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Includes the following (see below for detailed descriptions of each addon):
IBM SPSS Base 27
IBM SPSS Advanced Statistics(a $1200 value)
IBM SPSS Regression(a $1200 value)
IBM SPSS Custom Tables (a $1200 value)
IBM SPSS Data Preparation (a $1200 value)
IBM SPSS Missing Values (a $1200 value)
IBM SPSS Forecasting (a $1200 value)
IBM SPSS Decision Trees (a $1200 value)
IBM SPSS Direct Marketing (a $1200 value)
IBM SPSS Complex Sampling (a $1200 value)
IBM SPSS Conjoint (a $1200 value)
IBM SPSS Neural Networks (a $1200 value)
IBM SPSS Bootstrapping (a $1200 value)
IBM SPSS Categories (a $1200 value)
IBM SPSS Exact Tests (Windows only)
 No limitation on the number of variables or cases
 System requirements are at the bottom of this product description
 Windows Vista
 Windows XP
 Chromebooks
 IPads
 Android tablets
 Smartphones
New in Version 27
 New Power Analysis and Weighted Kappa procedures
 Enhancements to effect size, MATRIX, and Quantile regression procedures
 Usability and productivity enhancements, including the ability to autorecover files in the event of an unexpected application shutdown, new search capabilities, and output improvements
 SPSS Statistics Extensions give you a new way to access and work with open source and thirdparty programming extensions:
 SPSS Statistics Extensions Hub is a new interface to manage extensions. It provides an online storelike experience.
 With SPSS Statistics Custom Dialog Builder for Extensions, it is now easier than ever to create and share extensions based on R/Python and SPSS Syntax for your customized needs.
 A redesigned experience while importing and exporting the most popular file types enables smarter data management.
 Many enhancements to the SPSS Custom Tables module offer improved productivity.
 Gain deeper predictive insights from large and complex datasets.
 Use the Temporal Causal Modeling (TCM) technique to uncover hidden causal relationships among large numbers of time series and automatically determine the best predictors.
 Integrate, explore and model location and time data, and capitalize on new data sources to solve new business problems
 The SpatioTemporal Prediction (STP) technique can fit linear models for measurements taken over time at locations in 2D and 3D space.
 The Generalized Spatial Association Rule (GSAR) finds associations between spatial and nonspatial attributes.
 Embed analytics into the enterprise to speed deployment and return on investment.
 Completely redesigned web reports offer more interactivity, functionality and web server support.
 Enhanced categorical principal component analysis (CATPCA) capabilities.
 Bulk load data for faster performance.
 Stata 13 users can import, read and write Stata 913 files within SPSS Statistics.
 Enterprise users can access SPSS Statistics using their identification badges and badge readers.
 A wider range of R programming options enables developers to use a fullfeatured, integrated R development environment within SPSS Statistics.
 Quickly access and analyze massive datasets
 Easily prepare and manage your data for analysis
 Analyze data with a comprehensive range of statistical procedures
 Easily build charts with sophisticated reporting capabilities
 Discover new insights in your data with tables, graphs, cubes and pivoting technology
 Quickly build dialog boxes or let advanced users create customized dialog boxes that make your organization’s analyses easier and more efficient
Descriptive Statistics
 Crosstabulations – Counts, percentages, residuals, marginals, tests of independence, test of linear association, measure of linear association, ordinal data measures, nominal by interval measures, measure of agreement, relative risk estimates for case control and cohort studies.
 Frequencies – Counts, percentages, valid and cumulative percentages; central tendency, dispersion, distribution and percentile values.
 Descriptives – Central tendency, dispersion, distribution and Z scores.
 Descriptive ratio statistics – Coefficient of dispersion, coefficient of variation, pricerelated differential and average absolute deviance.
 Compare means – Choose whether to use harmonic or geometric means; test linearity; compare via independent sample statistics, paired sample statistics or onesample t test.
 ANOVA and ANCOVA – Conduct contrast, range and post hoc tests; analyze fixedeffects and randomeffects measures; group descriptive statistics; choose your model based on four types of the sumofsquares procedure; perform lackoffit tests; choose balanced or unbalanced design; and analyze covariance with up to 10 methods.
 Correlation – Test for bivariate or partial correlation, or for distances indicating similarity or dissimilarity between measures.
 Nonparametric tests – Chisquare, Binomial, Runs, onesample, two independent samples, kindependent samples, two related samples, krelated samples.
 Explore – Confidence intervals for means; Mestimators; identification of outliers; plotting of findings.
Tests to Predict Numerical Outcomes and Identify Groups:
IBM SPSS Statistics Base contains procedures for the projects you are working on now and any new ones to come. You can be confident that you’ll always have the analytic tools you need to get the job done quickly and effectively.
 Factor Analysis – Used to identify the underlying variables, or factors, that explain the pattern of correlations within a set of observed variables. In IBM SPSS Statistics Base, the factor analysis procedure provides a high degree of flexibility, offering:
 Seven methods of factor extraction
 Five methods of rotation, including direct oblimin and promax for nonorthogonal rotations
 Three methods of computing factor scores. Also, scores can be saved as variables for further analysis
 Kmeans Cluster Analysis – Used to identify relatively homogeneous groups of cases based on selected characteristics, using an algorithm that can handle large numbers of cases but which requires you to specify the number of clusters. Select one of two methods for classifying cases, either updating cluster centers iteratively or classifying only.
 Hierarchical Cluster Analysis – Used to identify relatively homogeneous groups of cases (or variables) based on selected characteristics, using an algorithm that starts with each case in a separate cluster and combines clusters until only one is left. Analyze raw variables or choose from a variety of standardizing transformations. Distance or similarity measures are generated by the Proximities procedure. Statistics are displayed at each stage to help you select the best solution.
 TwoStep Cluster Analysis – Group observations into clusters based on nearness criterion, with either categorical or continuous level data; specify the number of clusters or let the number be chosen automatically.
 Discriminant – Offers a choice of variable selection methods, statistics at each step and in a final summary; output is displayed at each step and/or in final form.
 Linear Regression – Choose from six methods: backwards elimination, forced entry, forced removal, forward entry, forward stepwise selection and R2 change/test of significance; produces numerous descriptive and equation statistics.
 Ordinal regression—PLUM – Choose from seven options to control the iterative algorithm used for estimation, to specify numerical tolerance for checking singularity, and to customize output; five link functions can be used to specify the model.
 Nearest Neighbor analysis – Use for prediction (with a specified outcome) or for classification (with no outcome specified); specify the distance metric used to measure the similarity of cases; and control whether missing values or categorical variables are treated as valid values.

