"The sum of r and 7" can be represented as r + 7
Answer:
a) The probability that the airline will lose no bags next monday is 0.1108
b) The probability that the airline will lose 0,1, or 2 bags next Monday is 0.6227
c) I would recommend taking a Poisson model with mean 4.4 instead of a Poisson model with mean 2.2
Step-by-step explanation:
The probability mass function of X, for which we denote the amount of bags lost next monday is given by this formula

a)

The probability that the airline will lose no bags next monday is 0.1108.
b) Note that
. And

Therefore, the probability that the airline will lose 0,1, or 2 bags next Monday is 0.6227.
c) If the double of flights are taken, then you at least should expect to loose a similar proportion in bags, because you will have more chances for a bag to be lost. WIth this in mind, we can correctly think that the average amount of bags that will be lost each day will double. Thus, i would double the mean of the Poisson model, in other words, i would take a Poisson model with mean 4.4, instead of 2.2.
Answer:
Bias is the difference between the average prediction of our model and the correct value which we are trying to predict and variance is the variability of model prediction for a given data p[oint or a value which tells us the spread of our data the variance perform very well on training data but has high error rates on test data on the other hand if our model has small training sets then it's going to have smaller variance & & high bias and its contribute more to the overall error than bias. If our model is too simple and has very few parameters then it may have high bias and low variable. As the model go this is conceptually trivial and is much simpler than what people commonly envision when they think of modelling but it helps us to clearly illustrate the difference bewteen bias & variance.