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Due to ease of use, the regression model is preferred over the neural network model.
However, if standard regression procedures of model construction and criticism are employed, then a strictly model-based estimator does better.
However, its use should be considered only when traditional linear regression models or other simpler methods do not show good results.
Open accessJournal ArticleDOI
07 Oct 2013-Građevinar
25 Citations
Consequently, the regression model can be adopted by practitioners.
In addition to improved accuracy, the use of nonlinear forms also expands the scope of regression analysis.

Related Questions

When to use regression?5 answersRegression analysis is utilized when predicting continuous variables based on multivariate input variables. It helps in understanding cause-effect relationships between different variables, estimating the impact of independent variables on dependent ones. Regression is crucial in various fields like clinical research for assessing relationships among variables. Additionally, it is employed in analyzing numerical model experiments to test the effects of different model settings, such as in coupled atmosphere-ocean models to predict seasonal variations. The technique is also valuable in making predictions, correcting errors, and estimating unknown factors in biomedical science and healthcare, aiding in decision-making processes. Overall, regression is beneficial when seeking to understand and quantify the influence of one variable on another, making it a versatile tool in research, modeling, and forecasting across different domains.
What is regression used for?5 answersRegression is a statistical technique used to understand the relationship between variables and predict future outcomes. It is commonly used in various fields such as medical research, business marketing, financial forecasting, and environmental modeling. Regression models can be used to estimate the collective relationship between variables, test hypotheses, and predict continuous values based on input variables. Different types of regression models exist, including linear regression, logistic regression, ridge regression, polynomial regression, and more. These models help researchers and decision-makers gain insights into the cause-effect relationship between variables and make informed predictions.
What are the advantages and disadvantages of using the 95% confidence interval for a regression?5 answersThe 95% confidence interval for a regression has several advantages and disadvantages. One advantage is that it provides a range of values within which the true parameter is likely to fall, giving a measure of the precision of the estimate. Additionally, it allows for easy comparison between different methods and provides an estimate of the actual clinical effect. However, there are also disadvantages. The width of the confidence interval depends on factors such as the desired confidence level, sample size, and variability in the sample. Furthermore, the coverage probability of the confidence interval may be slightly lower than the nominal value if the covariates are highly correlated. It is also important to check the assumptions of the regression model and the methodology used to calculate the confidence interval, as combining statistical methods without checking assumptions can lead to deflated confidence levels.
When is it better to use association testing instead of regression?5 answersAssociation testing is better than regression when the goal is to evaluate the relationship between variables and determine if there is a true association between an outcome of interest and a group of variants or microbiome profiles. Regression analysis is commonly used to assess the correlation between variables, but it may not be suitable for identifying associations between groups of variants or microbiome profiles and disease phenotypes. Association testing, on the other hand, allows for the examination of multiple variants or microbiome profiles simultaneously and can provide meaningful association information. It is particularly useful when dealing with rare variants or when the association occurs at a different level than the mean, such as the median or variance.
How to use Regression approach in research?4 answersRegression analysis is a statistical technique used to assess the relationship between variables in research. It helps in predicting continuous variables based on multivariate input variables. Different types of regression models, such as linear regression, polynomial regression, and logistic regression, are used depending on the type and scale of the response variables. Regression models are widely used in various fields, including climate prediction, medical research, and communication networks. They provide insights into the association between independent and dependent variables and help in making predictions and understanding causal relationships. Researchers can implement regression models using software like SPSS, STATA, and R, and interpret the output to gain a better understanding of the computational implementation of different regression models.
Why we Cannot use our regression model to predict the life expectancy of one particular individual?7 answers

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