Multivariate Statistical Methods: A Primer, Third Edition

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CRC Press, Jul 6, 2004 - Mathematics - 224 pages
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Multivariate methods are now widely used in the quantitative sciences as well as in statistics because of the ready availability of computer packages for performing the calculations. While access to suitable computer software is essential to using multivariate methods, using the software still requires a working knowledge of these methods and how they can be used.

Multivariate Statistical Methods: A Primer, Third Edition introduces these methods and provides a general overview of the techniques without overwhelming you with comprehensive details. This thoroughly revised, updated edition of a best-selling introductory text retains the author's trademark clear, concise style but includes a range of new material, new exercises, and supporting materials on the Web.

New in the Third Edition:

  • Fully updated references
  • Additional examples and exercises from the social and environmental sciences
  • A comparison of the various statistical software packages, including Stata, Statistica, SAS Minitab, and Genstat, particularly in terms of their ease of use by beginners

    In his efforts to produce a book that is as short as possible and that enables you to begin to use multivariate methods in an intelligent manner, the author has produced a succinct and handy reference. With updated information on multivariate analyses, new examples using the latest software, and updated references, this book provides a timely introduction to useful tools for statistical analysis.
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    This book is as the title states: a primer on multivariate statistics. The coverage is good with enough details to understand the different methods. There isn't much hard-core theory.

    Selected pages

    Contents

    The material of multivariate analysis
    vii
    12 Preview of multivariate methods
    12
    13 The multivariate normal distribution
    14
    14 Computer programs
    15
    16 Chapter summary
    16
    Matrix algebra
    17
    23 Operations on matrices
    19
    24 Matrix inversion
    21
    Exercise
    103
    Discriminant function analysis
    105
    83 Canonical discriminant functions
    107
    84 Tests of significance
    108
    85 Assumptions
    109
    86 Allowing for prior probabilities of group membership
    114
    88 Jackknife classification of individuals
    116
    810 Logistic regression
    117

    25 Quadratic forms
    22
    27 Vectors of means and covariance matrices
    23
    28 Further reading
    25
    References
    26
    Displaying multivariate data
    27
    33 The draftsmans plot
    29
    34 The representation of individual data points
    30
    35 Profiles of variables
    32
    36 Discussion and further reading
    33
    37 Chapter summary
    34
    Tests of significance with multivariate data
    35
    the multivariate case
    37
    44 Multivariate versus univariate tests
    41
    the singlevariable case
    42
    47 Comparison of means for several samples
    46
    48 Comparison of variation for several samples
    49
    49 Computer programs
    54
    Exercise
    55
    References
    57
    Measuring and testing multivariate distances
    59
    53 Distances between populations and samples
    62
    54 Distances based on proportions
    67
    55 Presence absence data
    68
    56 The Mantel randomization test
    69
    57 Computer programs
    72
    59 Chapter summary
    73
    Exercise
    74
    Principal components analysis
    75
    62 Procedure for a principal components analysis
    76
    63 Computer programs
    84
    64 Further reading
    85
    Exercises
    87
    References
    90
    Factor analysis
    91
    72 Procedure for a factor analysis
    93
    73 Principal components factor analysis
    95
    74 Using a factor analysis program to do principal components analysis
    97
    75 Options in analyses
    100
    76 The value of factor analysis
    101
    78 Discussion and further reading
    102
    811 Computer programs
    122
    813 Chapter summary
    123
    Exercises
    124
    Cluster analysis
    125
    93 Hierarchic methods
    127
    94 Problems of cluster analysis
    129
    96 Principal components analysis with cluster analysis
    130
    97 Computer programs
    134
    98 Discussion and further reading
    135
    99 Chapter summary
    136
    Exercises
    137
    References
    141
    Canonical correlation analysis
    143
    102 Procedure for a canonical correlation analysis
    145
    103 Tests of significance
    146
    104 Interpreting canonical variates
    148
    105 Computer programs
    158
    107 Chapter summary
    159
    References
    161
    Multidimensional scaling
    163
    112 Procedure for multidimensional scaling
    165
    113 Computer programs
    172
    114 Further reading
    174
    Exercise
    175
    Ordination
    177
    122 Principal components analysis
    178
    123 Principal coordinates analysis
    181
    124 Multidimensional scaling
    189
    125 Correspondence analysis
    191
    126 Comparison of ordination methods
    196
    127 Computer programs
    197
    129 Chapter summary
    198
    Epilogue
    201
    133 Missing values
    202
    References
    203
    Computer packages for multivariate analyses
    205
    References
    207
    Author Index
    209
    Subject Index
    211
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