Not necessarily.
A bell shaped probability distribution curve is NOT necessarily a normal distribution.
bell shaped
the bell curve shape? anonymous
The Normal curve is a graph of the probability density function of the standard normal distribution and, as is the case with any continuous random variable (RV), the probability that the RV takes a value in a given range is given by the integral of the function between the two limits. In other words, it is the area under the curve between those two values.
Yes. The total area under any probability distribution curve is always the probability of all possible outcomes - which is 1.
A bell shaped probability distribution curve is NOT necessarily a normal distribution.
There is no histogram below.However, the area under the curve for any histogram is the total frequency.
bell shaped
A normalized probability distribution curve has an area under the curve of 1.Note: I said "normalized", not "normal". Do not confuse the terms.
the bell curve shape? anonymous
The Normal curve is a graph of the probability density function of the standard normal distribution and, as is the case with any continuous random variable (RV), the probability that the RV takes a value in a given range is given by the integral of the function between the two limits. In other words, it is the area under the curve between those two values.
Yes. The total area under any probability distribution curve is always the probability of all possible outcomes - which is 1.
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.
100%. And that is true for any probability distribution.
We prefer mostly normal distribution, because most of the data around us follows normal distribution example height, weight etc. will follow normal. We can check it by plotting the graph then we can see the bell curve on the histogram. The most importantly by CLT(central limit theorem) and law of large numbers, we can say that as n is large the data follows normal distribution.
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.
A normal distribution is defined by its mean and standard deviation, which are sufficient to describe the entire curve. Once you know these two parameters, you can use the standard normal table (Z-table) to find probabilities for any normal distribution by standardizing values. This process involves converting any normal variable to a standard score (Z-score), which allows you to utilize the same table for all normal distributions. Therefore, only one normal table is needed for any probability under the normal curve.