I counted it b1 <- pnorm(q=0.02,mean=100,sd=15)

but I think there is some mistake cause the answer is 1.320327e-11

Hi Tove, why do you think that is a mistake? I get the same value with your code. Could you say a bit more about what you expect and why you have selected the parameters for the code?

Thank you for help.

I doubted my result, cause next step is simulate 500 observations, mean = 100, sd=15 and calculate how big part of observations is bigger than b1. And result of that would be something very big.

I should count b1 value that makes: P(Y≥b1) = 0.02

in the next part i need to use b1 as a seed (set.seed(b1)), and simulate 500 observation from normal distribution (mean= 100, sd =15) and check how big part of observations are bigger than b1.

So it seems I'm correct and the q param for pnorm was incorrect for you by a factor of 100

I still don't get it.

How i can count b1 from

P(Y>=b1)=0,002

what do i actually get from plot(pnorm(q=0:100, mean= 100, sd=15)?

normal distribution for those parameters?

i assume that plot command draws scatterplot that maybe it was there just to visualize it to me?

And the pnorm(q=0:100, mean=100, sd=15) gives my Y.

How can I count b1 from P(Y>=b1)=0,02? Cant find any way to do it with R

I think i can calculate it with qbinom:

b1= qnorm(p=0.2, mean=100,sd=15, lower.tail=TRUE)

hope I got it right now!

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Hi Tove, I thought I should highlight this as you seem to have missed it?

We have no context for why you calculate b1, and why you chose 0.02 in particular.

consider

```
plot(pnorm(q = 0:100,100,15))
```

I wonder if perhaps 0.02 was assuming 2% quantile, whereas that would be 2, as q would be in the range 0 to 100 rathen than 0 to 1 ( I think ?)