Correlation-regression analysis and its wide application in the economy

The main statistical methods have been used for a long time in all areas of human activity. However, the most important role is played by statistics for the economy. After all, it is this scientific branch that regulates the socio-economic relations of business entities, analyzes and processes a huge amount of information.

Very often economic research finds a solution to a certain problem in identifying factors that determine the level, dynamics of the process in the economy. This problem is often solved by correlation-regression analysis. To achieve the reliability of the analysis, it is necessary not only to identify certain relationships, but also to quantify these indicators.

Correlation-regression analysis solves such a problem as testing the hypothesis of statistics about the presence and strength of the correlation relationship. A sufficient number of factors affecting the processes in the economy are not random variables. It is this fact that serves as a prerequisite for the analysis of economic phenomena in the aspect of connections between random and nonrandom variables. These relationships are called regression and, accordingly, the statistical method that studies them - regression analysis.

Thanks to the continuous development of computer technology, the use of computer technology is increasingly used in statistical calculations. Thus, the use of certain computer programs for the processing of statistical information allows you to quickly solve the problem of studying the relationship of various indicators using correlation-regression analysis.

Thus, correlation-regression analysis (an example can be cited) clearly demonstrates its use with the help of the Microsoft Excel program when processing data on exchange rates.

The package itself Microsoft Excel allows you to solve complex statistical and engineering problems using a special set of data analysis tools. Correlation-regression analysis in Excel is performed with mandatory indication of input data and choice of initial parameters. The analysis itself is performed using a statistical macro function (it is possible to use the engineering function), the result is placed in the output range that can be set by the user. If you use other tools of the program, you can get the result in a graphical form.

With the help of a graphic image, the analyst can see visually the representation of statistical data. This mode significantly facilitates the perception of the results and their understanding.

For example, when statistics are aggregated in a table, it is sometimes difficult to detect errors or inaccuracies. The presentation in the form of a graph of data allows you to quickly and easily detect abnormalities and anomalies, a sharp increase or fall of data, although nothing bad foresees such negative aspects.

Correlation is one of the tools of the Microsoft Excel package. Can be used to quantify the relationship between several sets of data. Correlation analysis allows you to establish the relationship of data sets by magnitude. So, there are such concepts: "positive" correlation (large values of one data array are associated with the same large values of another array), a "negative" correlation (small values of one data array are associated with analogous values of another array) and a correlation equal to zero (data Two arrays are not connected to each other). Regression analysis in Microsoft Excel is to construct a graph using a statistical method such as least squares.

Thus, correlation-regression analysis is much easier to carry out with the use of modern computer technologies, allowing to obtain the desired result in the shortest possible time.