The Institute for Statistics Education at             Statistics.com

is the leading provider of online education in statistics.
We offer 100+ courses in introductory and advanced statistical subjects.
Students from around the world study with leading authorities via private discussion boards
on flexible schedules.
Teaching assistants provide individual responses in practical exercises.
Statistics.com confers CEU's, Records of Completion, and Certificates in advanced statistical study.

## Available courses

### Regression Analysis 011720 #### Aim of Course:

Regression, perhaps the most widely used statistical technique, estimates relationships between independent (predictor or explanatory) variables and a dependent (response or outcome) variable.

### Introduction to Bayesian Statistics 011720 #### Aim of Course:

This course will introduce you to the basic ideas of Bayesian Statistics. You will learn how to perform Bayesian analysis for a binomial proportion, a normal mean, the difference between normal means, the difference between proportions, and for a simple linear regression model.

### Statistics 3: Anova and Regression 011720 #### Aim of Course:

To provide an easy introduction to ANOVA and multiple linear regression through a series of practical applications.

### TESU DSI 604: Predictive Analytics 1: R 011020 #### Aim of Course:

In this online course, “Predictive Analytics 1 - Machine Learning Tools - with R,” you will be introduced to the basic concepts in predictive analytics, also called predictive modeling, the most prevalent form of data mining. This course covers the two core paradigms that account for most business applications of predictive modeling: classification and prediction. In both cases, predictive modeling takes data where a variable of interest (a target variable) is known and develops a model that relates this variable to a series of predictor variables, also called features. In classification, the target variable is categorical ("purchased something" vs. "has not purchased anything").

### TESU DSI 601: Predictive Analytics 1: Python 011020

#### Aim of Course:

In this online course, “Predictive Analytics 1 - Machine Learning Tools - with Python,” you will be introduced to the basic concepts in predictive analytics, also called predictive modeling, the most prevalent form of data mining.

### Predictive Analytics 1 011020

#### Aim of Course:

This course will introduce you to the basic concepts in predictive analytics, also called predictive modeling, the most prevalent form of data mining.

### TESU DSI 506: Programming 1: R 011020 #### Aim of Course:

In this online course, “R Programming Intro 1,” you will be introduced to basic concepts in computer programming via R - it is for those who have had little or no experience in programming.

### TESU DSI 505: Programming 1: Python 011020 #### Aim of Course:

The goal of this course is to introduce the basics of programming in Python, on either Windows or Mac.  You will use both Jupyter notebooks and standard script editors, and work through simple arithmetic operations, statistical operations, variables, keywords, lists, arrays, and dictionaries.  You'll use conda to install modules and close with some data visualizations.

### Logistic Regression 011020 #### Aim of Course:

Logistic regression extends ordinary least squares (OLS) methods to model data with binary (yes/no, success/failure) outcomes. Rather than directly estimating the value of the outcome, logistic regression allows you to estimate the probability of a success or failure.

### Optimization-Linear Programming 011020

#### Aim of Course:

The course introduces the use of mathematical models for managerial decision making and covers how to formulate linear programming models for decision problems where multiple decisions need to be made in the best possible way while simultaneously satisfying a number of logical conditions (or constraints). You will learn how to use spreadsheet software to implement and solve these linear programming problems.

### R Programming - Advanced 010320

#### Aim of Course:

The aim of the course is to give you the skills to work with a variety of data types and data sources in R.

### TESU DSI 603: Predictive Analytics 3 -Dimension Reduction, Clustering, and Association Rules: Python 010320 #### Aim of Course:

This course continues the work of Predictive Analytics 1, and introduces you to additional techniques in predictive analytics, also called predictive modeling, the most prevalent form of data mining.

### TESU DSI 606: Predictive Analytics 3 -Dimension Reduction, Clustering, and Association Rules: R 010320

#### Aim of Course:

This course continues the work of Predictive Analytics 1, and introduces you to additional techniques in predictive analytics, also called predictive modeling, the most prevalent form of data mining.

### Biostatistics 1 010320

#### Aim of Course:

This course covers sensitivity-specificity and predictive values of medical tests, confidence intervals, medical vs. statistical significance, and chi-square, Student's t and ANOVA F-tests, including multiple comparisons.

### Statistics 1: Probability and Study Design 010320

#### Aim of Course:

To provide an easy introduction to statistical inference for a single variable.

### Intro Stats Preview 121319

#### Aim of Course:

Try out our introductory statistics series for one week - you can learn stats!

### Introduction to Statistical Issues in Clinical Trials 120619

#### Aim of Course:

This course covers the basic statistical principles in the design and analysis of randomized controlled trials.

### Statistics 2: Inference and Association 120619

#### Aim of Course:

This course, the second in a three-course sequence, provides an easy introduction to inference and association through a series of practical applications, based on the resampling/simulation approach.

### Maximum Likelihood Estimation 112219

#### Aim of Course:

After successfully completing this course, you will understand the role that MLE plays in statistical models, and be able to assess both the advantages and disadvantages of using a maximum likelihood estimate in a particular situation.

### Matrix Algebra Review 112219

#### Aim of Course:

This course will provide the basics of vector and matrix algebra and operations necessary to understand multivariate statistical methods, including the notions of the matrix inverse, generalized inverse and eigenvalues and eigenvectors.