Michigan State University :: Department of Statistics and Probability
Pervasiveness and utility of data in modern society. Obtaining and managing data. Summarizing and visualizing data. Ethical issues in data science. Communication with data. Fundamentals of probability and statistics.
Data analysis, probability models, random variables, estimation, tests of hypotheses, confidence intervals, and simple linear regression.
Calculus-based course in probability and statistics. Probability models, and random variables. Estimation, confidence intervals, tests of hypotheses, and simple linear regression with applications in sciences.
This course introduces students to the R programming environment. Students will learn to manipulate data objects, produce graphics, clean data, analyse data using practical statistical methods, and generate reproducible statistical reports using R markdown. Please see the daily course schedule for more details on the specific topics that will be covered.
A first course in probability and statistics primarily for business majors. Data analysis, probability models, random variables, confidence intervals, and tests of hypotheses with business applications.