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Born in 1915, Harold Guetzkow might have been a child of the
Roaring Twenties. But in fact Professor Guetzkow is much more a
child of the depression (to use his own term). A complication of
essays by scholars who took time and energy to pen their work in
honor of Harold Guetzkow. The chapters that follow represent a real
contribution to the study of international relations and document
the influence of Harold Guetzkow in catalyzing that study over the
last thirty years.
Born in 1915, Harold Guetzkow might have been a child of the
"Roaring Twenties." But in fact Professor Guetzkow is much more a
"child of the depression" (to use his own term). A complication of
essays by scholars who took time and energy to pen their work in
honor of Professor Harold Guetzkow. A representation and
contribution to the study of international relations and document
the influence of Harold Guetzkow in catalyzing that study over the
last thirty years.
This book chronicles the creation, evolution, and multifaceted
impact of the University of Alabama HuntsvilleFoundation (UAHF). It
traces the growth of the city of Huntsville and the area
surrounding it as an internationally acclaimed center for research
and development in science, engineering, and technology. The author
describes the concurrent evolution of The University of Alabama in
Huntsville (UAH) as the pre-eminent academic center in Alabama for
research in engineering, physical science, environmental science,
and computational science. He recounts theseminal contributions
that the UAH Foundation, and the outstanding men and women who have
constituted its members, has made in helping to make some very good
things to happen in Huntsville, Alabama. The UAHF is a rarity among
university-related foundations in that the UAHF existed even before
the university it now serves was created. The books chapters detail
how Huntsville leaders came together to form the predecessor of the
UAHF as landholding operation supporting the orderly development of
Cummings Research Park, of which UAH occupies the easternmost
extremity. Later chapters describe how the UAHF gradually took on
greater and greater responsibility for the support of UAH itself,
becoming, ultimately, UAH's independent, university-related
foundation.
This book chronicles the creation, evolution, and multifaceted
impact of the University of Alabama HuntsvilleFoundation (UAHF). It
traces the growth of the city of Huntsville and the area
surrounding it as an internationally acclaimed center for research
and development in science, engineering, and technology. The author
describes the concurrent evolution of The University of Alabama in
Huntsville (UAH) as the pre-eminent academic center in Alabama for
research in engineering, physical science, environmental science,
and computational science. He recounts theseminal contributions
that the UAH Foundation, and the outstanding men and women who have
constituted its members, has made in helping to make some very good
things to happen in Huntsville, Alabama. The UAHF is a rarity among
university-related foundations in that the UAHF existed even before
the university it now serves was created. The books chapters detail
how Huntsville leaders came together to form the predecessor of the
UAHF as landholding operation supporting the orderly development of
Cummings Research Park, of which UAH occupies the easternmost
extremity. Later chapters describe how the UAHF gradually took on
greater and greater responsibility for the support of UAH itself,
becoming, ultimately, UAH's independent, university-related
foundation.
Spatial Regression Models illustrates the use of spatial analysis
in the social sciences within a regression framework and is
accessible to readers with no prior background in spatial analysis.
The text covers different modeling-related topics for continuous
dependent variables, including mapping data on spatial units,
creating data from maps, analyzing exploratory spatial data,
working with regression models that have spatially dependent
regressors, and estimating regression models with spatially
correlated error structures. Using social science examples based on
real data, the authors illustrate the concepts discussed, and show
how to obtain and interpret relevant results. The examples are
presented along with the relevant code to replicate all the
analysis using the R package for statistical computing. Users can
download both the data and computer code to work through all the
examples found in the text. New to the Second Edition is a chapter
on mapping as data exploration and its role in the research
process, updates to all chapters based on substantive and
methodological work, as well as software updates, and information
on estimation of time-series, cross-sectional spatial models.
This volume provides a practical introduction to the method of
maximum likelihood as used in social science research. Ward and
Ahlquist focus on applied computation in R and use real social
science data from actual, published research. Unique among books at
this level, it develops simulation-based tools for model evaluation
and selection alongside statistical inference. The book covers
standard models for categorical data as well as counts, duration
data, and strategies for dealing with data missingness. By working
through examples, math, and code, the authors build an
understanding about the contexts in which maximum likelihood
methods are useful and develop skills in translating mathematical
statements into executable computer code. Readers will not only be
taught to use likelihood-based tools and generate meaningful
interpretations, but they will also acquire a solid foundation for
continued study of more advanced statistical techniques.
This volume provides a practical introduction to the method of
maximum likelihood as used in social science research. Ward and
Ahlquist focus on applied computation in R and use real social
science data from actual, published research. Unique among books at
this level, it develops simulation-based tools for model evaluation
and selection alongside statistical inference. The book covers
standard models for categorical data as well as counts, duration
data, and strategies for dealing with data missingness. By working
through examples, math, and code, the authors build an
understanding about the contexts in which maximum likelihood
methods are useful and develop skills in translating mathematical
statements into executable computer code. Readers will not only be
taught to use likelihood-based tools and generate meaningful
interpretations, but they will also acquire a solid foundation for
continued study of more advanced statistical techniques.
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