Sampling dan estimasi titik

Anda memiliki akses ke riwayat perdagangan singkat Bitcoin (BTC) dan S&P 500 (SP500). Anda memutuskan untuk memilih sembilan puluh hari berturut-turut untuk menganalisis pertumbuhan persentase masing-masing aset pada periode yang sama.

Anda akan mulai dengan memilih nomor baris awal. Untuk memastikan Anda mendapatkan sampel 90 baris berurutan, Anda perlu memilih nomor baris awal ini dari rentang nilai mulai dari nol hingga panjang btc_sp_df, dengan mengecualikan 90 baris terakhir. Tujuan Anda adalah menggunakan sampel ini untuk lebih memahami pertumbuhan kedua aset secara umum.

Data perdagangan telah dimuat ke dalam btc_sp_df, begitu juga paket pandas sebagai pd dan NumPy sebagai np.

Latihan ini adalah bagian dari kursus

Dasar-dasar Inferensi di Python

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

# Select a random starting row number, not including the last 90 rows
initial_row_number = np.random.____(____(len(____) - ____))

# Use initial_row_number to select the next 90 rows from that row number
sample_df = btc_sp_df.____[____:(____ + ____)]

Edit dan Jalankan Kode

Latihan ini adalah bagian dari kursus

Dasar-dasar Inferensi di Python

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4.9+

Mulai Kursus Gratis

In this chapter, we'll explore the relationship between samples and statistically justifiable conclusions. Choosing a sample is the basis of making sound statistical decisions, and we’ll explore how the choice of a sample affects the outcome of your inference.

Exercise 1: Inferensi statistik dan pengambilan sampel acak Exercise 2: Sampling dan estimasi titik

Latihan Saat Ini

Exercise 3: Pengambilan sampel berulang, estimasi titik, dan inferensi Exercise 4: Pengambilan Sampel dan Bias Exercise 5: Memvisualisasikan sampel Exercise 6: Inferensi dan bias Exercise 7: Interval kepercayaan dan pengambilan sampel Exercise 8: Distribusi sampling normal Exercise 9: Menghitung interval kepercayaan Exercise 10: Menarik kesimpulan dari sampel

Learn all about applying normality tests, correlation tests, and parametric and non-parametric tests for sound inference. Hypothesis tests are tools, and choosing the right tool for the job is critical for statistical decision-making. While you may be familiar with some of these tests in introductory courses, you'll go deeper to enhance your inferential toolkit in this chapter.

Exercise 1: Normality tests Exercise 2: Testing for normality Exercise 3: Distribution of errors Exercise 4: Fitting a normal distribution Exercise 5: Correlation tests Exercise 6: Testing for correlation Exercise 7: Autocorrelation Exercise 8: Explained variance Exercise 9: Parametric tests Exercise 10: Equal variance Exercise 11: Normality of groups Exercise 12: ANOVA Exercise 13: Non-parametric tests Exercise 14: Comparing rankings Exercise 15: Comparing medians

In this chapter, you'll measure and interpret effect size in various situations, encounter the multiple comparisons problem, and explore the power of a test in depth. While p-values tell you if a significant effect is present, they don't tell you how strong that effect is. Effect size measures how strong an effect a treatment has. Master the factors underpinning effect size in this chapter.

Exercise 1: Effect size Exercise 2: Effect size for means Exercise 3: Effect size for correlations Exercise 4: Effect size for categorical variables Exercise 5: Multiple comparisons and corrections Exercise 6: Multiple comparisons problem Exercise 7: Bonferonni-Holm correction Exercise 8: Power of a test Exercise 9: What is power anyway?Exercise 10: Power for experimental design Exercise 11: Computing power and sample sizes

You’ll expand your inferential statistics toolkit further with a look at bootstrapping, permutation tests, and methods of combining evidence from p-values. Bootstrapping will provide you with a first look at statistical simulation. In the lesson meta-analysis, you’ll learn all about combining results from multiple studies. You’ll end with a look at permutation tests, a powerful and flexible non-parametric statistical tool.

Exercise 1: Bootstrapping Exercise 2: Bootstrap confidence intervals Exercise 3: Bootstrapping vs. normality Exercise 4: Combining evidence from p-values Exercise 5: Fisher's method in SciPy Exercise 6: Inference using Fisher's method Exercise 7: Summarizing Fisher's method Exercise 8: Permutation tests Exercise 9: Permutation tests for correlations Exercise 10: Permutation tests and bootstrapping Exercise 11: Analyzing skewed data with a permutation test Exercise 12: Course wrap-up video