Answer:
the y-intercept is (0,-6)
the x-intercept is (8,0)
Step-by-step explanation:
<em>Solving for the y-intercept:</em>
Since the equation 'y=3/4x - 6' is in the 'y=mx + c' form (where m is the slope and c is the y-intercept):
3/4 is m (which is the slope)
-6 is c (which is the y-intercept)
<em>Solving for the x-intercept:</em>
When the line intersects with the x-axis, the y-value is 0. Hence, the coordinate is (x,0). Substituting this into 'y=3/4x - 6':
0 = 3/4x - 6
3/4x - 6 + 6 = 6
3/4x = 6
x = 4/3 * 6
= 8
Hence,
the y-intercept is (0,-6)
the x-intercept is (8,0)
<em>Hope this helps and be sure to have a wonderful time ahead at Brainly! :D</em>
1. 15*13=195
2. 7*13=91/2=45.5
3. 7*13=91/2=45.5
4. 195+45.5+45.5=286 units squared
hope this helps!
6xy in simplest form is simply 6 to the second power
Hope this helps!
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
