| 词汇 | example_english_binomial |
| 释义 | Examples of binomialThese examples are from corpora and from sources on the web. Any opinions in the examples do not represent the opinion of the Cambridge Dictionary editors or of Cambridge University Press or its licensors. The results are produced using a negative binomial model, with standard errors adjusted for clustering on individual senators. There have been few attempts at modelling parasite abundance data using the negative binomial distribution to describe the error variance. Confidence limits (lines) are based on the negative binomial distribution with k l 4n96. The negative binomial distribution is widely used for empirical description of the dispersion of parasites among hosts. Each of the simulated distributions was overdispersed and well fitted by a negative binomial distribution. However, the negative binomial distribution does not allow ready extension to additional levels of nested random variation in the data. The process of recovery and counting of larvae is described by a model based on the binomial distribution. Neither elementary acquisition nor adolescent acquisition were significant in the binomial regression analysis conducted on the group of speakers as a whole. A success rate beyond chance would be above .67 by the binomial theorem. Fitted by the second method, the negative binomial distribution has its average burstiness matching exactly that of the data it is fitted to. First, for e2e2t < 1, the terms involving 1 + e2(e2t- 1) can be expanded in binomial series. Confidence limits for proportions were obtained from the binomial distribution. The probability calculations used in this application are based on standard lot acceptance sampling techniques using the binomial distribution. The binomial type of germination data required transformation before using procedures for mean comparisons or regression to achieve linearity. The results are produced using a negative binomial model, with standard errors adjusted for clustering on senators. The response variable was the number of trees flushing leaves per plot per week and the binomial totals were the corresponding total numbers of individuals. Using a binomial test, we obtain z = 19.86 corresponding to a probability level that is far too small to compute. Section 4 uses a binomial logit regression framework to estimate these relationships. We now formulate the correlated probit model for the binomial proportion data. Statistical significance was assessed by performing z-tests using standard errors calculated from binomial and large-sample variances. The binomial nomenclature system provides a convenient means by which plant enthusiasts throughout the world can share botanical information for generations to come. An exact binomial 95 % confidence interval was calculated. The number of cases has a binomial distribution. The model then simulated the total number of cases (all ages) by sampling from a negative binomial distribution. The confidence intervals were calculated by the exact binomial method. Also, the binomial distribution and the z-test were used for comparison of two proportions. Fitting the negative binomial distribution to biological data and a note on the efficient fitting of the negative binomial. Here, we present a method for analysing correlated binomial proportion data using the correlated probit model. If more larvae were found on a plant than eggs inserted, survival was bounded at 100% to permit the use of binomial errors. However, since data overdispersion was detected, a negative binomial regression model was used to account for this dispersion. We note that this has the following corollary for binomial random variables. Owing to this fact, we expect the negative-binomial transform to be the most useful new transform presented in the paper. Despite this no model gave a good fit to these data when using the binomial distribution. At every age, participants responded ' yes ' more often on spontaneous than caused movement trials, ps 0n01 by binomial tests. The result is an overdispersed distribution, mirroring that observed in natural infections, to which a negative binomial distribution provides an empirical description (k l 0n68). Confidence limits (lines) are based on the negative binomial distribution with k l 2n52. Also, percentages of fruit damage were transformed to angular values because proportions or percentages form a binomial rather than a normal distribution27. The negative binomial model was used because of the highly skewed distributions of the scale scores. The binary coding of binomial heaps is more space economical. The frequency distribution was equally well described by the negative binomial distribution. As rises significantly above one, it shows that the data are over-dispersed and a negative binomial regression model is required. The binomial theorem and genetic inference from pooled data are particularly well described. Preliminary analyses showed that variables which were not significant in the logistic regression model were also not significant in the logistic binomial model. Further, assuming the normal approximation to the binomial the magnitude of the deviations is not significant. The model is built on the basic tenet of binomial dichotomy. Note that these results using the expected match rates from the random re-orderings were confirmed using the binomial model. Reasoning in terms of (37), however, is again unnecessarily complicated, because of the use of the indices and the occurrence of the binomial coefficient. The expected numbers, for the households with each number of children examined, were calculated from the binomial distribution. Second, for each question, to explore related factors, multinomial or binomial logistic regression analysis was