Procedures Included:
General linear models (GLM) – Provides you with more flexibility to describe the relationship between a dependent variable and a set of independent variables. The GLM gives you flexible design and contrast options to estimate means and variances and to test and predict means. You can also mix and match categorical and continuous predictors to build models. Because GLM doesn’t limit you to one data type, you have options that provide you with a wealth of modelbuilding possibilities.
 Linear mixed models, also known as hierarchical linear models (HLM)
 Fixed effect analysis of variance (ANOVA), analysis of covariance (ANOVA), multivariate analysis of variance (MANOVA) and multivariate analysis of covariance (MANCOVA)
 Random or mixed ANOVA and ANCOVA
 Repeated measures ANOVA and MANOVA
 Variance component estimation (VARCOMP)
The linear mixed models procedure expands the general linear models used in the GLM procedure so that you can analyze data that exhibit correlation and nonconstant variability. If you work with data that display correlation and nonconstant variability, such as data that represent students nested within classrooms or consumers nested within families, use the linear mixed models procedure to model means, variances and covariances in your data.
Its flexibility means you can formulate dozens of models, including splitplot design, multilevel models with fixedeffects covariance, and randomized complete blocks design. You can also select from 11 nonspatial covariance types, including firstorder antedependence, heterogeneous, and firstorder autoregressive. You’ll reach more accurate predictive models because it takes the hierarchical structure of your data into account.
You can also use linear mixed models if you’re working with repeated measures data, including situations in which there are different numbers of repeated measurements, different intervals for different cases, or both. Unlike standard methods, linear mixed models use all your data and give you a more accurate analysis.
 Generalized linear models (GENLIN): GENLIN covers not only widely used statistical models, such as linear regression for normally distributed responses, logistic models for binary data, and loglinear model for count data, but also many useful statistical models via its very general model formulation. The independence assumption, however, prohibits generalized linear models from being applied to correlated data.
 Generalized estimating equations (GEE): GEE extend generalized linear models to accommodate correlated longitudinal data and clustered data.
 General models of multiway contingency tables (LOGLINEAR)
 Hierarchical loglinear models for multiway contingency tables (HILOLINEAR)
 Loglinear and logit models to count data by means of a generalized linear models approach (GENLOG)
 Survival analysis procedures:
 Cox regression with timedependent covariates
 KaplanMeier
 Life Tables
IBM SPSS Regression Overview, Features and Benefits  Linear mixed models, also known as hierarchical linear models (HLM)
More Statistics for Data Analysis
 Expand the capabilities of IBM® SPSS® Statistics Base for the data analysis stage in the analytical process. Using IBM SPSS Regression with IBM SPSS Statistics Base gives you an even wider range of statistics so you can get the most accurate response for specific data types.IBM SPSS Regression includes:
 Multinomial logistic regression (MLR): Regress a categorical dependent variable with more than two categories on a set of independent variables. This procedure helps you accurately predict group membership within key groups.
You can also use stepwise functionality, including forward entry, backward elimination, forward stepwise or backward stepwise, to find the best predictor from dozens of possible predictors. If you have a large number of predictors, Score and Wald methods can help you more quickly reach results. You can access your model fit using Akaike information criterion (AIC) and Bayesian information criterion (BIC; also called Schwarz Bayesian criterion, or SBC).  Binary logistic regression: Group people with respect to their predicted action. Use this procedure if you need to build models in which the dependent variable is dichotomous (for example, buy versus not buy, pay versus default, graduate versus not graduate). You can also use binary logistic regression to predict the probability of events such as solicitation responses or program participation.
With binary logistic regression, you can select variables using six types of stepwise methods, including forward (the procedure selects the strongest variables until there are no more significant predictors in the dataset) and backward (at each step, the procedure removes the least significant predictor in the dataset) methods. You can also set inclusion or exclusion criteria. The procedure produces a report telling you the action it took at each step to determine your variables.  Nonlinear regression (NLR) and constrained nonlinear regression (CNLR): Estimate nonlinear equations. If you are you working with models that have nonlinear relationships, for example, if you are predicting coupon redemption as a function of time and number of coupons distributed, estimate nonlinear equations using one of two IBM SPSS Statistics procedures: nonlinear regression (NLR) for unconstrained problems and constrained nonlinear regression (CNLR) for both constrained and unconstrained problems.
NLR enables you to estimate models with arbitrary relationships between independent and dependent variables using iterative estimation algorithms, while CNLR enables you to: Use linear and nonlinear constraints on any combination of parameters
 Estimate parameters by minimizing any smooth loss function (objective function)
 Compute bootstrap estimates of parameter standard errors and correlations
 Weighted least squares (WLS): If the spread of residuals is not constant, the estimated standard errors will not be valid. Use Weighted Least Square to estimate the model instead (for example, when predicting stock values, stocks with higher shares values fluctuate more than low value shares.)
 Twostage least squares (2LS): Use this technique to estimate your dependent variable when the independent variables are correlated with the regression error terms.
For example, a book club may want to model the amount they crosssell to members using the amount that members spend on books as a predictor. However, money spent on other items is money not spent on books, so an increase in crosssales corresponds to a decrease in book sales. TwoStage LeastSquares Regression corrects for this error.  Probit analysis: Probit analysis is most appropriate when you want to estimate the effects of one or more independent variables on a categorical dependent variable.
For example, you would use probit analysis to establish the relationship between the percentage taken off a product, and whether a customer will buy as the prices decreases. Then, for every percent taken off the price you can work out the probability that a consumer will buy the product.  IBM SPSS Regression includes additional diagnostics for use when developing a classification table
IBM SPSS Custom Tables
IBM® SPSS® Custom Tables helps you easily understand your data and quickly summarize your results in different styles for different audiences.
More than a simple reporting tool, IBM SPSS Custom Tables combines comprehensive analytical capabilities with interactive tablebuilding features to help you learn from your data and communicate the results of your analyses as professionallooking tables that are easy to read and interpret.
 Compare means or proportions for demographic groups, customer segments, time periods or other categorical variables when you include inferential statistics
 Select summary statistics – from simple counts for categorical variables to measures of dispersion – and sort categories by any summary statistic used
 Choose from three significance tests: Chisquare test of independence, comparison of column means (t test), or comparison of column proportions (z test)
 Drag and drop variables onto the interactive table builder to create results as pivot tables
 Preview tables in real time and modify them as you create them
 Exclude specific categories, display missing value cells and add subtotals to your tables
 Export tables to Microsoft® Word, Excel®, PowerPoint® or HTML for use in reports
IBM SPSS Custom Tables is an analytical tool that helps you augment your reports with information your readers need to make more informed decisions.
Use inferential statistics—also known as significance testing—in your tables to perform common analyses: Compare means or proportions for demographic groups, customer segments, time periods, or other categorical variables; and identify trends, changes, or major differences in your data. IBM SPSS Custom Tables includes the following significance tests:
 Chisquare test of independence
 Comparison of column means (t test)
 Comparison of column proportions (z test)
You can also choose from a variety of summary statistics, which include everything from simple counts for categorical variables to measures of dispersion. Summary statistics are included for:
 Categorical variables
 Multiple response sets
 Scale variables
 Custom total summaries for categorical variables
When your analysis is complete, you can use IBM SPSS Custom Tables to create customized tabular reports suitable for a variety of audiences—including those without a technical background.
IBM SPSS Data Preparation Overview, Features, and Benefits
IBM® SPSS® Data Preparation gives analysts advanced techniques to streamline the data preparation stage of the analytical process. All researchers have to prepare their data before analysis. While basic data preparation tools are included in IBM SPSS Statistics Base, IBM SPSS Data Preparation provides specialized techniques to prepare your data for more accurate analyses and results.
With IBM SPSS Data Preparation, you can:
 Quickly identify suspicious or invalid cases, variables and data values
 View patterns of missing data
 Summarize variable distributions
 Optimally bin nominal data
 More accurately prepare your data for analysis
 Use Automated Data Preparation (ADP) to detect and correct quality errors and impute missing values in one efficient step
 Get recommendations and visualizations to help you determine which data to use
 Multinomial logistic regression (MLR): Regress a categorical dependent variable with more than two categories on a set of independent variables. This procedure helps you accurately predict group membership within key groups.
Expand your Data Preparation Techniques with IBM SPSS Data Preparation

