Answer:
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Step-by-step explanation:
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
1440 will be your least common multiple
Answer:
Step-by-step explanation:
6 + 12 + 24 + 48 + 96 =
= 6 + 6 * 2 + 6 * 4 + 6 * 8 + 6 * 16
= 6 + 6 * 2^2 + 6 * 2^2 + 6 * 2^3 + 6 * 2 * 4
= 6 * 2^(n - 1)
Answer:
Step-by-step explanation:
mcccccccccccccccccccccccccccccccccccccccccccccc
