In this video I introduce the single exam question that will be used to teach this entire chapter. There is very little variation possible with simple linear regression questions, so this question will do a great job of preparing you for any short answer questions from Chapter 16 that may appear on your exam.
Even though the F-test replaces performing multiple t-tests to determine whehter or not a model is valid, the t-tests still have an important function. They are used to tell us WHICH of the independent variables are related to y.
Remember – Large amounts of variation in the time series data makes forecasting with accuracy difficult to achieve. Some forms of variation (like seasonal) are not random, but rather follow regular patterns that can be removed from the data to increase the reliability of our forecasts. This video shows you how to create a seasonal forecast model from data with seasonal variations and how to use it to make forecasts for time periods falling in different seasons.
Regression is all about making predictions. When you make a prediction, you can expect to make mistakes (errors). The Standard Error of the Estimate measures how big of an error we can typically expect. I go through the calculation and the interpretation and introduce the Sum of Squares Error (SSE) along the way.
This is where Chapter 4 becomes very challenging! The problem occurs when the given probabilities in a question do not match with the format required for using the decision tree to solve the problem. Sometimes you get P(X|si) when what's needed is P(si|X). Here's how you identify which version you've been given.
The questions in this chapter involve A LOT of calculations. I'll teach you a system that will allow you to get through them quickly and correctly. It all starts with setting up a table of the data, and finding the sum of each column...