يشرح هذا المقطع التوزيع الطبيعي Normal Distribution مع أمثلة وكيفية احتسابه من الجداول الاحصائية
The normal distribution, also known as the Gaussian distribution or bell curve, is a probability distribution that is widely used in statistics and probability theory. It is a continuous probability distribution that is symmetric and bell-shaped, with most of the values clustered around the mean.
The normal distribution is characterized by two parameters: the mean (μ) and the standard deviation (σ). The mean represents the center of the distribution, and the standard deviation represents the spread or dispersion of the values around the mean.
The probability density function (PDF) of the normal distribution is given by:
f(x) = (1 / (σ * sqrt(2π))) * exp(-(x-μ)^2 / (2σ^2))
where x is a random variable, σ is the standard deviation, μ is the mean, π is the mathematical constant pi, and exp() is the exponential function.
The normal distribution has several important properties, including the following:
1) It is symmetric, with half of the values below the mean and half above the mean.
2) It has a bell-shaped curve, with most of the values clustered around the mean.
3) It is continuous, meaning that there are an infinite number of possible values that the random variable can take.
4) It is widely used in statistics and probability theory to model many natural phenomena, such as heights and weights of people, IQ scores, and errors in measurements.
5) It has several important applications, such as in hypothesis testing, confidence intervals, and regression analysis.
#normaldistribution
6 дек 2019
