Regression model variable parameters. Medical search. Frequent questions

The number of predicted device days is calculated using multivariable logistic regression models generated from nationally aggregated data during a baseline time period. (cdc.gov)
Descriptive statis- tics and multivariable logistic regression models were performed. (who.int)

Counts of sites per physician were then modeled in a negative binomial regression. (jabfm.org)

Thereafter, Multinomial Logistic Regression (MNLR) and Gradient Boosting Algorithm (GBM), were utilized to spatially classify the salinity levels in the region. (researchgate.net)
On the basis of the data from the clinical evaluations and the confirmed diagnosis, a diagnostic model was developed using multinomial logistic regression methods. (bmj.com)
In statistics , multinomial logistic regression is a classification method that generalizes logistic regression to multiclass problems , i.e. with more than two possible discrete outcomes. (wikipedia.org)
Multinomial logistic regression is known by a variety of other names, including polytomous LR , [2] [3] multiclass LR , softmax regression , multinomial logit ( mlogit ), the maximum entropy ( MaxEnt ) classifier, and the conditional maximum entropy model . (wikipedia.org)
Multinomial logistic regression is used when the dependent variable in question is nominal (equivalently categorical , meaning that it falls into any one of a set of categories that cannot be ordered in any meaningful way) and for which there are more than two categories. (wikipedia.org)
Multinomial logistic regression is a particular solution to classification problems that use a linear combination of the observed features and some problem-specific parameters to estimate the probability of each particular value of the dependent variable. (wikipedia.org)

i is always non-negative and is represented by a loglinear function of explanatory variables x ji related to geometry, traffic, and other highway characteristics. (dot.gov)
First, the main explanatory variables in the wage model, i.e., measures of work experience and time-out-of-work, are endogenous. (repec.org)
Regression modelling where explanatory variables are measured with error is a common problem in applied sciences. (whiterose.ac.uk)
SAID also processes acoustic parameters to be used as explanatory variables for SSC. (usgs.gov)
Explanatory variables were the number of fast-food restaurants, full-service restaurants, convenience stores, small food stores, grocery stores, and large supermarkets within varying distances (0.25 to 3.0 miles) from the survey respondent's residence. (cdc.gov)

It is also used as a quality measure of binary choice or ordinal regression (e.g., logistic regressions) and credit scoring models. (wikipedia.org)

Weight logistic regression analysis was adopted to investigate the association of hypertension with DII. (frontiersin.org)

In addition, traditional regressions were used to estimate these parameters (de Leva, 1996). (cdc.gov)
1993) used a model of Poisson type to estimate accidents along highway segments. (dot.gov)
The landscape of practical deployments of these formulations is dominated by forward regression models that estimate the parameters of a function mapping a set of p covariates, x, to a response variable, y. (wisc.edu)
To account for the endogeneity induced by correlation between factors and lagged dependent variable/regressors, we first estimate factors consistently via an iterative principal component analysis. (lse.ac.uk)
In the second step, we run a quantile regression for the augmented model with estimated factors and estimate the slope parameters. (lse.ac.uk)
This paper investigates the impact of parameter uncertainty on capital estimate in the well-known extended Loss Given Default (LGD) model with systematic dependence between default and recovery. (repec.org)
For each measurement, separate regression models were derived to estimate the mean, standard deviation and reference percentiles at each week of gestational age for this cohort. (who.int)

Poisson regression models provide an easy linkage to probability, as opposed to other commonly used models such as multiple linear regression. (dot.gov)
This study employed a mixed-methods design, where quantitative variables were examined for relationships and effect size interactions using multiple linear regression techniques and the wild bootstrap technique. (who.int)

