Allan J. Rossman, Ann R. Cannon, Bradley A. Hartlaub, George W. Cobb, Jeffrey A. Witmer, Julie M. Legler, Robin H. Lock, Thomas L. Moore
W. H. Freeman
Beyond what they have learnt in AP Statistics or Stat 101 undergraduate courses, STAT2 introduces them to statistical modeling. Expanding on the fundamental ideas and techniques covered in that course, STAT2 gives students the tools they need to analyze larger datasets with more variables and a wider range of research issues.
Beyond successfully completing their first statistics course, there are no qualifications other than having a working knowledge of exponential and logarithmic functions. In order to facilitate everyone's entry into the course, Chapter 0 reviews fundamental statistical terms and employs the well-known two-sample t-test as an example of how to specify, estimate, and test a statistical model.
Students will use STAT2 to:
• Create and apply models with both quantitative and categorical response variables, as well as models with numerous explanatory factors, building on their knowledge from Stat 101. According to the kind of response and kind of predictors, STAT2 Chapters are divided into units that consider models.
• Learn how to employ an interactive procedure in statistical modeling practice. All statistical modeling in STAT2 follows a four-step process: select a model form, fit the model to the data, evaluate how well the model explains the data, and apply the model to the relevant topic.
• Learn how to apply their growing statistical modeling judgment. In a situation that students experienced in their Stat 101 course, STAT2 begins by introducing the concept of building statistical models. As students come across fresh and steadily more complex cases throughout the course, the emphasis on modeling remains constant.
• Real-world data analysis and conclusion-making are essential for educating students to utilize statistical modeling in the workplace. STAT2 includes authentic, detailed data all throughout the article. Students are more likely to study statistics if they are using real data to answer relevant research issues. The multivariate nature of most datasets and interesting contexts across a range of fields both contribute to the richness.