Inverse variation: y=k/x
1. plug in the values: (15,1)
1=k/15
2. solve for k
3. Now, plug in what you can for the second point: (n,5)
5=k/n (if you do the step above, you will know “k”.
4. Solve for “n”
Answer:
Discarding the influential outlying cases when detected is also known as flagging outliers in a data set, and this is because outliers do not follow the rest of the dataset's pattern. if this outliers are not discarded they would have a negative effect on any model attached to the dataset
Step-by-step explanation:
In a regression class ; If extremely influential outlying cases are detected in a Data set, discarding this influential outlying cases is the right way to go about it
Discarding the influential outlying cases when detected is also known as flagging outliers in a data set, and this is because outliers do not follow the rest of the dataset's pattern. if this outliers are not discarded they would have a negative effect on any model attached to the dataset
Answer:
(x,y) ⇒ (x - 3) (y - 4)
C(5, -1)
Step-by-step explanation:
let x=k
D=dog
T=Taxes
I=increase P=population
solve k
d=23,000
t=2000
d=2.3e^
divide both sides by 2
after year is,2000+k2-2.3
=1.999.7
k2- 23,000=
k-x
or
k2-2
2x(x-1) has the least common denominator
