PDQ Statistics, third edition offers an expert overview of major statistical methods, giving the reader a thorough understanding of statistics and how they are used in research articles.
The book covers the major categories — variable and descriptive statistics, parametric statistics, non-parametric statistics, and multivariate statistics. The explanations are clear, succinct, and loaded with practical examples.
PDQ Statistics serves as a supplemental text to introductory and advanced statistics courses. Without using algebra, calculus, calculations, or jargon, Professors Norman and Streiner decode biostatistics for you. You don’t need a technical dictionary. Nor do you have to do any math. All you need to understand the numbers is PDQ Statistics.
Featuring humorous examples, such as Convoluted Reasoning or Anti-intellectual Pomposity Detectors (C.R.A.P. Detectors), the text helps the reader identify statistical analyses with basic flaws in design or research.
This third edition includes new chapters on hierarchical and logistic regression, path analysis, and structural equation modeling. PDQ Statistics also helps the reader identify those statistical analyses with basic flaws in design or research. The book’s attractive design and humorous writing style make the subject matter accessible and engaging.
**Please note that this title is no longer accompanied by CD.
Part One: Variables and Descriptive Statistics
1 Names and Numbers: Types of Variables
2 Describing Data
Part Two: Parametric Statistics
3 Statistical Inference
4 Comparison of Means of Two Samples: The t Test
5 Comparison among Many Means: Analysis of Variance
6 Relationship between Interval and Ratio Variables: Linear and Multiple Regression
7 Analysis of Covariance
8 Variations on Linear Regression: Logistic Regression, General Linear Model, and Hierarchical Linear Models
9 Time Series Analysis
Part Three: Nonparametric Statistics
10 Nonparametric Tests of Significance
11 Nonparametric Measures of Association
12 Advanced Nonparametric Methods
Part Four: Multivariate Statistics
13 Introduction to Multivariate Statistics
14 Multivariate Analysis of Variance
15 Discriminant Function Analysis
16 Exploratory Factor Analysis
17 Path Analysis and Structural Equation Modeling
18 Cluster Analysis
19 Canonical Correlation
21 Research Designs