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 Johnson, Richard A. (Richard Arnold), 1937 author.
 Sixth edition.  [New York, NY] : Pearson Education, Inc., [2019]
 Description
 Book — xviii, 773 pages ; 24 cm.
 Summary

For courses in Multivariate Statistics, Marketing Research, Intermediate Business Statistics, Statistics in Education, and graduatelevel courses in Experimental Design and Statistics. Appropriate for experimental scientists in a variety of disciplines, this marketleading text offers a readable introduction to the statistical analysis of multivariate observations. Its primary goal is to impart the knowledge necessary to make proper interpretations and select appropriate techniques for analyzing multivariate data. Ideal for a junior/senior or graduate level course that explores the statistical methods for describing and analyzing multivariate data, the text assumes two or more statistics courses as a prerequisite.
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STATS20601
 Course
 STATS20601  Applied Multivariate Analysis
 Instructor(s)
 Hastie, Trevor John
2. Statistics : principles and methods [2014]
 Johnson, Richard A. (Richard Arnold), 1937
 7th ed.  Hoboken, N.J. : Wiley, c2014.
 Description
 Book — xv, 716 pages : ill ; 24 cm
 Summary

Statistics: Principles and Methods, 7th Edition provides a comprehensive, accurate introduction to statistics for business professionals who need to learn how to apply key concepts. The chapters include realworld data, designed to make the material more relevant. The numerous examples clearly demonstrate the important points of the methods. New What Will We Learn opening paragraphs set the stage for the material being discussed. Using Statistics Wisely boxes summarize key lessons. In addition, Statistics in Context sections give business professionals an understanding of applications in which a statistical approach to variation is needed.
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QA276.12 .J63 2014  Unknown 
3. Statistics : principles and methods [2010]
 Johnson, Richard A. (Richard Arnold), 1937
 6th ed.  Hoboken, NJ : John Wiley & Sons, c2010.
 Description
 Book — xvii, 686 p., 1 folded sheet ([8] p.) : ill. (some col.) ; 24 cm.
 Summary

