
• Intermediate Statistics with R (Greenwood)
Introductory statistics courses prepare students to think statistically but cover relatively few statistical methods. Building on the basic statistical thinking emphasized in an introductory course, a second course in statistics at the undergraduate level can explore a large number of statistical methods. This text covers more advanced graphical summaries, One-Way ANOVA with pair-wise comparisons, Two-Way ANOVA, Chi-square testing, and simple and multiple linear regression models.
• Analysis of Variance and Design of Experiments
This is a graduate-level course that provides a thorough introduction to statistical methods used to analyze data resulting from a wide range of experimental designs. The concepts of comparative experiments, randomization, replication, repeated measures, blocking, and factorial designs will be discussed. The main goal of the course will be to develop problem-solving skills for identifying a variety of designs and making inferences on associated parameters.
• Time Series Analysis (Aue)
A time series is an ordered sequence of values of a variable at equally spaced time intervals. Time series analysis accounts for the fact that data points taken over time may have an internal structure (such as autocorrelation, trend or seasonal variation) that should be accounted for.

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