A large outlier could affect the data set because it could make the mean larger, which just means that the average number of the set would be a lot bigger than it already is.
An example would be: 1, 3, 5, 19
19 is obviously the large outlier, so to find the mean, we would add everything together then divide by 4.
The mean would be 28/4=7 So 7 is our mean, but if the large outlier were, let's say 50, then the mean would be way larger than it already is.
~Hope this helped!~
Two fraction between 0 and 1/2 and 1/3 and 1/6. 1/3 = .33 and 1/6= .16. we know that 1/3 is greater than 1/6 because the value is larger. .33 > .16
.33 is definitely greater than 1.6
"The intersection (∩) of a pair of sets (G and H) is a third set (I) composed by the elements that belong, at the same time, to both given sets."
According to this definition:
Given the sets:
G = {3, 7, 8, 9}
H = {2, 5, 7, 8}
The Intersection is:
G ∩ H = I = {7, 8}
:-)
Given that the population can be modeled by P=22000+125t, to get the number of years after which the population will be 26000, we proceed as follows:
P=26000
substituting this in the model we get:
26000=22000+125t
solving for t we get:
t=4000/125
t=32
therefore t=32 years
This means it will take 32 years for the population to be 32 years. Thus the year in the year 2032
