Introduction to Normal Distributions: Difference between revisions
												
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Latest revision as of 10:49, 3 June 2014
- Most of the statistical analyses presented are based on the bell-shaped or normal distribution
 - Methods for calculating probabilities based on the normal distribution
 - A frequently used normal distribution is called the Standard Normal distribution (AKA Gauss Distribution)
 - The binomial distribution can be approximated by a normal distribution
 
Bell Curve
- Gaussian curve is a special case of a Normal Distribution (μ = 0, σ = 1)
 - Although Gauss played an important role in its history, de Moivre first discovered the normal distribution
 - There is no "the normal distribution" since there are many normal distributions which differ in their means and standard deviations
 
Probability Density Function
=NORMDIST(1,0,1,FALSE) = 0.2419707245
- All normal distributions are symmetric with relatively more values at the centre of the distribution and relatively few in the tails
 
The density of the normal distribution
- The density of the normal distribution is the height for a given value on the x axis
 - The parameters μ and σ are the mean and standard deviation, respectively, and define the normal distribution
 - The symbol e is the base of the natural logarithm and π is the constant pi.
 
Normal Cumulative Distribution
Features of Normal Distributions
- Normal distributions are symmetric around their mean.
 - The mean, median, and mode of a normal distribution are equal.
 - The area under the normal curve is equal to 1.0.
 - Normal distributions are denser in the center and less dense in the tails.
 - Normal distributions are defined by two parameters, the mean (μ) and the standard deviation (σ).
 - 68% of the area of a normal distribution is within one standard deviation of the mean.
 - Approximately 95% of the area of a normal distribution is within two standard deviations of the mean.
 
Quiz