İyileşme için bir ölçüt

İlk analizinde, 50 m yarışlarında yüzücülerin yarı final ve finalde düşük numaralı kulvarlardan (1-3) yüksek numaralı kulvarlara (6-8) geçerken sürelerinin nasıl değiştiğini inceleyeceksin. Önceki bölümde yarı final ile final performansı arasında çok az fark olduğunu göstermiştik; bu yüzden final ve yarı final olmasından kaynaklanan farkları göz ardı edeceksin.

Mümkün olduğunca çok veri kullanmak istediğin için, hem erkekler hem kadınlar yarışmaları için dört kulvar stilinin tamamını kullan. Buna göre, tek bir yüzücü için bir turdan sonraki tura iyileşmeyi ölçmek adına iyi bir ölçüt ne olurdu? Burada t_a düşük numaralı kulvardaki yüzme süresi, t_b ise yüksek numaralı kulvardaki yüzme süresidir.

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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: Akıntı tartışmasına giriş Exercise 2: İyileşme için bir ölçüt

Geçerli Egzersiz

Exercise 3: Düşükten yükseğe kulvar geçişindeki iyileşmenin ECDF'si Exercise 4: Ortalama iyileşmenin kestirimi Exercise 5: Hipotezi nasıl test etmeliyiz?Exercise 6: Hipotez testi: Kulvar ataması performansı etkiler mi?Exercise 7: 2015 etkinliğinde de bu sorun var mıydı?Exercise 8: Zikzak etkisi Exercise 9: Hangi bölümleri dikkate almalıyız?Exercise 10: EDA: tek ve çift bölünmeler arasındaki ortalama farklar Exercise 11: Akım etkisi kulvar konumuna nasıl bağlı?Exercise 12: Hipotez testi: Bu tesadüf olabilir mi?Exercise 13: Yüzme analizinin özeti

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: Variations in earthquake frequency and seismicity Exercise 2: EDA: Plotting earthquakes over time Exercise 3: Estimates of the mean interearthquake times Exercise 4: Hypothesis test: did earthquake frequency change?Exercise 5: How to display your analysis Exercise 6: Earthquake magnitudes in Oklahoma Exercise 7: EDA: Comparing magnitudes before and after 2010 Exercise 8: Quantification of the b-values Exercise 9: How should we do a hypothesis test on differences of the b-value?Exercise 10: Hypothesis test: are the b-values different?Exercise 11: What can you conclude from this analysis?Exercise 12: Closing comments