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Introduction to Statistical Methods and Data Analysis

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ISBN-10: 053437123X

ISBN-13: 9780534371234

Edition: 5th 2001

Authors: Longnecker Ott, Micheal Longnecker, R. Lyman Ott

List price: $74.95
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Description:

Provides worked-out solutions to odd-numbered exercises.
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Book details

List price: $74.95
Edition: 5th
Copyright year: 2001
Publisher: Brooks/Cole
Publication date: 1/30/2002
Binding: Paperback
Pages: 208
Size: 7.50" wide x 9.00" long x 0.50" tall
Weight: 0.792
Language: English

Introduction
What is Statistics?
Introduction
Why Study Statistics?
Some Current Applications of Statistics
What Do Statisticians Do?
Quality and Process Improvement
A Note to the Student
Summary
Supplementary Exercises
Collecting the Data
Using Surveys and Scientific Studies to Collect Data
Introduction
Surveys
Scientific Studies
Observational Studies
Data Management: Preparing Data for Summarization and Analysis
Summary
Summarizing Data
Data Description
Introduction
Describing Data on a Single Variable: Graphical Methods
Describing Data on a Single Variable: Measures of Central Tendency
Describing Data on a Single Variable: Measures of Variability
The Box Plot
Summarizing Data from More than One Variable
Calculators, Computers, and Software Systems
Summary
Key Formulas
Supplementary Exercises
Tools and Concepts
Probability and Probability Distributions
How Probability Can Be Used in Making Inferences
Finding the Probability of an Event
Basic Event Relations and Probability Laws
Conditional Probability and Independence
Bayes's Formula
Variables: Discrete and Continuous
Probability Distributions for Discrete Random Variables
A Useful Discrete Random Variable: The Binomial
Probability Distributions for Continuous Random Variables
A Useful Continuous Random Variable: The Normal Distribution
Random Sampling
Sampling Distributions
Normal Approximation to the Binomial
Summary
Key Formulas
Supplementary Exercises
Analyzing Data: Central Values, Variances, and Proportions
Inferences on a Population Central Value
Introduction and Case Study
Estimation of ?
Choosing the Sample Size for Estimating ?
A Statistical Test for ?
Choosing the Sample Size for Testing ?
The Level of Significance of a Statistical Test
Inferences about ? for Normal Population, s Unknown
Inferences About the Population Median
Summary
Key Formulas
Supplementary Exercises
Comparing Two Population Central Values
Introduction and Case Study
Inferences about ?1 - ?2: Independent Samples
A Nonparametric Alternative: The Wilcoxon Rank Sum Test
Inferences About ?1 - ?2: Paired Data
A Nonparametric Alternative: The Wilcoxon Signed-Rank Test
Choosing Sample Sizes for Inferences About ?1 - ?2
Summary
Key Formulas
Supplementary Exercises
Inferences about Population Variances
Introduction and Case Study
Estimation and Tests for a Population Variance
Estimation and Tests for Comparing Two Population Variances
Tests for Comparing k>2 Population Variances
Summary
Key Formulas
Supplementary Exercises
Inferences About More Than Two Population Central Values
Introduction and Case Study
A Statistical Test About More Than Two Population Means
Checking on the Assumptions
Alternative When Assumptions are Violated: Transformations
A Nonparametric Alternative: The Kruskal-Wallis test
Summary
Key Formulas
Supplementary Exercises
Mulitple Comparisons
Introduction and Case Study
Planned Comparisons Among Treatments: Linear Contrasts
Which Error Rate is Controlled
Mulitple Comparisons with the Best Treatment
Comparison of Treatments to a Control
Pairwise Comparison on All Treatments
Summary
Key Formulas
Supplementary Exercises
Categorical Data
Introduction and Case Study
Inferences About a Population Proportion p
Comparing Two Population Proportions p1 - p2
Probability Distributions for Discrete Random Variables
The Multinomial Experiment and Chi-Square Goodness-of-Fit Test
The Chi-Square Test of Homogeneity of Proportions
The Chi-Square of Independence of Two Nominal Level Variables
Fisher's Exact Test, a Permutation Test
Measures of Association
Combining Sets of Contingency Tables
Summary
Key Formulas
Supplementary Exercises
Analyzing Data: Regression Methods, Model Building
Simple Linear Regression and Correlation
Linear Regression and the Method of Least Squares
Transformations to Linearize Data
Correlation
A Look Ahead: Multiple Regression
Summary of Key Formulas
Supplementary Exercises
Inferences Related to Linear Regression and Correlation
Introduction and Case Study
Diagnostics for Detecting Violations of Model Conditions
Inferences About the Intercept and Slope of the Regression Line
Inferences About the Population Mean for a Specified Value of the Explanatory Variable
Predications and Prediction Intervals
Examining Lack of Fit in the Model
The Inverse Regression Problem (Calibration): Predicting Values for x for a Specified Value of y
Multiple Regression and the General Linear Model
The General Linear Model
Least Squares Estimates of Parameters in the General Linear Model
Inferences About the Parameters in the General Linear Model
Inferences About the Population Mean and Predictions from the General Linear Model
Comparing the Slope of Several Regression Lines
Logistic Regression
Matrix Formulation of the General Linear Model
Building Regression Models with Diagnostics
Selecting the Variables (Step 1)
Model Formulation (Step 2)
Checking Model Conditions (Step 3)
Analyzing Data: Design of Experiments and Anova
Design Concepts for Experiments and Studies
Experiments, Treatments, Experimental Units, Blocking, Randomization, and Measurement Units
How Many Replications
Studies for Comparing Means Versus Studies for Comparing Variances
Analysis of Variance for Standard Designs
Completely Randomized Design with Single Factor
Randomized Block Design
Latin Square Design
Factorial Experiments in a Completely Randomized Design
The Estimation of Treatment Differences and Planned Comparisons in the Treatment Means
Checking Model Conditions
Alternative Analyses: Transformation and Friedman's Rank Based Test
Analysis of Covariance
A Completely Randomized Design with One Covariate
The Extrapolation Problem
Multiple Covariates and More Complicated Designs
Analysis of Variance for Some Unbalanced Designs
A Randomized Block Design with One or More Mission Observations
A Latin Square Design with Missing Data
Incomplete Block Designs
A Factorial Experiment with Missing Factors
Analysis of Variance for Some Fixed Effects, Random Effects and Mixed Effects Models
A One-Factor Experiment with Random Treatment Effects
Extensions of Random-Effects Models
A Mixed Model: Experiments with Both Fixed and Random Treatment Effects
Models with Nested Factors
Rules for Obtaining Expected Mean Squares
Split-Plot Designs and Experiments with Repeated Measures
Split-Plot Designs
Single-Factor Experiments with Repeated Measures
Two-Factor Experiments with Repeated Measures on One of the Factors
Crossover Design
Communicating and Documenting the Results of a Study or Experiment
Communicating and Documenting the Results of a Study or Experiment
Introduction
The Difficulty of Good Communication
Communication Hurdles: Graphical Distortions
Communication Hurdles: Biased Samples
Communication Hurdles
Sample Size
The Statistical Report
Documentation and Storage of Results
Summary
Supplementary Exercises