Topic

# Population proportion

About: Population proportion is a(n) research topic. Over the lifetime, 247 publication(s) have been published within this topic receiving 4099 citation(s).

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Book
01 Oct 1998
TL;DR: This chapter discusses Graphical Descriptive Techniques for Quantitative Data, which focuses on the art and science of Graphical Presentations, and Hypothesis Testing, and its applications to Statistics.
Abstract: 1. WHAT IS STATISTIC?. Introduction to Statistics. Key Statistical Concepts. How Managers Use Statistics. Statistics and the Computer. World Wide Web and Learning Center. Part I. DESCRIPTIVE TECHNIQUES AND PROBABILITY. 2. Graphical Descriptive Techniques. Introduction. Types of Data. Graphical Techniques for Quantitative Data. Scatter Diagrams. Pie Charts, Bar Charts, and Line Charts. Summary. Case 2.1 Pacific Salmon Catches. Case 2.2 Bombardier Inc. Case 2.3 The North American Free Trade Agreement (NAFTA). Appendix 2.A Minitab Instructions. Appendix 2.B Excel Instructions. 3. Art and Science of Graphical Presentations. Introduction. Graphical Excellence. Graphical Deception. Summary. Case 3.1 Canadian Federal Budget. 4. Numerical Descriptive Measures. Introduction. Measures of Central Location. Measures of Variability. Interpreting Standard Deviation. Measures of Relative Standing and Box Plots. Measures of Association. General Guidelines on the Exploration of Data. Summary. Appendix 4.A Minitab Instructions. Appendix 4.B Summation Notation. 5. Data Collection and Sampling. Introduction. Sources of Data. Sampling. Sampling Plans. Errors Involved in Sampling. Use of Sampling in Auditing. Summary. 6. Probability and Discrete Probability Distributions. Introduction. Assigning Probabilities to Events. Probability Rules and Trees. Random Variables and Probability Distributions. Expected Value and Variance. Bivariate Distributions. Binomial Distribution. Poisson Distribution. Summary. Case 6.1 Let's Make a Deal. Case 6.2 Gains from Market Timing. Case 6.3 Calculating Probabilities Associated with the Stock Market. Appendix 6.A Minitab Instructions. Appendix 6.B Excel Instructions. 7. Continuous Probability Distributions. Introduction. Continuous Probability Distributions. Normal Distribution. Exponential Distribution. Summary. Appendix 7.A Minitab Instructions. Appendix 7.B Excel Instructions. Part II. STATISTICALl INFERENCE. 8. Sampling Distributions. Introduction. Sampling Distribution of the Mean. Summary. 9. Introduction to Estimation. Introduction. Concepts of Estimation. Estimating the Population Mean When the Population Variance Is Known. Selecting the Sample Size. Summary. Appendix 9.A Minitab Instructions. Appendix 9.B Excel Instructions. 10. Introduction to Hypothesis Testing. Introduction. Concepts of Hypothesis Testing. Testing the Population Mean When the Population Variance Is Known. The p-Value of a Test of Hypothesis. Calculating the Probability of a Type II Error. The Road Ahead. Summary. Appendix 10.A Minitab Instructions. Appendix 10.B Excel Instructions. 11. Inference about the Description of a Single Population. Introduction. Inference about a Population Mean When the Population Variance Is Unknown. Inference about a Population Variance. Inference about a Population Proportion. The Myth of the Law of Averages. Case 11.1 Number of Uninsured Motorists. Case 11.2 National Patent Development Corporation.

791 citations

Book
01 Jan 1978

311 citations

Journal ArticleDOI
Abstract: Post-stratification is a common technique in survey analysis for incorporating population distributions of variables into survey estimates. The basic technique divides the sample into post-strata, and computes a post-stratification weight w ih = rP h /r h for each sample case in post-stratum h, where r h is the number of survey respondents in post-stratum h, P h is the population proportion from a census, and r is the respondent sample size. Survey estimates, such as functions of means and totals, then weight cases by w h . Variants and extensions of the method include truncation of the weights to avoid excessive variability and raking to a set of two or more univariate marginal distributions. Literature on post-stratification is limited and has mainly taken the randomization (or design-based) perspective, where inference is based on the sampling distribution with population values held fixed. This article develops Bayesian model-based theory for the method. A basic normal post-stratification mod...

