Sentence examples for interrelationship of variables from inspiring English sources

ISM used the collective wisdom of the team members which includes Lean practitioners and consultants to convert mental model into a structural model by considering the interrelationship of variables involved in the process or system.

Therefore, we checked the underlying structure of the data matrix by analyzing the interrelationships of variables and using the information collected to explain the interrelationships.

These models are essentially the same as simple regression analysis, except that the multiple regression analysis equation describes the interrelationship of many variables and allows us to evaluate the joint effect of these variables on the outcome variable in question.

In the remainder of the article we report the results of the regularized partial correlation networks, which provide information about the unique interrelationships of two variables while correcting for all other variables35,36,37.

Once more data are available, it will be possible to examine BPA exposure in greater detail and possibly also consider interrelationships of personal variables and sources of exposure.

This article focuses on the interrelationships of anthropometric variables used to characterize overweight and adiposity, and the effect of age on these relations.

Simulation models have been used as a convenient and rapid method of exploring the outcome of different screening policies and of demonstrating the importance and interrelationships of the variables concerned.

The dominant neo-Keynesian (see economics: Keynesian economics) and monetarist schools of economics make predictions of the macroscopic behaviour of economies (see macroeconomics) based the interrelationship of a few variables; money supply, rate of inflation, and rate of unemployment jointly determine the rate of economic growth.

In diabetes exists a complex interrelationship of various inflammatory variables with metabolic disorders and their effect on cardiovascular system.

Identification of vital and significant variables and establishment of interrelationship between variables is very important for accurate model development using ISM.

Principal component analysis is a statistical method designed to analyze the interrelationships within a set of variables by reducing the complex information to an easily interpretable form.