Instructor(s): |
David Eaton |

Course: |
P A 397 - Introduction to Quantitative Analysis (previously Applied Quantitative Analysis I) |

Unique Number: |
65010 |

Day & Time: |
Tuesdays, 6:00 PM - 9:00 PM |

Room: |
SRH 3.102 |

Waitlist Information: | For LBJ Students: UT Waitlist Information |

**Description:** This course develops basic competence and skills in problem solving and quantitative methods applied to public policy analysis. It emphasizes the art and skill of converting problem descriptions into quantitative models as well as the analysis and interpretation of these models.

The first portion of the class will study mathematical optimization as a way to search for the best solution to a decision problem which may be constrained or unconstrained. This will include using linear programming techniques as well as calculus-based techniques for non-linear functions. The class will discuss how to apply multiobjective optimization models to public policy problems.

Then the class will study how quantitative inference methods can be applied to support decision-making. A section on decision analysis will model decisions using decision matrices and decision trees to assess decisions made under conditions of certainty, risk, and uncertainty. The class will also consider the value and cost of obtaining additional information when making decisions; how to incorporate the decision maker's value judgments into the decision model; and the sensitivity of the decision to changes in the problem conditions.

A third portion of the class will review basic concepts of probability, probability distributions, and descriptive statistics for describing quantitative data. Following a brief discussion of data sources and sampling methods, the class will study the use of sample data to make estimates of, and inferences about, the parameters of larger populations. Both parametric statistics and nonparametric statistics will be studied. Using these inference skills, the class will develop and test linear regression models to describe relationships between a dependent variable that describes a sample or population and one or more explanatory variables, based on sample data. Building on these regression skills, the class will use historical data to estimate and forecast trends and seasonality.

The course will emphasize application and interpretation of quantitative modeling and analysis methods in policy evaluation and decision-making. Students will be expected to use of Microsoft Excel (or other vendors’) computer spreadsheets for homework assignments, applications exercises, and exams.