Internet packets can be classified as
video (V) or as generic data ( D). Based
on a lot of observations taken by the Internet
service provider, we have the following
probability model: P[V] = 3/4, P[D] =
1/4. Data packets and video packets occur
independently of one another. The random
variable Kn is the number of video packets
in a collection of n packets.
a) What is E[K100], the expected number
of video packets in a set of 100 packets?
b) What is standard dev. K 100?
c) Use the central limit theorem to estimate
P[K100>18].
d) Use the central limit theorem to estimate
P[ 16 <= K100 <= 24 ].

Internet packets can be classified as video (V) or as generic data ( D). Based on a lot of observations taken by the Internet service provider, we have the following probability model: P[V] = 3/4, P[D...
9.5.2 Internet packets can be classified as video (V) or as generic data (D). Based on a lot of observations taken by the In ternet service provider, we have the follow ing probability model: PV] 3/4, P[D]- 1/4. Data packets and video packets occu.r independently of one another. The random variable Kn is the number of video packets in a collection of n packets. (a) What is E[K100], the expected number of video packets in a set of 100 packets? (b) What is σκ100? (c) Use the central limit theorem to est mate P[K10 18]. (d) Use the central limit theorem to esti- mate P[16 24]
9.5.2 Internet packets can be classified as video (V) or as generic data (D). Based on a lot of observations taken by the In ternet service provider, we have the follow ing probability model: PV] 3/4, P[D]- 1/4. Data packets and video packets occu.r independently of one another. The random variable Kn is the number of video packets in a collection of n packets. (a) What is E[K100], the expected number of video packets in a set of 100 packets? (b) What is σκ100? (c) Use the central limit theorem to est mate P[K10 18]. (d) Use the central limit theorem to esti- mate P[16 24]