Nothing

```
## ----setup, echo = FALSE-------------------------------------------------
knitr::opts_chunk$set(echo = TRUE, message = FALSE, warning = FALSE, fig.align = 'center', fig.width = 4.65, fig.height = 4)
## ---- include=FALSE------------------------------------------------------
library("bookdown")
## ---- fig.show='hold', fig.cap = "Contaminated normal distribution Y", include=FALSE, eval=FALSE----
# #Setting $\pi = 0.3$ and $\sigma^2 = 1$ produces the following distribution for $n = 10000$:
# library("SimCorrMix")
# Nstcum <- calc_mixmoments(mix_pis = c(0.3, 0.7), mix_mus = c(0, 0),
# mix_sigmas = c(3, 1), mix_skews = c(0, 0), mix_skurts = c(0, 0),
# mix_fifths = c(0, 0), mix_sixths = c(0, 0))
# Nmix <- contmixvar1(n = 10000, "Polynomial", Nstcum[1], Nstcum[2]^2,
# mix_pis = c(0.3, 0.7), mix_mus = c(0, 0), mix_sigmas = c(3, 1),
# mix_skews = c(0, 0), mix_skurts = c(0, 0), mix_fifths = c(0, 0),
# mix_sixths = c(0, 0))
# plot_simpdf_theory(sim_y = Nmix$Y_mix[, 1], overlay = FALSE,
# legend.position = "none", title = "PDF of Y")
# qqnorm(Nmix$Y_mix[, 1], main = "Q-Q plot for Y")
# qqline(Nmix$Y_mix[, 1], col = 2)
# #The Q-Q plot shows that the assumption of homogeneous variance for the error distribution would not be valid in this situation
```

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