If the triangle has a angle of 90°, you can solved this exercise by applying the Pythagorean Theorem, which is:
h²=a²+b²
h=√(a²+b²)
h: It is the hypotenuse
(The opposite side of the right angle and the longest side of the triangle).
a and b: They are the legs
(The sides that form the right angle).
The result of h=√(a²+b²), should be 17.1 (The longest side given in the problem). So, let's substitute the values of the legs into the Pythagorean equation:
h=√(a²+b²)
h=√((9.2)²+(14.5)²)
h=17.1
Therefore, the answer is:
Yes, the given measures can be the lengths of the sides of a triangle.
The answer is D.
y = 3x - 4
(5, 11)
11 = 3(5) - 4
11 = 15 - 4
11 = 11
(2, 2)
y = 3x - 4
2 = 3(2) - 4
2 = 6 - 4
2 = 2
Answer: D:(-1,4)
R:(-4,4)
Step-by-step explanation:
I hope that correct
1. Introduction. This paper discusses a special form of positive dependence.
Positive dependence may refer to two random variables that have
a positive covariance, but other definitions of positive dependence have
been proposed as well; see [24] for an overview. Random variables X =
(X1, . . . , Xd) are said to be associated if cov{f(X), g(X)} ≥ 0 for any
two non-decreasing functions f and g for which E|f(X)|, E|g(X)|, and
E|f(X)g(X)| all exist [13]. This notion has important applications in probability
theory and statistical physics; see, for example, [28, 29].
However, association may be difficult to verify in a specific context. The
celebrated FKG theorem, formulated by Fortuin, Kasteleyn, and Ginibre in
[14], introduces an alternative notion and establishes that X are associated if
∗
SF was supported in part by an NSERC Discovery Research Grant, KS by grant
#FA9550-12-1-0392 from the U.S. Air Force Office of Scientific Research (AFOSR) and
the Defense Advanced Research Projects Agency (DARPA), CU by the Austrian Science
Fund (FWF) Y 903-N35, and PZ by the European Union Seventh Framework Programme
PIOF-GA-2011-300975.
MSC 2010 subject classifications: Primary 60E15, 62H99; secondary 15B48
Keywords and phrases: Association, concentration graph, conditional Gaussian distribution,
faithfulness, graphical models, log-linear interactions, Markov property, positive