Menafsirkan ECDF dan alur ceritanya

Meskipun analisis distribusi yang lebih mendalam sering diperlukan untuk analisis yang cermat, Anda sudah bisa mendapatkan gambaran tentang distribusi dan alur cerita di balik data dengan mengamati ECDF secara sekilas. Pernyataan mana berikut ini yang paling masuk akal tentang bagaimana panjang bout aktif terdistribusi dan proses seperti apa yang mungkin mendasari keluarnya ikan dari bout aktif untuk beristirahat?

Jika Anda perlu penyegaran, Anda dapat menonton video dari Statistical Thinking in Python (Bagian 1) tentang cerita di balik distribusi probabilitas.

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Studi Kasus dalam Pemikiran Statistik

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Latihan ini adalah bagian dari kursus

Studi Kasus dalam Pemikiran Statistik

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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: Aktivitas ikan zebra dan melatonin Exercise 2: EDA: Plot ECDF untuk panjang active bout Exercise 3: Menafsirkan ECDF dan alur ceritanya

Latihan Saat Ini

Exercise 4: Interval kepercayaan bootstrap Exercise 5: Estimasi parameter: active bout length Exercise 6: Uji hipotesis permutasi dan bootstrap Exercise 7: Uji permutasi: wild type versus heterozigot Exercise 8: Uji hipotesis bootstrap Exercise 9: Regresi linear dan pairs bootstrap Exercise 10: Menilai laju pertumbuhan Exercise 11: Memplot kurva pertumbuhan

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: 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