Use the specialized data preparation techniques in IBM SPSS Data Preparation to facilitate data preparation in the analytical process. IBM SPSS Data Preparation easily plugs into IBM SPSS Statistics Base so you can seamlessly work in the IBM SPSS environment.
Perform Data Checks

Data validation has typically been a manual process. You might run a frequency on your data, print the frequencies, circle what needs to be fixed and check for case IDs. This approach is time consuming and prone to errors. And since every analyst in your organization could use a slightly different method, maintaining consistency from project to project may be a challenge.
To eliminate manual checks, use the IBM SPSS Data Preparation Validate Data procedure. This enables you to apply rules to perform data checks based on each variable’s measure level (whether categorical or continuous).
For example, if you’re analyzing data that has variables on a fivepoint Likert scale, use the Validate Data procedure to apply a rule for fivepoint scales and flag all cases that have values outside of the 15 range. You can receive reports of invalid cases as well as summaries of rule violations and the number of cases affected. You can specify validation rules for individual variables (such as range checks) and crossvariable checks (for example, “retired 30 yearolds”).
With this knowledge you can determine data validity and remove or correct suspicious cases at your discretion before analysis.
Quickly Find Multivariate Outliers

Prevent outliers from skewing analyses when you use the IBM SPSS Data Preparation Anomaly Detection procedure. This searches for unusual cases based upon deviations from similar cases, and gives reasons for such deviations. You can flag outliers by creating a new variable. Once you have identified unusual cases, you can further examine them and determine if they should be included in your analyses.
Preprocess Data before Model Building

In order to use algorithms that are designed for nominal attributes (such as Naïve Bayes and logit models), you must bin your scale variables before model building. If scale variables aren’t binned, algorithms such as multinomial logistic regression will take an extremely long time to process or they might not converge. This is especially true if you have a large dataset. In addition, the results you receive may be difficult to read or interpret.
IBM SPSS Data Preparation Optimal Binning, however, enables you to determine cutpoints to help you reach the best possible outcome for algorithms designed for nominal attributes.
With this procedure, you can select from three types of binning for pre processing data:
 Unsupervised — create bins with equal counts
 Supervised — take the target variable into account to determine cutpoints. This method is more accurate than unsupervised; however, it is also more computationally intensive.
 Hybrid approach — combines the unsupervised and supervised approaches. This method is particularly useful if you have a large number of distinct values.
IBM SPSS Missing Values
IBM® SPSS® Missing Values is used by survey researchers, social scientists, data miners, market researchers and others to validate data.
Missing data can seriously affect your models – and your results. Ignoring missing data, or assuming that excluding missing data is sufficient, risks reaching invalid and insignificant results. To ensure that you take missing values into account, make IBM SPSS Missing Values part of your data management and preparation.
Uncover Missing Data Patterns

