Characteristics of a Normal Distribution
1) Continuous Random Variable.
2) Mound or Bell-shaped curve.
3) The normal curve extends indefinitely in both directions, approaching, but never touching, the horizontal axis as it does so.
4) Unimodal
5) Mean = Median = Mode
6) Symmetrical with respect to the mean
That is, 50% of the area (data) under the curve lies to the left of
the mean and 50% of the area (data) under the curve lies
to the right of the mean.
7) (a) 68% of the area (data) under the curve is within one
standard deviation of the mean
(b) 95% of the area (data) under the curve is within two
standard deviations of the mean
(c) 99.7% of the area (data) under the curve is within three
standard deviations of the mean
8) The total area under the normal curve is equal to 1.
False. A normalized distribution curve (do not confuse normalized with normal), by definition, has an area under the curve of exactly 1. That is because the probability of all possible events is also always exactly 1. The shape of the curve does not matter.
The normal distribution, also known as the Gaussian distribution, has a familiar "bell curve" shape and approximates many different naturally occurring distributions over real numbers.
It is a symmetrical, "bell-shaped" curve. The tails are infinitely long.
The mean must be 0 and the standard deviation must be 1. Use the formula: z = (x - mu)/sigma
Yes. By definition. A normal distribution has a bell-shaped density curve described by its mean and standard deviation. The density curve is symmetrical(i.e., an exact reflection of form on opposite sides of a dividing line), and centered about (divided by) its mean, with its spread (width) determined by its standard deviation. Additionally, the mean, median, and mode of the distribution are equal and located at the peak (i.e., height of the curve).
No, the normal curve is not the meaning of the Normal distribution: it is one way of representing it.
A bell shaped probability distribution curve is NOT necessarily a normal distribution.
It could be a Gaussian curve (Normal distribution) rotated through a right angle.It could be a Gaussian curve (Normal distribution) rotated through a right angle.It could be a Gaussian curve (Normal distribution) rotated through a right angle.It could be a Gaussian curve (Normal distribution) rotated through a right angle.
The normal distribution would be a standard normal distribution if it had a mean of 0 and standard deviation of 1.
Because the domain of the normal distribution is infinite - in both directions.
yup, it's a bell curve
A bell curve describes the graphed curve that normal distribution produces for a set of data. The curve slopes upward before returning downward after the point of the mean.
Your question makes no sense. Significant is a word related to tests. The normal curve is a distribution, not a test.
The domain of the Normal distribution is the whole of the real line. As a result the horizontal axis is asymptotic to the Normal distribution curve. The curve gets closer and closer to the axis but never, ever reaches it.
The mean of a standard normal curve is 0. This curve, which is a type of probability distribution known as the standard normal distribution, is symmetric and bell-shaped, centered around the mean. Additionally, the standard deviation of a standard normal curve is 1, which helps define the spread of the data around the mean.
normal curve
Neither. It is symmetrical.