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Stat 210 syllabus

Statistical Reasoning and Applications 

Course Description: An introduction to modern statistics designed to provide the student with a solid foundation in statistical concepts, reasoning and applications. Emphasis given to problem identification, methodology selection and interpretation of results. Analysis of data accomplished by extensive use of statistical computer software, thereby minimizing manual computation. Coverage includes descriptive statistics, probability models, estimation, and hypothesis testing. High School level algebra is recommended. Computer Lab fee. NOTE: ST 210 is intended for students in all disciplines except Engineering and Computer Science. Credit for both ST 210 and ST 315 not allowed. May be offered for Honors Credit.

Prerequisites: High School level algebra or equivalent

Textbook: Interactive Statistics: Informed Decisions Using Data, by Michael Sullivan, III and George Woodbury, First Edition, Pearson. There is no physical copy of this text. It will only be available as an eText in MyLabsPlus (http://usouthal.mylabsplus.com)

Coverage: Chapter 1 (1.1-1.3, 1.5, 1.6), Chapter 2 (2.1, 2.2, 2.4), Chapter 3 (3.1, 3.2, 3.4, 3.5), Chapter 4 (4.1, 4.2), Chapter 5 (5.1-5.4), Chapter 6 (6.1, 6.2), Chapter 7 (7.1-7.3), Chapter 8 (8.1, 8.2), Chapter 9 (9.1-9.3), Chapter 10 (10.1-10.4), Chapter 11 (11.1-11.4)

 

Learning outcomes: The emphasis of this course is to understand statistical terminology, procedures, and interpretation of results. Skill with formulas, graphs, tables, interpretation of numeric results, and use of software are all needed for success in this course. Upon the successful completion of the course a student will:

  • Organize, display, and summarize data.
  • Understand probability distributions and applications.
  • Understand sampling distributions and applications.
  • Understand inferential statistics including confidence intervals and hypothesis testing.
  • Evaluate and explain the results of quantitative analysis of data.
  • Evaluate important assumptions in estimation, modeling, and data analysis.