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An Introduction to Statistical Methods and Data Analysis

ISBN: 9780534251222 | 0534251226
Edition: 5th
Format: Hardcover
Publisher: Cengage Learning
Pub. Date: 12/20/2000

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SummaryTable of Contents
Statistics is a thought process. In this comprehensive introduction to statistical methods and data analysis, the process is presented utilizing a four-step approach: 1) gathering data, 2) summarizing data, 3) analyzing data, and 4) communicating the results of data analyses.
PART 1 Introduction1(17)
What Is Statistics?
2(15)
Introduction
2(4)
... MORE
Why Study Statistics?
6(1)
Some Current Applications of Statistics
6(4)
What Do Statisticians Do?
10(2)
Quality and Process Improvement
12(2)
A Note to the Student
14(1)
Summary
14(3)
Supplementary Exercises
14(3)
PART 2 Collecting Data17(22)
Using Surveys and Scientific Studies to Gather Data
18(21)
Introduction
18(1)
Surveys
19(8)
Scientific Studies
27(7)
Observational Studies
34(1)
Data Management: Preparing Data for Summarization and Analysis
35(3)
Summary
38(1)
PART 3 Summarizing Data39(82)
Data Description
40(81)
Introduction
40(1)
Calculators, Computers, and Software Systems
41(2)
Describing Data on a Single Variable: Graphical Methods
43(26)
Describing Data on a Single Variable: Measures of Central Tendency
69(12)
Describing Data on a Single Variable: Measures of Variability
81(15)
The Boxplot
96(5)
Summarizing Data from More Than One Variable
101(8)
Summary
109(12)
Key Formulas
110(1)
Supplementary Exercises
110(11)
PART 4 Tools and Concepts121(70)
Probability and Probability Distributions
122(69)
How Probability Can Be Used in Making Inferences
122(3)
Finding the Probability of an Event
125(3)
Basic Event Relations and Probability Laws
128(3)
Conditional Probability and Independence
131(5)
Bayes' Formula
136(5)
Variables: Discrete and Continuous
141(1)
Probability Distributions for Discrete Random Variables
142(2)
A Useful Discrete Random Variable: The Binomial
144(10)
Probability Distributions for Continuous Random Variables
154(3)
A Useful Continuous Random Variable: The Normal Distribution
157(9)
Random Sampling
166(5)
Sampling Distributions
171(11)
Normal Approximation to the Binomial
182(3)
Minitab Instructions
185(1)
Summary
186(5)
Key Formulas
187(1)
Supplementary Exercises
187(4)
PART 5 Analyzing Data: Central Values, Variances, and Proportions191(338)
Inferences about Population Central Values
192(71)
Introduction and Case Study
192(4)
Estimation of μ
196(8)
Choosing the Sample Size for Estimating μ
204(3)
A Statistical Test for μ
207(12)
Choosing the Sample Size for μ
219(5)
The Level of Significance of a Statistical Test
224(4)
Inferences about μ for a Normal Population, σ Unknown
228(15)
Inferences about the Median
243(7)
Summary
250(13)
Key Formulas
251(2)
Supplementary Exercises
253(10)
Inferences Comparing Two Population Central Values
263(78)
Introduction and Case Study
263(4)
Inferences about μ1 - μ2: Independent Samples
267(20)
A Nonparametric Alternative: The Wilcoxon Rank Sum Test
287(12)
Inferences about μ1 - μ2: Paired Data
299(9)
A Nonparametric Alternative: The Wilcoxon Signed-Rank Test
308(6)
Choosing Sample Sizes for Inferences about μ1 - μ2
314(2)
Summary
316(25)
Key Formulas
317(2)
Supplementary Exercises
319(22)
Inferences about Population Variances
341(38)
Introduction and Case Study
341(3)
Estimation and Tests for a Population Variance
344(11)
Estimation and Tests for Comparing Two Population Variances
355(10)
Tests for Comparing t > 2 Population Variances
365(8)
Summary
373(6)
Key Formulas
373(1)
Supplementary Exercises
374(5)
Inferences about More Than Two Population Central Values
379(48)
Introduction and Case Study
379(5)
A Statistical Test about More Than Two Population Means: An Analysis of Variance
384(10)
The Model for Observations in a Completely Randomized Design
394(2)
Checking on the AOV Conditions
396(7)
An Alternative Analysis: Transformations of the Data
403(7)
A Nonparametric Alternative: The Kruskal-Wallis Test
410(4)
Summary
414(13)
Key Formulas
415(1)
Supplementary Exercises
416(11)
Multiple Comparisons
427(42)
Introduction and Case Study
427(4)
Linear Contrasts
431(7)
Which Error Rate Is Controlled?
438(2)
Fisher's Least Significant Difference
440(4)
Tukey's W Procedure
444(3)
Student-Newman-Keuls Procedure
447(3)
Dunnett's Procedure: Comparison of Treatments to a Control
450(2)
Scheffe's S Method
452(6)
Summary
458(11)
Key Formulas
459(1)
Supplementary Exercises
