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SPSS Statistics Extensions give you a new way to access and work with open source and third-party programming extensions: Usability and productivity enhancements, including the ability to auto-recover files in the event of an unexpected application shutdown, new search capabilities, and output improvements Enhancements to effect size, MATRIX, and Quantile regression procedures New Power Analysis and Weighted Kappa procedures
A redesigned experience while importing and exporting the most popular file types enables smarter data management. With SPSS Statistics Custom Dialog Builder for Extensions, it is now easier than ever to create and share extensions based on R/Python and SPSS Syntax for your customized needs. It provides an online store-like experience.
Embed analytics into the enterprise to speed deployment and return on investment. The Generalized Spatial Association Rule (GSAR) finds associations between spatial and non-spatial attributes. The Spatio-Temporal Prediction (STP) technique can fit linear models for measurements taken over time at locations in 2D and 3D space. Integrate, explore and model location and time data, and capitalize on new data sources to solve new business problems Use the Temporal Causal Modeling (TCM) technique to uncover hidden causal relationships among large numbers of time series and automatically determine the best predictors. Gain deeper predictive insights from large and complex datasets.
Compare means – Choose whether to use harmonic or geometric means test linearity compare via independent sample statistics, paired sample statistics or one-sample t test. Descriptive ratio statistics – Coefficient of dispersion, coefficient of variation, price-related differential and average absolute deviance. Descriptives – Central tendency, dispersion, distribution and Z scores. Frequencies – Counts, percentages, valid and cumulative percentages central tendency, dispersion, distribution and percentile values.
You can be confident that you’ll always have the analytic tools you need to get the job done quickly and effectively. Explore – Confidence intervals for means M-estimators identification of outliers plotting of findings.Tests to Predict Numerical Outcomes and Identify Groups:IBM SPSS Statistics Base contains procedures for the projects you are working on now and any new ones to come. Nonparametric tests – Chi-square, Binomial, Runs, one-sample, two independent samples, k-independent samples, two related samples, k-related samples. Correlation – Test for bivariate or partial correlation, or for distances indicating similarity or dissimilarity between measures.
K-means Cluster Analysis – Used to identify relatively homogeneous groups of cases based on selected characteristics, using an algorithm that can handle large numbers of cases but which requires you to specify the number of clusters. Also, scores can be saved as variables for further analysis Three methods of computing factor scores. Five methods of rotation, including direct oblimin and promax for nonorthogonal rotations In IBM SPSS Statistics Base, the factor analysis procedure provides a high degree of flexibility, offering:
Statistics are displayed at each stage to help you select the best solution. Distance or similarity measures are generated by the Proximities procedure. Analyze raw variables or choose from a variety of standardizing transformations. Hierarchical Cluster Analysis – Used to identify relatively homogeneous groups of cases (or variables) based on selected characteristics, using an algorithm that starts with each case in a separate cluster and combines clusters until only one is left.
Linear mixed models, also known as hierarchical linear models (HLM) Nearest Neighbor analysis – Use for prediction (with a specified outcome) or for classification (with no outcome specified) specify the distance metric used to measure the similarity of cases and control whether missing values or categorical variables are treated as valid values. Ordinal regression—PLUM – Choose from seven options to control the iterative algorithm used for estimation, to specify numerical tolerance for checking singularity, and to customize output five link functions can be used to specify the model. Linear Regression – Choose from six methods: backwards elimination, forced entry, forced removal, forward entry, forward stepwise selection and R2 change/test of significance produces numerous descriptive and equation statistics. Discriminant – Offers a choice of variable selection methods, statistics at each step and in a final summary output is displayed at each step and/or in final form.
You can also select from 11 non-spatial covariance types, including first-order ante-dependence, heterogeneous, and first-order autoregressive. If you work with data that display correlation and non-constant variability, such as data that represent students nested within classrooms or consumers nested within families, use the linear mixed models procedure to model means, variances and covariances in your data.Its flexibility means you can formulate dozens of models, including split-plot design, multi-level models with fixed-effects covariance, and randomized complete blocks design. Variance component estimation (VARCOMP)The linear mixed models procedure expands the general linear models used in the GLM procedure so that you can analyze data that exhibit correlation and non-constant variability.
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