Have you ever needed forecasting, regression, quality control charts, or other statistical analyses beyond the basics that are provided with Excel? Have you ever doubted the accuracy of some of Excel’s statistical results? StatTools addresses both of these issues, providing a new, powerful statistics toolset to Excel.
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StatTools covers the most commonly used statistical procedures, and offers unprecedented capabilities for adding new, custom analyses. 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. Over 30 wide-ranging statistical procedures plus 9 built-in data utilities include forecasts, time series, 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.
New in Version 6 – Scatter Plot Improvements and More
StatTools now includes a matrix that lets you view multiple scatter plots between variables in a single report. This consolidates data, saving a lot time. You can also choose to select categorical variables to color your data points, identifying which category the points fall into. Furthermore, Spearman rank-order correlations have been added to the StatTools correlation and covariance procedure. This makes it easy to determine the Spearman rank-order (non-linear) correlations from your data in StatTools for use in @RISK or other modeling.
How StatTools Works
First, you define your data in StatTools. Then, you perform any of over 30 different types of analysis on your data set, depending on your situation.
StatTools provides a comprehensive and intuitive data set and variable manager right in Excel. 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 2007-2010. Plus, you can define variables that span multiple worksheets.
Once your data sets have been defined, choose a procedure from the StatTools menu or write your own, custom procedure. To write your own, 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, which allows you to utilize StatTools's built-in data management, charting and reporting tools.
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: 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.
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. 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.
• Sample Size Selection
• Moving Averages
• Discriminant Analysis
• Categorical Data
Summary Analyses and Graphs
Nonparametric Tests (Pro only)
• The Sign Test
StatTools comes in Professional and Industrial editions. The Industrial edition of StatTools adds Expanded Data Sets. With Expanded Data Sets, you can define variables that span multiple worksheets. The Professional edition allows 10,000 data points for a single variable.
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Part of the DecisionTools Suite
StatTools is available by itself or as part of the DecisionTools Suite, Palisade’s complete risk and decision analysis toolkit. The DecisionTools Suite includes @RISK for risk analysis with Monte Carlo simulation, NeuralTools for predictions using neural networks, Evolver for optimization, PrecisionTree for decision trees, and more. StatTools is fully compatible with all DecisionTools programs and can be combined with them for greater insight and analysis. For example:
Combine StatTools with @RISK to assess confidence intervals on simulation results. You can also apply @RISK functions to StatTools time-series forecast results to simulate possible outcomes with more precision than the times-series analysis alone. Run RISKOptimizer on your model using the same @RISK functions in order to find the best combination of factors to maximize your forecasted profits.
Save Over 50% When You Buy the Suite
When you buy the DecisionTools Suite, you save over 50% versus buying all components individually. The best analyses at a great price–with the DecisionTools Suite.
Licensing and Training
StatTools is available through a variety of licensing options, including corporate, network, and academic licenses. Training, consulting, and books can be bundled with your software to ensure you and your staff get the most out of your investment.
With your StatTools purchase, you should also consider a copy of Learning Statistics with StatTools, a practical, straightforward guide to getting the most out of StatTools and statistics generally. Much more than a software manual, this book shows you how to apply statistics to a variety of real-world problems. Each chapter discusses a set of statistical procedures, grouped as they are in the StatTools menu structure. Example models are also included that you can customize for your own needs.
StatTools calculations are performed 100% within Excel,supported by Palisade sampling and statistics proven inover twenty years of use. Palisade does not attempt to rewrite Excel in an external recalculator to gain speed. Asingle recalculation from an unsupported or poorly reproduced macro or function can dramatically change your results. Where will it occur, and when? Get accurate results using StatTools!
COMPATIBILITY: @RISK and DecisionTools Suite software is compatible with all 32-bit and 64-bit versions of Microsoft Windows XP-8, Excel 2003-2013, and Project 2003-2013.
64-bit technology enables Excel and DecisionTools software to access much more computer memory than ever before. This allows for vastly larger models and greater computational power.