KEA: Depremleri zamana göre görselleştirme

1980 ile 2017’nin ilk yarısı arasında Oklahoma’daki tüm depremler için, y ekseninde büyüklük, x ekseninde zaman olacak şekilde bir grafik oluştur. Grafikteki her nokta tek bir depremi temsil eder. Depremlerin zamanları ondalık yıl biçiminde NumPy dizisi time içinde, büyüklükler ise NumPy dizisi mags içinde saklanıyor.

Bu egzersiz

İstatistiksel Düşünmede Vaka Çalışmaları

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Kursu Görüntüle

Egzersiz talimatları

plt.plot() kullanarak büyüklüğü (mags) zamana (time) karşı çiz. Anahtar argümanlar olarak marker='.' ve linestyle='none' kullan. Ayrıca üst üste binen noktaları daha iyi görmek için noktaları yarı saydam yapmak amacıyla alpha=0.1 anahtar argümanını da ekle.
x eksenini 'time (year)', y eksenini 'magnitude' olarak etiketle ve grafiği göster.

Uygulamalı interaktif egzersiz

Bu örnek kodu tamamlayarak bu egzersizi bitirin.

# Plot time vs. magnitude


# Label axes and show the plot

Kodu Düzenle ve Çalıştır

Bu egzersiz

İstatistiksel Düşünmede Vaka Çalışmaları

kursunun bir parçasıdır

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Kursa Ücretsiz Başlayın

To begin, you'll use two data sets from Caltech researchers to rehash the key points of Statistical Thinking I and II to prepare you for the following case studies!

Exercise 1: Activity of zebrafish and melatonin Exercise 2: EDA: Plot ECDFs of active bout length Exercise 3: Interpreting ECDFs and the story Exercise 4: Bootstrap confidence intervals Exercise 5: Parameter estimation: active bout length Exercise 6: Permutation and bootstrap hypothesis tests Exercise 7: Permutation test: wild type versus heterozygote Exercise 8: Bootstrap hypothesis test Exercise 9: Linear regressions and pairs bootstrap Exercise 10: Assessing the growth rate Exercise 11: Plotting the growth curve

In this chapter, you will practice your EDA, parameter estimation, and hypothesis testing skills on the results of the 2015 FINA World Swimming Championships.

Exercise 1: Introduction to swimming data Exercise 2: Graphical EDA of men's 200 free heats Exercise 3: 200 m free time with confidence interval Exercise 4: Do swimmers go faster in the finals?Exercise 5: EDA: finals versus semifinals Exercise 6: Parameter estimates of difference between finals and semifinals Exercise 7: How to do the permutation test Exercise 8: Generating permutation samples Exercise 9: Hypothesis test: Do women swim the same way in semis and finals?Exercise 10: How does the performance of swimmers decline over long events?Exercise 11: EDA: Plot all your data Exercise 12: Linear regression of average split time Exercise 13: Hypothesis test: are they slowing down?

Some swimmers said that they felt it was easier to swim in one direction versus another in the 2013 World Championships. Some analysts have posited that there was a swirling current in the pool. In this chapter, you'll investigate this claim! References - <a href="https://qz.com/761280/researchers-believe-certain-lanes-in-the-olympic-pool-may-have-given-some-swimmers-an-advantage/" target="_blank">Quartz Media</a>, <a href="https://www.washingtonpost.com/news/wonk/wp/2016/09/01/these-charts-clearly-show-how-some-olympic-swimmers-may-have-gotten-an-unfair-advantage/?utm_term=.dba907006ba1" target="_blank">Washington Post</a>, <a href="https://swimswam.com/rio-olympic-test-event-showed-same-pool-bias-2-0/" target="_blank">SwimSwam</a> (and also <a href="https://swimswam.com/problem-rio-pool/" target="_blank">here)</a>, and <a href="https://www.ncbi.nlm.nih.gov/pubmed/25003776" target="_blank">Cornett, et al</a>.

Exercise 1: Introduction to the current controversy Exercise 2: A metric for improvement Exercise 3: ECDF of improvement from low to high lanes Exercise 4: Estimation of mean improvement Exercise 5: How should we test the hypothesis?Exercise 6: Hypothesis test: Does lane assignment affect performance?Exercise 7: Did the 2015 event have this problem?Exercise 8: The zigzag effect Exercise 9: Which splits should we consider?Exercise 10: EDA: mean differences between odd and even splits Exercise 11: How does the current effect depend on lane position?Exercise 12: Hypothesis test: can this be by chance?Exercise 13: Recap of swimming analysis

Herein, you'll use your statistical thinking skills to study the frequency and magnitudes of earthquakes. Along the way, you'll learn some basic statistical seismology, including the Gutenberg-Richter law. This exercise exposes two key ideas about data science: 1) As a data scientist, you wander into all sorts of domain specific analyses, which is very exciting. You constantly get to learn. 2) You are sometimes faced with limited data, which is also the case for many of these earthquake studies. You can still make good progress!

Exercise 1: Introduction to statistical seismology and the Parkfield experiment Exercise 2: Parkfield earthquake magnitudes Exercise 3: Computing the b-value Exercise 4: The b-value for Parkfield Exercise 5: Timing of major earthquakes and the Parkfield sequence Exercise 6: Interearthquake time estimates for Parkfield Exercise 7: When will the next big Parkfield quake be?Exercise 8: How are the Parkfield interearthquake times distributed?Exercise 9: Computing the value of a formal ECDF Exercise 10: Computing the K-S statistic Exercise 11: Drawing K-S replicates Exercise 12: The K-S test for Exponentiality

Of course, earthquakes have a big impact on society, and recently are connected to human activity. In this final chapter, you'll investigate the effect that increased injection of saline wastewater due to oil mining in Oklahoma has had on the seismicity of the region.

Exercise 1: Deprem sıklığı ve sismisitedeki değişimler Exercise 2: KEA: Depremleri zamana göre görselleştirme

Geçerli Egzersiz

Exercise 3: Depremler arası ortalama süre tahminleri Exercise 4: Hipotez testi: deprem sıklığı değişti mi?Exercise 5: Analizini nasıl sunmalısın Exercise 6: Oklahoma’daki deprem büyüklükleri Exercise 7: EDA: 2010 öncesi ve sonrası büyüklüklerin karşılaştırılması Exercise 8: b-değerlerinin nicelenmesi Exercise 9: b-değerlerindeki farklar için hipotez testini nasıl yapmalıyız?Exercise 10: Hipotez testi: b-değerleri farklı mı?Exercise 11: Bu analizden ne çıkarabilirsin?Exercise 12: Kapanış yorumları