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. Data on the amount of the customers' shopping by using an account card and whether they decided to upgrade their account from silver status to platinum status after receiving the upgrade offer is shown in Table 2.
2. Construct the k nearest neighbors scheme for predicting upgrades based on the data given in Table 2, and interpret the confusion matrix.

1. According to the data given in part 1, estimate the probability of upgrade for each division of the purchase amount, using Bayes' theorem.