PASW Statistics gives its users advanced statistical analysis, whether they are an experienced statistician or just getting started.
Support for direct Microsoft Excel access
SAS v7-v9 Support
SPSS PASW Statistics also offers the following functionality:
PASW Statistics Base: The procedures within PASW Statistics Base will enable you to get a quick look at your data and formulate hypotheses for additional testing. It will then carry out a number of procedures to help clarify relationships between variables, create clusters, identify trends and make predictions.
PASW Advanced Statistics: Allows the user to move beyond basic analysis, build flexible data models, and utilize a wide range of modeling techniques.
PASW Bootstrapping: Whether you conduct academic or scientific research, study issues in the public sector, or provide the analyses that support business decision-making, bootstrapping is a useful technique for testing model stability. And PASW Bootstrapping makes it simple and easy to do.
Quickly and easily estimate the sampling distribution of an estimator by re-sampling with replacement from the original sample
Estimate the standard errors and confidence intervals of a population parameter
Estimate the mean, median, proportion, odds ratio, correlation coefficient, regression coefficient and numerous others
Create thousands of alternate versions of your dataset for more accurate analysis
PASW Bootstrapping helps you reduce the impact of outliers and anomalies that can degrade the accuracy or applicability of your analysis. As a result, you have a clearer view of your data for creating the model you are working with.
PASW Categories: With this feature, you are no longer hampered by categorical or highly-dimensional data. These techniques ensure you have all the tools you need to easily analyze and interpret your multivariate data and its relationships more completely. For example, use PASW Categories to understand which characteristics consumers relate most closely to your product or brand, or to determine customer perception of your products compared to other products that you or your competitors offer.
PASW Complex Samples: Get a more accurate picture of your data when working with large-scale surveys as well as achieve more statistically valid inferences for populations. This feature allows you to reach correct point estimates for statistics such as: totals, means, ratios, and obtain standard errors of these statistics. In addition to this, you can predict numerical and categorical outcomes from non-simple random samples.
PASW Conjoint: Thoroughly understand customer preferences, tradeoffs, and price sensitivity with PASW Conjoint Analysis. By using conjoint analysis, you can uncover more information about how customers compare products in the marketplace and measure how individual product attributes affect consumer behavior. Armed with this knowledge, you can design, price and market products and services tailored to your customers' needs.
PASW Custom tables: Enables you to display your analyses as presentation-quality, production-ready tables. It gives you all the tools you need to easily create and work with tabular reports. Generate the table you envision using the advanced Graphical User Interface. You can easily work with output; present survey results by using nesting, stacking, and multiple response categories; handle missing values; and change labels or formats. You can even include missing values in your results.
PASW Data Preparation: Enables you to easily identify suspicious and invalid cases, variables, and data values - view patterns of missing data and summarize variable distributions. You can streamline the data preparation process so that you can get ready for analysis faster and reach more accurate conclusions.
PASW Decision Trees: Creates classification and decision trees to identify groups, discover relationships and predict future events. By creating visual trees, you are able to present results in an intuitive manner - so you can more clearly explain results to non-technical audiences.
PASW Direct Marketing: Lets you analyze the success of your direct marketing tactics. Its intuitive user interface guides you through the whole process so that you can classify customers in a few easy steps.
Classify customers and prospects based on identifying characteristics
Compare campaign performance
Create responder profiles
Generate propensity-to-purchase scores
Analyze responses by postal code
Refine individual customer lists
PASW Forecasting: Time-series analysis is the most powerful procedure you can use to analyze historical information, build models, predict trends and forecast future events. PASW Forecasting is the best way to quickly create powerful forecasts with confidence. With better forecasts, long-term goals can be set - with insight on how to achieve them - based on your organizations' past performance and knowledge of your industry. PASW Forecasting has the advanced statistical techniques you need in order to work with time-series data. You can analyze historical data and predict trends faster, and deliver information in ways that your organizations' decision makers can understand and use.
PASW Missing Values: When you ignore or exclude missing data, you risk obtaining biased or insignificant results. Use PASW Missing Values to impute your missing data and draw more valid conclusions. PASW Missing Values is a critical tool for anyone concerned about data validity. You can easily examine your data to uncover missing data patterns. Then, estimate summary statistics and impute missing values through statistical algorithms.
PASW Statistics Developer: A new product from SPSS Inc. that allows R and Python programmers to “wrap” procedures in PASW Statistics syntax so that they can be accessible to a wider range of users. PASW Statistics Developer includes all of the core functionality found in every PASW Statistics module – crucially, the data access and management capabilities, programmability options, Custom Dialog Builder feature, and report creation, charting and deployment functionality – except for the analytical procedures that the modules contain.