 1. Introduction
 1. What is Statistics?
 2. Statistics in Our Everyday Life
 3. Statistics in Aid of Scientific Inquiry
 4. Two Basic Concepts Population and Sample
 5. The Purposeful Collection of Data
 6. Statistics in Context
 7. Objectives of Statistics
 2. Organization and Description of Data
 1. Introduction
 2. Main Types of Data
 3. Describing Data by Tables and Graphs
 4. Measures of Center
 5. Measures of Variation
 6. Checking the Stability of the Observations over Time
 7. More on Graphics
 8. Statistics in Context
 3. Descriptive Study of Bivariate Data
 1. Introduction
 2. Summarization of Bivariate Categorical Data
 3. A Designed Experiment for Making a Comparison
 4. Scatter Diagram of Bivariate Measurement Data
 5. The Correlation Coefficient A Measure of Linear Relation
 6. Prediction of One Variable from Another (Linear Regression)
 4. Probability
 1. Introduction
 2. Probability of an Event
 3. Methods of Assigning Probability
 4. Event Relations and Two Laws of Probability
 5. Conditional Probability and Independence
 6. Bayes' Theorem
 7. Random Sampling from a Finite Population
 5. Probability Distributions
 1. Introduction
 2. Random Variables
 3. Probability Distribution of a Discrete Random Variable
 4. Expectation (Mean) and Standard Deviation of a Probability Distribution
 5. Success and Failures Bernoulli Trials
 6. The Binomal Distribution
 7. The Binomal Distribution in Context
 6. The Normal Distribution
 1. Probability Model for a Continuous Random Variable
 2. The Normal DistributionIts General Features
 3. The Standard Normal Distribution
 4. Probability Calculations with Normal Distributions
 5. The Normal Approximation to the Binomial
 6. Checking the Plausibility of a Normal Model
 7. Transforming Observations to Attain Near Normality
 7. Variation in Repeated SamplesSampling Distribution
 1. Introduction
 2. The Sampling Distribution of a Statistic
 3. Distribution of the Sample Mean and the Central Limit Theorem
 4. Statistics in Context
 8. Drawing Inferences From Large Samples
 1. Introduction
 2. Point Estimation of Population Mean
 3. Confidence Interval for a Population Mean
 4. Testing Hypotheses about a Population Mean
 5. Inferences about a Population Proportion
 9. SmallSample Inferences for Normal Populations
 1. Introduction
 2. Student's t Distribution
 3. Inferences about Small Sample Size
 4. Relationship between Tests and Confidence Intervals
 5. Inferences About the Standard Deviation o (The ChiSquare Distribution)
 6. Robustness of Inference Procedures
 10. Comparing Two Treatments
 1. Introduction
 2. Independent Random Samples from Two Populations
 3. Large Samples Inference about Difference of Two Means
 4. Inferences from Small Samples: Normal Populations with Equal Variances
 5. Inferences from Small Samples: Normal Populations but Unequal Variances
 6. Randomization and its Role in Inference
 7. Matched Pairs Comparisons
 8. Choosing Between Independent Samples and a Matched Pairs Sample
 9. Comparing Two Population Proportions
 11. Regression Analysis I (Simple Linear Regression)
 1. Introduction
 2. Regression with a Single Predictor
 3. A StraightLine Regression Model
 4. The Method of Least Squares
 5. The Sampling Variability of the Least Squares EstimatorsTools for Inference
 6. Important Inference Problems
 7. The Strength of a Linear Relation
 8. Remarks About the Straight Line Model Assumption
 12. Regression Analysis II Multiple Linear Regression and Other Topics
 1. Introduction
 2. Nonlinear Relations and Linearizing Transformations
 3. Multiple Linear Regression
 4. Residual Plots to Check the Adequacy of a Statistical Model
 5. Review Exercises
 13. Analysis of Categorical Data
 1. Introduction
 2. Pearson's x^2 Test for Goodness of Fit
 3. Contingency Table with One Margin Fixed (Test of Homogeneity)
 4. Contingency Table with Neither Margin Fixed (Test of Independence)
 5. Review Exercises
 14. Analysis of Variance (ANOVA)
 1. Introduction
 2. Comparison of Several Treatments The Completely Randomized Design
 3. Population Model and Inferences for a Completely Randomized Design
 4. Simultaneous Confidence Intervals
 5. Graphical Diagnostics and Displays to Supplement ANOVA
 6. Randomized Block Experiments for Comparing k Treatments
 7. Review Exercises
 15. Nonparametric Inference
 1. Introduction
 2. The Wilcoxon RankSum Test for Comparing Two Treatments
 3. Matched Pair Comparisons
 4. Measure of Correlation Based on Ranks
 5. Concluding Remarks
 6. Using Statistics Wisely
 7. Key Ideas and Formulas
 8. Technology
 9. Review Exercises
 Appendix A1 Summation Notation Appendix A2 Rules for Counting Appendix A3 Expectation and Standard DeviationProperties Appendix A4 The Expected Value and Standard Deviation of X Appendix B Tables.
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QA276.12 .J63 2010  Available 
 Johnson, Richard A. (Richard Arnold), 1937
 6th ed.  Upper Saddle River, N.J. : Pearson Prentice Hall, c2007.
 Description
 Book — xviii, 773 p. : ill. ; 25 cm.
 Summary