 Easily examine data from several different angles using one of six diagnostic reports, then estimate summary statistics and impute missing values
 Quickly diagnose serious missing data imputation problems
 Replace missing values with estimates
 Display a snapshot of each type of missing value and any extreme values for each case
 Remove hidden bias by replacing missing values with estimates to include all groups ¬– even those with poor responsiveness
Uncover Missing Data Patterns
 With IBM SPSS Missing Values, you can easily examine data from several different angles using one of six diagnostic reports to uncover missing data patterns. You can then estimate summary statistics and impute missing values through regression or expectation maximization algorithms (EM algorithms).IBM SPSS Missing Values helps you to:
 Diagnose if you have a serious missing data imputation problem
 Replace missing values with estimates — for example, impute your missing data with the regression or EM algorithms
Quickly and Easily Diagnose Your Missing Data
 Quickly diagnose a serious missing data problem using the data patterns report, which provides a casebycase overview of your data. This report helps you determine the extent of missing data; it displays a snapshot of each type of missing value and any extreme values for each case.
Reach More Valid Conclusions
 Replace missing values with estimates and increase the chance of receiving statistically significant results. Remove hidden bias from your data by replacing missing values with estimates to include all groups in your analysis – even those with poor responsiveness.
Use Multiple Imputation to Replace Missing Data Values
 IBM SPSS Missing Values’ multiple imputation procedure will help you understand patterns of “missingness” in your dataset and enable you to replace missing values with plausible estimates. It offers a fully automatic imputation mode that chooses the most suitable imputation method based on characteristics of your data, while also allowing you to customize your imputation model.Several complete datasets are generated (typically, three to five), each with a different set of replacement values. Next, you can model the individual datasets, using techniques such as linear regression, to produce parameter estimates for each dataset. Then you can obtain final parameter estimates. This involves pooling the individual sets of parameter estimates obtained in step two and computing inferential statistics that take into account variation within and between imputations.Analysis of the individual datasets and pooling of the results are supported via existing IBM SPSS Statistics procedures such as REGRESSION. When operating on datasets with imputed values, existing procedures will automatically produce pooled parameter estimates.
Fill in the Blanks for Improved Data Management
 IBM SPSS Missing Values has the statistics you need to fill in missing data:
 Univariate: compute count, mean, standard deviation, and standard error of mean for all cases excluding those containing missing values, count and percent of missing values, and extreme values for all variables
 Listwise: compute mean, covariance matrix, and correlation matrix for all quantitative variables for cases excluding missing values
 Pairwise: compute frequency, mean, variance, covariance matrix, and correlation matrix
 Expectation maximization (EM) algorithm
 Estimate the means, covariance matrix, and correlation matrix of quantitative variables with missing values, assuming normal distribution, t distribution with degrees of freedom, or a mixednormal distribution with any mixture proportion and any standard deviation ratio
 Impute missing data and save the completed data as a file
 Regression algorithm
 Estimate the means, covariance matrix, and correlation matrix of variables set as dependent; set number of predictor variables; set random elements as normal, t, residuals, or none
IBM SPSS Missing Values also has features that enable you to analyze patterns and manage data, including the ability to:
 Display missing data and extreme cases for all cases and all variables using the data patterns table
 Determine differences between missing and nonmissing groups for a related variable with the separate t test table
 Assess how much missing data for one variable relates to the missing data of another variable using the percent mismatch of patterns table
IBM SPSS Forecasting
IBM® SPSS® Forecasting enables analysts to predict trends and develop forecasts quickly and easily — without being an expert statistician.
Reliable forecasts can have a major impact on your organization’s ability to develop and implement successful strategies. Unlike spreadsheet programs, IBM SPSS Forecasting has the advanced statistical techniques needed to work with timeseries data regardless of your level of expertise.
 Analyze historical data and predict trends faster, and deliver information in ways that your organization’s decision makers can understand and use
 Automatically determine the bestfitting ARIMA or exponential smoothing model to analyze your historic data
 Model hundreds of different time series at once, rather than having to run the procedure for one variable at a time
 Save models to a central file so that forecasts can be updated when data changes, without having to reset parameters or reestimate models
 Write scripts so that models can be updated with new data automatically
IBM SPSS Decision Trees
IBM SPSS Forecasting offers a number of capabilities that enable both novice and experienced users to quickly develop reliable forecasts using timeseries data. It is a fully integrated module of IBM SPSS Statistics, giving you all of IBM SPSS Statistics’ capabilities plus features specifically designed to support forecasting.
New to Building Models from Timeseries Data?
 IBM SPSS Forecasting helps you by:
 Generating reliable models, even if you’re not sure how to choose exponential smoothing parameters or ARIMA orders, or how to achieve stationarity
 Automatically testing your data for seasonality, intermittency and missing values, and selecting appropriate models
 Detecting outliers and preventing them from influencing parameter estimates
 Generating graphs showing confidence intervals and the model’s goodness of fit
You’re an Experienced IBM SPSS Statistics User?
 IBM SPSS Forecasting allows you to:
 Control every parameter when building your data model
 Use IBM SPSS Forecasting Expert Modeler recommendations as a starting point or to check your work
Procedures and Statistics for Analyzing Timeseries Data
 Using IBM SPSS Forecasting with IBM SPSS Statistics Base gives you a selection of statistical techniques for analyzing timeseries data and developing reliable forecasts.
Techniques Tailored to Timeseries Analysis
 IBM SPSS Statistics has the procedures you need to realize the most benefit from your timeseries analysis. It generates statistics and normal probability plots so that you can easily judge model fit. You can even limit output to see only the worstfitting models — those that require further examination. Automatically generated highresolution charts enhance your output.Procedures available in IBM SPSS Forecasting include:
 TSMODEL – Use the Expert Modeler to model a set of timeseries variables, using either ARIMA or exponential smoothing techniques
 TSAPPLY – Apply saved models to new or updated data
 SEASON – Estimate multiplicative or additive seasonal factors for periodic time series
 SPECTRA – Decompose a time series into its harmonic components, which are sets of regular periodic functions at different wavelengths or periods
The IBM® SPSS® Decision Trees module helps you better identify groups, discover relationships between them and predict future events.
This module features highly visual classification and decision trees. These trees enable you to present categorical results in an intuitive manner, so you can more clearly explain categorical analysis to nontechnical audiences.
IBM SPSS Decision Trees enables you to explore results and visually determine how your model flows. This helps you find specific subgroups and relationships that you might not uncover using more traditional statistics. The module includes four established treegrowing algorithms.
Use IBM SPSS Decision Trees if you need to identify groups and subgroups. Applications include:
 Database marketing
 Market research
 Credit risk scoring
 Program targeting
 Marketing in the public sector
IBM SPSS Direct Marketing
IBM® SPSS® Direct Marketing helps you understand your customers in greater depth, improve your marketing campaigns and maximize the ROI of your marketing budget.
Conduct sophisticated analyses of your customers or contacts easily – and with a high level of confidence in your results. Choose from recency, frequency and monetary value (RFM) analysis, cluster analysis, prospect profiling, postal code analysis, propensity scoring and control package testing. The software’s intuitive interface enables you to:
 Identify which customers are likely to respond to specific promotional offers
 Develop a marketing strategy for each customer group
 Compare the effectiveness of direct mail campaigns
 Boost profits and reduce costs by mailing only to those customers most likely to respond
 Prevent spam complaints by monitoring the frequency of emails sent to each customer group
 Select potential business locations
 Connect to Salesforce.com to extract customer information, collect details on opportunities and perform analyses
