Statistical Analysis

Statistical Analysis

Introduction

The probability is an assessment of a probable event. In mathematics, the study of probability is a matter of great importance resulting in many applications. The probability of an event is a real number between 0 and 1. The higher the number, the greater the risk that the event occurring is high. If we consider that the probability of a coin flip gives stack is equal to 1/2, this means that if you throw a huge number of times this room, the frequency of cells will probably tend towards 1/2, without prejudice to the regularity of their distribution. This notion will be defined more rigorously empirical in the body of this paper.

The probability theory is the part of mathematics that studies the random phenomena. These must be weighed against deterministic phenomena which are unique results and / or predictable experiments under the same conditions determined, for example, if water is heated to 100 degrees Celsius at sea level is obtained steam. Random phenomena, however, are those which are obtained as a result of experiments conducted, again under the same conditions determined but as a result have a set of possible alternatives, for example, the release of a die or a dart.

Many natural phenomena are random, but some such as throwing a die, where the phenomenon is not repeated in the same conditions, because the characteristics of the material does not exist in the same symmetry and repetition do not guarantee a probability defined. In the real processes are modeled using probability distributions corresponding to complex models where there are known a priori all parameters involved, this is one reason why the statistics , which seeks to determine these parameters is not reduced immediately the probability theory itself.

Central Tendencies

In describing groups of observations, it is often convenient to summarize the information with a single number. This number, to that end, usually located towards the center of the distribution of data is called a measure of central tendency or parameter or centralization. When referring only to the position of these parameters within the distribution, whether it is more or less focused, speaking of these measures as measures of position.

The measures of central tendency (mean, median and mode) serve as points of reference for interpreting the scores obtained in a test. In summary, the purpose of the measures of central tendency is:

Show in what place is the average or typical person in the group. It serves as a method to compare or interpret any score in relation to the central or typical score.

It serves as a method for comparing the score obtained by the same person on two different occasions.

It serves as a method for comparing the average results obtained by two or more groups.

The measures of central tendency most common are:

The arithmetic mean: commonly known as the mean or average

The median: which is the score that fits at the center of a distribution

Mode: that is the score that occurs most frequently in a distribution

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