**LHS** continuous and **RHS** continuous is not problematic so long as the diagnostics get proper attention and overfitting is avoided.

**LHS** continuous and **RHS** binary had the awkward situation of a regression curve that has an intercept and slope for only the beginning and end, with nothing in between, as there is no Schrödinger’s binary provided for or a quantum regression methodology to go with it.

**LHS** binary and **RHS** continuous is an issue discussed recently here with one view being that it should never be used over logistic regression and the other that so long as R^2 is ignored ordinary least squares may be used.

**LHS** binary and **R** binary under logit regression

```
summary(fit5 <- glm(vs ~ am, data = mtcars, family = binomial(link = logit)))
#>
#> Call:
#> glm(formula = vs ~ am, family = binomial(link = logit), data = mtcars)
#>
#> Deviance Residuals:
#> Min 1Q Median 3Q Max
#> -1.2435 -0.9587 -0.9587 1.1127 1.4132
#>
#> Coefficients:
#> Estimate Std. Error z value Pr(>|z|)
#> (Intercept) -0.5390 0.4756 -1.133 0.257
#> am 0.6931 0.7319 0.947 0.344
#>
#> (Dispersion parameter for binomial family taken to be 1)
#>
#> Null deviance: 43.860 on 31 degrees of freedom
#> Residual deviance: 42.953 on 30 degrees of freedom
#> AIC: 46.953
#>
#> Number of Fisher Scoring iterations: 4
par(mfrow = c(2,2))
plot(fit5)
```

appears to have the same diagnostic plot issues as the `lm()`

example. Should different diagnostics be used? Is it simply the arbitrary selection of variables? Insufficient data? Are non-parametric required?