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.
- Teacher: Iain Pardoe
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
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.
Teaching assistants provide individual responses in practical exercises.
Statistics.com confers CEU's, Records of Completion, and Certificates in advanced statistical study.
Available courses
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.
- Teacher: Bill Bolstad
Aim of Course:
To provide an easy introduction to ANOVA and multiple linear regression through a series of practical applications.
- Teacher: Meena Badade
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").
- Teacher: Kuber Deokar
- Teacher: Inbal Yahav
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.
- Teacher: Peter Gedeck
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.
- Teacher: Anthony Babinec
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.
- Teacher: Tal Galili
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.
- Teacher: Stan Blank
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.
- Teacher: James Hardin
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.
- Teacher: Cliff Ragsdale
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.
- Teacher: Daniel Chen
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.
- Teacher: Peter Gedeck
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.
- Teacher: Kuber Deokar
- Teacher: Inbal Yahav
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.
- Teacher: Abhaya Indrayan
Aim of Course:
To provide an easy introduction to statistical inference for a single variable.
- Teacher: Peter Bruce
Aim of Course:
Try out our introductory statistics series for one week - you can learn stats!
- Teacher: Peter Bruce
- Teacher: Kuber Deokar
Aim of Course:
This course covers the basic statistical principles in the design and analysis of randomized controlled trials.
- Teacher: Nand Kishore Rawat
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.
- Teacher: Meena Badade
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.
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.