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# 1 - 10 of 35 results for: STATS ; Currently searching autumn courses. You can expand your search to include all quarters

## STATS 32:Introduction to R for Undergraduates

This short course runs for weeks one through five of the quarter. It is recommended for undergraduate students who want to use R in the humanities or social sciences and for students who want to learn the basics of R programming. The goal of the short course is to familiarize students with R's tools for data analysis. Lectures will be interactive with a focus on learning by example, and assignments will be application-driven. No prior programming experience is needed. Topics covered include basic data structures, File I/O, data transformation and visualization, simple statistical tests, etc, and some useful packages in R. Prerequisite: undergraduate student. Priority given to non-engineering students. Laptops necessary for use in class.
Terms: Aut | Units: 1
Instructors: Zhang, I. (PI)

## STATS 60:Introduction to Statistical Methods: Precalculus (PSYCH 10, STATS 160)

Techniques for organizing data, computing, and interpreting measures of central tendency, variability, and association. Estimation, confidence intervals, tests of hypotheses, correlation, and regression.
Terms: Aut, Win, Spr | Units: 5 | UG Reqs: GER:DB-Math, WAY-AQR, WAY-FR

## STATS 110:Introduction to Statistics for Engineering and the Sciences

Introduction to statistics with examples drawn from various fields, including the sciences, engineering, and social sciences. Collecting data (random sampling, randomized experiments); describing data (numerical and graphical summaries); discrete and continuous probability models; statistical inference (hypothesis tests and confidence intervals). Use of software to conduct probability simulations and data analysis. Prerequisite: MATH 20 or AP Calculus AB. Please note that students must enroll in one section in addition to the main lecture.
Terms: Aut | Units: 5 | UG Reqs: GER:DB-Math, WAY-AQR, WAY-FR
Instructors: Sun, D. (PI)

## STATS 117:Introduction to Probability Theory

Introduction to probability theory, including probability axioms, conditional probability, independence, random variables, and expectation. Joint, marginal, and conditional distributions. Discrete models (binomial, hypergeometric, Poisson) and continuous models (normal, exponential). Prerequisites: MATH 21 or AP Calculus BC.
Terms: Aut, Spr | Units: 3

## STATS 118:Probability Theory for Statistical Inference

Continuation of STATS 117, with a focus on probability topics useful for statistical inference. Sampling distributions of sums, means, variances, and order statistics of random variables. Convolutions, moment generating functions, and limit theorems. Probability distributions useful in statistics (gamma, beta, chi-square, t, multivariate normal). Applications to estimation and hypothesis testing. Prerequisites: Math 51, MATH 52 (may be taken concurrently), STATS 117. See https://statistics.stanford.edu/course-equiv for equivalent courses in other departments that satisfy these prerequisites.
Terms: Aut, Win | Units: 4
Instructors: Kim, G. (PI)

## STATS 160:Introduction to Statistical Methods: Precalculus (PSYCH 10, STATS 60)

Techniques for organizing data, computing, and interpreting measures of central tendency, variability, and association. Estimation, confidence intervals, tests of hypotheses, correlation, and regression.
Terms: Aut, Win, Spr | Units: 5

## STATS 200:Introduction to Theoretical Statistics

Terms: Aut, Win | Units: 4

## STATS 202:Statistical Learning and Data Science

Overview of supervised learning, with a focus on regression and classification methods. Syllabus includes: linear and polynomial regression, logistic regression and linear discriminant analysis; cross-validation and the bootstrap, model selection and regularization methods (ridge and lasso); nonlinear models, splines and generalized additive models; tree-based methods, random forests and boosting; support-vector machines; Some unsupervised learning: principal components and clustering (k-means and hierarchical). Prerequisites: STATS 117, CS 106A, MATH 51. Recommended: STATS 191 or STATS 203. See https://statistics.stanford.edu/course-equiv for equivalent courses in other departments that satisfy these prerequisites.
Terms: Aut, Spr | Units: 3
Instructors: Walther, G. (PI)

## STATS 203:Regression Models and Analysis of Variance

Modeling and interpretation of observational and experimental data using linear and nonlinear regression methods. Model building and selection methods. Multivariable analysis. Fixed and random effects models. Experimental design. Prerequisites: Math 51, Math 104, STATS 200. See https://statistics.stanford.edu/course-equiv for equivalent courses in other departments that satisfy these prerequisites.
Terms: Aut, Spr | Units: 3
Instructors: Green, A. (PI)
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