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: 0.0000805
Step-by-step explanation:
You move the decimal 5 places to the left of 8.05.
Answer:
c=
−4
3
s−t+
−4
3
Step-by-step explanation:
Let's solve for c.
3s+2t−3c−7s−5t=4
Step 1: Add 4s to both sides.
−3c−4s−3t+4s=4+4s
−3c−3t=4s+4
Step 2: Add 3t to both sides.
−3c−3t+3t=4s+4+3t
−3c=4s+3t+4
Step 3: Divide both sides by -3.
−3c
−3
=
4s+3t+4
−3
c=
−4
3
s−t+
−4
3
