Answer:
![\lambda \geq 6.63835](https://tex.z-dn.net/?f=%5Clambda%20%5Cgeq%206.63835)
Step-by-step explanation:
The Poisson Distribution is "a discrete probability distribution that expresses the probability of a given number of events occurring in a fixed interval of time or space if these events occur with a known constant mean rate and independently of the time since the last event".
Let X the random variable that represent the number of chocolate chips in a certain type of cookie. We know that ![X \sim Poisson(\lambda)](https://tex.z-dn.net/?f=X%20%5Csim%20Poisson%28%5Clambda%29)
The probability mass function for the random variable is given by:
![f(x)=\frac{e^{-\lambda} \lambda^x}{x!} , x=0,1,2,3,4,...](https://tex.z-dn.net/?f=f%28x%29%3D%5Cfrac%7Be%5E%7B-%5Clambda%7D%20%5Clambda%5Ex%7D%7Bx%21%7D%20%2C%20x%3D0%2C1%2C2%2C3%2C4%2C...)
And f(x)=0 for other case.
For this distribution the expected value is the same parameter ![\lambda](https://tex.z-dn.net/?f=%5Clambda)
![E(X)=\mu =\lambda](https://tex.z-dn.net/?f=E%28X%29%3D%5Cmu%20%3D%5Clambda)
On this case we are interested on the probability of having at least two chocolate chips, and using the complement rule we have this:
![P(X\geq 2)=1-P(X](https://tex.z-dn.net/?f=P%28X%5Cgeq%202%29%3D1-P%28X%3C2%29%3D1-P%28X%5Cleq%201%29%3D1-%5BP%28X%3D0%29%2BP%28X%3D1%29%5D)
Using the pmf we can find the individual probabilities like this:
![P(X=0)=\frac{e^{-\lambda} \lambda^0}{0!}=e^{-\lambda}](https://tex.z-dn.net/?f=P%28X%3D0%29%3D%5Cfrac%7Be%5E%7B-%5Clambda%7D%20%5Clambda%5E0%7D%7B0%21%7D%3De%5E%7B-%5Clambda%7D)
![P(X=1)=\frac{e^{-\lambda} \lambda^1}{1!}=\lambda e^{-\lambda}](https://tex.z-dn.net/?f=P%28X%3D1%29%3D%5Cfrac%7Be%5E%7B-%5Clambda%7D%20%5Clambda%5E1%7D%7B1%21%7D%3D%5Clambda%20e%5E%7B-%5Clambda%7D)
And replacing we have this:
![P(X\geq 2)=1-[P(X=0)+P(X=1)]=1-[e^{-\lambda} +\lambda e^{-\lambda}[]](https://tex.z-dn.net/?f=P%28X%5Cgeq%202%29%3D1-%5BP%28X%3D0%29%2BP%28X%3D1%29%5D%3D1-%5Be%5E%7B-%5Clambda%7D%20%2B%5Clambda%20e%5E%7B-%5Clambda%7D%5B%5D)
![P(X\geq 2)=1-e^{-\lambda}(1+\lambda)](https://tex.z-dn.net/?f=P%28X%5Cgeq%202%29%3D1-e%5E%7B-%5Clambda%7D%281%2B%5Clambda%29)
And we want this probability that at least of 99%, so we can set upt the following inequality:
![P(X\geq 2)=1-e^{-\lambda}(1+\lambda)\geq 0.99](https://tex.z-dn.net/?f=P%28X%5Cgeq%202%29%3D1-e%5E%7B-%5Clambda%7D%281%2B%5Clambda%29%5Cgeq%200.99)
And now we can solve for ![\lambda](https://tex.z-dn.net/?f=%5Clambda)
![0.01 \geq e^{-\lambda}(1+\lambda)](https://tex.z-dn.net/?f=0.01%20%5Cgeq%20e%5E%7B-%5Clambda%7D%281%2B%5Clambda%29)
Applying natural log on both sides we have:
![ln(0.01) \geq ln(e^{-\lambda}+ln(1+\lambda)](https://tex.z-dn.net/?f=ln%280.01%29%20%5Cgeq%20ln%28e%5E%7B-%5Clambda%7D%2Bln%281%2B%5Clambda%29)
![ln(0.01) \geq -\lambda+ln(1+\lambda)](https://tex.z-dn.net/?f=ln%280.01%29%20%5Cgeq%20-%5Clambda%2Bln%281%2B%5Clambda%29)
![\lambda-ln(1+\lambda)+ln(0.01) \geq 0](https://tex.z-dn.net/?f=%5Clambda-ln%281%2B%5Clambda%29%2Bln%280.01%29%20%5Cgeq%200)
Thats a no linear equation but if we use a numerical method like the Newthon raphson Method or the Jacobi method we find a good point of estimate for the solution.
Using the Newthon Raphson method, we apply this formula:
![x_{n+1}=x_n -\frac{f(x_n)}{f'(x_n)}](https://tex.z-dn.net/?f=x_%7Bn%2B1%7D%3Dx_n%20-%5Cfrac%7Bf%28x_n%29%7D%7Bf%27%28x_n%29%7D)
Where :
![f(x_n)=\lambda -ln(1+\lambda)+ln(0.01)](https://tex.z-dn.net/?f=f%28x_n%29%3D%5Clambda%20-ln%281%2B%5Clambda%29%2Bln%280.01%29)
![f'(x_n)=1-\frac{1}{1+\lambda}](https://tex.z-dn.net/?f=f%27%28x_n%29%3D1-%5Cfrac%7B1%7D%7B1%2B%5Clambda%7D)
Iterating as shown on the figure attached we find a final solution given by:
![\lambda \geq 6.63835](https://tex.z-dn.net/?f=%5Clambda%20%5Cgeq%206.63835)