Answers:
- A) Ray QS or Ray QR
- B) Line segment QS or SQ
- C) Plane QSR
- D) Line QS or RQ
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Explanation:
Part A)
When naming a ray, always start at the endpoint. This is the first letter and we'll start with point Q.
The second letter is the point that is on the ray where the ray aims at. We have two choices S and R as they are both on the same ray. That's why we can name this Ray QS and Ray QR.
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Part B)
A segment is named by its endpoints. The order of the endpoints doesn't matter so that's why segment QS is the same as segment SQ. To me, it seems more natural to read from left to right, so QS seems better fitting (again the order doesn't matter).
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Part C)
When forming a plane, you need 3 noncollinear points. The term "collinear" means the points all fall on the same line. So these three points cannot all fall on the same straight line. In other words, we must be able to form a triangle of some sort.
So that's how we get the name "Plane QSR". The order of the letters doesn't matter.
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Part D)
To name a line, we just need to pick two points from it. Any two will do. The order doesn't matter. So that's how we get Line QS and Line RQ as two aliases for this same line. It turns out that there are 6 different ways to name this line.
- Line QR
- Line QS
- Line RQ
- Line RS
- Line SQ
- Line SR
Answer:
Definition of Midpoint
Step-by-step explanation:
Since T is the midpoint then it is equadistant from R to T and T to S
Answer: It would be approximately 2000 ft.
Step-by-step explanation:
Answer: It is usefull.
Step-by-step explanation:
The regression squared talks to us about how well the model fits in the experimental data, where 0.0 means that the model does not fit at all, and 100% means that the model fits perfectly.
This is always true? well, really not, there are cases where you can have a regression square of 0.98, which would imply that the model is correct, but when you see the residual vs fit the plot, you may see some pattern, which implies that there is a problem with the model (you always expect to see randomness when you look at this graph). While for a prediction, this actually may work (at least in the range of the data points, outside this range the model and the data may not coincide at all)
Now, it still is useful in a certain range, so we can actually conclude that if R^2 = 0.949 represents a model that is useful for predicting the exam marks.
