Verklaarde variantie

Je zag dat huurprijzen tussen Houston en Las Vegas gecorreleerd zijn. Maar in hoeverre kan de prijsverandering in de ene stad de prijsverandering in de andere stad verklaren? Door R-kwadraat te berekenen kun je dit precies kwantificeren.

Een NumPy-array met huren is geladen voor Las Vegas (lasvegas_rents) en Houston (houston_rents), evenals de dates die bij elke meting horen. De pakketten pandas als pd, NumPy als np, Matplotlib als plt en het stats-pakket uit SciPy zijn al voor je geladen.

Deze oefening maakt deel uit van de cursus

Basis van inferentie in Python

Cursus bekijken

Oefeninstructies

Bereken de Pearson-correlatiecoëfficiënt tussen houston_rents en lasvegas_rents.
Print het kwadraat van de correlatiecoëfficiënt.

Praktische interactieve oefening

Probeer deze oefening eens door deze voorbeeldcode in te vullen.

# Compute the correlation between Houston and Las Vegas
r, p_value = ____

# Print R-squared
____

Code bewerken en uitvoeren

Deze oefening maakt deel uit van de cursus

Basis van inferentie in Python

SkillTag.level.advancedSkillTag.label

4.9+

Begin de cursus gratis

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

Huidige oefening

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 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