EDA grafis untuk heat 200 bebas putra

Dalam heat, semua peserta berenang, yang sangat cepat maupun yang sangat lambat. Untuk menelusuri bagaimana sebaran waktu renang, plot ECDF untuk nomor 200 meter gaya bebas putra.

Latihan ini merupakan bagian dari kursus

Studi Kasus dalam Pemikiran Statistik

Instruksi latihan

Hasilkan nilai x dan y untuk ECDF menggunakan dcst.ecdf(). Waktu renang pada heat disimpan dalam numpy array mens_200_free_heats.
Plot ECDF sebagai titik. Ingat untuk menentukan marker dan linestyle yang sesuai.
Beri label sumbu dan tampilkan plot. Gunakan 'time (s)' sebagai label sumbu x dan 'ECDF' sebagai label sumbu y.

Latihan interaktif langsung praktik

Cobalah latihan ini dengan melengkapi kode contoh ini.

# Generate x and y values for ECDF: x, y


# Plot the ECDF as dots


# Label axes and show plot

Edit dan Jalankan Kode

Latihan ini merupakan bagian dari kursus

Studi Kasus dalam Pemikiran Statistik

SkillTag.level.intermediateSkillTag.label

4.9+

Mulai Kursus Gratis

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: Pengantar data renang Exercise 2: EDA grafis untuk heat 200 bebas putra

Latihan Saat Ini

Exercise 3: Waktu 200 m gaya bebas dengan interval kepercayaan Exercise 4: Apakah perenang lebih cepat di final?Exercise 5: EDA: final versus semifinal Exercise 6: Estimasi parameter perbedaan antara final dan semifinal Exercise 7: Cara melakukan uji permutasi Exercise 8: Membuat sampel permutasi Exercise 9: Uji hipotesis: Apakah gaya renang putri sama di semifinal dan final?Exercise 10: Bagaimana performa perenang menurun pada nomor jarak jauh?Exercise 11: EDA: Plot seluruh data Anda Exercise 12: Regresi linear waktu split rata-rata Exercise 13: Uji hipotesis: apakah mereka melambat?

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