INTRODUCTION TO STATISTICAL METHODS FOR PUBLIC POLICY ANALYSIS

Name: INTRODUCTION TO STATISTICAL METHODS FOR PUBLIC POLICY ANALYSIS
Availability: OutOfStock
Rating: 4.3 (866 reviews)

PUBAFFR 818

과목 설명

An introduction to the statistical methods used in public policy. Covers the basics of probability, statistics, and quantitative methods in public policy analysis. Stresses interpretation and presentation of data as well as theory.

선수과목

Declared in Public Affairs MPA or International Public Affairs MIPA

충족 요건

AAE/ENVIRST/POPHLTH/PUBAFFR 881 , POLISCI/PUBAFFR 871 , PUBAFFR 856 , and PUBAFFR 819

학점

미보고

개설 시기

미보고

평점

3.3

-3.82% 과거 데이터 대비

수료율

100%

1.17% 과거 데이터 대비

A 비율

39.39%

4.33% 과거 데이터 대비

학급 규모

-27.6% 과거 데이터 대비

Cumulative Grade Distribution

강사 (2026 Summr)

다음 사이트의 평점순으로 정렬 Rate My Professors

유사 과목

ANALYTIC TOOLS FOR PUBLIC POLICY

PUBAFFR 380

Provides training in the analytical skills and tools needed to comprehend, assess, and address complex public policy decisions. Overview of the theory and application of quantitative and qualitative analysis methods relevant to public policy decision-making and policy analysis.

INTRODUCTION TO POLICY ANALYSIS

PUBAFFR 873

Develops skills in policy analysis in two ways. First, sets out the conceptual foundations for understanding the role of policy analysis in democratic politics. Second, provides training and experience in policy analysis craft.

ADVANCED STATISTICAL METHODS FOR PUBLIC POLICY ANALYSIS

PUBAFFR 819

A review of intermediate statistics, and an introduction to analytical, quantitative and computer techniques applicable to the analysis of public policy.

INTRODUCTION TO STATISTICAL METHODS IN POLITICAL SCIENCE

POLISCI 812

Introductory statistics course for Ph.D. students. Emphasis on probability theory, inference, and the foundations of multivariate least squares and maximum likelihood.

STATISTICS FOR RESEARCH AND EVALUATION

EDPOL 602

An introduction to statistical analysis for research and evaluation. Draw on current events and policy debates when possible. Describe data and distributions; internal/external validity; sampling, inference, and evaluation designs (RCT, regression, DiD). Expect to use real data and computer software to complete assignments.