Membuat sampel permutasi

Dalam video, Anda mempelajari bahwa pengambilan sampel permutasi adalah cara yang sangat baik untuk mensimulasikan hipotesis bahwa dua variabel memiliki sebaran probabilitas yang identik. Ini sering kali merupakan hipotesis yang ingin Anda uji, sehingga pada latihan ini, Anda akan menulis fungsi untuk membuat sampel permutasi dari dua himpunan data.

Ingat, sebuah sampel permutasi dari dua array yang masing-masing memiliki n1 dan n2 entri dibentuk dengan menggabungkan kedua array tersebut, mengacak isi array gabungan, lalu mengambil n1 entri pertama sebagai sampel permutasi untuk array pertama dan n2 entri terakhir sebagai sampel permutasi untuk array kedua.

Latihan ini adalah bagian dari kursus

Pemikiran Statistik dengan Python (Bagian 2)

Petunjuk latihan

Gabungkan kedua array masukan menjadi satu menggunakan np.concatenate(). Pastikan Anda memberikan data1 dan data2 sebagai satu argumen (data1, data2).
Gunakan np.random.permutation() untuk melakukan permutasi pada array gabungan.
Simpan len(data1) entri pertama dari permuted_data sebagai perm_sample_1 dan len(data2) entri terakhir dari permuted_data sebagai perm_sample_2. Dalam praktiknya, ini dapat dilakukan dengan menggunakan :len(data1) dan len(data1): untuk melakukan slicing pada permuted_data.
Kembalikan perm_sample_1 dan perm_sample_2.

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

def permutation_sample(data1, data2):
    """Generate a permutation sample from two data sets."""

    # Concatenate the data sets: data
    data = ____

    # Permute the concatenated array: permuted_data
    permuted_data = ____

    # Split the permuted array into two: perm_sample_1, perm_sample_2
    perm_sample_1 = permuted_data[____]
    perm_sample_2 = permuted_data[____]

    return perm_sample_1, perm_sample_2

Edit dan Jalankan Kode

Latihan ini adalah bagian dari kursus

Pemikiran Statistik dengan Python (Bagian 2)

SkillTag.level.intermediateSkillTag.label

4.8+

Mulai Kursus Gratis

When doing statistical inference, we speak the language of probability. A probability distribution that describes your data has parameters. So, a major goal of statistical inference is to estimate the values of these parameters, which allows us to concisely and unambiguously describe our data and draw conclusions from it. In this chapter, you will learn how to find the optimal parameters, those that best describe your data.

Exercise 1: Optimal parameters Exercise 2: How often do we get no-hitters?Exercise 3: Do the data follow our story?Exercise 4: How is this parameter optimal?Exercise 5: Linear regression by least squares Exercise 6: EDA of literacy/fertility data Exercise 7: Linear regression Exercise 8: How is it optimal?Exercise 9: The importance of EDA: Anscombe's quartet Exercise 10: The importance of EDA Exercise 11: Linear regression on appropriate Anscombe data Exercise 12: Linear regression on all Anscombe data

To "pull yourself up by your bootstraps" is a classic idiom meaning that you achieve a difficult task by yourself with no help at all. In statistical inference, you want to know what would happen if you could repeat your data acquisition an infinite number of times. This task is impossible, but can we use only the data we actually have to get close to the same result as an infinitude of experiments? The answer is yes! The technique to do it is aptly called bootstrapping. This chapter will introduce you to this extraordinarily powerful tool.

Exercise 1: Generating bootstrap replicates Exercise 2: Getting the terminology down Exercise 3: Bootstrapping by hand Exercise 4: Visualizing bootstrap samples Exercise 5: Bootstrap confidence intervals Exercise 6: Generating many bootstrap replicates Exercise 7: Bootstrap replicates of the mean and the SEM Exercise 8: Confidence intervals of rainfall data Exercise 9: Bootstrap replicates of other statistics Exercise 10: Confidence interval on the rate of no-hitters Exercise 11: Pairs bootstrap Exercise 12: A function to do pairs bootstrap Exercise 13: Pairs bootstrap of literacy/fertility data Exercise 14: Plotting bootstrap regressions

You now know how to define and estimate parameters given a model. But the question remains: how reasonable is it to observe your data if a model is true? This question is addressed by hypothesis tests. They are the icing on the inference cake. After completing this chapter, you will be able to carefully construct and test hypotheses using hacker statistics.

Exercise 1: Merumuskan dan mensimulasikan sebuah hipotesis Exercise 2: Membuat sampel permutasi

Latihan Saat Ini

Exercise 3: Memvisualisasikan pengambilan sampel permutasi Exercise 4: Statistik uji dan p-value Exercise 5: Statistik uji Exercise 6: Apa itu p-value?Exercise 7: Membuat replika permutasi Exercise 8: Berhati-hatilah sebelum melompat: EDA sebelum pengujian hipotesis Exercise 9: Uji permutasi pada data katak Exercise 10: Uji hipotesis dengan bootstrap Exercise 11: Uji hipotesis bootstrap satu-sampel Exercise 12: Uji hipotesis bootstrap dua-sampel untuk selisih rata-rata

As you saw from the last chapter, hypothesis testing can be a bit tricky. You need to define the null hypothesis, figure out how to simulate it, and define clearly what it means to be "more extreme" in order to compute the p-value. Like any skill, practice makes perfect, and this chapter gives you some good practice with hypothesis tests.

Exercise 1: A/B testing Exercise 2: The vote for the Civil Rights Act in 1964 Exercise 3: What is equivalent?Exercise 4: A time-on-website analog Exercise 5: What should you have done first?Exercise 6: Test of correlation Exercise 7: Simulating a null hypothesis concerning correlation Exercise 8: Hypothesis test on Pearson correlation Exercise 9: Do neonicotinoid insecticides have unintended consequences?Exercise 10: Bootstrap hypothesis test on bee sperm counts

Every year for the past 40-plus years, Peter and Rosemary Grant have gone to the Galápagos island of Daphne Major and collected data on Darwin's finches. Using your skills in statistical inference, you will spend this chapter with their data, and witness first hand, through data, evolution in action. It's an exhilarating way to end the course!

Exercise 1: Finch beaks and the need for statistics Exercise 2: EDA of beak depths of Darwin's finches Exercise 3: ECDFs of beak depths Exercise 4: Parameter estimates of beak depths Exercise 5: Hypothesis test: Are beaks deeper in 2012?Exercise 6: Variation in beak shapes Exercise 7: EDA of beak length and depth Exercise 8: Linear regressions Exercise 9: Displaying the linear regression results Exercise 10: Beak length to depth ratio Exercise 11: How different is the ratio?Exercise 12: Calculation of heritability Exercise 13: EDA of heritability Exercise 14: Correlation of offspring and parental data Exercise 15: Pearson correlation of offspring and parental data Exercise 16: Measuring heritability Exercise 17: Is beak depth heritable at all in G. scandens?Exercise 18: Final thoughts