IBM SPSS Statistics Grad Pack 20.0 STANDARD -(IN STOCK NOW!) Windows or Mac - 12 month license - can install on up to 2 computers
PLEASE NOTE: For college student use only. Teachers, schools, and K-12 students are not eligible and may not purchase this product. We can ship within the 50 states of the U.S. only. You may install the software on up to two (2) computers. License is good for 12 months. If needed you can order the 2 or 3 year version by clicking here. Runs on Windows and Mac OS 10.6 and 10.7 (Lion) computers. For a comparison of all IBM SPSS versions, please click here. If you need the new version 21, click here. Shipping: ships out next business day after you place your order. Includes: IBM SPSS Base 20 Overview, Features and Benefits
IBM® SPSS® Statistics Base is easy to use and forms the foundation for many types of statistical analyses.
The procedures within IBM SPSS Statistics Base will enable you to get a quick look at your data, formulate hypotheses for additional testing, and then carry out a number of statistical and analytic procedures to help clarify relationships between variables, create clusters, identify trends and make predictions.
Descriptive StatisticsTests 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.
What's New in IBM SPSS Base 19Faster processing - run tests faster. Automatic Linear Models – A new family of algorithms makes it possible for business analysts and analytic professionals to build powerful linear models in an easy and automated manner. Syntax Editor – More than a dozen performance and ease-of-use enhancements for writing syntax in the syntax editor based on customer feedback, including tooltip tip displaying the “name,” improved scrolling, improved indentation of lines, toggle commenting “on” or “off”, the ability to split the syntax editor window, and many more. Default Measurement Level – When a data file is opened, a measurement level is automatically assigned so business analysts can focus on solving their business problem rather than manually setting the measurement level. Faster Performance – Save time when creating reports that involve large tables or a large number of smaller tables. Creating pivot tables in the output is now up to 200% times faster than before. In addition, tables will also take up less memory
With IBM SPSS Statistics Base you can be confident in your analytic results. This comprehensive software solution includes a wide range of procedures and tests to solve your business and research challenges.IBM Advanced Statistics - More Accurately Analyze Complex Relationships Using Powerful Univariate and Multivariate Analysis
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 model-building possibilities.
IBM SPSS Regression Overview, Features and Benefits
IBM® SPSS® Regression enables you to predict categorical outcomes and apply a wide range of nonlinear regression procedures. You can apply IBM SPSS Regression to many business and analysis projects where ordinary regression techniques are limiting or inappropriate: for example, studying consumer buying habits or responses to treatments, measuring academic achievement, and analyzing credit risks.
IBM SPSS Regression includes the following procedures:Multinomial logistic regression: Predict categorical outcomes with more than two categories Binary logistic regression: Easily classify your data into two groups Nonlinear regression and constrained nonlinear regression (CNLR): Estimate parameters of nonlinear models Weighted least squares: Gives more weight to measurements within a series Two-stage least squares: Helps control for correlations between predictor variables and error terms Probit analysis: Evaluate the value of stimuli using a logit or probit transformation of the proportion responding
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
Operating system: * 10.6x (Snow Leopard™). (32-bit and 64-bit) and 10.7 (LION).
- Intel processor (32 and 64 bit)
- Memory: 1GB RAM or more recommended
- Minimum free drive space: 800MB***
- DVD drive
- Super VGA (800x600) or higher-resolution monitor
- Web browser: Mozilla® Firefox® 2.x and 3.x