Using the following data on the Observations 10, 13, 4, and 20 confirm that the complete linkage distance between the cluster containing 《10, 13) and the cluster containing (4, 20) s 2.577 units as displayed in the dendrogram Observation
13 20 0.032 0.195 -0510 0.466 0.741 0.8750.207 0.474 0.700 0.748 -0.004 -0.490 -0.892 0.735 0.219 0.655 -0.1731.013 0.943 0.083 -0.693 -0.489-0.702 -0.458 1.620 2.275 1328 1.733 -0.863 1.035 0.724 0.721 10 Income/Debt Return Cost Load Peak Sales TotalFuelCosts
If required, round your answers to three decimal places. Do not round intermediate calculations
1. Distance from Observation 10 and Observation 4:
2. Distance from Observation 10 and Observation 20:
3. Distance from Observation 13 and Observation 4:
4. Distance from Observation 13 and Observation 20:

Step-by-step explanation:

The distance between:

10 and 4: 1.492

10 and 20: 2.055

13 and 4: 2.577

13 and 20: 2.226

The R code:

#Convert your datafile into csv and make sure your row names are 10,13,4 and 20

data=read.csv(file.choose())

data

row.names(data)=c(10,13,4,20)

data

d=dist(data,method="euclidean")

d

fit=hclust(d,method="complete")

plot(fit)

groups=cutree(fit,k=2)

rect.hclust(fit,k=2,border="red")

answer: 80,000

step-by-step explanation:

the population doubles every day.

10,000*2=20,000 day 4

20,000*2=40,000 day 5

40,000*2=80,000 day 6

85.560%

step-by-step explanation:

conditional probability is defined as:

p(a|b) = p(a∩b) / p(b)

in other words, the probability that a occurs, given that b has occurred = the probability that both a and b occur / the probability that b occurs.

in this example:

p(turns 64 | turns 44) = p(turns 64 & turns 44) / p(turns 44).

if someone turns 64, they must have already turned 44, so:

p(turns 64 | turns 44) = p(turns 64) / p(turns 44)

on page 42, we see that of the original 100,000, 94944 turn 44, and 81234 turn 64.   therefore:

p(turns 64 | turns 44) = 81234 / 94944

p(turns 64 | turns 44) = 0.85560