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# i thought it was just me amazon

According to the authors, the text is to help students âforming a foundation of statistical thinking and methods,â unfortunately, some basic... No display issues with the devices that I have. The order of the topics seemed appropriate and not unlike many alternatives, but there was the issue of the term highlight boxes terms mentioned above. I am not necessarily in disagreement with the authors, but there is a clear voice. The learner can’t capture what is logistic regression without a clear definition and explanation. Logistic regression. "
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"Well written, clearly organized, and comprehensive . . Since this particular textbook relies heavily on the use of scenarios or case study type examples to introduce/teach concepts, the need to update this information on occasion is real. read more. The consistency of this text is quite good. I think that these features make the book well-suited to self-study. I did not see any inaccuracies in the book. Comes in pdf, tablet friendly pdf, and printed (15 dollars from amazon as of March, 2019). I think it would work well for liberal arts/social science students, but not for economics/math/science students who would need more mathematical rigor.

, Reviewed by Paul Murtaugh, Associate Professor, Oregon State University on 7/15/14, The text has a thorough introduction to data exploration, probability, statistical distributions, and the foundations of inference, but less complete discussions of specific methods, including one- and two-sample inference, contingency tables,... The traditional approach to Applied Logistic Regression, Third Edition. I did not find any issues with consistency in the text, though it would be nice to have an additional decimal place reported for the t-values in the t-table, so as to make the presentation of corresponding values between the z and t-tables easier to introduce to students (e.g., tail p of .05 corresponds to t of 1.65 - with rounding - in large samples; but the same tail p falls precisely halfway between z of 1.64 and z of 1.65). ISBN-13: 978-0471208266. Additionally, as research and analytical methods evolve, then so will the need to cover more non-traditional types of content i.e mixed methodologies, non parametric data sets, new technological research tools etc. The sections seem easily labeled and would make it easy to skip particular sections, etc. Hosmer. More extensive coverage of contingency tables and bivariate measures of association would... As well, the authors define probability but this is not connected as directly as it could be to the 3 fundamental axioms that comprise the mathematical definition of probability. The text provides enough examples, exercises and tips for the readers to understand the materials. I find this method serves to give the students confidence in knowing that they understand concepts before moving on to new material. Examples stay away from cultural topics. . While it would seem that the data in a statistics textbook would remain relevant forever, there are a few factors that may impact such a textbook's relevance and longevity. The structure and organization of this text corresponds to a very classic treatment of the topic. The index and table of contents are clear and useful. The terms and notation are consistent throughout the text. It is accurate. For example, the inference for categorical data chapter is broken in five main section. Reviewed by Greg McAvoy, Professor, University of North Carolina at Greensboro on 12/5/16, The book covers the essential topics in an introductory statistics course, including hypothesis testing, difference of means-tests, bi-variate regression, and multivariate regression. Some of the sections have only a few exercises, and more exercises are provided at the end of chapters. Basic linear algebra will be used. Applied Logistic Regression, Third Edition is a must-have guide for professionals and researchers who need to model nominal or ordinal scaled outcome variables in public health, medicine, and the social sciences as well as a wide range of other fields and disciplines. The title of Chapter 5, "Inference for numerical data", took me by surprise, after the extensive use of numerical data in the discussion of inference in Chapter 4. The text is in PDF format; there are no problems of navigation. For example, types of data, data collection, probability, normal model, confidence intervals and inference for single proportions. The common brushtail possum of the Australia region is a bit cuter than its distant cousin, the American opossum (see Figure 7.5 on page 318). David M. Diez, Harvard School of Public Health, Christopher D. Barr, Harvard School of Public Health, Reviewed by Kendall Rosales, Instructor and Service Level Coordinator, Western Oregon University on 8/20/20, There is more than enough material for any introductory statistics course. The final chapter (8) gives superficial treatments of two huge topics, multiple linear regression and logistic regression, with insufficient detail to guide serious users of these methods. The basic theory is well covered and motivated by diverse examples from different fields. Notation, language, and approach are maintained throughout the chapters. . I think that the book is fairly easy to read. While the examples did connect with the diversity within our country or i.e. This was not necessarily the case with some of the tables in the text. The book is very consistent from what I can see.

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It would be nice if the authors can start with the big picture of how people perform statistical analysis for a data set. Free shipping . United States: Duxbury, 2008. The graphs and tables in the text are well designed and accurate. Get solutions . The most accurate open-source textbook in statistics I have found. —Choice "Well written, clearly organized, and comprehensive . Anyone here that can recommend me something? However, even with this change, I found the presentation to overall be clear and logical. We haven't found any reviews in the usual places. The text is easily reorganized and re-sequenced. The authors are sloppy in their use of hat notation when discussing regression models, expressing the fitted value as a function of the parameters, instead of the estimated parameters (pp. This could make it easier for students or instructors alike to identify practice on particular concepts, but it may make it more difficult for students to grasp the larger picture from the text alone. Great job overall. The students can easily see the connections between the two types of tests.

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For example: "Researchers perform an observational study when they collect data in a way that does not directly interfere with how the data arise" (p. 13). No grammatical errors have been found as of yet. The book provides readers with stateâ€“ofâ€“theâ€“art techniques for building, interpreting, and assessing the performance of LR models. . The text covers the foundations of data, distributions, probability, regression principles and inferential principles with a very broad net. It is difficult for a topic that in inherently cumulative to excel at modularity in the manner that is usually understanding. In addition all of the source code to build the book is available so it can be easily modified. No solutions are given for Exercises, Projects, or Case Studies. Most contain glaring conceptual and pedagogical errors, and are painful to read (don't get me started on percentiles or confidence intervals). Reads more like a 300-level text than 100/200-level. We consider 104 brushtail possums from two regions in Australia, where the possums may be considered a random sample from the population. It does a more thorough job than most books of covering ideas about data, study design, summarizing data and displaying data. Some examples are related to United States. Each chapter consists of 5-10 sections. David M. Diez is a Quantitative Analyst at Google where he works with massive data sets and performs statistical analyses in areas such as user behavior and forecasting. Display of graphs and figures is good, as is the use of color. . the authors carefully walk the reader through the estimation of interpretation of coefficients from a wide variety of logistic regression models . I was sometimes confused by tables with missing data or, as was the case on page 11, when the table was sideways on the page. Hosmer and Lemeshow have used very little mathematics, have presented difficult concepts heuristically and through illustrative examples, and have included references." For example, a goodness of fit test begins by having readers consider a situation of whether or not the ethnic representation of a jury is consistent with the ethnic representation of the area. The book is not culturally offensive.

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