Which statements are true about the standard normal distribution? The standard normal distribution is______.
(Select all that apply.)

(A) defined by the two parameters μ = 0, σ =1
(B) symmetric.
(C) defined by the two parameters μ = 1, σ = 0
(D) a density curve.
(E) unimodal.
(F) referred to as a bell-curve.(G) skewed.(H) multimodal.

Respuesta :

Answer:

A) defined by the two parameters μ = 0, σ =1

(B) symmetric

(D) a density curve.

(E) unimodal

(F) referred to as a bell-curve

Step-by-step explanation:

We have to state the properties of a standard normal distribution.

A) defined by the two parameters μ = 0, σ =1

The standard normal distribution has a mean 0 and standard deviation of 1.

(B) symmetric

The normal distribution is symmetric around the mean that is area under the curve on the left side of mean is same as the area on the right side of mean.

(C) defined by the two parameters μ = 1, σ = 0

The given statement is false in regard with standard normal distribution.

(D) a density curve.

A density curve is a curve that represents the probability for a distribution and area under the curve is 1. The standard normal distribution is a density curve.

(E) unimodal

The standard normal distribution is unimodal that is it have only one value that occurs most frequently and for standard normal distribution the modal value is 0.

(F) referred to as a bell-curve

The standard normal distribution curve has a shape similar to a bell and referred to as bell-curve.

(G) skewed

The standard normal distribution is not skewed. It is symmetric.

(H) multimodal

The standard normal distribution has a unique mode.

The standard normal distribution is defined by the two parameters μ = 0, σ =1, symmetric, skewed and referred to as a bell curve.

What is a normal distribution?

The standard normal distribution is a normal distribution with mean μ = 0 and standard deviation σ = 1. The standard normal distribution is referred to as a bell-curve. The normal distribution is symmetric and has a skewness of zero. Z score is used to determine by how many standard deviations the raw score is above or below the mean in a normal distribution.

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