Correlations have been found between chemical and mineralogical characteristics of phyllite clays and permeability. Phyllite clays are applied as a layer on a surface to be waterproofed and subsequently compacted. For this purpose, phyllite clays deposits can be grouped by their chemical and mineralogical characteristics, and these characteristics difference between correlation and regression analysis pdf be connected with their properties, mainly permeability, in order to select those deposits with the lowest permeability values.
52 samples determined by XRF, mineralogical analysis by XRD and permeability are reported. Permeability, a characteristic physical property of phyllite clays, was calculated using the results for experimental nitrogen gas adsorption and nitrogen adsorption-desorption permeability dependence. According to the results, permeability values differentiated two groups, i. 1 and group 2, with two subgroups in the latter. The influence of chemical as well as mineralogical characteristics on the permeability values of this set of phyllite clays was demonstrated using a multiple linear regression model. Two regression equations were deduced to describe the relationship between adsorption and desorption permeability values, which support this correlation. This was an indication of the statistical significance of each chemical and mineralogical variable, as it was added to the model.
The statistical tests of the residuals suggested that there was no serious autocorrelation in the residuals. Check if you have access through your login credentials or your institution. Regression analysis is also used to understand which among the independent variables are related to the dependent variable, and to explore the forms of these relationships. Many techniques for carrying out regression analysis have been developed. Since the true form of the data-generating process is generally not known, regression analysis often depends to some extent on making assumptions about this process. These assumptions are sometimes testable if a sufficient quantity of data is available. Regression models for prediction are often useful even when the assumptions are moderately violated, although they may not perform optimally.
Gaussian, but the joint distribution need not be. In this respect, Fisher’s assumption is closer to Gauss’s formulation of 1821. In the 1950s and 1960s, economists used electromechanical desk calculators to calculate regressions. Before 1970, it sometimes took up to 24 hours to receive the result from one regression. Regression methods continue to be an area of active research. The sample is representative of the population for the inference prediction.
The independent variables are measured with no error. It is important to note that actual data rarely satisfies the assumptions. That is, the method is used even though the assumptions are not true. Variation from the assumptions can sometimes be used as a measure of how far the model is from being useful. Many of these assumptions may be relaxed in more advanced treatments. Reports of statistical analyses usually include analyses of tests on the sample data and methodology for the fit and usefulness of the model.