BUS350_Simple and Multiple Regression

• Why do we build regression models?


  1. Method of modeling relationships between a response variable Y and one or more predictors X

  2. Explain one variable Y with one or more variables X

  3. A way of “fitting a line through the data”
• Relationship Analysis

  Regression helps in understanding the relationship between variables. It can show whether and how strongly one variable (dependent variable) is affected by changes in another variable or multiple variables (independent variables)
• Predictive Modeling

  One of the primary reasons is to make predictions or forecasts based on historical data. For example, predicting sales based on advertising spending or predicting housing prices based on features like location, size, and amenities.
• Goal of Fitting a Line


  The goal of regression analysis is to find the best-fitting line that summarizes the relationship between the independent variable(s) and the dependent variable. This line is called the regression line or the line of best fit.
• Regression Modeling Steps:

  1. Specify (hypothesize) the form of the model
  2. Estimate unknown model parameters
  3. Evaluate the model for: percentage of variation in Y explained (R^2) and statistical significance of the relationship between Y and X
  4. Check the basic assumptions using residual plots
  5. Use the model for prediction and estimation