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Applied Statistics I: Basic Bivariate Techniques spends less time on statistical significance testing, and moves in the direction of the new statistics by focusing more on confidence intervals and effect size. Instructors of upper undergraduate or beginning graduate level courses will find that the greater focus on basic concepts such as partition of variance and effect size is more useful to students, particularly as preparation for more advanced courses. Spending less time on statistical significance testing allows for more time to be devoted to more interesting and useful statistics that students will see in journal articles (such as correlation and regression). The author includes examples in SPSS, together with datasets on an accompanying website. A companion study guide reproducing the exercises and examples in R will also be available.
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Applied Statistics I: Basic Bivariate Techniques spends less time on statistical significance testing, and moves in the direction of the new statistics by focusing more on confidence intervals and effect size. Instructors of upper undergraduate or beginning graduate level courses will find that the greater focus on basic concepts such as partition of variance and effect size is more useful to students, particularly as preparation for more advanced courses. Spending less time on statistical significance testing allows for more time to be devoted to more interesting and useful statistics that students will see in journal articles (such as correlation and regression). The author includes examples in SPSS, together with datasets on an accompanying website. A companion study guide reproducing the exercises and examples in R will also be available.