\(x^2\) Programming and problem solving in a high-level \(x^2\) programming language. Data structures including arrays and linked-lists. Modularity, abstraction and abstract data types including stacks, queues and lists. Introduction to searching and sorting, recursion, algorithm analysis and object-orientation. \(y=mx+x^{22} a^[b]\) and \(\sqrt[n]{x}\)

Description and comparison of data sets, linear regression analysis, basic probability theory, discrete probability distributions, binomial and normal distributions, Central Limit Theorem, confidence intervals and hypothesis tests on means and proportions, properties of t-, F- and chi-squared distributions, analysis of variance, inference on regression. Emphasis on interpretation of numerical results for all topics. interpretation of numerical results for all topics. interpretation of numerical results for all topics. interpretation of numerical results for all topics. interpretation of numerical results for all topics. interpretation of numerical results for all topics. interpretation of numerical results for all topics. interpretation of numerical results for all topics.