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
4x² + 2x - 30 = 0
<span>
factor out the GCF:
</span>2(2x² + x - 15) = 0
<span>
factor the trinomial completely:
2x</span>² + x - 15 = 0
2x² + 6x - 5x - 15 = 0
2x(x + 3) - 5(x + 3) = 0
(2x - 5)(x + 3) = 0
<span>use the zero product property and set each factor equal to zero and solve:
2x - 5 = 0 or x + 3 = 0
2x = 5 x = -3
x = 2.5
</span><span>The roots of the function are x=-3, x=2.5</span>
The answer is 7.07 times, with rounding, still 7.07.
Answer:
375 each descent
Step-by-step explanation:
1500/4 = 375
