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
Everything = 180 degrees
2X = 180 - 40 - 40 -30
2x = 70
X= 35
Answer:
6÷41 equals 0.14634146341
6÷21 equals 0.28571428571
6÷43 equals 0.13953488372
6÷37 equals 0.16216216216
6÷20 equals 0.3
Step-by-step explanation:
Hope this helps
Answer:whichever # is closest to 0.6 round up to 1 or 60%
Step-by-step explanation:
