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Advanced Statistics for the Behavioral Sciences - A Computational Approach with R (Hardcover, 1st ed. 2018)
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Advanced Statistics for the Behavioral Sciences - A Computational Approach with R (Hardcover, 1st ed. 2018)
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This book demonstrates the importance of computer-generated
statistical analyses in behavioral science research, particularly
those using the R software environment. Statistical methods are
being increasingly developed and refined by computer scientists,
with expertise in writing efficient and elegant computer code.
Unfortunately, many researchers lack this programming background,
leaving them to accept on faith the black-box output that emerges
from the sophisticated statistical models they frequently use.
Building on the author's previous volume, Linear Models in Matrix
Form, this text bridges the gap between computer science and
research application, providing easy-to-follow computer code for
many statistical analyses using the R software environment. The
text opens with a foundational section on linear algebra, then
covers a variety of advanced topics, including robust regression,
model selection based on bias and efficiency, nonlinear models and
optimization routines, generalized linear models, and survival and
time-series analysis. Each section concludes with a presentation of
the computer code used to illuminate the analysis, as well as
pointers to packages in R that can be used for similar analyses and
nonstandard cases. The accessible code and breadth of topics make
this book an ideal tool for graduate students or researchers in the
behavioral sciences who are interested in performing advanced
statistical analyses without having a sophisticated background in
computer science and mathematics.
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