Statistical Analysis I

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. 

UPON COMPLETION OF THE COURSE, THE STUDENT WILL BE COMPETENT IN:

• 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.