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Statistics Without Math

ISBN-10: 0878935061
ISBN-13: 9780878935062
Edition: 2004
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Book details

Copyright year: 2004
Publisher: Sinauer Associates, Incorporated
Binding: Paperback
Pages: 136
Size: 7.50" wide x 9.00" long x 0.50" tall
Weight: 0.572
Language: English

Introduction
What Is Sampling Design?
What We Hope You Get from This Book
Outline of the book
Flow Charts and Scientific Questions
Constructing an Initial Model
Three types of questions
How Big Is Your Problem?
Where to go from here?
Describing Things: Some "Scientific" Conventions and Some Useful Techniques
How to Give a False Impression with a Computer
How Much Evidence Is Enough?
How Good Is Your Information?
When Highly Improbable Means Very Likely
How Textbooks Tell the Story
How Statisticians Count Independent Observations
Understanding the Statistical World with Ease
How to Avoid Accumulating Risk in Simple Comparisons
What Sort of Risk Are We Worried About?
Using Variability to Recognize a Difference
An Important Assumption
Partitioning Variance
Analyses for a World with All Shades of Gray
What Sort of Risk Are We Worried About?
Putting the World into Boxes
Describing a Straight World
How Good a Fit Is the Model?
Real-World Problems: More Than One Factor
Adding Things Together
Adding Partitioned Variability
Checking Assumptions with Partial Plots
Interactions
Which Variables Should I Analyze Statistically?
Artificial Intelligence
Computer Generated Phantom Variables
More Complex Models: How to String Things Together
Estimating Direct Effects
Estimating Indirect Effects
Some Problems with Path Analysis
Straightenening the World: Transformations and Other Tricks
Trial and Error Estimates without Transformation
Other Deviant Methods
General Linear Models
Maximum Likelihood
Pitfalls in Nonlinear Estimation
Multivariate Statistics: Cutting Down the Trees to Better See the Forest
Graphs of Gradients
Hypothetical Gradients
More Than One Dimension
Eigenvector Analyses
Deep Culture: Significance Tests
Association Matrix (Mantel) Analyses
Canonical Analyses
Discriminating between Groups
Categories that Grow on Trees
Selecting Variables
Multivariate Indices that Masquerade as Univariate Variables
Know What You Are Looking for before You Start
How to Write Better Backwards
A Simple Conceptual Scheme
Annoying Yet Important Details
"Why" Questions
A Final Comment
Tips for Teachers

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