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