Answer:
im pretty sure its 5
Step-by-step explanation:
x, y, x, y, z
Hope this helps!
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
Step-by-step explanation:
are in the solution:
(3,1)
(-1,2)
(6,0)
(0,0)
are not
(-2,-1)
(0,-4)
Answer:
1, 10
Step-by-step explanation:
