Showing papers on "Population proportion published in 2004"

Using R for Introductory Statistics

Abstract: DATA What Is Data? Some R Essentials Accessing Data by Using Indices Reading in Other Sources of Data UNIVARIATE DATA Categorical Data Numeric Data Shape of a Distribution BIVARIATE DATA Pairs of Categorical Variables Comparing Independent Samples Relationships in Numeric Data Simple Linear Regression MULTIVARIATE DATA Viewing Multivariate Data R Basics: Data Frames and Lists Using Model Formula with Multivariate Data Lattice Graphics Types of Data in R DESCRIBING POPULATIONS Populations Families of Distributions The Central Limit Theorem SIMULATION The Normal Approximation for the Binomial for loops Simulations Related to the Central Limit Theorem Defining a Function Investigating Distributions Bootstrap Samples Alternates to for loops CONFIDENCE INTERVALS Confidence Interval Ideas Confidence Intervals for a Population Proportion, p Confidence Intervals for the Population Mean, u Other Confidence Intervals Confidence Intervals for Differences Confidence Intervals for the Median SIGNIFICANCE TESTS Significance Test for a Population Proportion Significance Test for the Mean (t-Tests) Significance Tests and Confidence Intervals Significance Tests for the Median Two-Sample Tests of Proportion Two-Sample Tests of Center GOODNESS OF FIT The Chi-Squared Goodness-of-Fit Test The Chi-Squared Test of Independence Goodness-of-Fit Tests for Continuous Distributions LINEAR REGRESSION The Simple Linear Regression Model Statistical Inference for Simple Linear Regression Multiple Linear Regression ANALYSIS OF VARIANCE One-Way ANOVA Using lm() for ANOVA ANCOVA Two-Way ANOVA TWO EXTENSIONS OF THE LINEAR MODEL Logistic Regression Nonlinear Models APPENDIX A: GETTING, INSTALLING, AND RUNNING R Installing and Starting R Extending R Using Additional Packages APPENDIX B: GRAPHICAL USER INTERFACES AND R The Windows GUI The Mac OS X GUI Rcdmr APPENDIX C: TEACHING WITH R APPENDIX D: MORE ON GRAPHICS WITH R Low- and High-Level Graphic Functions Creating New Graphics in R APPENDIX E: PROGRAMMING IN R Editing Functions Using Functions Using Files and a Better Editor Object-Oriented Programming with R INDEX

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.

On Estimating a Population Proportion via Ranked Set Sampling

Abstract: This paper explores the use of the rank set sampling (RSS) protocol as it pertains to the estimation of a population proportion. The maximum likelihood estimator (MLE) and the sample proportion, both based on the RSS data, are discussed and their corresponding asymptotic distributions are derived. Based on these results the MLE is found to be uniformly more efficient than the sample proportion. Nevertheless, both estimators are more efficient than the simple random sample proportion. The greatest gains in efficiency are obtained at the center of the parameter space. Finally, these results remain valid in the presence of judgment error. (© 2004 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim)

Concomitant-based rank set sampling proportion estimates.

Abstract: This paper discusses the rank set sampling (RSS) protocol as it pertains to the estimation of a population proportion. The ranking process is based on a concomitant variable. The concomitant-based RSS estimate is asymptotically normal so standard inference procedures can still be implemented. This is illustrated using a real data set from the medical literature. The performance of the estimator is studied in terms of relative efficiency. Generally speaking, the concomitant-based RSS estimator is more efficient than the proportion of successes in a simple random sample. The greatest gains in efficiency are obtained when the correlation between the Bernoulli and concomitant variable is large in absolute value.

Using Randomized Response to Estimate the Proportion and Truthful Reporting Probability in a Dichotomous Finite Population

Abstract: In this paper, an alternative randomized response procedure is given that allows us to estimate the population proportion in addition to the probability of providing a truthful answer. It overcomes a difficulty associated with traditional randomized response techniques. Properties of the proposed estimators as well as sample size allocations are studied. In addition, an efficiency comparison is carried out to investigate the performance of the proposed technique.

Unknown Repeated Trials in the Unrelated Question Randomized Response Model

Abstract: This paper proposes a modified randomization device for collecting information on sensitive issues. The estimator based on the suggested strategy is found to be unbiased for population proportion and is better than the Greenberg et. al.'s (1969) estimator. (© 2004 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim)

Improved estimation of population proportion possessing sensitive attribute with unknown repeated trials in randomized response sampling

Abstract: This paper proposes an estimator for population proportion p possessing sensitive attribute under unknown repeated trials model envisaged by Singh and Joarder (1997). The exact bias and mean square error of the proposed estimators are worked out. The superiority of the suggested estimator over Warner (1965) estimator and Singh and Joarder (1997) estimator have been discussed through numerical illustrations. An approximate expression for MSE is also given.

Can't miss: conquer any number task by making important statistics simple. Part 4. Confidence intervals with t distributions, standard error, and confidence intervals for proportions.

Abstract: Healthcare quality professionals need to understand and use inferential statistics to interpret sample data from their organizations. Since in quality improvement and healthcare research studies, all the data from a population often are not available, investigators take samples and make inferences about that population using inferential statistics. This series of six articles will give readers an understanding of the concepts of inferential statistics, as well as the specific tools for calculating confidence intervals and tests of statistical significance for samples of data. The statistical principles are equally applicable to quality improvement and healthcare research studies. This article, Part 4, starts with a review of the information contained in Parts 1, 2, and 3, which appeared in the July/August 2003 issue of the Journal for Healthcare Quality. This article describes t distributions and how these are used to calculate confidence intervals for estimating a population mean based on a sample mean of a continuous variable. Part 4 concludes with a discussion of standard error, margin of error, and confidence intervals for estimating a population proportion based on a sample proportion from a binomial variable.