 DRAFT (NOTE: Each chapter begins with an Introduction, and concludes with Exercises and References.) I. GETTING STARTED.
 1. Aspects of Multivariate Analysis. Applications of Multivariate Techniques. The Organization of Data. Data Displays and Pictorial Representations. Distance. Final Comments.
 2. Matrix Algebra and Random Vectors. Some Basics of Matrix and Vector Algebra. Positive Definite Matrices. A SquareRoot Matrix. Random Vectors and Matrices. Mean Vectors and Covariance Matrices. Matrix Inequalities and Maximization. Supplement 2A Vectors and Matrices: Basic Concepts.
 3. Sample Geometry and Random Sampling. The Geometry of the Sample. Random Samples and the Expected Values of the Sample Mean and Covariance Matrix. Generalized Variance. Sample Mean, Covariance, and Correlation as Matrix Operations. Sample Values of Linear Combinations of Variables.
 4. The Multivariate Normal Distribution. The Multivariate Normal Density and Its Properties. Sampling from a Multivariate Normal Distribution and Maximum Likelihood Estimation. The Sampling Distribution of 'X and S. LargeSample Behavior of 'X and S. Assessing the Assumption of Normality. Detecting Outliners and Data Cleaning. Transformations to Near Normality. II. INFERENCES ABOUT MULTIVARIATE MEANS AND LINEAR MODELS.
 5. Inferences About a Mean Vector. The Plausibility of ...m0 as a Value for a Normal Population Mean. Hotelling's T 2 and Likelihood Ratio Tests. Confidence Regions and Simultaneous Comparisons of Component Means. Large Sample Inferences about a Population Mean Vector. Multivariate Quality Control Charts. Inferences about Mean Vectors When Some Observations Are Missing. Difficulties Due To Time Dependence in Multivariate Observations. Supplement 5A Simultaneous Confidence Intervals and Ellipses as Shadows of the pDimensional Ellipsoids.
 6. Comparisons of Several Multivariate Means. Paired Comparisons and a Repeated Measures Design. Comparing Mean Vectors from Two Populations. Comparison of Several Multivariate Population Means (OneWay MANOVA). Simultaneous Confidence Intervals for Treatment Effects. TwoWay Multivariate Analysis of Variance. Profile Analysis. Repealed Measures, Designs, and Growth Curves. Perspectives and a Strategy for Analyzing Multivariate Models.
 7. Multivariate Linear Regression Models. The Classical Linear Regression Model. Least Squares Estimation. Inferences About the Regression Model. Inferences from the Estimated Regression Function. Model Checking and Other Aspects of Regression. Multivariate Multiple Regression. The Concept of Linear Regression. Comparing the Two Formulations of the Regression Model. Multiple Regression Models with Time Dependant Errors. Supplement 7A The Distribution of the Likelihood Ratio for the Multivariate Regression Model. III. ANALYSIS OF A COVARIANCE STRUCTURE.
 8. Principal Components. Population Principal Components. Summarizing Sample Variation by Principal Components. Graphing the Principal Components. LargeSample Inferences. Monitoring Quality with Principal Components. Supplement 8A The Geometry of the Sample Principal Component Approximation.
 9. Factor Analysis and Inference for Structured Covariance Matrices. The Orthogonal Factor Model. Methods of Estimation. Factor Rotation. Factor Scores. Perspectives and a Strategy for Factor Analysis. Structural Equation Models. Supplement 9A Some Computational Details for Maximum Likelihood Estimation.
 10. Canonical Correlation Analysis Canonical Variates and Canonical Correlations. Interpreting the Population Canonical Variables. The Sample Canonical Variates and Sample Canonical Correlations. Additional Sample Descriptive Measures. Large Sample Inferences. IV. CLASSIFICATION AND GROUPING TECHNIQUES.
 11. Discrimination and Classification. Separation and Classification for Two Populations. Classifications with Two Multivariate Normal Populations. Evaluating Classification Functions. Fisher's Discriminant Function...nSeparation of Populations. Classification with Several Populations. Fisher's Method for Discriminating among Several Populations. Final Comments.
 12. Clustering, Distance Methods and Ordination. Similarity Measures. Hierarchical Clustering Methods. Nonhierarchical Clustering Methods. Multidimensional Scaling. Correspondence Analysis. Biplots for Viewing Sample Units and Variables. Procustes Analysis: A Method for Comparing Configurations. Appendix. Standard Normal Probabilities. Student's tDistribution Percentage Points. ...c2 Distribution Percentage Points. FDistribution Percentage Points. FDistribution Percentage Points (...a = .10). FDistribution Percentage Points (...a = .05). FDistribution Percentage Points (...a = .01). Data Index. Subject Index.
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QA278 .J63 2007  Unknown On reserve at Li and Ma Science Library 2hour loan 
STATS20601
 Course
 STATS20601  Applied Multivariate Analysis
 Instructor(s)
 Hastie, Trevor John
 Johnson, Richard A. (Richard Arnold), 1937
 7th ed.  Upper Saddle River, NJ : Pearson Prentice Hall, c2005.
 Description
 Book — xi, 642 p. : ill. ; 25 cm. + 1 CDROM (4 3/4 in.)
 Summary

