Answer:
Check the explanation
Step-by-step explanation:
The odds are 4 to 1 against, so we can estimate the probability of success (p) as
![\frac{p}{q}=\frac{p}{1-p}=\frac{1}{4}\\\\4p=1-p\\\\5p=1\\\\p=0.2](https://tex.z-dn.net/?f=%5Cfrac%7Bp%7D%7Bq%7D%3D%5Cfrac%7Bp%7D%7B1-p%7D%3D%5Cfrac%7B1%7D%7B4%7D%5C%5C%5C%5C4p%3D1-p%5C%5C%5C%5C5p%3D1%5C%5C%5C%5Cp%3D0.2)
The expected pay for every success is 3 to 1, so we lose $1 for every lose and we gain $3 for every win.
The number of winnings in the 100 rounds to be even can be calculated as:
![W+L=100\\\\L=100-W\\\\\\Payoff=0=3*W-1*L=3W-1*(100-W)=3W+W-100\\\\0=4W-100\\\\W=25](https://tex.z-dn.net/?f=W%2BL%3D100%5C%5C%5C%5CL%3D100-W%5C%5C%5C%5C%5C%5CPayoff%3D0%3D3%2AW-1%2AL%3D3W-1%2A%28100-W%29%3D3W%2BW-100%5C%5C%5C%5C0%3D4W-100%5C%5C%5C%5CW%3D25)
We have to win at least 25 rounds to have a positive payoff.
As the number of rounds is big, we will approximate the binomial distribution to a normal distribution with parameters:
![\mu=np=100*0.2=20\\\\\ \sigma=\sqrt{npq}=\sqrt{100*0.2*0.8}=4](https://tex.z-dn.net/?f=%5Cmu%3Dnp%3D100%2A0.2%3D20%5C%5C%5C%5C%5C%20%5Csigma%3D%5Csqrt%7Bnpq%7D%3D%5Csqrt%7B100%2A0.2%2A0.8%7D%3D4)
The z-value for x=25 is
![z=\frac{X-\mu}{\sigma}=\frac{25-20}{4}=1.25](https://tex.z-dn.net/?f=z%3D%5Cfrac%7BX-%5Cmu%7D%7B%5Csigma%7D%3D%5Cfrac%7B25-20%7D%7B4%7D%3D1.25)
The probability of z>1.25 is
P(X>25)=P(z>1.25)=0.10565
There is a 10.5% chance of having a positive payoff.
NOTE: if we do all the calculations for the binomial distribution, the chances of having a net payoff are 13.1%.