik ringkasan dari sampel berbagai distribusi. Pada distribusi mana teorema limit pusat tidak berlaku?

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Pengantar Statistika di Python

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Latihan ini adalah bagian dari kursus

Pengantar Statistika di 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: Distribusi normal Exercise 2: Distribusi penjualan Amir Exercise 3: Probabilitas dari sebaran normal Exercise 4: Mensimulasikan penjualan dalam kondisi pasar baru Exercise 5: Pasar mana yang lebih baik?Exercise 6: Teorema limit pusat Exercise 7: Memvisualisasikan sebaran penarikan contoh

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Exercise 8: CLT dalam praktik Exercise 9: Rata-rata dari rata-rata Exercise 10: Distribusi Poisson Exercise 11: Mengidentifikasi lambda Exercise 12: Melacak respons prospek Exercise 13: Distribusi probabilitas lainnya Exercise 14: Seret dan lepas distribusi Exercise 15: Memodelkan waktu antar lead Exercise 16: Distribusi t

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!