This co-requisite course contains topics which directly support the content in STAT 100: Introduction to Statistics (4 units). It contains support for mathematical skills and knowledge used in STAT 100: Introduction to Statistics (4 units), supplemental instruction on STAT 100: Introduction to Statistics (4 units) content, and study skill development. Remedial Available. (Coreq: STAT 100: Introduction to Statistics (4 units))
Units: 1 — 1
Includes organization and classification of data, graphic representation, measures of central tendency and variability, percentiles, normal curves, standard scores, correlation and regression, introduction to statistical inference, and the use of computers for statistical calculations. (Prereq: MATH 99: Mathematics Review II (4 units) or ELMT Score of 50)
Units: 4 — 4
Self-paced seminar series to learn and apply statistical computing software. Recommended as a co or pre-requisite for STAT 250: Applied Stat:Sci Tech (4 units), MATH 320: Applied Probability and Statistics (4 units) or STAT 320: Nonparametric Statistics (4 units). (Credit/No Credit Available)
Units: 1 — 1
Provides the models and methods used in a career in technology, engineering, and natural and biological sciences. Emphasizes the use of tables, graphs, and elementary descriptive statistical applications. Introduces statistical inferences through parameter estimation and regression modeling. Introduces students to the basic skills for using computers in statistical analyses. (Prereq: MATH 150: Calculus I (4 units))
Units: 4 — 4
Reviews basic introductory statistics with an overview of basic probability theory. Extends the scope of statistics by teaching theory through application with a focus on nonparametric statistical methods for hypothesis tests, empirical distribution estimation, bootstrap, estimation theory, chi-square tests, one-way ANOVA, and simple linear regression using statistical computing and programming. (Prereq: STAT 100: Introduction to Statistics (4 units) or STAT 250: Applied Stat:Sci Tech (4 units) or MATH 320: Applied Probability and Statistics (4 units))
Units: 4 — 4
Studies the design, analysis, and follow-up procedures of experiments across disciplines. Includes one-way and two-way analysis of variance, completely randomized designs, factorial designs, Latin Squares, nested and split-plot design, repeated measures, block designs, analysis of covariance, multiple comparison procedures, and incomplete designs. (Prereq: MATH 320: Applied Probability and Statistics (4 units) or STAT 250: Applied Stat:Sci Tech (4 units) or STAT 100: Introduction to Statistics (4 units))
Units: 4 — 4
Studies the design, analysis, and follow-up procedures of sampling finite populations. Includes survey design, random, stratified, cluster, systematic sampling designs, analysis of quantitative and qualitative data collected through surveys and sampling. Emphasis on statistical considerations of sampling and non-sampling error. (Prereq: MATH 320: Applied Probability and Statistics (4 units) or STAT 250: Applied Stat:Sci Tech (4 units) or STAT 100: Introduction to Statistics (4 units))
Units: 4 — 4
Student and faculty member select topic of study and number of credits.
Units: 1 — 6
Studies the use of explanatory, confirmatory, and predictive linear models in data-driven decision making. Includes simple linear regression, multiple linear regression, variable selection methods, model comparison methods, generalized linear model, logistic regression, Poisson regression, principle component analysis, mixed-effects models, times series models, and residual analysis using statistical computing packages. (Prereq: STAT 250: Applied Stat:Sci Tech (4 units) or STAT 320: Nonparametric Statistics (4 units) or MATH 320: Applied Probability and Statistics (4 units) or Instructor Consent)
Units: 4 — 4
Theory focused probability tools for statistics: description of discrete and absolutely continuous distributions, expected values, moments, moment generating functions, transformation of random variables, marginal and conditional distributions, independence, order statistics, multivariate distributions, concept of random sample, and derivation of many sampling distributions. (Prereq: MATH 320: Applied Probability and Statistics (4 units) and MATH 322: Foundations of Modern Math (4 units))
Units: 4 — 4
Theory focused framework for statistical inference: point estimators: biased and unbiased, minimum variance unbiased, least mean square error, maximum likelihood and least squares, asymptotic properties. Interval estimators and tests of hypotheses: confidence intervals, power functions, Neyman-Pearson lemma, likelihood ratio tests, unbiasedness, efficiency and sufficiency. (Prereq: STAT 420: Statistical Theory I (4 units))
Units: 4 — 4
Studies the Bayesian approach to data analysis. Includes Bayes theorem, basic concept of Bayesian statistics, prior and posterior distributions, conjugacy, credible intervals, generalized linear models, statistical inference (with comparison to frequentist approach), prior elicitation, computational methods and applications to real world problems. [(Prereq: STAT 410: Applied Statistics Methods: Linear Models (4 units) and STAT 420: Statistical Theory I (4 units)) or (Instructor Consent)]
Units: 4 — 4