 1. Introduction.
 2. Treatment of Data.
 3. Probability.
 4. Probability Distributions.
 5. Probability Densities.
 6. Sampling Distribution.
 7. Inferences Concerning Means.
 8. Inferences Concerning Variances.
 9. Inferences Concerning Proportions.
 10. Nonparametric Tests.
 11. Curve Fitting.
 12. Analysis of Variance.
 13. Factorial Experimentation.
 14. The Statistical Content of QualityImprovement Programs.
 15. Applications to Reliability and Life Testing. Bibliography. Statistical Tables. Answers to OddNumbered Exercises. Index.
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Engineering Library (Terman)
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TA340 .M5 2005  Unknown 
 Johnson, Richard A. (Richard Arnold), 1937
 Madison, WI (One Gifford Pinchot Dr., Madison 537262398) : U.S. Dept. of Agriculture, Forest Service, Forest Products Laboratory, [2003]
 Description
 Book — 8 p. ; 28 cm.
Green Library
Green Library  Status 

Find it Bing Wing lower level: Microform cabinets  
A 13.78:FPLRP606  Inlibrary use 
 Johnson, Richard A. (Richard Arnold), 1937
 5th ed.  Upper Saddle River, N.J. : Prentice Hall, c2002.
 Description
 Book — xviii, 767 p. : ill ; 24 cm. + 1 CDROM (4 3/4 in.)
 Summary

