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|a 519.535
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|a Johnson, Richard A.
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| 245 |
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|a Applied Multivariate Statistical Analysis.
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| 250 |
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|a 6th ed.
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| 264 |
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|a Harlow, United Kingdom :
|b Pearson Education, Limited,
|c 2019.
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| 300 |
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|a 1 online resource (775 pages)
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| 336 |
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|a text
|b txt
|2 rdacontent
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|a computer
|b c
|2 rdamedia
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|a online resource
|b cr
|2 rdacarrier
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|a Cover -- Table of Contents -- Chapter 1: Aspects of Multivariate Analysis -- 1.1 Introduction -- 1.2 Applications of Multivariate Techniques -- 1.3 The Organization of Data -- Arrays -- Descriptive Statistics -- Graphical Techniques -- 1.4 Data Displays and Pictorial Representations -- Linking Multiple Two-Dimensional Scatter Plots -- Graphs of Growth Curves -- Stars -- Chernoff Faces -- 1.5 Distance -- 1.6 Final Comments -- Exercises -- References -- Chapter 2: Sample Geometry and Random Sampling -- 2.1 Introduction -- 2.2 The Geometry of the Sample -- 2.3 Random Samples and the Expected Values of the Sample Mean and Covariance Matrix -- 2.4 Generalized Variance -- Generalized Variance Determined by and its Geometrical Interpretation -- Another Generalization of Variance -- 2.5 Sample Mean, Covariance, and Correlation as Matrix Operations -- 2.6 Sample Values of Linear Combinations of Variables -- Exercises -- References -- Chapter 3: Matrix Algebra and Random Vectors -- 3.1 Introduction -- 3.2 Some Basics of Matrix and Vector Algebra -- Vectors -- 3.3 Positive Definite Matrices -- 3.4 A Square-Root Matrix -- 3.5 Random Vectors and Matrices -- 3.6 Mean Vectors and Covariance Matrices -- Partitioning the Covariance Matrix -- The Mean Vector and Covariance Matrix for Linear Combinations of Random Variables -- Partitioning the Sample Mean Vector and Covariance Matrix -- 3.7 Matrix Inequalities and Maximization -- Supplement 3A: Vectors and Matrices: Basic Concepts -- Vectors -- Matrices -- Exercises -- References -- Chapter 4: The Multivariate Normal Distribution -- 4.1 Introduction -- 4.2 The Multivariate Normal Density and its Properties -- Additional Properties of the Multivariate Normal Distribution -- 4.3 Sampling from a Multivariate Normal Distribution and Maximum Likelihood Estimation -- The Multivariate Normal Likelihood.
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|a Maximum Likelihood Estimation of μ and Σ -- Sufficient Statistics -- 4.4 The Sampling Distribution of X and S -- Properties of the Wishart Distribution -- 4.5 Large-Sample Behavior of X and S -- 4.6 Assessing the Assumption of Normality -- Evaluating the Normality of the Univariate Marginal Distributions -- Evaluating Bivariate Normality -- 4.7 Detecting Outliers and Cleaning Data -- Steps for Detecting Outliers -- 4.8 Transformations to Near Normality -- Transforming Multivariate Observations -- Exercises -- References -- Chapter 5: Inferences About a Mean Vector -- 5.1 Introduction -- 5.2 The Plausibility of μ0 as a Value for a Normal Population Mean -- 5.3 Hotelling's T2 and Likelihood Ratio Tests -- General Likelihood Ratio Method -- 5.4 Confidence Regions and Simultaneous Comparisons of Component Means -- Simultaneous Confidence Statements -- A Comparison of Simultaneous Confidence Intervals with One-at-a-Time Intervals -- The Bonferroni Method of Multiple Comparisons -- 5.5 Large Sample Inferences about a Population Mean Vector -- 5.6 Multivariate Quality Control Charts -- Charts for Monitoring a Sample of Individual Multivariate Observations for Stability -- Control Regions for Future Individual Observations -- Control Ellipse for Future Observations -- T2-Chart for Future Observations -- Control Charts Based on Subsample Means -- Control Regions for Future Subsample Observations -- 5.7 Inferences about Mean Vectors When Some Observations are Missing -- 5.8 Difficulties Due to Time Dependence in Multivariate Observations -- Supplement 5A: Simultaneous Confidence Intervals and Ellipses as Shadows of the p-Dimensional Ellipsoids -- Exercises -- References -- Chapter 6: Comparisons of Several Multivariate Means -- 6.1 Introduction -- 6.2 Paired Comparisons and a Repeated Measures Design -- Paired Comparisons.
