393  mtcars-simple2

Schlüsselwörter

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

393.1 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.

Compute the explained variance (point estimate) for the above model!

Notes:

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

393.2 Solution

Compute Model:

lm1_freq <- lm(mpg ~ hp + cyl + disp, data = mtcars)

library(rstanarm)
lm1_bayes <- stan_glm(mpg ~ hp + cyl + disp, data = mtcars, refresh = 0)

Get R2:

library(easystats)

r2(lm1_freq)
## # R2 for Linear Regression
##        R2: 0.768
##   adj. R2: 0.743

r2(lm1_bayes)
## # Bayesian R2 with Compatibility Interval
## 
##   Conditional R2: 0.746 (95% CI [0.593, 0.849])

The coefficient is estimated as about 0.77.

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Categories:

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