Answer:
x ∈ [-2, 7]
Step-by-step explanation:
The given equation ...
x^2 -5x -4 ≤ 10
can be rewritten as ...
x^2 -5x -14 ≤ 0
and factored as ...
(x +2)(x -7) ≤ 0
Clearly, the "or equal to" condition will be met when x=-2 and x=7. For values of x between these numbers, one factor is negative and the other is positive. Hence the product will be negative. So, numbers in that interval are the solution set.
x ∈ [-2, 7]
Answer:
98
Step-by-step explanation:
Integers are whole numbers or opposite of whole numbers.
The slope is 1.
How?
Change of y is 100-1=99.
Change of x is 100-1=99.
The slope is 99/99=1 or 1/1.
So if we start at (1,1) and we rise 1 and run 1 right, we get (2,2).
If we do that again from (2,2) we get (3,3).
Following the pattern of going up 1 and right from each new location discovered we get all the of these points:
Start (1,1)
(2,2)
(3,3)
(4,4)
(5,5)
(6,6)
....
(96,96)
(97,97)
(98,98)
(99,99)
end (100,100)
So we just need to count all the numbers from 2 to 99.
99-2+1
97+1
98
The possible outcomes of a random experiment and the probability of each outcome is called "a Probability Distribution."
<h3>What is a Probability Distribution?</h3>
A probability is a statistical formula that indicates all of the potential values and probability distributions for a random variable within a specified range.
Some characteristics regarding the Probability Distribution are-
- The range will be bounded by the minimum and greatest possible values, but the precise location of the possible value just on probability distribution relies on a number of factors.
- These variables include the mean (average), standard deviation, skewness, & kurtosis of the distribution.
- Although other regularly used probability distributions exist, the normal distribution, called "bell curve," is perhaps the most common.
- Typically, the technique of generating data for a phenomenon will influence its probability distribution. This is known as the probability density function.
- Likelihood distributions can also be used to generate cumulative distribution functions (CDFs), that cumulatively build up the probability of occurrences and always begin at zero and end at 100%.
To know more about Probability Distribution, here
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