Although IBM SPSS Direct Marketing relies on powerful analytics, you don’t need to be a statistician or programmer to use it. The intuitive interface guides you every step of the way, and the new Scoring Wizard makes it easy to build models to score your data. After you run an analysis, the significance of the output is clearly explained.IBM SPSS Direct Marketing includes a combination of specifically chosen procedures that enable database and direct marketers to conduct data preparation and analysis activities. You can do this using only IBM SPSS Direct Marketing, or you can use it in conjunction with other applications in the IBM SPSS Statistics product family.
 RFM Analysis: Score customers according to the recency, frequency and monetary value of their purchases.
 Segment customers or contacts: Create “clusters” of those who are like each other, and distinctly different from others.
 Profile customers or contacts: Identify shared characteristics, to improve the targeting of marketing offers and campaigns.
 Identify those who are likely to purchase: Develop propensity scores and improve the focus and timing of your campaigns.
 Test control packages: Find out which new (test) packages outperform your existing (control) package.
 Know where responses come from: Identify by postal code the responses to your campaigns.
 Integrate response data with Salesforce.com to track leads and report on sales pipeline.
IBM SPSS Complex Samples
IBM® SPSS® Complex Samples helps make more statistically valid inferences by incorporating the sample design into survey analysis.
IBM SPSS Complex Samples provides the specialized planning tools and statistics you need when working with complex sample designs, such as stratified, clustered or multistage sampling.
This module of IBM SPSS Statistics is an indispensable for survey and market researchers, public opinion researchers or social scientists seeking to reach more accurate conclusions when working with sample survey methodology. You can more accurately work with numerical and categorical outcomes in complex sample designs using two algorithms for analysis and prediction. In addition, you can use this module’s techniques to predict time to an event
Only IBM® SPSS® Complex Samples makes understanding and working with your complex sample survey results easy. Through the intuitive interface, you can analyze data and interpret results. Choose from one of several wizards to make it easier to create plans, analyze data and interpret results.
When you’re finished, you can publish publicuse datasets and include your sampling and analysis plans. These plans act as a template and allow you to save all the decisions made when creating the plan – define it once and you’re done. This saves time and improves accuracy for yourself and others who may want to plug your plans into the data to replicate results or pick up where you left off.
Use the following types of sample design information with IBM SPSS Complex Samples:
 Stratified sampling – Increase the precision of your sample or ensure a representative sample from key groups by choosing to sample within subgroups of the survey population.
 Clustered sampling – Select clusters, which are groups of sampling units, for your survey. Clustering often helps makes surveys more costeffective.
 Multistage sampling – Select an initial or firststage sample based on groups of elements in your population; then create a secondstage sample by drawing a subsample from each selected unit in the firststage sample. By repeating this option, you can select a higherstage sample.
Everything You Need for Planning
 To help you through the planning stage in the analytical process, IBM SPSS Complex Samples provides you with specialized tools and procedures for working with sample survey data:
 IBM SPSS Complex Samples Plan (CSPLAN) – Use this procedure to specify the sampling frame to create a complex sample design or analysis specification used by companion procedures in IBM SPSS Complex Samples.
 Sampling Plan Wizard – If you are creating your own samples, use the Sampling Plan Wizard to define the scheme and draw the sample.
 Analysis Preparation Wizard – If you’re using publicuse datasets that already have samples, use the Analysis Plan Wizard to specify how the samples were defined and how standard errors should be estimated.
 Plan files – Once you have created plan files, you can save them and treat them as templates. This allows you to save all the decisions you made when creating the plan. This saves time and improves accuracy for yourself and others who may want to plug your plans into the data to replicate results or pick up where you left off.
Everything You Need for Data Management
 IBM SPSS Complex Samples provides what you need for the data management stage when working with sample survey data. And it easily plugs into other IBM SPSS Statistics modules so you can seamlessly work in the IBM SPSS Statistics environment.IBM SPSS Complex Samples Selection (CSSELECT) procedure — Enables you to select complex, probabilitybased samples from a population while mitigating the risk in doing so (e.g. over or underrepresenting a subgroup). CSSELECT chooses units according to a sample design created through the CSPLAN procedure.With this procedure, you can:
 Control the scope of execution and specify a seed value with the CRITERIA subcommand
 Control whether or not usermissing values of classification (stratification and clustering) variables are treated as valid variables with the CLASSMISSING subcommand
 Specify general options concerning input and output files with the DATA subcommand
 Write sampled units to an external file using an option to keep/drop specified variables
 Automatically save firststage joint inclusion probabilities to an external file when the plan specifies a probability proportionate to size (PPS) without replacement (WR) sampling method
 Opt to generate text files containing a rule that describes characteristics of selected units
Everything You Need for Data Analysis
 Performing data analysis in IBM SPSS Complex Samples helps you to achieve more statistically valid inferences for populations measured in your complex sample data. IBM SPSS Complex Samples provides you with better results because, unlike most conventional statistical software, it incorporates the sample design into survey analysis.IBM SPSS Complex Samples features five procedures to analyze data from sample survey data:
 IBM SPSS Complex Samples Descriptives (CSDESCRIPTIVES) – Estimates means, sums and ratios, and computes standard errors, design effects, confidence intervals hypothesis tests for samples drawn by complex methods.
 IBM SPSS Complex Samples Tabulate (CSTABULATE) – Displays oneway frequency tables or twoway crosstabulations and associated standard errors, design effects, confidence intervals and hypothesis tests for samples drawn by complex sampling methods.
 IBM SPSS Complex Samples General Linear Models (CSGLM) – Enables you to build linear regression, analysis of variance (ANOVA), and analysis of covariance (ANCOVA) models for samples drawn by complex sampling methods.
 IBM SPSS Complex Samples Logistic Regression (CSLOGISTIC) – Performs binary logistic regression analysis, as well as multiple logistic regression (MLR) analysis, for samples drawn by complex sampling methods.
 IBM SPSS Complex Samples Cox Regression (CSCOXREG) – Applies Cox proportional hazards regression to analysis of survival times; that is, the length of time before the occurrence of an event for samples drawn by complex sampling methods.
 IBM SPSS Complex Samples Plan (CSPLAN) – Use this procedure to specify the sampling frame to create a complex sample design or analysis specification used by companion procedures in IBM SPSS Complex Samples
IBM SPSS Conjoint
IBM® SPSS® Conjoint gives you a realistic way to measure how individual product attributes affect people’s preferences.
When you use both conjoint analysis and competitive product market research for your new products, you are less likely to overlook product dimensions that are important to your customers or constituents, and more likely to successfully meet their needs.
With IBM SPSS Conjoint, you can easily measure the tradeoff effect of each product attribute in the context of a set of product attributes – as consumers do when making purchasing decisions.
For example, you can answer critical product market research questions:
 What product attributes do my customers care about?
 What are the most preferred attribute levels?
 How can I most effectively perform pricing and brand equity studies?
You can answer all of your questions before you spend valuable resources trying to bring products or services to market. Use IBM SPSS Conjoint to focus your efforts on the service or product development that has the best chance of succeeding.
IBM SPSS Conjoint gives you all the tools you need for developing product and service attribute ratings. You can use its three procedures to:
 Generate designs easily – use Orthoplan, the design generator, to produce an orthogonal array of alternative potential products or services that combine different product/service features at specified levels
 Print “cards” to elicit respondents’ preferences – use Plancards to quickly generate cards that respondents can sort to rank alternative products
 Get informative results – analyze your data using Conjoint, a procedure that’s a specially tailored version of regression. Find out which product/service attributes are important and at which levels they are most preferred. You can also perform simulations that tell you the market share of preference for alternative products
Conduct intelligent planning