220 citations

Book
01 Jan 2004
TL;DR: This chapter discusses programming in R using Functions using Files and a Better Editor Object-Oriented Programming with R, and discusses low- and high-Level Graphic Functions and Confidence Intervals.
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

202 citations

Book
01 Jan 2005
Abstract: Part 1: Gathering and Exploring Data 1. Statistics: The Art and Science of Learning from Data 1.1 Using Data to Answer Statistical Questions 1.2 Sample Versus Population 1.3 Using Calculators and Computers Chapter Summary Chapter Problems 2. Exploring Data with Graphs and Numerical Summaries 2.1 Different Types of Data 2.2 Graphical Summaries of Data 2.3 Measuring the Center of Quantitative Data 2.4 Measuring the Variability of Quantitative Data 2.5 Using Measures of Position to Describe Variability 2.6 Recognizing and Avoiding Misuses of Graphical Summaries Chapter Summary Chapter Problems 3. Association: Contingency, Correlation, and Regression 3.1 The Association Between Two Categorical Variables 3.2 The Association Between Two Quantitative Variables 3.3 Predicting the Outcome of a Variable 3.4 Cautions in Analyzing Associations Chapter Summary Chapter Problems 4. Gathering Data 4.1 Experimental and Observational Studies 4.2 Good and Poor Ways to Sample 4.3 Good and Poor Ways to Experiment 4.4 Other Ways to Conduct Experimental and Nonexperimental Studies Chapter Summary Chapter Problems Part 1 Review Part 1 Questions Part 1 Exercises Part 2: Probability, Probability Distributions, and Sampling Distributions 5. Probability in Our Daily Lives 5.1 How Probability Quantifies Randomness 5.2 Finding Probabilities 5.3 Conditional Probability: The Probability of A Given B 5.4 Applying the Probability Rules Chapter Summary Chapter Problems 6. Probability Distributions 6.1 Summarizing Possible Outcomes and Their Probabilities 6.2 Probabilities for Bell-Shaped Distributions 6.3 Probabilities When Each Observation Has Two Possible Outcomes Chapter Summary Chapter Problems 7. Sampling Distributions 7.1 How Sample Proportions Vary Around the Population Proportion 7.2 How Sample Means Vary Around the Population Mean 7.3 The Binomial Distribution Is a Sampling Distribution (Optional) Chapter Summary Chapter Problems Part 2 Review Part 2 Questions Part 2 Exercises Part 3: Inferential Statistics 8. Statistical Inference: Confidence Intervals 8.1 Point and Interval Estimates of Population Parameters 8.2 Constructing a Confidence Interval to Estimate a Population Proportion 8.3 Constructing a Confidence Interval to Estimate a Population Mean 8.4 Choosing the Sample Size for a Study 8.5 Using Computers to Make New Estimation Methods Possible Chapter Summary Chapter Problems 9. Statistical Inference: Significance Tests about Hypotheses 9.1 Steps for Performing a Significance Test 9.2 Significance Tests about Proportions 9.3 Significance Tests about Means 9.4 Decisions and Types of Errors in Significance Tests 9.5 Limitations of Significance Tests 9.6 The Likelihood of a Type II Error (Not Rejecting H0, Even Though It's False) Chapter Summary Chapter Problems 10. Comparing Two Groups 10.1 Categorical Response: Comparing Two Proportions 10.2 Quantitative Response: Comparing Two Means 10.3 Other Ways of Comparing Means and Comparing Proportions 10.4 Analyzing Dependent Samples 10.5 Adjusting for the Effects of Other Variables Chapter Summary Chapter Problems Part 3 Review Part 3 Questions Part 3 Exercises Part 4: Analyzing Association and Extended Statistical Methods 11. Analyzing the Association Between Categorical Variables 11.1 Independence and Association 11.2 Testing Categorical Variables for Independence 11.3 Determining the Strength of the Association 11.4 Using Residuals to Reveal the Pattern of Association 11.5 Small Sample Sizes: Fisher's Exact Test Chapter Summary Chapter Problems 12. Analyzing the Association Between Quantitative Variables: Regression Analysis 12.1 Model How Two Variables Are Related 12.2 Describe Strength of Association 12.3 Make Inference About the Association 12.4How the Data Vary Around the Regression Line 12.5 Exponential Regression: A Model for Nonlinearity Chapter Summary Chapter Problems 13. Multiple Regression 13.1 Using Several Variables to Predict a Response 13.2 Extending the Correlation and R-squared for Multiple Regression 13.3 Using Multiple Regression to Make Inferences 13.4 Checking a Regression Model Using Residual Plots 13.5 Regression and Categorical Predictors 13.6 Modeling a Categorical Response Chapter Summary Chapter Problems 14. Comparing Groups: Analysis of Variance Methods 14.1 One-Way ANOVA: Comparing Several Means 14.2 Estimating Differences in Groups for a Single Factor 14.3 Two-Way ANOVA Chapter Summary Chapter Problems 15. Nonparametric Statistics 15.1 Compare Two Groups by Ranking 15.2 Nonparametric Methods For Several Groups and for Matched Pairs Chapter Summary Chapter Problems PART 4 Review Part 4 Questions Part 4 Exercises Tables Answers Index Index of Applications Photo Credits

150 citations

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##### Performance
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No. of papers in the topic in previous years
YearPapers
202112
202017
201914
201813
201713
201613