Implementation example of normal distribution of R language

Statistical Distribution Each distribution has four functions:d―density (density function), p―distribution function, q―quantile function, r―random number function.The normal curve is bell-shaped, with both ends low and high in the middle. It is symmetrical on the left and right because its curve is bell-shaped, so people often call it bell-shaped curve.

1. rnorm

Generate random numbers of normal distributions

rnorm(n, mean = 0, sd = 1)

rnorm(100)
rnorm(10,2,5)

2. dnorm

Probability density distribution

dnorm(x, mean = 0, sd = 1, log = FALSE)

dnorm(1)    # Probability when x=1 in the standard normal distribution.# Make a picturex &lt;- seq(-1,1,0.01)
plot(x,dnorm(x))

3. pnorm

Cumulative probability

pnorm(q, mean = 0, sd = 1,  = TRUE,  = FALSE)

pnorm(0) # In normal distribution, the cumulative probability of x from negative infinite to 0 (integral)pnorm(1.644854) # Default =TRUE, P[X ≤ x]
pnorm(1.644854, =FALSE) #P[X &gt; x]

This function takes the probability value and gives the number whose accumulated value matches the probability value, the inverse function of pnorm.

qnorm(p, mean = 0, sd = 1,  = TRUE,  = FALSE)

qnorm(0.95)  # x value with accumulated value of 0.95qnorm(c(0.5,0.8,0.6,0.3))

qnorm(pnorm(0))

5. Normal distribution test

It can be used to detect whether the data is a normal distribution through density maps, QQ maps and normal distribution assumptions.

# P<0.05, then the distribution is a non-normal distribution.x1 &lt;- rnorm(50)
x2 &lt;- runif(30)
(x1)
(x2)

(rnorm(100, mean = 5, sd = 3))
(runif(100, min = 2, max = 4))

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