Expand the capabilities of IBM SPSS Statistics Base with IBM SPSS Conjoint. Make better decisions about your data and gain knowledge in the planning stage that you can carry throughout the analytical process.
Save time and money by generating a set of conjoint experimental trials that are a fraction of all possible combinations and attribute levels. You’ll quickly learn how your respondents rank their preferences when you create and print cards they can sort. And, with the results from the Conjoint procedure, you’ll learn how your respondents rank product attributes. Here are more details on each procedure:
 Orthoplan enables you to generate orthogonal main effects fractional factorial designs and display results in pivot tables.
 Plancards enables you to produce printed cards for a conjoint experiment.
 Conjoint enables you to perform an ordinary leastsquares analysis of preference or rating, working with a plan file generated through Plancards or with one inputted from a data list. Various graphing and printing options are available.
IBM SPSS Neural Networks (a $1200 value)
IBM® SPSS® Neural Networks offers nonlinear data modeling procedures that enable you to discover more complex relationships in your data.
Using the procedures in IBM SPSS Neural Networks, you can develop more accurate and effective predictive models. The result? Deeper insight and better decision making.
What is a neural network?
 A computational neural network is a set of nonlinear data modeling tools consisting of input and output layers plus one or two hidden layers. The connections between neurons in each layer have associated weights, which are iteratively adjusted by the training algorithm to minimize error and provide accurate predictions.
Complement traditional statistical techniques
 The procedures in IBM SPSS Neural Networks complement the more traditional statistics in IBM SPSS Statistics Base and its modules. Find new associations in your data with Neural Networks and then confirm their significance with traditional statistical techniques
How can you use IBM SPSS Neural Networks?

