Plus, StatTools is developed by Palisade Corporation, the world leader in analytical solutions that add-in to Microsoft Excel.
New in StatTools 5.7
New StatTools 5.7 Is fully compatible with 64-bit Excel 2010. 64-bit technology enables Excel and StatTools to access much more computer memory than ever before. This allows for vastly larger models and greater computational power.
Excel Ease of Use
StatTools is a true add-in to Microsoft Excel, integrating completely with your spreadsheet. Browse, define, analyze – while never leaving Excel. StatTools replaces Excel’s built-in statistics functions with its own calculations. The accuracy of Excel’s built-in statistics calculations has often been questioned, so StatTools doesn’t use them. All StatTools functions are true Excel functions, and behave exactly as native Excel functions do. StatTools calculations are optimized through the use of C++ .DLLs, not macro calculations.
How StatTools Works
StatTools covers the most commonly used statistical procedures, and offers unprecedented capabilities for adding new, custom analyses. Over 30 wide-ranging statistical procedures plus 9 built-in data utilities include forecasts, descriptive statistics, normality tests, group comparisons, correlation, regression analysis, quality control, nonparametric tests, and more.
StatTools features live, "hot-linked" statistics calculations. Change a value in your dataset and your statistics report automatically updates. There is no need to manually re-run your analyses.
The Best in Data Management
StatTools provides a comprehensive data set and variable manager right in Excel, just as you would expect from a stand-alone statistics package. You can define any number of data sets, each with the variables you want to analyze, directly from your data in Excel. StatTools intelligently assesses your blocks of data, suggesting variable names and locations for you. Your data sets and variables can reside in different workbooks, allowing you to organize your data as you see fit. Run statistical analyses that refer to your variables, instead of re-selecting your data over and over again in Excel. StatTools fully supports the expanded worksheet size in Excel 2010. Plus, you can define variables that span multiple worksheets.
Excel Developer Kit (XDK)
StatTools includes a complete, object-oriented, programming interface—the Excel Developer Kit (XDK). Custom statistical procedures may be added using Excel's built-in VBA programming language. Utilize StatTools's built-in data management, charting and reporting tools.
StatTools Analyses : Stattool Analysis group
The statistical procedures available in StatTools come in the following natural groups.
Statistical Inference: This group performs the most common statistical inference procedures of confidence intervals and hypothesis tests.
Forecasting: StatTools gives you several methods for forecasting a time series variable. You can also deseasonalize the data first, using the ratio-to-moving-averages method and a multiplicative seasonality model. Then use a forecasting method to forecast your deseasonalized data, and finally “reseasonalize” the forecasts to return to original units.
The outputs include a set of new columns to show the various calculations (for example, the smoothed levels and trends for Holt’s method, the seasonal factors from the ratio-to-moving-averages method, and so on), the forecasts, and the forecast errors. Summary measures such as MAE, RMSE and MAPE are also included for tracking the fit of the model to the observed data. Finally, several time series plots are available, including a plot of the original series, a plot of the series with forecasts superimposed, and a plot of the forecast errors. In cases using deseasonalized data, these plots are available for the original and deseasonalized series.
Classification Analysis: StatTools provides both discriminant analysis and logistic regression. Discriminant analysis predicts which of several groups a variable will fall in, and logistic regression is a nonlinear type of regression analysis where the response variable is 0 or 1 for “failure” or “success.” You can then estimate the probability of success.
Data Management: This group allows you to manipulate your data set in various ways, either by rearranging the data or by creating new variables. These operations are typically performed before running a statistical analysis.
Summary Analyses: This group allows you to calculate several numerical summary measures for single variables or pairs of variables.
Tests for Normality: Because so many statistical procedures assume that a set of data are normally distributed, it is useful to have methods for checking this assumption. StatTools provides three commonly used checks: Chi-square, Lilliefors, and Q-Q plot.
Regression Analysis: For each of these analyses, the following outputs are given: summary measures of each regression equation run, an ANOVA table for each regression, and a table of estimated regression coefficients and other statistics. In addition, StatTools gives you the option of creating two new variables: the fitted values and residuals. Plus, you can create a number of diagnostic scatterplots.
Quality Control Charts: This set of procedures produces control charts that allow you to see whether a process is in statistical control. Each of the procedures takes time series data and plots them in a control chart. This allows you to see whether the data stay within the control limits on the chart. You can also tell if other nonrandom behavior is present, such as long runs above or below the centerline. Each of these procedures provides the option of using all the data or only part of the data for constructing the chart. Furthermore, each lets you base the control limits on the given data or on limits from previous data.
Nonparametric Tests (Industrial edition only): Nonparametric tests are statistical procedures which can be used to make successful inferences when there is little available data. They are more robust than many of the widely known parametric hypothesis tests. Nonparametric tests do not always need the parametric assumptions—such as normality—or generalized assumptions regarding the underlying distribution. In most cases, the nonparametric tests are much easier to apply and provide clearer interpretation than traditional parametric tests.
List of StatTools analyses