More inference

Next, we’ll use the inference() function for evaluating whether there is a difference between the average birth weights of babies born to smoker and non-smoker mothers.

Let’s pause for a moment to go through the arguments of this custom function:

The first argument is y, which is the response variable that we are interested in: nc$weight.
The second argument is the grouping variable, x, which is the explanatory variable – the grouping variable across the levels of which we’re comparing the average value for the response variable, smokers and non-smokers: nc$habit.
The third argument, est, is the parameter we’re interested in: "mean" (other options are "median", or "proportion".)
Next we decide on the type of inference we want: a hypothesis test ("ht") or a confidence interval("ci").
When performing a hypothesis test, we also need to supply the null value, which in this case is 0, since the null hypothesis sets the two population means equal to each other.
The alternative hypothesis can be "less", "greater", or "twosided".
Lastly, the method of inference can be "theoretical" or "simulation" based.

Run the code and inspect the plots that are drawn.

Exercise

More inference

Instructions