Regression analysis courses can help you learn statistical modeling, hypothesis testing, and data interpretation. You can build skills in understanding relationships between variables, making predictions, and assessing model accuracy. Many courses introduce tools like R, Python, and Excel, which are commonly used for performing regression analysis and visualizing data trends.

Johns Hopkins University
Skills you'll gain: Regression Analysis, Statistical Analysis, Statistical Modeling, Logistic Regression, Data Analysis, Model Evaluation, Probability & Statistics, Statistical Inference
Mixed · Course · 1 - 4 Weeks

Skills you'll gain: Regression Analysis, Logistic Regression, Statistical Hypothesis Testing, Data Analysis, Advanced Analytics, Statistical Analysis, Correlation Analysis, Analytical Skills, Business Analytics, Statistical Modeling, Model Evaluation, Variance Analysis, Predictive Modeling, Machine Learning, Python Programming
Advanced · Course · 1 - 3 Months

Corporate Finance Institute
Skills you'll gain: Regression Analysis, Statistical Modeling, Statistical Analysis, Predictive Modeling, Data Analysis, Scikit Learn (Machine Learning Library), Microsoft Excel, Linear Algebra, Model Evaluation, Supervised Learning, Exploratory Data Analysis
Advanced · Course · 1 - 3 Months

Duke University
Skills you'll gain: Regression Analysis, R (Software), Data Analysis Software, Statistical Analysis, R Programming, Statistical Modeling, Statistical Inference, Correlation Analysis, Model Evaluation, Exploratory Data Analysis, Mathematical Modeling, Statistics, Predictive Modeling, Probability & Statistics
Beginner · Course · 1 - 4 Weeks

University of Colorado Boulder
Skills you'll gain: Regression Analysis, Supervised Learning, Model Evaluation, Logistic Regression, Statistical Analysis, Data Analysis, Statistical Modeling, Predictive Modeling, Machine Learning Methods, Feature Engineering, Data Preprocessing, Exploratory Data Analysis
Intermediate · Course · 1 - 3 Months
Duke University
Skills you'll gain: Bayesian Statistics, Statistical Hypothesis Testing, Sampling (Statistics), Statistical Inference, Exploratory Data Analysis, Peer Review, Regression Analysis, R (Software), Statistical Reporting, Probability Distribution, Statistical Analysis, Statistics, Data Analysis Software, Data Analysis, Probability & Statistics, Probability, R Programming, Statistical Modeling, Statistical Methods, Data Visualization
Beginner · Specialization · 3 - 6 Months

University of Pittsburgh
Skills you'll gain: NumPy, Matplotlib, Linear Algebra, Pandas (Python Package), Data Manipulation, Applied Mathematics, Data Visualization, Python Programming, Data Analysis, Data Science, Regression Analysis, Data Visualization Software, Mathematics and Mathematical Modeling, Probability & Statistics, Statistics, Numerical Analysis, Mathematical Modeling, Machine Learning, Computational Logic, Logical Reasoning
Build toward a degree
Beginner · Course · 1 - 4 Weeks

University of Colorado Boulder
Skills you'll gain: Statistical Inference, Statistical Modeling, Regression Analysis, Data Ethics, Statistical Methods, Data Science, R Programming, Data Modeling, Statistical Analysis, Predictive Modeling, Mathematical Modeling, Probability & Statistics, Model Evaluation, Feature Engineering
Build toward a degree
Intermediate · Course · 1 - 3 Months

Johns Hopkins University
Skills you'll gain: Biostatistics, Regression Analysis, Logistic Regression, Statistical Methods, Public Health, Probability & Statistics, Statistical Analysis, Statistical Inference, Advanced Analytics, Statistical Modeling, Predictive Modeling, Model Evaluation
Beginner · Course · 1 - 4 Weeks

Illinois Tech
Skills you'll gain: Model Evaluation, Statistical Analysis, Bayesian Statistics, Data Visualization, Statistical Inference, Exploratory Data Analysis, Data Cleansing, Regression Analysis, Analytics, Data Analysis, Logistic Regression, Machine Learning, R Programming, Data Preprocessing, Statistical Modeling, Data Validation, R (Software), Data Mining, Statistical Programming, Predictive Modeling
Build toward a degree
Intermediate · Specialization · 3 - 6 Months

Skills you'll gain: Data Wrangling, Exploratory Data Analysis, Data Analysis, Model Evaluation, R Programming, Data Manipulation, Data Visualization, Predictive Analytics, Statistical Analysis, Data Science, Regression Analysis, Statistical Modeling, Data Cleansing, Data Preprocessing, Feature Engineering, Forecasting
Intermediate · Course · 1 - 3 Months

University of California, Irvine
Skills you'll gain: Predictive Modeling, Predictive Analytics, Regression Analysis, Decision Tree Learning, Supervised Learning, Data Modeling, Data Analysis, Logistic Regression, Descriptive Analytics, Forecasting, Machine Learning, Model Evaluation, Classification Algorithms, Unsupervised Learning, Statistical Analysis
Intermediate · Course · 1 - 4 Weeks
Regression analysis is a statistical method used to understand the relationship between variables. By modeling the relationship between a dependent variable and one or more independent variables, regression analysis helps in predicting outcomes and making informed decisions. Its importance lies in its wide application across various fields, including economics, healthcare, and social sciences, where it aids in identifying trends, forecasting future events, and optimizing processes.
A background in regression analysis can open doors to various job opportunities. Positions such as data analyst, statistician, market researcher, and financial analyst often require skills in regression techniques. Additionally, roles in healthcare analytics, business intelligence, and academic research also value expertise in regression analysis, as it is crucial for interpreting data and making evidence-based decisions.
To effectively learn regression analysis, you should focus on several key skills. Proficiency in statistical software such as R or Python is essential, as these tools are commonly used for data analysis. Understanding fundamental statistical concepts, including hypothesis testing and correlation, is also important. Additionally, developing skills in data visualization can help you present your findings clearly and effectively.
There are numerous online courses available to help you learn regression analysis. Some of the best options include Regression Analysis, which covers the fundamentals, and Modern Regression Analysis in R, which focuses on practical applications using R. For those interested in public health, Multiple Regression Analysis in Public Health is a great choice.
Yes. You can start learning regression analysis on Coursera for free in two ways:
If you want to keep learning, earn a certificate in regression analysis, or unlock full course access after the preview or trial, you can upgrade or apply for financial aid.
To learn regression analysis, start by selecting a course that matches your current skill level and learning goals. Engage with the course materials, complete assignments, and practice with real datasets. Additionally, seek out online forums or study groups to discuss concepts and share insights. Consistent practice and application of the techniques will reinforce your understanding and build confidence.
Typical topics covered in regression analysis courses include simple and multiple regression techniques, model fitting, residual analysis, and the interpretation of regression coefficients. Courses may also explore advanced topics such as logistic regression, time series analysis, and the use of regression in predictive modeling, providing a comprehensive understanding of how to analyze and interpret data relationships.
For training and upskilling employees in regression analysis, courses like Predictive Modeling, Model Fitting, and Regression Analysis and Regression Analysis - Fundamentals & Practical Applications are excellent choices. These courses provide practical insights and applications that can enhance the analytical capabilities of your workforce.