How to use FSBforecast Excel add-in for regression analysis

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paste) values for the independent variables into the cells at the right end of the forecast row, as in the shaded cells in the table below, and then click the Forecasting button. The forecast and its confidence limits will then be computed and displayed in the cells to the left. Two plots of the forecasts are also produced. The first one shows only the forecast(s), together with 95% confidence limits for both means and forecasts. (A 95% confidence interval for the mean is a confidence interval for the true height of the regression line for given values of the independent variables. A 95% confidence interval for the forecast is a confidence interval for a prediction that is based on the regression line. The latter confidence interval also takes into account the unexplained variations of the data around the regression line, so it is wider.) The second plot shows the actual and predicted values from the sample to which the model was fitted, together with the forecasts and 95% confidence intervals for forecasts. (The latter plot is always produced, even if there are no forecasts.) How to generate forecasts “manually”: enter values for the independent variables in one or more rows at the right end of the forecast table, below the variable names, then hit the Forecasting button on the toolbar. The forecasts and confidence limits will be displayed at the left end of the same row(s), and they will also be plotted. In the automatic forecasting approach, which is more systematic and more suitable for generating many forecasts at once, define your variables up front so that they include rows for out‐of‐sample data from which forecasts are to be computed later. FSBforecast will automatically generate forecasts for any rows where all of the independent variables have values but the dependent variable is missing (i.e., has a blank cell). The variables must all be ranges with the same length, but the dependent variable will have some empty cells at the bottom or elsewhere. The advantage of this approach is that you only need to enter the forecast data once, at the time the data file is first created, and it will automatically be transformed if you apply any data transformations to the same variables later. Also, when using this method it is possible for forecasts to be generated in the middle of the data set if missing values of the dependent variable happen to occur there. The file used in the example above contains an extra row of data at the bottom for a “hypothetical car” whose mileage is to be predicted. It has values for all the numeric variables other than MPG_City, so any model fitted to MPG_City will generate a forecast for this row automatically, without the need for you to type values for the independent variables in the forecast table. Only one forecast is shown in this example, but you can generate any number of forecasts in this way by including 8
additional rows with out‐of‐sample data for the independent variables. You can also use this feature to do out‐ofsample testing of a model by removing the values of the dependent variable from a large block of rows and then comparing the forecasts to the actual values afterward. A forecast is also generated automatically for any row of data where the dependent variable is missing and all independent variables are present. Viewing tables and charts in your regression output: Each model worksheet provides a number of standard tables and charts, and they can be maximized or minimized by clicking the +’s or –’s on the left sidebar of the worksheet. At the time you run the model you have the option for “full” regression output (which is the default) or “brief” output (which you get by checking the box). If you allow full output to be produced, much of it will be minimized to start with, and you will need to go down the left sidebar of the sheet checking the +’s to see the complete results. As noted earlier, full output includes scatterplots of the dependent variable versus each of the independent variables and plots of the residuals versus each of the independent variables. These are all minimized by default because they take up a lot of room when there are many variables. Full output also includes a normal probability plot (a diagnostic test for normally distributed errors) as well as the usual histogram plot of the residuals. In the special case of a simple regression model, you also get a line fit plot (the regression line and confidence bands around it) in both brief‐output and full‐output mode. See the last page of this handout for an example. Choosing the output to display: click the “‐” symbol to minimize (hide) a table or chart and click “+“ to maximize (unhide) it. Model summary worksheet: An innovative feature of FSBforecast is that it maintains a separate “Model Summary” worksheet that shows side‐by‐side summary statistics and model coefficients for all regression models that have been fitted in the same workbook. This allows easy comparison of models, and it also provides an “audit trail” for all of the models you have fitted so far. Here’s an example of the model summary worksheet that was obtained after fitting two more models in which less‐significant variables were successively removed: 9
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How to use FSBforecast Excel add-in for regression analysis

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