 (NOTE: Each chapter begins with an Introduction, and concludes with Exercises and References.) I. GETTING STARTED.
 1. Aspects of Multivariate Analysis. Applications of Multivariate Techniques. The Organization of Data. Data Displays and Pictorial Representations. Distance. Final Comments.
 2. Matrix Algebra and Random Vectors. Some Basics of Matrix and Vector Algebra. Positive Definite Matrices. A SquareRoot Matrix. Random Vectors and Matrices. Mean Vectors and Covariance Matrices. Matrix Inequalities and Maximization. Supplement 2A Vectors and Matrices: Basic Concepts.
 3. Sample Geometry and Random Sampling. The Geometry of the Sample. Random Samples and the Expected Values of the Sample Mean and Covariance Matrix. Generalized Variance. Sample Mean, Covariance, and Correlation as Matrix Operations. Sample Values of Linear Combinations of Variables.
 4. The Multivariate Normal Distribution. The Multivariate Normal Density and Its Properties. Sampling from a Multivariate Normal Distribution and Maximum Likelihood Estimation. The Sampling Distribution of 'X and S. LargeSample Behavior of 'X and S. Assessing the Assumption of Normality. Detecting Outliners and Data Cleaning. Transformations to Near Normality. II. INFERENCES ABOUT MULTIVARIATE MEANS AND LINEAR MODELS.
 5. Inferences About a Mean Vector. The Plausibility of ...m0 as a Value for a Normal Population Mean. Hotelling's T 2 and Likelihood Ratio Tests. Confidence Regions and Simultaneous Comparisons of Component Means. Large Sample Inferences about a Population Mean Vector. Multivariate Quality Control Charts. Inferences about Mean Vectors When Some Observations Are Missing. Difficulties Due To Time Dependence in Multivariate Observations. Supplement 5A Simultaneous Confidence Intervals and Ellipses as Shadows of the pDimensional Ellipsoids.
 6. Comparisons of Several Multivariate Means. Paired Comparisons and a Repeated Measures Design. Comparing Mean Vectors from Two Populations. Comparison of Several Multivariate Population Means (OneWay MANOVA). Simultaneous Confidence Intervals for Treatment Effects. TwoWay Multivariate Analysis of Variance. Profile Analysis. Repealed Measures, Designs, and Growth Curves. Perspectives and a Strategy for Analyzing Multivariate Models.
 7. Multivariate Linear Regression Models. The Classical Linear Regression Model. Least Squares Estimation. Inferences About the Regression Model. Inferences from the Estimated Regression Function. Model Checking and Other Aspects of Regression. Multivariate Multiple Regression. The Concept of Linear Regression. Comparing the Two Formulations of the Regression Model. Multiple Regression Models with Time Dependant Errors. Supplement 7A The Distribution of the Likelihood Ratio for the Multivariate Regression Model. III. ANALYSIS OF A COVARIANCE STRUCTURE.
 8. Principal Components. Population Principal Components. Summarizing Sample Variation by Principal Components. Graphing the Principal Components. LargeSample Inferences. Monitoring Quality with Principal Components. Supplement 8A The Geometry of the Sample Principal Component Approximation.
 9. Factor Analysis and Inference for Structured Covariance Matrices. The Orthogonal Factor Model. Methods of Estimation. Factor Rotation. Factor Scores. Perspectives and a Strategy for Factor Analysis. Structural Equation Models. Supplement 9A Some Computational Details for Maximum Likelihood Estimation.
 10. Canonical Correlation Analysis Canonical Variates and Canonical Correlations. Interpreting the Population Canonical Variables. The Sample Canonical Variates and Sample Canonical Correlations. Additional Sample Descriptive Measures. Large Sample Inferences. IV. CLASSIFICATION AND GROUPING TECHNIQUES.
 11. Discrimination and Classification. Separation and Classification for Two Populations. Classifications with Two Multivariate Normal Populations. Evaluating Classification Functions. Fisher's Discriminant Function...nSeparation of Populations. Classification with Several Populations. Fisher's Method for Discriminating among Several Populations. Final Comments.
 12. Clustering, Distance Methods and Ordination. Similarity Measures. Hierarchical Clustering Methods. Nonhierarchical Clustering Methods. Multidimensional Scaling. Correspondence Analysis. Biplots for Viewing Sample Units and Variables. Procustes Analysis: A Method for Comparing Configurations. Appendix. Standard Normal Probabilities. Student's tDistribution Percentage Points. ...c2 Distribution Percentage Points. FDistribution Percentage Points. FDistribution Percentage Points (...a = .10). FDistribution Percentage Points (...a = .05). FDistribution Percentage Points (...a = .01). Data Index. Subject Index.
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QA278 .J63 2002  Unknown 
 Johnson, Richard A. (Richard Arnold), 1937
 5th ed.  Upper Saddle River, N.J. : Prentice Hall, c2002.
 Description
 Book — xviii, 767 p. : ill. ; 24 cm. + 1 computer optical disc (4 3/4 in.)
 Summary

