The phrase "a normal model for" is correct and usable in written English.
It can be used when discussing statistical models or frameworks that are considered standard or typical for a particular analysis or situation.
Example: "In our research, we used a normal model for the distribution of test scores to analyze the data effectively."
Alternatives: "a standard model for" or "a typical model for".
In addition, they developed a log-normal model for the observational component and a normal model for the prior component.
However, the results also revealed some severe limitations of a normal model for meta-analysis of binary data, as now discussed.
In simulation experiments, it has been shown that, in general, it is better to work with such a binomial model rather than with a normal model for transformed observed proportions [ 18].
Our simulation results highlight that a normal model for BRMA is preferable to two separate URMAs for estimating the pooled endpoints, and our results are consistent with previous findings that show how the inclusion of correlation allows the 'borrowing of strength' across endpoints [ 1, 10, 19].
In this paper we have used analytic reasoning, two applied examples and a realistic simulation study to highlight the benefits of a normal model for BRMA over two separate URMAs, and explain why the between-study correlation is often estimated as +1 or -1.
To incorporate the ppw-1 -flanking marker interval as a covariate, we used a parametric normal model for the interval mapping.
For the imputation we used a multivariate normal model for PM10 and ozone levels containing all variables from the final temperature model.
We fitted a two-level mixed logistic regression model conditional on the sensitivity and the specificity of each study and a bivariate normal model for the sensitivity and specificity between studies [ 17].
In humans, the mixture model fit the data significantly better than a simple normal model for both color dimensions (a* and b* P = 0.000010 JPEG and RAW); the two modes in humans may represent the influence of the main effect SNP.
Although the GMDR method is not necessarily likelihood-based (least-squares regression or other statistical methods for non-normal continuous traits can be employed as well, in theory), continuous phenotypes were only investigated in terms of a normal model, and the software implementation for continuous traits relies on the classical linear regression paradigm to build the score statistics.
"The President's Stealth Nominee" (editorial, Oct. 4) says, "The normal model for a Supreme Court nominee is a judge, usually from a federal appeals court, who has served long enough to develop and demonstrate judicial excellence".
