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Introduction
Statistics is a seemingly very mysterious yet necessary subject at graduate level. Many graduate students are required to use statistics to carry out their research, be their study in science, medicine, engineering, business, or social sciences. Most universities provide basic courses in statistics for students at undergraduate and graduate level, including research design and the analysis of data. However, most courses are not long enough to cover statistics beyond a scattering of basic tests, and more advanced statistical methods are usually not explained in such a way as to be understood by novice statistics students, especially those without a mathematical leaning.
How does this book teach statistics ?
In common with other books in the ‘Statistics without Mathematics’ series, each test is accompanied by a worked example. In particular, April Liu gives a running explanation of how the R functions are used, so that relatively new users of R should be able to dip into any chapter and reuse the code therein to examine their own datasets. She also recommends reading materials should the reader wish to study a test in greater depth. It should be emphasized that this book keeps it light, superficial even, in order for the test user to get started on data analysis with advanced statistical methods without becoming bogged down in theory and equations. April explains any complexities of the test in simple language which a non-statistician can easily follow.
The contents of the book
It could be argued that this book should be called Beyond Regression, in that many of the tests included here are devoted to doing things which multiple regression cannot, or building on top of its magnificent edifice.
Foreword.
Structural Equation Modeling.
Time Series Analysis.
Survival Analysis.
Longitudinal Analysis.
Multivariate Analysis.
Miscellaneous methods.
References.
Index