![]() For more information on partitioning, please see the Data Mining Partitioning chapter.Ĭlick Rescale Data to normalize one or more features in your data during the data preprocessing stage. If partitioning has already occurred on the dataset, this option will be disabled. Analytic Solver Data Mining will partition your dataset (according to the partition options you set) immediately before running the prediction method. See below, for option explanations included on the Linear Regression Parameters dialog.Īnalytic Solver Data Mining includes the ability to partition a dataset from within a classification or prediction method by clicking Partition Data on the Parameters dialog. Select the variable whose outcome is to be predicted here. ![]() A record with a large weight influences the model more than a record with a smaller weight. Analytic Solver Data Mining offers an opportunity to provide a Weight Variable, which allocates a weight to each record. One major assumption of Multiple Linear Regression is that each observation provides equal information. This classification algorithm will accept both numeric and non-numeric categorical variables. Place categorical variables from the Variables listbox to be included in the model by clicking the > command button. Variables listed here will be utilized in the output. ![]() Linear Regression Dialog, Data tabĪll variables in the data set are listed here. The following options appear on the four Multiple Linear Regression dialogs. ![]()
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