Regression Analysis
Regression analysis is a statistical method employed to predict continuous outcomes by examining relationships between variables. It covers both simple and multiple linear regression techniques using Python, emphasizing model fitting and evaluation metrics for effective predictive performance.
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Sections
Navigate through the learning materials and practice exercises.
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3.1Example
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4.2Example
What we have learnt
- Regression is used to predict continuous values.
- Simple regression involves one input, while multiple regression utilizes several independent variables.
- Scikit-learn simplifies the processes of model fitting and evaluation.
- Evaluation metrics such as MAE, MSE, and R-squared are essential for assessing model performance.
- Visualizing regression and verifying model assumptions are critical for accurate predictions.
Key Concepts
- -- Regression
- A statistical method used to examine the relationship between variables, particularly to predict a continuous outcome.
- -- Simple Linear Regression
- A regression method that models the relationship between a single independent variable and a dependent variable.
- -- Multiple Linear Regression
- A regression approach that uses two or more independent variables to predict a dependent variable.
- -- Evaluation Metrics
- Statistical measures such as MAE, MSE, and R-squared that assess the performance of regression models.
- -- Assumptions of Regression
- Conditions such as linearity, homoscedasticity, and absence of multicollinearity that must be validated for reliable predictions.
Additional Learning Materials
Supplementary resources to enhance your learning experience.