Statistical Analysis IBack to Course Guide
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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.
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.