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STAT2 introduces students to statistical modeling beyond what they
have learned in a Stat 101 college course or an AP Statistics
course. Building on basic concepts and methods learned in that
course, STAT2 empowers students to analyze richer datasets that
include more variables and address a broader range of research
questions. Other than a working understanding of exponential and
logarithmic functions, there are no prerequisites beyond successful
completion of their first statistics course. To help all students
make a smooth transition to this course, Chapter 0 reminds students
of basic statistical terminology and also uses the familiar
two-sample t-test as a way to illustrate the approach of
specifying, estimating, and testing a statistical model. Using
STAT2, students will: Go beyond their Stat 101 experience by
learning to develop and apply models with both quantitative and
categorical response variables, and with multiple explanatory
variables. STAT2 Chapters are grouped into units that consider
models based on the type of response and type of predictors.
Discover that the practice of statistical modeling involves
applying an interactive process. STAT2 employs a four-step process
in all statistical modeling: Choose a form for the model, fit the
model to the data, assess how well the model describes the data,
and use the model to address the question of interest. Learn how to
apply their developing judgment about statistical modeling. STAT2
introduces the idea of constructing statistical models at the very
beginning, in a setting that students encountered in their Stat 101
course. This modeling focus continues throughout the course as
students encounter new and increasingly more complicated scenarios.
Analyze and draw conclusions from real data, which is crucial for
preparing students to use statistical modeling in their
professional lives. STAT2 incorporates real and rich data
throughout the text. Using real data to address genuine research
questions helps motivate students to study statistics. The richness
stems not only from interesting contexts in a variety of
disciplines, but also from the multivariable nature of most
datasets.
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