227-2012: Executing a PROC from a DATA Step - …

2 Using RUN_MACRO to execute a PROC from a DATA step requires writing three blocks of code, described in the Introduction: a macro to execute a PROC, a user-written function to execute the macro, and a DATA step to call the

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338-2011: An Introduction to Survival Analysis …

2 (Censored) Study Ends (From Applied Survey Data Analysis, p. 306) Figure 1. Prospective View of Event History Survey Data Time can be regarded as continuous or discrete and this basic distinction affects the analytic approach selected.

  Analysis, Introduction, Survival, Continuous, An introduction to survival analysis

249-2012: A Tutorial on the SAS® Macro Language

1 Paper 249-2012 A Tutorial on the SAS® Macro Language John J. Cohen, Advanced Data Concepts LLC, Newark, DE ABSTRACT The SAS macro language is another language layering on top of regular SAS code.

  Language, Macro, Tutorials, Tutorial on the sas, 174 macro language, Sas macro

027-2009: Tips and Tricks for Creating the Reports …

Paper 027-2009 TIPS AND TRICKS FOR CREATING THE REPORTS YOUR CLIENTS NEED TO SEE Michael J Molter, INC Research, Raleigh, NC ABSTRACT Advanced reporting tools such as PROC REPORT have progressed by leaps and …

  Your, Report, Creating, Tricks for creating the reports, Tricks, Tricks for creating the reports your clients, Clients

Step-by-Step Programming with Base SAS Software

The correct bibliographic citation for this manual is as follows: SAS Institute Inc. 2001. Step-by-Step Programming with Base SAS® Software.Cary, NC: SAS Institute Inc.

  Programming, With, Base, Step, Software, By step programming with base sas software, By step programming with base sas, 174 software

149-2012: Queries, Joins, and WHERE Clauses, Oh …

Paper 149-2012 Queries, Joins, and WHERE Clauses, Oh My!! Demystifying PROC SQL Christianna S. Williams, Chapel Hill, NC ABSTRACT Subqueries, inner joins, outer joins, HAVING expressions, set operators…just the terminology of PROC SQL might

  Where, Corps, Queries, Joins, And where clauses, Clauses

158-2010: How to Use Arrays and DO Loops: Do I …

1 Paper 158-2010 How to Use ARRAYs and DO Loops: Do I DO OVER or Do I DO i? Jennifer L Waller, Medical College of Georgia, Augusta, GA ABSTRACT

  Array, Loops, Use arrays and do loops

248-2009: Learning When to Be Discrete: …

1 Paper 248–2009 Learning When to Be Discrete: Continuous vs. Categorical Predictors David J. Pasta, ICON Clinical Research, San Francisco, CA ABSTRACT

  Discrete, Continuous, Continuous vs, Categorical predictors, Categorical, Predictors

Dongsheng Yang and Jarrod E. Dalton - SAS

1 Paper 335-2012 A unified approach to measuring the effect size between two groups using SAS® Dongsheng Yang and Jarrod E. Dalton Departments of Quantitative Health Sciences and Outcomes Research

  Yang, Dongsheng yang and jarrod e, Dongsheng, Jarrod

SAS/STAT 9.2 User's Guide: Introduction to Power …

SAS/STAT ® 9.2 User’s Guide Introduction to Power and Sample Size Analysis (Book Excerpt) SAS® Documentation

  Analysis, Introduction, Power, Samples, Book, Size, Introduction to power and sample size analysis, Book excerpt, Excerpt

Missing Data & How to Deal: An overview of missing data

Missing Data Mechanisms Missing Completely at Random (MCAR) Missing value (y) neither depends on x nor y Example: some survey questions asked of a simple random sample of original sample Missing at Random (MAR) Missing value (y) depends on x, but not y Example: Respondents in service occupations less likely to report income Missing not at Random (NMAR)

  Data, Missing, Missing data

312-2012: Handling Missing Data by Maximum Likelihood

Many traditional missing data techniques are valid only if the MCAR assumption holds. A considerably weaker (but still strong) assumption is that data are missing at random (MAR). Again, this is most easily defined in the case where only a single variable Y has missing data, and another set of variables X has no missing data.

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Handling missing data in Stata: Imputation and likelihood …

Missing Data Mechanisms The classic typology of missing data mechanisms, introduced by Rubin: Missing completely at random (MCAR) Missingness on x is unrelated to observed values of other variables and the unobserved values of x Missing at random (MAR) Missingness on x uncorrelated with the unobserved value of x, after adjusting for observed ...

  Data, Missing, Missing data

Sensitivity Analysis in Multiple Imputation for Missing Data

A data set that contains the variables Y 1, Y 2, ..., Y p (in that order) is said to have a monotone missing pattern when the event that a variable Y j is missing for a particular individual implies that all subsequent variables Y k, k> j, are missing for that individual. For data sets that have monotone missing patterns, the variables that contain missing values can be imputed

  Data, Missing, Missing data

Exploratory Data Analysis for Feature Selection in Machine …

6.2.1 Data type and missing value 21 6.2.2 Numerical attributes 22 6.2.3 Categorical attributes 24 6.3 Correlation analysis 25 6.3.1 Categorical versus categorical 25 6.3.2 Numerical versus numerical 26 6.3.3 Categorical versus numerical 28 Appendix 29 A. Hypothesis testing 29 B. Pearson correlation coefficient 30 C. Student T-test 31

  Feature, Data, Missing

Unpaid Care Work: The missing link in the analysis of gender …

Issues Paper: Unpaid Care Work: The missing link in the analysis of gender gaps in labour outcomes OECD 2014 Using time use data, this policy brief analyses the impact of gender gaps in time devoted to unpaid care activities on gender gaps in labour outcomes. The first section provides an overview of gender

  Code, Data, Missing

Missing-data imputation - Department of Statistics

MISSING-DATA METHODS THAT DISCARD DATA 531 Censoring and related missing-data mechanisms can be modeled (as discussed in Section 18.5) or else mitigated by including more predictors in the missing-data model and thus bringing it closer to missing at random. For example, whites and persons with college degrees tend to have higher-than-average ...

  Data, Missing, Imputation, Missing data imputation

PUBLIC SPACE - UN-Habitat

TARGET 11.7: By 2030, provide universal access to safe, inclusive and accessible, green and public spaces, in particular for women and children, older persons and persons with disabilities Indicator 11.7.1: Average share of the built-up area of cities that is open space for public use for all, by sex, age and persons with disabilities