|
Key words: spss version 12.0, spss 12, spss 8.0, spss 9.0, spss 10.0, spss 10, spss 10.1, spss 11.01, spss version 11, spss 11 serial, spss 11.5 serial, spss 11.0 serial spss, spss 11.0 for windows, spss version 10
SPSS 16.0 Base Data Analysis
A broad choice of statistics for data analysis
SPSS for Windows
can help you analyze data better because it gives you the statistical depth needed to solve a variety of business and research problems - not just the problem for which you initially purchased the software. SPSS
empowers you with a wide range of statistics so you can get the most accurate response for specific data types. Add-on modules and other software provide you with even more analytical power and they easily plug into
SPSS Base. This means you can add as much analytical capability to your system as you need and work confidently, moving seamlessly from one product to the next.
Statistical highlights for SPSS Base:
Linear Regression: Explore the relationships between predictors and what you want to predict, for example, predict sales using price and customer type.
Factor Analysis:
Identify underlying variables or factors that explain correlations within a set of observed variables. For example, use this procedure in data reduction to identify a small number of factors that explain most of the variance observed in a much larger number of manifest variables. Factor Analysis has a high degree of flexibility, giving you a number of methods for factor extraction, rotation and factor score computation.
TwoStep Cluster Analysis:
Work with very large datasets using this scalable cluster analysis algorithm. This algorithm can handle both continuous and categorical variables or attributes and requires only one data pass in the procedure. In the first step of the procedure, you pre-cluster the records into many small sub-clusters. Then, you cluster the sub-clusters created in the pre-cluster step into the desired number of clusters. If the desired number of clusters is unknown, TwoStep Cluster analysis automatically finds the proper number of clusters. By using TwoStep Cluster analysis, you can group data so that records within a group are similar. For example, you can apply it to data that describe customer buying habits, gender, age, income, etc. Then tailor your marketing and product development strategy to each consumer group to increase sales and build brand loyalty.
 |
Use TwoStep Cluster analysis to get the most accurate identification of your clusters. This state-of
-the art algorithm enables you to find clusters in large datasets and mixed datasets with continuous- (such as income) and categorical-level (such as job type) variables. TwoStep Cluster
analysis also provides you with the flexibility to pre-specify the number of clusters or to have the algorithm automatically find the proper number of clusters.
K-means Cluster Analysis: Group data from larger datasets, such as customer mailing lists. This procedure assumes data fall into a known number
of clusters. Given this number, the procedure will assign cases to clusters. You can select one of two methods to classify cases - either update cluster
centers iteratively or classify only. Save cluster memberships, distance information and final cluster centers. A market researcher, for example, might
want to cluster cities into homogeneous groups using K-means Cluster Analysis to find comparable cities to test marketing strategies.
Hierarchical Cluster Analysis: Take clusters from a single record and form groups until all clusters are merged. You can choose from over 40 measures of
similarity or dissimilarity, standardize data using several methods and cluster cases or variables. You can also analyze raw variables or choose from a
variety of standardizing transformations. Generate distance or similarity measures using the proximities procedure. Display statistics at each stage to
help you select the best solution. This procedure is recommended for datasets that are smaller in number, for example, focus group lists. A market researcher
could use Hierarchical Cluster Analysis to identify types of television shows that attract similar audiences for each show type. The organization could
cluster TV shows into homogenous groups based on viewer characteristics to identify segments for advertising.
SPSS Base provides you with:
Descriptive statistics
- Cross tabulations
- Frequencies
- Descriptives
- Explore
- Descriptive Ratio Statistics
Bivariate statistics
- Means
- t-tests
- ANOVA
- Correlation
- Bivariate
- Partial
- Distances
- Non-parametric tests
Prediction for numerical outcomes
Prediction for identifying groups
- Factor Analysis
- TwoStep Cluster Analysis
- K-means Cluster Analysis
- Hierarchical Cluster Analysis
- Discriminant
More statistics for more powerful data analysis
Add-on modules and stand-alone software from SPSS Inc. offer much more for the data analysis stage, including these statistics:
Download the SPSS 15.0 spec sheet brochure (PDF file - zipped)) for more information about SPSS Base and its capabilities for data access.
Back to SPSS Base page.
Back to SPSS software main page.
|