We present a review on the implementation of regularization methods for the estimation of additive nonparametric regression models with instrumental variables. (degruyter.com)
2003. "Efficient Estimation of Models with Conditional Moment Restrictions Containing Unknown Functions. (degruyter.com)
2015. "Adaptive Estimation of Functionals in Nonparametric Instrumental Regressions. (degruyter.com)
Large Sample Sieve Estimation of Semi-Nonparametric Models. (degruyter.com)
2015. Optimal Uniform Convergence Rates and Adaptive Estimation of Nonparametric Instrumental Variables Models . (degruyter.com)
2012. "Estimation of Nonparametric Conditional Moment Models With Possibly Nonsmooth Generalized Residuals. (degruyter.com)
The goal of this paper is to show how Inverse Regression in the ``abundant'' feature setting (i.e., many subsets of features are associated with the target label or response, as is the case for images), together with a statistical construction called Sufficient Reduction, yields highly flexible models that are a natural fit for model estimation tasks in vision. (wisc.edu)
This work deals with estimation and diagnostic analytics in regression modelling based on the Birnbaum-Saunders distribution using additive measurement errors. (whiterose.ac.uk)
The maximum pseudo-likelihood and regression calibration methods are used for parameter estimation. (whiterose.ac.uk)

A mixed linear regression model was performed (a = 0.05). (cdc.gov)
Cluster analysis, discriminant analysis, factor analysis, and linear regression were applied in stepwise fashion. (dot.gov)
It fits a univariate left, right, or interval censored linear regression model with autoregressive errors, considering the normal or the Student-t distribution for the innovations. (r-project.org)
Quantile Regression as introduced by Koenker and Bassett (1978) seeks to complement classical linear regression analysis. (wikibooks.org)
Central hereby is the extension of "ordinary quantiles from a location model to a more general class of linear models in which the conditional quantiles have a linear form" (Buchinsky (1998), p. 89). (wikibooks.org)
2010. "Thresholding Projection Estimators in Functional Linear Models. (degruyter.com)
For different groups of tree species and different places, with simple linear regression models using DBH as an independent variable, Hilbert et al. (researchgate.net)
p=0.001) was found in a multivariable linear regression. (ersjournals.com)
The best description of growth was obtained when using available proteins and total lipids as the independent variables of simple linear regression models. (int-res.com)
Statistical models such as linear regression drive numerous applications in computer vision and machine learning. (wisc.edu)
In a statistics textbook I saw that the linear simple regression model is defined as \begin{equation} Y = \alpha + \beta x + e \end{equation} where $x$ is a value of the independent variable, $Y$ is the response, $\alpha$ and $\beta$ are the parameters, and $e$ is a random error with a mean of 0. (stackexchange.com)
This will be the last post in the series on Linear Regression. (pugetsystems.com)
There are a couple more fundamentals that are well illustrated with linear regression. (pugetsystems.com)
You can use non-linear feature variable terms in your model function! (pugetsystems.com)
The "Linear" in Linear Regression is referring to the model parameters, $a$ The feature varaibles can be most anything that makes sense for the data you are trying to fit your model to. (pugetsystems.com)
You can use terms like $x^2$, $\sqrt{x}$, $\exp{(-x^2)}$, $(1 - \exp{(-x)})^2$, … as long as the parameters $a$ are just simple scaler multipliers you can use linear regression. (pugetsystems.com)
Non-Linear Regression is also very useful and you can use "least-squares" as a cost function but the problem is then non-linear optimization and there are extra difficulties to consider. (pugetsystems.com)
I'll probably do a non-linear regression post sometime in the future. (pugetsystems.com)
One of the things we are trying to achieve with non-linear feature variables is getting a better fit to our data. (pugetsystems.com)
The linear-regression procedure gives parameters for the model that minimize that. (pugetsystems.com)
If a non-linear feature variable is used to improve the model then the value of the cost function using the optimized parameters should decrease i.e. less error. (pugetsystems.com)
In multivariate linear regression models, mRNA expression of AdipoR1, but not AdipoR2, was a determinant of first-phase insulin secretion independent of insulin sensitivity and body fat. (diabetesjournals.org)
Linear regression analysis was used to examine the association with hospitalization costs and predictors. (biomedcentral.com)
Linear regression analysis showed that LoS, hospital level, and previous surgery were key determinants of hospitalization costs in all immobile patients with stroke. (biomedcentral.com)
The Surrogate Analysis and Index Developer (SAID) standalone tool, under development by the U.S. Geological Survey (USGS), assists in the creation of linear regression models that relate constituent and surrogate parameters by providing visual and quantitative diagnostics to the user. (usgs.gov)
Linear regression models with stepwise selection were employed to Author Manuscript examine factors associated with serum PCBs. (cdc.gov)
Variable selection in high-dimensional linear regression via advanced regularization methods. (lu.se)
In particular, I wil talk about the problem of selecting significant variables in high-dimensional linear regression and generalized linear models. (lu.se)
Analyses of differences in outcomes at both the patient and clinician levels will include a linear mixed model to compare change in outcomes from baseline to each post-baseline assessment between the randomized study arms. (cdc.gov)

