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.

Descriptives ibm

Central tendency, dispersion, distribution and Z scores. Descriptive ratio statistics — Coefficient of dispersion, coefficient of spss,

price-related differential and average ibm

deviance, 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.

Also, 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, ibm spss

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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, ibm spss

.

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 spss

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