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Confidence intervals and p-values for delivery to the end user

Author

Listed:
  • Roger Newson

    (King's College London)

Abstract

Statisticians make their living producing confidence intervals and pvalues. However, those in the Stata log are not ready for delivery to the end user, who usually wants to see statistical output either as a plot or as a table. This article describes a suite of programs used to convert Stata results to one or other of these forms. The eclplot package creates plots of estimates with conÞdence intervals, and the listtex package outputs a Stata dataset in the form of table rows that can be inserted into a plain TEX, LATEX, HTML, or word processor table. To create a Stata dataset that can be output in these ways, we can use the parmest, dsconcat, and lincomest packages to create datasets with one observation per estimated parameter; the sencode, tostring, ingap, and reshape packages to process these datasets into a form ready to be output; and the descsave and factext packages to reconstruct, in the output dataset, categorical predictor variables represented by dummy variables in regression models. Copyright 2003 by StataCorp LP.

Suggested Citation

  • Roger Newson, 2003. "Confidence intervals and p-values for delivery to the end user," Stata Journal, StataCorp LP, vol. 3(3), pages 245-269, September.
    • Handle: RePEc:tsj:stataj:v:3:y:2003:i:3:p:245-269
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    Citations

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    Cited by:

    1. Ulrich Kohler & Stephanie Eckman, 2011. "Stata tip 103: Expressing confidence with gradations," Stata Journal, StataCorp LP, vol. 11(4), pages 627-631, December.
    2. Michael Lokshin & Zurab Sajaia, 2008. "Creating print-ready tables in Stata," Stata Journal, StataCorp LP, vol. 8(3), pages 374-389, September.
    3. Ben Jann, 2005. "Making regression tables from stored estimates," Stata Journal, StataCorp LP, vol. 5(3), pages 288-308, September.
    4. Giovanni L. Lo Magno, 2013. "Sar: Automatic generation of statistical reports using Stata and Microsoft Word for Windows," Stata Journal, StataCorp LP, vol. 13(1), pages 39-64, March.
    5. Stephen P. Jenkins, 2020. "Comparing distributions of ordinal data," Stata Journal, StataCorp LP, vol. 20(3), pages 505-531, September.
    6. Roger B. Newson, 2010. "Frequentist q-values for multiple-test procedures," Stata Journal, StataCorp LP, vol. 10(4), pages 568-584, December.
    7. Ben Jann, 2005. "Tabulation of multiple responses," Stata Journal, StataCorp LP, vol. 5(1), pages 93-122, March.
    8. Ben Jann, 2013. "Plotting regression coefficients and other estimates in Stata," University of Bern Social Sciences Working Papers 1, University of Bern, Department of Social Sciences, revised 18 Sep 2017.
    9. Maarten L. Buis, 2007. "Stata tip 53: Where did my p-values go?," Stata Journal, StataCorp LP, vol. 7(4), pages 584-586, December.
    10. Rosa Gini & Jacopo Pasquini, 2006. "Automatic generation of documents," Stata Journal, StataCorp LP, vol. 6(1), pages 22-39, March.
    11. Roger B. Newson, 2012. "From resultssets to resultstables in Stata," Stata Journal, StataCorp LP, vol. 12(2), pages 191-213, June.
    12. Roger Newson, 2008. "parmest and extensions," United Kingdom Stata Users' Group Meetings 2008 07, Stata Users Group.
    13. Roger B. Newson, 2013. "Bonferroni and Holm approximations for Sidak and Holland–Copenhaver q-values," Stata Journal, StataCorp LP, vol. 13(2), pages 379-381, June.
    14. Dang, Hai-Anh & Lanjouw, Peter & Luoto, Jill & McKenzie, David, 2014. "Using repeated cross-sections to explore movements into and out of poverty," Journal of Development Economics, Elsevier, vol. 107(C), pages 112-128.

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