Download: 100 Statistical Tests N D Lewis eBook (ePub, KINDLE, PDF) + Audio Version

  • File Size: 796 KB
  • Print Length: 479 pages
  • Publisher: Heather Hills Press (December 31, 2013)
  • Publication Date: December 31, 2013
  • Language: English

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This book is a really good compendium of some important statistical tests. I found it useful because it outlined many I was not really acquainted with (such as Unit Underlying tests). I would have loved a brief mathematical synopsis which may save time digging in the materials. Also a bit more justification of the R-output would have helped as well. Important also could have already been one or more hyperlinks to actual data models of relevant data. If you want to understand these tests better then you'll need to do some exploring. The particular Kindle Edition gets you directly to each test so you can move around quickly. So, for the price, it's a good book., The book serves well as a guide point and a good source for learning various statistical tests with their implementation in R. One needs some background in Statistics and R to profit from most of the publication; the more the better. The author has organized each section (approximately) as follows: first comes test name, the question it answers and its purpose, next practical applications are presented, then comes implementation in R illustrated by examples along with their interpretations, all backed up by a well researched literature. Most important topics are covered so when relevant alternative tests are presented. Overall, the author's details are clear, concise and conductive to learning. About the downside, there are few occasional typos and confusing errors (example: test 84, page 385, How to Calculate in Ur mistakenly repeats earlier test resettest lmtest, as opposed to white. test tseries ). One main problem with this book is the lack of an over-all index at the end. This makes difficult to get tests related to specific topics, unless you already know a test's name (for example when looking to test the usefulness of a time series in prediction of another, or on the whole browsing for time series related tests). The provided checks classification table (on web page 13) may give a clue, but still not very useful when one is totally blind to a subject. Also, an index of R packages used in the book could have already been useful. Additionally, as another critics pointed out, I found the font sizes and text layout on published book somewhat inconvenient. General a good work with some room for development. The current price (. 99) makes it a good deal!, This could have been a useful simplifié of a broad range of common and not so common statistical checks. Granted, this material can be acquired for free by looking up each of the vignette at the R site for every single of those tests. Because you know, those vignettes vary greatly in quality. Many are great and clear. Others are not so great and not so clear. Thus, the author quite clear style could have added value by providing a certain consistency to the clarity of the codes required to run those tests.

Yet , the author makes way too many errors in R unique codes, tests description, test model, and reference to the wrong packages with this publication to earn a fairly neutral to good star score (3 or above). Earlier, I had documented some of the errors out of the first 31 tests I actually had studied through this book. You can see those documented errors in the following paragraphs. I actually found much more errors as I went through all 100 tests. I am not going to document them all for the sake of sanity within this publication review. But, you get my point. This book was not professionally edited by someone with the adequate expertise in R to clean up this book.

Inside this paragraph, I file some of the errors I noticed within the very first 31 tests I researched throughout the book. Within the section “How to estimate in R” when talking about the Ramsey test (83) and the White Neural Network test (84) he mistakenly refers to the Harvey-Collier test (82). All those are huge typos. Ur is confusing enough as is without being lead explicitly down the wrong path. When describing the Phillips-Ouliaris test (87), he states the test result reject the null speculation of no cointegration between the variables (p-value < 0. 9). Which is wrong conclusion. In this circumstance, you should reject the null hypothesis of no cointegration when p-value < 0. 05. Regarding the Elliott, Rothenberg & Share test (89), the author now makes a blunder of test interpretation in the opposite direction. He declares the test fails to reject the null speculation suggesting the occurrence of a unit root because the p-value > zero. 01. This is too stringent an alpha threshold level for this type of test. It also contradicts his own explained threshold throughout the publication that if a p-value < 0. 05 you can reject the null hypothesis. That is certainly still the situation even if a p-value > zero. 01. Regarding the Spearman Rank Correlation test (2), the coding of the Spearman test using the " pspearman" package is incorrect. You have to embed the c() perform a couple of times to improve the coding. Inside the one sample t-test (12), to take a single observation of any trial and compare it to an hypothetical mean, a pair of his examples are wrong. Within pairwise t-test for difference in sample means, he repeats the same mistake. One of the example illustrates a combined t-test (he even brands it effectively within the specific example). However combined t-test as described is a different testing framework than pairwise. Indeed, the latter describe situations where you take two observations (pre and post) of the same sample. This is a different test: paired t-test. Inside his description of two sample t-test for the difference in means (15), in one of the examples he describes a result as having a p-value of -0. sixty two. This is simply not possible. P-values are probabilities that are limited to values between 0% and 100%. They can't be negative by description. The Bartlett's test of sphericity (7) has an error in coding. A person should remove the " ncol=3" term for the code to work. The particular Jennrich test (8) got incomplete coding. You need to include the trial size of n1 and n2., I purchased both the digital and paper variations of this book. The team uses R on a regular basis to fit both linear and logistic regression models. Using the recent popularity of model risk management, conventional record tests are becoming a necessity for practicing predictive modelers. The book provides a one-stop-shop for the " finest hits" of conventional speculation testing. Each test is organized as follows: elaborate the null hypothesis; when should you use the test; applications from literature; R program code (this is the selling point of the book); and academic references., Great overview of available checks with short explanations and examples. This is good addition to other textbooks, since it just lists all the tests and provides a kind of recipe which one is appropriate.
However, the layout is awful, looks like a e book was just printed out. This book could be much far better and more useful if Font sizes and text layout were chosen in a more innovative manner., My review talks about the Kindle edition only. The text makes recommendations to " over 300 diagrams and figures. " The Kindle edition does not appear to have any. Also, the written text has no layout or typographic differentiation to distinguish between Ur code and narrative. The layout makes the text nearly unreadable. I recommend you try " R for Dummies" if you are searching for a good book on the Kindle., Simple, but have to have a good index. Or at least to add, as chapters (heading1) the test specialized niche and the to group the test inside. With the current book, you have to go to page 13, then visit the index to find the pages and then go to the desired chapter., It's here somewhere. A lot of piles of statistics books...

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100 Statistical Tests N D Lewis
Average Rating: 4.45
Votes: 14
Reviews: 1