Linear models, as their name implies, relates an outcome to a set of predictors of interest using linear assumptions. Regression models, a subset of linear models, are the most important statistical analysis tool in a data scientist’s toolkit. This course covers regression analysis, least squares and inference using regression models. Special cases of the regression model, ANOVA and ANCOVA will be covered as well. Analysis of residuals and variability will be investigated. The course will cover modern thinking on model selection and novel uses of regression models including scatterplot smoothing.
Course Description
Regression Models
Provider
coursera
Target
- Data scientists
- Statisticians
- Researchers
- Analysts
- Learners in statistics or data-related fields
Sector
- Data Science
- Statistics
- Academic research
- Business analytics
- Healthcare analytics
Area
- Predictive modeling
- Statistical analysis
- Data interpretation
- Research methodologies
- Quantitative analysis
Method
Online
Certification
Yes
Duration
53 hours to complete /3 weeks at 17 hours a week
Assessment
No
Learning Outcomes
- Use regression analysis, least squares and inference
- Understand ANOVA and ANCOVA model cases
- Investigate analysis of residuals and variability
- Describe novel uses of regression models such as scatterplot smoothing
- Predictive modeling
- Statistical analysis
- Data interpretation
- Research methodologies
- Quantitative analysis
Learning Content
- Week 1: Least Squares and Linear Regression
- Week 2: Linear Regression & Multivariable Regression
- Week 3: Multivariable Regression, Residuals, & Diagnostics
- Week 4: Logistic Regression and Poisson Regression