Entwurf

349  stand-reg

regression
Veröffentlichungsdatum

2. Juli 2024

Schlüsselwörter

Aufgaben, Statistik, Prognose, Modellierung, R, Datenanalyse, Regression

349.1 Aufgabe

Gegeben sei das folgende lineare Modell.

library(tidyverse)
library(easystats)
library(rstanarm)
data(mariokart, package = "openintro")

mariokart_z <-
  mariokart |>
  select(total_pr, wheels) |> 
  standardise()

model1 <- stan_glm(total_pr ~ wheels, data = mariokart_z)
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Hier sind die Ergebnisse des Modells:

parameters(model1)
plot(parameters(model1))

Welche Aussage zum Modell model1 ist korrekt?

  1. Für eine Einheit Unterschied in wheels ändert sich total_pr um etwa 0 Einheiten.
  2. Für eine Einheit Unterschied in wheels ändert sich total_pr um etwa 0.33 Einheiten.
  3. Für eine SD-Einheit Unterschied in wheels ändert sich total_pr um etwa 0 Einheiten.
  4. Für eine SD-Einheit Unterschied in wheels ändert sich total_pr um etwa 0 SD-Einheiten.
  5. Für eine SD-Einheit Unterschied in total_pr ändert sich wheels um etwa 0 SD-Einheiten.

Hinweise:

  • Beachten Sie die üblichen Hinweise des Datenwerks.

349.2 Lösung

D

Da beide variablen, die UV und die AV, z-standardisiert sind, bedeutet das, dass die statistischen Effekte (Regressionsgewichte) als Unterschiede in z-Werten zu verstehen sind.

Hinweis: Es gibt viele Möglichkeiten, die Variablen zu (z-)standardisieren. Hier ist eine andere Option:

model2 <- stan_glm(scale(total_pr) ~  scale(wheels), data = mariokart)
parameters(model2)
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