RegressIt features for practitioners
If you are a practitioner who uses linear or logistic regression models, RegressIt can be a valuable addition to your toolkit. If you are already using an Excel add-in for this purpose, RegressIt will peacefully coexist with it on your Excel menu while both are active, and it will give you better tools for regression analysis during your session. (RegressIt can be installed so that it loads automatically with Excel, but it doesn't have to be. It can be launched in a session as needed by just opening its xlam file.) You can obtain a copy of RegressIt on the free download page and get started with analysis in just a few minutes. The RegressIt slide show provides a pictorial overview of its features.
Hopefully you haven't been using Excel's own data analysis add-in (the Analysis Toolpak) for regression modeling. If you have been using it, go here to see what's wrong. For detailed comparisons of the regression analysis outputs of a number of other different add-ins (XLSTAT, Analyse-It, StatPro, SigmaXL, XLminer, Unistat), go to the links at the bottom of the data page on this site. See which one you prefer!
If you are not currently using Excel for regression analysis, you may want to consider it. You'll find that RegressIt is fun to use while playing around with alternative models, and even if most of your analysis is carried out with other software, RegressIt can be a useful end-of-the day tool for reproducing results in an environment that is better for presenting and sharing.
RegressIt's descriptive analysis procedure meshes nicely with the regression procedure, making it easy to do the appropriate descriptive analysis (looking at correlations, scatterplots, time series plots and histograms) before starting to fit models. One of the options in the descriptive analysis procedure is to produce a table of correlations and squared correlations versus the dependent variable, which can be used to interactively select variables for the first regression model. And one of the options in the regression procedure is to interactively sort the coefficient table on p-values (or any other statistic) and then de-select insignificant variables directly from the table rather than by unchecking boxes in the main variable list. And blue-to-red color coding can be interactively applied to highlight the sign and significance of correlations, autocorrelations, and regression coefficients.
The table and chart output of each regression model is presentation-quality and nicely arranged on a single worksheet within the Excel file. RegressIt's novel ribbon interface, pictured above, makes it easy to navigate among the models and study them side-by-side. By default it produces a journal-article-style table of model comparisons on a separate worksheet. It also can display a pop-up window with an historical list of all models in the file, showing key facts about each one and allowing you to jump directly to its worksheet. These tools provide a interactive audit trail of your work for your own records and for others who may want to survey it or extend it, and they are also good for live demonstrations that involve comparisons among models. Model parameters such as confidence levels and cutoff levels can be varied interactively for demonstration. The complete details of regression model output can also be easily viewed and browsed with an Excel file viewer on a cell phone. You can tap on tabs to flip among models and scroll up and down and zoom in and out to see clearly readable tables and charts. This makes it easy to share your work with colleagues.
If you use logistic regression, you should enjoy the novel interactive table and chart output that you get with the logistic version of RegressIt (which runs on PCs only). It's great for live demonstrations, helping you to visualize how logistic models work. In particular, the cutoff value for binary classification can be varied after a model has been fitted, and you can watch how the numbers change in the classification tables (both in- and out-of-sample) and track your position on the ROC curve. This feature is self-contained in each logistic model worksheet and can be used even if the program is not running. See the Titanic analysis file for an illustration.
If you are an R user (or even if you're not), RegressIt's R interface will give you access to more advanced tools such as stepwise variable selection and several forms of out-of-sample testing, and it produces detailed, high-quality output in both RStudio and Excel. It can also handle much larger data sets than can be fitted within Excel. It is effectively a new package for R that provides additional modeling tools for fitting and testing linear and logistic regression models, and its output is better designed than what you get with R's own standard tools. You can use this feature without any knowledge of R programming: all that is necessary to do in RStudio is to hit a couple of keys to run a script that RegressIt has written. You can install R and RStudio and get up and running with this tool in about 15 minutes.
Another very important tool that RegressIt provides is a very comprehensive variable transformation procedure, which makes it easy to deal in a systematic way with nonlinear relationships, time patterns, and categorical predictors. A transformation can be performed with a couple of clicks, and the new variable is automatically given a descriptive name such as X.Ln for natural log of X.
These are just a few of the features that you may find helpful. The RegressIt slide show provides a more comprehensive view. (You're encouraged to read the manual and the other documentation on the web site, but the slide show will probably be enough get up and running.) To use RegressIt on your own data, you just need to start with a clean Excel data file in which variables are arranged columnwise with variable names in the first row.
Go to the free download page and take it out for a test drive!
