Normal distribution

• Relying on previous works of Pascal (combinatoric triangle and Binomial distribution) he identified the Normal distribution by  Johann Friedrich Gauss and name it Gaussian distribution
• Mode:mean:median Is equal
• The first region have a area of 34.1 % The second region have a area of 13.6 %

  The third region have a area of 2.1 %
• The normal distribution: also known as the Gaussian distribution, is a continuous random variables characterized by a bell shaped curve.
• The normal distribution is symmetrical around its mean, with the majority of data points clustered around the mean. •It is defined by two parameters: mean (μ) and standard deviation (σ). •It is asymptotical:meaning the tails of the distribution approach but never touch the horizontal axis.
• μ represents the mean (average) of the distribution, around which the data is centered.
• σ represents the standard deviation, which measures the dispersion or spread of the data points from the mean.
• The probability density function of the normal distribution forms a symmetrical bell shaped curve.
• The total area under the normal distribution curve is equal to 1.
• The normal distribution can be used to model many real-world phenomena such as heights, weights, test scores, etc.
• The standard normal distribution is a special case of the normal distribution where the mean (μ) is 0 and the standard deviation (σ) is 1
• The Z score, also known as the standard score, measures the number of standard deviations a data point is from the mean of a normal distribution.