So students opting for Math 111 would normally want to take that course before STAT 112. Students learn how data are used to draw conclusions about populations and processes. Statistical experimental design is the art and science of producing reliable statistical data. The course discusses the principal causes for bias in the collection of data and in the design of experiments, as well as the techniques available to avoid bias. This analysis involves the concepts of bias, controls, randomization, variability, and statistical significance.
Probability analyzes randomness.
Its use in statistical analysis allows for the quantification of statistical significance, accuracy, and confidence. Statistical inference involves the application of descriptive statistics and probability to the drawing of statistically significant conclusions about populations and processes. For example, using statistical tests and confidence intervals, statisticians can estimate population parameters precisely, and give measures of both the precision of the estimates and the confidence they have in these estimates. See the course syllabus for more details. The text comes with a CD with additional material including sample quizzes, extra chapters, and additional examples and projects in the form of a browser.