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:
f(g(5)) = 16.5
Step-by-step explanation:
To calculate f(g(5)), evaluate g(5) then substitute the value obtained into f(x)
g(5) = 
× 5 = 2.5 , then
f(2.5) = 5(2.5) + 4 = 12.5 + 4 = 16.5
Use gradient formula, rise over run.
y2-y1/x2-x1
Answer:
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Step-by-step explanation:
Answer:
so this year she grew 14 tomatoes and 16 roma tomatoes so if u add 14 plus 16 your answer would be 30 she grew 30 tomatoes this year.
Step-by-step explanation:
