Language Overview

• Plots packages

using Plots

x = collect(1:10)
y = [xx+randn() for xx in x]

plot(x,y,seriestype=:scatter)

• two Monte Carlo method examples and the usage of @time marco

# The First Method
using Statistics

@time begin
    data = Float64[];
    for _ in 1:10^6
        group = Float64[];
        for _ in 1:5*10^2
            push!(group, rand())
        end
        push!(data, mean(group))
    end
    println("98% of the means lie in the estimamted range:",
        (quantile(data,0.01)), (quantile(data,0.99))
    )
end

# The Second Method is faster than the first
using Statistics

@time begin
    data = [mean(rand(5*10^2)) for _ in 1:10^6]
    println("98% of the means lie in the estimamted range:",
        (quantile(data,0.01)), (quantile(data,0.99))
    )
end

• the example of variable scope

data = [1,2,3]
s = 0
β,γ = 2,1

for i in 1:length(data)
    print(i," ")
    global s


    s += β*data[i]
    data[i] *= -1
end

println("\nSum of data in extrnal scope: ", s)

function sumData(β)
    s = 0
    for i in 1:length(data)
        s += data[i] + γ
    end
    return s
end
println("Sum of data in a function: ", sumData(β/2))
@show s