With the advancement of science, knowledge and technology, new and comprehensive methods for measuring, collecting and recording information have been invented, which has led to the emergence and development of high-dimensional data. High-dimensional data sets, i.e. data sets in which the number of explanatory variables is much larger than the number of observations, cannot be analyzed simply and with traditional and classical methods, such as the ordinary least squares method, and their interpretability will be very complex. Although ordinary least squares estimation is the best estimation method in regression analysis if the basic assumptions are valid, it cannot be used for high-dimensional data and in these circumstances we need to use new methods. In this article, first, the problems of classical methods in analyzing high-dimensional data are mentioned and then, the introduction and explanation of common and modern regression analysis methods such as principal component analysis and weighted analysis methods for high-dimensional data are discussed. Finally, a simulation study is conducted to examine and compare the mentioned methods in high-dimensional data.