我们仍在积极开发此网站。如果您遇到任何问题，请报告给我们！ - 报告问题

INTRODUCTION TO BIOSTATISTICS

BMI/STAT 541
课程描述

Course designed for the biomedical researcher. Topics include: descriptive statistics, hypothesis testing, estimation, confidence intervals, t-tests, chi-squared tests, analysis of variance, linear regression, correlation, nonparametric tests, survival analysis and odds ratio. Biomedical applications used for each topic.

先修课程

Graduate/professional standing. Not open to students with credit for STAT 511 or BMI/POPHLTH 551

满足要求
学分

未报告

开课时间

未报告

平均绩点
3.66

3.15% 相比历史数据

完成率
97.47%

-0.55% 相比历史数据

A率
74.68%

21.08% 相比历史数据

班级规模
79

24.77% 相比历史数据

Cumulative Grade Distribution

教师 (2026 Summr)

按评分排序，数据来自 Rate My Professors

相似课程

SPECIAL TOPICS IN BIOSTATISTICS AND BIOMEDICAL INFORMATICS

BMI 826

Covers advanced topics in the areas of biostatistics and biomedical informatics. Includes reading and discussion of original literature and individual student projects.

INTRODUCTION TO BIOSTATISTICS FOR POPULATION HEALTH

BMI/POPHLTH 551

Designed for population health researcher. Topics include descriptive statistics, elementary probability, probability distributions, one- and two-sample normal inference (point estimation, hypothesis testing, confidence intervals), power and sample size calculations, one- and two-sample binomial inference, underlying assumptions and diagnostic work.

STATISTICAL METHODS FOR EPIDEMIOLOGY

BMI/STAT 642

Methods for analysis of case-control, cross sectional, and cohort studies. Covers epidemiologic study design, measures of association, rates, classical contingency table methods, and logistic and Poisson regression.

STATISTICAL METHODS FOR CLINICAL TRIALS

BMI/STAT 641

Statistical issues in the design of clinical trials, basic survival analysis, data collection and sequential monitoring.

INTRODUCTION TO CLINICAL TRIALS I

BMI/STAT 542

Intended for biomedical researchers interested in the design and analysis of clinical trials. Topics include definition of hypotheses, measures of effectiveness, sample size, randomization, data collection and monitoring, and issues in statistical analysis.