Somers' D plays a central role in rank statistics and is the parameter behind many nonparametric methods. (wikipedia.org)
Methods for fitting such models include logistic and probit regression. (wikipedia.org)
The aim of this study was to investigate differences in body segment parameters derived using two methods: (1) a dual-energy x-ray absorptiometry (DXA) subject-specific method (Chambers et al. (cdc.gov)
Death rates calculated using bridging via the NHIS models were similar to those calculated using other methods, except for the American Indian/Alaska Native group, which included a large proportion of multiple-race reporters. (cdc.gov)
In recent years, advanced regularization methods such as the SLOPE method have been gaining popularity in this area, e.g. it has been proven that the SLOPE method with an appropriate sequence of regularization parameters controls the FDR at a given level for orthogonal and 'nearly' orthogonal design matrices. (lu.se)
Methods: Study was motivated by a large TEDDY dietary longitudinal and time-to-event data with a continuous time-varying vitamin B 12 intake as the exposure of interest and development of Islet Autoimmunity (IA) as the response variable. (lu.se)

Within the multi-lane segments, the significant variables identified by discriminant analysis were different from those identified by stepwise regression. (dot.gov)

The model developed is checked for its adequacy by regression by using the F-test and Analysis of Variance (ANOVA). (amrita.edu)
The assumption of linearity in the regression analysis is simplistic and should be refined. (dot.gov)
To determine the applicability of the model trained at the reference site to the target area, a Multivariate Environmental Similarity Surface (MESS) analysis was conducted. (researchgate.net)
Regression analysis is the study of the relationship between one or several predictors (independent variables) and the response (dependent variable). (originlab.com)
To perform regression analysis on a dataset, a regression model is first developed. (originlab.com)
So we may also refer to regression analysis as "curve fitting" or "surface fitting. (originlab.com)
In particular, we adopt a smoothed quantile regression analysis where the quantile loss function is smoothed to have well-defined derivatives. (lse.ac.uk)

Article: Modelling HSLA steel product quality under multi-stage manufacturing set up using multi-block partial least square regression Journal: International Journal of Productivity and Quality Management (IJPQM) 2019 Vol.27 No.2 pp.177 - 195 Abstract: In order to have an understanding about the quality of steel product, involving multiple stages of manufacturing process, adequate assessment about the input-output relationship is necessary to ensure high-quality product. (inderscience.com)

Candidate predictors for multivariate logistic regression were selected by expert opinion based on clinical significance. (bmj.com)

Quantile Regression goes beyond this and enables one to pose such a question at any quantile of the conditional distribution function. (wikibooks.org)
The following seeks to introduce the reader to the ideas behind Quantile Regression. (wikibooks.org)
Finally, Quantile Regression is presented, along with an example utilizing the Boston Housing data set. (wikibooks.org)
Dynamic Quantile Panel Data Models with Interactiv. (lse.ac.uk)
We propose a simple two-step procedure for estimating the dynamic quantile panel data model with unobserved interactive effects. (lse.ac.uk)