You can combine Neural Networks with other statistical procedures to gain clearer insight in a number of areas:
Market research

 Create customer profiles
 Discover customer preferences
Database marketing

 Segment your customer base
 Optimize campaigns
Financial analysis

 Analyze applicants’ creditworthiness
 Detect possible fraud
Operational analysis

 Manage cash flow
 Improve logistics planning
Healthcare

 Forecast treatment costs
 Perform medical outcomes analysis
Use data mining techniques

IBM SPSS Neural Networks provides a complementary approach to the data analysis techniques available in IBM SPSS Statistics Base and its modules. From the familiar IBM SPSS Statistics interface, you can “mine” your data for hidden relationships, using either the Multilayer Perceptron (MLP) or Radial Basis Function (RBF) procedure.
Both of these are supervised learning techniques – that is, they map relationships implied by the data. Both use feedforward architectures, meaning that data moves in only one direction, from the input nodes through the hidden layer or layers of nodes to the output nodes.
Your choice of procedure will be influenced by the type of data you have and the level of complexity you seek to uncover. While the MLP procedure can find more complex relationships, the RBF procedure is generally faster.
With either of these approaches, the procedure operates on a training set of data and then applies that knowledge to the entire dataset, and to any new data.
Control the process from start to finish

After selecting a procedure, you specify the dependent variables, which may be scale, categorical or a combination of the two. You adjust the procedure by choosing how to partition the dataset, what sort of architecture you want and what computation resources will be applied to the analysis.
Finally, you choose whether you want to display results in tables or graphs, save optional temporary variables to the active dataset and/or export models in XMLbased file format to score future data.
IBM SPSS Bootstrapping (a $1200 value)
IBM® SPSS® Bootstrapping makes it simple to test the stability and reliability of your models so that they produce accurate, reliable results.
Whether you conduct academic or scientific research, study issues in the public sector or provide the analyses that support business decisions, it’s important that your models are stable. Test model stability quickly and easily with IBM SPSS Bootstrapping.
IBM SPSS Bootstrapping provides an efficient way to ensure that your models are stable and reliable, so your analysis generates more accurate results. With IBM SPSS Bootstrapping, you can:
 Quickly and easily estimate the sampling distribution of an estimator by resampling with replacement from the original sample
 Estimate the standard errors and confidence intervals of a population parameter such as 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
IBM SPSS Bootstrapping helps 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.
 Fast, easy resampling — estimate the sampling distribution of an estimator in a snap
 Reduce the impact of outliers and anomalies — ensure the stability and reliability of your models
 Bootstrap many analytical procedures — test a wide range of the descriptive and modeling procedures found in the IBM SPSS Statistics product family
IBM SPSS Bootstrapping works with a number of analytical procedures in the IBM SPSS Statistics product family, including:
Descriptive Procedures Product Descriptives IBM SPSS Statistics Base Frequencies IBM SPSS Statistics Base Examine IBM SPSS Statistics Base Means IBM SPSS Statistics Base Crosstabs IBM SPSS Statistics Base t tests IBM SPSS Statistics Base Correlations/Nonparametric correlations IBM SPSS Statistics Base Partial Correlations IBM SPSS Statistics Base Modeling Procedures Product Oneway IBM SPSS Statistics Base UniAnova IBM SPSS Statistics Base Linear Regression IBM SPSS Statistics Base Discriminant IBM SPSS Statistics Base General Linear Models IBM SPSS Advanced Statistics Linear Mixed Models IBM SPSS Advanced Statistics Cox Regression IBM SPSS Advanced Statistics Nominal Regression IBM SPSS Regression Logistic Regression, Binary, Multinomial IBM SPSS Regression Logistic and Ordinal Regression IBM SPSS Regression IBM SPSS Categories (a $1200 value)
IBM® SPSS® Categories provides you with all the tools you need to obtain clear insight into complex categorical and numeric data, as well as highdimensional data.
Use IBM SPSS Categories to understand which characteristics consumers relate most closely to your brand, or to determine customer perception of your products compared to other products you or your competitors offer.
 Discover underlying relationships through perceptual maps, bi plots and tri plots
 Work with and understand nominal (e.g. salary) and ordinal (e.g. education level) data with procedures similar to conventional regression, principal components and canonical correlation to predict outcomes and reveal relationships
 Visually interpret datasets and see how rows and columns relate in large tables of scores, counts, ratings, rankings or similarities
 Deal with nonnormal residuals in numeric data or nonlinear relationships between predictor variables (e.g. customer or product attributes) and the outcome variable (e.g. purchase/nonpurchase)
 Use Ridge Regression, the Lasso, the Elastic Net, variable selection and model selection for both numeric and categorical data
Unleash the full potential of your data through predictive analysis, statistical learning, perceptual mapping, preference scaling and dimension reduction techniques –including optimal scaling of your variables.
Graphically display underlying relationships

IBM SPSS Categories’ dimension reduction techniques enable you to clarify relationships in your data by using perceptual maps and biplots:
 Perceptual maps are highresolution summary charts that graphically display similar variables or categories close to each other. They provide you with unique insight into relationships between more than two categorical variables.
 Biplots and triplots enable you to look at the relationships among cases, variables and categories. For example, you can define relationships between products, customers and demographic characteristics.
By using the preference scaling feature, you can further visualize relationships among objects. The breakthrough algorithm on which this procedure is based enables you to perform nonmetric analyses for ordinal data and obtain meaningful results. The proximities scaling procedure allows you to analyze similarities between objects, and incorporate characteristics for objects in the same analysis.
Turn qualitative variables into quantitative ones

