Grafische EDA van de series 200 m vrije slag mannen

In de series zwemmen alle deelnemers, zowel de hele snelle als de hele langzame. Om te verkennen hoe de zwemtijden zijn verdeeld, maak je een ECDF van de 200 meter vrije slag bij de mannen.

Deze oefening maakt deel uit van de cursus

Casestudies in statistisch denken

Oefeninstructies

Genereer x- en y-waarden voor de ECDF met dcst.ecdf(). De zwemtijden van de series staan in de numpy-array mens_200_free_heats.
Plot de ECDF als punten. Vergeet niet de juiste marker en linestyle in te stellen.
Label de assen en toon de plot. Gebruik 'time (s)' als label voor de x-as en 'ECDF' als label voor de y-as.

Interactieve oefening met praktijkervaring

Probeer deze oefening door deze voorbeeldcode aan te vullen.

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


# Plot the ECDF as dots


# Label axes and show plot

Code bewerken en uitvoeren

Deze oefening maakt deel uit van de cursus

Casestudies in statistisch denken

SkillTag.level.intermediateSkillTag.label

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Begin gratis met de cursus

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: Introductie tot zwemdata Exercise 2: Grafische EDA van de series 200 m vrije slag mannen

Huidige oefening

Exercise 3: 200 m vrije slag met betrouwbaarheidsinterval Exercise 4: Zwemmen sporters sneller in de finale?Exercise 5: EDA: finales versus halve finales Exercise 6: Parameterinschattingen van het verschil tussen finales en halve finales Exercise 7: Hoe voer je de permutatietoets uit Exercise 8: Permutatiesteekproeven genereren Exercise 9: Hypothesetoets: Zwemmen vrouwen hetzelfde in halve finales en finales?Exercise 10: Hoe neemt de prestatie van zwemmers af bij lange nummers?Exercise 11: EDA: Plot al je data Exercise 12: Lineaire regressie van gemiddelde splittijd Exercise 13: Hypothesetoets: gaan ze langzamer?

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