Hints for conference #6

# You can complete the following pieces of code

###########
### 7.1 ###
###########

### a

f.shots <- function(p.success=.6, hits.stop=2){
shots <- 0
missed <- 0
success <- 0
while ( ### condition to complete ### ){

### actions to complete here ###

}

# result:
c(shots=shots,success=success)
}
f.shots()

### b
# initialize a vector of size 1000 with missing values
sim.shots <- rep(NA,1000)
# fill in the missing values...

mean(sim.shots)
sd(sim.shots)

# do the same with both shots and successes:
sim.shots.success <- data.frame(shots=rep(NA,1000), success=rep(NA,1000))


mean(sim.shots.success$shots)
mean(sim.shots.success$success)
sd(sim.shots.success$shots)
sd(sim.shots.success$success)

### c
# plot the result


###########
### 7.3 ###
###########

# let's assume all random variables are independent, continuous and normally distributed

f.savings <- function(m.unit=5, sd.unit=4, m.market=40000, s.market=10000){
# calculate market size:
market.size <-

# calculate vector of savings and sum it

f.savings()

# initialize
total.savings <- rep(NA,1000)
# fill in

mean(total.savings)
sd(total.savings)

# another story:
# let's assume that there are 40000 firms
# each firm will independently enter the market with probability 1/2501
# each firm which enters the market will buy 2501 widgets
# otherwise it will buy 0 widgets

# do the same

###########
### 7.8 ###
###########

# create function according to the procedure p.143
# use rchisq and rnorm
unidim.sim <- function(estimate, sd.estimate, df, n){
}

# try it
unidim.sim(estimate=600, sd.estimate=400, df=50, n=5)

# plot the result with 1000 points

# 1000 simulations:
cost.eff <- rep(NA,1000)
# fill in

# histogram
hist(cost.eff)

# use function quantile for the confidence

# try again with different values