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Intermediate Statistics For Dummies
How This Book Is Organized
This book is organized into five major parts that explore the main topic areas
in intermediate statistics, along with one bonus part that offers a series of
quick top-ten references for you to use. Each part contains chapters that
break down the part’s major objective into understandable pieces.
Part I: Data Analysis and
Model-Building Basics
This part goes over the big ideas of descriptive and inferential statistics
and simple linear regression in the context of model building and decision
making. Some material from introductory statistics receives a quick review.
I also present you with the typical jargon of intermediate statistics.
Part II: Making Predictions
by Using Regression
Here, you can review and extend the ideas of simple linear regression to that
of using more than one predictor variable. This part presents techniques for
dealing with data that follows a curve (nonlinear models) and models for yes
or no data used to make predictions about whether or not an event will happen
(logistic regression). It includes all you need to know about conditions, diag-
nostics, model building, data-analysis techniques, and interpreting results.
Part III: Comparing Many
Means with ANOVA

You may want to compare the means of more than two populations. In this
case, you use analysis of variance (ANOVA). I discuss the basic conditions
required, the F-test, one-way and two-way ANOVA, and multiple comparisons.
The final goal of these analyses is to show whether the means of the given
populations are different and if so, which ones are higher or lower than
the rest.