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Fall 2019
May 08, 2021
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Information Select the Course Number to get further detail on the course. Select the desired Schedule Type to find available classes for the course. The Schedule Type links will be available only when the schedule of classes is available for the selected term.

STAT 51100 - Statistical Methods
Credit Hours: 3.00. Descriptive statistics; elementary probability; sampling distributions; inference, testing hypotheses, and estimation; normal, binomial, Poisson, hypergeometric distributions; one-way analysis of variance; contingency tables; regression. For statistics majors and minors, credit should be allowed in no more than one of STAT 30100, STAT 35000, STAT 50100, and in no more than one of STAT 50300 and STAT 51100. Prerequisite: Two semesters of college calculus. Typically offered Fall Spring.
3.000 Credit hours

Syllabus Available
Levels: Graduate, Professional, Undergraduate
Schedule Types: Distance Learning, Lecture

Offered By: College of Science
Department: Statistics

Course Attributes:
Upper Division

May be offered at any of the following campuses:     
      West Lafayette Continuing Ed
      PU Fort Wayne
      IUPUI
      Northwest- Westville
      Northwest- Hammond
      West Lafayette

Learning Outcomes: 1. Understand the difference between population parameters and sample statistics. 2. Understand practical data displays: meaning and interpretation of common data displays in the media. 3. Appreciate various interpretations of probability and where they enter into statistical studies. 4. Understand statistical distributions: difference between discrete and continuous random variables. Computing the mean and variance using various important probability distributions such as the Binomial, Hypergeometric, Poisson, Normal, Exponential, Gamma. Computing probabilities using these distributions. 5. Understand statistical distributions of two or more random variables: Sample statistics and their distributions. Understanding the Central Limit Theorem. 6. Understand statistical inference: to understand what this means and what are some practical and important applications. Examples include Confidence Intervals, Tests of Hypotheses for one, two or more populations. Linear Regression with emphasis on the difference between causation and relationships.



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