The cumulative distribution function f(x) of a discrete random variable x is given by f(0) =. 30, f(1) =. 70, f(2) =. 90 and f(3
1 answer:
Correlation between x & y is 0.6125.
In probability theory and statistics, the cumulative distribution function of a real-valued random variable X, or simply the distribution function of X weighted by x, is the probability that X takes a value less than or equal to x.
The cumulative distribution function (CDF) of a random variable X is defined as FX(x)=P(X≤x) for all x∈R. Note that the subscript X indicates that this is the CDF of the random variable X. Also note that the CDF is defined for all x∈R. Let's look at an example.
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Answer:
Proved CA=CB
Step-by-step explanation:
Given,
In ΔABC, CP is perpendicular to AB.
And CP bisects AB.
So, AP=PB and ∠CPA=∠CPB=90°
The figure of the triangle is in the attachment.
Now, In ΔACP and ΔBCP.
AP = PB(given)
∠CPA = ∠CPB = 90°(perpendicular)
CP = CP(common)
So, By Side-Angle-Side congruence property;
ΔACP ≅ ΔBCP
According to the property of congruence;
"If two triangles are congruent to each other then their corresponding sides are also equal."
Therefore, CA = CB (corresponding side of congruent triangle)
CA = CB Hence Proved
