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On the basis of experimental measurements, regression models were created for conveyor belts with and without the supporting system.
A repeated measurements regression model was constructed, using backward selection procedures for identifying relevant maternal and fetal characteristics.
In order to investigate whether baseline LV and arterial elastance measurements correlated with baseline levels of NT-proBNP and troponine or changes in these measurements, regression analysis was performed failing to demonstrate a significant relation between the measurements.
Once a man has accumulated a number of measurements, regression will provide a better indication of the overall trend in his PSA level, but will become increasingly insensitive to any upturn in PSA level.
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Consider a repeated measurement regression model yij= g xi +εij where i= 1,…,n, j= 1,…,m, yij's are responses, g is an unknown function, xi's are design points, εij's are random errors with a one-way error component structure, i.e. εij= μi+νij, μi and νij's are i.i.d random variables with mean zero, variance σ2μ and σ2ν, respectively.
Change in CGM use over time was evaluated with a repeated-measurements regression model.
Repeated-measurement regression was applied.
For this analysis, we used repeated measurement regression models, which take into account the correlation between repeated growth measurements of the same participant.
Discriminant function analysis(DFA) is then used to generate equations for modeling the measurements and regression analysis to classify the samples into their respective classes.
Outcome was calculated with repeated measurements logistic regression every 6 months.
To incorporate all BMD measurements, a regression slope was calculated using all available measurements for each patient.
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