This is the fourth entry in our regression analysis and modeling series. In this tutorial, we continue the analysis discussion we started earlier and leverage an advanced technique –regression stability test - to help us detect deficiencies in the selected model, and thus the reliability of the forecast.
Again, we will use a sample data set gathered from 20 different sales persons. The regression model attempts to explain and predict weekly sales for each salesperson (dependent variable) using two explanatory variables: intelligence (IQ) and extroversion.
This is the first entry in what will become an ongoing series on principal component analysis in Excel (PCA). In this tutorial, we will start with the general definition, motivation and applications of a PCA, and then use NumXL to carry on such analysis. Next, we will closely examine the different output elements in an attempt to develop a solid understanding of PCA, which will pave the way to a more advanced treatment in future issues.
In this tutorial, we’ll demonstrate the steps to compute seasonal adjusted time series using the functions NumXl and X12 ARIMAi in Excel.
The generalized linear model (GLMi) is a flexible generalization of ordinary linear regression. By allowing the linear model to be related to the response variable via a link function, GLM in Excel generalizes linear regression. It also allows the magnitude of the variance of each measurement to be a function of its' predicted value.