conducted with background factors as explanatory variables. Our experimentation with the negative binomial showed that it is capable of providing good fit for distributions of content entities as well. However, the neutral value varies according to whether the data are put to a binomial or a multinomial analysis. Larger values (above 0.500 for binomial analysis) imply stronger promoting effects, and smaller values (below 0.500) imply stronger inhibitory effects. Each simulation randomly draws samples of the same size coming from simulated negative binomial distributions with the same mean and overdispersion as the ones calculated. Sampling theory of the negative binomial and logarithmic series distributions. Exact 95% confidence intervals for rates were calculated using the binomial distribution. A binomial distribution and a logit link function were used in both analyses. Given that 60% of cones have zero transgenes maintained, the number having 1, 2, 3, etc. to be approximated from the binomial distribution. The binomial probability density function describing the percentage of photoreceptors expressing the viral transgene long term. The binomial test was used to determine the 95% confidence limits around chance performance based on the number of test trials. Using this binomial numerical procedure, we solve the optimal stopping problem assuming different lengths of the gestation period. A probability distribution derived from the binomial distribution by regarding the probability of success as variable between the sets of trial. The goodness of fit to the negative binomial distribution was tested by x 2. The random trees are constructed with a bounded height, and the number of children (out-degree) at each node is determined randomly, using a binomial distribution. I first show the inflation in the simplest case - binomial voting - then generalize to other models. In order to examine if age and year of sampling could be confounders, a negative binomial regression model was adjusted for these variables. In table 4 the results of the binomial regression model adjusted for host age classes and for year of sampling are shown. Variations in the binomial distribution parameter k tended to parallel the change in prevalence values, with a higher prevalence generating a lower degree of over-dispersion. Negative binomial regression was used in all analyses to account for over-dispersion in the data (the variance being greater than the mean) [22]. The assumption of a logit link function and binomial error distribution, which are usually appropriate for a dichotomous dependent variable, produces a logit-linear model. The standard error was computed from the binomial variance of the recombination frequency. The negative binomial is so-named by analogy with the binomial. The first class are much more bursty than the binomial would predict. Consequently, the variance is less than the mean in the binomial, and greater than the mean in the negative binomial. According to the binomial distribution scores equal to or above 12 indicate that responses are significantly above chance. Such attempts to interpret or apply the binomial formula may be seen in two ways. The coherence of the whole system manifested itself in a single mathematical theorem, namely, the binomial or polynomial theorem, which governed most transformations. Nominal regression is a method of maximum likelihood estimation where the basic random variable of interest is a dichotomous variable with a binomial distribution. The binomial evaluations proved very fractionally less conservative, and will not be presented separately here. The model was binomial with a logit link function. The dashed line is the fit of the binomial model to the data (see text). The pro is also a con: binomial heaps do not adapt to the input data. Error bars represent standard errors based on the binomial distribution. The authoritarian+totalitarian binomial has been an extremely useful classi®catory tool for students of twentieth-century dictatorships. The negative binomial's utility for fitting the distributions of content words and phrases comes from its appropriate functional form. Binomial tests do not contain any kinetic information, only equilibrium information. The comparison (a) is straightforward by either the standard binomial or survival techniques. The randomness coming from the binomial random variables is irrelevant for the order of precision we are after. Although many plant names continued to be published under different classification schemes, botanists began to show a definite preference for the binomial system. Vertical segments show standard errors computed assuming a binomial distribution. The analysis uses the logistic binomial model, which allows for correlation of mortality risks within groups such as families. Equation 4 is a probabilistic equation where prevalences are related to cumulated probabilities of the negative binomial probability function. Level 1 variation is constrained by assumption to be binomial variation. Our simulation results suggest that the probit model can be used for the binomial proportional data. The lack of independence among the individual respondents will result in a larger variability than can be explained by the binomial model. Another important point is that as a formal binomial approach the potential for ichnospecies exists in this scheme. In 1987 seven squares showed significantly high activity (using the binomial test). The errors were assumed to have a binomial distribution. These examples are from corpora and from sources on the web. Any opinions in the examples do not represent the opinion of the Cambridge Dictionary editors or of Cambridge University Press or its licensors. |
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