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INTRODUCTORY NONPARAMETRIC STATISTICS

STAT 351
과목 설명

Distribution free statistical procedures or methods valid under nonrestrictive assumptions: basic tools; counting methods; order statistics, ranks, empirical distribution functions; distribution free tests and associated interval and point estimators; sign test; signed rank tests; rank tests; Mann Whitney Wilcoxon procedures; Kolmogorov Smirnov tests; permutation methods; kernel density estimation; kernel and spline regression estimation; computer techniques and programs; discussion and comparison with parametric methods.

선수과목

STAT 333 , STAT 340 , graduate/professional standing, or declared in Statistics VISP

충족 요건

This course does not satisfy any prerequisites.

학점

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개설 시기

미보고

평점
3.46

-1.43% 과거 데이터 대비

수료율
100%

0.89% 과거 데이터 대비

A 비율
29.31%

-45.61% 과거 데이터 대비

학급 규모
58

2.47% 과거 데이터 대비

Cumulative Grade Distribution

강사 (2026 Summr)

다음 사이트의 평점순으로 정렬 Rate My Professors

유사 과목

INTRODUCTION TO STATISTICAL METHODS

STAT 301

Distributions, measures of central tendency, dispersion and shape, the normal distribution; experiments to compare means, standard errors, confidence intervals; effects of departure from assumption; method of least squares, regression, correlation, assumptions and limitations; basic ideas of experimental design.

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.

INTRODUCTION TO STATISTICAL INFERENCE

STAT 610

Conditioning, distribution theory, approximation to distributions, modes of convergence, limit theorems, statistical models, parameter estimation, comparision of estimators, confidence sets, theory of hypothesis tests, introduction to Bayesian inference and nonparametric estimation.

STATISTICAL METHODS FOR BIOSCIENCE I

F&WECOL/STAT 571

Descriptive statistics, distributions, one- and two-sample normal inference, power, one-way ANOVA, simple linear regression, categorical data, non-parametric methods; underlying assumptions and diagnostic work.

NONPARAMETRIC STATISTICS AND MACHINE LEARNING METHODS

STAT 841

Statistical function estimation and classification; reproducing kernel machines, support vector machines; high dimensional model selection and estimation; Bayesian, empirical Bayesian interpretation of nonparametric learning methods; log density ANOVA and graphical models; tree ensemble methods including bagging, boosting, and random forest.