## Friday, July 31, 2015

### Finding Statistics Time- Consuming and Calculative, Get Solutions Instantly!

Statistics is a very common subject in graduation and generally included in most of the academic curriculums like humanities, commerce, business etc. A very common topic is the normal distribution which appears in a majority of statistical applications. One reason behind this is the central limit theorem. This theorem tells that sums of random variables are approximately normally distributed if there is large number of observations. Normal distribution is one of the common and very general topics which students learn in their academic course work. But many application based problems strikes when we talk about statistics exams and related assignments & homework.
There are some of the characteristics and properties of normal curves and the normal distribution mentioned below:
The normal curve is symmetrical and well shaped in its appearance. If the curves are folded along its vertical axis, then the two halves will go inside. In a normal distribution the number of cases below the mean is equal to the number of cases above the mean which provides that the mean and median coincide. The height of the curve for a positive deviation of three units is the exactly same as the height of the curve for negative deviation of three units.

The height of the normal curve is at maximum in the mean. Therefore the mean and mode of normal distribution coincide or overlap. Thus in the case of normal distribution mean, median and mode, all are equal.
There is one highest point of the curve which occurs at the mean and then the height of the curve declines as one goes in either direction from the mean. The curve approaches nearer and nearer to the base but it will never touch it i.e. the curve is mot to meet to the base on either side. Therefore its range is limitless and infinite in both directions.
As there is only one maximum point, the normal curve is said to be unimodal.

The point of inflection, this means, and the points where the change in the curvature occurs are X ± s.
The variable distribution as according the normal curve is continuous which is different from binomial and poisson distribution where the variable is discrete.

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