07 (7%) if Responsive to Calls and Ease of Communications are both equal to 5, the probability of a satisfied customer is. If Responsive to Calls and Ease of Communications are both equal to 3, the probability of a satisfied customer is only. Scroll over to display the Event Probability calculator: Reselect the Binary Logistic Report sheet.The reasons for these discrepancies should be explored further but we will not do so here. The large negative residuals have high Responsive to Calls and Ease of Communications but dissatisfied customers. The -6.6 value indicates that the predicted event probability was high (.98) but the actual result was a 0. The +3.4 value indicates that the predicted event probability was low (.08) but the actual result was a 1. There are 3 such outliers here: rows 24, 67, and 77 in the residuals table.

Pearson Residual as theĪny Standardized Pearson Residual value that is less than -3 or greater than +3 is considered extreme and should be investigated. Use Entire Data Table to select the Residuals data, click

The Likelihood Ratio p-value Graphical Tools > Run Chart, check.

This is common when tracking daily quality data or performing design of experiments where each run contains a response of the number of defects and sample size.īinary Logistic Regression report is shown: Should be used when each record contains both the number of occurrences along with associated sample size. Note that Response Count (Y)/Sample Size (Trials) Responsive to Calls and Ease of Communications, click Continuous Predictors (X) > selectĬustomer Type, click Categorical Predictors (X) >.

Select Sat-Discrete, click Binary Response (Y) > select.We will analyze the familiar Customer Satisfaction data using Y=Discrete Satisfaction where the values have been coded such that an Overall Satisfaction score >= 3.5 is considered a 1, and scores Statistical Tools > Regression > Binary Logistic Regression. The model coefficient p-values would indicate which X’s are significant and the odds-ratios would provide the relative change in risk for each unit change in X. The odds-ratios identify change in likelihood of the event for one unit change in X.Īn example application from medical research would be Y=Disease (Yes/No) and X’s = Age, Smoker (Yes/No), Number Years of Smoking and Weight. Statistical tests of hypothesis and odds-ratios are provided for the model coefficients. SigmaXL uses the method of maximum likelihood to solve for the model coefficients and constant term. This equation can be used to predict an event probability Y value for a given set of input X values. It is used to discover the relationship between the variables and create an empirical equation of the form: Binary Logistic Regression is used to analyze the relationship between one binary dependent variable (Y) and multiple independent numeric and/or discrete variables (X's).

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