Summary
In this chapter, we introduced the concept of trend lines and their significance in visualizing patterns in datasets. We explored the least squares method for estimating the line of best fit, discussed the importance of understanding residuals, and explained how to interpret the slope and intercept of the regression line. Finally, we covered how to evaluate a model’s goodness of fit using R-squared and RMSE. This knowledge has equipped you to carry out (or interpret from your team) regression analysis and apply it to various business scenarios. These scenarios could include forecasting sales, optimizing advertising budgets, and assessing the impact of different factors on key performance indicators, leading to informed data-driven decisions and business growth.
As we transition into Part 2 of this guide, we’re about to open a new dimension of analytical capabilities: machine learning. You’ll learn how to move from understanding relationships between...
