(71 +29) x 26 HAKSJA HELP RN
2 answers:
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Answer:
The driving distance for meeting at the three centres can be written as
d1=columbus
d2=Des moines
d3=Boise
The distance from des moines to columbus to 650 miles
from Boise to des moines is 1350
Therefore the distance from columbus to Boise
if the meeting is holding at columbus
0+650+1350=2000
Pr(columbus)=1/3
Pr(Des moines)=1/3
Pr(Boise)=1/3
a. Probability distribution is
capital c DM B
di 0 650 2000
Pr(di) 1/3 1/3 1/3
b. Expected value is multiplication of the probability of d1 and the outcome
E(x)=0*1/3=0
c. find the variance of d1 first
Var=(x-E(x))^2*Pr(d1)
Var=(0-0)^2*1/3
Var=0
the square root of var=standard deviation
S.D=0
d. probability distribution of d2 and d3 is equal to the probability distribution of d1 , because they all have a probability of 1/3(the likelihood that an event will occur is 1/3 for the meeting \location
e. d1=0
d2=650
d1+d2=650
pr(d1+d2)=1/3+1/3=2/3
Pr(d1+d2) will be on the vertical axis, while d1+d2 will be plotted on the horizontal axis of the probability distribution graph
Step-by-step explanation:
The driving distance for meeting at the three centres can be written as
d1=columbus
d2=Des moines
d3=Boise
The distance from des moines to columbus to 650 miles
from Boise to des moines is 1350
Therefore the distance from columbus to Boise
if the meeting is holding at columbus
0+650+1350=2000
Pr(columbus)=1/3
Pr(Des moines)=1/3
Pr(Boise)=1/3
a. Probability distribution is
capital c DM B
di 0 650 2000
Pr(di) 1/3 1/3 1/3
b. Expected value is multiplication of the probability of d1 and the outcome
E(x)=0*1/3=0
c. find the variance of d1 first
Var=(x-E(x))^2*Pr(d1)
Var=(0-0)^2*1/3
Var=0
the square root of var=standard deviation
S.D=0
d. probability distribution of d2 and d3 is equal to the probability distribution of d1 , because they all have a probability of 1/3(the likelihood that an event will occur is 1/3 for the meeting \location
e. d1=0
d2=650
d1+d2=650
pr(d1+d2)=1/3+1/3=2/3
Pr(d1+d2) will be on the vertical axis, while d1+d2 will be plotted on the horizontal axis of the probability distribution graph