 (NOTE: Each chapter begins with an Introduction, and concludes with Exercises and References.) I. GETTING STARTED.
 1. Aspects of Multivariate Analysis. Applications of Multivariate Techniques. The Organization of Data. Data Displays and Pictorial Representations. Distance. Final Comments.
 2. Matrix Algebra and Random Vectors. Some Basics of Matrix and Vector Algebra. Positive Definite Matrices. A SquareRoot Matrix. Random Vectors and Matrices. Mean Vectors and Covariance Matrices. Matrix Inequalities and Maximization. Supplement 2A Vectors and Matrices: Basic Concepts.
 3. Sample Geometry and Random Sampling. The Geometry of the Sample. Random Samples and the Expected Values of the Sample Mean and Covariance Matrix. Generalized Variance. Sample Mean, Covariance, and Correlation as Matrix Operations. Sample Values of Linear Combinations of Variables.
 4. The Multivariate Normal Distribution. The Multivariate Normal Density and Its Properties. Sampling from a Multivariate Normal Distribution and Maximum Likelihood Estimation. The Sampling Distribution of 'X and S. LargeSample Behavior of 'X and S. Assessing the Assumption of Normality. Detecting Outliners and Data Cleaning. Transformations to Near Normality. II. INFERENCES ABOUT MULTIVARIATE MEANS AND LINEAR MODELS.
 5. Inferences About a Mean Vector. The Plausibility of ...m0 as a Value for a Normal Population Mean. Hotelling's T 2 and Likelihood Ratio Tests. Confidence Regions and Simultaneous Comparisons of Component Means. Large Sample Inferences about a Population Mean Vector. Multivariate Quality Control Charts. Inferences about Mean Vectors When Some Observations Are Missing. Difficulties Due To Time Dependence in Multivariate Observations. Supplement 5A Simultaneous Confidence Intervals and Ellipses as Shadows of the pDimensional Ellipsoids.
 6. Comparisons of Several Multivariate Means. Paired Comparisons and a Repeated Measures Design. Comparing Mean Vectors from Two Populations. Comparison of Several Multivariate Population Means (OneWay MANOVA). Simultaneous Confidence Intervals for Treatment Effects. TwoWay Multivariate Analysis of Variance. Profile Analysis. Repealed Measures, Designs, and Growth Curves. Perspectives and a Strategy for Analyzing Multivariate Models.
 7. Multivariate Linear Regression Models. The Classical Linear Regression Model. Least Squares Estimation. Inferences About the Regression Model. Inferences from the Estimated Regression Function. Model Checking and Other Aspects of Regression. Multivariate Multiple Regression. The Concept of Linear Regression. Comparing the Two Formulations of the Regression Model. Multiple Regression Models with Time Dependant Errors. Supplement 7A The Distribution of the Likelihood Ratio for the Multivariate Regression Model. III. ANALYSIS OF A COVARIANCE STRUCTURE.
 8. Principal Components. Population Principal Components. Summarizing Sample Variation by Principal Components. Graphing the Principal Components. LargeSample Inferences. Monitoring Quality with Principal Components. Supplement 8A The Geometry of the Sample Principal Component Approximation.
 9. Factor Analysis and Inference for Structured Covariance Matrices. The Orthogonal Factor Model. Methods of Estimation. Factor Rotation. Factor Scores. Perspectives and a Strategy for Factor Analysis. Structural Equation Models. Supplement 9A Some Computational Details for Maximum Likelihood Estimation.
 10. Canonical Correlation Analysis Canonical Variates and Canonical Correlations. Interpreting the Population Canonical Variables. The Sample Canonical Variates and Sample Canonical Correlations. Additional Sample Descriptive Measures. Large Sample Inferences. IV. CLASSIFICATION AND GROUPING TECHNIQUES.
 11. Discrimination and Classification. Separation and Classification for Two Populations. Classifications with Two Multivariate Normal Populations. Evaluating Classification Functions. Fisher's Discriminant Function...nSeparation of Populations. Classification with Several Populations. Fisher's Method for Discriminating among Several Populations. Final Comments.
 12. Clustering, Distance Methods and Ordination. Similarity Measures. Hierarchical Clustering Methods. Nonhierarchical Clustering Methods. Multidimensional Scaling. Correspondence Analysis. Biplots for Viewing Sample Units and Variables. Procustes Analysis: A Method for Comparing Configurations. Appendix. Standard Normal Probabilities. Student's tDistribution Percentage Points. ...c2 Distribution Percentage Points. FDistribution Percentage Points. FDistribution Percentage Points (...a = .10). FDistribution Percentage Points (...a = .05). FDistribution Percentage Points (...a = .01). Data Index. Subject Index.
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QA278 .J63 2002  Available 
9. Statistics : principles and methods [2001]
 Johnson, Richard A. (Richard Arnold), 1937
 4th ed.  New York : John Wiley, c2001.
 Description
 Book — xii, 723 p. : ill. (some col.) ; 25 cm.
 Summary

