Univariate

About: Univariate is a research topic. Over the lifetime, 11255 publications have been published within this topic receiving 593457 citations.

Using multivariate statistics

Abstract: In this Section: 1. Brief Table of Contents 2. Full Table of Contents 1. BRIEF TABLE OF CONTENTS Chapter 1 Introduction Chapter 2 A Guide to Statistical Techniques: Using the Book Chapter 3 Review of Univariate and Bivariate Statistics Chapter 4 Cleaning Up Your Act: Screening Data Prior to Analysis Chapter 5 Multiple Regression Chapter 6 Analysis of Covariance Chapter 7 Multivariate Analysis of Variance and Covariance Chapter 8 Profile Analysis: The Multivariate Approach to Repeated Measures Chapter 9 Discriminant Analysis Chapter 10 Logistic Regression Chapter 11 Survival/Failure Analysis Chapter 12 Canonical Correlation Chapter 13 Principal Components and Factor Analysis Chapter 14 Structural Equation Modeling Chapter 15 Multilevel Linear Modeling Chapter 16 Multiway Frequency Analysis 2. FULL TABLE OF CONTENTS Chapter 1: Introduction Multivariate Statistics: Why? Some Useful Definitions Linear Combinations of Variables Number and Nature of Variables to Include Statistical Power Data Appropriate for Multivariate Statistics Organization of the Book Chapter 2: A Guide to Statistical Techniques: Using the Book Research Questions and Associated Techniques Some Further Comparisons A Decision Tree Technique Chapters Preliminary Check of the Data Chapter 3: Review of Univariate and Bivariate Statistics Hypothesis Testing Analysis of Variance Parameter Estimation Effect Size Bivariate Statistics: Correlation and Regression. Chi-Square Analysis Chapter 4: Cleaning Up Your Act: Screening Data Prior to Analysis Important Issues in Data Screening Complete Examples of Data Screening Chapter 5: Multiple Regression General Purpose and Description Kinds of Research Questions Limitations to Regression Analyses Fundamental Equations for Multiple Regression Major Types of Multiple Regression Some Important Issues. Complete Examples of Regression Analysis Comparison of Programs Chapter 6: Analysis of Covariance General Purpose and Description Kinds of Research Questions Limitations to Analysis of Covariance Fundamental Equations for Analysis of Covariance Some Important Issues Complete Example of Analysis of Covariance Comparison of Programs Chapter 7: Multivariate Analysis of Variance and Covariance General Purpose and Description Kinds of Research Questions Limitations to Multivariate Analysis of Variance and Covariance Fundamental Equations for Multivariate Analysis of Variance and Covariance Some Important Issues Complete Examples of Multivariate Analysis of Variance and Covariance Comparison of Programs Chapter 8: Profile Analysis: The Multivariate Approach to Repeated Measures General Purpose and Description Kinds of Research Questions Limitations to Profile Analysis Fundamental Equations for Profile Analysis Some Important Issues Complete Examples of Profile Analysis Comparison of Programs Chapter 9: Discriminant Analysis General Purpose and Description Kinds of Research Questions Limitations to Discriminant Analysis Fundamental Equations for Discriminant Analysis Types of Discriminant Analysis Some Important Issues Comparison of Programs Chapter 10: Logistic Regression General Purpose and Description Kinds of Research Questions Limitations to Logistic Regression Analysis Fundamental Equations for Logistic Regression Types of Logistic Regression Some Important Issues Complete Examples of Logistic Regression Comparison of Programs Chapter 11: Survival/Failure Analysis General Purpose and Description Kinds of Research Questions Limitations to Survival Analysis Fundamental Equations for Survival Analysis Types of Survival Analysis Some Important Issues Complete Example of Survival Analysis Comparison of Programs Chapter 12: Canonical Correlation General Purpose and Description Kinds of Research Questions Limitations Fundamental Equations for Canonical Correlation Some Important Issues Complete Example of Canonical Correlation Comparison of Programs Chapter 13: Principal Components and Factor Analysis General Purpose and Description Kinds of Research Questions Limitations Fundamental Equations for Factor Analysis Major Types of Factor Analysis Some Important Issues Complete Example of FA Comparison of Programs Chapter 14: Structural Equation Modeling General Purpose and Description Kinds of Research Questions Limitations to Structural Equation Modeling Fundamental Equations for Structural Equations Modeling Some Important Issues Complete Examples of Structural Equation Modeling Analysis. Comparison of Programs Chapter 15: Multilevel Linear Modeling General Purpose and Description Kinds of Research Questions Limitations to Multilevel Linear Modeling Fundamental Equations Types of MLM Some Important Issues Complete Example of MLM Comparison of Programs Chapter 16: Multiway Frequency Analysis General Purpose and Description Kinds of Research Questions Limitations to Multiway Frequency Analysis Fundamental Equations for Multiway Frequency Analysis Some Important Issues Complete Example of Multiway Frequency Analysis Comparison of Programs

The central role of the propensity score in observational studies for causal effects