In practice, Somers' D is most often used when the dependent variable Y is a binary variable, i.e. for binary classification or prediction of binary outcomes including binary choice models in econometrics. (wikipedia.org)
It provides estimates and standard errors of the parameters, predicts future observations, and supports missing values on the dependent variable. (r-project.org)
[1] That is, it is a model that is used to predict the probabilities of the different possible outcomes of a categorically distributed dependent variable , given a set of independent variables (which may be real-valued, binary-valued, categorical-valued, etc. (wikipedia.org)
They all have in common a dependent variable to be predicted that comes from one of a limited set of items that cannot be meaningfully ordered, as well as a set of independent variables (also known as features, explanators, etc.), which are used to predict the dependent variable. (wikipedia.org)

Poisson regression provides one of the most suitable models because vehicle accidents are discrete rare events and accident counts are nonnegative integers. (dot.gov)
With respect to the underlying Poisson assumption that the mean equals the variance, the model for two-lane rural segments is not very satisfactory since the estimated ratio of variance to mean, 1.36, is not close to one. (dot.gov)
The regression coefficients in the negative binomial model are similar to those of the Poisson model. (dot.gov)
Poisson and negative binomial modeling techniques are believed to be robust and quite suitable for accident modeling. (dot.gov)
Offset variables are most often used to scale the modeling of the mean in Poisson regression situations with a log link. (jmp.com)

This tutorial will walk you through the steps of solving a regression problem with MATLAB for any dataset, while showing examples for each step using a sample dataset. (mathworks.com)

In many scientific experiments, the regression model has only one or two predictors, and the aim of regression is to fit a curve or a surface to the experimental data. (originlab.com)
The derived SPAR model combining both predictors (SPO 2 and ARthritis) increased the prediction rate from 25.5% to 91.7% with an area under the curve (95% CI) of 0.83 (0.73 to 0.93). (bmj.com)
Descriptive statistics and logistic regression models tested the association between predictors and trifecta outcome. (bvsalud.org)
In summary, demographic variables and past consumption of locally produced foods were found to be the most important predictors of PCB concentrations in residents living in the vicinity of a former PCB manufacturing facility. (cdc.gov)

In the study, soil salinity classes are the outcome variables. (researchgate.net)
Specifically, two logit functions are needed in the three-outcome category model. (researchgate.net)
Main outcome measures Diagnosis of one of three key types of serious bacterial infection (urinary tract infection, pneumonia, and bacteraemia), and the accuracy of both our clinical decision making model and clinician judgment in making these diagnoses. (bmj.com)
Thus, the relationship between the variables pointed out that family members' income and education and joint activities between adults and children were the factors most associated with the outcome of language, which highlights the importance of monitoring language skills and the quality factors of the environments. (bvsalud.org)
Outcome variables were BMI, weight status of a BMI of 25.0 or more and a BMI of 30.0 or more, and the number of times per week the following were consumed: fruits, vegetables, sugar- sweetened soft drinks, fried potatoes, and fast food. (cdc.gov)

In the last two decades several Software Reliability models have been developed in the literature showing that the relationship between the testing time and the corresponding number of faults removed is either Exponential or S-shaped or a mix of the two [ 1 - 7 ]. (hindawi.com)
These models can describe both exponential and S-shaped growth curves and therefore are termed as flexible models [ 2 , 5 , 6 ]. (hindawi.com)
The first type is modeled by an Exponential model of Goel and Okumoto [ 4 ]. (hindawi.com)

Least Absolute Shrinkage and Selection Operator (LASSO) regression was carried out to screen the most important dietary factors associated with the risk of hypertension. (frontiersin.org)
Given that I have over 100 variables related to ESG scores I want to run an adaptive LASSO/Elastic Net in order to penalize coefficients and get a few ESG scores which impact firm value the most. (stackexchange.com)

Differences in geriatric anthropometric data between DXA-based subject-specific estimates and non-age-specific traditional regression models. (cdc.gov)
The obesity and gender differences that we observed translate into differences associated with using traditional regressions to predict anthropometric variables in an aging population. (cdc.gov)
Instead, the SUR allows users to summarize data by more than a single variable (for example, location or medical school affiliation), adjusting for differences in the use of each device type among other variables of importance. (cdc.gov)
The Datathon task is to train a model that predicts building energy consumption based on regional differences in building energy efficiency, as this could help determine the best targets for retrofitting. (mathworks.com)

