# Core Knowledge Area: Quantitative Analysis

Learning Outcome: Demonstrate understanding of quantitative analysis.

Quantitative analysis enables one to understand and analyze quantitative information presented in various formats. It involves reasoning by symbolic, numerical, or geometrical means; determining various ways to solve problems; and predicting possible consequences.

Competencies: By way of example, Loyola graduates should be able to:

• Represent and interpret quantitative information symbolically, graphically, numerically, verbally, and in written form.
• Recognize the limitations of mathematical and statistical models.
• Develop an understanding of the nature and history of mathematics, its role in scientific inquiry and technological progress, and its importance in dealing with issues in the public realm.
• Develop an understanding of the rudiments of statistics, including sampling and hypothesis testing, and the uses of statistical reasoning in everyday life.

## Quantitative Analysis Courses (1 course required)

 CJC 206: Criminal Justice Statistics  (Formerly CRMJ 206 and CRMJ 316) This course provides an introduction and overview of statistical analysis methods and techniques used in the study of delinquency, crime and the operation/management of the criminal justice system.Outcome: Students will be able to effectively perform and interpret statistical analyses and identify the appropriate use of these statistics in the analysis of crime and criminal justice system performance. COMP 125: Visual Information Processing This course provides an introduction to computer programming using a language well-suited to beginning programmers and practical applications, for example Visual Basic .Net.Outcome: Students will be able to represent and interpret quantitative information symbolically, graphically, numerically, verbally, and in written form. COMP 150: Introduction to Computing This course will introduce both majors and non-majors to the range of studies, experimentation, and practice embodied in computer science. Outcome: Students will understand the field and foundations of computer science, and be able to demonstrate basic tools of the field. COMP 163: Discrete Structures This course will cover topics in discrete mathematics relevant to computer science, with particular emphasis on foundational knowledge needed for design and analysis of algorithms. Outcome: Students will understand the field and foundations of computer science, and be able to demonstrate basic tools of the field. ISOM 241: Business Statistics This course examines the steps and procedures required to solve problems in science, social science, and business where data are useful - from definition of the managerial problem to the use of statistical analysis to address the problem. Outcome: Students will be able to demonstrate understanding of statistical thinking and data analysis techniques for decision-making purposes. MATH 108: Real World Modeling with Mathematics This course covers material selected from the mathematics of the management sciences, statistics, the digital revolution, social choice, and consumer finance models. Outcome: Students will be able to demonstrate understanding particular topics, including: networks, planning and scheduling, linear programming, generating and analyzing statistical data, probability, statistical inference, identification numbers, data encryption, voting procedures, weighted voting systems, fair division, apportionment, models for saving and for borrowing. PHIL 174: Logic This course is a detailed study of the methods and principles of correct reasoning, both deductive and inductive and from both the traditional and modern point of view. Outcome: Students will be able to demonstrate a complete, symbolic formal system utilizing a comprehensive and entirely symbolic language and containing a complete set of formal laws of logic. STAT 103: Fundamentals of Statistics This course is an introduction to the fundamentals of descriptive and inferential statistics. Outcome: Students will be able to demonstrate understanding of particular topics, including: design of experiments, observational studies, histograms, the average and standard deviation, normal approximations, chance error and bias, basic probability, chance processes, expected value and standard error, probability histograms, surveys, accuracy of percentages and averages, tests of significance, and correlation and regression.