"Maybe you're a girl looking for a boyfriend, but the boy you're interested in refuses to date anyone who "isn't Bayesian". What matters is that Bayes is cool, and if you don't know Bayes, you aren't cool."
trying to introduce people to Bayesian reasoning is that the existing online explanations are too abstract. Bayesian reasoning is very counterintuitive. People do not employ Bayesian reasoning intuitively, find it very difficult to learn Bayesian reason
As just about every statistics student can attest, Simpson's Paradox — a statistical phenomenon where an apparent trend is reversed when you look at subgroups — is notoriously hard to explain. You can look at examples — say, the fact that US wages are rising overall, but dropping within every educational group — but that don't really help to explain the paradox. But it's not really paradox at all, but simply the fact that the disparate rate at which members of the study join the subgroups isn't accounted for in the analysis. To demonstrate this effect, the Visualizing Urban Data...
"An American Self-Portrait. This new series looks at contemporary American culture through the austere lens of statistics. Each image portrays a specific quantity of something."
free package for analyzing data from complex samples, especially large-scale assessments, as well as non-assessment survey data. Has sophisticated stats, easy drag & drop interface, and integrated help system that explains the statistics as well as how to
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Aktuelle Samsung-Studie nimmt die Fotoleidenschaft der Deutschen unter die Lupe
Donnerstag, 20. Juni 2013
AideRSS is an intelligent assistant that saves time and keeps you on top of the latest news. We research every story and filter out the noise, allowing you to focus on what matters most
The AGing Integrated Database (AGID) is an on-line query system based on AoA-related data files and surveys, and includes population characteristics from the Census Bureau for comparison purposes. The system allows users to produce descriptive information in graphical or tabular form, at the level of detail most suited for their needs. The four options or paths through AGID provide different levels of focus and aggregation of the same data – from individual data elements within Data-at-a-Glance to full database access within Data Files.
Advances in Pure Mathematics (APM) is an international journal dedicated to the latest advancement of ordered algebraic structures. The goal of this journal is to provide a platform for scientists and academicians all over the world to promote, share, and discuss various new issues and developments in different areas of ordered algebraic structures.
Online book to take your R (programming) skills to the next next level. The authors is quite influencial in the R community and "knows what he's talking about". This is for advanced R users!
Data on Academic Workforce and a survey to gather more data. view historic information about staffing patterns at individual institutions of higher education. view the numbers and percentages of tenure-track, full-time non-tenure-track, and part-time faculty members at institutions in 1995 and in 2009.
compiled by the EBSS Reference Sources & Services Committee[1], a division of the Association of College and Research Libraries. We created this directory with the goal of creating a resource for librarians who regularly need to find statistics for their patrons. We welcome additions or comments. General United States Statistics, Education Statistics, Gerontology Statistics, Psychology Statistics, Social Work Statistics
Using data gathered from US government agencies, anthropologist Felix Pharand-Deschenes has created a collection of maps that illustrate the various circulatory systems that connect humanity: cities, roads, railways, power lines, pipelines, cable Internet, airlines, and shipping lanes. The maps are remarkable cartographic documents of our current age, but also serve deeper research and educational purposes.
The RsquareV macro provides an R-square measure for models with a well-defined variance function such as generalized linear and generalized additive models.
R2 is a popular measure of fit used for ordinary regression models. The RsquareV macro provides the R_V^2 statistic proposed by Zhang (2016) for use with any model based on a distribution with a well-defined variance function. This includes the class of generalized linear models and generalized additive models based on distributions such as the binomial for logistic models, Poisson, gamma, and others. It also includes models based on quasi-likelihood functions for which only the mean and variance functions are defined. A partial R2 is provided when comparing a full model to a nested, reduced model. Partial R can be obtained from this when the difference between the full and reduced model is a single parameter. A penalized R2 is also available adjusting for the additional parameters in the full model.
The NLEstimate macro allows you to estimate one or more linear or nonlinear combinations of parameters from any model for which you can save the model parameters and their variance-covariance matrix. Most modeling procedures which offer ESTIMATE, CONTRAST, or LSMEANS statements only provide for estimating or testing linear combinations of model parameters. However, common estimation problems often involve nonlinear combinations, particularly in generalized models with nonidentity link functions such as logistic and Poisson models.