 Organization and Description of Data Descriptive Study of Bivariate Data Probability Probability Distributions The Normal Distribution Variation in Repeated Samples Sampling Distributions Drawing Inferences from Large Samples SmallSample Inferences for Normal Populations Comparing Two Treatments Regression Analysis I (Simple Linear Regression) Regression Analysis II: Multiple Linear Regression and Other Topics Analysis of Categorical Data Analysis of Variance (ANOVA) Nonparametric Inference Appendices Data Bank Answers to Selected OddNumered Exercises Index.
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10. Applied multivariate statistical analysis [1998]
 Johnson, Richard A. (Richard Arnold), 1937
 4th ed.  Upper Saddle River, NJ : PrenticeHall, c1998.
 Description
 Book — xvi, 816 p. : ill. ; 25 cm. + 1 computer disk (3 1/2 in.)
 Summary

 *(NOTE: Each chapter begins with an Introduction, and concludes with Exercises and References)*I. GETTING STARTED*Aspects of Multivariate Analysis*Matrix Algebra and Random Vectors*Sample Geometry and Random Sampling*The Multivariate Normal Distribution*II. INFERENCES ABOUT MULTIVARIATE MEANS AND LINEAR MODELS*Inferences about a Mean Vector*Comparisons of Several Multivariate Means*Multivariate Linear Regression Models*III. ANALYSIS OF COVARIANCE STRUCTURE*Principal Components*Factor Analysis and Inference for Structured Covariance Matrices*Canonical Correlation Analysis*IV. CLASSIFICATION AND GROUPING TECHNIQUES*Discrimination and Classification*Clustering, Distance Methods and Ordination*Appendix*Data Index*Subject Index.
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QA278 .J63 1998  Available 
11. Applied multivariate statistical analysis [1998]
 Johnson, Richard A. (Richard Arnold), 1937
 4th ed.  Upper Saddle River, N.J. : Prentice Hall, c1998.
 Description
 Book — xvi, 816 p. : ill ; 25 cm. + 1 computer disk (3 1/2 in.)
 Summary

 *(NOTE: Each chapter begins with an Introduction, and concludes with Exercises and References)*I. GETTING STARTED*Aspects of Multivariate Analysis*Matrix Algebra and Random Vectors*Sample Geometry and Random Sampling*The Multivariate Normal Distribution*II. INFERENCES ABOUT MULTIVARIATE MEANS AND LINEAR MODELS*Inferences about a Mean Vector*Comparisons of Several Multivariate Means*Multivariate Linear Regression Models*III. ANALYSIS OF COVARIANCE STRUCTURE*Principal Components*Factor Analysis and Inference for Structured Covariance Matrices*Canonical Correlation Analysis*IV. CLASSIFICATION AND GROUPING TECHNIQUES*Discrimination and Classification*Clustering, Distance Methods and Ordination*Appendix*Data Index*Subject Index.
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QA278 .J63 1998  Unknown 
 Johnson, Richard A. (Richard Arnold), 1937
 1st ed.  New York : J. Wiley, c1997.
 Description
 Book — xiii, 769 p. : ill. (some col.) ; 27 cm.
 Summary

 Describing Patterns in Data. Organizing Data: Association and Relationships. Collecting Data. Probability. Random Variables and Probability Distributions. Continuous Random Variables and Sampling Distributions. From Samples to Populations: Inferences About Means. Comparing Means. Analyzing Count Data. Simple Linear Regression. Multiple Linear Regression and Time Series Models. Management and Statistics. Appendices. Answers to Selected Exercises. Index.
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HD30.215 .J64 1997  Available 
13. Statistics : principles and methods [1996]
 Johnson, Richard A. (Richard Arnold), 1937
 3rd ed.  New York : John Wiley and Sons, Inc., c1996.
 Description
 Book — xv, 720 p. : ill. ; 24 cm.
 Summary