If you are a practitioner who uses linear or logistic regression models, RegressIt can be a valuable addition to your toolkit. If you are already using an Excel add-in for this purpose, RegressIt will peacefully coexist with it on your Excel menu while both are active, and it will give you better tools for regression analysis during your session. (RegressIt can be installed so that it loads automatically with Excel, but it doesn't have to be. It can be launched in a session as needed by just opening its xlam file.) You can obtain a copy of RegressIt on the free download page and get started with analysis in just a few minutes. The RegressIt slide show provides a pictorial overview of its features.
Hopefully you haven't been using Excel's own data analysis add-in (the Analysis Toolpak) for regression modeling. If you have been using it, go here to see what's wrong. For detailed comparisons of the regression analysis outputs of a number of other different add-ins (XLSTAT, Analyse-It, StatPro, SigmaXL, XLminer, Unistat), go to the links at the bottom of the data page on this site. See which one you prefer!
If you are not currently using Excel for regression analysis, you may want to consider it. You'll find that RegressIt is fun to use while playing around with alternative models, and even if most of your analysis is carried out with other software, RegressIt can be a useful end-of-the day tool for reproducing results in an environment that is better for presenting and sharing.
RegressIt's descriptive analysis procedure meshes nicely with the regression procedure, making it easy to do the appropriate descriptive analysis (looking at correlations, scatterplots, time series plots and histograms) before starting to fit models. One of the options in the descriptive analysis procedure is to produce a table of correlations and squared correlations versus the dependent variable, which can be used to interactively select variables for the first regression model. And one of the options in the regression procedure is to interactively sort the coefficient table on p-values (or any other statistic) and then de-select insignificant variables directly from the table rather than by unchecking boxes in the main variable list. And blue-to-red color coding can be interactively applied to highlight the sign and significance of correlations, autocorrelations, and regression coefficients.
The table and chart output of each regression model is presentation-quality and nicely arranged on a single worksheet within the Excel file. RegressIt's novel ribbon interface, pictured above, makes it easy to navigate among the models and study them side-by-side. By default it produces a journal-article-style table of model comparisons on a separate worksheet. It also can display a pop-up window with an historical list of all models in the file, showing key facts about each one and allowing you to jump directly to its worksheet. These tools provide a interactive audit trail of your work for your own records and for others who may want to survey it or extend it, and they are also good for live demonstrations that involve comparisons among models. Model parameters such as confidence levels and cutoff levels can be varied interactively for demonstration. The complete details of regression model output can also be easily viewed and browsed with an Excel file viewer on a cell phone. You can tap on tabs to flip among models and scroll up and down and zoom in and out to see clearly readable tables and charts. This makes it easy to share your work with colleagues.
If you use logistic regression, you should enjoy the novel interactive table and chart output that you get with the logistic version of RegressIt (which runs on PCs only). It's great for live demonstrations, helping you to visualize how logistic models work. In particular, the cutoff value for binary classification can be varied after a model has been fitted, and you can watch how the numbers change in the classification tables (both in- and out-of-sample) and track your position on the ROC curve. This feature is self-contained in each logistic model worksheet and can be used even if the program is not running. See the Titanic analysis file for an illustration.
If you are an R user (or even if you're not), RegressIt's R interface will give you access to more advanced tools such as stepwise variable selection and several forms of out-of-sample testing, and it produces detailed, high-quality output in both RStudio and Excel. It can also handle much larger data sets than can be fitted within Excel. It is effectively a new package for R that provides additional modeling tools for fitting and testing linear and logistic regression models, and its output is better designed than what you get with R's own standard tools. You can use this feature without any knowledge of R programming: all that is necessary to do in RStudio is to hit a couple of keys to run a script that RegressIt has written. You can install R and RStudio and get up and running with this tool in about 15 minutes.
Another very important tool that RegressIt provides is a very comprehensive variable transformation procedure, which makes it easy to deal in a systematic way with nonlinear relationships, time patterns, and categorical predictors. A transformation can be performed with a couple of clicks, and the new variable is automatically given a descriptive name such as X.Ln for natural log of X.
These are just a few of the features that you may find helpful. The RegressIt slide show provides a more comprehensive view. (You're encouraged to read the manual and the other documentation on the web site, but the slide show will probably be enough get up and running.) To use RegressIt on your own data, you just need to start with a clean Excel data file in which variables are arranged columnwise with variable names in the first row.
Go to the free download page and take it out for a test drive!