The problem is to find how many students have either blond hair or glasses and then divide that by the total number of students; that will find the probability.
Since 7 have blonde hair and 9 have glasses, that would be a total of 16 persons if there were no overlap; that is, no blonde wears glasses and nobody who wears glasses is a blonde.
But there are 4 persons who have both blond hair and wears glasses. That means, of the 7 who are blondes, 3 of them do not wear glasses. And, of the 9 who wear glasses, 5 of them are not blondes.
So we have 3 who are blondes without glasses, 5 who wear glasses who are not blondes, and 4 blonds with glasses, for a total of 12 persons.
The probability is, therefore, 12/26 or 6/13.
Answer:
The steps that can be done to both sides of the equation is: Add 9.6, then divide by 3.2
Step-by-step explanation:
Solve the equation:
-First you add both sides 9.6:
-Then, you divide both sides by 3.2:
Answer:
I think the answer is 1
Step-by-step explanation:
-49÷7=-7
-7÷-7=1
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
(x -1)(x +4) = 0
x² +3x -4 = 0 . . . . . . probably the form you're looking for
