The basic statistical methods have long been used in all areas of human life. However, statistics play the most important role for the economy. Indeed, it is this scientific branch that regulates the socio-economic relations of business entities, is engaged in analysis and processing of a huge amount of information.
Very often in economic studies they find a solution to a particular problem in identifying factors that determine the level and dynamics of the process in the economy. This problem is often solved by correlation and regression analysis. To achieve the reliability of the analysis, it is necessary not only to identify certain relationships, but also to give a quantitative assessment of these indicators.
Correlation-regression analysis solves such a problem as testing the hypothesis of statistics on the presence and strength of correlation. A sufficient number of factors that influence 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 the relationship between random and nonrandom variables. These relationships are called regression and, accordingly, the statistical method that studies them is regression analysis.
Due to the constant development of computer technology, the use of computer technology is increasingly used in statistical calculations. So, the use of certain computer programs for processing statistical information allows you to quickly solve the problem of studying the relationship of various indicators using correlation and regression analysis.
So, correlation-regression analysis (an example can be given) clearly demonstrates its use with the help of Microsoft Excel in processing exchange rate data.
The Microsoft Excel package itself allows you to solve complex statistical and engineering problems using a special set of data analysis tools. Correlation-regression analysis in Excel is carried out with the obligatory indication of the input data and the choice of initial parameters. The analysis itself is carried out using statistical macro function (it is possible to use an engineering function), the result is placed in the output range, which can be set by the user. If you use other program tools, you can get the result in graphical form.
Using a graphical image, the analyst can visually see the statistics. This mode greatly facilitates the perception of results and their understanding.
For example, when compiling statistics in a table, it is sometimes difficult to detect errors or inaccuracies. Presentation in the form of a graph of data allows you to quickly and easily detect deviations and anomalies, a sharp increase or decrease in data, although nothing negative on the table portends such negative points.
Correlation is one of the tools of the Microsoft Excel package. It can be used to quantify the relationship of multiple data sets. Correlation analysis allows you to establish the relationship of data sets in magnitude. So, such concepts exist: “positive” correlation (large values of one data array are associated with the same large values of another array), “negative” correlation (small values of one data array are associated with the same values of another array) and zero correlation (data two arrays are not interconnected). Regression analysis in Microsoft Excel consists in plotting using a statistical method such as least squares.
Thus, correlation and regression analysis is much easier to carry out using modern computer technologies, allowing to obtain the desired result in the shortest time.
