Operating a probability distribution in R

Probability distribution and statistics analysis are closely related to each other. For statistics analysis, analysts make predictions based on a certain population, which is mostly under a probability distribution. Therefore, if you find that the data selected for a prediction does not follow the exact assumed probability distribution in the experiment design, the upcoming results can be refuted. In other words, probability provides the justification for statistics. The following examples will demonstrate how to generate probability distribution in R.

Getting ready

Since most distribution functions originate from the stats package, make sure the library stats are loaded.

How to do it...

Perform the following steps:

1. For a normal distribution, the user can use dnorm, which will return the height of a normal curve at 0:

        > dnorm(0)
        Output:
    
       [1] 0.3989423  

2. Then, the user can change the mean and the standard deviation in the argument...