Analogi waktu di situs web

Ternyata Anda sudah melakukan uji hipotesis yang analog dengan uji A/B ketika Anda tertarik pada berapa lama waktu yang dihabiskan di situs web sebelum dan sesudah kampanye iklan. Gaya dorong lidah katak (besaran kontinu seperti waktu di situs web) adalah analoginya. "Sebelum" = Katak A dan "sesudah" = Katak B. Mari kita latih kembali dengan sesuatu yang benar-benar merupakan skenario sebelum/sesudah.

Kita kembali ke himpunan data no-hitter. Pada tahun 1920, Major League Baseball menerapkan perubahan aturan penting yang mengakhiri apa yang disebut era dead ball. Yang penting, pelempar tidak lagi diizinkan meludahi atau menggores bola, suatu tindakan yang sangat menguntungkan pelempar. Dalam soal ini Anda akan melakukan uji A/B untuk menentukan apakah perubahan aturan ini menghasilkan laju no-hitter yang lebih lambat (yaitu, rata-rata waktu antartanpa-pemukul yang lebih panjang) dengan menggunakan selisih rerata waktu antartanpa-pemukul sebagai statistik uji. Waktu antartanpa-pemukul untuk masing-masing era disimpan dalam array nht_dead dan nht_live, di mana "nht" merupakan singkatan dari "no-hitter time" (waktu tanpa pemukul).

Karena Anda akan menggunakan fungsi draw_perm_reps() dalam latihan ini, ada baiknya Anda mengingat kembali tanda tangan pemanggilannya: draw_perm_reps(d1, d2, func, size=1) atau bahkan merujuk kembali ke latihan bab 3 saat Anda mendefinisikannya.

Latihan ini adalah bagian dari kursus

Pemikiran Statistik dengan Python (Bagian 2)

Petunjuk latihan

Hitung selisih teramati pada rerata waktu antartanpa-pemukul menggunakan diff_of_means().
Hasilkan 10.000 replikasi permutasi dari selisih rerata menggunakan draw_perm_reps().
Hitung dan cetak p-value.

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

# Compute the observed difference in mean inter-no-hitter times: nht_diff_obs
nht_diff_obs = ____

# Acquire 10,000 permutation replicates of difference in mean no-hitter time: perm_replicates
perm_replicates = ____


# Compute and print the p-value: p
p = ____
print('p-val =', p)

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: Formulating and simulating a hypothesis Exercise 2: Generating a permutation sample Exercise 3: Visualizing permutation sampling Exercise 4: Test statistics and p-values Exercise 5: Test statistics Exercise 6: What is a p-value?Exercise 7: Generating permutation replicates Exercise 8: Look before you leap: EDA before hypothesis testing Exercise 9: Permutation test on frog data Exercise 10: Bootstrap hypothesis tests Exercise 11: A one-sample bootstrap hypothesis test Exercise 12: A two-sample bootstrap hypothesis test for difference of means

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: Pengujian A/B Exercise 2: Pemungutan suara untuk Civil Rights Act tahun 1964 Exercise 3: Mana yang setara?Exercise 4: Analogi waktu di situs web

Latihan Saat Ini

Exercise 5: Apa yang seharusnya Anda lakukan terlebih dahulu?Exercise 6: Uji korelasi Exercise 7: Mensimulasikan hipotesis nol terkait korelasi Exercise 8: Uji hipotesis pada korelasi Pearson Exercise 9: Apakah insektisida neonicotinoid memiliki konsekuensi yang tidak diinginkan?Exercise 10: Uji hipotesis bootstrap pada jumlah sperma lebah

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