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Are these true of normal probability distribution IIt is symmetric about the mean TTotal area under the normal distribution curve is equal to 1 DDistribution is totally described by two quantities?
Wiki User
∙ 14y ago
Yes, it is true; and the 2 quantities that describe a normal distribution are mean and standard deviation.
Wiki User
∙ 14y ago
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A binomial probability distribution with 5 as the probability of a success will have a distribution with a shape best described by?
Nothing since it is impossible. No event can have 5 as the probability of success.
Find the Z score location of vertical line that separate a normal distribution as described in each of the followinf 20 percent in the tail on the left?
Z = -0.8416
Is normal distribution symmetrical?
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).
if a given value yields three output values that relationship can be best described a function?
No, it is described as a relation.
How are the parts of a region described by a fraction like the segment of a number line described by a fraction?
because there brocken apart
A binomial probability distribution with 5 as the probability of a success will have a distribution with a shape best described by?
Nothing since it is impossible. No event can have 5 as the probability of success.
The probability density of the standardized normal distribution is?
The probability density of the standardized normal distribution is described in the related link. It is the same as a normal distribution, but substituted into the equation is mean = 0 and sigma = 1 which simplifies the formula.
What is the probability distribution that can be describe by just one parameter?
There are probably many probability distributions that have just one parameter. The most important one for statistical analysis is probably the Student t distribution.This probability distribution is fully described by a single parameter which is often called "degrees of freedom". The parameter describes the scale of the distribution, and not the location, since the Student t distribution is always centered at zero (unlike the normal distribution, which has a scale parameter, the variance, and a location parameter, the mean).Another example of a distribution that is described with a single parameter is the exponential distribution. Unlike the Student t distribution, it is a distribution that takes only positive values.
How many types of quantities?
there are three types of quantities:-1.Scalar quantities - Scalarsare quantities that are fully described by a magnitude (or numerical value) alone.2.vector quantities - Vectorsare quantities that are fully described by both a magnitude and a direction.3.Tensor quantities - tensors are quantities that are fully described by magnitude, direction and the plane thecomponent acts on.
What is Solar and vector quantities?
Vector quantities are those that must be described with both a magnitude and direction. Scalar quantities can be described with only a single value.
Derived quantities are the?
All other quantities which described in terms of base quantities are called base quantities.
What are derived quantities?
All other quantities which described in terms of base quantities are called base quantities.
Write differences between scaler and vector quantities?
Scaler Quantity- quantities which are described only by magnitude.Vector Quantity- quantities which are described by both magnitude as well as direction.
A complete probability distribution is always an objective listing of all possible events Since it is impossible to list all the possible outcomes from a single event probability distributions are o?
Your question is not clear, but I will attempt to interpret it as best I can. When you first learn about probability, you are taught to list out the possible outcomes. If all outcomes are equally probable, then the probability is easy to calculate. Probability distributions are functions which provide probabilities of events or outcomes. A probability distribution may be discrete or continuous. The range of both must cover all possible outcomes. In the discrete distribution, the sum of probabilities must add to 1 and in the continuous distribtion, the area under the curve must sum to 1. In both the discrete and continuous distributions, a range (or domain) can be described without a listing of all possible outcomes. For example, the domain of the normal distribution (a continuous distribution is minus infinity to positive infinity. The domain for the Poisson distribution (a discrete distribution) is 0 to infinity. You will learn in math that certain series can have infinite number of terms, yet have finite results. Thus, a probability distribution can have an infinite number of events and sum to 1. For a continuous distribution, the probability of an event are stated as a range, for example, the probability of a phone call is between 4 to 10 minutes is 10% or probability of a phone call greater than 10 minutes is 60%, rather than as a single event.
What is solar quantities?
Scalar quantities are physical quantities that can be described with a single value. They are unlike vector quantities which require both magnitude and direction.
What is the vector quantities?
those quantities in which we are need are both magnitude and direction for complete described is called a Victor
What is the difference between a probability distribution and sampling distribution?
A sampling distribution function is a probability distribution function. Wikipedia gives this definition: In statistics, a sampling distribution is the probability distribution, under repeated sampling of the population, of a given statistic (a numerical quantity calculated from the data values in a sample). I would add that the sampling distribution is the theoretical pdf that would ultimately result under infinite repeated sampling. A sample is a limited set of values drawn from a population. Suppose I take 5 numbers from a population whose values are described by a pdf, and calculate their average (mean value). Now if I did this many times (let's say a million times, close enough to infinity) , I would have a relative frequency plot of the mean value which will be very close to the theoretical sampling pdf.
