![]() Linear mixed models, also known as hierarchical linear models (HLM).Because GLM doesn’t limit you to one data type, you have options that provide you with a wealth of model-building possibilities. You can also mix and match categorical and continuous predictors to build models. The GLM gives you flexible design and contrast options to estimate means and variances and to test and predict means. General linear models (GLM) – Provides you with more flexibility to describe the relationship between a dependent variable and a set of independent variables. ![]() IBM Advanced Statistics – More Accurately Analyze Complex Relationships Using Powerful Univariate and Multivariate Analysis 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.
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