APPLIED REGRESSION ANALYSIS

Name: APPLIED REGRESSION ANALYSIS
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Rating: 4.2 (1518 reviews)

STAT 333

과목 설명

An introduction to regression with emphasis on the practical aspects. Topics include: straight-line model, role of assumptions, residual analysis, transformations, multiple regression (with some use of matrix notation), multicollinearity, subset selection, and a brief introduction to mixed models.

선수과목

(STAT 240 , STAT 301 , 302, STAT 312 , STAT 324 , STAT 371 , ECON 310 , BME 325 , or ISYE 210 ) and (STAT 327 or STAT 303 , or concurrent enrollment)

충족 요건

FINANCE 325 , BMI/STAT 620 , GENBUS 656 , ACTSCI 652 , ACTSCI 650 , STAT 349 , STAT 351 , COMPSCI/STAT 471 , FINANCE 330 , ECON 570 , STAT 575 , ACTSCI 654 , STAT 456 , ECON/FINANCE 320 , ACTSCI 657 , ACTSCI 300 , ACTSCI 655 , ME 536 , STAT 433 , COMPSCI 566 , STAT 461 , STAT 443 , COMPSCI 541 , ACTSCI 640 , STAT 411 , ECON 695 , and STAT 421

학점

미보고

개설 시기

미보고

평점

3.77

12.92% 과거 데이터 대비

수료율

96.61%

0.52% 과거 데이터 대비

A 비율

84.75%

80.43% 과거 데이터 대비

학급 규모

16.6% 과거 데이터 대비

Cumulative Grade Distribution

강사 (2026 Summr)

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유사 과목

ADVANCED STATISTICAL METHODS

STAT 849

Gain thorough grounding in modern statistical methods and theory. Classical and modern statistical techniques, including linear regression models, diagnostic tools, prediction, generalized linear regression models, random effect models, experimental design, and model selection. Apply these techniques to dataset in specific contexts.

STATISTICS FOR SOCIOLOGISTS II

C&ESOC/SOC 361

Applied linear regression modeling for social scientists. Bivariate and multiple regression, dummy variables, interactions, nonlinear relationships, indirect effects and omitted variable bias, outliers, heteroskedasticity, and mulitcollinearity; associated diagnostics and corrections. Use of Stata and/or SAS for dataset creation and analysis.

STATISTICAL METHODS FOR DATA SCIENCE

STAT 613

Provides a thorough grounding in modern statistical methods. Introduces statistical techniques and methods of data analysis, including data description, linear regression models, diagnostic tools, prediction and model selection, and experimental design.

REGRESSION MODELS IN EDUCATION

EDPSYCH 763

An applied introduction to regression techniques is given, covering nonparametric regression, multiple linear regression and logistic regression. All applied analyses are done in the statistical programming language R.

APPLIED ECONOMETRIC ANALYSIS I

AAE 636

Introduction to the standard linear regression model with an emphasis on application issues. Includes statistical foundation, hypothesis testing, functional form, model selection and procedures for handling violations of model assumption.