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|a A Repeated Measures Design for Comparing Treatments -- 6.3 Comparing Mean Vectors from Two Populations -- Assumptions Concerning the Structure of the Data -- Further Assumptions When n1 and n2 are Small -- Simultaneous Confidence Intervals -- The Two-Sample Situation When Σ1 ≠ Σ2 -- An Approximation to the Distribution of T2 for Normal Populations When Sample Sizes are Not Large -- 6.4 Comparing Several Multivariate Population Means (One-Way Manova) -- Assumptions about the Structure of the Data for One-Way Manova -- A Summary of Univariate Anova -- Multivariate Analysis of Variance (Manova) -- 6.5 Simultaneous Confidence Intervals for Treatment Effects -- 6.6 Testing for Equality of Covariance Matrices -- 6.7 Two-Way Multivariate Analysis of Variance -- Univariate Two-Way Fixed-Effects Model with Interaction -- Multivariate Two-Way Fixed-Effects Model with Interaction -- 6.8 Profile Analysis -- 6.9 Repeated Measures Designs and Growth Curves -- 6.10 Perspectives and a Strategy for Analyzing Multivariate Models -- Exercises -- References -- Chapter 7: Multivariate Linear Regression Models -- 7.1 Introduction -- 7.2 The Classical Linear Regression Model -- 7.3 Least Squares Estimation -- Sum-of-Squares Decomposition -- Geometry of Least Squares -- 7.4 Inferences About the Regression Model -- Inferences Concerning the Regression Parameters -- Likelihood Ratio Tests for the Regression Parameters -- 7.5 Inferences from the Estimated Regression Function -- Estimating the Regression Function at Z0 -- Forecasting a New Observation at Z0 -- 7.6 Model Checking and Other Aspects of Regression -- Does the Model Fit? -- Leverage and Influence -- Additional Problems in Linear Regression -- 7.7 Multivariate Multiple Regression -- Other Multivariate Test Statistics -- Predictions from Multivariate Multiple Regressions -- 7.8 The Concept of Linear Regression.
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|a 7.9 Comparing the Two Formulations of the Regression Model -- Mean Corrected Form of the Regression Model -- Relating the Formulations -- 7.10 Multiple Regression Models with Time Dependent Errors -- Supplement 7A: The Distribution of the Likelihood Ratio for the Multivariate Multiple Regression Model -- Exercises -- References -- Chapter 8: Principal Components -- 8.1 Introduction -- 8.2 Population Principal Components -- Principal Components for Covariance Matrices with Special Structures -- 8.3 Summarizing Sample Variation by Principal Components -- The Number of Principal Components -- Interpretation of the Sample Principal Components -- Standardizing the Sample Principal Components -- 8.4 Graphing the Principal Components -- 8.5 Large Sample Inferences -- Large Sample Properties of λi and ei -- Testing for the Equal Correlation Structure -- 8.6 Monitoring Quality with Principal Components -- Checking a Given Set of Measurements for Stability -- Controlling Future Values -- Supplement 8A: The Geometry of the Sample Principal Component Approximation -- The p-Dimensional Geometrical Interpretation -- The n-Dimensional Geometrical Interpretation -- Exercises -- References -- Chapter 9: Factor Analysis and Inference for Structured Covariance Matrices -- 9.1 Introduction -- 9.2 The Orthogonal Factor Model -- 9.3 Methods of Estimation -- The Principal Component (and Principal Factor) Method -- A Modified Approach-the Principal Factor Solution -- The Maximum Likelihood Method -- A Large Sample Test for the Number of Common Factors -- 9.4 Factor Rotation -- Oblique Rotations -- 9.5 Factor Scores -- The Weighted Least Squares Method -- The Regression Method -- 9.6 Perspectives and a Strategy for Factor Analysis -- Supplement 9A: Some Computational Details for Maximum Likelihood Estimation -- Recommended Computational Scheme.
