Statistical analysis for public and nonprofit managers
著者
書誌事項
Statistical analysis for public and nonprofit managers
Praeger, 1990
大学図書館所蔵 全4件
  青森
  岩手
  宮城
  秋田
  山形
  福島
  茨城
  栃木
  群馬
  埼玉
  千葉
  東京
  神奈川
  新潟
  富山
  石川
  福井
  山梨
  長野
  岐阜
  静岡
  愛知
  三重
  滋賀
  京都
  大阪
  兵庫
  奈良
  和歌山
  鳥取
  島根
  岡山
  広島
  山口
  徳島
  香川
  愛媛
  高知
  福岡
  佐賀
  長崎
  熊本
  大分
  宮崎
  鹿児島
  沖縄
  韓国
  中国
  タイ
  イギリス
  ドイツ
  スイス
  フランス
  ベルギー
  オランダ
  スウェーデン
  ノルウェー
  アメリカ
注記
Includes bibliographical references (p. [191]-204) and index
内容説明・目次
内容説明
Designed for use by professionals and graduate-level students in public administration and not-for-profit management, this is a comprehensive, clearly written guide to the use of statistical analysis in the management of nonprofit organizations. The volume emphasizes statistical models that use more than one variable and is unique in presenting multivariate statistics specifically with the public and nonprofit manager in mind. Examples throughout have been chosen to be relevant to the not-for-profit organization and each chapter contains several real-life illustrations of how statistical techniques can be used in actual practice. In addition to explaining statistical methods and techniques in detail, the author focuses on why statistics should be used and helps the reader obtain an intuitive grasp of the rationale behind the statistics. Because she stresses the logic of proofs and the limitations of results rather than the pure mathematics of statistical derivation, the volume is accessible to students and managers with only a little statistical background.
Following a chapter that introduces the concept of multivariate analysis, Stiefel explains simple and multiple regression models in detail. Later chapters discuss other techniques that are becoming widely used in not-for-profit organizations: logit and probit analysis, time-series models, and simultaneous equation models. Two types of examples are used to make the material immediately relevant to the not-for-profit manager: real-world examples culled from professional journals and reports in a variety of fields including health care, education, finance, budgeting, and administrative science; and examples of results obtained using statistical programs run on a personal computer. Thus the book enables the reader to understand and interpret both the statistics used in professional articles and statistical results as they appear on computer printouts. Four appendixes review basic statistical methods such as simple summation operators, the Pearson Correlation Coefficient, and hypothesis testing for the sample mean.
目次
Preface Introduction The Bivariate or Simple Regression Model The Multiple Regression Model, Part I--Estimators, Statistical Properties, and Significance Tests The Multiple Regression Model, Part II--Importance of Variables, Model Building, and Forecasting Dummy Variables and Nonlinear and Nonadditive Relationships Basic Assumptions and Common Problems in Regression Models Qualitative Dependent Variables Some Advanced Topics: Pooled Time-Series and Cross-Section Analysis, Lagged Variables, Missing Data, Time-Series Analysis, and Multiequation Systems An Overview Appendices Bibliographical Essay Index
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