CLO #1 - Distinguish the concept, types, and the role of descriptive and inferential statistics; qualitative or quantitative; discrete or continuous; and the values associated with the practice of statistics.

CLO #2 - Given a managerial problem and accompanying data set, construct graphs (following principles of ethical data presentation), and calculate and interpret numerical summaries appropriate for the situation. Use the graphs and numerical summaries as aids in determining a course of action relative to the problem at hand.

CLO #3 - Given a managerial problem, utilize basic probability concepts and standard probability distributions; for example, binomial, normal, as is appropriate, to formulate a course of action that addresses the practical examples.

CLO #6 - Utilize the Bayesian statistics by incorporating conditionality in the inferential methodology.

CLO #7 - Create machine learning schemes based on the K - nearest neighbor approach to assist in making business decisions which entail incorporation of many determinant influences.

1. Explain the k-nearest neighbors algorithm for prediction, accompanied by a simple example.
2. Explain Bayes' algorithm for estimating probability of an outcome, accompanied by a simple example.