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|a Maximum Likelihood Estimators of p = LzLz + ψz -- Exercises -- References -- Chapter 10: Canonical Correlation Analysis -- 10.1 Introduction -- 10.2 Canonical Variates and Canonical Correlations -- 10.3 Interpreting the Population Canonical Variables -- Identifying the Canonical Variables -- Canonical Correlations as Generalizations of Other Correlation Coefficients -- The First r Canonical Variables as a Summary of Variability -- A Geometrical Interpretation of the Population Canonical Correlation Analysis -- 10.4 The Sample Canonical Variates and Sample Canonical Correlations -- 10.5 Additional Sample Descriptive Measures -- Matrices of Errors of Approximations -- Proportions of Explained Sample Variance -- 10.6 Large Sample Inferences -- Exercises -- References -- Chapter 11: Discrimination and Classification -- 11.1 Introduction -- 11.2 Separation and Classification for Two Populations -- 11.3 Classification with Two Multivariate Normal Populations -- Classification of Normal Populations When Σ1 = Σ2 = Σ -- Scaling -- Fisher's Approach to Classification with Two Populations -- Is Classification a Good Idea? -- Classification of Normal Populations When Σ1 ≠ Σ2 -- 11.4 Evaluating Classification Functions -- 11.5 Classification with Several Populations -- The Minimum Expected Cost of Misclassification Method -- Classification with Normal Populations -- 11.6 Fisher's Method for Discriminating among Several Populations -- Using Fisher's Discriminants to Classify Objects -- 11.7 Logistic Regression and Classification -- Introduction -- The Logit Model -- Logistic Regression Analysis -- Classification -- Logistic Regression with Binomial Responses -- 11.8 Final Comments -- Including Qualitative Variables -- Classification Trees -- Neural Networks -- Selection of Variables -- Testing for Group Differences -- Graphics.
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| 505 |
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|a Practical Considerations Regarding Multivariate Normality.
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| 520 |
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|a For courses in Multivariate Statistics, Marketing Research, Intermediate Business Statistics, Statistics in Education, and graduate-level courses in Experimental Design and Statistics.Appropriate for experimental scientists in a variety of disciplines, this market-leading text offers a readable introduction to the statistical analysis of...
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| 526 |
0 |
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|a CS241 - Bachelor of Science (Hons.) Statistics
|z Syllabus Programme
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| 526 |
0 |
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|a CS249 - Bachelor of Science (Hons.) Mathematics
|z Syllabus Programme
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| 526 |
0 |
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|a CS291 - Bachelor of Science (Hons.) Statistics and Bachelor of Entrepreneurship (Logistics & Distributive Trade)
|z Syllabus Programme
|
| 588 |
|
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|a Description based on publisher supplied metadata and other sources.
|
| 590 |
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|a Electronic reproduction. Ann Arbor, Michigan : ProQuest Ebook Central, 2021. Available via World Wide Web. Access may be limited to ProQuest Ebook Central affiliated libraries.
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| 650 |
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0 |
|a Multivariate analysis.
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| 655 |
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4 |
|a Electronic books.
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| 700 |
1 |
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|a Wichern, Dean W.
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| 776 |
0 |
8 |
|i Print version:
|a Johnson, Richard A.
|t Applied Multivariate Statistical Analysis: Pearson New International Edition
|d Harlow, United Kingdom : Pearson Education, Limited,c2013
|z 9781292024943
|
| 797 |
2 |
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|a ProQuest (Firm)
|
| 856 |
4 |
0 |
|u https://ezaccess.library.uitm.edu.my/login?url=https://ebookcentral.proquest.com/lib/uitm-ebooks/detail.action?docID=5174865
|z View fulltext via EzAccess
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| 966 |
0 |
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|a 2021
|b ProQuest Ebook Central
|c UiTM Library
|d Nor Hazreeni Hamzah
|e Faculty Computer and Mathematical Sciences
|f ProQuest
|