Predict values in sas jmp
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In order to have a knowledge of the customer data set, I used “graph explore “ statexplore” so as to perform some descriptive analysis ( mean, median, Normal distribution). The data is originally in an excel spread sheet, so I used “file import” node to import the data into the work flow and this was followed by using “save data” node to save the imported files as SAS data file. I decided to use a supervised learning approach because the data collected already had a past record of customers who had churned already from the network. SAS EMiner evaluates the remaining (not rejected) variables using a forward stepwise R-square regression. In light of this I decided to do a quick demonstration of how Churn could be predicted in telecoms by applying data mining techniques using SAS Enterprise Miner. SAS EMiner computes the squared correlation for each variableand then assigns the Rejected role to those variables that have a value less than the squared correlation criterion. Note that all of the predictor variables are fully observed, i.e., the predictors contain no missing values. This really gave me a concern as identifying customers who are likely to churn and preventing them from churning is cost effective. Im using SAS Proc GLM to make predictions for a dependent variable with some missing values.
Predict values in sas jmp free#
Statistics in Medicine 26:2170-2183.There had been some incentives in form of free credits given out by one of the leading telecoms in Nigeria to some CHURNING customers.
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JMP also features predictive modeling with cross-validation, model comparison and averaging features, exact tests, and one-click bootstrapping.
Predict values in sas jmp pro#
When these data are entered click Test or press Enter to see the results. SAS JMP Pro predictive analytics software provides advanced algorithms for building, assessing, and managing predictive models. Input of these numbers will enable MedCalc to calculate 95% confidence intervals for the positive and negative predictive values. These are the number of cases included in the study in which sensitivity and specificity were established. Optionally you can enter the number of cases in the diseased and normal groups. Required inputĮnter the sensitivity and specificity of a test (expressed as percentages), and the disease prevalence (also expressed as a percentage). This course also discusses selecting variables and interactions, recoding categorical variables based on the smooth weight of evidence, assessing models, treating missing values, and using efficiency techniques for massive data sets. When you do have access to the raw data to perform ROC curve analysis, you can still calculate positive and negative predictive values for a test when the sensitivity and specificity of the test as well as the disease prevalence (or the pretest probability of disease) are known, using Bayes' theorem. This course covers predictive modeling using SAS/STAT software with emphasis on the LOGISTIC procedure. If you are a customer seeking information about SAS analytics solutions and services, we refer you to our primary site: sas.com. ROC curve analysis: predictive values Command: If you are an employee, a fan of SAS, or an agency seeking to learn more about us and how we express our brand in tangible assets, you have come to the right place.