Answer:
-35
Step-by-step explanation:
- 4ac = (1)(1) - 4(-3)(3) = 1 - 36 = -35
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:
Step-by-step explanation:
0.001y^12 has only one term and is thus a monomial. Are you sure you've shared the entire question?
<h3>
Answer: 83.85%</h3>
This value is approximate.
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Explanation:
Let's compute the z score for x = 40
z = (x-mu)/sigma
z = (40-47)/7
z = -1
We're exactly one standard deviation below the mean.
Repeat these steps for x = 68
z = (x-mu)/sigma
z = (68-47)/7
z = 3
This score is exactly three standard deviations above the mean.
Now refer to the Empirical Rule chart below. We'll add up the percentages that are between z = -1 and z = 3. This consists of the two pink regions (each 34%), the right blue region (13.5%) and the right green region (2.35%). These percentages are approximate.
34+34+13.5+2.35 = 83.85
<u>Roughly 83.85%</u> of the one-mile roadways have between 40 and 68 potholes.