Statistics Courses Available for Tutoring

Covers data display, normal distribution, regression, probability, confidence intervals, and hypothesis testing with real-world applications.

Provides a foundation in statistical methods, including data summarization, probability distributions, and basic inference techniques.

Explores data collection, summarization, probability, and drawing conclusions, with a focus on conceptual understanding.

Covers summarizing data, probability distributions, confidence intervals, hypothesis testing, and regression analysis using statistical software.

Introduces applied statistical techniques, including data analysis, correlation, and basic inferential methods for practical applications.

Covers probability spaces, discrete and continuous random variables, expectations, conditional distributions, and limit theorems.

Builds on introductory statistics, covering advanced descriptive statistics, sampling distributions, and hypothesis testing.

Focuses on intermediate statistical methods, including regression models, ANOVA, and data interpretation techniques.

Explores Gaussian linear regression models, residual analysis, variable selection, and non-linear regression.

Covers single-factor and multi-factor ANOVA, randomized block design, Latin squares, and split-plot designs.

Introduces mathematical statistics, covering probability calculus, random variables, expectation, central limit theorem, and hypothesis testing.

Covers statistical methods for actuarial science, including probability models and risk analysis techniques.

Explores probability spaces, random variables, transformations, expectations, generating functions, and limit theorems.

Covers distribution-free tests for location, dispersion, one- and two-way layouts, independence, regression slopes, and resampling methods.

Introduces statistical modeling techniques, including linear regression, model diagnostics, and variable selection.

Covers data reduction, maximum likelihood, least squares, Bayesian inference, and fundamental statistical theory.

A project-driven course focusing on hands-on data analysis, covering statistics, computation, and social issues like privacy and design.

Introduces statistics and R programming, covering mean, variance, quantiles, hypothesis testing, and data management.

Focuses on programming for statistical analysis, including data manipulation and visualization using software like R or Python.

Covers computational statistics, including optimization, Monte Carlo methods, and machine learning techniques.

Covers programming for data science, including shell programming, SQL, R, and handling large-scale data.

Introduces data mining, classification, model building, decision trees, and cluster analysis techniques.