Ranglijsten vergelijken

Verschillende organisaties rangschikken universiteiten op factoren zoals onderzoek, onderwijs en studenttevredenheid, naast andere factoren. Drie van zulke organisaties zijn de Times Higher Education World (THEW) University Ranking, de Academic Ranking (ARW) of World Universities en de Center for World (CW) University Rankings. Het is echter gebruikelijk dat deze organisaties verschillende posities toekennen aan dezelfde universiteit. Hoe kun je de mate van overeenstemming of afwijking tussen ranglijsten kwantitatief meten? Eén aanpak is een niet-parametrische rangcorrelatiecoëfficiënt zoals Kendall’s tau te gebruiken.

Een DataFrame met deze drie sets ranglijsten staat in rankings_df. De pakketten pandas als pd, NumPy als np en het pakket stats uit SciPy zijn al voor je ingeladen.

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Basis van inferentie in Python

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# Compute Kendall's tau between the THEW and ARW rankings
tau_thew_arw, p_value_thew_arw = ____

# Compute Kendall's tau between the THEW and CW rankings
tau_thew_cw, p_value_thew_cw = ____

# Compute Kendall's tau between the ARW and CW rankings
tau_arw_cw, p_value_arw_cw = ____

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Basis van inferentie in Python

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In this chapter, we'll explore the relationship between samples and statistically justifiable conclusions. Choosing a sample is the basis of making sound statistical decisions, and we’ll explore how the choice of a sample affects the outcome of your inference.

Exercise 1: Statistical inference and random sampling Exercise 2: Sampling and point estimates Exercise 3: Repeated sampling, point estimates and inference Exercise 4: Sampling and bias Exercise 5: Visualizing samples Exercise 6: Inference and bias Exercise 7: Confidence intervals and sampling Exercise 8: Normal sampling distributions Exercise 9: Calculating confidence intervals Exercise 10: Drawing conclusions from samples

Learn all about applying normality tests, correlation tests, and parametric and non-parametric tests for sound inference. Hypothesis tests are tools, and choosing the right tool for the job is critical for statistical decision-making. While you may be familiar with some of these tests in introductory courses, you'll go deeper to enhance your inferential toolkit in this chapter.

Exercise 1: Normaliteitstoetsen Exercise 2: Toetsen op normaliteit Exercise 3: Verdeling van fouten Exercise 4: Een normale verdeling fitten Exercise 5: Correlatietests Exercise 6: Correlatie testen Exercise 7: Autocorrelatie Exercise 8: Verklaarde variantie Exercise 9: Parametrische toetsen Exercise 10: Gelijke variantie Exercise 11: Normaliteit per groep Exercise 12: ANOVA Exercise 13: Niet-parametrische toetsen Exercise 14: Ranglijsten vergelijken

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Exercise 15: Mediaanwaarden vergelijken

In this chapter, you'll measure and interpret effect size in various situations, encounter the multiple comparisons problem, and explore the power of a test in depth. While p-values tell you if a significant effect is present, they don't tell you how strong that effect is. Effect size measures how strong an effect a treatment has. Master the factors underpinning effect size in this chapter.

Exercise 1: Effect size Exercise 2: Effect size for means Exercise 3: Effect size for correlations Exercise 4: Effect size for categorical variables Exercise 5: Multiple comparisons and corrections Exercise 6: Multiple comparisons problem Exercise 7: Bonferonni-Holm correction Exercise 8: Power of a test Exercise 9: What is power anyway?Exercise 10: Power for experimental design Exercise 11: Computing power and sample sizes

You’ll expand your inferential statistics toolkit further with a look at bootstrapping, permutation tests, and methods of combining evidence from p-values. Bootstrapping will provide you with a first look at statistical simulation. In the lesson meta-analysis, you’ll learn all about combining results from multiple studies. You’ll end with a look at permutation tests, a powerful and flexible non-parametric statistical tool.

Exercise 1: Bootstrapping Exercise 2: Bootstrap confidence intervals Exercise 3: Bootstrapping vs. normality Exercise 4: Combining evidence from p-values Exercise 5: Fisher's method in SciPy Exercise 6: Inference using Fisher's method Exercise 7: Summarizing Fisher's method Exercise 8: Permutation tests Exercise 9: Permutation tests for correlations Exercise 10: Permutation tests and bootstrapping Exercise 11: Analyzing skewed data with a permutation test Exercise 12: Course wrap-up video