Fundamental concepts for data analysis and experimental design. Descriptive and inferential statistics, including classical and nonparametric methods, regression, correlation, and analysis of variance. Statistical software. Prerequisite: Minimum Junior standing or STAT 141 or STAT 143 and Instructor permission.
This course is currently listed as a MIXED course, but it is likely to be changed to entirely REMOTE. Required Materials: • Textbook: OpenIntro Statistics, 4th Edition by Diez, Barr and Cetinkaya-Rundel available as a free download at https://www.openintro.org/stat/textbook.php. Paperback copies can be ordered for $20. • Calculator: No specific calculator is required, but I recommend a graphing calculator with the normal, t, Chi-square, F and binomial distributions. (Many students use the TI 83 or TI 84.) • Computer with R: You will use a free statistical software package called R to analyze data outside of class. Information will be provided to help you get R up and running on your computer. Course Description: STAT 211 introduces students to the discipline of statistics as a science of understanding and analyzing data. Students learn how to collect and analyze data to make inferences and conclusions about real world phenomena. The learning goals are: • Recognize the importance of data collection and how the methods used to collect data affect the scope of the inference that can be made. • Summarize data effectively using numerical summaries, graphical methods and appropriate statistical language. • Calculate probabilities of events using basic rules of probability and common distributions such as binomial and normal distributions. • Apply appropriate estimation and testing methods to understand natural phenomena and make data-based decisions. • Build basic regression models to describe relationships between variables. • Interpret statistical results correctly, effectively, and in context. • Critique data-based claims and evaluate data-based decisions. • Use R to perform basic statistical analyses with clean datasets.
Reading the text and participating in class will be essential parts of this course. Class meeting times will be used for brief topic specific lectures interspersed with problem solving, discussions and activities around key concepts. To get the most out of class, it is important to be prepared and engaged.
Grades will be based on: Homework Assignments R Labs Tests Project Attendance and Participation
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to on Tuesday and Thursday
Note: These dates may change before registration begins.
Note: These dates may not be accurate for select courses during the Summer Session.
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