Categorical data: Logit, Cohen’s kappa, Pearson’s chi-square test, Fisher’s exact test, Iterative proportional fitting, Logistic regression

Posted by admin on December 10, 2011 Comments Off

Categorical data: Logit, Cohens kappa, Pearsons chi-square test, Fishers exact test, Iterative proportional fitting, Logistic regression
Please note that the content of this book primarily consists of articles available from Wikipedia or other free sources online. Pages: 34. Chapters: Logit, Cohen’s kappa, Pearson’s chi-square test, Fisher’s exact test, Iterative proportional fitting, Logistic regression, Contingency table, McNemar’s test, Fleiss’ kappa, Poisson regression, Categorical distribution, Goodness of fit, Cochran-Armitage test for trend, Positive predictive value, Probabilistic latent semantic analysis, Good-Turing frequency estimation, Qualitative variation, G-test, Latent class model, Multinomial test, Cramér’s V, Phi coefficient, Pareto chart, Gamma test, Negative predictive value, Yates’ correction for continuity, Count data, Goodman and Kruskal’s lambda, Ordered logit, Barnard’s test, Polychoric correlation, Additive smoothing, Logit analysis in marketing, Ordered probit, Cross tabulation, Qualitative data, Nominal category, Cochran-Mantel-Haenszel statistics, List of analyses of categorical data, Multinomial probit. Excerpt: Cohen’s kappa coefficient is a statistical measure of inter-rater agreement or inter-annotator agreement for qualitative (categorical) items. It is generally thought to be a more robust measure than simple percent agreement calculation since κ takes into account the agreement occurring by chance. Some researchers (e.g. Strijbos, Martens, Prins, & Jochems, 2006) have expressed concern over κ’s tendency to take the observed categories’ frequencies as givens, which can have the effect of underestimating agreement for a category that is also commonly used; for this reason, κ is considered an overly conservative measure of agreement. Others (e.g., Uebersax, 1987) contest the assertion that kappa “takes into account” chance agreement. To do this effectively would require an explicit model of how chance affects rater decisions. The so-called chance adjustment of kappa statistics supposes that, when not completely certain, raters simply guess-a very unrealistic scena…
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Metamorphosis: from statistics into cockroaches, a response to Professor Cohen’s a study of invidious racial discrimination in admissions at Thomas Jefferson … 2003): An article from: Albany Law Review

Posted by admin on December 3, 2011 Comments Off

This digital document is an article from Albany Law Review, published by Albany Law School on September 22, 2003. The length of the article is 2152 words. The page length shown above is based on a typical 300-word page. The article is delivered in HTML format and is available in your Amazon.com Digital Locker immediately after purchase. You can view it with any web browser.

Citation Details
Title: Metamorphosis: from statistics into cockroaches, a response to Professor Cohen’s a study of invidious racial discrimination in admissions at Thomas Jefferson High School for Science and Technology: Monty Python and Frank Kafka meet a probit regression.(response to Lloyd Cohen, Albany Law Review, vol. 66, p. 447, 2003)
Author: Daniel A. Domenech
Publication: Albany Law Review (Refereed)
Date: September 22, 2003
Publisher: Albany Law School
Volume: 67 Issue: 1 Page: 279(6)

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Straw men, fibs, and other academic sins.(response to article by Daniel A. Domenech in this issue, p. 279): An article from: Albany Law Review

Posted by admin on November 15, 2011 Comments Off

This digital document is an article from Albany Law Review, published by Albany Law School on September 22, 2003. The length of the article is 6844 words. The page length shown above is based on a typical 300-word page. The article is delivered in HTML format and is available in your Amazon.com Digital Locker immediately after purchase. You can view it with any web browser.

Citation Details
Title: Straw men, fibs, and other academic sins.(response to article by Daniel A. Domenech in this issue, p. 279)
Author: Lloyd Cohen
Publication: Albany Law Review (Refereed)
Date: September 22, 2003
Publisher: Albany Law School
Volume: 67 Issue: 1 Page: 285(16)

Distributed by Thomson Gale
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Repetition: Past Lives, Life, and Rebirth

Posted by admin on November 14, 2011 Comments Off


This fascinating book by Doris Eliana Cohen, Ph.D., was written to help us create a shift in our own consciousness as well as that of humanity. In order to heal from traumas, we unknowingly repeat the stories of our lives again and again, reliving them in different scenarios in this life as well as in other lifetimes. This repetition of our behavior patterns is neither neurotic nor pathological. It is absolutely necessary, because painful though it may be, repetition offers us multiple opportunities for facing our issues, making new choices, and healing ourselves at last.

All of us have a God-given gift of free choice, although we may be unaware of it at times. Only when we acknowledge and take full responsibility for the choices weâ??ve made in our current and past lives can we begin to change our stories and end the suffering weâ??ve been causing ourselves.

This material is based on Dorisâ??s 30 years of clinical experience with patients, using traditional therapy techniques combined with past-life regression therapy. It is guided and inspired by her communication with Guides and Angels of the Light, who have accompanied her for many years.

       Within these pages, Doris presents the 7 Steps of Rebirth, which provide a profound yet swift and simple route to change our lives and heal ourselves. Her 4 Steps of Joy offer a powerful tool for accessing the Light swiftly and easily. Remembering the events of our past lives provides a rich and fascinating tapestry of our journey, resulting in the humbling and uplifting realization that our souls are on a grand adventure. In owning our stories, we move from seeing ourselves as victims of life to empowering ourselves as co-creators of our destiny.


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Application of two regression-based methods to estimate the effects of partial harvest on forest structure using Landsat data [An article from: Remote Sensing of Environment]

Posted by admin on November 12, 2011 Comments Off


This digital document is a journal article from Remote Sensing of Environment, published by Elsevier in . The article is delivered in HTML format and is available in your Amazon.com Media Library immediately after purchase. You can view it with any web browser.

