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Autor:   •  September 25, 2014  •  Case Study  •  1,071 Words (5 Pages)  •  969 Views

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Statistics

Introduction

Data analysis is done to convert the raw data into meaningful information. Statistics is all about this data analysis. Before actually working on data analysis in real world, it is important that the basic knowledge is possessed by everybody involved in this process. The following paper seeks to provide basic knowledge in some specific areas of statistical data analysis.

Descriptive statistics

As the name suggests, descriptive statistics means analysis of data to describe or summarize data in a meaningful manner. The analysis helps in determination of patterns that exist in the data. Descriptive statistics simply describes the data but not present an analysis beyond the data. One cannot make any conclusions about any hypothesis using this.

The data, when presented to users, is difficult to comprehend in its raw form. Sometimes the sheer size and volume of data may render it impossible to understand what it represents. Descriptive statistics helps in proper presentation of data such that it can be understood and analyzed by the user. For example, if we had the data about the returns from stocks of 100 companies, then that data would make no sense as it would be difficult to comprehend. But if that data is arranges, classified industry-wise, an average is calculated for each industry and for all the stocks, and then the interpretation would be simpler. Here, descriptive statistics comes as a savior. Following are two most common types of statistics used to describe data:

Measures of central tendency: these are measures which are at the central position of the frequency distribution and which are representative of the whole distribution. Some of the measures of central tendency are mean, mode and median.

Measures of dispersion: these are measures that show how far the actual values are from the measure of central tendency. For example, the average return from all the 100 stocks may be 5% but individual stocks will not exactly earn 5% each. Each stock will earn a different rate of return. Measures of dispersion find out that variation in the actual returns and the average returns. Range, standard deviation, quartiles, absolute deviation and variance are some of the measures of dispersion.

This means that simple measures of mean and standard deviation can be used to describe the whole data of stock returns of 100 stocks.

In descriptive statistics, it is beneficial to use tables (tabulated description), graphs, charts (graphical description) and discussion of the results (statistical

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