The distribution function of the univariate random variable x is continuous at x if and only if , F (x) = P (X ≤ x)
Continuous univariate statistical distributions are functions that describe the likelihood that a random variable, say, X, falls within a given range. Let P (a Xb) represent the probability that X falls within the range [a, b].
A numerically valued variable is said to be continuous if, in any unit of measurement, whenever it can take on the values a and b. If the random variable X can assume an infinite and uncountable set of values, it is said to be a continuous random variable.
If X can take any specific value on the real line, the probability of any specific value is effectively zero (because we'd have a=b, which means no range). As a result, continuous probability distributions are frequently described in terms of their cumulative distribution function, F(x).
To learn more about univariated data
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17x + 16x + 12x =180
45x = 180
x = 4
Largest angle = 17*4 = 68 degrees
Alright, so the sum of two days is going to equal zero.
The variable here is what Jesse got on the first day. Let's call it "x".
x-6=0
x is the first day's score, -6 is the second day's score, and the total score is zero.
Now, add 6 to both sides.
x-6=0
+6 +6
x=6
Jesse's score for the first day was (+)6.
Answer: Function 1 has a greater rate of change than function two.
Answer:
3, -3, 0
Step-by-step explanation:
The exlcusions of x are where the denominator =0
So we can write the following equations
x²-9=0
and
5x=0
Let's solve the first one
x²-9=0
x²=9
x= ±3
and
5x=0
x=0
Therefore x cannot equal 3, -3, 0