Even though the word "regression is used there it's actually a classification algorithm … that's just the name used for it. (pugetsystems.com)

the receiver operating characteristic (ROC) curve was used to evaluate the diagnostic power of the nomogram model for screening hypertension risk. (frontiersin.org)
Physicians' diagnoses of bacterial infection had low sensitivity (10-50%) and high specificity (90-100%), whereas the clinical diagnostic model provided a broad range of values for sensitivity and specificity. (bmj.com)
A clinical diagnostic model could improve decision making by increasing sensitivity for detecting serious bacterial infection, thereby improving early treatment. (bmj.com)
The best values of the parameters for a given problem are usually determined from some training data (e.g. some people for whom both the diagnostic test results and blood types are known, or some examples of known words being spoken). (wikipedia.org)

Descriptive statistical parameters were computed for the values of the eight chosen digital covariates within both the reference and target regions. (researchgate.net)

Our data point to a need to consider age, obesity, and gender when utilizing anthropometric data sets and to develop regression models that accurately predict body segment parameters in the geriatric population, considering gender and obesity. (cdc.gov)
We used a transmission dynamic model to predict the burden of human papillomavirus related disease for the number of cervical screens, treatments for precancerous abnormalities of the cervix, and cases of diagnosed cancer and anogenital warts expected before and after vaccination. (bmj.com)

The use of acoustic and other parameters as surrogates for suspended-sediment concentrations (SSC) in rivers has been successful in multiple applications across the Nation. (usgs.gov)
Tools to process and evaluate the data are critical to advancing the operational use of surrogates along with the subsequent development of regression models from which real-time sediment concentrations can be made available to the public. (usgs.gov)
Statistically significant positive associations were observed between serum PCB concentrations and age, race, residential variables, current smoking, and local fish consumption, as was a negative association with education level. (cdc.gov)

As a result, applying traditional estimators may lead to inconsistent parameter estimates. (repec.org)

Identifying visit-level outliers from longitudinal data with continuous time-dependent covariates is important when the distribution of such variable is highly skewed. (lu.se)

However, according to the textbook, $A+Bx$ is an estimator of $Y$ . I don't understand that $A+Bx$ can be an estimator of $Y$ , since $Y$ is a random variable but not a parameter. (stackexchange.com)
Note that $\alpha+\beta x+e$ is a random variable and it equals $Y$ . If you oppose to calling $A+Bx$ and estimator of $Y$ , you should oppose calling it an estimator of $\alpha+\beta x+e$ for the sake of internal consistency. (stackexchange.com)
The proposed two-step estimator is consistent and asymptotically normally distributed, but subject to asymptotic bias due to the incidental parameters. (lse.ac.uk)
To assess the impact of detected outliers, data were analyzed using the extended time-dependent Cox model with robust sandwich estimator. (lu.se)

Second, empirical evidence on the gender wage gap hinges on estimates of the parameters of interest. (repec.org)
Instrumental Variables Estimates of the Effect of Subsidized Training on the Quantiles of Trainee Earnings ," Econometrica , Econometric Society, vol. 70(1), pages 91-117, January. (repec.org)
Instrumental Variables Estimates of the Effect of Subsidized Training on the Quantiles of Trainee Earnings ," Working papers 99-16, Massachusetts Institute of Technology (MIT), Department of Economics. (repec.org)
Parameter estimates differed between the NHIS models for the multiple-race groups. (cdc.gov)

In this study, a high-strength low-alloy (HSLA) steel product quality, comprising of two stages of manufacturing, is modelled using partial least square regression (PLSR) and multi-block PLSR (MBPLSR) approaches. (inderscience.com)

The producer's accuracy, user's accuracy, and Tau index parameters were used to evaluate the model's accuracy, and spatial confusion indices were computed to assess uncertainty. (researchgate.net)

