Population proportion

About: Population proportion is a research topic. Over the lifetime, 247 publications have been published within this topic receiving 4099 citations.

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.

Bayesian penalized spline model-based inference for finite population proportion in unequal probability sampling.

Abstract: We propose a Bayesian Penalized Spline Predictive (BPSP) estimator for a finite population proportion in an unequal probability sampling setting. This new method allows the probabilities of inclusion to be directly incorporated into the estimation of a population proportion, using a probit regression of the binary outcome on the penalized spline of the inclusion probabilities. The posterior predictive distribution of the population proportion is obtained using Gibbs sampling. The advantages of the BPSP estimator over the Hajek (HK), Generalized Regression (GR), and parametric model-based prediction estimators are demonstrated by simulation studies and a real example in tax auditing. Simulation studies show that the BPSP estimator is more efficient, and its 95% credible interval provides better confidence coverage with shorter average width than the HK and GR estimators, especially when the population proportion is close to zero or one or when the sample is small. Compared to linear model-based predictive estimators, the BPSP estimators are robust to model misspecification and influential observations in the sample.

A survey technique for estimating the proportion and sensitivity in a dichotomous finite population

Abstract: In this paper, a simple survey technique to measure the sensitivity of survey issues is presented. It can be applied to estimate the population proportion as well as the probability that a respondent truthfully states that he or she bears a sensitive character when experienced in a direct response survey. An unbiased estimator of mean square error for direct response survey is obtainable so as to be able to judge the effect on the accuracy in estimation. It is also found that the proposed technique is more efficient than some traditional techniques. A simple extension for polychotomous situations can be developed as well.