How to use FSBforecast Excel add-in for regression analysis

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2. Hit the Create‐From‐Selection button on the FSBforecast menu and check (only) the “Top row” box in the dialog box. To define the variables for analysis, highlight the table of data (including the first row with the variable names) and hit the “Create From Selection” button. Check only the “Top row” box for creating names. You can have any number of named ranges in your workbook, although you cannot use more than 50 variables at one time in the Data Analysis or Regression procedures. You can have up to 32,000 rows of data, although the graphs will take a long time to draw if you have a huge number of rows, and the row limit is somewhat less for regressions with large numbers of variables. A 50‐variable regression is limited to about 18,000 rows. The regression procedure has a “brief‐output” mode that suppresses some of the chart output to speed up the analysis of large data sets and keep file sizes from getting too large when many models are fitted. In brief‐output mode, a regression with 50 variables and 18,000 rows of data will run in about 30 seconds on most PC’s, which is as fast or faster than most other regression software such as SPSS. Data analysis: The Data Analysis procedure provides descriptive statistics, correlations, series plots, and scatterplots for a selected group of variables. Simply click the Data Analysis button on the FSBforecast toolbar and check the boxes for the variables you wish to include. The variable list that you see will only include variables containing at least some rows of numeric data. In this example, the variables Make and Type do not appear on the list of variables available for analysis because they have only text values. Model does appear because a few of its values are numeric (e.g., for the Audi 90 and 100 models), but you would not choose it for analysis. 2 In the Data Analysis procedure, select the variables you want to analyze and choose the plot options.
If you check the Show Series Plots box, you will also get a plot of each variable versus row number. We recommend that you always ask for series plots in at least one of your data analysis runs, no matter how large the data set. These plots give you a visual impression of each variable by itself and are vitally important if the variables are time series (although in this example they are not). If your variables are time series (i.e., measurements of the same quantities performed at different periods in time and arranged in chronological order), then you should check the Time Series Data box. This will provide an additional table of statistics, namely the autocorrelations of the variables, i.e., their correlations with their own prior values, going back as far as 12 periods into the past depending on the amount of history available. Also, the series plots are drawn with connecting lines when the Time Series box is checked. Here is a picture of the top portion of the Data Analysis report for the variables selected above, showing the descriptive statistics and series plots. (Only two of the 7 series plots in this analysis are shown.) Notice that the Cylinders variable has only a small number of possible values and they are all integers (4, 5, 6, 8), and there are only two cars with 5 cylinders and only seven cars with 8 cylinders in the sample. This is an example of the properties of your data that you can clearly see when you look at the series plots. The results of running the procedure are stored on a new worksheet. Descriptive stats and optional series plots appear at the top. If the “Time series data” box is checked, you also get a table of autocorrelations and the series plots have connecting lines. Sample sizes may vary if any values are missing: Be aware that on any given run the data analysis procedure ignores rows where any of the selected variables have missing values or text values, so that the sample size is the same for all the variables. (In some data files, missing values may be coded as text labels such as “NA”, meaning “not available.”) This means that the sample sizes and the values of the sample statistics may vary from one data analysis run to another if you add or drop variables that have missing or text values in different positions. If the sample size (“Count”) is less than you expected or if it varies from one run to another, you should look carefully at the data matrix to see if there are unsuspected missing or text values scattered around among the variables. In this data set, if you choose Model as one of the variables to be analyzed, you will only get a sample size of 7, because there are only 7 cars whose model names consist of numbers (like the Audi 90 and 100). The reason for following this convention is that it keeps the data analysis sheet in synch with a regression model sheet that uses the same set of variables—e.g., the correlation matrix on both sheets will be the same. When 3
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How to use FSBforecast Excel add-in for regression analysis

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