The results are robust to uncertainty in many parameters and processes. (bmj.com)

Influence diagnostics for censored regression models with autoregressive errors. (r-project.org)

Since the model will be predicting a building's energy consumption, which is a quantitative result, this problem could be solved using a regression model. (mathworks.com)

Both the modelling approaches are found to be useful for this purpose. (inderscience.com)

Variable natural history and accompanying clinical features of TBM may confuse the clinician. (medscape.com)
Objectives To identify the predictive clinical characteristics and establish a prediction model for the progression of mild interstitial lung disease (ILD) in patients with systemic sclerosis (SSc). (bmj.com)
Conclusions The evidence-based SPAR prediction model developed in our study might be helpful for the risk stratification of patients with mild SSc-ILD in clinical practice and cohort enrichment for future clinical trial design. (bmj.com)
CONCLUSION: Clinical and laboratory parameters are associated with an increased risk of sepsis after decompression. (bvsalud.org)
The aim of this study is detecting to prove bacterial spread into irrigation fluid and bloodstream during percutaneous nephrolithotomy (PCNL) and to correlate these findings with clinical and microbiological parameters and infectious complications (IC). (bvsalud.org)

Slope - adaptive variable selection via convex optimization. (lu.se)

We first trained a statistical model for obtaining relative brain age (RBA), a metric describing a subject's brain age relative to peers, based on whole-brain anatomical measurements, from training set subjects (n = 5,193). (nature.com)

2013. "On the Testability of Identification in Some Nonparametric Models with Endogeneity. (degruyter.com)
Data Driven Selection of the Regularization Parameter in Nonparametric Instrumental Regressions. (degruyter.com)
2015. "Sieve Wald and QLR Inferences on Semi/Nonparametric Conditional Moment Models. (degruyter.com)
2014. "Local Identification of Nonparametric and Semiparametric Models. (degruyter.com)
2011. "Nonparametric Instrumental Regression. (degruyter.com)

The process parameters are optimized to get desired weld bead geometry. (amrita.edu)

If the multinomial logit is used to model choices, it relies on the assumption of independence of irrelevant alternatives (IIA), which is not always desirable. (wikipedia.org)

If you have simple data and a simple model you may be able to make a plot and just look at how well your model is fitting the data. (pugetsystems.com)

We demonstrate how the uncertainty can be quantified using the full posterior distribution of model parameters obtained from Bayesian inference via Markov chain Monte Carlo (MCMC). (repec.org)
Results show that the parameter uncertainty and its impact on capital can be very significant. (repec.org)

The proposed approach generalizes the portfolio model used in Pillar 1 for the calculation of the capital requirement, removing the assumptions of the existence of one systematic risk factor and of an infinitely granular portfolio. (repec.org)

Hotelling's T 2 statistic is used for diagnosis of faults in batches of heat along with developing fault detection system through significant input variables. (inderscience.com)
In such a situation, we can model the software fault detection process as a stochastic process with continuous state space. (hindawi.com)
Ohba [ 6 ] refined the Goel-Okumoto model by assuming that the fault detection/removal rate increases with time and that there are two types of faults in the software. (hindawi.com)
He suggested that the fault removal process for each module is modeled separately and that the total fault removal phenomenon is the addition of the fault removal process of all the modules. (hindawi.com)

Several statistics can be used to quantify the quality of such models: area under the receiver operating characteristic (ROC) curve, Goodman and Kruskal's gamma, Kendall's tau (Tau-a), Somers' D, etc. (wikipedia.org)

This study highlighted the effectiveness of model transfer as a means of identifying and evaluating the management of regions facing significant salinity-related challenges. (researchgate.net)
The present study was conducted using response surface methodology to optimize tray-drying temperatures (50-80°C) and slice thicknesses (2-8 mm) on the basis of phytochemical and physical parameters of dried green banana slices. (hindawi.com)
The present study was derived from a research project of the National Health and Family Planning Commission of the People's Republic of China, which aimed to develop a standardized nursing intervention model (SNIM) among immobile patients with stroke. (biomedcentral.com)
This study will provide much needed evidence on the effectiveness of a technology-enabled multi-disciplinary team-based care model for the management of Long COVID, ME/CFS, and other PIFI within a federally qualified health center. (cdc.gov)

