Intermediate Statistics for Dummies

A light- reading book, with concise chapters in various topics of statistics. Totally worth the choice.

I read many traditional books in statistics, to include intro and advanced ones. Most of them either focus too much on the algebraic details with almost no discussion of the essential statistical concepts or they focus too much on abstract theorizing with little technical and practical content. Prof. Rumsey's book on the other hand, does a great job keeping the discussion to an easy to follow level of understanding without sacrificing important details and the complexity and subtleties that essential statistical concepts and techniques require (e.g., testing models vis-à-vis hypothesis testing, confidence intervals, multiple regressions, nonparametric methods, discussion of Simpson's paradox). This book can be a good supplemental reading for those students from humanities, the social and behavioral sciences, as well as anyone interested in having a quick review of the fundamentals of stats plus a good walk through of the highly demanded (and often misunderstood and abused) statistical tools for everyday practice in applied fields. I recommend this book, and I wish other statisticians could explain as well as Prof. Rumsey does.

I did not find any use for my college statistics when I started working (at least for my function), but just recently, I had to relearn, what I've forgotten to keep pace with the demands of my job. This book is heavenly! Whether you're someone looking for a refresher course without having to pay huge sums to pick-up on stuff you know you've already learned or someone who wants the basics of statistics, this is the book for you.

I'm glad the author has provided us with a good sequel to her previous "for dummies" books. This book addresses "intermediate" topics like linear and non-linear regression, ANOVA, and non-parametric tests. Also, the author uses again her successful formula of providing useful tips and hints at the end of the book (see Part VI). Book Contents: Part I: Data Analysis and Model-Building Basics. Chapter 1: Beyond Number Crunching: The Art and Science of Data Analysis. Chapter 2: Sorting through Statistical Techniques. Chapter 3: Building Confidence and Testing Models. Part II: Making Predictions by Using Regression. Chapter 4: Getting in Line with Simple Linear Regression. Chapter 5: When Two Variables Are Better than One: Multiple Regression. Chapter 6: One Step Forward and Two Steps Back: Regression Model Selection. Chapter 7: When Data Throws You a Curve: Using Nonlinear Regression. Chapter 8: Yes, No, Maybe So: Making Predictions By Using Logistic Regression. Part III: Comparing Many Means with ANOVA. Chapter 9: Going One-Way with Analysis of Variance. Chapter 10: Pairing Things Down with Multiple Comparisons. Chapter 11: Getting a Little Interaction with Two-Way ANOVA. Chapter 12: Rock My World: Relating Regression to ANOVA. Part IV: Building Strong Connections with Chi-Square Tests. Chapter 13: Forming Associations with Two-Way Tables. Chapter 14: Being Independent Enough for the Chi-Square Test. Chapter 15: Using Chi-Square Tests for Goodness-of-Fit (Your Data, Not Your Jeans). Part V: Rebels without a Distribution. Chapter 16: Going Nonparametric. Chapter 17: The Sign Test and Signed Rank Test. Chapter 18: Pulling Rank with the Rank Sum Test. Chapter 19: Do the Kruskal-Wallis and Rank the Sums with Wilcox. Chapter 20: Pointing Out Correlations with Spearman's Rank. Part VI: The Part of Tens. Chapter 21: Ten Errors in Statistical Conclusions. Chapter 22: Ten Practice Problems. Appendix: Tables for Your Reference. Index. In short, a good introductory book at a fair price.

I'm not familiar with Rumsy's "Statistics for Dummies," but this book is a nice overview of a second statistics course. The chapters on multiple regression techniques are especially good. The writing style is clear. Negatives: Only Minitab is used to illustrate data analysis. No SPSS. There might have been more data sets for the various techniques.