Spss 23
IBM SPSS Base 23 Overview, Features and Benefits 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. Installation Procedure SPSS 23,22, or 21 (Windows) Start Windows (Boot up the computer) Locate the SPSS zip file that you downloaded from the Campus Software Library. It should like something like this: Right click on this file and select the 'Extract All' option. A window will then pop up and ask you to choose an extraction location.
Speed up research and analysis to make better decisions with SPSS Statistics
Speed up research and analysis to make better decisions with SPSS StatisticsRegister for webcasts
Why IBM® SPSS® Statistics?
IBM® SPSS® Statistics is a powerful statistical software platform. It delivers a robust set of features that lets your organization extract actionable insights from its data.
With SPSS Statistics you can:
- Analyze and better understand your data, and solve complex business and research problems through a user friendly interface.
- Understand large and complex data sets quickly with advanced statistical procedures that help ensure high accuracy and quality decision making.
- Use extensions, Python and R programming language code to integrate with open source software.
- Select and manage your software easily, with flexible deployment options.
SPSS Statistics is available for Microsoft Windows and the Mac operating system.
Explore what’s new with SPSS Statistics 28
Explore what’s new with SPSS Statistics 28Read the blog
SPSS Statistics 28: Latest Release
SPSS Statistics introduces new statistical algorithms, procedural enhancements and usability improvements to help boost data analysis.
SPSS Statistics Tech-talk series
The SPSS webinar series helps both statistics novices and experts unlock richer insights from data with tips around SPSS Statistics 28.
New learning guide for SPSS Statistics
Spss 23 Amos
Explore videos, product tours, tutorials and more learning resources to help you accelerate data analysis with SPSS Statistics.
A powerful statistical analysis software platform
Easier to use
Perform powerful analysis and more easily build visualizations and reports through a point-and-click interface, and without any coding experience.
Efficient data conditioning
Reduce data preparation time by identifying invalid values, viewing patterns of missing data and summarizing variable distributions.
Quick and reliable
Analyze large data sets and prepare data in a single step with automated data preparation.
Comprehensive
Run advanced and descriptive statistics, regression and more with an integrated interface. Plus, you can automate common tasks through syntax.
Open source integration
Enhance SPSS syntax with R and Python using a library of extensions or by building your own.
Data security
Store files and data on your computer rather than in the cloud with SPSS that’s installed locally.
Take a closer look at IBM SPSS Statistics
Explore advanced statistical procedures with SPSS Statistics
Advanced statistics
Use univariate and multivariate modeling for more accurate conclusions in analyzing complex relationships.
Custom tables
Regression
Predict categorical outcomes and apply nonlinear regression procedures.
Decision trees
Use classification and decision trees to help identify groups and relationships and predict outcomes.
Direct marketing
Identify the right customers easily and improve campaign results.
Spss 23 Mac
Forecasting
Build time-series forecasts regardless of your skill level.
Neural networks
Discover complex relationships and improve predictive models.
Categories
Predict outcomes and reveal relationships using categorical data.
Complex samples
Analyze statistical data and interpret survey results from complex samples.
Conjoint
Understand and measure purchasing decisions better.
Exact tests
Reach more accurate conclusions with small samples or rare occurrences.
Missing values
Uncover missing data patterns, estimate summary statistics and impute missing values.
Which option is right for you?
