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:
9cups of dried split peas
8 qts of water
3 cups of chopped onion
4 cups chopped celery
8 lbs Ham shank
Step-by-step explanation:
The recipe serves 8 people, so to figure out how much you need to serve 32 people you multiply each measurment by 4 because 8 x 4=32.
2 1/4 x 4 = 9
2 x 4 = 8
3/4 x 4 = 3
1 x 4 = 4
2 x 4 = 8
Answer:
1295/36
Step-by-step explanation:
the statement tell us:
(7-(5/6))*(7-(7/6))
we have:
(7-(5/6))=((6*7)-5)/6=(42-5)/6=37/6
and we have:
(7-(7/6))=((6*7)-7)/6=(42-7)/6=35/6
we need multiply both terms:
(37/6)*(35/6)=(37*35)/(6*6)
finally we have
1295/36
Answer:
5x=55
Divide both sides by 5 to solve for x
5x/5=55/5
x=11
