What is regression?

1. Data Collection: You collect data on the variables of interest. This involves gathering observations of both the dependent variable and the independent variables from your target population or sample.
2. Data Exploration: You explore the data to understand its characteristics, such as distributions, relationships between variables, and potential outliers.
3. Model Selection: Based on the nature of your data and the research question, you select an appropriate regression model. This could be linear regression, logistic regression, polynomial regression, etc.
4. Model Training: You use your dataset to estimate the parameters of the regression model. This involves fitting the model to the data in order to find the best-fitting line, curve, or surface that describes the relationship between the variables.
5. Model Evaluation: Once the model is trained, you evaluate its performance. This could involve assessing how well the model fits the data, checking for violations of assumptions, and using appropriate metrics to evaluate predictive accuracy.
6. Inference or Prediction: Depending on your goals, you can use the regression model for two main purposes:

• Inference: You can make inferences about the population parameters based on the sample data. For example, you might use regression to understand the relationship between smoking and lung cancer risk in a population.
• Prediction: You can use the regression model to make predictions about the dependent variable for new or unseen data. For example, you might use regression to predict house prices based on features like square footage, number of bedrooms, etc.

Regression analysis provides valuable insights into the relationships between variables and allows us to make informed decisions based on data. It's widely used across various fields including economics, finance, marketing, healthcare, social sciences, and many others.