Chapter 6 explained: Analysis and Communication

Lessons in this chapter (3 lessons)

1. Lesson 6-1: Statistical Inference

   Estimate with a point or an interval (confidence level = how often the method captures the truth); test by comparing a p-value with a significance level to reject or keep the null. Watch Type I / II errors, and never confuse statistical significance with practical importance.

2. Lesson 6-2: Use and Evaluation of Regression Analysis

   Regression predicts a response variable from one (simple, y = ax + b) or several (multiple) explanatory variables, fitted by least-squares. Judge it with R2, RMSE, and MAE together — and never read a good fit as proof of cause.

3. Lesson 6-3: Data Visualization and Communication

   Match the graph to the data (heatmap for a value across two axes; mosaic plot for two categories by area; scatter/bar/line for one or two variables), then tell a data story: one message, only the elements that serve it, honest labels — and check it lands with the audience.

Pick the Chart