Command For Standard Error In Stata
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Stata Standard Deviation Function
Customer service Register Stata online Change registration Change address Subscribe to Stata News Subscribe to email alerts International resellers Careers Our sites Statalist The Stata Blog Stata Press Stata Journal Advanced search Site index Purchase Products Training Support Company >> Home >> Resources & support >> FAQs >> summarize and aweights and pweights Why doesn’t summarize accept pweights? What does summarize generate residuals stata calculate when you use aweights? Title Probability weights, analytic weights, and summary statistics Author William Sribney, StataCorp Question My data come with probability weights (the inverse of the probability of an observation being selected into the sample). I am trying to compute various summary statistics, including the mean, standard deviation, and various percentiles of the data. Is there any way to compute the mean, standard deviation, and percentiles of a variable with probability weights? Short answer It is important to distinguish among an estimate of the population mean (mu), an estimate of the population standard deviation (sigma), and the standard error of the estimate of the population mean. The command svy: mean provides an estimate of the population mean and an estimate of its standard error. When computing the standard error, consider the effect of clustering and stratification as well as the effect of sampling weights. An estimate of the population standard deviation can be obtained from estat sd after svy: mean. An estimate of the population standard deviation (sigma) is given by estimate of sigma = sqrt( n * V_srs ) where V_srs is an estimate of the var
Chapter 4 - Beyond OLS Chapter Outline 4.1 Robust Regression Methods 4.1.1 Regression with Robust Standard Errors 4.1.2 Using the Cluster Option 4.1.3 Robust Regression 4.1.4 Quantile Regression 4.2 confidence interval stata Constrained Linear Regression 4.3 Regression with Censored or Truncated Data 4.3.1 Regression
Variance Stata
with Censored Data 4.3.2 Regression with Truncated Data 4.4 Regression with Measurement Error 4.5 Multiple Equation Regression Models
T Test Stata
4.5.1 Seemingly Unrelated Regression 4.5.2 Multivariate Regression 4.6 Summary 4.7 Self assessment 4.8 For more information In this chapter we will go into various commands that go beyond OLS. This chapter is http://www.stata.com/support/faqs/statistics/weights-and-summary-statistics/ a bit different from the others in that it covers a number of different concepts, some of which may be new to you. These extensions, beyond OLS, have much of the look and feel of OLS but will provide you with additional tools to work with linear models. The topics will include robust regression methods, constrained linear regression, regression with censored and truncated data, regression with measurement error, and multiple equation models. 4.1 Robust Regression http://www.ats.ucla.edu/stat/stata/webbooks/reg/chapter4/statareg4.htm Methods It seems to be a rare dataset that meets all of the assumptions underlying multiple regression. We know that failure to meet assumptions can lead to biased estimates of coefficients and especially biased estimates of the standard errors. This fact explains a lot of the activity in the development of robust regression methods. The idea behind robust regression methods is to make adjustments in the estimates that take into account some of the flaws in the data itself. We are going to look at three approaches to robust regression: 1) regression with robust standard errors including the cluster option, 2) robust regression using iteratively reweighted least squares, and 3) quantile regression, more specifically, median regression. Before we look at these approaches, let's look at a standard OLS regression using the elementary school academic performance index (elemapi2.dta) dataset. use http://www.ats.ucla.edu/stat/stata/webbooks/reg/elemapi2 We will look at a model that predicts the api 2000 scores using the average class size in K through 3 (acs_k3), average class size 4 through 6 (acs_46), the percent of fully credentialed teachers (full), and the size of the school (enroll). First let's look at the descriptive statistics for these variables. Note the missing values for acs_k3 and acs_k6. summarize api00 acs_k3 acs_46 full enroll Variable | Obs Mean Std. Dev. Min Max ---------+----------------------------------------------------- api00 | 400 647.6225 142.249 369 940 acs_k3 |
material JEL Classification NEP reports Subscribe to new research Search Pub compilations Reading lists MyIDEAS More options are now at bottom of page IDEAS is a service hosted by the Research Division https://ideas.repec.org/c/boc/bocode/s456742.html of the Federal Reserve Bank of St. Louis Cannot find something on IDEAS? Encourage the publisher to index it! Instructions. Printed from https://ideas.repec.org/ Share: MyIDEAS: Log in (now much improved!) to save this software component SEMEAN: Stata module to compute standard error of mean (optionally from transformed data) Contents:Author info Abstract Bibliographic info Download info Related research References Citations Lists Statistics Corrections standard error Author Info Christopher F Baum() (Boston College)
Registered author(s): Christopher F Baum Abstractsemean computes the standard error of the mean. It may be called with a numeric function, in which case the function is applied to the data before computation of the mean and its standard error. The standard error of the mean can also be computed with N.J. Cox's egenmore package, which stata standard deviation includes a semean function. Download Info If you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large. File URL: http://fmwww.bc.edu/repec/bocode/s/semean.adoFile Function: program codeDownload Restriction: no File URL: http://fmwww.bc.edu/repec/bocode/s/semean.hlpFile Function: help fileDownload Restriction: no Bibliographic Info Software component provided by Boston College Department of Economics in its series Statistical Software Components with number S456742. as HTML HTML with abstract plain text plain text with abstract BibTeX RIS (EndNote, RefMan, ProCite) ReDIF JSON in new window Size: Programming language: Stata Requires: Stata version 9.0 Date of creation: 30 Jun 2006 Date of revision: Handle: RePEc:boc:bocode:s456742 Note: This module should be installed from within Stata by typing "ssc install semean". Windows users should not attempt to download these files with a web browser. Contact details of provider: Postal: Boston College, 140 Commonwealth Avenue, Chestnut Hill MA 02467 USAPhone: 617-552-3670Fax: +1-617-552-2308Web page: http://fmwww.bc.edu/EC/Email: More information through EDIRC Order Information: Web: http://repec.org/docs/ssc.php Related rese