Abstract: : The results of observational studies are often disputed because of nonrandom treatment assignment. For example, patients at greater risk may be overrepresented in some treatment group. This paper discusses the central role of propensity scores and balancing scores in the analysis of observational studies. The propensity score is the (estimated) conditional probability of assignment to a particular treatment given a vector of observed covariates. Both large and small sample theory show that adjustment for the scalar propensity score is sufficient to remove bias due to all observed covariates. Applications include: matched sampling on the univariate propensity score which is equal percent bias reducing under more general conditions than required for discriminant matching, multivariate adjustment by subclassification on balancing scores where the same subclasses are used to estimate treatment effects for all outcome variables and in all subpopulations, and visual representation of multivariate adjustment by a two-dimensional plot. (Author)

Past: paleontological statistical software package for education and data analysis

Abstract: A comprehensive, but simple-to-use software package for executing a range of standard numerical analysis and operations used in quantitative paleontology has been developed. The program, called PAST (PAleontological STatistics), runs on standard Windows computers and is available free of charge. PAST integrates spreadsheet-type data entry with univariate and multivariate statistics, curve fitting, timeseries analysis, data plotting, and simple phylogenetic analysis. Many of the functions are specific to paleontology and ecology, and these functions are not found in standard, more extensive, statistical packages. PAST also includes fourteen case studies (data files and exercises) illustrating use of the program for paleontological problems, making it a complete educational package for courses in quantitative methods.

Density estimation for statistics and data analysis

Abstract: Introduction. Survey of Existing Methods. The Kernel Method for Univariate Data. The Kernel Method for Multivariate Data. Three Important Methods. Density Estimation in Action.

Multiple imputation for nonresponse in surveys

Abstract: Tables and Figures. Glossary. 1. Introduction. 1.1 Overview. 1.2 Examples of Surveys with Nonresponse. 1.3 Properly Handling Nonresponse. 1.4 Single Imputation. 1.5 Multiple Imputation. 1.6 Numerical Example Using Multiple Imputation. 1.7 Guidance for the Reader. 2. Statistical Background. 2.1 Introduction. 2.2 Variables in the Finite Population. 2.3 Probability Distributions and Related Calculations. 2.4 Probability Specifications for Indicator Variables. 2.5 Probability Specifications for (X,Y). 2.6 Bayesian Inference for a Population Quality. 2.7 Interval Estimation. 2.8 Bayesian Procedures for Constructing Interval Estimates, Including Significance Levels and Point Estimates. 2.9 Evaluating the Performance of Procedures. 2.10 Similarity of Bayesian and Randomization--Based Inferences in Many Practical Cases. 3. Underlying Bayesian Theory. 3.1 Introduction and Summary of Repeated--Imputation Inferences. 3.2 Key Results for Analysis When the Multiple Imputations are Repeated Draws from the Posterior Distribution of the Missing Values. 3.3 Inference for Scalar Estimands from a Modest Number of Repeated Completed--Data Means and Variances. 3.4 Significance Levels for Multicomponent Estimands from a Modest Number of Repeated Completed--Data Means and Variance--Covariance Matrices. 3.5 Significance Levels from Repeated Completed--Data Significance Levels. 3.6 Relating the Completed--Data and Completed--Data Posterior Distributions When the Sampling Mechanism is Ignorable. 4. Randomization--Based Evaluations. 4.1 Introduction. 4.2 General Conditions for the Randomization--Validity of Infinite--m Repeated--Imputation Inferences. 4.3Examples of Proper and Improper Imputation Methods in a Simple Case with Ignorable Nonresponse. 4.4 Further Discussion of Proper Imputation Methods. 4.5 The Asymptotic Distibution of (Qm,Um,Bm) for Proper Imputation Methods. 4.6 Evaluations of Finite--m Inferences with Scalar Estimands. 4.7 Evaluation of Significance Levels from the Moment--Based Statistics Dm and Dm with Multicomponent Estimands. 4.8 Evaluation of Significance Levels Based on Repeated Significance Levels. 5. Procedures with Ignorable Nonresponse. 5.1 Introduction. 5.2 Creating Imputed Values under an Explicit Model. 5.3 Some Explicit Imputation Models with Univariate YI and Covariates. 5.4 Monotone Patterns of Missingness in Multivariate YI. 5.5 Missing Social Security Benefits in the Current Population Survey. 5.6 Beyond Monotone Missingness. 6. Procedures with Nonignorable Nonresponse. 6.1 Introduction. 6.2 Nonignorable Nonresponse with Univariate YI and No XI. 6.3 Formal Tasks with Nonignorable Nonresponse. 6.4 Illustrating Mixture Modeling Using Educational Testing Service Data. 6.5 Illustrating Selection Modeling Using CPS Data. 6.6 Extensions to Surveys with Follow--Ups. 6.7 Follow--Up Response in a Survey of Drinking Behavior Among Men of Retirement Age. References. Author Index. Subject Index. Appendix I. Report Written for the Social Security Administration in 1977. Appendix II. Report Written for the Census Bureau in 1983.