 Organization and Description of Data Descriptive Study of Bivariate Data Probability Probability Distributions The Normal Distribution Variation in Repeated Samples  Sampling Distributions Drawing Inferences from Large Samples SmallSample Inferences for Normal Populations Comparing Two Treatments Regression Analysis Analysis of Categorical Data Analysis of Variance Nonparametric Inference Summation Notation Expectation and Standard Deviation  Properties The Expected Values and Standard Deviation of X Bar.
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QA276.12 .J63 1996  Available 
14. Applied multivariate statistical analysis [1992]
 Johnson, Richard A. (Richard Arnold), 1937
 3rd ed.  Englewood Cliffs, N.J. : Prentice Hall, c1992.
 Description
 Book — 642 p.
 Summary

 Part 1 Getting started: aspects of multivariate analysis matrix algebra and random vectors sample geometry and random sampling the multivariate normal distribution.
 Part 2 Inferences about multivariate means and linear models: inferences about a mean vector comparisons of several multivariate means multivariate linear regression models.
 Part 3 Analysis of covariance structure: principal components factor analysis and inference for structured covariance analysis canonical correlation analysis.
 Part 4 Classification and grouping techniques: discrimination and classification clustering.
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QA278 .J63 1992  Available 
15. Applied multivariate statistical analysis [1988]
 Johnson, Richard A. (Richard Arnold), 1937
 2nd ed.  Englewood Cliffs, N.J. : PrenticeHall, c1988.
 Description
 Book — xvi, 607 p. : ill. ; 25 cm.
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QA278 .J63 1988  Available 
16. Applied multivariate statistical analysis [1982]
 Johnson, Richard A. (Richard Arnold), 1937
 Englewood Cliffs, N.J. : PrenticeHall, c1982.
 Description
 Book — xiii, 594 p. : ill. ; 25 cm.
 Online
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QA278 .J63 1982  Available 
QA278 .J63 1982  Available 
17. Statistical concepts and methods [1977]
 Bhattacharyya, Gouri K., 1940
 New York : Wiley, c1977.
 Description
 Book — xv, 639 p. : ill. ; 24 cm.
 Summary

 Introduction. Descriptive Study of Data. Elements of Probability. Random Variables and Probability Distributions. Distributions for Counts. Basic Concepts of Testing Hypotheses. The Normal Distribution and Random Samples. Inferences about a Population. Comparing Two Treatments. Regression Analysis: Simple Linear Relation. Regression Analysis: Model Checking and Multiple Linear Regression. Correlation: A Measure of Linear Relationship. Analysis of Categorized Data. Design of Experiments and Analysis of Variance. Nonparametric Inference. Sample Surveys.
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HA29 .B46 1977  Available 
 Probability and statistics for engineers
 Miller, Irwin, 1928
 6th ed.  Upper Saddle River, NJ : Prentice Hall, c2000.
 Description
 Book — xii, 622 p. : ill. ; 24 cm. + 1 computer disk (3 1/2 in.)
 Summary

 1. Introduction.
 2. Treatment of Data.
 3. Probability.
 4. Probability Distributions.
 5. Probability Densities.
 6. Sampling Distribution.
 7. Inferences Concerning Means.
 8. Inferences Concerning Variances.
 9. Inferences Concerning Proportions.
 10. Nonparametric Tests.
 11. Curve Fitting.
 12. Analysis of Variance.
 13. Factorial Experimentation.
 14. The Statistical Content of QualityImprovement Programs.
 15. Applications to Reliability and Life Testing. Bibliography. Statistical Tables. Answers to OddNumbered Exercises. Index.
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Engineering Library (Terman)
Engineering Library (Terman)  Status 

Stacks


TA340 .M5 2000  Unknown 
 Evans, James W., 1943
 [Madison, Wis.] : U.S Dept. of Agriculture, Forest Service, Forest Products Laboratory, [1989]
 Description
 Book — 27 p. : ill. ; 28 cm.
 Online
Green Library
Green Library  Status 

Find it US Federal Documents  
A 13.78:FPLRP493  Unknown 
 Verrill, S. P.
 Madison, WI : U.S. Dept. of Agriculture, Forest Service, Forest Products Laboratory, [2009]
 Description
 Book — 1 online resource (17 p.) : ill.
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