459(10)
Categorical Data
469(60)
Introduction and Case Study
469(2)
Inferences about a Population Proportion π
471(11)
Inferences about the Difference between Two Population Proportions, π1 - π2
482(6)
Inferences about Several Proportions: Chi-Square Goodness-of-Fit Test
488(9)
The Poisson Distribution
497(4)
Contingency Tables: Tests for Independence and Homogeneity
501(9)
Measuring Strength of Relation
510(6)
Odds and Odds Ratios
516(4)
Summary
520(9)
Key Formulas
520(1)
Supplementary Exercises
521(8)
PART 6 Analyzing Data: Regression Methods and Model Building529(300)
Linear Regression and Correlation
531(86)
Introduction and Case Study
531(9)
Estimating Model Parameters
540(17)
Inferences about Regression Parameters
557(10)
Predicting New Y Values Using Regression
567(9)
Examining Lack of Fit in Linear Regression
576(6)
The Inverse Regression Problem (Calibration)
582(8)
Correlation
590(10)
Summary
600(17)
Key Formulas
602(1)
Supplementary Exercises
603(14)
Multiple Regression and the General Linear Model
617(88)
Introduction and Case Study
617(8)
The General Linear Model
625(2)
Estimating Multiple Regression Coefficients
627(19)
Inferences in Multiple Regression
646(11)
Testing a Subset of Regression Coefficients
657(9)
Forecasting Using Multiple Regression
666(4)
Comparing the Slopes of Several Regression Lines
670(5)
Logistic Regression
675(8)
Some Multiple Regression Theory (Optional)
683(4)
Summary
687(18)
Key Formulas
688(1)
Supplementary Exercises
689(16)
More on Multiple Regression
705(124)
Introduction and Case Study
705(2)
Selecting the Variables (Step 1)
707(20)
Formulating the Model (Step 2)
727(31)
Checking Model Assumptions (Step 3)
758(24)
Summary
782(47)
Key Formulas
783(1)
Supplementary Exercises
783(46)
PART 7 Design of Experiments and Analysis of Variance829(261)
Design Concepts for Experiments and Studies
830(23)
Introduction
830(1)
Types of Studies
831(1)
Designed Experiments: Terminology
832(4)
Controlling Experimental Error
836(4)
Randomization of Treatments to Experimental Units
840(5)
Determining the Number of Replications
845(4)
Summary
849(4)
Supplementary Exercises
849(4)
Analysis of Variance for Standard Designs
853(90)
Introduction and Case Study
853(2)
Completely Randomized Design with Single Factor
855(4)
Randomized Complete Block Design
859(20)
Latin Square Design
879(12)
Factorial Treatment Structure in a Completely Randomized Design
891(23)
Factorial Treatment Structure in a Randomized Complete Block Design
914(2)
Estimation of Treatment Differences and Comparisons of Treatment Means
916(6)
Summary
922(21)
Key Formulas
923(1)
Supplementary Exercises
924(19)
The Analysis of Covariance
943(32)
Introduction and Case Study
943(3)
A Completely Randomized Design with One Covariate
946(13)
The Extrapolation Problem
959(3)
Multiple Covariates and More Complicated Designs
962(8)
Summary
970(5)
Supplementary Exercises
971(4)
Analysis of Variance for Some Fixed-, Random-, and Mixed-Effects Models
975(50)
Introduction and Case Study
975(3)
A One-Factor Experiment with Treatment Effects Random: A Random-Effects Model
978(5)
Extensions of Random-Effects Models
983(9)
Mixed-Effects Models
992(8)
Rules for Obtaining Expected Mean Squares
1000(10)
Nested Sampling and the Split-Plot Design
1010(9)
Summary
1019(6)
Supplementary Exercises
1020(5)
Repeated Measures and Crossover Designs
1025(26)
Introduction and Case Study
1025(4)
Single-Factor Experiments with Repeated Measures
1029(2)
Two-Factor Experiments with Repeated Measures on One of the Factors
1031(9)
Crossover Designs
1040(4)
Summary
1044(7)
Supplementary Exercises
1045(6)
Analysis of Variance for Some Unbalanced Designs
1051(26)
Introduction and Case Study
1051(2)
A Randomized Block Design with One or More Missing Observations
1053(6)
A Latin Square Design with Missing Data
1059(4)
Balanced Incomplete Block (BIB) Designs
1063(9)
Summary
1072(5)
Key Formulas
1072(2)
Supplementary Exercises
1074(3)
Communicating and Documenting the Results of Analyses
1077(13)
Introduction
1077(1)
The Difficulty of Good Communication
1078(1)
Communication Hurdles: Graphical Distortions
1079(3)
Communication Hurdles: Biased Samples
1082(1)
Communication Hurdles: Sample Size
1083(1)
Preparing Data for Statistical Analysis
1084(3)
Guidelines for a Statistical Analysis and Report
1087(1)
Documentation and Storage of Results
1088(1)
Summary
1089(1)
Supplementary Exercise
1089(1)
Appendix: Statistical Tables1090(40)
References1130(3)
Index1133

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