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Statistical information necessary to compare equations is rarely presented, and few publications include the necessary information to create regression prediction intervals, so generation of pseudodata representing the true level of variation in predictions is not possible.
The spiked samples were extracted and analysed under the same conditions as the experimental samples to create regression lines.
In PLS, which is used here, one ore more dependent variables are selected in order to create regression models describing the relationships between the dependent vs. the independent variables.
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On the basis of experimental measurements, they were created regression models of the impact of external factors acting on the readability of the RFID transponder.
Strong covariation between H and O isotope ratios of animals in the single-source experiments created regression lines that extended backwards through isotopic water sources, or very close to these sources, despite differences in temperature and humidity.
To better understand the relationship among daily mortality, mean temperature, and cumulative rainfall, we created regression models for mortality, stratifying by age and sex.
This technique also allows for the identification of a best model and creates regression coefficients based on all the possible models (model-averaged coefficients), which are more robust than typical regression coefficients.
For each missing data item, we will create a regression model (typically logistic regression for missing dichotomous items and linear regression for missing continuous data) that predicts the missing item based on related non-missing items from each subject, and general trends seen in the non-missing data from all subjects.
Next, we paired this data with aboveground carbon measurements at 219 sites to create boosted regression tree models and map aboveground carbon across the city.
MINITAB® v.15.1.30 was also used to create the regression fitted line plots for deposition analysis using fluorescence and comparisons to QCM deposition (Figure 5); data set was at least n = 4 for all dilutions tested.
This test also gives the weights for each question to create a regression equation to be used with the reduced questionnaire to optimise classification.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com