What I am trying to do: In American roulette, the payout for winning on green is $17. This means that if you bet $1 and it lands on green, you get $17 as a prize.
The output I desire: Create a sampling model to predict winnings from betting on green.
# Use the `set.seed` function to make sure your answer matches the expected result after random sampling.
set.seed(1)
# The variables 'green', 'black', and 'red' contain the number of pockets for each color
green <- 2
black <- 18
red <- 18
# Assign a variable `p_green` as the probability of the ball landing in a green pocket
p_green <- green / (green+black+red)
# Assign a variable `p_not_green` as the probability of the ball not landing in a green pocket
p_not_green <- 1 - p_green
#Create a model to predict the random variable `X`, your winnings from betting on green.
X <- sample(17,1,prob = p_green)
#> Error in sample.int(x, size, replace, prob): incorrect number of probabilities
# Print the value of `X` to the console
X
#> Error in eval(expr, envir, enclos): object 'X' not found
Having trouble defining the "X" random variable. Can someone hint me the correct syntax ? thanks
the payout is either 17 or 0. So you really want to sample from a vector of 2 values: c(17,0)
try this:
sample(c(17,0),1,prob = c(p_green, 1-p_green))
The other thing to consider is sampling a draw from the list of possible outcomes with even odds:
n <- 1
sample(0:35,n, replace=TRUE)
I think the range is 0:35... but you get the idea...
then you could set up a metadata table with columns: value, color, odd, even, etc
join your random draws to the metadata table to determine the characteristics of each draw.
I think you are just figuring this all out and are using gambling games as tractable examples. I think that's a great learning method.
yes, thats the one. i just installed it and will be trying it out. there is also a separate data science program there as well