Statistical Analysis I

This course presents quantitative decision-making techniques applying principles of probability and statistical analysis to managerial decision-making.  The course emphasizes conceptual understanding rather than mathematical proofs. Key activities include distinguishing between variables, random sampling and understanding descripting and inferential statistics.  

PREREQUISITE: Three semester hours of mathematics.

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

• Distinguishing between independent and dependent variables.
• Identifying and applying the concept of a random variable.
• Differentiating between discrete and continuous random variables.
• Identifying random sampling techniques and describing the importance of sampling distributions.
• Illustrating and utilizing descriptive and inferential statistics.
• Calculating the common measures of central tendency.
• Calculating the variance and standard deviation for a population and for a sample.
• Determining a standard score and finding percentages under the normal curve.
• Recognizing the general properties of probability, binomial, and normal distributions.
• Applying  the laws governing probability principles.
• Identifying and stating the null 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.
• Discerning 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 its importance in statistical inference.
• Utilizing Artificial Intelligence for collecting, organizing and analyzing raw data to gather important information.

ACQUIRED SKILLS   

• Critiquing a Problem Solving Model
• Developing a Personal Critical Thinking Algorithm