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Unlike some other reproductions of classic texts (1) We have not
used OCR(Optical Character Recognition), as this leads to bad
quality books with introduced typos. (2) In books where there are
images such as portraits, maps, sketches etc We have endeavoured to
keep the quality of these images, so they represent accurately the
original artefact. Although occasionally there may be certain
imperfections with these old texts, we feel they deserve to be made
available for future generations to enjoy.
Unlike some other reproductions of classic texts (1) We have not
used OCR(Optical Character Recognition), as this leads to bad
quality books with introduced typos. (2) In books where there are
images such as portraits, maps, sketches etc We have endeavoured to
keep the quality of these images, so they represent accurately the
original artefact. Although occasionally there may be certain
imperfections with these old texts, we feel they deserve to be made
available for future generations to enjoy.
Although nearly all major social science departments offer graduate
students training in quantitative methods, the typical sequencing
of topics generally delays training in regression analysis and
other multivariate techniques until a student's second year.
William Berry and Mitchell Sanders's Understanding Multivariate
Research fills this gap with a concise introduction to regression
analysis and other multivariate techniques. Their book is designed
to give new graduate students a grasp of multivariate analysis
sufficient to understand the basic elements of research relying on
such analysis that they must read prior to their formal training in
quantitative methods. Berry and Sanders effectively cover the
techniques seen most commonly in social science
journals--regression (including nonlinear and interactive models),
logit, probit, and causal models/path analysis. The authors draw on
illustrations from across the social sciences, including political
science, sociology, marketing and higher education. All topics are
developed without relying on the mathematical language of
probability theory and statistical inference. Readers are assumed
to have no background in descriptive or inferential statistics, and
this makes the book highly accessible to students with no prior
graduate course work.
Manufacturing Planning & Control for Supply Chain Management,
6e by Jacobs, Berry, and Whybark (formerly Vollmann, Berry,
Whybark, Jacobs) is a comprehensive reference covering both basic
and advanced concepts and applications for students and practicing
professionals. The text provides an understanding of supply chain
planning and control techniques with topics including purchasing,
manufacturing, warehouse, and logistics systems. Manufacturing
Planning & Control for Supply Chain Management, 6e continues to
be organized in a flexible format, with the basic coverage in
chapters 1-8 followed by the last four chapters that focus on the
integration of manufacturing with the supply chain. Each chapter
provides a managerial issues overview, a detailed technical
presentation related to the topic, company examples, and concluding
principles. This book is the essential desk reference for Supply
Chain Planning and Control techniques.
Although nearly all major social science departments offer graduate
students training in quantitative methods, the typical sequencing
of topics generally delays training in regression analysis and
other multivariate techniques until a student's second year.
William Berry and Mitchell Sanders's "Understanding Multivariate
Research" fills this gap with a concise introduction to regression
analysis and other multivariate techniques. Their book is designed
to give new graduate students a grasp of multivariate analysis
sufficient to understand the basic elements of research relying on
such analysis that they must read prior to their formal training in
quantitative methods. Berry and Sanders effectively cover the
techniques seen most commonly in social science
journals--regression (including nonlinear and interactive models),
logit, probit, and causal models/path analysis. The authors draw on
illustrations from across the social sciences, including political
science, sociology, marketing and higher education. All topics are
developed without relying on the mathematical language of
probability theory and statistical inference. Readers are assumed
to have no background in descriptive or inferential statistics, and
this makes the book highly accessible to students with no prior
graduate course work.
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