The reason is because specific features are not for sale. So, you’ll want to consult the documentation to figure out what’s included with the Regression feature. I know “IBM SPSS Regression” might not mean much to you. On the list of feature codes, you’ll notice that every feature code lists each of the items included for that feature. IBM SPSS Visualization Designer (Windows only) This makes it easy to find the version that’s right for you. Definitions of each feature code are available to make this easier. Be sure to cross-check the feature code information and the table below to determine the features you need. This handy table outlines each of the three different versions and the features associated with them. This will help you make an informed decision regarding the version to purchase for your class. The aim of this post is to clarify the differences between each version of IBM SPSS Statistics. Because, if you purchase the Base version, you will not get those features. This is where choosing the correct student version becomes so important. For example, your class might require access to binary logistics or a regression feature. You will find that each course requires very specific software features. Since back to school time is quickly approaching, we wanted to share this helpful guide to picking out the best version for you. All contents under (CC) BY-NC-SA license, unless otherwise noted.Studica offers three versions of IBM SPSS Statistics student software: Grad Pack Base, Grad Pack Standard, and Grad Pack Premium. Missing value analysis (with multiple imputation) to address issues of “dirty data” for more complete analysis and better decision-makingĪdvanced data preparation to identify anomalies and the other data that can skew resultsĭecision trees to better identify groups, discover relationships between groups, and predict future eventsįorecasting to predict trends and build expert time-series forecasts quickly and easilyĬategories to obtain clear insight into complex categorical and numeric data, as well as high dimensional data.īootstrapping to test the stability and reliability of predictive modelsĪdvanced sampling assessment and testing proceduresĭirect marketing and product decision-making procedures to identify best customers and the product attributes that appeal to themĬopyright © Melinda Higgins, Ph.D. High-end charts, graphs and mapping capabilities to aid analysis and reporting Simulation modeling to build better models and assess risk when inputs are uncertainĬustomized tables to analyze and report on numerical and categorical data (not available in Statistics Standard Grad Pack Edition) Nonlinear regression, including MLR, Binary Logistic Regression, NLR, CNLR and Probit Analysis, to improve the accuracy of predictions Seamless integration with R, Python and other environments to easily and effectively expand statistical capabilities and programmabilityĪdvanced statistical procedures, including GLM, GLMM, HLM, GENLIN and GEE to more accurately identify and analyze complex relationships Here is a quick comparison between the 3 editions (available at this reseller ) FeaturesĬore statistical and graphics capabilities to take standard analytic projects from start to finish However, it is recommended to purchase the Premium Grad Pack which also includes bootstrapping, missing data analysis, customized tables, and other helpful tools. The Standard edition would be the minimal version to purchase as it has the necessary statistical modeling procedures included. The Premium edition (approximately $89).The Standard edition (approximately $49) and.There are several links at this website for purchasing students versions of SPSS. Go to IBM’s website for student grad pack versions at Discriminant Analysis/MANOVA, Mediators and SEM Multilevel (Mixed or Nested) Linear Models (MLM) Dependent/Paired data and Repeated Measures Logistic & Poisson Regression - Generalized Linear Regression Modeling Covariates and Interaction/Moderator Effects Interactions, Moderators, Covariates, Factors 14,15 Regression Diagnostics and Variable Selection (cont'd). Multivariate Regression & Variable Selection
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