true - Distributions that have the same shape on both sides of the center are called symmetric
Symmetry is when something has exactly the same shape on either side of an axis line. T is a vertical symmetrical shape.
The probability density functions are different in shape and the domain. The domain of the beta distribution is from 0 to 1, while the normal goes from negative infinite to positive infinity. The shape of the normal is always a symmetrical, bell shape with inflection points on either sides of the mean. The beta distribution can be a variety of shapes, symmetrical half circle, inverted (cup up) half circle, or asymmetrical shapes. Normal distribution has many applications in classical hypothesis testing. Beta has many applications in Bayesian analysis. The uniform distribution is considered a specialized case of the beta distribution. See related links.
yes it is a symmetrical shape
Asymmetry is when a shape is not symmetrical.
I have included two links. A normal random variable is a random variable whose associated probability distribution is the normal probability distribution. By definition, a random variable has to have an associated distribution. The normal distribution (probability density function) is defined by a mathematical formula with a mean and standard deviation as parameters. The normal distribution is ofter called a bell-shaped curve, because of its symmetrical shape. It is not the only symmetrical distribution. The two links should provide more information beyond this simple definition.
symmetrical
Normalll APEX!!
Symmetry is when something has exactly the same shape on either side of an axis line. T is a vertical symmetrical shape.
It is a symmetrical, "bell-shaped" curve. The tails are infinitely long.
The probability density functions are different in shape and the domain. The domain of the beta distribution is from 0 to 1, while the normal goes from negative infinite to positive infinity. The shape of the normal is always a symmetrical, bell shape with inflection points on either sides of the mean. The beta distribution can be a variety of shapes, symmetrical half circle, inverted (cup up) half circle, or asymmetrical shapes. Normal distribution has many applications in classical hypothesis testing. Beta has many applications in Bayesian analysis. The uniform distribution is considered a specialized case of the beta distribution. See related links.
False. It can be skewed to the left or right or be symmetrical.
Only objects that have the exact size, shape, mass and density distribution can have the same center of mass. Any variation and the center of gravity would move. Furthermore, only objects that are geometrically symmetrical (think sphere) can have a center of gravity at their geometric center.
yes it is a symmetrical shape
Either can be used for symmetrical distributions. For skewed data, the median may be more a appropriate measure of the central tendency - the "average".
Asymmetry is when a shape is not symmetrical.
yes it can be a circle has no straight sides, but is symmetrical
I have included two links. A normal random variable is a random variable whose associated probability distribution is the normal probability distribution. By definition, a random variable has to have an associated distribution. The normal distribution (probability density function) is defined by a mathematical formula with a mean and standard deviation as parameters. The normal distribution is ofter called a bell-shaped curve, because of its symmetrical shape. It is not the only symmetrical distribution. The two links should provide more information beyond this simple definition.