bike05

probability

Published

December 18, 2025

1 Aufgabe

Laden Sie die Daten day.csv aus dem Bike Sharing Datensatz.

Berechnen Sie folgendes Modell: cnt ~ workingday, data = day.

Nennen Sie dann den Punktschätzer für \(\beta1\).
Interpretieren Sie diesen Wert.
Können wir die \(H_0\) für \(\beta_1\) ablehnen?
Visualisieren Sie den Gruppenunterschied mit einem geeigneten Plot.

Hinweise:

Beachten Sie die üblichen Hinweise des Datenwerks.

2 Lösung

Pakete starten:

library(tidyverse)
library(easystats)
library(rstanarm)

Daten importieren:

Achtung! Die herunter geladenen Daten müssen zuerst entzippt werden.

Bei mir liegen diese Daten hier:

day <- read_csv("C:/Users/sebastian.sauer/Downloads/bike+sharing+dataset/day.csv")

Bei Ihnen liegen die Daten in einem Ordner mit einem anderen Namen.

Falls der Datensatz im gleichen Ordner liegt wie Ihre Quarto-Datei, dann können Sie die Daten ohne Pfadangabe importieren:

day <- read_csv("day.csv")

Das ist auch eine gute Methode, um Daten zu importieren:

day <- read_csv(paste0(here::here(),"/data/bike+sharing+dataset/day.csv"))

3 Modell berechnen

m_cnt_workingday <-
  stan_glm(cnt ~ workingday, data = day)


SAMPLING FOR MODEL 'continuous' NOW (CHAIN 1).
Chain 1: 
Chain 1: Gradient evaluation took 1.8e-05 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.18 seconds.
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SAMPLING FOR MODEL 'continuous' NOW (CHAIN 2).
Chain 2: 
Chain 2: Gradient evaluation took 7e-06 seconds
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SAMPLING FOR MODEL 'continuous' NOW (CHAIN 3).
Chain 3: 
Chain 3: Gradient evaluation took 6e-06 seconds
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SAMPLING FOR MODEL 'continuous' NOW (CHAIN 4).
Chain 4: 
Chain 4: Gradient evaluation took 4e-06 seconds
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parameters(m_cnt_workingday)

Parameter	Median	CI	CI_low	CI_high	pd	Rhat	ESS_tail	Prior_Distribution	Prior_Location	Prior_Scale
(Intercept)	4331.9401	0.95	4074.4574	4585.8089	1.00	0.9999662	1935	normal	4504.349	4843.029
workingday	248.3911	0.95	-47.2145	564.7314	0.95	0.9999054	2418	normal	0.000	10409.891

coef(m_cnt_workingday)[2]

workingday 
  248.3911

4 Interpretation

Bei mir Werte zwischen 250 und 260 zusätzlich vermietete Fahrräder an Arbeitstagen im Vergleich zu Nicht-Arbeitstagen.

5 Nullhypothese

Wir können die \(H_0\) für \(\beta_1\) NICHT ablehnen: Die Null ist im Konfidenzintervall enthalten.

parameters(m_cnt_workingday) |> plot()

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