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Although partial harvests are common in many forest types globally, there has been little assessment of the potential to map the intensity of these harvests using Landsat data. We modeled basal area removal and percent cover change in a study area in central Washington (northwestern USA) using biennial Landsat imagery and reference data from historical aerial photos and a system of inventory plots. First, we assessed the correlation of Landsat spectral bands and associated indices with measured levels of forest removal. The variables most closely associated with forest removal were the shortwave infrared (SWIR) bands (5 and 7) and those strongly influenced by SWIR reflectance (particularly Tasseled Cap Wetness, and the Disturbance Index). The band and indices associated with near-infrared reflectance (band 4, Tasseled Cap Greenness, and the Normalized Difference Vegetation Index) were only weakly correlated with degree of forest removal. Two regression-based methods of estimating forest loss were tested. The first, termed ”state model differencing” (SMD), involves creating a model representing the relationship between inventory data from any date and corresponding, cross-normalized spectral data. This ”state model” is then applied to imagery from two dates, with the difference between the two estimates taken as estimated change. The second approach, which we called ”direct change modeling” (DCM), involves modeling forest structure changes as a single term using re-measured inventory data and spectral differences from corresponding image pairs. In a leave-one-out cross-validation process, DCM-derived estimates of harvest intensity had lower root mean square errors than SMD for both relative basal area change and relative cover change. The higher measured accuracy of DCM in this project must be weighed against several operational advantages of SMD relating to less restrictive reference data requirements and more specific resultant estimates of change.
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Statistical Power Analysis for the Behavioral Sciences (2nd Edition)

Posted by admin on November 10, 2011 Comments Off


Statistical Power Analysis is a nontechnical guide to power analysis in research planning that provides users of applied statistics with the tools they need for more effective analysis. The Second Edition includes:
* a chapter covering power analysis in set correlation and multivariate methods;
* a chapter considering effect size, psychometric reliability, and the efficacy of “qualifying” dependent variables and;
* expanded power and sample size tables for multiple regression/correlation.

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Comparison of regression and geostatistical methods for mapping Leaf Area Index (LAI) with Landsat ETM+ data over a boreal forest [An article from: Remote Sensing of Environment]

Posted by admin on November 8, 2011 Comments Off


This digital document is a journal article from Remote Sensing of Environment, published by Elsevier in 2005. The article is delivered in HTML format and is available in your Amazon.com Media Library immediately after purchase. You can view it with any web browser.

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This study compared aspatial and spatial methods of using remote sensing and field data to predict maximum growing season leaf area index (LAI) maps in a boreal forest in Manitoba, Canada. The methods tested were orthogonal regression analysis (reduced major axis, RMA) and two geostatistical techniques: kriging with an external drift (KED) and sequential Gaussian conditional simulation (SGCS). Deterministic methods such as RMA and KED provide a single predicted map with either aspatial (e.g., standard error, in regression techniques) or limited spatial (e.g., KED variance) assessments of errors, respectively. In contrast, SGCS takes a probabilistic approach, where simulated values are conditional on the sample values and preserve the sample statistics. In this application, canonical indices were used to maximize the ability of Landsat ETM+ spectral data to account for LAI variability measured in the field through a spatially nested sampling design. As expected based on theory, SGCS did the best job preserving the distribution of measured LAI values. In terms of spatial pattern, SGCS preserved the anisotropy observed in semivariograms of measured LAI, while KED reduced anisotropy and lowered global variance (i.e., lower sill), also consistent with theory. The conditional variance of multiple SGCS realizations provided a useful visual and quantitative measure of spatial uncertainty. For applications requiring spatial prediction methods, we concluded KED is more useful if local accuracy is important, but SGCS is better for indicating global pattern. Predicting LAI from satellite data using geostatistical methods requires a distribution and density of primary, reference LAI measurements that are impractical to obtain. For regional NPP modeling with coarse resolution inputs, the aspatial RMA regression method is the most practical option.
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Applied Multiple Regression/Correlation Analysis for the Behavioral Sciences, Second Edition

Posted by admin on November 6, 2011 Comments Off

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Statistics and Data with R: An Applied Approach Through Examples

Posted by admin on November 4, 2011 Comments Off


R, an Open Source software, has become the de facto statistical computing environment. It has an excellent collection of data manipulation and graphics capabilities. It is extensible and comes with a large number of packages that allow statistical analysis at all levels â?? from simple to advanced â?? and in numerous fields including Medicine, Genetics, Biology, Environmental Sciences, Geology, Social Sciences and much more. The software is maintained and developed by academicians and professionals and as such, is continuously evolving and up to date. Statistics and Data with R presents an accessible guide to data manipulations, statistical analysis and graphics using R.

Assuming no previous knowledge of statistics or R, the book includes:

  • A comprehensive introduction to the R language.
  • An integrated approach to importing and preparing data for analysis, exploring and analyzing the data, and presenting results.
  • Over 300 examples, including detailed explanations of the R scripts used throughout.
  • Over 100 moderately large data sets from disciplines ranging from Biology, Ecology and Environmental Science to Medicine, Law, Military and Social Sciences.
  • A parallel discussion of analyses with the normal density, proportions (binomial), counts (Poisson) and bootstrap methods.
  • Two extensive indexes that include references to every R function (and its arguments and packages used in the book) and to every introduced concept.

An accompanying Wiki website, http://turtle.gis.umn.edu includes all the scripts and data used in the book. The website also features a solutions manual, providing answers to all of the exercises presented in the book. Visitors are invited to download/upload data and scripts and share comments, suggestions and questions with other visitors. Students, researchers and practitioners will find this to be both a valuable learning resource in statistics and R and an excellent reference book.
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