Applied Statistical Analysis of the Linear Regression Models

Abstract

The aim of the study is to achieve a statistical analysis of linear regression models of specific industrial processes data. The work strategy involves the regression analysis which is the most widely used statistical tools to understand which among the independent variables are related to the dependent variable, and to explore the forms of these relationships. This application focused on the fitting and checking of linear regression models, using small and large data sets, with pocket calculators or computers. The performance of regression analysis methods in practice depends on the form of the data generating process, and how it relates to the regression approach being used. It was used some statistical criteria as: Cochran criteria; Student criteria and Fischer criteria. After solving statistical analysis of the linear regression models, in the end there was obtained an applied statistical analysis of the linear regression model through the use of the classical method with a pocket computer. The same data were calculated with C++ software. By using this software we obtained more accurate results and the application time was reduced by several hours to 2-3 minutes.