Showing papers on "Population proportion published in 2008"

Medical statistics from scratch : an introduction for health professionals

Abstract: Preface to the 1st Edition Preface to the 2nd Edition Introduction I Some fundamental stuff 1 First things first - the nature of data Learning objectives Variables and data The good, the bad and the ugly - types of variable Categorical variables Metric variables II Descriptive statistics 2 Describing data with tables Learning objectives What is descriptive statistics? The frequency table 3 Describing data with charts Learning objectives Picture it! Charting nominal and ordinal data Charting discrete metric data Charting continuous metric data Charting cumulative data 4 Describing data from its distributional shape Learning objectives The shape of things to come 5 Describing data with numeric summary values Learning objectives Numbers R us Summary measures of location Summary measures of spread Standard deviation and the Normal distribution III Getting the data 6 Doing it right first time - designing a study Learning objectives Hey ho! Hey ho! It's off to work we go Collecting the data - types of sample Types of study Confounding Matching Comparing cohort and case-control designs Getting stuck in - experimental studies IV From little to large - statistical inference 7 From samples to populations - making inferences Learning objectives Statistical inference 8 Probability, risk and odds Learning objectives Chance would be a fine thing - the idea of probability Calculating probability Probability and the Normal distribution Risk Odds Why you can't calculate risk in a case-control study The link between probability and odds The risk ratio The odds ratio Number needed to treat V The informed guess - confidence interval estimation 9 Estimating the value of a single population parameter - the idea of confidence intervals Learning objectives Confidence interval estimation for a population mean Confidence interval for a population proportion Estimating a confidence interval for the median of a single population 10 Estimating the differences between two population parameters Learning objectives What's the difference? Estimating the difference between the means of two independent populations - using a method based on the two-sample t test Estimating the difference between two matched population means - using a method based on the matched-pairs t test Estimating the difference between two independent population proportions Estimating the difference between two independent population medians - the Mann-Whitney rank-sums method Estimating the difference between two matched population medians - Wilcoxon signed-ranks method 11 Estimating the ratio of two population parameters Learning objectives Estimating ratios of means, risks and odds VI Putting it to the test 12 Testing hypotheses about the difference between two population parameters Learning objectives The research question and the hypothesis test A brief summary of a few of the commonest tests Some examples of hypothesis tests from practice Confidence intervals versus hypothesis testing Nobody's perfect - types of error The power of a test Maximising power - calculating sample size Rules of thumb 13 Testing hypotheses about the ratio of two population parameters Learning objectives Testing the risk ratio Testing the odds ratio 14 Testing hypotheses about the equality of two or more proportions Learning objectives Of all the tests in all the worldthe chi-squared (chi 2 ) test VII Getting up close 15 Measuring the association between two variables Learning objectives Association The correlation coefficient 16 Measuring the agreement between two variables Learning objectives To agree or not agree: that is the question Cohen's kappa Measuring agreement with ordinal data - weighted kappa Measuring the agreement between two metric continuous variables VIII Getting into a relationship 17 Straight-line models - linear regression Learning objectives Health warning! Relationship and association The linear regression model Model building and variable selection 18 Curvy models - logistic regression Learning objectives A second health warning! The logistic regression model IX Two more chapters 19 Measuring survival Learning objectives Introduction Calculating survival probabilities and the proportion surviving: the Kaplan-Meier table The Kaplan-Meier chart Determining median survival time Comparing survival with two groups 20 Systematic review and meta-analysis Learning objectives Introduction Systematic review Publication and other biases The funnel plot Combining the studies Appendix: Table of random numbers Solutions to Exercises References Index

Ratio Estimators in Simple Random Sampling Using Information on Auxiliary Attribute

Abstract: Some ratio estimators for estimating the population mean of the variable under study, which make use of information regarding the population proportion possessing certain attribute, are proposed Under simple random sampling without replacement (SRSWOR) scheme, the expressions of bias and mean-squared error (MSE) up to the first order of approximation are derived The results obtained have been illustrated numerically by taking some empirical population considered in the literature

Confidence intervals for a population proportion based on a ranked set sample

Abstract: This article examines several approximate methods to formulate confidence intervals for a single population proportion based on a ranked set sample (RSS). All of the intervals correspond to certain test statistics. That is, the confidence intervals are obtained by inverting the Wald, Wilson, score, and likelihood ratio tests. The Wald and Wilson intervals are based on the asymptotic distributions of two point estimators; the method of moments (MM) estimator and the maximum likelihood (ML) estimator. Continuity corrected versions of these intervals are also discussed. The R statistical software program is used to both calculate and evaluate the proposed intervals. For instance, an actual data set is analyzed for the sake of illustration. Furthermore, a simulation study which compares the intervals via expected widths and coverage probabilities is presented. The study indicates that the confidence intervals derived from the ML methodology generally outperform those based on MM procedures. Additiona...

Comparison of Bayesian priors for highly reliable limit models

Abstract: Limit standards are probability interval requirements for proportions. Simulation literature has focused on finding the confidence interval of the population proportion, which is inappropriate for limit standards. Further, some frequentist approaches cannot be utilized for highly reliable models, or models which produce no or few non-conforming trials. Bayesian methods provide approaches that can be utilized for all limit standard models. We consider a methodology developed for Bayesian reliability analysis, where historical data is used to define the a priori distribution of proportions p, and the customer desired a posteriori maximum probability is utilized to determine sample size for a replication.

Divergence-based confidence intervals in false-positive misclassification model

Abstract: In this article, we introduce minimum divergence estimators of parameters of a binary response model when data are subject to false-positive misclassification and obtained using a double-sampling plan. Under this set up, the problem of goodness-of-fit is considered and divergence-based confidence intervals (CIs) for a population proportion parameter are derived. A simulation experiment is carried out to compare the coverage probabilities of the new CIs. An application to real data is also given.

On Estimation of Finite Population Proportion

Abstract: In this paper, we study the classical problem of estimating the proportion of a finite population. First, we consider a fixed sample size method and derive an explicit sample size formula which ensures a mixed criterion of absolute and relative errors. Second, we consider an inverse sampling scheme such that the sampling is continue until the number of units having a certain attribute reaches a threshold value or the whole population is examined. We have established a simple method to determine the threshold so that a prescribed relative precision is guaranteed. Finally, we develop a multistage sampling scheme for constructing fixed-width confidence interval for the proportion of a finite population. Powerful computational techniques are introduced to make it possible that the fixed-width confidence interval ensures prescribed level of coverage probability.