394  mtcars-simple3

Schlüsselwörter

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

394.1 Exercise

We will use the dataset mtcars in this exercise.

Assume your causal model of your research dictates that fuel economy is a linear function of horse power, cylinder count and displacement of the engine.

Which of the predictors in the above model has the weakest causal impact on the output variable?

Notes:

• Use can either use frequentist or bayesian modeling.
• Use R for all computations.
• There are multiple ways to find a solution.

394.2 Answerlist

• cyl
• hp
• disp
• All are equally strong
• none of the above

394.3 Solution

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

In order to gauge the relative importance of the predictors, we need to make sure they are on the same scale:

mtcars2 <-
  standardise(mtcars)

Compute Model:

lm1_freq <- lm(mpg ~ hp + cyl + disp, data = mtcars2)
lm1_bayes <- stan_glm(mpg ~ hp + cyl + disp, data = mtcars2, refresh = 0)

Get parameters:

parameters(lm1_bayes)

Note that the absolute value of the coefficient’s estimate is what we are after.

The predictors with the strongest impact is disp, and cyl. The weakest influence has hp.

394.4 Answerlist

• wrong
• correct
• wrong
• wrong
• wrong

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Categories:

• regression
• en
• bayes
• frequentist
• qm1
• stats-nutshell