An ideal choice for data analysis, IBM® SPSS® Statistics enables you to take the analytical process from start to finish. In addition to the data preparation, data management, output management and charting features, SPSS Statistics offers deep data visualization capabilities, including charts, plots and animations.
These visual displays communicate complex data relationships and data-driven insights in a way that is simple to understand.
Identify patterns and trends within large data sets through visualization capabilities such as scatterplots, line charts and heat maps. Understand correlations between variables or detect seasonality in time-series data.
Enhance data analysis quality by accurately pinpointing outliers within data sets for more reliable statistical insights and interpretations.
Visualize temporal trends with time series plots and trend lines to track changes in data accurately. This helps to forecast future outcomes more precisely and understand the impact of interventions.
Use simulation techniques such as Exact Test and Monte Carlo methods to build robust models, visualize probability distributions, and assess risk when inputs are uncertain.
Employ histograms, density plots and box plots to analyze data distribution and variability. Visualizing data distribution helps in assessing normality, skewness or kurtosis to identify appropriate statistical analysis techniques.
Communicate complex data-driven insights effectively by including customized charts and graphs in research papers, presentations and reports, improving the presentation of findings.
- 3D bar: Simple, cluster and stacked.
- Bar: Simple, cluster, stacked, dropped shadow and 3D.
- Line: Simple, multiple and drop-line.
- Area: Simple and stacked.
- Pie: Simple, exploding and 3D effect.
- High-low: High-low-close, difference area and range bar.
- Box plot: Simple and clustered.
- Error bar: Simple and clustered.
- Error bars: Add to bar, line and area charts; confidence level; S.D.; or S.E. Dual-Y axes and overlay subgroups, display spikes to line.
- Fit lines: Linear, quadratic or cubic regression; confidence interval control for total or bivariate statistics.
- Population pyramids: Mirrored axis to compare distributions, with or without a normal curve.
- Dot charts: Stacked dots show distribution: Symmetric, stacked and linear.
- Histograms: With or without normal curve; custom binning options.
Choose from a subscription or one-time purchase, with multiple options for capabilities based on your needs.
Purchase SPSS Statistics and the add-ons you need online, whether monthly or annually, and start your analysis right away.
Select between Base, Standard, Professional and Premium packages with options to customize your configuration, whether it is a perpetual or 12-month subscription license.
Choose the edition that meets your requirements and buy it from IBM-selected vendors to receive the best support.
FAQs
The earliest form of data visualization can be traced back to the Egyptians. As time progressed, people used data visualizations for broader applications, such as in economic, social and health disciplines. Today, common visualization techniques include:
- Tables: This consists of rows and columns that are used to compare variables.
- Pie charts and stacked bar charts: These graphs are divided into sections that represent parts of a whole. They provide a simple way to organize data and compare the size of each component to one other.
- Line charts and area charts: These visuals show changes in one or more quantities by plotting a series of data points over time and are frequently used within predictive analytics. Line graphs use lines to demonstrate these changes, while area charts connect data points with line segments, stacking variables on top of one another and using color to distinguish between variables.
- Histograms: This graph plots a distribution of numbers by using a bar chart (with no spaces between the bars), representing the quantity of data that falls within a particular range. This visual makes it simple for a user to identify outliers within a particular data set.
- Scatterplots: These visuals are beneficial in revealing the relationship between two variables, and they are commonly used within regression data analysis.
- Heat maps: These graphical representation displays help visualize behavioral data by location.
- Treemaps: Display hierarchical data as a set of nested shapes, typically rectangles. Treemaps are great for comparing the proportions between categories via their area sizes.
Data visualization can be used for various purposes, and not just by data scientists. Management teams can use these tools to detail organizational structure and hierarchy. Data visualization exercises are also used commonly to spur idea generation across teams to convey ideas, tactics or processes, and capture key concepts, trends and hidden relationships within unstructured data.
Footnotes
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