Syntax - Stata

2cluster kmeans and kmedians— Kmeans and kmedians cluster analysis Options Main k(#) is required and indicates that # groups are to be formed by the cluster analysis.

  Syntax

Syntax Description - Stata

2substr()— Extract substring Diagnostics In substr(s, b, l) and substr(s, b), if b describes a position before the beginning of the string or after the end, "" is returned.

  Descriptions, Stata, Syntax, Syntax description

Title stata.com generate — Create or change …

Title stata.com generate — Create or change contents of variable SyntaxMenuDescriptionOptions Remarks and examplesMethods and formulasReferencesAlso see Syntax Create new variable generate type newvar

  Change, Content, Variable, Create, Stata, Generate, Generate create or change, Generate create or change contents of variable

SyntaxDescriptionRemarks and examplesAlso see

macro— Macro definition and manipulation 3 Macro extended functions related to matrices rownamesjcolnamesjrowfullnamesjcolfullnames matname roweqjcoleq

  Syntaxdescriptionremarks and examplesalso see, Syntaxdescriptionremarks, Examplesalso

Title stata.com graph box — Box plots

graph box— Box plots 3 Menu Graphics > Box plot Syntax graph box yvars if in weight, options graph hbox yvars if in weight, options where yvars is a varlist options Description

  Plot, Box plot

Title stata

area options — Options for specifying the look of special areas 3 Also see [G-2] graph dot — Dot charts (summary statistics)

  Title, Stata, Title stata

www.stata.com

2cluster dendrogram— Dendrograms for hierarchical cluster analysis The height of the vertical lines and the range of the (dis)similarity axis give visual clues about the

Title stata.com putexcel — Export results to an Excel …

putexcel— Export results to an Excel file 3 export options Description Main overwritefmt overwrite existing cell formatting when exporting new content

  Putexcel

destring — Convert string variables to numeric ... - …

Title stata.com destring — Convert string variables to numeric variables and vice versa SyntaxMenuDescription Options for destringOptions for tostringRemarks and …

  Title, Variable, Stata, Numeric, String, Convert, Title stata, Destring convert string variables to numeric, Destring

Title stata.com gettoken — Low-level parsing

Title stata.com gettoken — Low-level parsing SyntaxDescriptionOptionsRemarks and examplesAlso see Syntax gettoken emname1 emname2: emname3, parse("pchars") quotes qed(lmacname) match(lmacname) bind

  Title, Stata, Title stata

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.

  Data, Missing, Missing data

Multiple Imputation for Missing Data: Concepts and New …

complete data sets. Ignorable Missing-Data Mechanism Let Y be the n×p matrix of complete data, which is not fully observed, and denote the observed part of Y by Y obs and the missing part by Y mis. The SAS multiple imputation procedures assume that the missing data are missing at random (MAR), that is, the probability that an observation is

  Data, Multiple, Missing, Imputation, Missing data, Multiple imputation, Sas multiple imputation

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

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