Consider the function f(x) = x2 – 331. Chivonne claims a domain restriction x ≥ 0 produces the inverse function f–1(x) = StartRo
ot x minus 331 EndRoot . Which statement describes whether Chivonne is correct? The expression for f–1(x) is correct, but the domain restriction is not. The domain restriction is correct, but the expression for f–1(x) is not. Both the expression for f–1(x) and the domain restriction are correct. Neither the domain restriction nor the expression for f–1(x) is correct.
2 answers:
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Answer:
The Normal distribution is a continuous probability distribution with possible values all the reals. Some properties of this distribution are:
Is symmetrical and bell shaped no matter the parameters used. Usually if X is a random variable normally distributed we write this like that:
The two parameters are:
who represent the mean and is on the center of the distribution
who represent the standard deviation
One particular case is the normal standard distribution denoted by:
Example: Usually this distribution is used to model almost all the practical things in the life one of the examples is when we can model the scores of a test. Usually the distribution for this variable is normally distributed and we can find quantiles and probabilities associated
Step-by-step explanation:
The Normal distribution is a continuous probability distribution with possible values all the reals. Some properties of this distribution are:
Is symmetrical and bell shaped no matter the parameters used. Usually if X is a random variable normally distributed we write this like that:
The two parameters are:
who represent the mean and is on the center of the distribution
who represent the standard deviation
One particular case is the normal standard distribution denoted by:
Example: Usually this distribution is used to model almost all the practical things in the life one of the examples is when we can model the scores of a test. Usually the distribution for this variable is normally distributed and we can find quantiles and probabilities associated