Statistics and Population
In our analysis of these data we will treat housing satisfaction as an ordered response, with categories low, medium and high, and the other three factors as explanatory variables. 6.5.2 Cumulative Link Models All of the models to be considered in this section arise from focusing on the cumulative distribution of the response. Let \( \pi_{ij} = \Pr\{Y_i=j\} \) denote the probability that the respo
Statistics and Population
7.5 Discrete Time Models We discuss briefly two extensions of the proportional hazards model to discrete time, starting with a definition of the hazard and survival functions in discrete time and then proceeding to models based on the logit and the complementary log-log transformations. 7.5.1 Discrete Hazard and Survival Let \( T \) be a discrete random variable that takes the values \( t_1 < t_2
Statistics and Population
Digit Preference The datasets section has the age distribution of the Philippines in 1990 from ages 0 to 99, which appears as table 7.7 in Siegel and Swanson (2004), p. 103. (Thanks to Tom Pullum for providing an electronic version as well as a do file to analyze it.) We’ll calculate some measures of digit preference using Stata and R. Stata R We first read the data and plot the age distribution i
Statistics and Population
This tutorial is an introduction to Stata emphasizing data management, tables and graphics. A PDF version is available here. The web pages and PDF file were all generated using the markstat command to combine Markdown and Stata, as described here. See also the computing logs for the statistics courses on this site. 1 Introduction Stata is a powerful statistical package with smart data-management f
Statistics and Population
The purpose of these notes is to provide a quick introduction to R, particularly as a tool for fitting linear and generalized linear models. A PDF version is available here. The web pages and PDF were created using rmarkdown as explained in the last section. You will find many applications in the computing logs for the statistics courses on this site. 1 Introduction R is a powerful environment for
Statistics and Population
Using MLwiN’s Command Interface In class we have illustrated the use of MLwiN using the Graphical User Interface (GUI). For serious work you should (1) log the results, and (2) use the Command Interface so you can store all your commands on a file and ensure that you can reproduce your results at a later time. The online help has a useful section on Using the command interface. Here are some hints
Statistics and Population
Random Effects Logits using WinBUGS This page is an html version of a winBUGS compound document that I first produced in the Spring of 2001. The document is available on this website, right click on hospBug.odc and select ‘Save as …’ from the context menu. Then open it in WinBUGS. The file hospital.txt with the data is also available here. To see how to run WinBUGS in batch mode and import results
Statistics and Population
These data have also been analyzed by Long and Freese (2006), and are available from the Stata website: . use https://www.stata-press.com/data/lf2/couart2,clear (Academic Biochemists / S Long) . summarize art Variable │ Obs Mean Std. dev. Min Max ─────────────┼───────────────────────────────────────────────────────── art │ 915 1.692896 1.926069 0 19 . di r(Var) 3.7097416 The mean number of article
Statistics and Population
7. Survival Models Our final chapter concerns models for the analysis of data which have three main characteristics: (1) the dependent variable or response is the waiting time until the occurrence of a well-defined event, (2) observations are censored, in the sense that for some units the event of interest has not occurred at the time the data are analyzed, and (3) there are predictors or explanat
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