Method type was significant in every variable of interest except forearm segment mass. (cdc.gov)
By backward elimination method, reduced model is obtained from the full model. (amrita.edu)
Through Monte Carlo simulations, we discuss the finite sample properties of each regularization method for different smoothness properties of the regression function. (degruyter.com)
Then the best fit parameters are estimated using something like the least-square method. (originlab.com)
The method further includes generating, by the controller, a regression model for each of the plants in the production system. (google.com)
The method further includes adjusting, by the controller, one or more control variables for each plant based on a target output by using machine learning. (google.com)
The sediment acoustic method utilizes acoustic parameters from fixed-mount stationary equipment. (usgs.gov)

A prediction model for ILD progression was established in the derivation cohort and validated in the multinational validation cohort. (bmj.com)

Response Surface Methodology (RSM) is used to develop a mathematical model to correlate the bead geometry with controllable process variables. (amrita.edu)

Background: Outliers can influence regression model parameters and change the direction of the estimated effect, over-estimating or under-estimating the strength of the association between a response variable and an exposure of interest. (lu.se)
Objectives: The primary objective was to identify potential outliers at follow-up visits using interquartile range (IQR) statistic and assess their influence on estimated Cox regression parameters. (lu.se)
Results: Extreme vitamin B 12 observations that were cases of IA had a stronger influence on the Cox regression model than non-cases. (lu.se)

Finally, the quality of the model is assessed using one or more hypothesis tests. (originlab.com)

Weight logistic regression showed that an increment of DII was strongly associated with hypertension after adjusting for confounding factors. (frontiersin.org)

1991) applied regression techniques to develop accident models for two-lane roads. (dot.gov)
The Software Reliability Growth Model (SRGM) is a tool of SRE that can be used to evaluate the software quantitatively, develop test status, schedule status, and monitor the changes in reliability performance [ 1 ]. (hindawi.com)
Objectives To evaluate current processes by which young children presenting with a febrile illness but suspected of having serious bacterial infection are diagnosed and treated, and to develop and test a multivariable model to distinguish serious bacterial infections from self limiting non-bacterial illnesses. (bmj.com)

that is, each independent variable has a single value for each case. (wikipedia.org)
This allows the choice of K alternatives to be modeled as a set of K -1 independent binary choices, in which one alternative is chosen as a "pivot" and the other K -1 compared against it, one at a time. (wikipedia.org)

Anatomy1

Organisms1

Diseases29

Chemicals and Drugs9

Analytical, Diagnostic and Therapeutic Techniques and Equipment95

Psychiatry and Psychology18

Phenomena and Processes51

Disciplines and Occupations5

Anthropology, Education, Sociology and Social Phenomena20

Humanities1

Information Science22

Named Groups10

Health Care111

Geographicals26

Multivariable logistic regression models2

Negative binomial regression1

Multinomial Logistic Regression6

Explanatory5

Ordinal regression1

Logistic regression analysis1

Estimate7

Multiple linear reg2

Estimation9

Linear29

Methods6

Stepwise1

Analysis7

Abstract1

Multivariate1

Quantile5

Dependent variable4

Poisson5

Dataset1

Predictors4

Outcome5

Exponential3

LASSO2

Differences4

Algorithm1

Diagnostic4

Descriptive1

Predict2

Concentrations3

Estimators1

Covariates1

Estimator4

Estimates4

Partial1

Spatial1

Robust1

Diagnostics1

Quantitative1

Approaches1

Clinical5

Slope1

Statistical1

Nonparametric5

Process parameters1

Assumption1

Plot1

Uncertainty2

Generalizes1

Fault4

Curve1

Study4

Method7

Cohort1

Mathematical1

Influence3

Hypothesis1

Weight1

Develop3

Independent2