The SELECT macro performs model selection methods for categorical-response models that can be fit in PROC LOGISTIC. These include models using the logit, probit, cloglog, cumulative logit, or generalized logit links. The macro supports binary as well as ordinal and nominal multinomial models.
Standard model selection is done by choosing candidate effects for entry to or removal from the model according to their significance levels. After completion, the set of models selected at each step of this process is sorted on the selected criterion - AUC, R-square, max-rescaled R-square, AIC, or BIC. The requested number of best models on the selected criterion is displayed.
The %CLUSTERGROUPS macro creates a custom template that combines a dendrogram and a blockplot to highlight each of the specified number of clusters with a different color.
The %CLUSTERGROUPS macro enhances dendrograms produced in SAS by adding color to highlight the clusters. You specify the number of clusters desired as input to the macro.
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This sample combines macro programming with PROC FREQ and DATA Step logic to count the number of missing and non-missing values for every variable in a data set. The results are stored in a data set.
This sample illustrates one method of counting the number of missing and non-missing values for each variable in a data set. Two methods for structuring the resulting data set are shown.
This sample creates four adverse event with relative risk plots. An adverse event with relative risk plot is a two-panel display of the most frequently occurring adverse events sorted by relative risk for a clinical study.
The sample requires a macro that can be downloaded from the Downloads tab. After downloading the program, the sample code on the Full Code tab can be submitted from your SAS session.
What we present here is a macro that will automatically check all the numeric variables in a SAS data set for a specific data value, and produce a report showing which variables contain this special value and how many times it appeared. The macro is called FIND_VALUE
Many of us are presented with SAS data sets where codes such as 9999 are intermingled with real data values. Sometimes these codes represent missing values; sometimes they represent other non-data values.
If you run SAS procedures on numeric variables in such a data set, you will, obviously, produce nonsense. What we present here is a macro that will automatically check all the numeric variables in a SAS data set for a specific data value, and produce a report showing which variables contain this special value and how many times it appeared.
The macro is called FIND_VALUE and is presented below. You can download this macro and many other useful macros from the SAS Companion Web Site: support.sas.com/publishing. Search for my book, Cody's Data Cleaning Techniques, Second Edition, and then click on the link to download the programs and data files from the book.
Overview
This sample shows one way of computing Mahalanobis distance in each of the following scenarios:
from each observation to the mean
from each observation to a specific observation
from each observation to all other observations (all possible pairs)
The %CumIncid macro for estimating and plotting cumulative incidence functions with competing risks is discussed.
This version of the CUMINCID macro applies only to SAS 9.1 which is available on the Downloads tab. For SAS 9.2 and later, refer to the Autocall macro library.
The CUMINCID macro computes the crude cumulative-incidence function estimates for homogeneous (no covariates) survival data whose endpoints are subjected to competing risks: see Kalbfleish and Prentice(1980). Standard errors and pointwise confidence limits are also computed. The estimated crude cumulative-incidence curve is displayed as a step function using ODS Graphics.
NOTE: Beginning in SAS 9.2, the QIC statistic is produced by PROC GENMOD. Beginning in SAS 9.4 TS1M2, QIC is available in PROC GEE.
PURPOSE:
The %QIC macro computes the QIC and QICu statistics proposed by Pan (2001) for GEE (generalized estimating equations) models. These statistics allow comparisons of GEE models (model selection) and selection of a correlation structure.
NOTE: This macro is obsolete beginning with SAS 8.0. Use the STDIZE procedure in SAS/STAT software beginning in that release.
PURPOSE:
The %STDIZE macro standardizes one or more numeric variables in a SAS data set by subtracting a location measure and dividing by a scale measure. A variety of location and scale measures are provided, including estimates that are resistant to outliers and clustering
The %INTRACC macro calculates reliabilities for intraclass correlations. The macro calculates the six intraclass correlations discussed in Shrout and Fleiss (1979). Additionally it calculates two intraclass correlations using formulae from Winer (1971) which are identical to two of the six from Shrout and Fleiss. It also calculates the reliability of the mean of nrater ratings, where nrater is a parameter of the macro, using the Spearmen-Brown prophecy formula so that one can examine the effect obtaining more raters would have on the reliability of a mean.
J. Wermter, и U. Hahn. 44th Annual Meeting of the Association for Computational Linguistics, стр. 785--792. Sydney, Australia, Association for Computational Linguistics, (июля 2006)