Statistical Analysis I

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The course presents quantitative decision-making techniques applying principles of probability and statistical analysis to managerial decision-making.  The course places emphasis on conceptual understanding rather than mathematical proofs.

PREREQUISITE: Six semester hours of mathematics. 


  • Distinguishing between independent and dependent variables.
  • Defining and applying the idea of a random variable.
  • Differentiating between discrete and continuous random variables.
  • Identifying random sampling techniques and describing the importance of sampling distributions.
  • Defining, describing, and giving examples of descriptive and inferential statistics.
  • Communicating important information contained in a set of data by means of graphs and frequency distributions.
  • Calculating and describing characteristics of the common measures of central tendency: mean, median, and mode.
  • Defining the sum of the squares and square of sum concepts.
  • Calculating the variance and standard deviation for a population and for a sample.
  • Calculating a standard score and determining percentages under the normal curve.
  • Determining the general properties of probability, binomial, and normal distributions.
  • Explaining the rules governing probability concepts.
  • Identifying and differentiating between null hypotheses and alternative hypotheses.
  • Describing what is meant by the level of significance and the region of rejection.
  • Differentiating between one-tailed and two-tailed tests for hypotheses.
  • Describing the general procedures for testing statistical hypotheses including the definition of sampling error, the differentiation of Type I and Type II errors, and the use of the Z and T distributions.
  • Explaining the central limit theorem and the concept of degrees of freedom and discussing their importance in statistical inference.

Course Syllabi

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