Before we dive into SLR models (Simple linear regression models) there are a few things we have to discuss and explain.
- What are dependent and independent variables
- What is a "Time series"?
- What is seasonality?
- What's a trend?
- What are residuals?
- What's a random walk? or what is a white noise?
- What's cyclicality?
Of course we will briefly explain what a p value is and how you have to interpret the out put of a regression. Once we are this far that we have explained all the rules and concepts of a simple linear regression then we will show how to forecast with a SLR model. Remember always to have enough data in order to make a regression. The more data the better the quality, well in most cases.
We will continue with the 7 main concepts this week and by the end of next week we will be touching up on forecasting (if everything goes according to schedule).