Organizations of all types rely on SPSS Statistics to increase revenue, outmaneuver competitors, conduct research and make better decisions. With decades of built-in expertise and innovation, it’s a leading choice for reliable statistical analysis.

• The most critical part of any data analysis is the initial data entry. If you enter the data the wrong way, you won’t be able to analyze it properly. While you can use a wide range of options for data entry,  entering the data into IBM SPSS is often the best choice. IBM SPSS offers a simple spreadsheet format for data entry that is intuitive and easy to start with. More importantly, IBM SPSS provides a broad range of data documentation (especially value labels) that will help you to ensure consistency in your data entry.
• Before you start your data analysis, you need to understand how your data behaves. This is best done graphically. IBM SPSS provides scatterplots, boxplots, and histograms that help you to see patterns in your data. You shouldn’t publish findings based solely on an intuitive interpretation of graphics, of course. Rather, these graphics will provide you with a general framework for understanding your data, so that you will be able to interpret the complex inferential procedures that follow better.
• At the start of a research project you often won’t know what statistical models would be best suited for your particular project. Sometimes you will have a general idea, but the statistical model can change as you start examining your data. Or you will want to run an alternate analysis as a quality check for the originally planned analysis. IBM SPSS offers a broad range of highly flexible statistical models: most notably the general linear model and a variety of logistic regression models. These allow you to have a single program that will meet virtually all your data analysis needs. Although some might need to supplement IBM SPSS with another program like R, for most, the IBM SPSS will be the only statistical software package they need.
• Many of the competitive statistical software programs, such as R, SAS, and Stata, are run primarily as a programming language. While a programming language offers some important advantages, it takes much longer to learn. Furthermore, the complexity often discourages the ursers from trying a new and different approach.
• Support business decisions with data-based analytics for improved outcomes.
• Be more confident in your results by incorporating data from many different sources, including geospatial information, in your analysis and using proven, tested techniques to perform your analysis.
• Save time and effort with capabilities that enable experienced analysts to develop procedures or dialogues that others can use to speed through repetitive tasks.
• Give results greater impact by using visualization capabilities that clearly show the significance of your findings to others.