The IBM® SPSS® software platform offers advanced statistical analysis, a vast library of machine learning algorithms, text analysis, open-source. IBM SPSS Statistics is software for advanced statistical analysis. All facets of the analytics lifecycle are included, from data preparation and management. SPSS Statistics is a statistical software suite developed by IBM for data management, advanced analytics, multivariate analysis, business intelligence, and criminal investigation. Long produced by SPSS Inc., it was acquired by IBM in
IBM SPSS Base Ibm, Features and Benefits Descriptive Statistics Crosstabulations spss Counts, ibm spss, percentages, residuals, ibm spss, marginals, tests of independence, test of linear association, measure
ibm linear association, ibm spss, ordinal data measures, nominal
ibm interval measures, ibm spss, measure of agreement, relative risk estimates for case control and cohort studies.
Frequencies — Counts, ibm spss, percentages, ibm and cumulative percentages; central tendency, ibm spss, dispersion, distribution and percentile values.
ibm Central tendency, dispersion, distribution and Z scores. Descriptive ratio statistics — Coefficient of dispersion, coefficient of
spss, price-related differential and average
ibm spss. Compare means —
Spss whether to use harmonic or geometric means; test ibm compare via independent sample statistics, ibm spss, paired sample statistics or one-sample t test.
ANOVA and ANCOVA — Conduct contrast, spss and post hoc tests; spss fixed-effects and random-effects ibm group descriptive statistics; choose your spss based on ibm types of the sum-of-squares procedure; perform lack-of-fit tests; choose balanced or unbalanced design; and analyze covariance with up to 10 spss. Correlation — Test for bivariate ibm partial correlation, or for spss indicating similarity or dissimilarity between measures.
Nonparametric tests — Chi-square, Binomial, Runs, ibm spss, one-sample, two independent spss, k-independent spss, two related samples, k-related samples. Explore — Confidence spss for ibm M-estimators; identification of
ibm plotting of findings. Factor Analysis — Used to identify the underlying variables, ibm spss, or factors, ibm spss, that explain the pattern of correlations within a set of observed ibm. In IBM SPSS Statistics Base,
ibm spss, the factor analysis procedure provides a high degree of flexibility, offering: Seven spss of factor extraction Five methods of rotation, including ibm oblimin and promax for nonorthogonal rotations Three methods of computing factor scores.
ibm spss, scores can be saved as variables for
ibm analysis Ibm Cluster Analysis — Used to identify ibm homogeneous groups of cases based on selected
spss, using an algorithm that can handle large numbers of cases but which requires you to spss the number of clusters.
Select one of two ibm for classifying cases, ibm spss, either updating cluster centers iteratively spss classifying only. Hierarchical Cluster Analysis — Used to ibm relatively homogeneous groups of cases or spss based on selected characteristics, ibm spss, using spss algorithm that spss with each case in a separate cluster and combines clusters until only one is left.
Analyze raw variables or choose from spss variety of standardizing
spss. Distance or similarity measures are generated by the Proximities procedure. Statistics are displayed at each stage to help you select the best ibm. TwoStep Cluster Analysis — Group observations into clusters spss on nearness criterion, with either categorical or continuous level spss specify the number of clusters or let the number be chosen automatically.
Ordinal regression—PLUM — Choose from seven options to control the iterative algorithm used for ibm, to specify numerical tolerance for checking singularity, ibm spss, and to customize
spss five link functions can be used to specify the model. Nearest Neighbor analysis — Use for prediction
ibm a specified outcome or
ibm 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 ibm as valid values.
The GLM gives you flexible design and contrast options to estimate means and variances and to test and predict spss. You can also ibm and match categorical and continuous predictors to ibm models.
If you work with data that display correlation and non-constant ibm, such as data that represent students nested within ibm or consumers nested within families, use spss linear mixed models procedure to model means, variances and spss in your spss. Its flexibility means you can formulate spss of models, ibm spss, including split-plot design, multi-level models with fixed-effects covariance, and randomized complete blocks design.
You can also select from 11 spss covariance types, including first-order ante-dependence, spss, and first-order autoregressive. Unlike standard methods, linear mixed models use all your
ibm and give you ibm more accurate analysis.
Generalized linear models GENLIN : GENLIN covers not only widely used statistical ibm, such as linear regression for normally distributed responses, logistic models for binary data, and loglinear model for count data, ibm spss, ibm also many useful statistical models via its very general model spss. The
ibm assumption, ibm spss, however, prohibits generalized linear
ibm from being applied
spss correlated data,
Generalized estimating equations GEE : GEE extend generalized linear models to accommodate correlated longitudinal ibm and clustered data. You can apply IBM SPSS Regression
spss many business and analysis projects where spss regression
ibm are limiting or inappropriate: for example, ibm spss, studying consumer spss habits or responses to treatments, ibm spss, ibm spss, measuring academic achievement, and analyzing credit risks, ibm spss.
This procedure helps you accurately predict group membership within key groups. You can also
spss stepwise functionality, ibm spss, including forward entry, backward elimination, forward stepwise or backward stepwise, to find the best predictor from spss of possible predictors. If you have a large number of predictors, ibm spss, Score ibm Wald ibm can help you more quickly reach results, ibm spss, ibm spss.
Binary logistic regression: Group people with
ibm to their predicted action. Use this procedure if you need to build models in
spss the dependent variable is dichotomous for example, buy versus spss buy, ibm spss, pay versus default, graduate versus not ibm. You can also use binary logistic spss to predict the probability of events such as solicitation responses or program participation, ibm spss.
With binary logistic regression, you can select variables spss six types of stepwise methods, ibm spss, including forward the procedure selects the ibm variables until there are no more significant predictors in the dataset and backward at each step, spss procedure removes the least significant predictor in the dataset methods.
You can also set inclusion or exclusion criteria, ibm spss. The procedure produces a report telling ibm the action
spss took at each step to determine your variables. If you are you working with models that have nonlinear relationships, for example, if you are predicting coupon redemption as a function of time and number of coupons distributed,
ibm spss, estimate nonlinear equations using one of two IBM SPSS Statistics procedures: nonlinear regression NLR for unconstrained problems and constrained nonlinear regression CNLR for both constrained and unconstrained problems, ibm spss.
NLR enables you to estimate models with arbitrary relationships between spss and dependent variables using iterative estimation algorithms, ibm spss, while CNLR enables you to: Use linear and nonlinear constraints
ibm any combination of parameters Estimate parameters ibm minimizing any
ibm loss function objective function Compute bootstrap estimates of parameter standard errors and correlations Weighted least squares WLS
spss If the spread of residuals is not constant, the ibm standard errors will not be valid, ibm spss.
Spss Weighted Least Square to estimate the model instead spss example, when predicting stock values, stocks with higher shares values fluctuate more than low value shares. Two-stage least squares 2LS : Use this technique to estimate ibm dependent variable when the independent variables are correlated with spss regression error terms, ibm spss. For example,
ibm spss, a book club may want to model the amount they cross-sell to members using the amount that members spss on books as a predictor,
However, ibm spss, money spent ibm other items is money not spent on ibm, so an increase in cross-sales corresponds to ibm decrease in book sales. Two-Stage Least-Squares Regression corrects for this error, ibm spss.
Probit analysis: Probit ibm is most appropriate when you want to estimate spss effects of one or more independent variables on a categorical dependent variable.
For example, ibm spss, you would use probit analysis spss establish the relationship between the percentage taken off a product, and whether a customer will buy as the prices decreases. Then, for every percent taken off the price you can work out the probability that a consumer will buy the product.
Please Note: Please ibm or email us for