National College Credit Recommendation Service

Organization

Credit Course Categories:

Descriptions and credit recommendations for all evaluated learning experiences

Length:

Varies (self-study; self-paced).

Dates:

October 2022 - Present.

Objectives:

Upon successful completion of the course, students will be able to: appropriately gather, organize, tabulate and present data; find various central tendencies and variances including mean, weighted means, median, mode, mean absolute deviation and standard deviation; display data in various graphs including: bar graphs, line graphs, pictographs, histograms, cumulative frequency histograms, frequency polygons and ogives; understand and work with various binomial distributions including the binomial distribution, hyper geometric distribution, geometric distribution, multinomial distribution, poisson distribution and normal distribution; design appropriate samples; perform hypothesis tests; estimate population parameters based on samples statistics; test claims about means and proportions; make inferences about two means and two proportions; determine the significance of colorations; and construct confidence intervals; find and use a regression line.

Instruction:

This course develops the statistical skills of collecting, organizing, summarizing, and analyzing information to draw conclusions or answer questions. Major topics include descriptive statistics, frequency distributions, probability, binomial and normal distributions, statistical inference, linear regression, and correlation.

Credit recommendation:

In the lower division baccalaureate/associate degree category, 3 semester hours in Statistics, Mathematics or as an elective in Business, Computer Science, Social Sciences, Education, Engineering, or Health Sciences (3/23).

Length:

Varies (self-study; self-paced).

Dates:

October 2022 - Present.

Objectives:

Upon successful completion of the course, students will be able to: define business problems and effect optimal solutions utilizing well-reasoned modeling techniques; create solutions to problems having common business applications; recognize both the effectiveness and limitations in using models as replications of real-world situations; and apply appropriate forecasts and simulations as a basis for managerial decisions.

Instruction:

This course introduces students to some of the important models and problem-solving techniques used in business decision making. Topics include learning curves, forecasting, utility theory, statistical decision theory, queuing theory, linear and integer programming, transportation and assignment models, graph theory, project management, process mapping and network modeling.

Credit recommendation:

In the lower division baccalaureate/associate degree category, 3 semester hours in Business, Quantitative Analysis, or Decision Science (3/23).