Perform additional statistical operations on categorical data with the advanced procedures available in IBM SPSS Categories:
 Use optimal scaling procedures to assign units of measurement and zeropoints to your categorical data
 Choose from stateofthe art procedures for model selection and regularization
 Perform correspondence and multiple correspondence analyses to numerically evaluate similarities between two or more nominal variables in your dataset
 Summarize your data according to important components by using principal components analysis
 Quantify your ordinal and nominal variables with an optimal scaling correlation matrix
 Use nonlinear canonical correlation analysis to incorporate and analyze variables of different measurement levels
Procedures and statistics for analyzing categorical data

Using IBM SPSS Categories with IBM SPSS Statistics Base gives you a selection of statistical techniques for analyzing highdimensional or categorical data, including:
 Categorical regression that predicts the values of a nominal, ordinal or numerical outcome variable from a combination of categorical predictor variables. Optimal scaling techniques are used to quantify variables. Three regularization methods: Ridge regression, the Lasso and the Elastic Net, improve prediction accuracy by stabilizing the parameter estimates.
 Correspondence analysis that enables you to analyze twoway tables that contain some measurement of correspondence between rows and columns, as well as display rows and columns as points in a map.
 Multiple correspondence analysis which is used to analyze multivariate categorical data by allowing the use of more than two variables in your analysis. With this procedure, all the variables are analyzed at the nominal level (unordered categories).
 Categorical principal components analysis uses optimal scaling to generalize the principal components analysis procedure so that it can accommodate variables of mixed measurement levels.
 Nonlinear canonical correlation analysis uses optimal scaling to generalize the canonical correlation analysis procedure so that it can accommodate variables of mixed measurement levels. This type of analysis enables you to compare multiple sets of variables to one another in the same graph, after removing the correlation within sets.
 Multidimensional scaling performs multidimensional scaling of one or more matrices with similarities or dissimilarities (proximities).
 Preference scaling visually examines relationships between two sets of objects, for example, consumers and products. Preference scaling performs multidimensional unfolding in order to find a map that represents the relationships between these two sets of objects as distances between two sets of points
IBM SPSS Exact Tests (a $1200 value)
IBM® SPSS® Exact Tests (formerly PASW® Exact Tests) gives you what’s needed to more accurately work with small samples and analyze rare occurrences in large datasets.
IBM SPSS Exact Tests enables you to use small samples and still feel confident about the results. With the money saved using smaller sample sizes, you can conduct surveys or test direct marketing programs more often. Stay ahead of the competition by using these resources to find new opportunities.
Easily Interpret and Apply Exact Tests

IBM SPSS Exact Tests is easy to use. You can perform a test any time, with just a click of a button – during your original analysis or when you rerun it. With IBM SPSS Exact Tests, there is no steep learning curve, because you don’t need to learn any new statistical theories or procedures. You simply interpret the exact tests results the same way you already interpret the results in IBM SPSS Statistics Base.
You’ll always have the right statistical test for your data situation. IBM SPSS Exact Tests provides more than 30 exact tests, which cover the entire spectrum of nonparametric and categorical data problems for small or large datasets. These tests include onesample, twosample and Ksample tests on independent or related samples, goodnessoffit tests, tests of independence in RxC contingency tables and on measures of association.
And, with the release of IBM SPSS Statistics 19, both the client and server versions of IBM SPSS Exact Tests are available on Mac® and Linux®, as well as on Windows® operating systems
More Statistics for Data Analysis

Expand the capabilities of IBM SPSS Statistics Base for the data analysis stage in the analytical process. Using IBM SPSS Exact Tests with IBM SPSS Statistics Base gives you an even wider range of statistics, so you can get the most accurate response when:
 Working with a small number of cases
 Working with variables that have a high percentage of response in one category
 Dividing your data into fine breakdowns
 Searching for rare occurrences in large datasets (such as sales above $1 million)
IBM SPSS Exact Tests easily plugs into other IBM SPSS Statistics modules so you can seamlessly work in the IBM SPSS Statistics environment.
Get greater value from your data: with IBM SPSS Exact Tests, you can slice and dice your data into breakdowns, which can be as fine as you want, so you learn more by extending your analysis to subgroups. You aren’t limited by required expected counts of five or more per cell for correct results. And you can even rely on IBM SPSS Exact Tests when you’re searching for rare occurrences within large datasets.
Keep your original categories: don’t lose valuable information by collapsing categories to meet the assumptions of traditional tests. With IBM SPSS Exact Tests, you can keep your original design or natural categories—for example, regions, income, or age groups—and analyze what you intended to analyze.
IBM SPSS Exact Tests has the tests and statistics you need get the more insight from your small samples and rare occurrences within large databases. These procedures include:
 Pearson Chisquared test
 Linearbylinear association test
 Contingency coefficient
 Uncertainty coefficient—symmetric or asymmetric
 Wilcoxon signedrank test
 Cochran’s Q test
 Binomial test
 And many more
System Requirements:
 License Term: 2 years
 Windows Operating system: Microsoft Windows 8, 10 or higher
 Hardware: Intel® or AMD x86 processor running at 1GHz or higher
Memory: 1GB RAM or more recommended
Minimum free drive space: 800MB***  Mac: Operating system: * Apple® Mac 10.13 or higher
 Hardware:
 Intel processor
 Memory: 1GB RAM or more recommended
 Minimum free drive space: 800MB***
 Windows Vista
 Windows XP
 Chromebooks
 IPads
 Android tablets
 Smartphones