STAT 281 Applied Statistics for Management (3)

Catalog Description:

Topics in probability and statistics: descriptive measures, distributions, one-sample estimation and hypothesis testing, correlation, simple linear regression, and categorical data. (Offered each semester.)


Prerequisites:


MATH 107 Applied Mathematics for Management, MATH 108 Intuitive Calculus with Applications, or MATH 111 Calculus I

Required Course Materials:


James T. McClave, P. George Benson, Terry Sincich, Statistics for Business and Economics, 12th edition, Pearson, 2012 (ISBN: 978-0-321-82623-7)

Course Coordinator:


L. Marlin Eby, Ph.D., Professor of Mathematics and Statistics

Course Audience:


Students who are required to take this course:

  1. Majors housed in the Department of Management and Business,
  2. Interdisciplinary majors: Art Business, Chinese Business, Music Business, Spanish Business, Theater Business, Computer and Information Science with Business Information Systems concentration, Computer and Information Science with Software Development concentration (option), Nutrition and Food Services Management (option)

Course Objectives:


Students who complete this course will be able to:

  1. Become familiar with both descriptive and inferential analyses.
  2. Use probability in applied models and as the bridge between descriptive and inferential analysis.
  3. Intuitively understand each concept – intuition should not be confused with theory.
  4. Integrate topics by identifying commonalties.
  5. Understand the limitations of each analysis through consideration of conditions for validity.
  6. Express general concepts in terms of the application.
  7. Communicate results, clearly and completely, in a manner appropriate to nonquantitative audiences.
  8. Be introduced to the computer's capabilities in solving practical problems, using the computer for analysis only after understanding how to perform the analysis manually.

Topics:

  1. Introduction: Types of Variables, Levels of Measurement
  2. Descriptive Measures:
    • Tables, Graphs
    • Measures of Location
    • Measures of Dispersion
    • Measures of Relative Standing
    • Application
  3. Descriptive Measures:
    • Tables, Graphs
  4. Probability
    • Mean, Variance and Standard Deviation of Probability Distributions
    • Other Probability Distributions
      • Binomial
      • Hypergeometric
      • Poisson
      • Normal
      • Approximations to Exact Techniques
      • Sampling
      • Application
  5. Single-Sample
    • Estimation – Point and Interval
    • Sample Size
    • Hypothesis Testing of Means
    • Hypothesis Testing of Proportions
    • Application
  6. Correlation, Spearman’s Rank Correlation
  7. Simple Linear Regression
    • Method of Least Squares
    • Measuring Accuracy of Prediction
    • Statistical Inference in Regression Analysis
    • Application
  8. Multiple Regression
    • Measuring Accuracy of Prediction
    • Statistical Inference in Multiple Regression Analysis
    • Dummy Variables
    • Residuals
    • Application
  9. Ethics is integrated throughout the course.

 

 

Revised: October 2013 (textbook); September 2011; September 2010 (YM)

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