Ibm Spss |verified| May 2026
Advanced Statistics
: Includes univariate and multivariate modeling, such as GLM , logistic regression , survival analysis, and Bayesian statistics .
Descriptives
| Category | Examples | |----------|----------| | | Frequencies, cross-tabs, means, skewness, kurtosis. | | Bivariate | Pearson/Spearman correlation, t-tests, ANOVA, chi-square. | | Regression | Linear, logistic, multinomial, ordinal, nonlinear. | | Advanced | GLM, mixed models, generalized linear models, loglinear. | | Multivariate | Factor analysis, PCA, cluster analysis, discriminant analysis. | | Nonparametric | Mann-Whitney, Wilcoxon, Kruskal-Wallis, Friedman. | | Time series | ARIMA, exponential smoothing, autocorrelation. | | Survival | Kaplan-Meier, Cox regression. | ibm spss
3. Getting Data into SPSS
Compute new variable
The platform addresses the entire analytical process, from initial data collection to final reporting. | | Regression | Linear, logistic, multinomial, ordinal,
In today's data-driven world, organizations across various industries rely on data analysis to inform their decisions, drive business outcomes, and stay ahead of the competition. One of the most popular and widely used statistical software packages for data analysis is IBM SPSS. In this article, we will explore the features, benefits, and applications of IBM SPSS, as well as its role in unlocking insights and driving business success. Defining and Managing Variables 1. Overview