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tutorial
X12-ARIMA in NumXL Techinal Note
Starting with version 1.57, NumXL will support U.S. Census X12-ARIMA modeling including seasonal adjustment, trend filtering, and model identification and forecasting.
In this paper, we will go over the approach followed by NumXL to implement this model. 
Regression stability Test
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.
Influential Data Analysis
This is the third entry in our regression analysis and modeling series. In this tutorial, we continue the analysis discussion we started earlier by leveraging a more advanced technique – influential data analysis - to help us improve the model, and, as a result, the reliability of the forecast.
Stepwise Regression in Excel
This is the second entry in our regression analysis and modeling series. In this tutorial, we continue the analysis discussion we started earlier and leverage an advanced technique – stepwise regression - to help us find an optimal set of explanatory variables for the model.
Kernel Density Estimation (KDE) Tutorial
In this tutorial, we’ll carry on the problem of probability density function inference, but using another method: Kernel density estimation.
Principal Component Analysis (PCA)
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.
X-12-ARIMA
In this tutorial, we’ll demonstrate the steps to compute seasonal adjusted time series using the functions NumXl and X12 ARIMAi in Excel.
Module 3 - Smoothing
In this module, we will walk you through time series smoothing in Excel using NumXL functions and tools. For sample data, we’ll use the S&P 500 weekly closing prices between January 2009 and July 2012.
Generalized Linear Models (GLM)
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.
Forecasting
To perform a forecast using NumXL, select the cell of the model's table (i.e. AirLine). Click on the "Forecast" icon in the toolbar. The forecast wizard will pop up. Select the realized observations (i.e. tail points of your sample data), and the forecast horizon in units of steps.
