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.
Palisade StatTools-Industrial
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- 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
