Aim of Course:
This course continues the study begun in the prior course "Structural Equation Modeling" and covers many popular advanced SEM models with practical exercises.
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:
Rasch analysis constructs linear measures from scored observations, such as responses to multiple-choice questions, Likert scales and quality-of-life assessments. This course covers the practical aspects of data setup, analysis, output interpretation, fit analysis, differential item functioning, dimensionality and reporting.
- Teacher: Everett Smith
Aim of Course:
Try out our introductory statistics series for one week - you can learn stats!
- Teacher: Anthony Babinec
- Teacher: Kuber Deokar
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: 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: Kuber Deokar
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:
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
Aim of Course:
This course will introduce basic concepts in computer programming via R - it is for those who have had little or no experience in programming.
- Teacher: Paul Murrell
Aim of Course:
In this course, participants will learn how to use the ggplot2 R Project to make, format, label and adjust graphs using R.
- Teacher: Randall Pruim
This course provides an introduction to the Python programming language for the complete beginner. We will set up a Python programming environment from download to writing programs, taking small steps along the way. The programming environment will feature the Jupyter notebook interface.
- Teacher: Stan Blank
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:
You will have a chance to try the first week of our course “Predictive Analytics 1 - Machine Learning
Tools.” You will be introduced to the basic concepts in predictive
analytics, also called predictive modeling, the most prevalent form of
data mining. The full course covers the two core paradigms that account for
most business applications of predictive modeling: classification and
prediction.
- Teacher: Anthony Babinec
- Teacher: Kuber Deokar
Aim of Course:
This course covers key unsupervised learning techniques: association rules, principal components analysis, and clustering. The course will include an integration of supervised and unsupervised learning techniques.
- Teacher: Kuber Deokar
Aim of Course:
This course covers key unsupervised learning techniques: association rules, principal components analysis, and clustering. The course will include an integration of supervised and unsupervised learning techniques.
- Teacher: Peter Gedeck
Aim of Course:
This course covers key unsupervised learning techniques: association rules, principal components analysis, and clustering. The course will include an integration of supervised and unsupervised learning techniques.
- Teacher: Kuber Deokar
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:
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:
This course will introduce 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:
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