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    article we can study bell curve normal distribution, which figure most significantly in statistical theory and in application. Normal distribution is also called as the Normal probability distribution. Let us see how to calculate normal distribution. The normal distribution looks like a bell shaped curve. Hence it is also known as normal curve of distribution. Definition of normal distribution: A continuous random variable X is said to follows a normal distribution with parameter μ and σ (or μ and

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    Normal distributions are very informative in statistics, it is type of continuous distribution. It is often used in both natural and social sciences to help shed light on random variables where their distri-bution is not known. The three features of normal distribution are 1. It has a bell shaped curve. 2. The total areas under the curve is equal to 1. 3. The bell shape is symmetrical. 2. How is the average of a normal distribution measured and what should be the relationship be-tween the three

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    models for a specific objective, which might be one of the followings; Problem solving, Research, Education. Modeling and Simulation is a discipline which consists of many branches such as; Discrete distributions, Continuous distributions, Monte Carlo modeling and simulation, Probability distributions. Modeling any system, for example, communications system requires the analysis of the input data, to analyze the input data we have to introduce the use of MATLAB. MATLAB is defined as a high-level programming

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    Probability Distribution Functions I summarize here some of the more common distributions utilized in probability and statistics. Some are more consequential than others, and not all of them are utilized in all fields.For each distribution, I give the denomination of the distribution along with one or two parameters and betoken whether it is a discrete distribution or a perpetual one. Then I describe an example interpretation for a desultory variable X having that distribution. Each discrete

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    probit plots and by computing univariate and multivariate measures of skewness and kurtosis. Histograms, stem-and-leaf and probit plots indicate the symmetric distribution of variables or sets of variables. Tabachnick and Fidell (1996) suggest the value of skewness and kurtosis is equal to zero if the distribution of a variable is normal. Chou and Bentler (1995) emphases the absolute values of univariate skewness indices greater than 3 can be described as extremely skewed. Meanwhile, a threshold

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    Spherical Coordinate System

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    standard deviation. Standard deviation is the part of measurement of the z-score. It allows association of clarification from dissimilar normal distributions, which is made normally in examine. z score Standard scores are as well call z-values, z-scores, normal scores; the make use of Z be since the normal distribution be too known while the Z distribution.

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    Measures of spread and dispersion Measures of central tendency are not the only statistics used to summarise a distribution . We also have to identify the spread of the distribution of the data set. Spread defines how widely the observations are spread out around the measure of central tendency. Note that the words, spread, dispersion and variation denote the same meaning. The most commonly used measures of spread are range, variance and standard deviation. The scales of measurement appropriate

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    mathematical medthos

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    (i)Gaussian distribution (steady state , continuous release , puff model ,variable wind speed) (ii) Satistical ( non-uniform distribution , monte-carlo simulation) (iii)Box model (iv)Eulerian model Long range (i) Lagrangian (ii)Gaussian (iii) Box model (iv)Eulerian model Short or minimum range: Gaussian models: from past 20 years air dispersion modelling is totally dominated by Gaussian model this is also known as pasquill-gifford equations. According to this model assumptions of normal distribution of the

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    Meteorological drought is referred as a precipitation deficiency, in comparison to normal or base line condition. We use Standardized Precipitation index (SPI-n, where n = 3, 6, 9 and 12 months accumulation period) as an index of meteorological drought. SPI represents a statistical z-score or the number of standard deviations (following a probability distribution, usually Gamma and back transformed to standard normal distribution) above or below that an event is from the mean (McKee et al. 1993; Sims et

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    Theorem: n If the sample size is large enough, the distribution of the sample mean is approximately Normal. n The variance of the distribution of the sample mean is equal to the variance of the sample mean divided by the sample size. These are true whatever the distribution of the parent population. The Central Limit Theorem allows predictions to be made about the distribution of the sample mean without any knowledge of the distribution of the parent population, as long as the sample is

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